CITY PLANNING LABS - World Bank

CITY PLANNING LABS

A CONCEPT FOR STRENGTHENING CITY PLANNING CAPACITY

IN INDONESIA

PREPARED BY THE CITY FORM LAB, SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN (SUTD)

FOR WORLD BANK INDONESIA

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© 2013 The International Bank of Reconstruction and Development/ The World Bank East Asia and Pacific Region/East Asia Infrastructure Sector (EASIS) 1818 H St., NW Washington, DC 20433 USA All rights reserved This volume is a joint publication of the staff of the International Bank for Reconstruction and Development/The World Bank and the Australian Aid. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Executive Directors of the World Bank, the governments they represent or of Australian Aid. The World Bank does not guarantee the accuracy of the data included in this work. Rights and Permissions The material in this publication is copyrighted. Copying and/or transmitting portions or all of this work without permission may be a violation of applicable law. The International Bank for Reconstruction and Development/The World Bank encourages dissemination of its work and will normally grant permission to reproduce portions of the work promptly. All queries should be addressed to the Task Team Leader, Thalyta Yuwono: The World Bank Jakarta Office Indonesia Stock Exchange Building Tower II, 12th Floor. Jalan Jenderal Sudirman Kav. 52-53, Jakarta 12190, Indonesia e-mail: [email protected]. Disclaimer The views expressed in this publication are those of the authors and not necessarily those of the Australian Aid.

CITY PLANNING LABS

A CONCEPT FOR STRENGTHENING CITY PLANNING CAPACITY

IN INDONESIA

PREPARED BY THE CITY FORM LAB, SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN (SUTD)

FOR WORLD BANK INDONESIA

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TABLE OF CONTENTS

Acknowledgement ................................................................................................................................ v

Abbreviation and Acronyms ............................................................................................................. vi

Executive Summary ............................................................................................................................ vii

Introduction ............................................................................................................................................ 1

1.1 Background .......................................................................................................................... 3

1.2 Rationale ............................................................................................................................... 5

1.3 Objectives ............................................................................................................................. 6

1.4 Scope of Activities ............................................................................................................... 7

Sector Module A: City Planning Labs and Spatial Growth Analytics ........................................ 9

2.1 Background ....................................................................................................................... 11

2.1.1 Context ...................................................................................................................... 11

2.1.2 Implementing P3N Technical Assistance through City Planning Labs ............. 11

2.2 Objectives .......................................................................................................................... 12

2.3 Scope of Activities ............................................................................................................ 13

2.3.1 Establishing the City Planning Lab ........................................................................ 14

2.3.2 Spatial Growth and Change Analytics ................................................................ 18

2.3.3 Planning Enforcement .............................................................................................. 21

2.4 Risks .................................................................................................................................... 24

2.5 Outputs ............................................................................................................................... 24

2.6 Team and Timeline ........................................................................................................... 26

Sector Module B: City Economic Competitiveness ....................................................................... 27

3.1 Background ....................................................................................................................... 29

3.2 Objectives .......................................................................................................................... 29


3.4 Risks and Mitigation ......................................................................................................... 37

3.5 Outputs ............................................................................................................................... 37

3.6 Team ................................................................................................................................... 37

3.7 Resource Allocation and Timeline .................................................................................. 38

Annex Module B: Assessment of Data Environment ................................................................ 39

Sector Module C: Slum Analytics and Management Systems ................................................... 41

4.1 Background ....................................................................................................................... 43

4.2 Objectives .......................................................................................................................... 44



4.5 Outputs ............................................................................................................................... 49

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4.6 Team ................................................................................................................................... 50

4.7 Timeline .............................................................................................................................. 50

Annex Module C: Data Collection ............................................................................................. 51

Sector Module D: Disaster and Climate Resilient Planning Analytics ...................................... 55

5.1 Background ....................................................................................................................... 57

5.2 Objectives .......................................................................................................................... 58



5.5 Outputs ............................................................................................................................... 63

5.6 Team ................................................................................................................................... 64

5.7 Timeline .............................................................................................................................. 64

Annex Module D: Data Collection ............................................................................................. 65

Sector Module E: Monitoring Land and Real Estate Markets ................................................... 67

6.1 Background ....................................................................................................................... 69

6.2 Objective ........................................................................................................................... 70



6.5 Outputs ............................................................................................................................... 75

6.6 Team ................................................................................................................................... 76

6.7 Timeline .............................................................................................................................. 76

References .......................................................................................................................................... 77

Annex 1: Demonstration Report of Spatial Growth Analytics Module ..................................... ix

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LIST OF TABLES

Table 1. Characteristics of Pilot Cities ............................................................................................. 5

Table 2. Scope of Activities and Timeline .................................................................................... 30

Table 3. Example of Analysis: Ratings of Indonesian Cities on Economic Performance

(2000 – 2010) .................................................................................................................................. 35

Table 4. Risks and Mitigation of Economic Competitiveness Module ...................................... 37

Table 5. Inventory of Data for Economic Competitiveness Module ......................................... 39

Table 6. Inventory of Data for Disaster and Climate Resilient Module ................................. 65

Table 7. Example Dataset: Price Range of Flats Offered by Housing Development Board

in Singapore (in Thousand SGD) .................................................................................................... 72

LIST OF FIGURES

Figure 1. Screen Capture of a QGIS Open-source Data Platform Work Environment ...... 15

Figure 2. City Planning Lab Partnership Framework ................................................................. 16

Figure 3. City Planning Lab Staffing ............................................................................................. 17

Figure 4. Example Analysis Output: Accessibility to Jobs ......................................................... 18

Figure 6. Methodology Illustration: Export Performance Tool.................................................. 35

Figure 7. Methodology Illustration: Value Chain Mapping Tool .............................................. 36

Figure 8. Methodology Illustration: Cost Structure Analysis Tool ............................................. 36

Figure 9 A,B. Example of Analysis: Sao Paulo HABISP Online Housing Information System

............................................................................................................................................................. 47

Figure 10 A,B. The InaSAFE Tool .................................................................................................... 59

Figure 11. Example Analysis: Distribution of Building Types in Singapore ........................... 71

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ACKNOWLEDGEMENT The City Planning Labs provide a concept to build capacity for an integrated, evidence-based spatial planning and investment decision making to help cities in Indonesia to achieve sustainable and inclusive economic growth. This concept was prepared through a consultative process in Indonesia which included meetings with Central and Local Government authorities and site visits to pilot cities. The City Planning Lab Concept has been prepared by a core team led by Thalyta Yuwono (EASIS) in collaboration with The City Form Lab, Singapore University of Technology and Design. Andres Sevtsuk and Reza Amindarbari from The City Form Lab were the main authors of this concept. Inputs have been provided by Chandan Deuskar (EASIN), Renata Simatupang (EASIS), Connor Spreng (EASFP), and Pranav Kumar (FCDKP), under the guidance of Taimur Samad (EASIS) and Nathan Belete (Sector Manager, EASIS). Wilmar Salim and Ari Kuncoro, consultants, provided important contribution to the preparation of this concept. The team benefited from wide range of consultation with the Government of Indonesia: Ms. Hayu Parasati (National Planning Agency/Bappenas), Mr. Basuki Hadimuljono (Ministry of Public Works), Mr. Dadang Sumantri Mochtar (Ministry of Home Affairs), Mr. Dodi Sukmayadi Wiradisastra (Geospatial Information Agency/BIG); and also with the Mayors and local agencies in Surabaya, Denpasar, Balikpapan and Palembang. The team greatly appreciates technical contribution from various stakeholders who were consulted during the preparation of this concept. Finally, the team would like to acknowledge the generous support provided by Australian Aid.

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ABBREVIATION AND ACRONYMS

Bappeda Badan Perencanaan Daerah/ Local Planning Agency

Bappenas Badan Perencanaan Nasional/ National Planning Agency

BMKG Badan Meteorologi, Klimatologi, dan Geofisika/ National Agency for

Meteorology, Climatology, and Geophysics

BNPB Badan Nasional Penanggulangan Bencana/ National Agency for Disaster

Management

BIG Badan Informasi Geospasial/ National Agency of Geospatial Information

BPBD Badan Penanggulangan Bencana Daerah/ Regional Agency for Disaster

Management

BPN Badan Pertanahan Nasional/ National Land Agency

BPS Badan Pusat Statistik/ Statistics Indonesia

C/K City (urban municipality) and Kabupaten (rural municipality)

CCA Climate Change Adaptation

CPL City Planning Labs

DRR Disaster Risk Reduction

GDP Gross Domestic Product

GIS Geographic Information System

GRDP Gross Regional Domestic Product

IDR Indonesian Rupiah

IFC International Financial Corporation

KPI Key Performance Indicators

KPPOD Komite Pemantauan Pelaksanaan Otonomi Daerah/ Regional Autonomy Watch

LMA Land Market Assessment

MOF Ministry of Finance

MoU Memorandum of Understanding

MP3EI Masterplan Percepatan dan Perluasan Pembangunan Ekonomi Indonesia/

Masterplan for Acceleration and Expnsion of Indonesian Economic Development

MPW Ministry of Public Works

MUDP Metropolitan and Urban Development Program, or P3N

NGO Non-governmental Organization

P3N Program Pembangunan Perkotaan Nasional, or MUDP

RPJMN Rencana Pembangunan Jangka Menengah Nasional/ National Midterm

Development Plan

RT Rukun Tetangga

RW Rukun Warga

SGD Singapore Dollar

SME Small and Medium Enterprises

SNDB Subnational Doing Business Report

SNG Subnational Government

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EXECUTIVE SUMMARY

The cities that emerge from Indonesia’s rapid urbanization will be key determinants of the country’s overall economic development and competitiveness, as well as their inclusiveness and environmental sustainability. However, without strategically planned investments, policy interventions, and institutional capacity, mismanaged urbanization could become an obstacle to sustainable growth. Indonesia has been no exception to the rapid urbanization experienced in many East Asian countries. With average annual urbanization rate estimated at 4.2% between 1993 and 2007, Indonesia is urbanizing faster than its Asian counterparts. This has made Indonesia one of the most urbanized countries in Asia, with an urban population share of 51% in 2011. Projections of urbanization suggest that this figure will increase to 68 % by 2025. However, Indonesia has yet to achieve the economic returns to urbanization that other countries have achieved. For every additional 1% that the country urbanizes, it achieves just 2% of additional GDP growth, whereas other countries in the region achieve 6-10% GDP growth per 1% of urbanization. Under the Metropolitan and Urban Development Program (MUDP/P3N), currently under preparation, the World Bank is engaging directly with large cities through investments in transformative infrastructure. The Bank has initiated direct engagements with local governments, targeting large and medium cities and metropolitan areas with populations over 500,000 to prepare and facilitate investments in transformative infrastructure. In addition to investment support, a key component P3N is building technical and institutional capacity in cities and metropolitan authorities, which will take the form of City Planning Labs. The City Planning Lab (CPL) is envisioned as the driver of improved integrated and evidence-based spatial, development and investment planning. The City Planning Labs core module will be initially implemented in four cities: Surabaya, Palembang, Denpasar and Balikpapan, with two additional modules in each city. In the short term, the CPL will (i) provide “just in time”, demand driven data and analysis that can feed into immediate decisions, and (ii) streamline ongoing urban management functions, such as building permitting and tax-related functions. In the medium term, it will provide cost-effective analytics to cities that can feed into planning and investment decisions, reducing the expense involved in contracting consultants during each planning cycle. In the long term, the CPL will build local technical capacity, by gathering expertise from Indonesia and international sources to work closely with local staff. Over time, external involvement will diminish as local capacity strengthens.

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The proposed activities of the CPL will be conducted in modular fashion, each pertaining to a different sector. The proposed sector modules are: A. Instituting the City Planning Lab & Spatial Growth Analytics (Core Module) B. City Economic Competitiveness C. Slum Analytics and Management Systems D. Climate and Risk Resilience Planning Systems E. Monitoring Land and Real Estate Markets

While the details of the activities will differ, they will all take a common approach, which will involve (i) data gathering; (ii) inputting new and existing data into an integrated cross-sectoral data platform; (iii) using data in ongoing urban management functions; (iv) analyzing the data; and (v) working with city leaders to help them use the insights from data analysis in planning and decision-making.

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INTRODUCTION

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1.1 BACKGROUND The cities that emerge from Indonesia’s rapid urbanization will be key determinants of the country’s overall economic development and competitiveness, as well as their inclusiveness and environmental sustainability. There is reason to be cautiously optimistic about Indonesia’s urban future. However, without strategically planned investments, policy interventions, and institutional capacity, mismanaged urbanization could become an obstacle to sustainable growth. Indonesia has been no exception to the rapid urbanization experienced in many East Asian countries. With average annual urbanization rate estimated at 4.2% between 1993 and 2007, Indonesia is urbanizing faster than its Asian counterparts, such as China (3.8%), India (3.1%) and Thailand (2.8%). This has made Indonesia one of the most urbanized countries in Asia, with an urban population share of 51% in 2011. Projections of urbanization suggest that this figure will increase to 68 % by 2025. These statistics tell a powerful story of structural transition in Indonesian society, from predominantly rural and agricultural society into more urban, manufacture and service based economy. However, Indonesia has yet to achieve the economic returns to urbanization that other countries have achieved. For every additional 1% that the country urbanizes, it achieves just 2% of additional GDP growth, whereas other countries in the region achieve 6-10% GDP growth per 1% of urbanization. Under the Metropolitan and Urban Development Program (MUDP/P3N), currently under preparation, the World Bank is engaging directly with large cities through investments in transformative infrastructure. The Bank has initiated direct engagements with local governments, targeting large and medium cities and metropolitan areas with populations over 500,000 to prepare and facilitate investments in transformative infrastructure. In addition to investment support, a key component P3N is building technical and institutional capacity in cities and metropolitan authorities, which will take the form of City Planning Labs. The City Planning Labs core module will be initially implemented in four cities: Surabaya, Palembang, Denpasar and Balikpapan, with two additional modules in each city. CITIES: Surabaya: Surabaya is the second largest city in Indonesia, and the capital of East Java Province. The city has become one of the main ports of Java, which connects the western to eastern part of Indonesia. Surabaya comprises of 31 kecamatan (sub district), with total area of 326.81 Km2. The city is the core of Gerbangkertosusila metropolitan (Gresik, Bangkalan, Mojokerto, Surabaya, Sidoarjo and Lamongan), with estimated total metro population of 9.1 million people. In 2010, the population of Surabaya was 2.76 million people, with population density of 8,462 people per Km2. The average population growth rate from 2000 to 2010 is

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0.63% annually, and average size of household is 3.6 people per household. Its unemployment rate in 2010 was 10%, which was higher than the national average. The economy of Surabaya is dominated by hotel, trade and restaurant sector (43%), followed by manufacture (22%) and transport and communication (10%). GRDP per capita in current price for 2010 was IDR 64,279,710 (USD 6,766), which was significantly higher than Indonesia’s GDP per capita (USD 2,850). The city has experience relatively constant economic growth in the last five years. In 2010, the economic growth was 7.1%, which was higher than national growth rate of 6.1%. The economy is expected to continue to grow, albeit at slightly lower rate, since Surabaya is struggling to create jobs for the existing work force and immigrants that come into the city. Palembang: Palembang is the capital of South Sumatera province. The city comprises 16 kecamatan (sub-district), with a total area of 400.61 Km2. Palembang borders Kabupaten Banyu Asin to the east, west and north, and Muara Enim to the south. The topography of Palembang is mostly flat lowlands, located at 8 meter above sea level. There are four rivers passing through the city: Musi (the largest), Komering, Ogan, and Keramasan, with total of 108 tributaries. In 2011, the population of Palembang was 1,481,814 people, with an average annual growth rate of 1.76% over the last decade. The population density is 3,698 people per Km2. The unemployment rate of Palembang in 2011 was 10%, and most of the population works in tertiary sector. The economy of Palembang is dominated by manufacture sector (43.8%), trade, hotel and restaurant sector (17%), followed by service sector (12.8%). GRDP per capita in current price for 2010 was IDR 32.6 million (USD 3,430), which is higher than the national GDP per capita (USD 2,850). The economy grew at 7.4% in 2010, and 10.8% in 2011. Oil refinery and fertilizer are the most prominent industries of the city. As with other oil related economies, Palembang is also susceptible to energy price fluctuation and was deeply affected by 2008 global economy crisis. Denpasar: Denpasar is the capital of Bali province, making it an important hub to other tourism sites in Bali island. The city comprises 4 kecamatan (sub-district), with total area of 127.98 Km2. The city had reclaimed land of 380 Ha, or 2.27% of its total area. Denpasar is bordered by Kabupaten Badung to the west and north, and Kabupaten Gianyar to the east, with Badung Strait to the south. The topography of Denpasar is mostly sloping to the south, between 0 – 75 meter above sea level. Denpasar has 10 Km of coastline, which is prone to abrasion. The city also makes an effort to maintain the 10 rivers that pass through the city through community participation in keeping the rivers clean. In 2010, the population of Denpasar was 788,589 people, with a population density of 6,171people per Km2. 31% of the population lives in Kecamatan Denpasar Selatan (South Denpasar), 29% lives in Denpasar Barat (West Denpasar), while North and East Denpasar house 22% and 15.5% of total population, respectively. The unemployment rate of Denpasar in 2011 was 6%, and 79.8% of the population works in tertiary sector.

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The economy of Denpasar is dominated by trade, hotel and restaurant sector (37.4%), followed by finance sector (14%) and transport and communication (12.8%). GRDP per capita in current price for 2010 was IDR 15.85 million (USD 1,668), which is lower than national GDP per capita (USD 2,850). The economy grew rapidly at 16.2% in 2010, and has always been growing above 13% annually over the last 4 years. As the economy relies heavily on tourism, it is susceptible to global economic downturn and security issues.

Table 1. Characteristics of Pilot Cities

City Province Area (Km2)

Population (2010)

Population Density

(people/ Km2)

GRDP per capita

(2010, USD)

Surabaya East Java 327 2,765,908 8,462 6,766

Palembang South Sumatera 401 1,481,814 3,698 3,430

Denpasar Bali 128 788,589 6,171 1,668

Balikpapan East Kalimantan 503 557,579 1,108 4,721 Source: BPS, 2011

Balikpapan: Balikpapan is the second largest city in East Kalimantan province, which gains its economic importance as the oil refinery and base operation for multinational mining service companies. The city comprises of 5 kecamatan (sub districts), with a total area of 503.3 Km2. Balikpapan is bordered by Kabupaten Kutai to the north, with Makassar Strait to the south and east side, and Kabupaten Penajam Paser Utara to the west. 85% of Balikpapan’s area is hilly, while flat planes are mostly located along the coast. Due to its topography, the land is prone to erosion. To avoid landslide, the government of Balikpapan plan to limit development to only 48% of its area, leaving 52% as green space (Spatial Plan 2012-2032). In 2010, the population of Balikpapan was 557,579 people, with a population density of 1,108 people per Km2 and average annual population growth of 2.1% in the last five years. Most of Balikpapan’s population is in the productive age group (15-64 years old), where the workforce constituted of 46.5% of population. The economy of Balikpapan is dominated by manufacture sector (51%), followed by trade, hotel and restaurant (16%) and construction (15%). GRDP per capita in current price for 2010 was IDR 44,850,051 (USD 4,721), which was significantly higher than Indonesia’s GDP per capita (USD 2,850). As a refinery and mining services city, Balikpapan’s economy is susceptible to global energy prices. Economic growth has fluctuated heavily during the last five years, with growth at 12.4% in 2008, followed by 1.7% in 2009, due to global oil crisis. The economy has bounced back, with 5.19% growth rate in 2010 and 9.7% (preliminary figure) in 2011.

1.2 RATIONALE The City Planning Lab (CPL) is envisioned as the driver of improved integrated and evidence-based spatial, development and investment planning. Local governments in Indonesia understand the importance of improved data and technical analysis for strategic, evidence-based, integrated planning and decision-making. In the

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attempt to address this need, technical assistance to cities usually takes the form of isolated studies which, while they may be helpful in the short term, often do not systematically increase cities’ technical capacity, or improve urban management on an ongoing basis. Instead, in order to make technical assistance under P3N more sustainable, it will be anchored in a dedicated facility in each partner city, called the City Planning Lab. The CPLs aim to establish technical capacity at the municipal level to provide reliable analytic support to a city’s planning, policy and infrastructure decisions, and to enable access to leading technical assistance in urban management, analytics and planning systems. The focus of the facilities will be to build up technical and institutional capacity in city planning and regulatory agencies to produce reliable and up-to-date data about the cities, well-informed plans, effective public investments, and to support the enforcement of development regulations. The facilities will operate by offering a menu of technical engagements for immediate as well as long-term projects on a demand-driven basis. CPLs will seek strong support and cooperation from the City Government with the aim of becoming technically and materially self-sustainable within two to three years. By acting as a single ‘nerve center’ or focal point for analytical work across a range of sectors, touching on spatial growth, land use, land markets, slums, economic competitiveness, and climate and risk resilience, the CPL will help to habituate city leaders to thinking about urban management in an integrated, holistic way, allowing them to meet a range of needs through select but strategic interventions. As described in detail under the ‘core’ module, the CPL will facilitate coordination through various agencies, with the Directorate General of Spatial Planning, Ministry of Public Works (MPW) at the center of the technical engagement at the national level, and Bappenas playing an important coordinating and advisory role, and donor support from the World Bank. At the local level, the CPL will have dedicated staff from various local government agencies, as well as external experts with long-term commitments to working with the Lab. It will also establish working relationships with academic and research institutions. It will conduct technical studies in modular form to respond to immediate needs, while also serving as the venue for the transfer of technical knowledge and the building of local capacity in the longer term.

1.3 OBJECTIVES In the short term, the CPL will (i) provide “just in time”, demand driven data and analysis that can feed into immediate decisions, and (ii) streamline ongoing urban management functions, such as building permitting and tax-related functions. In the medium term, it will provide cost-effective analytics to cities that can feed into planning and investment decisions, reducing the expense involved in contracting consultants during each planning cycle. In the long term, the CPL will build local technical capacity, by gathering expertise from Indonesia and international sources to work closely with local staff. Over time, external involvement will diminish as local capacity strengthens.

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MPW will facilitate an ongoing objective alongside those mentioned above will be to work with MPW to demonstrate and disseminate the value of this approach more broadly to local governments throughout the country.

1.4 SCOPE OF ACTIVITIES The proposed activities of the CPL will be conducted in modular fashion, each pertaining to a different sector. The proposed sector modules are: F. Instituting the City Planning Lab & Spatial Growth Analytics (core module) G. City Economic Competitiveness H. Slum Analytics and Management Systems I. Climate and Risk Resilience Planning Systems J. Monitoring Land and Real Estate Markets

While the details of the activities will differ, they will all take a common approach, which will involve (i) data gathering; (ii) inputting new and existing data into an integrated cross-sectoral data platform; (iii) using data in ongoing urban management functions; (iv) analyzing the data; and (v) working with city leaders to help them use the insights from data analysis in planning and decision-making. In addition, the Ministry of Public Works will lead an overarching component involving three key activities: (i) preparation of guidelines for establishing CPL, (ii) a capacity building program beyond the core cities, and (iii) dissemination activities. The detailed outputs, budget, timeline and potential risks for each module are discussed separately. A summary is provided below: A. City Planning Labs & Spatial Growth Analytics (core module)

Four cities (Surabaya, Palembang, Denpasar, and Balikpapan)

Major outputs:

Geospatial database

Support to detail planning process

Pilot of a new permitting decision support platform.

Report on spatial accessibility of urban services

Report on urban expansion trends, 2000-2010

Report on land value impacts of infrastructure

Report on infrastructure demands over 10 years

B. City Economic Competitiveness Two cities

Major outputs:

City economic competitiveness review

City economic planning and decision support capacity building

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2-4 Workshops for public-private dialogues

City economic competitiveness dashboard C. Slum Analytics and Management Systems

Two cities

Major outputs:

Slum Information Database, incorporating all collected data

Survey materials

Report outlining slum management strategies

Planning of pilot implementing programs for selected sites

Report outlining (a) the process of slum formation, as observed through case studies; and (b) recommendations for strategies for preventing slum growth in specified areas

D. Climate and Risk Resilience Planning Systems Two cities

Major outputs:

Data inputs on disaster risk into city’s geospatial database

Customization of the InaSAFE software tool based on user needs

Report outlining the drivers of disaster and climate risk to core sectors and areas/neighborhoods, with risk-sensitive micro zoning maps, and recommendations for resilient land use and infrastructure investment planning

E. Monitoring Land and Real Estate Markets

Two Cities Major outputs:

Cadastral real-estate database, showing each land parcel with its associated buildings, occupants’ demographics, accessibility characteristics and valuation estimates.

Land and property market assessment report

Housing segmentation study report

Impact analysis report, documenting the observed real estate value impacts of selected infrastructure investment projects

Real Estate Financing Analysis

Hedonic pricing analysis, explaining variations in land and real estate values based on the spatial attributes and accessibility

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SECTOR MODULE A: CITY PLANNING LABS AND SPATIAL GROWTH

ANALYTICS

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2.1 BACKGROUND 2.1.1 CONTEXT As Indonesia urbanizes, the forms of its metropolitan areas will have profound and long-lasting socio-economic and environmental consequences. Present urban expansion can, on the one hand, foster economic growth, offer better opportunities to citizens and improve regional and international connectivity. On the other hand, rapid urban expansion also brings about important challenges, such poor integration of complementary land uses, exhaustion of urban resources and social inequality. In order to overcome such challenges and harness the opportunities, Indonesian cities need a capacity to analyze the current growth trends, understand their underlying forces and forecast their future consequences. At present, a number of medium and large-scale cities in Indonesia, where a large share of urban growth is occurring, lack the analytic capacity to examine how and much they are growing, what factors drive the growth and change, where and what types of public infrastructure investments are needed, how well past policies and investments have performed, and how future plans can be informed by current development trends. This leads to uncoordinated planning and enforcement efforts, inefficient use of scarce resources, and poor returns on infrastructure investments. The lack of basic urban information systems impedes the necessary information sharing across different city departments, making decision coordination and planning enforcement difficult to achieve. Without reliable information and analytics, scarce public resources cannot be effectively allocated and policies cannot be effectively designed nor enforced to address key urbanization issues. In order to support efficient, sustainable and equitable urban growth in the next decade, it is critical for Indonesia’s cities to invest into new information, analytic and regulatory systems of urban planning and development. 2.1.2 IMPLEMENTING P3N TECHNICAL ASSISTANCE THROUGH CITY PLANNING LABS As part of the Metropolitan and Urban Development Program (P3N), the World Bank aims to support the Government of Indonesia in establishing City Planning Labs (CPLs) in medium and large-scale cities, starting with four pilot cities in 2013 – Denpasar, Palembang, Surabaya and Balikpapan1. The CPLs will house a number of planning support activities for a wide range of urban problems that are divided into several modules. The central focus of the labs is to provide reliable Urban Spatial Growth Analytics and to upgrade information management for Regulatory Enforcement Systems. The focus of the Spatial Growth Analytics Module will be on analysis that provides a clear practical benefit to cities, which can serve as inputs into decision-making around policies and investments, and which can eventually be carried on by the cities independently. Provisioning the right amount of land and utility systems for future housing needs, for instance, can reduce the development of slums and save costly

1 Denpasar (metro population 1.8 million), Palembang (metro population 1.6 million), Balikpapan

(population 0.6 million) and Surabaya (metro population 5.6 million) have been selected due to their their

existing planning efforts, their fair results in coordinating planning efforts with the central government, and

their keen interest in the initiative.

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legal land readjustments later. Positioning key infrastructure, such as new roads, in places that generate the greatest and the most equitable benefits to landowners, can lead to a rise in land values and rental incomes that greatly exceed the initial investment. Paralleling analytic work, the technical facility will also assist city planning enforcement agencies to transition into a transparent, electronic permitting and enforcement workflows. A great deal of planning and development regulation today is paper based and fragmented between different approval processes, making it difficult to have a holistic overview of developments that are being approved. A number of spatial planning agencies have voiced that the present enforcement system also fails to integrate critical information between enforcement and planning groups in local governments, hampering their capacity to carry out approved plans. These two activities are described as part of the core CPL concept note below. Additional CPL activity modules are described in separate concept notes as follows: A) Land and Real Estate Market Monitoring Module; B) City Economic Competitiveness Analytics Module; C) Slum Analytics and Management Systems Module; D) Climate and Risk Resilience Planning Systems Module. Human resources, technical infrastructure and data management systems will be hosted by a single CPL facility in each city and shared by the activities of all analytic modules. Section three of this note describes three related steps of the proposed CPL implementation process:

i. Developing City Planning Labs as institutionalized municipal platforms for spatial analysis, integrated and evidence-based spatial development and investment planning.

ii. Implementing core urban spatial growth analytics (as well as other analytic modules described in separate concept notes) and using the outputs in planning activities.

iii. Establishing an effective data exchange system between spatial planning and enforcement agencies for an improved and automated planning enforcement framework for core urban land use and construction permitting functions.

2.2 OBJECTIVES

The primary objective of setting up the support facilities at municipal governments is to establish technical capacity to measure, analyze and respond to urban development pressures in an evidence-based and timely manner. By supporting evidence-based decision making, capacity building in urban analytics and more seamless information sharing across city departments, we expect the CPLs to lead to substantial cost savings in spatial management and enforcement, plans that are aligned with the city’s aspirations, more effective enforcement of planning goals, as well as greater multiplier effects on infrastructure investments in the medium and long run. The initial core activity of the facility is urban growth analysis, the objectives of which include to:

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Project future growth, based on existing trends, and forecast the future demand for land uses and amenities.

Help integrate projected demographic and economic changes into Masterplans and Detail Plans.

Communicate information relating to future spatial plans over web-based maps to other related agencies to strengthen regulatory enforcement.

Foresee infrastructure requirements from current trends and help avoid supply shortages by proposing possible planning responses.

Conduct spatial cost-benefit analyses of public investment decisions.

Evaluate the social and environmental impacts of public investments.

Assess the equality of public investment distribution across all demographic and income groups.

Provide accurate and reliable geospatial data to private sector developers and individual stakeholders.

The upgrading of regulatory enforcement systems module of the facility aims to improve information sharing and information capture between planning and regulating arms of the local government in order to develop a more effective and transparent decision chain for carrying out the city’s planning intentions. The objectives of the proposed regulatory technical assistance are to:

Analyze the present paper-based regulatory processes for building permits and change-of-use permits in local spatial planning offices.

Develop a comprehensive action plan to upgrade the present permitting system to computerized databases that allow permitting officers to instantly access approved planning information about parcels under question via a simple web interface.

Implement a pilot data capture system for building permits and change-of-use permits that will record each approved permit in a database and automatically update the city’s GIS parcel and building map layers with accurate information.

Display Masterplan and Detailed plan information to landowners publicly over a web-based map server, without requiring personal consultations to find out the allowable buildable volumes on site.

Evaluate the effectiveness of the above pilot schemes with respect to more effective regulation and adjust the system implementation accordingly.

The CPLs will additionally offer the World Bank and other donor organizations a valuable platform for predicting and tracking the impacts of transformative infrastructure investments in Indonesian cities.

2.3 SCOPE OF ACTIVITIES

Addressing the goals and challenges discussed above, the three steps to implement the CPL activities outlined in this note are:

i. Establishing City Planning Labs: This involves providing assistance to the city on

institutional setup, data collection, software and hardware and human capacity.

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ii. Implementing Spatial Growth and Change Analytics: This involves developing the preliminary analytical work on spatial growth monitoring, and proposing activities for future phases, to be conducted by the City Planning Lab with external assistance.

iii. Improving Planning Enforcement Systems: This involves assisting the cities’ spatial regulatory agencies to implement computerized information and permitting systems that are synchronized with spatial information with other city agencies.

2.3.1 ESTABLISHING THE CITY PLANNING LAB

2.3.1.1 Software and Data Platform To fulfill their primary goal of assembling, maintaining and distributing large geospatial databases, the City Planning Labs need a data platform that satisfies four fundamental requirements. The platform should:

Allow the data to be stored and management in a well-organized way

Allow the data to be shared across different departments or with members of the public over internet browsers

Enable all data management operations to be performed from a local networked computer

Enable the end-users to interact with the datasets, by querying their attributes, overlaying different data layers, using simple base-maps to situate the information, and sharing personal information layers on published maps.

The capacity to operate basic spatial functions (e.g. spatial search, measurement or proximity search, attribute table joining etc.), would be desirable additional functions for the end users, though not a first-order priority. Combined, these basic requirements necessitate setting up a GIS map server platform. There is a considerable list of open source and proprietary GIS server technologies. Proprietary technologies include ArcGIS Server, ArcGIS Online and MapInfo Spatial Server, while open source options include GeoServer, GeoNode, and PostGIS. The World Bank’s Platform for Urban Management and Analysis (PUMA), currently under development, is also a potential open source option for the City Planning Labs. Based on the vital and desired functionalities, cost and budget limits, and the platform’s flexibility for scaling up, a few options will be introduced to the Lab. While setting up the data platform should be tackled at the outset of the lab, its maintenance and potential expansion – given the envisioned collaboration with a larger number of government departments – will continue throughout later phases. It is possible, for instance, to start off with a proprietary off-the-shelf system that requires little setup time, such as ArcGIS Online, while the staff are technically trained to set up a more long-term open-source system. Apart from the platform for geographic data, general software (e.g. text editors) and operating system, the lab requires two types of desktop software tools for assembling data and conducting analysis:

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Spreadsheet software with basic statistical analysis capabilities (e.g. Microsoft Office Excel, Access; Open Office Calc, Base)

GIS desktop software (e.g. ArcGIS, MapInfo, QGIS) These desktop tools are available both as proprietary and open source, with different functionality. The potential options will be introduced to the lab, based on the required capacities.

Figure 1. Screen Capture of a QGIS Open-source Data Platform Work Environment

2.3.1.2 Institutional Arrangements Organizational location: A few different options are available in terms of situating the City Planning Lab within the existing local government. The exact institutional setup would be tailored to the preferences of the local governments. An effective institutional model would be to have the Lab located within Bappeda, who would provide the physical space and some of the basic investments in setting up the Lab. It is recommended that both

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Bappeda, as well as the Department of Spatial Planning, would provide two full time staff members to work as part of the Lab team. In order to ensure coordination across agencies, it is recommended that the Lab be advised by an Advisory Committee convened by the Mayor, with members from Bappeda, Spatial Planning, Public Works, Revenue, BPS, BPN and other planning related agencies or city departments. The committee may also include representatives from neighboring jurisdictions or regional governments, in order to ensure coordination across the whole metropolitan area. In addition to coordinating between city agencies and departments, the advisory committee will liaise between CPL and the Ministry of Public Works in order to inform the national level spatial planning by local analysis, data and plans. CPL in each pilot city will also assist the local governments by informing their planning enforcement systems of national plans. This committee would likely meet once every month or two in order to set the strategic direction for the work of the Lab. It is not recommended that the advisory committee intervene with the daily operations of the lab, which could be done more efficiently by the CPL staff.

Figure 2. City Planning Lab Partnership Framework

Partnerships: The Lab would establish institutional partnerships with external entities in order to facilitate knowledge exchange. For example, there may be MoUs signed with Indonesian universities to foster collaborative projects between students and the Lab, internships or part-time positions for students who may work at the Lab for short periods, or research projects conducted by universities that complement Lab activities. MoUs may also be signed with agencies at other levels of government, including data sharing agreements with BPS or BPN. In addition, consultants would be hired to work closely on specific analytical areas on a project basis. During the first two years of the implementation phase, outside consultants and partner organizations will be required to collaborate closely and transfer knowledge and skills to the CPLs. The World Bank team would play an ongoing advisory role, which would phase out over time. In this way the Lab would gradually become technically proficient and self-sufficient to support all

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necessary spatial analytic support function for the cities, and form the means by which the city interacts with key partners in urban planning and management.

Figure 3. City Planning Lab Staffing

Staffing: In the first phase, the Lab is recommended to have 6-7 full time staff. This would include the following:

i. Director: responsible for managing the daily activities of the Lab. Ideally the Director would be an individual with a Master’s or higher degree in urban planning or a related field, and approximately ten years of experience in urban planning in Indonesia, who is familiar both with the kinds of analytical tools and approaches that the Lab will use, as well as with the functioning of local governments in Indonesia, and has experience in starting up new institutions or ventures. This individual would most likely be hired from outside the government.

ii. Representative of Bappeda: responsible for coordinating with Bappeda’s spatial planning activities. This would be someone with at least 3 years of experience in the local government, who is familiar with the operating procedures of Bappeda. He or she would be assigned to work with the Lab full time.

iii. Representative of Department of Spatial Planning: responsible for coordinating with Department of Spatial Planning activities. This would be someone with at least 3 years of experience in the local government, who is familiar with the operating procedures of the department. He or she would be assigned to work with the Lab full time.

iv. 2-3 technical staff: responsible for data gathering, managing databases, and using software tools to perform the analysis. These individuals would need to be highly proficient in ArcGIS and AutoCAD. They should have some background in urban planning, policy, geography, architecture or other relevant field. At least one of these should have experience in setting up and managing data servers. These individuals would most likely be hired from outside the government.

v. An administrative assistant. These individuals would be involved in the functioning of the Lab full time from its establishment onwards. In subsequent phases, more staff may be added as necessary.

City Planning Lab

Director

Technical Staff

(GIS, databases, web)

Civil Servants

(Bappeda, Spatial Planning)

Admin. Assistant

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Equipment and Space: In its first phase, it is recommended that the Lab be situated in a space of approximately 40 sq. m., with a desk and a computer station for each full-time staff as well as one additional work station for visiting consultants, and a small meeting area. The equipment necessary would include a computer for each work station, a laser color printer / scanner, a 36-in color plotter, a large-format scanner, and a 46-in flat-screen display for presentations.

Figure 4. Example Analysis Output: Accessibility to Jobs

Source: City Form Lab Note: Accessibility to jobs within a 10 minutes walking range from each building in Cambridge and Sommervile, MA, USA

2.3.2 SPATIAL GROWTH AND CHANGE ANALYTICS

2.3.2.1 Analytics A core objective of the SP Module is to provide spatial analyses and evidence-based decision support to different city agencies and outside constituencies. The CPL will play an important role here. The spatial information gathered and analyzed by the CPL should enable the city to keep track of the growth and changes in its overall development, to monitor its land and real-estate markets, and to forecast and monitor the impacts of its planning interventions. The analytics performed by the CPL will be used as a basis for the city’s Masterplanning and detailed planning efforts, for setting the priorities and predicting the impacts of public financing and infrastructure investments, and for making reliable spatial information available to various planning and enforcement decision

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makers (i.e. building permitting office) on a continuous basis. The Bank’s staff and outside technical experts will work closely with the local CPL teams over the first two years to transfer technical knowledge and to build up the skills needed to perform the information management and analytics autonomously. For the first year of operations, the CPL aims to achieve the following analytic outcomes: Phase 1 (Months 1 to 6):

- Creating interactive geospatial databases: Existing spatial datasets are uploaded to an online map server for interactive viewing by different city departments. The interactive viewing should be web browser-based, not require any additional software from end users. This will allow stakeholders to overlay different spatial data (e.g. current built-out areas and the existing Masterplan) and to query simple attributes about the map elements by clicking on them (e.g. click on a parcel to see its area, ID, etc.). The exact list of existing datasets to be uploaded will be decided together with the city planning agencies based on availability (e.g. high-resolution satellite image, street centerlines, building footprints, parcels, schools, hospitals etc.).

- Urban growth analysis: The growth of the metropolitan area and its corresponding population from year 2000 to 2010 will be obtained (from the World Bank’s ongoing East Asia and Pacific Urban Flagship activity) and used to analyze the spatial extent and rate of the city’s growth in the past decade. The previous decade’s expansion areas will be overlaid with current building and land-use data in order to analyze how much land was consumed by different land-use categories. This analysis, combined with regional economic and demographic forecasts, will subsequently be used as a reference to develop likely estimates for growth in the current decade, from 2010 to 2020.

Phase 2 (Months 7 to 12):

- Accessibility analysis: Existing spatial information on public facilities and resources (e.g. drinking water sources; drainage points; schools; hospitals; markets; transit stops) will be used to estimate accessibility to these resources in different parts of the city. This analysis should illustrate underserved areas and provide an empirical basis for future public investments.

- Support to planning: As the planning agencies (Bappeda) of the participating cities engage in developing detailed plans (1:5,000 scale) from their current Masterplans (1:25,000 scale), the CPL will help develop the supporting spatial analysis required to achieve the goals of detailed plans. Palembang planners indicated that they need to develop 16 detailed plans for the different parts of the city, indicating the allowable land-uses, building heights, building coverage, infrastructure changes and buildable areas in different parts of the city. CPL analyses will help choose the areas in need for public investments (i.e. new roads, transit stops, schools, flood protection, etc.); for determining the likely economic growth poles in the city; and for forecasting the needs for different land-uses at the detailed plan scale during the next five years. The planning agency (Bappeda) can integrate these inputs to detailed plans and associated legal development regulations.

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- Impact analysis: CPL will additionally develop impact analyses for ongoing public investment projects, such as choosing the exact location for the second bridge in Palembang, for locating sanitation and water facilities in Denpasar, collaborating with Public Works in choosing the placement of a new toll road, etc. on a per need basis.

Phase 3 (Months 13 to 18):

- Projections: More accurate and up-to-date spatial data will allow the CPL to start developing more accurate forecasts for near-term and long-term projections on land use requirements, housing needs, transportation demand, infrastructure needs etc. Analytics outlining such needs will help the cities prepare for potential problems (i.e. housing shortages, congestion) before they occur in the future. CPL staff will a long term (20 year) forecast for the city’s growth and start analyzing planning and policy responses needed to accommodate the projected growth.

2.3.2.2 Data Spatial and development plans cannot achieve their envisioned goals without accurate projections of supply and demand for housing, infrastructure and services, and forecasts for broader socio-economic and environmental situations to which planners must respond. Private sector developers and individuals can also make better decisions and contribute to the progress of the city if they have access to accurate data on how the city is growing and changing, and potential risks and bottlenecks. One of the primary objectives of the City Planning Lab in the four pilot cities is piecing together a comprehensive geospatial database from both the existing data and new data sources. A large body of data currently exists in local and national agencies; however, the absence of a well-structured collaborative information system has obstructed the flow of appropriate information among the government departments and the public. A considerable amount of data has not yet been digitized, prohibiting the data from being shared or used for computer-based analysis. The City Planning Lab aims to fill this gap by assembling existing data through a close collaboration with municipal agencies, and initiating collection mechanisms for new datasets. The Lab will develop an online platform to which government departments can contribute data they collect or record. The contributors, in return, will have access to more comprehensive and linked datasets, benefiting their own operations.

Apart from continuous updating of the database, another important task for database maintenance is the verification of the accuracy of data. Accuracy verification will be a continuous task for CPL staff. The first phase of data collection will involve identifying existing datasets, obtaining data from multiple departments, and integrating the data to standardized formats. This process will involve a significant digitization effort – e.g. generating GIS maps with useful attribute tables from the current paper maps showing allowable building regulations. Some early data collection activities will require external support. In the second phase, the Lab will start to build databases by joining different datasets together – e.g. adding land

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values, land uses and establishment locations to the building dataset. The third phase will be mostly dedicated to field surveys for filling in the missing data and collecting new data. The use of government accounting and registries (e.g. data recorded for permitting or land and real estate transaction taxation) constitute an important future source of reliable and up-to-date data. Piloting the collection of such data is discussed further below.

2.3.2.3 Communicating Planning Goals Beyond performing analytic work to support ongoing planning, development and regulation work in the city, the CPL also aims to gather, document and visualize the planning goals that form the basis of Masterplans, Detail Plans and other spatial development initiatives. CPL’s analytic work is impactful only if it is well aligned with the city’s goals and initiatives. Yet such goals are often unclear and dispersed among multiple agencies. CPL could provide a venue that collects and visualizes the different initiatives and goals graphically in order to help disseminate the ideas across departments and to the general public. This can be done through web-maps, info-graphics and printed publications that are shared across the city’s departments. 2.3.3 PLANNING ENFORCEMENT

2.3.3.1 Restructuring the Planning Enforcement Procedures: Any government accounting and registry procedure naturally leaves a trace of data behind, which could be effectively used if a proper structure for the flow of data is developed. The structure of the existing planning enforcement systems in Indonesian cities, however, does not allow for the effective and efficient utilization of these registry and accounting records. Planning enforcement procedures are still paper-based and the lack of a standardized national addressing system makes it difficult to integrate them with other spatial databases. This concept note proposes restructuring three planning enforcement procedures – building permitting, change-of-use permitting and the communication of zoning regulations – as a pilot initiative in the four cities. CPLs will develop a detailed assessment of the current enforcement mechanisms at the local spatial planning agencies and propose comprehensive improvements to digitize and streamline development-permitting processes. CPLs will carry out a pilot implementation of a data capture system in building permitting and change-of-use application procedures that will demonstrate an integrated information flow for keeping a city’s geospatial building and land-use data up to date. Building and Change-of-Use Permitting (Phases 1 and 2):

Building and change-of-use permits are potentially the best source of data for keeping a city’s spatial database up to date, as such permits capture changes in all legal development activities. In order to actively harness these data, the permit issuance procedures in Indonesian cities need to be restructured.

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During the first two phases, the CPL will perform a detailed assessment of the present permitting procedures in the Department of Spatial Planning and develop a plan for updating the processes to digital standards. The new procedures will allow each permit to be recorded in a digital database, which can be referenced via parcel and address indicators and geographic coordinates to other existing databases (e.g. parcel, building and business location databases). This is expected to produce two important benefits. First, linking permitting with existing geospatial data will allow permitting officers to instantaneously retrieve approved planning information about the permit sites under question, eliminating an information gap between planning goals and enforcement. Second, a continuous updating of building and use data based on permitting procedures will also significantly lower the on-ground or aerial surveys required in the future for data updating. Phase 1 (Months 1 to 6) deliverables:

- Assessment of new building and change-of-use permitting. CPL will document and evaluate the current procedures for issuing new building permits and change-of-use permits at the local spatial planning agencies, producing a report of the current workflows and potential opportunities for improvement. The report will also outline the success rate of the current planning enforcement system, overlaying legal spatial plans with issued permits on the ground.

Phase 2 (Months 7 to 12) deliverables:

- Recommendations and activity plan for a new permitting decision support platform. CPL will produce a report outlining recommendations for a new, digital permitting decision support platform that will allow permitting and enforcement agents to seamlessly access cross-linked information about planning regulations for parcels, buildings, zones in the city. The report will also outline a proposal for making general planning and zoning regulations accessible to land-owners and developers via an online portal.

Zoning Regulations and Spatial Plans (Phase 3):

Zoning regulations and spatial plans are currently not fully shared with the public, which has imposed an unnecessary work load on the Department of Spatial Planning, who communicates this information on a case-by-case basis to interested property owners. Prior to applying for any building permit, property owners are required to submit an inquiry about allowable coverage, height, use, and setbacks for each property. A planning officer retrieves this information from paper-based documents and communicates back to the requestor in written form. Such zoning information, which is publicly available in most developed countries, can also be made publicly available in Indonesia. Integrating Registry and Accounting Records into the cities Spatial Database (Phase 3)

As discussed above, one of the primary objectives of The City Planning Labs is to piece together and maintain a comprehensive geospatial database. In addition to readily available data, additional spatial information harnessed from the government accounting and registry documents offer important potential sources for expanding the datasets and

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keeping them up-to-date. Building permits, for instance, can be used for updating the building dataset in real time and in the most accurate and cost-efficient way. This requires proper digital and spatially referenced registries that could be linked with the CPL databases. Transaction data from local tax services or notary offices could allow land and real estate value datasets to be updated each time a transaction is made. Such mechanisms are common in developed countries’ planning systems. The City Planning Labs will not only be collectors of data, but they will also provide participating government departments (local and national) with integrated and updated geospatial databases, built upon the data provided by individual agencies themselves. Bappeda, for instance, will benefit significantly from registry and permit data from the Department of Spatial Planning (Dinas Tata Kota), which can be used for preparing the detail plan of sub-districts. As the permit information is currently not transferred to Bappeda in a ready-to-use manner (it is not digital nor spatially referenced), Bappeda instead uses open-source satellite images to update its building datasets, leading to outdated and inaccurate information.

Phase 3 (Months 12 to 18) deliverables:

- Planning enforcement portal. By the end the third phase the CPL, in collaboration with Bappeda and Department of Spatial Planning, will prepare and publish currently available maps of zoning regulations and spatial plans to the general public on designated websites. Since the information is legal and explicitly stated, this upgrade is expected to relieve an unnecessary burden of private consultancies.

- Pilot program for permitting decision support platform. CPL will implement a pilot program for permitting decision support that will test an integrated digital workflow for permitting officers. The platform should allow permitting officers to instantaneously retrieve approved planning information about the permit sites under question, providing a more integrated planning and reinforcement workflow. Each issued permit should automatically update building and land-use data in the city’s building and parcel databases. This pilot program will be implemented on two permitting procedures in each city: new building permits and change-of-use permits. The pilot program seeks to understand the existing data flows, and the required procedures for integrating and maintaining a real-time database between different city departments. After evaluating the first phase pilots, CPL aims to scale such efforts up in the second phase.

2.3.3.2 Digitizing Historic Data While keeping track of the registry and accounting records offers an up-to-date capture of the existing condition of a city, it is not sufficient for understanding the current trends and forecasting their future changes. This requires several datasets of spatial conditions over time. These snapshots can be collected gradually over time. The existing planning enforcement systems have been collecting valuable data, although many of them are not digital or in an appropriate format for analytical purposes. During the first and second phases of the project, the City Planning Lab will collaborate with municipal government departments and

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agencies to first map available historic data, and to then digitize and integrate these historic data with current conditions.

2.3.3.3 Planning Tools The availability of close partnerships with outside institutions (e.g. CPLs in other cities, the World Bank, outside consultants) also offers a unique opportunity to collect and document information about planning tools and implementation mechanisms in other successful cases. Such tools may include zoning regulations, incentive systems, building guidelines etc., which could potentially be implemented in the city as part of planning initiatives. CPL can help disseminate knowledge about such tools to different stakeholders in online and print publications. Keeping an up-to-date overview of planning goals and their related implementation tools will help CPL ensure that the analytics performed are aligned with the city’s needs.

2.4 RISKS Potential risks include the following:

i. Difficulty in transferring skills in a sustainable manner: Local governments in Indonesia

often lack the technical expertise necessary to perform the kinds of analytical work proposed for the Lab. For this reason, much of the work in the early phases will be done by external consultants. There is a risk that knowledge will not be sufficiently transferred to the local government counterparts involved in the Lab. In order to address this risk, the Lab will involve local officials as key team members from the beginning, and will be overseen by the mayor or a local government agency. Any external consultants will be required to work closely with the local officials in the Lab. Every technical assistance activity will have the dual objective of producing the analytical output itself while simultaneously training local staff to perform such analysis. This will ensure sustainability of skills in the Lab.

ii. Lack of coordination with other agencies: There is a risk that while the local staff directly involved in the Lab will adopt new analytical approaches, the overall urban planning and management systems will carry on with business as usual. This risk will be most effectively mitigated if there is a high-level champion for the Lab, ideally the mayor, the head of Bappeda, or a board consisting of heads of various departments (see section on institutional arrangements), to ensure the proliferation of analytical approaches and operating procedures developed in Lab throughout the rest of the government.

2.5 OUTPUTS PHASE 1 At the end of Phase 1, the Lab should have a physical space, with hardware and software equipment with full-time staff set up. The outputs of the phase 1 analytical activities will be as follows:

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- An interactive online geospatial database, featuring datasets that are already available for the lab, will be ready for use for different city departments on web-browsers.

- A short report will outline the 2000-2010 urban expansion increase in the given city and the likely growth scenario for the current decade based on urban extent and population data from World Bank’s ongoing East Asia and Pacific Urban Flagship activity.

- A report assessing the current procedures for issuing new building permits and change-of-use permits at the local spatial planning agencies, describing the current workflows and potential opportunities for improvement.

PHASE 2

- A spatial accessibility databases will be analyzed at the individual building level, illustrating how easily households in different parts of the city can access critical urban resources – drinking water sources; drainage points; schools; hospitals; markets; transit stops. The results will be described in a short report and in graphic material (e.g. paper-based and online maps) that can be shared with various city departments.

- Based on collaboration with the detailed planning team in the respective city, CPL staff will support the development of the detailed plans with spatial analyses. CPL analyses can help choose the areas in need for public investments (i.e. new roads, transit stops, schools, flood protection, etc.); for determining the likely economic growth poles in the city; and for forecasting the needs for different land-uses at the detailed plan scale during the next five years. These analyses will be determined on a per-need basis and documented in written and online reports with supporting geospatial evidence.

- CPL will produce a report and supporting geospatial data, outlining the likely land-value impacts of ongoing public investment projects, such as the addition of a second bridge in Palembang, for locating sanitation and water facilities in Denpasar, choosing the placement of a new toll road, etc.

- Recommendations and activity plan for a new permitting decision support platform. CPL will produce a report outlining recommendations for a new, digital permitting decision support platform that will allow permitting and enforcement agents to seamlessly access cross-linked information about planning regulations for parcels, buildings, zones in the city.

PHASE 3

- CPL will produce a report, which analyzes the directions and magnitudes of the effects that different land improvement strategies have on land and real-estate values in the respective cities. The report, based on hedonic price models, will indicate how access to critical infrastructure (roads, water, transit) and land-use linkages (commerce, jobs, parks) affect land prices and real estate sales.

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- Forecasts will be prepared in the form of a written report and supporting graphic material to describe land use requirements, housing needs, transportation demand and infrastructure needs for the next 10 years in the city.

- CPL will compose a report and hold a workshop with various city planning related departments to describe the results of the digital data integration and capture pilot program through planning enforcement mechanisms. The report will outline the successes and shortcoming of the pilot program and make concrete recommendations for the expansions and automation of the data capture system in the future.

- CPL, in collaboration with Bappeda and Department of Spatial Planning, will prepare and publish currently available maps of zoning regulations and spatial plans to the general public on designated websites.

- CPL will implement a pilot program for permitting decision support that will test an integrated digital workflow for permitting officers. The platform should allow permitting officers to instantaneously retrieve approved planning information about the permit sites under question, providing a more integrated planning and reinforcement workflow. Each issued permit should automatically update building and land-use data in the city’s building and parcel databases. This pilot program will be implemented on two permitting procedures in each city: new building permits and change-of-use permits.

2.6 TEAM AND TIMELINE This module will be carried out in three phases of six months each. In addition to full time staff members listed above, additional expertise required for providing consultation to the spatial growth analytics module will include:

i. IT/GIS Server Specialist ii. Urban Economist iii. Urban and Regional Planner

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SECTOR MODULE B: CITY ECONOMIC COMPETITIVENESS

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3.1 BACKGROUND Indonesia’s cities remain a challenge – and a major opportunity. The Indonesian economy has performed strongly over the past decade. The country has also been rapidly urbanizing, but has been unable to fully capture the productivity benefits from agglomeration. Improved capabilities at the municipal/urban level are the key to unlocking the potential for improved economic competitiveness of Indonesian cities, since i. The size and diversity of Indonesia calls for customized strategies for sustained

economic growth. This is true particularly in the current political environment where risk aversion among decision makers at the national level ahead of the 2014 elections means that reforms at the city-level have a greater chance for success.

ii. Cities can influence the business environment considerably, thanks to decentralization and partial devolution of regulatory authority. According to the Subnational DB study (2012), obtaining a construction permit in the city of Bandung, for example, takes on average 44 days, while in the capital Jakarta, less than 150 Km away, the same procedure takes on average 158 days, more than 3 times as long. To start a business in the city of Palangkaraya, 27 days are needed for the official procedures, while the same steps in Jakarta take almost twice as long, 45 days. These variations indicate that cities have the ability to improve the regulatory environment independent of reforms (or lack thereof) at the national level.

iii. City land use and infrastructure planning can directly and significantly impact the cost of doing business especially rental cost, ease and cost of brownfield expansion of businesses and access to last mile infrastructure (road, power, broadband, etc)

Examples of notable success (and failure) of international cities can provide guidance as to what can be done to enhance city competitiveness and make best use of comparative advantages. Locally appropriate policies are needed to provide the simple, transparent, and supportive operating environment that businesses need to succeed and grow.

3.2 OBJECTIVES The objective of the City Economic Competitiveness Module of the Metropolitan and Urban Development Program (P3N) is to enable client cities to actively guide and foster their municipal and regional economic development through superior planning and decision making and deep consultation with private sector stakeholders. Success indicators include the number and quality of jobs available in the city (and surrounding areas, as applicable), the improvement of productivity in targeted sectors, and the inclusiveness of economic growth (target measure TBD) within the city. The module’s components will be achieved by helping cities (i) get smarter about understanding trends in their regional economy, and the impacts that public policy, investment and planning decisions have on their economic prospects; and (ii) work with the private sector to leverage this newfound understanding to initiate a series of local reforms/policies/investments. Activities will focus on building institutional capacity to

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significantly strengthen the targeted cities’ competitiveness planning and analysis capability, as part of the City Planning Labs (CPL), as well as on market sensing and working closely with the private sector to ensure that the cities’ efforts on planning and analysis are well targeted. Since the achievement of this component’s objectives ultimately depends on the private sector’s response to the cities’ improved planning, the involvement of and dialogue with the private sector from the start is critical.

3.3 SCOPE OF ACTIVITIES In keeping with the other CPL modules, the activities in this module will be conducted in three phases of six months each. The module comprises four components, which will span all three phases, though at varying intensity. The module is designed on the basis on World Bank’s past experience with such programs, latest literature on the subject, and our own initial assessment of the cities. An overview of the key products that will be produced under the four components is in the table below.

i. City Economic Competitiveness Review: city-level economic review and comparison

across cities, sectoral studies and in-depth analysis.

ii. Capacity building: building city economic planning and decision support capacity at a local level

iii. Consultative workshops and Public-Private Dialogue (PPD): 2-4 workshops for public-private dialogues; from early consultations to institutionalized, regular dialogue and fine-tuning of policy initiatives.

iv. City initiatives and dashboard: from considering external case studies, to finalizing decisions on policy initiatives and implementation, including joint action plan with private sector and dashboard to communicate initiatives and monitor progress.

Table 2. Scope of Activities and Timeline

Phase 1: 0-6 months

Phase 2: 7-12 months

Phase 3: 12-18 months

City economic competitiveness review

City Economic Competitiveness Review of pilot cities including comparative analysis

Sectoral studies, possibly based on additional data

In-depth analysis

Capacity building

Integrating city economic data with the core spatial planning platform; map economic analysis to core spatial data platform

Building analytical capacity at CPL; map economic analysis to decisions at city level

Transferring lead of activities to CPL

Consultative workshops / PPD

Early consultations with private sector; formulating top hypotheses on constraints and policy levers

Decision on key sectors and policy options

Institutionalizing dialogue; decision on action plan for policy initiative

City initiatives and dashboard

External case studies Policy options Policy finalization and decision, incl. action plan

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Component 1: City Economic Competitiveness Review Local government intervention to boost competitiveness should start with a clear understanding of the market and the main spatial and sectoral drivers of city economic growth. This report aims to do an in-depth study of the city’s economy. Description: The City Economic Competitiveness Review attempts to answer the following questions – (a) what is the state of the local city economy in terms of GRDP, GRDP per capita (as a proxy for productivity), GRDP mix, total jobs, average wages, and exports and their growth over last 10 - 20 years; (b) how does the economic performance of the city compare to peers and what are city’s strengths and weaknesses including education and skill level of workforce, land pricing, regulatory environment; (c) what are the spatial and sectoral drivers of city’s economic performance and competitiveness; (d) which are the sectors where the city can be considered or has potential to be nationally and globally competitive; (e) for the top sectors, what are the market failures and major barriers to growth across regulation/policy, skills, infrastructure (including land), technology and access to finance? Methodology: To answer the different questions stated above, the work on the report will consist of both quantitative and qualitative studies. It will build on existing work and on expertise by the World Bank and development partners. It will also involve collaboration with stakeholders from different government agencies, private sector, universities, and industry associations.

Trends in city’s economic performance will be based on data from statistical agency (BPS) and data available with local government agencies. For example, it is instructive to contrast the rate of growth in jobs, productivity and exports of a given city with its peers in Indonesia and potentially from outside Indonesia to determine a city’s strengths and weaknesses and their key drivers. Based on data from BPS, we have compared the growth trajectories of top Indonesian cities in Figure 1. Between 2000 and 2010, Palembang’s real GRDP grew 3.3 percent per annum (behind most cities) due to only 2.1 percent per annum growth in productivity (using the proxy of real GRDP/capita). Denpasar’s productivity growth was even lower at 1.7 percent per annum between 2000 and 2010. Denpasar’s GRDP growth is more respectable at 6 percent per annum driven by 4.2 percent per year growth in population.

Sector specific analysis: To analyze drivers of economic performance, sector specific data will be used. In particular, we would like to identify the traded and resource based industries2 that are true source of competitiveness. World Bank has developed

2 Michael Porter's work on competitiveness and clusters (e.g., US Cluster mapping project) talks about 3

types of industries -

Local industries - customers are local e.g., construction, retail

Traded industries - goods and services traded across regions/nations due to higher productivity achieved in the cluster/city (e.g., Auto components)

Resource based industries - raw materials and other resources are local (e.g., Oil and gas)

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tools and surveys which can be used to drive this analysis. Some of the major tools, which we are likely to use for this analysis will include Sector feasibility checklist, Enterprise Survey, Value chain mapping, Porter’s five forces, Market trends, and Cost Analysis (same as cost-structure benchmarking). Figure 2, 3 and 4 are illustrative slides on methodology used for sector analysis.

Leveraging Sub-national Doing Business (SNDB) database and insights, which was earlier work done by World Bank and IFC with the pilot cities

Interviews with mayors, planners on top economic priorities, initiatives underway, challenges faced, city administration organization structure, decision rights, KPIs and incentives

Interviews with industry associations, major private sector players, investors, banking analysts, and central bank

Literature and case study survey of work done by World Bank, donors, partner institutions, academics, Indonesian think tanks

Study of government masterplans including MP3EI (Masterplan for Acceleration of Regional Economic Development), RPJMN and city plans

Additional, targeted data collection for in-depth and sectoral studies, if needed and as agreed to with all stakeholders

Timeline and phases: The work on the City Economic Competitiveness Review will span across all three phases. However, the first version of the report, based on existing data and key informant interviews only, will be completed during Phase 1, in order to support the dialogue and generation of policy options during the subsequent phases. The analysis will then be deepened and focused on agreed-upon sectors during phases 2 and 3. This may include additional data collection in the cities, if it turns out that such data is needed to complete the analytical work. Component 2: Capacity building Description: This part of the module links CPL outputs to the planning process, budget and resource allocation, land use, governance, and private sector decision support. The completion of the City Economic Competitiveness Review should translate into an ongoing capability and become an input into all the planning related to the cities’ economic development. The new analysis and insights from the review should be linked to action on the ground by (a) agreement on immediate initiatives and (b) permanent linking of new data and analyses to ongoing decision making by the city government and other players.

Competitiveness may come initially by exploiting resource advantage (resource based industries), and may come sustainably by developing traded industries. One way to analyze these local versus non-local industry concentration in a given city or cluster is to look at industry share of total output of the cluster and compare it to national averages. Palembang’s share of rubber and palm oil industry output to total Palembang GRDP would be way above the national average across clusters – making them Palembang’s competitive sectors.

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This can be achieved by capacity building at Bappeda and other relevant institutions through on-the-job personnel training, setting up appropriate decision analysis tools, and institutional changes to ensure application of results. Methodology:

Map all controllable decisions at a city level, current data and facts used for those decisions, current information gaps, and specific lab outputs that will bridge this gap. This step will use process maps/ decision rights and other tools

One-time intervention to agree on immediate initiatives for private sector development followed by other institutional interventions that improve decision making to support private sector development on an going basis. This is essentially transforming the way Bappeda works and will be typically achieved through internal decision making workshops involving all relevant stakeholders.

Timeline and phases: The work on capacity building in this module will be especially closely coordinated with the activities of other modules, which also have capacity building as a key component across the three phases. It should be noted that the goal is to transfer the lead of the city economic competitiveness work to the CPL during Phase 3. Support of the work and of the CPL can continue, but the aim is to have the city government fully in the driver seat by that time. Component 3: Consultative workshops and Public-Private Dialogue (PPD) Description: The consultation with the private sector is critical throughout, since the success of this module is particularly dependent on the private sector’s reaction to the policy initiatives (and, later on, to the associated infrastructure investments). This module envisions, in addition to ongoing, informal consultations, 3-4 workshops that will involve all key stakeholders, including government agencies, private sector players of companies of different sizes (large, medium and small), representation from all major sectors, industry associations and other experts and academics. The workshops should serve the purpose of both information sharing (from the City Economic Competitiveness Review) and getting feedback. Timeline and phases: Proposed workshops and touch points (including timeline) include:

During Phase 1: initial work shop to discuss project setup, objective and vision sharing, collecting top hypothesis on challenges faced and solutions (in terms of controllable local decisions)

During Phase 2: work shop to discuss initial results from the City Economic Competitiveness Review, including comparison to peers; seeking input and agreement on sector prioritization. In addition, methodology and approach for remaining work can be shared and feedback sought

During Phase 3: After the expanded version of the City Economic Competitiveness Review has been finalized, this workshop is the major event that discusses results,

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implications, city’s plans going forward and gets feedback from players. Subsequent formal periodic PPD program will be established.

Component 4: City initiatives and dashboard Description: This component represents in effect the culmination and combination of the first three components. The output of this component puts the insights and recommendations into practice in the form of policy decisions and other initiatives that are agreed upon and an implementation plan to execute the initiatives. The proposed dashboard is a tool to achieve these objectives by bringing the critical information together, while the content of the actual policy decisions and initiatives will of course be customized to each city. Eventually, it is expected that the City Planning Lab will help prioritize and structure the investment projects for the city. The dashboard will have three sections:

city overall economic indicators;

city top sectors indicators;

city economic competitiveness initiatives scorecard While the final policy formulation itself is an output that will be developed during Phase 3, this component offers the opportunity to demonstrate what the City Economic Competitiveness module can do from the very beginning. During Phase 1, case studies of successful cities can be discussed with stakeholders to illustrate what might be possible and to motivate the stakeholders’ further engagement. During phases 2 and 3, the development, discussion and finalization of policy options are a culmination of the work done as part of the analysis, capacity building and dialogue. The dashboard itself will be generated starting during Phase 3 and periodically by CPL thereafter. There will be formal setting (perhaps a quarterly steering committee review) where the Mayor of the city will review the dashboard with all relevant stakeholders to take stock of progress and make important decisions. This could be combined with review of other modules of the P3N Methodology: For city wide economic indicators and sector specific indicators, we will leverage Bank’s deep experience in helping cities and creating Mayor’s dashboards along with deep consultation with all stakeholders. For the third aspect on initiatives, World Bank has significant experience in design and delivery of project monitoring and evaluation (M&E) framework. Typically, the framework includes metrics at input, output, outcome and impact level. Timeline and phases: While some discussion of case studies will take part in Phase 1, the work of this component will start in earnest during Phase 2 and intensify during Phase 3.

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Table 3. Example of Analysis: Ratings of Indonesian Cities on Economic Performance

(2000 – 2010)

Rank Real GRDP 2010 (IDR Tril)

Population 2010 (Mil)

GRDP/capita 2010

(IDR Mil)

Real GRDP growth (% p.a)

Population growth (% p.a)

GDRGP/cap growth (% p.a)

Jobs 2010 (Mil)

1 Jakarta (JKT) 396 JKT 9.6 JKT 41 BDG 16.1 BDG 8.1 MDN 9.3 JKT 4.3

2 Surabaya (SBY) 86 SBY 2.8 SBY 32 MDN 9.1 BTM 8.0 BDG 7.4 SBY 1.2

3 Medan (MDN) 36 BDG 2.4 BTM 30 SBY 6.5 DPS 4.2 SBY 5.9 BDG 1.0

4 Bandung (BDG) 32 MDN 2.1 MDN 17 DPS 6.0 BGR 2.4 JKT 4.2 MDN 0.8

5 Batam (BTM) 28 SMG 1.6 SMG 14 BTM 5.8 JKT 1.4 MKS 4.1 SMG 0.7

6 Semarang (SMG) 21 PLB 1.5 BDG 13 JKT 5.7 PLB 1.1 SMG 3.3 PLB 0.5

7 Palembang (PLB) 18 MKS 1.3 MKS 12 BGR 5.6 MKS 0.9 BGR 3.1 MKS 0.4

8 Makassar (MKS) 16 BGR 1.0 PLB 11 MKS 5.1 SBY 0.5 PLB 2.1 BGR 0.4

9 Denpasar (DPS) 6 BTM 0.9 DPS 7 PLB 3.3 MDN -0.2 DPS 1.7 BTM 0.4

10 Bogor (BGR) 5 DPS 0.8 BGR 5 SMG 1.2 SMG -2.0 BTM -2.0 DPS 0.4

Source: Team’s analysis from BPS data (various years)

Figure 5. Methodology Illustration: Export Performance Tool

An analysis of Indonesian cities reveals wide variation in economic performance. A more nuanced story of Indonesian growth over the last decade (2000 – 2010)

- Palembang real GRDP grew 3.3% p.a (lower than national average) driven one third by population growth and two third by productivity growth

- Denpasar real GRDP grew 6%, but most of it came from population growth

(4.2% p.a) with productivity growth declining (1.7% p.a)

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Figure 6. Methodology Illustration: Value Chain Mapping Tool

Figure 7. Methodology Illustration: Cost Structure Analysis Tool

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3.4 RISKS AND MITIGATION The project has a few key risks which have been highlighted in the table below, along with mitigation measures.

Table 4. Risks and Mitigation of Economic Competitiveness Module

Key Risks Potential Mitigation

The data for comprehensive analysis does not exist (i.e. quality of analysis is limited due to data gaps)

Creative combination of different data sources will seek to quantify any remaining data gaps and the resulting uncertainty in the analyses

The data exists but is not delivered, due to coordination failures

Getting a client owner, possibly at MoF or MPW, to act as influential coordinator between agencies

The data is of poor quality Data quality tests underway and design of project contingent on agreement on data quality

Delays or failure to adequately staff the CPL with right talent

Availability of CPL physical space and staff commitment to be used as an engagement criteria with clients

Project monitoring and governance risk (e.g. Mayor’s dashboard is not used in practice)

Bank will insist on formal institutional arrangements to ensure project monitoring discipline

3.5 OUTPUTS As indicated above, the following outputs will be produced as part of the City Economic Competitiveness Module: i. City economic competitiveness review

ii. City economic planning and decision support capacity building

iii. 2-4 Workshops for public-private dialogues

iv. City economic competitiveness initiative roll out and monitoring dashboard

3.6 TEAM In addition to the regular staff of the City Planning Lab, the expected composition of the technical assistance team specific to this activity is as follows: (i) Senior Economist, team leader, (ii) Finance and PSD Specialist, (iii) Economist, (iv) Competitive Industries Practice Specialist

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3.7 RESOURCE ALLOCATION AND TIMELINE Significant emphasis is being put on design and implementation rather than data analysis. Approximately two-third of cost is concentrated for travel and on the ground work, while a third is for pre and post mission desk based analytics. Implementation of the module emphasizes using local expertise complemented with the World Bank’s international knowledge and experience. The module envisages 8 week-long missions to the two cities. To maintain the momentum and continue to build the needed relationships, as well as to provide capacity building on the competitiveness module to the city planners in the City Planning Lab, a local consultant will be hired to be the person on the ground in both cities.

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ANNEX MODULE B: ASSESSMENT OF DATA ENVIRONMENT It must be noted that some of the non-government databases in the table below have solid

historical data but weak prospects of periodic updates in future (e.g. KPPOD).

Table 5. Inventory of Data for Economic Competitiveness Module

Area Description Coverage Period Source

General/ background information

City/Kabupaten in Figures; published annually

All cities and kabupaten, nationwide

1990 – 2010 BPS

General/ background information

Susenas (Household Survey) All cities and kabupaten, nationwide

1976 – 2010 BPS

Manufacturing sector

Statistik Industri (census of large and medium manufacturing, 20+ employees)

Nationwide. Data available at city/ kabupaten level

1990 – 2010 BPS


Survey of micro (1-4 employees) and small (5 – 19 employees) manufacturing companies

Nationwide. Data available at provincial level

2010, 2012 BPS


Number of small industry establishments and employees

Some cities/kabupaten Varies City/Kabupaten in Figures (BPS)

Service Survey on manufacturing and non-manufacturing firms in Economic Census

Nationwide 2006 BPS

Business climate SNG Doing Business assessment on metrics such as days, cost, number of procedures to obtain licenses and permits

14 cities (2010) 20 cities (2012)

2010, 2012 WB - IFC

Business climate Regulatory environment for doing business; economic governance

90 C/K (2001) 134 C/K (2002) 200 C/K (2003) 214 – 245 C/K (2004-2011)

2001 – 2005, 2007, 2011

KPPOD

Business climate C/K rankings and Autonomy Award on economic development, public service, and local political performance

C/K in East Java, Central Java, DI Yogyakarta, South Sulawesi

2009 – 2012 Jawa Pos Institute Pro-

Autonomy (JPIP)

Labor Sakernas (Labor Survey): survey of employment status, field of work

Nationwide 1976 – 2010 BPS

Banking Credit trend lines by sector and by size of firm

Select cities Bank Indonesia Regional Offices

Local economy GRDP by economic sector Nationwide. Data available at C/K level

1990 – 2010 BPS

Other Enterprise Survey Transportation Survey Manufacturing Survey SME Survey (underway)

Varies WB

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SECTOR MODULE C: SLUM ANALYTICS AND MANAGEMENT

SYSTEMS

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4.1 BACKGROUND Like many rapidly urbanizing countries, Indonesia has seen the growth of informal settlements in many of its cities. The Ministry of Public Work estimates that a quarter of the urban population (roughly 25 million people) lives in slums and informal settlements. While the growth of slums is an indicator of the economic draw of urban areas, it is also a sign of inefficient land and housing markets, and unequal access to urban services. Addressing existing slums is critical to alleviating urban inequality, while prevention of future slum growth and protection of land rights is essential to attracting investment to cities. Some Indonesian cities have taken innovative and progressive approaches to slum upgrading, and through policies and small investments have managed to upgrade slums into viable neighborhoods for poor urban communities. In-situ upgrading of slums is not always possible, however, since they are often located on risk-prone or contested land. Most cities are forced to try to address the issue of slums in the absence of vital information. Cities often have no systematic way to answer basic questions about slums, such as: i. What are the primary causes of slum formation in the city?

ii. How do slum dwellers make location choices?

iii. How have recent government policies or actions (e.g. housing policies, infrastructure

and service provision, slum upgrading, formalization, land sales, etc.) affected slum residents and the formation of new slums?

iv. How does a slum household’s intention to invest in or otherwise upgrade their dwelling correlate with other factors, such as tenure security, income, duration of residence, etc.?

v. What determines prices/ rents in slums, e.g. tenure security, distance from various amenities, etc.?

vi. How can slum areas be classified in terms of their origins, characteristics, or expectations of future growth, in order to devise the most appropriate government responses?

In order to answer these and other questions, slum analytics and management systems will be a key strategic activity of the City Planning Lab (CPL). The slum information database produced as part of this work will be an important input into decisions on investments in basic infrastructure and services, helping devise appropriate interventions and target them to areas of greatest need. It will also help cities devise more effective slum policies and regulations. Performing this activity as part of the broader City Planning Lab initiative will take advantage of several synergies, as the findings will both feed into and benefit from the analytical work in parallel modules. The findings on distribution of slums will support the work on spatial growth analytics, which in turn will help provide spatial context to the growth of slums. It will also be a strong indicator of where the demand for affordable

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land is likely to be highest, which will add value to the land market analytics module. The slum analytics and land market analytics together can help the city identify and proactivity respond to high demand for land before new slums emerge. The module on disaster and climate risk resilience will help identify vulnerable slums. Not only will these synergies provide efficiency through shared data and analysis, they will also ensure that the analysis done by the CPL as a whole puts special emphasis on the most disadvantaged populations.

4.2 OBJECTIVES The main objective of this module is to assist partner cities in improving the management of slum areas, using detailed information and mapping of slum areas and vacant lands. Technical assistance provided under the module will consist of three components with specific objective as follows: Component 1: Slum mapping and information systems: The objective of this component is to help the city develop and maintain a geo-referenced database of slums, using satellite imagery and other data sources to provide an overview of the slum situation, as well as a survey in selected areas recording attributes such as legal status, year of construction, quality of construction, disaster risk, price/rent, access to urban services, access to transportation networks, etc. This database would be managed and maintained within the City Planning Lab, and would allow slum-related policies to be informed by an empirical understanding of the needs of slum communities. Component 2: Slum management framework: The objective of this component is to use the analysis emerging from the City Planning Lab’s database developed in component 1 to formulate a medium-term program for a citywide strategy and investment program targeting existing slum areas. This includes identifying slum areas that are suitable for in-situ upgrading, and those which are vulnerable to disaster risk and where resettlement may be required. This slum management framework would outline strategies for community participation, institutional capacity building, and investments. Component 3: Managing new slum growth: The objective of this component is to work with city leaders to develop strategies to prevent growth of new slums in areas identified as vulnerable to disaster risk or planned for public use.

4.3 SCOPE OF ACTIVITIES In keeping with the other CPL modules, the activities in this module will be conducted in three phases of six months each. Component 1 will be completed at the end of phase 1. Components 2 and 3 will begin at the start of phase 2 and will carry over into phase 3 (see timeline below). The duration of the complete module is 18 months.

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Component 1: Slum Mapping and Information Systems This component will be divided into three stages, as follows: 1A: Creation of a basic Slum Information Database This stage will involve the creation of a basic version of the GIS database on slums (a part of the CPL’s broader database) populating it with all information on slums that can be derived from satellite imagery and field visits. The goal will be to develop a preliminary picture of the situation in the city with regard to slums. The tasks for this component in this stage would include:

i. Gathering existing spatial data on the location of slums in the city from government

and other sources; ii. Converting the above data into standard, non-proprietary formats (e.g. shape files,

KML files), digitizing paper maps where necessary, and recording the associated metadata;

iii. Using satellite imagery (e.g. Google Earth) to identify possible slum areas in the city; iv. Conducting field visits to these areas for verification (with photographic documentation

of slum areas during visits); v. Using data collected from all the preceding steps to create a GIS map of slums in the

city. Each slum area will be represented as a polygon depicting the boundaries of the slum area, and associated with a table of attributes reflecting all the available data for each slum; and

vi. Uploading all unclassified data to the local government website in the form of PDFs and GIS files, as well as to an open source web-based mapping service (e.g. OpenStreetMap).

1B: Additional secondary data collection This stage will involve gathering additional secondary data that can help develop a more complete picture of the characteristics of slums and slum households, and inputting this data into the Slum Information Database. Tasks during this phase will include:

i. Gathering and inputting into the database, in the standard format, all available data

on demographic and socio-economic characteristics of slum and non-slum households, citywide land ownership, zoning, disaster risk, transportation infrastructure and routes, utilities (water, sanitation, drainage), and other relevant data;

ii. Adding all unclassified data collected during this phase to the open source web-based mapping service used in component 1A.

1C: Primary data collection and analysis This stage will involve conducting household surveys to collect primary data in selected slum areas, to further develop the database and contribute to a more robust understanding of the slum areas. Tasks during this phase will include:

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i. Designing a methodology and questionnaire for a household survey, covering a significant number of random households in selected, high-priority slum areas. (See Annex section for an indicative list of the kinds of household attributes that may be included.)

ii. Gathering feedback on the survey design and site selection from local government counterparts and external stakeholders and refining it accordingly;

iii. Conducting the household survey, while also recording constraints faced while conducting the survey (e.g. households inaccessible, households declining to respond, gender/age bias in respondents, etc.);

iv. Adding all information to the Slum Information Database, with metadata; v. Analyzing the data obtained from all phases, through regression analysis and other

means, in order to answer the questions listed in the ‘Rationale’ section above and others.

Component 2: Slum Management Framework This component aims to formulate a medium-term program for a citywide strategy and investment program targeting existing slum areas. Activities in this component would include: i. Identifying slum areas that are suitable for in-situ upgrading, and those which are

vulnerable to disaster risk and where resettlement may be required.

ii. Creating the typology of existing slum areas based on its characteristics such as type of dwelling, dwellers, tenure status, land ownership, etc., identified and analyzed in Component 1 above.

iii. Developing strategies based on the typology of area, which includes site analysis, building and urban design, land management, financial assessment, and temporary shelter. This would utilize the phase 1 outputs of the other CPL modules.

iv. Outlining strategies for community participation, institutional capacity building, and investments for pilot sites, selected based on discussion with the city government.

v. Developing a program of future activities to implement the selected strategies, in coordination with related government agencies.

An example of output produced by similar activity is HABISP, a sophisticated housing information system in City of Sao Paulo, Brazil. The system features maps with data on slums and other low income housing.

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Figure 8 A,B. Example of Analysis: Sao Paulo HABISP Online Housing Information

System

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Component 3: Managing New Slum Growth This component aims to develop strategies to prevent the formation of new slums in areas of high risk or those reserved for public use. Activities in this component will include:

i. Together with the team working on the disaster risk module of the CPL, identifying all

currently vacant land in the city that is: (a) prone to disaster risk (using existing data); or (b) identified in the current city spatial plan as usable for public purposes, documenting the ownership status of all such land (private or public, and if public, which agency), and inputting this data into the Slum Information Database described above;

ii. Developing an understanding of the process by which land is encroached by slums in the city, using case studies or other means;

iii. On the basis of this understanding, making recommendations for strategies to safeguard the land identified in task (i) above. These recommendations should address:

a) reforms to regulations; b) reforms to enforcement procedures; c) capacity building of relevant institutions; d) reforms to the process of land use planning; e) public awareness strategies; and f) community-based prevention and participation strategies.

iv. Working with local government agencies to help them implement the recommended

strategies.

Workshops

In order to ensure that the technical assistance activity is useful to the city at every stage, the team will conduct workshops in order to share the results of the work done so far, as well as to receive guidance from government leaders on future directions.

i. A kick-off workshop will be held in order to discuss the work ahead and establish

working procedures;

ii. An interim workshop will be held in month 7, to share the findings and recommendations of all activities completed up to that point, including all of component 1, and to develop plans for the collaboration with the counterparts for the remaining duration.

iii. A wrap-up workshop will be held at the completion of all activities, to discuss plans for

government agencies and/or donors to carry on the work, and reflect on lessons learned.

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4.4 RISKS AND MITIGATION The primary risk with this activity is that the information from the database built as part of the first component and the recommendations that emerge from the second and third components will not be mainstreamed into either the day-to-day decision making with regard to municipal actions affecting slums, or into the long-term visioning and planning for the city. The team will address this potential risk by working closely with the staff of various city agencies during the various activities, under the City Planning Lab framework, as well as periodically consulting with city leaders through workshops, in order to ensure that the data collected and the recommendations for slum policy are relevant to the city’s needs.

4.5 OUTPUTS The following outputs are expected from the technical assistance:

Component 1: Slum Mapping and Information Systems

i. All data gathered during all three stages, with metadata, transferred to the Bank

team and to the relevant government agency as digital files in a standard format, and uploaded to an existing online mapping service;

ii. A report describing: (a) hosting options; and (b) future enhancements to the database.

iii. All materials associated with survey, including: a) completed questionnaires (may be in original language of survey, may be scanned

hard copies); b) a spreadsheet displaying the data collected; and c) a report briefly describing methodology and constraints, and summarizing the

findings.

Component 2: Slum Management Framework

i. A report outlining typology of existing slum areas in the city and strategies for its management.

ii. Selection of slum area sites as pilot projects for implementing strategies and the programming of activities for implementation.

Component 3: Managing New Slum Growth

i. Layers in the GIS database mapping vacant land, hazard-prone vacant land, and vacant land zoned for public use, with all available associated data, including ownership;

ii. A report outlining:

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a) the process of slum formation, as observed through case studies; and b) recommendations for strategies for preventing slum growth in disaster-prone or

strategic areas, as described earlier.

4.6 TEAM In addition to the regular staff of the City Planning Lab, the expected composition of the technical assistance team specific to this activity is as follows: i. Urban Planner as Team Leader ii. Social/Low-income Housing Specialist iii. Economist iv. Community Development Specialist v. GIS Specialist vi. Urban Design Specialist vii. Governance/Institutional Specialist

4.7 TIMELINE This module will be carried out in three phases of six months each.

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ANNEX MODULE C: DATA COLLECTION The following is an indicative list of the kind of information gathered from primary and secondary sources and input into the slum information database3: Slum attributes:

1. Name 2. Location – jurisdiction 3. Location – description (urban core or fringe area) 4. Year of establishment 5. Area of slum (sq. meters) 6. Land use surrounding slum (residential/ commercial/ industrial/ other) 7. Physical location of slum (along road/ along railway tracks/ riverside etc.) 8. Legal status of slum (provide details) 9. Ownership of land 10. Estimated population 11. Estimated Number of households 12. Primary source of water 13. Primary sanitation facility 14. Primary means of garbage disposal 15. Connectivity to citywide water supply system (fully/ partially/ not connected) 16. Connectivity to citywide storm-water drainage system 17. Connectivity to citywide water sewerage system 18. Flooding risk (no flooding/ floods 15 days a year/ 15-30 days/ more than 30

days) 19. Frequency of garbage disposal 20. Frequency of clearance of open drains 21. Condition of access road to slum (paved/ unpaved, motorable/ unmotorable) 22. Condition of internal roads 23. Distance from nearest motorable road 24. Street light availability 25. Distance to nearest pre-primary, primary, high school 26. Distance to nearest primary health care facility, public hospital, maternity center 27. Availability of communal facilities (meeting halls/training center/ night shelter,

etc.) 28. Active presence of NGOs

Household Attributes:

1. Name of slum 2. Address (house number, street) 3. Existence of formal street addressing 4. Number of family members 5. Number of school-age children

3 Adapted from “Formats and Guidelines for Survey and Preparation of Slum, Household and Livelihood Profiles of Cities/Towns”, Government of India Ministry of Housing and Urban Poverty Alleviation, National Buildings Organization.

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6. Number of disabled people 7. Land tenure status 8. Type of structure (permanent/ temporary) 9. Construction material used in floor 10. Construction material used in roof 11. Source of light 12. Source of cooking fuel 13. Source of drinking water 14. If piped water, duration of availability during the day 15. If outside source of water, distance from dwelling 16. Existence of toilet facility 17. Bathroom facility 18. Condition of road in front of house 19. Vehicle ownership (none/ bicycle/ motorcycle/ care/ truck) 20. Number of years in current dwelling 21. Migrated from (urban/ rural) 22. Reason for migration 23. Migration type (seasonal/ permanent) 24. Number of earning adult family members 25. Number of earning non-adult family members 26. Number of non-family adult members (specify if renter) 27. Average monthly income of household 28. Average monthly expenditure of household 29. Debt outstanding as on date of survey 30. Educational qualifications/ training of adult members 31. Employment status (self-employed/ salaried/ casual labor/ others) 32. Place of work (within/ outside slum) 33. Length of daily commute 34. Mode of daily commute 35. Monthly earning 36. Source of income 37. Income-generating activity within dwelling unit (home-based industry/ commerce) 38. If unemployed, main reason for unemployment 39. Acquisition of dwelling unit (self-built/ bought/ rented) 40. Price / rent of dwelling unit 41. Income from renting space in dwelling unit 42. Intention to invest/ upgrade dwelling 43. Intention to move away 44. Major constraints to formal housing

Business attributes

1. Type of business 2. Average monthly/ annual earnings 3. Seasonal/ regular 4. Number of household members employed 5. Number of hired employees 6. Resource needs of business (water/ power) 7. Waste produced by business

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8. Means of waste disposal 9. Spatial needs of business (must be easily accessible to public, e.g. in a market

place/ space needed for production or processing/ other) 10. Intention to expand (none/ more employees/ more space)

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SECTOR MODULE D: DISASTER AND CLIMATE RESILIENT

PLANNING ANALYTICS

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5.1 BACKGROUND Indonesia’s rapidly growing urban population is particularly vulnerable to natural disasters. More than 110 million people in roughly 60 cities, mostly located in coastal areas are exposed to hazards including tsunamis, earthquakes, flooding and impacts of climate change. With nearly 70 percent of Indonesia’s population expected to live in urban areas by 2025, coupled with the increasing wealth of the population, Indonesian cities are increasingly vulnerable to both large-scale and persistent natural hazard events. The limited capacity of urban centers to absorb new residents because of lack of fundamental infrastructure investments has also resulted in the creation of many unplanned settlements. Inadequate zoning and lax enforcement led to the occupation of many hazard-prone locations. The Ministry of Public Work estimates that a quarter of the urban population (roughly 25 million people) lives in slums and informal settlements. Indonesia’s unique geological setting and the complexity of its population settlements has generally led to more disasters causing greater damage (loss of life, economic impacts etc). Although hazardous natural events cannot be prevented, the severity of their consequences can be minimized or even avoided through disaster and climate sensitive urban development coupled with better community preparedness and enhanced coping capacity to achieve greater city/urban resilience. Climate change and variability in the near and long term can only increase the level of risk. In addition to higher intensity meteorological events such as floods and droughts, the climate also influences food production patterns and outputs, creating additional uncertainty in the event of a disaster that further exacerbates its impact. While there is growing awareness of the need to address the impact of climate variability and change, more accurate identification of vulnerability and evidence-based response and adaptation measures must be developed. Cities also often lack the fiscal capacity to initiate programs that require sophisticated technical expertise and dedicated investment. In preparation for addressing topic, World Bank team has engaged in disaster and climate risk reviews in six cities. The purpose was to take stock of the baseline information on climate and disaster risks and identify critical gaps in addressing the cities’ risk sensitive planning and investment needs thereby setting the priorities for this proposed module on disaster and climate resilient planning analytics. The current urban planning practice in Indonesia still consider hazards and risks from disaster and climate change only as constraining parameters in the selection of sites suitable for development. Where the risks originate and how existing growth trends and investment will impact or be impacted by the pattern of disasters have not been thoroughly analyzed during the planning process. As part of the objective of Metropolitan and Urban Development Program (P3N) to establish technical capacity to measure, analyze and respond to urban development pressures in evidence-based and timely manner, a Disaster and Climate Resilient Planning Module is needed to address the following challenges:

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The gap in high-resolution hazard and exposure information required for a detailed city-level planning;

The absence of policy instruments and practical guidelines to introduce disaster and climate resilient practices into detailed spatial plans and city level infrastructure investments decisions.

The lack of customized geospatial analytical tools to conduct risk analysis that can easily integrate various data sources and facilitate the implementation of risk sensitive planning;

Addressing these issues will be a key strategic activity of the City Planning Lab (CPL) to take advantage of several synergies, as the findings will both feed into and benefit from the analytical work in parallel modules. The Spatial Growth Analytics and Slum Analytics and Management Systems modules provide data that when combined with the climate and disaster risk analytics can provide valuable insights into the largest potential threats to the city’s future growth.

5.2 OBJECTIVES The primary objective of the Disaster and Climate Resilient Planning Module is to provide essential risk information and analytics to measure, analyze and identify options to address urban development pressures from disaster and climate related hazards. The overarching objective of the work across the three components will be building the capacity of local agencies in undertaking thorough and integrated but practical analysis incorporating disaster and climate risk management options into city investment program. The methods and approaches used during these activities may be adopted and continued by local agencies beyond the timeframe of this engagement and become standard practice in the city’s approach to resilient urban management inclusive of land use and infrastructure planning. The specific goals that will be fulfilled include: Component 1: Filling risk information and data gaps This component will compile or develop baseline hazard and asset exposure data as essential inputs for climate and disaster risk analyses that inform planning and investment decisions. Priority areas identified in the climate and disaster risk review through area-focused and risk-based approach as needing higher resolution data will be addressed developed by combining several potential information sources. Expert sources at technical agencies or universities, as well as participatory methods to engage the community and civil society are critical to developing robust hazard and exposure data. There will be an element focused on improved data sharing and management. In coordination with the core CPL, this will include both the platform software that can integrate with other P3N activities and the policies that can be established for city agencies in line with the guidelines set out in the National One Map Initiative of the Geospatial Information (BIG). Component 2: Establishing capacity to carry out detailed land use planning and infrastructure investment screening This component will specifically build the capacity of targeted cities to implement the three options for disaster and climate risk management through translating the preventive,

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avoidance, and adaptive approaches into practical targeted investment under the slum management and urban growth modules of the City Planning Lab of the Metropolitan and Urban Development Program (P3N). This is to enable the use of risk information to support resilience in key sectoral operations such as land-use zoning and infrastructure planning. In coordination with regional divisions of the Ministry Public Works’ DG Spatial Planning, a pilot of detailed risk-sensitive spatial planning will be carried out to showcase evidence-based planning enforcement/action instruments. Component 3: Developing tool for practical Climate and Disaster Risk Analysis This component will enable the integration of risk information into the City Planning Labs data platforms and analytical capabilities based on the guidelines developed in Component 2. The risk data can be accessed by analytical modules to support different planning functions within the city government (e.g., zoning, infrastructure, community actions/development). The current Indonesia Scenario Assessment for Emergencies (InaSAFE) which is still focused on contingency planning application will be expanded with additional analytical modules. InaSAFE tool supports better disaster risk reduction decision-making by providing a simple yet rigorous approach to analyzing the likely effects of future disaster events or climate change scenario. This component will use the baseline risk information generated in the first component as the data stream.

Figure 9 A,B. The InaSAFE Tool

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5.3 SCOPE OF ACTIVITIES The activities in this module will be conducted over a span of 18 months split into three month increments for initial planning purposes. Component 1: Baseline Risk Information and Participatory Mapping This component will be divided into three sub components, as follows: 1A. Baseline information on hazards This sub-component will involve the creation of a basic version of the GIS database, and populating it with all information on key hazards. National-level agencies such as BNPB, Badan Geologi, and BMKG as well as Universities are producing highly technical, scientific information on hazards and risk. However, with improved coordination and capacity development, cities can take better advantage of existing information and be aware of gaps and need to invest in better data to support local level resilience activities. New hazard information needs will be identified during phase 1 of the risk review. For example, if a city is planning micro drainage investments, ideally there needs to be a detailed flood hazard model to develop risk-sensitive design standards as well as the necessary micro zoning in the surrounding areas. The tasks for this component in this phase would include:

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i. Gather existing spatial data on hazards affecting the city from government and

other sources identified in the risk review; Convert the above data into standard, non-proprietary formats (e.g. shapefiles, KML files), digitizing paper maps if necessary, and recording the associated metadata;

ii. Develop new scenario or probabilistic hazard data based on the specific needs defined in scoping phase;

iii. Confirm that all hazard data is formatted in InaSAFE- compatible files; iv. Determine data sustainability issues including methods for updating, guidelines for

licensing and official usage. This activity would be coordinated with the core CPL components.

1B. Participatory mapping to develop baseline administrative boundaries, public asset inventory of critical infrastructure, and past hazard event or hazard prone data. This sub-component will involve gathering information to create a GIS enabled database of Kelurahan and RT-RW (ward/neighborhood) boundaries, public assets including critical infrastructure, and detailed GIS data of past hazard events. The methodology will follow BIG’s draft Standard Operating Procedures developed under the Participatory One Map Initiative (POMI). Tasks during this phase will include: i. Evaluate the resolution of freely available satellite imagery through OpenStreetMap

platform; ii. Establish working group of technical stakeholders for the participatory mapping,

provide training for group to learn OpenStreetMap tools and platform; iii. Gather existing spatial data on RW-RT, public assets from government and other

sources, perform basic validation and/or conversion into standardized GIS format; iv. Organize community workshop with OpenStreetMap training to gathering

information on critical infrastructure for baseline data; v. Collect data on past hazards to develop maps of hazard prone areas at the RW-RT

level; vi. Conduct quality assurance and validation of each data set. 1C. Institutional data management and sharing It is necessary to establish good protocols for data sharing between government stakeholders and with the broader community of civil society, private sector. i. Workshop with key stakeholders to review existing data sharing process and to

present options for implementing; ii. Customization of data sharing agreements based on workshop feedback.

Component 2: Resilient Land Use Planning and Infrastructure Investment Guidelines The risk-based and area focused land use planning component aims to: i) identify and mitigate the root cause of disaster risks embedded in existing land development practices through regulated use of land in hazard-prone areas and building codes, ii) promote controlled urban growth without generating new risks, ‘building back better’ through

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rebuilding and upgrading infrastructure using hazard-resistant construction in accordance with a comprehensive plan. In close coordination with an operationalized risk-based land use planning mechanism supported by the Ministry Public Work’s Directorate General of Spatial Planning, cities can be supported in make detailed spatial plan as the basis for locational decision of investments that have the primary purpose of risk reductions such as urban drainage and flood control or screening mechanisms that would introduce resilience criteria in infrastructure design, construction and economic development more broadly.

The activities under this component will include:

i. Dissemination of the detailed risk-sensitive spatial planning principles and guidelines and their practical implications to city operations and urban management;

ii. Visioning exercises and mentoring to catalyze a holistic analytical-based planning process informed by the data developed in Component 1 in which disaster and climate risk management serve as the norm for balancing city’s growth and community resilience (i.e., green development) for key investment in city services such as utility, transport/mobility and natural landscape and water management;

iii. Conduct participatory planning workshop targeting selected high-risk areas in the cities to present sectoral implications and options for rezoning, redevelopment and adaptive investment.. This exercise will reinforce the use of detailed spatial planning process as action instrument to ‘enforce’ the spatial plan;

iv. Develop risk-sensitive planning and investment guidelines through the translation of the vulnerability and site planning spatial analysis into detailed zoning map and its descriptive land use designation and restriction.

Component 3: Climate and Disaster Risk Analysis Tools Using the hazard and asset exposure data collected in Component 1, it will be possible to conduct a baseline risk analysis for the city. It is important that capacity be built within the CPL to easily use the results and conduct various secondary analyses related to planning and urban management. This project will leverage the InaSAFE tool which supports better disaster risk reduction decision-making by providing a simple yet rigorous approach to analyzing the likely effects of future disaster events or climate change scenarios. Under this component, this tool will be adapted and applied in support of analytics for various risk sensitive land use and infrastructure investment planning. The activities under this component will include: i. Expand user need assessment based on priorities identified in the risk review for

spatial analysis of disaster and climate risk impacts to support various city level sectoral and area-based planning;

ii. Design of user-friendly GIS functionality within the software architecture of InaSAFE that is compatible with the spatial data infrastructure of the CPL;

iii. Develop demonstration version testing of InaSAFE in the CPL to show results of baseline analysis;

iv. Customize modular tools to support integration of risk analytics into detailed spatial planning and infrastructure investment screening as defined by the guidelines in Component 2; and

v. Training and integration of the tool into the CPL’s core functions.

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Workshops In order to ensure that the technical assistance activity is useful to the city at every stage, the team will conduct overview workshops in order to share the results of the work done so far, as well as to receive guidance from government leaders on future directions. The workshops are also important opportunities to foster partnerships between the local government leaders, CPL, Universities, and Civil Society groups. These meetings will be in addition to the participatory activities embedded within individual components.

i. A kick-off workshop will be held in order to discuss the work ahead and establish

working procedures including government leadership for the participatory mapping exercises of Component 1;

ii. An interim workshop will be held in month 7, to share the findings and recommendations of all activities completed up to that point, including all of component 1 and 2, and to develop plans for the collaboration with the counterparts for the remaining duration.

iii. A wrap-up workshop will be held at the completion of all activities, to discuss plans for

government agencies and/or donors to carry on the work, and reflect on lessons learned.

5.4 RISKS AND MITIGATION The primary risk with this activity is that the information from the database built as part of the first component, the analytics from the second and the recommendations that emerge from third components will not be mainstreamed into either the day-to-day decision making with regard to municipal actions building disaster and climate resilience, or into the long-term visioning and planning for the city. The team will address this potential risk by working closely with the staff of various city agencies during the various activities, under the City Planning Lab framework, as well as periodically consulting with city leaders through workshops, in order to ensure that the disaster and climate risk data collected and resilient planning guidelines are relevant to the city’s needs.

5.5 OUTPUTS The following outputs are expected from the technical assistance: Component 1: Baseline Risk Information and Participatory Mapping i. All data gathered during all three stages, with metadata, transferred to the Bank

team and to the relevant government agency as (a) digital files in a standard format,

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(b) uploaded to an existing online mapping service, (c) selection of hard copy maps for key data sets professionally designed for display in city offices;

ii. Partnership agreements, data sharing between key data providers and the CPL.

Establish an extended network of technical experts to provide future advisory services on climate and disaster risk assessments.

iii. A report describing: (a) hazard modeling methodology, including resolution and limits of use for output hazard maps data, (b) strategy for the maintenance and/or updating of the data, (c) summary of the data sharing and management workshop;

iv. Materials associated with the participatory mapping exercise, including: a) Field survey templates; b) Extracted and GIS files; c) Customized training materials ; and d) A report briefly describing survey and quality assurance methodology, roster of

trained volunteers and city staff, and summarizing the findings. Component 2: Resilient Land Use Planning and Infrastructure Investment Guidelines

A report outlining:

a) the drivers of disaster and climate risk to core sectors and areas/neighborhoods; b) risk-sensitive micro zoning maps; and c) recommendations and practical roadmap for implementing the resilient landuse

and infrastructure investment guidelines.

Component 3: Climate and Disaster Risk Analysis Tools i. A report outlining the users’ needs assessment findings and design criteria for the

customization of the InaSAFE tool. ii. Users’ manual, support documentation, and detailed training materials for InaSAFE. iii. Fully deployed and bug tested installation of InaSAFE software on CPL servers.

5.6 TEAM In addition to the regular staff of the City Planning Lab, the expected composition of the technical assistance team specific to this activity is as follows: i. Disaster Risk Management Specialist as Team Leader ii. Climate and Natural Disaster Hazard Specialist x 2 iii. Community Mapping Specialist x 2 iv. GIS Specialist


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ANNEX MODULE D: DATA COLLECTION The following is an indicative list of the kind of information gathered from primary and secondary sources during the scoping phase:

Table 6. Inventory of Data for Disaster and Climate Resilient Module

Types of Data Source

Physical condition of the city

Geography Cipta Karya

Topography Cipta Karya

Geology Cipta Karya

Social Population BPS

Total population by sex BPS

Total population by age BPS

Density BPS

Growth and projection BPS

Urban population in coastal cities BPS

Economy PDRB BPS

Dominant economy activity BPS

Income BPS

Budget and subsidy (For DRR and CCA) Bappeda

Hazard Type of hazard BPBD

History BPBD

Intensity BPBD

Level of hazard BPBD

Damage (loss) BPBD

Vulnerability Main infrastructure Cipta Karya

Population welfare Bappeda

Vulnerability projection Cipta Karya/ Bappeda/ Related Agency

Risk Type of risk BPBD

Level of risk BPBD

Agriculture/food security Agriculture Agency

Forestry Forestry Agency

Water shortage Cipta Karya

Biodiversity Forestry Agency

Planning Spatial planning Cipta Karya

Midterm Development Planning Bappeda

Long Term Development Planning Bappeda

Climate Indicator Type(rainfall, temperature, La Nina, El Nino, etc) BMKG/Agriculture Agency

History BMKG/Agriculture Agency

Trend and projection BMKG/Agriculture Agency

Intensity BMKG/Agriculture Agency

Sea Level Rise Level of sea level rise Bappeda/Related Agency

Trend and projection Bappeda/Related Agency

Mitigation Program (type) Bappeda/Related Agency

Level of Mitigation Bappeda/Related Agency

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Types of Data Source

Mainstreaming to other planning document Bappeda/Related Agency

Adaptation Program (type) Bappeda/Related Agency

Level of adaptation Bappeda/Related Agency

Mainstreaming to other planning document Bappeda/Related Agency

Community education Bappeda

Community preparedness Bappeda

Institution Government agency (collaboration) Bappeda, BPBD

Local NGO Bappeda, BPBD

National NGO Bappeda, BPBD

International NGO Bappeda, BPBD

Local University/research center Bappeda, BPBD

Related research document Bappeda, University

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SECTOR MODULE E: MONITORING LAND AND REAL ESTATE

MARKETS

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6.1 BACKGROUND As in many rapidly urbanizing economies, the net worth of new constructions in the real estate market of Indonesia constitutes one of the largest sectors of annual investment and contributors to the GDP. The share of the construction sector in the GDP was 10.4 percent in 2012 – approximately a 90% increase from a 5.5-percent share in 2000 (Bank Indonesia, 2013). Real estate and construction sectors are among the major drivers of economic growth, along with transportation, communication and finance sectors. Growth in the construction sector, for instance, has outpaced the total annual GDP growth between 2000 and 2012 been by a factor of 1.45 times on average (Bank Indonesia, 2013). Between 2007 and 2011, 36 to 43 percent of all annual construction occurred in real estate products (Suraji, Pribadi & Ismono, 2012). Although an accurate assessment of the total value of the real estate market in Indonesia is not available due to lack of reliable data, a rudimentary estimation based on The Wealth of Nations Dataset of the World Bank (2005) suggests that urban land and structures that occupy it constitute approximately 20 percent of Indonesia’s $4.36 trillion total wealth.4 Given its durability, this large bundle of assets forms an important part of long-term national assets in Indonesia. Given the considerable importance of the land and real estate market for the economy, Indonesia’s cities would benefit from ensuring their efficient functioning. At present, however, most medium and large-scale municipal governments in Indonesia lack the institutional capacity to monitor the performance of their land and real estate market, or assess the impact of their policies and regulatory decisions on this market. The cities are unable to forecast the rapidly increasing demand for residential, commercial and industrial land for Masterplans and land use plans, and supply consequently does not meet demand. Resilient economic growth in Indonesia cannot be achieved without informed land and real estate policies that guarantee the availability of affordable space in demanded locations for living, working as well as recreation. As land and real estate markets provide space for all economic activities, they naturally impact various sectors of the economy. An inadequate provision of residential land, for instance, may inflate housing prices everywhere and trigger an increase in informal settlements, which in turn reduce the population’s spending capacity for transportation and other vital expenditures. In the absence of an understating of how real estate and land markets function, rapidly urbanizing cities of Indonesia expand on natural resources and agricultural land even before all existing urban land and infill development sites are exploited. Furthermore, lacking reliable data and analytics on land and real estate markets, Indonesian cities are unable to foresee and prevent abrupt fluctuations and bubbles in these markets. Lacking an empirical understanding of metropolitan growth, argues David E. Dowall (1995), leads to a “blind flight” for local governments and a failure to effectively deal with rapid population change and land development. In order to address these limitations and necessities, this concept note proposes to integrate a Land and Real Estate Market Monitoring Module to the planned activities for the P3N City Planning Lab facilities in two pilot cities in Indonesia. It discusses the needs

4 The total wealth estimate includes human and natural resources.

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and objectives for necessary land and real estate market monitoring and lay out the proposed activities. This module is envisioned to be closely integrated with CPL’s core Spatial Growth Analytics module, since the way that physical expansion and internal restructuring take place, is largely determined by and reflected in the land and real estate market. Cities grow or change internally when the supply side of the market responds to changes in demand for different land uses by a) converting peripheral non-urban land to urban use, b) changing land-use designations internally, c) adding infill development and densifying, or d) changing occupancy levels on existing land uses. All these scenarios are monitored in the proposed Spatial Growth Analytics module. The present module is also connected to the Slum Analytics and Management Systems module, which collects and analyzes data on the informal housing and commercial land uses.

6.2 OBJECTIVE The primary objective of CPL’s land and real estate market monitoring module is to enhance the resilience and efficiency of these markets through supporting evidenced-based planning, policy and investment decisions. This includes projecting future demand for land and different types of real estate products – residential, commercial, industrial, institutional – and integrating the projection into Masterplans, detailed plans and regulatory development policies. Reliable information about the projected demand will allow municipal planning agencies to make sure that enough affordable land and real estate will be supplied in desired locations, and that the agricultural to urban land use conversion along with the destruction of natural resources is not over exploited. The module will also help cities foresee and prevent potential bubbles and sudden fluctuations in the land and real estate market. This requires an understanding of how the land and real estate market performs in the context of the broader capital market, and how the real estate space and asset markets are related. The module will help local governments of the pilot cities foresee the impact of shifts in other sectors of the economy on the real estate market, and conversely predict shifts in other sectors of the economy triggered by real estate and land market changes. The module will also inform the local finance and tax agencies of the current and projected state of the land and real estate market, and of investment/revenue opportunities, which can help improve the efficiency of their mortgage plans and taxation systems respectively. CPL staff, together with infrastructure and transportation departments, will explore value capture taxation systems as potential ways of unlocking financing for much needed infrastructure improvements.

6.3 SCOPE OF ACTIVITIES Addressing the objectives above, the activities for the Land and Real Estate Market Monitoring module in the two pilot cities are proposed as follows:

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Phase 1: Compiling the land and real estate database (Months 1-6):

In line with the core module, the CPL will first assemble all real-estate and land market related datasets that already exist in different government departments, integrate them and host them for cross-departmental viewing on an online map server. Existing datasets that are important for monitoring the land and real estate market include:

Cadaster: land parcel dataset containing ownership, occupancy, use – by sub-type e.g. single family, multifamily and mixed residential – coverage, FAR, and assessed value data. Additional attributes, such as size, frontage and distance to nearby amenities can be calculated for each land parcel by CPL staff.

Buildings: building footprints containing ownership, occupancy, use (by built area and sub-types), past sales transactions and assessed values. A reliable building dataset that distinguishes building types is needed for evaluating the total supply of different types of real estate products on the market. Overlaying the building dataset with the cadaster will also provide the total supply of land that is available for development within the currently urbanized extent of each city.

Figure 10. Example Analysis: Distribution of Building Types in Singapore

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Table 7. Example Dataset: Price Range of Flats Offered by Housing Development Board in Singapore (in Thousand SGD)

Town

2 Rooms 3 Rooms 4 Rooms 5 Rooms

Selling price

Selling price less

AHG/SHG5

Selling price

Selling price less

AHG/SHG

Selling price

Selling price less AHG/SHG

Selling price

Selling price less AHG/SHG

Bukit Panjang

- - 137 – 189 107 – 159 217 – 298 207 – 288 274 – 386 274 – 386

Choa Chu Kang

- - 146 – 172 116 – 142 229 - 284 219 – 274 295 – 364 295 – 364

Punggol 85 - 111 25 - 51 150 – 242 120 – 212 257 – 390 247 – 380 335 – 484 335 – 484

Sembawang 92 - 116 32 - 56 158 – 191 128 – 161 255 – 310 245 – 300 - -

Sengkang 83 - 112 23 - 52 134 – 220 104 – 190 255 – 370 215 – 360 283 – 456 283 – 456

Yishun - - 156 – 193 126 – 163 234 – 296 224 – 286 277 – 381 277 – 381

Source: Singapore Housing Development Board

Road Network: the road network is essential for performing accessibility measurements for each building and parcel. Location, or more accurately the accessibility of a location, is the main indicator of land and real estate value.

Public Transit networks: In addition to road-level accessibility, land values also depend on available transit options. All forms of public transit (e.g. bus, minibus, regional lines) can impact land and real estate values.

Points of Interest: accessibility to amenities and businesses is also known to impact land and real estate values. Proximity to commercial destinations and other desirable venues or establishments, such as parks, hospitals, or museums – can be measured on the available road and transit networks in different parts of the city.

Census and Household Survey Data: these datasets are essential for estimating the demand side of the market.

Rents and prices: Available sales and rental prices for different property types (e.g. housing, retail) and subtypes (e.g. 1-Bedroom Apartment) will be collected from the main brokerage firms in each city. If possible, then each observed transaction should also indicate how long the unit was on the market and illustrate other general characteristics of the larger building complex the unit is part of. This data may be available at address level resolution, at a zone or street-level resolution.

Upcoming developments: Information should also be collected about all real estate development projects that are currently under construction or otherwise planned to be completed. Approximate type and size of each development should be listed and an approximate date of delivery recorded. This will allow the CPL staff to account for

5 AHG: The Additional Central Provident Fund/CPF Grant, given to eligible first-timer families who are applying to buy a 2-room or bigger flat who are able to meet the eligibility conditions. SHG: The Special CPF Housing Grant, given to eligible first-timer families who are applying to buy a 2-room, 3-room or 4-room flat in a non-mature estate and who are able to meet the eligibility conditions.

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future supply additions in estimating the needs for land and real estate products in five-year and twenty-year master plans.

Defining Analytic Zones: The first step for conducting property market analysis is to divide the cities into value zones based on their location, accessibility, uses, assessed values, and morphological properties – e.g. plot size, frontage, floor area ratio. The type of land and buildings in each zone should be as homogenous as possible. The census data and household survey data will be associated to each zone.

Supply and Demand Estimation: The data collection efforts in Phase One will be concluded by developing a supply and demand estimation for different real estate products in each zone, providing a fundamental basis for both real estate analysis and spatial planning. The supply of land includes non-developed lands that are available for development, as well as lands that can be converted to other uses or densities – e.g. conversion of single family housing to multifamily housing. The analysis will yield an estimated supply and demand overview for different real estate products in different parts of the city based on assessed values. The estimation of the demand side of the market, however, will rely on census data and household surveys (e.g. household size, and household income), as well as financing options. The latter will require collecting data from local banks and mortgage brokers. Although the assessed values may be significantly lower than the real values, they can provide an indication of spatial shifts in the market. The results will be later compared to and synthesized with the land and property market surveys in Phase Two.

Phase 2: Surveys (Months 6-12):

In the second phase of the project, CPL will carry out two surveys with local real estate brokers to compile a database of observed real estate market transactions and to develop an understanding of the segmentation of households by housing market access.

Land and Property Market Assessment: CPL will carry out a land and property market assessment survey using the methodology outlined in Dowall’s 1995 Land Market Assessment (LMA) and a simplified update from 2010. The survey involves interviewing experienced land brokers in each city to determine the prices for prototypical land parcels in different parts of the city. These property values are expected to differ from official assessment estimations, which often undervalue properties for tax reasons. One additional improvement to Dowall’s original LMA strategy is to exploit more easily accessed satellite imagery in the categorization of housing stock. Bertaud (2008) outlines this approach and draws attention to the importance of incorporating considerations for transportation infrastructure and urban growth patterns in LMA. CPL staff will be trained to carry out the survey periodically in the future and to use the results of the surveys as the basis for evidence based policy recommendations in the land and housing sectors, and to also enable infrastructure projects to be developed and financed in a more integrated manner from the outset.

Housing Market Segmentation Study: CPL will perform a household survey that assesses the mechanisms through which people access housing in different income groups. The study is expected to yield important information about actual housing demand and supply for different unit types. Disaggregating housing demand into market segments

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(based on income or on other criteria) is an important step in understanding how the housing market functions as a whole, and in identifying the distribution and trends of demand across segments. Different segments of the population access and combine the basic inputs into housing (Land, Finance, Materials and Labor) using a range of different methods. Analyzing these different variables and streams of supply, as well as the bottlenecks they face, is a crucial step in formulating more precise and targeted Government housing programs.

These two survey activities will follow the approach outlined in the recent World Bank document, “Land and Property Market Assessment - Housing Market Segmentation Study: Existing Tools and Survey Strategy”.

Phase 3: Land and Real Estate Analytics (Months 12-18):

The analytics proposed below will form the basis for real estate and spatial planning in the pilot cities. In addition to being a platform for conducting land and real estate analytics, CPL will assist the pilot cities in incorporating the analysis into their real estate and spatial planning.

Accessibility and Land/Real Estate Value analysis: Along with the accessibility analysis in the core module, CPL will analyze the impact of accessibility to surrounding land uses and amenities on land and real estate values. This analysis will extend in the final phase to a full hedonic pricing model that takes into account all major determinants of land and real estate value.

Impact analysis – before-after comparison: CPL will evaluate the impact of key infrastructure investments on land prices – e.g. a new road or a public hospital – by comparing historic land value data before and after development. Controlling for other factors that can affect land values (e.g. city-wide shifts, inflation), the before and after comparisons offers a useful methodology for evaluating the multiplier effects of public infrastructure, which can be used for supporting investment decisions in the future.

Hedonic Pricing Model: The full hedonic land price model is an extension of two pervious analyses – impact analysis and accessibility analysis described above. When a sufficient amount of spatial information and land / real-estate market data have been collected, CPL will be able to develop spatial hedonic pricing models to analyze variations in land and real estate values. Initially, the analysis could focus on explaining the direction and magnitude of infrastructure and service amenities on land values. How do new roads, sanitation facilities, transit systems, plot sizes and demographic characteristics impact land values? How far in space do such effects reach (e.g. how far can a parcel be from a paved road to have a value impact)? Such analyses should become regular activities at the CPL, accompanying all significant public investment projects and planning initiatives. Hedonic land value analyses can also form a basis for potential value capture regulations in the future.

Projections: Using the hedonic model and examining the current trends in the land and real estate markets, CPL staff will develop evidence-based forecasts for near-term and long-term changes in land and real estate values that are likely to result from

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foreseen developments. Additionally, using hedonic pricing models, CPL staff will work across municipal departments to investigate the financial feasibility of a pilot value capture taxation program around a planned infrastructure investment project.

6.4 RISKS AND MITIGATION The primary risk concerning the activities proposed above involve the reliability of the gathered land and real estate market data and the validity of the estimations that result from these data. In order to address this risk, we have proposed to collect the data from various sources, which will allow CPL staff to cross check the results. In Phase One, various datasets are collected from existing sources, including the assessed land and real estate values from DG Tax and BPN. In Phase Two, similar information is collected through personal surveys with experienced local real estate brokers. Even though the surveys can only cover limited parts of the city, consistent offsets and interpolations can be used to adjust all officially assessed data accordingly.

6.5 OUTPUTS The following outputs are expected from the land and real estate market module: A cadastral real-estate database. All spatial data gathered in the first phase of the project will be compiled into an online geodatabase, showing each land parcel with its associated buildings, occupants’ demographics, accessibility characteristics and valuation estimates. A land and property market assessment report. The report will present the findings on demand and supply for different real estate products (e.g. residential, commercial, industrial land) and provide the basis for evidence based policy recommendations in the land and real estate sectors. A housing segmentation study report. The report will outline the demand and supply for different types of housing units and outline discrepancies between availability and need. The segmentation study will enable policy makers to detect which demand categories (e.g. low income residents) are most burdened by inefficiencies in the market and point to solutions that can be used to address these inefficiencies. Impact Analysis Report. The report documents the observed real estate value impacts of selected infrastructure investment projects undertaken by the city. The exact choice of projects (e.g. road construction, bridge or a facility) will be made together with local planning agencies on a per-need basis. The results of the study are expected to inform what multiplier effects future investments could have and how the benefits are spatial distributed. Real Estate Financing Analysis. The study will outline existing options and conditions that are available for short term, medium term and long term real estate financing, outlining potential shortcomings and improvement opportunities for desirable financing options.

76

Hedonic Pricing Model Results. A report describing the controlled multivariate analysis results of land and real estate values in the respective cities. Hedonic price models explain variations in land and real estate values based on the spatial attributes and accessibility conditions of buildings and land parcels. These results can be used to estimate the likely market effects of future plans and infrastructure investments, forming the foundations of a sound real estate market policy.

6.6 TEAM

In addition to full time staff members listed above, additional expertise required for providing consultation to this module will include:

i. Urban Economist ii. Housing and Real Estate Planner iii. Market Analyst Specialist


77

REFERENCES

Bank Indonesia. Economic and Financial Data for Indonesia:

http://www.bi.go.id/sdds/series/NA/index_NA.asp , Accessed on April 8, 2013 Bertaud, A. 2008. Spatial Tools to Analyze the Impact of Land Markets on Affordability and

Urban Spatial Structures. Presentation at the World Bank, Washington, DC, February 28th.

Dowall, D. 2010. Literature Review and Proposed Methodological Approach, Land Markets

in Latin American and Caribbean Cities. Inter-American Development Bank: Washington, DC.

Dowall, D.E., 1995. The Land Market Assessment: A New Tool for Urban Management.

Washington, D.C.: Published for the Urban Management Programme by the World Bank.

Suraji, A, Pribadi SK., Ismono, (2012). The Indonesia Construction Sector. The Proceeding of

the Asia Construct Conference 18th, Singapore The World Bank, 2005. The Wealth of Nations Dataset:

http://data.worldbank.org/sites/default/files/total_and_per_capita_wealth_of_nations.xls , Accessed on April 8, 2013

The World Bank, 2013. Land and Property Market Assessment - Housing Market Segmentation Study: Existing Tools and Survey Strategy

ix

ANNEX 1: DEMONSTRATION REPORT OF

SPATIAL GROWTH ANALYTICS MODULE

DEM

ON

STR

ATI

ON

REP

OR

T SP

ATI

AL

GR

OW

TH A

NA

LYTI

CS

TE

CH

NIC

AL

SU

PP

OR

T F

AC

ILIT

Y

FO

R N

AT

ION

AL

UR

BA

N D

EV

ELO

PM

EN

T P

RO

GR

AM

IN IN

DO

NE

SIA

2

Con

tact

S

UT

D C

ity

For

m L

ab

20 D

ove

r D

rive

13

868

2 S

ing

apor

e +6

5-6

3036

60

0

city

form

lab

@m

it.e

du

Dat

e: J

uly

14, 2

013

And

res

Sevt

suk

PI,

Cit

y F

orm

Lab

S

ing

apo

re U

nive

rsit

y o

f T

echn

olo

gy

and

Des

ign

Rez

a A

min

dar

bar

i R

esea

rche

r, C

ity

Fo

rm L

ab

Sin

gap

ore

Uni

vers

ity

of

Tec

hno

log

y an

d D

esig

n Ta

imur

Sam

ad

Sen

ior

Urb

an E

cono

mis

t W

orl

d B

ank-

Ind

one

sia

Wilm

ar S

alim

U

nive

rsit

y o

f B

and

ung

C

hand

an D

eusk

ar

Urb

an D

evel

op

men

t A

naly

st

Wo

rld

Ban

k

Ren

ata

Sim

atup

ang

Eco

nom

ist

Wo

rld

Ban

k-In

do

nesi

a D

EMO

NST

RA

TIO

N O

F A

NA

LYTI

CS

SPA

TIA

L G

RO

WTH

AN

ALY

TIC

S

Aut

hors

: C

olla

bor

ator

s:

3

SEC

TIO

NS

1 IN

TR

OD

UC

TIO

N

2 G

EO

SP

AT

IAL

DA

TA

3 S

PA

TIA

L G

RO

WT

H A

ND

CH

AN

GE

AN

ALY

TIC

S

4

PLA

NN

ING

DE

CIS

ION

S S

UP

PO

RT

4

INTR

OD

UC

TIO

N

5

1. IN

TRO

DU

CTI

ON

A k

ey e

ngag

emen

t o

f th

e W

orl

d B

ank

und

er t

he N

atio

nal U

rban

Dev

elo

pm

ent

Pro

gra

m (

P3N

) is

tec

hnic

al a

nd i

nsti

tuti

ona

l ca

pac

ity

bui

ldin

g f

or

sup

po

rtin

g

evid

ence

-bas

ed s

pat

ial

and

dev

elo

pm

ent

pla

nnin

g i

n In

do

nesi

a. T

his

acti

vity

w

ill t

ake

pla

ce t

hro

ugh

new

per

man

ent

tech

nica

l as

sist

ance

fac

iliti

es –

Cit

y P

lann

ing

Lab

s (C

PLs

) –

that

are

pro

po

sed

wit

hin

Bap

ped

a in

fo

ur p

arti

cip

atin

g

citi

es –

Den

pas

ar, S

urab

aya,

Pal

emb

ang

and

Bal

ikp

apan

. Pro

po

sed

CP

L te

ams,

su

per

vise

d b

y a

dir

ecto

r, in

clud

e ci

vil s

erva

nts

fro

m B

app

eda,

Din

as T

ata

Ko

ta

and

oth

er r

elat

ed c

ity

dep

artm

ents

, an

d t

echn

ical

sta

ff w

ith

bac

kgro

und

s in

ur

ban

pla

nnin

g,

geo

gra

phi

c in

form

atio

n sy

stem

s (G

IS),

and

sp

atia

l an

alys

is

hire

d

fro

m

out

sid

e th

e lo

cal

go

vern

men

t.

In

ord

er

to

bui

ld

the

esse

ntia

l ca

pab

iliti

es

at

CP

Ls,

team

s o

f in

tern

atio

nal

Wo

rld

B

ank

cons

ulta

nts

will

co

llab

ora

te c

lose

ly w

ith

CP

Ls in

the

fir

st e

ight

een

mo

nths

of

the

pro

ject

.

CP

Ls’ p

rim

ary

go

al is

to

co

llect

geo

spat

ial d

ata,

and

to

co

nduc

t an

alys

es t

hat

can

info

rm s

pat

ial

pla

nnin

g d

ecis

ions

in

the

abo

vem

enti

one

d c

itie

s. C

PLs

’ an

alyt

ic

acti

viti

es

will

b

e st

ruct

ured

ar

oun

d

a co

re

mo

dul

e an

d

four

su

pp

lem

enta

ry m

od

ules

. The

co

re m

od

ule

aim

s to

est

ablis

h th

e C

PL

faci

litie

s,

inst

itut

iona

l str

uctu

res

and

dat

a p

latf

orm

s, a

nd t

o t

rain

CP

L st

aff

to im

ple

men

t b

asic

urb

an g

row

th a

nd c

hang

e an

alys

is i

n ea

ch r

esp

ecti

ve c

ity.

The

op

tio

nal

mo

dul

es i

nclu

de:

(1)

cit

y ec

ono

mic

co

mp

etit

iven

ess,

(2)

slu

m a

naly

tics

and

m

anag

emen

t sy

stem

s, (

3) c

limat

e an

d r

isk

resi

lienc

e p

lann

ing

sys

tem

s, (

4)

and

m

oni

tori

ng la

nd a

nd r

eal e

stat

e m

arke

ts.

A

sep

arat

e W

orl

d

Ban

k co

ncep

t no

te

des

crib

es

the

anal

ytic

ac

tivi

ties

p

rop

ose

d

for

each

m

od

ule.

T

he

pur

po

se

of

the

pre

sent

re

po

rt

is

to

dem

ons

trat

e th

e im

ple

men

tati

on

and

sp

ecifi

cati

on

of

the

anal

yses

pro

po

sed

in

the

core

urb

an g

row

th a

naly

sis

mo

dul

e. T

he i

ssue

s an

d o

pp

ort

unit

ies

that

In

do

nesi

an c

itie

s in

gen

eral

, an

d t

he f

our

pilo

t ci

ties

in

par

ticu

lar,

fac

e d

ue t

o

rap

id

urb

an

gro

wth

ar

e d

iver

se

and

lo

cally

sp

ecifi

c.

The

lis

t o

f an

alyt

ic

acti

viti

es n

eed

ed t

o u

nder

stan

d a

nd a

dd

ress

all

of

them

wit

h ap

pro

pri

ate

pla

nnin

g t

oo

ls a

nd p

olic

ies

is t

oo

exh

aust

ive

to b

e ca

ptu

red

her

e. T

his

rep

ort

fo

cuse

s o

n a

few

b

asic

, b

ut

usef

ul

spat

ial

anal

ysis

te

chni

que

s th

at

are

pro

po

sed

as

par

t o

f th

e co

re C

PL

mo

dul

e in

eac

h ci

ty.

Fig

ure

1. P

alem

ban

g i

s a

city

of

1.7M

inh

abit

ants

in

Sum

atra

. Due

to

the

pre

senc

e of

fre

que

nt e

arth

qua

kes

and

the

lac

k of

inv

estm

ent

in c

omm

erci

al r

eal

esta

te,

the

city

ce

nter

re

mai

ns

hori

zont

ally

d

ense

b

ut

vert

ical

ly lo

w r

ise.

6

The

re

po

rt

first

d

iscu

sses

es

sent

ial

dat

a re

qui

rem

ents

fo

r th

e p

rop

ose

d

anal

ytic

s an

d t

he n

eed

ed g

eosp

atia

l d

ata

pla

tfo

rm f

or

shar

ing

geo

gra

phi

c in

form

atio

n am

ong

go

vern

men

t ag

enci

es, s

take

hold

ers

and

the

gen

eral

pub

lic.

The

fir

st s

ecti

on

of

the

rep

ort

fo

cuse

s o

n d

ata

pre

par

atio

n, a

pp

rop

riat

e un

its

of

anal

ysis

, req

uire

d a

ttri

but

es f

or

each

dat

aset

, and

po

tent

ial s

our

ces

for

the

corr

esp

ond

ing

dat

a in

Ind

one

sia.

The

sec

ond

sec

tio

n co

ncen

trat

es o

n th

e an

alyt

ic a

ctiv

itie

s. T

he a

naly

tic

acti

viti

es w

e d

iscu

ss a

nd d

emo

nstr

ate

incl

ude

spat

ial

gro

wth

an

d

chan

ge

anal

ysis

, sp

atia

l ac

cess

ibili

ty

anal

ysis

, im

pac

t an

alys

is,

and

sp

atia

l-st

atis

tica

l m

od

els.

Tec

hnic

al i

mp

lem

enta

tio

n d

etai

ls a

nd

req

uire

d g

eo-p

roce

ssin

g t

oo

ls a

re d

iscu

ssed

thr

oug

h a

seri

es o

f ex

amp

les

usin

g d

ata

fro

m o

ther

cit

ies

and

co

untr

ies.

The

thi

rd a

nd f

inal

par

t o

f th

e re

po

rt f

ocu

ses

on

po

tent

ial

pla

nnin

g a

pp

licat

ions

of

the

pro

po

sed

ana

lysi

s te

chni

que

s.

Dev

elo

pin

g a

n ev

iden

ce-b

ased

pla

nnin

g d

ecis

ion

sup

po

rt s

yste

m t

hat

relie

s o

n em

pir

ical

and

up

-to

-dat

e d

ata

and

uti

lizes

po

wer

ful s

pat

ial a

naly

sis

too

ls c

oul

d

mak

e a

sig

nific

ant

cont

rib

utio

n to

war

ds

turn

ing

th

e ra

pid

ur

ban

izat

ion

in

Ind

one

sian

ci

ties

in

to

a va

st

op

po

rtun

ity

for

eco

nom

ic

gro

wth

, eq

uita

ble

re

sour

ce d

istr

ibut

ion

and

acc

ess

to h

uman

dev

elo

pm

ent

op

po

rtun

itie

s. T

he

dat

a an

d a

naly

sis

tech

niq

ues

des

crib

ed i

n th

is r

epo

rt a

re,

how

ever

, no

t o

nly

app

licab

le t

o I

ndo

nesi

an c

itie

s –

they

co

uld

als

o b

enef

it a

num

ber

of

oth

er

rap

idly

urb

aniz

ing

co

untr

ies

in S

out

h E

ast

Asi

a an

d b

eyo

nd. T

he C

ity

Pla

nnin

g

Lab

s p

roje

ct

in

Ind

one

sia

will

te

st

the

imp

lem

enta

tio

n o

f g

eosp

atia

l d

ata

syst

ems

and

ana

lysi

s te

chni

que

s in

fo

ur m

ediu

m-s

cale

cit

ies,

whi

ch w

ill o

ffer

a

uniq

ue o

pp

ort

unit

y to

lear

n an

d im

pro

ve f

rom

the

exp

erie

nce.

Ano

ther

rep

ort

w

ill b

e co

mp

iled

at

the

end

of

a 12

or

18 m

ont

h en

gag

emen

t w

ith

the

CP

Ls in

In

do

nesi

a, d

escr

ibin

g h

ow

the

im

ple

men

tati

on

unro

lled

and

wha

t co

uld

be

do

ne b

ette

r ne

xt t

ime.

GEO

SPA

TIA

L D

ATA

7

2.1.

GEO

SPA

TIA

L D

ATA

The

fir

st r

esp

ons

ibili

ty o

f C

PLs

is

to c

olle

ct a

nd d

isse

min

ate

geo

spat

ial

dat

a re

qui

red

fo

r su

pp

ort

ing

pla

nnin

g d

ecis

ions

in

citi

es.

A l

arg

e am

oun

t o

f d

ata

that

C

PLs

re

qui

re

for

thei

r an

alyt

ical

ac

tivi

ties

al

read

y ex

ists

in

d

iffer

ent

agen

cies

and

dep

artm

ents

at

the

loca

l an

d n

atio

nal

leve

l in

Ind

one

sia.

We

pro

vid

e a

tab

le a

t th

e en

d o

f th

is s

ecti

on

sho

win

g a

ll th

e d

atas

ets

that

are

re

com

men

ded

to

be

colle

cted

at

each

CP

L, w

here

we

also

ind

icat

e w

heth

er

and

whe

re w

e ha

ve w

itne

ssed

the

ava

ilab

ility

of

such

dat

a. N

ot

all

of

thes

e d

ata

will

be

avai

lab

le in

eac

h o

f th

e fo

ur c

itie

s. D

urin

g t

he f

irst

yea

r, t

he t

able

ca

n b

e us

ed a

s a

wis

h-lis

t fo

r g

eosp

atia

l dat

a fo

r th

e co

re C

PL

mo

dul

e.

CP

Ls w

oul

d b

enef

it f

rom

est

ablis

hing

a s

usta

inab

le l

ong

-ter

m c

olla

bo

rati

on

wit

h ag

enci

es w

ho g

ener

ate

or

harv

est

geo

spat

ial

dat

a in

Ind

one

sia,

in

ord

er

to h

ave

acce

ss t

o t

he m

ost

up

-to

-dat

e in

form

atio

n an

d t

o a

lso

sha

re t

he d

ata

they

co

mp

ile w

ith

oth

er a

gen

cies

. M

uch

of

the

exis

ting

dat

a, h

ow

ever

, is

no

t d

igit

ized

or

attr

ibut

ed f

or

GIS

. C

ons

truc

tio

n an

d c

hang

e-o

f-us

e p

erm

its,

lan

d

use

map

s, c

adas

tral

rec

ord

s, M

aste

rpla

ns a

nd s

urve

y re

cord

s ca

n b

e p

aper

-b

ased

, m

akin

g r

efer

enci

ng a

nd d

ata

shar

ing

diff

icul

t. C

AD

dra

win

gs,

whe

re

they

exi

st,

oft

en c

om

e w

itho

ut a

sp

atia

l re

fere

nce

for

geo

gra

phi

c lo

cati

on.

In

crea

sing

ly,

Ind

one

sian

cit

ies

have

sta

rted

co

llect

ing

the

se d

ata

dig

ital

ly a

nd

som

e ha

ve a

cces

s to

rem

ote

-sen

sing

inf

orm

atio

n, s

uch

as m

ediu

m o

r hi

gh-

reso

luti

on

aeri

al o

r sa

telli

te i

mag

ery,

LiD

AR

sca

ns, o

r sa

telli

te s

tere

o i

mag

ery.

A

nu

mb

er

of

citi

es

have

g

one

th

roug

h in

itia

l ef

fort

s to

d

igit

ize

bui

ldin

g

foo

tpri

nts,

st

reet

ne

two

rks

and

la

nd-u

se

clas

sific

atio

n zo

nes,

al

bei

t w

ith

rela

tive

ly l

imit

ed a

ttri

but

e in

form

atio

n d

escr

ibin

g t

he c

hara

cter

isti

cs o

f th

e g

eom

etri

c el

emen

ts.

In t

he f

irst

pro

ject

pha

se,

CP

L st

aff

will

nee

d t

o c

olle

ct,

org

aniz

e an

d s

tand

ard

ize

a b

road

ran

ge

of

exis

ting

dat

aset

s av

aila

ble

in e

ach

city

.

As

exis

ting

dat

a ar

e o

ften

par

tial

, C

PLs

will

als

o n

eed

to

ob

tain

so

me

dat

a,

such

as

re

mo

tely

se

nsed

to

po

gra

phi

c d

ata

thro

ugh

spec

ializ

ed

serv

ice

pro

vid

ers.

Gro

und

sur

veys

sho

uld

be

pre

par

ed f

or

bui

ldin

g u

se a

nd c

ond

itio

n m

aps

or

add

itio

nal

soci

o-e

cono

mic

d

ata

gat

heri

ng.

CP

Ls

sho

uld

d

evel

op

ro

utin

e p

roce

sses

fo

r im

ple

men

ting

an

nual

g

roun

d

surv

eys

for

accu

racy

Fig

ure

2. H

igh-

reso

luti

on s

atel

lite

imag

e, L

ond

on, O

xfor

d C

ircus

.

8

veri

ficat

ion

and

fo

r co

mp

leti

ng o

r up

dat

ing

exi

stin

g d

atas

ets.

The

Wo

rld

Ban

k co

nsul

tant

s in

thi

s p

roje

ct w

ill p

rop

ose

sta

ndar

ds

and

gui

del

ines

fo

r su

ch d

ata

colle

ctio

n d

urin

g t

he f

irst

imp

lem

enta

tio

n p

hase

.

2.1.

1. U

nits

of A

naly

sis

All

geo

spat

ial

dat

a is

ass

oci

ated

wit

h g

eom

etri

c sp

atia

l un

its

(e.g

. ad

dre

ss

po

ints

, b

uild

ing

o

r p

arce

l p

oly

go

ns,

stre

et

cent

erlin

es),

w

hich

ar

e g

eog

rap

hica

lly

refe

renc

ed.

Eac

h in

div

idua

l sp

atia

l un

it

cont

ains

ce

rtai

n at

trib

utes

ab

out

the

env

iro

nmen

tal

feat

ure

it s

ymb

oliz

es (

e.g

. b

uild

ing

flo

or

area

, cen

sus

blo

ck p

op

ulat

ion,

par

cel v

alue

etc

.). T

he s

pat

ial u

nits

fo

r av

aila

ble

d

ata

typ

ical

ly d

epen

d o

n b

oth

the

geo

met

ric

elem

ents

ob

serv

ed i

n th

e b

uilt

en

viro

nmen

t (s

tree

ts, b

uild

ing

s, p

arce

ls),

the

eas

e w

ith

whi

ch t

he d

ata

can

be

phy

sica

lly s

urve

yed

on

gro

und

(ce

nsus

blo

cks

can

be

typ

ical

ly s

urve

yed

in le

ss

then

a d

ay),

as

wel

l as

the

gra

phi

c re

pre

sent

atio

n co

nstr

aint

s (e

.g. s

lop

ing

land

is

usu

ally

sym

bo

lized

wit

h ho

rizo

ntal

ele

vati

on

lines

tha

t ar

e no

t w

itne

ssed

in

real

ity)

. F

or

pri

vacy

and

sec

urit

y re

aso

ns,

or

tech

nica

l lim

itat

ions

, ag

enci

es

that

co

llect

dat

a d

o n

ot

alw

ays

rele

ase

spat

ial i

nfo

rmat

ion

at t

he s

ame

spat

ial

reso

luti

on

or

wit

h th

e sa

me

unit

s o

f an

alys

is

that

d

ata

was

o

rig

inal

ly

asse

mb

led

. Sta

tist

ic In

do

nesi

a (B

PS

), f

or

inst

ance

, co

llect

s al

l cen

sus

dat

a at

a

cens

us b

lock

leve

l (o

ften

co

mp

arab

le t

o a

cit

y b

lock

), b

ut o

nly

rele

ases

dat

a at

th

e vi

llag

e, s

ub-d

istr

ict

and

hig

her

leve

ls.

Ori

gin

al s

pat

ial

unit

s o

f d

ata,

as

dis

sem

inat

ed b

y d

iffer

ent

dat

a co

llect

ion

agen

cies

, d

o

not

nece

ssar

y m

atch

th

e ne

eds

for

urb

an

gro

wth

an

alys

es

pro

po

sed

in

the

core

mo

dul

e.

An

imp

ort

ant

par

t o

f C

PL’

s ac

tivi

ties

will

be

ded

icat

ed

to

com

pili

ng

and

d

eriv

ing

ne

w

dat

aset

s,

usin

g

geo

-pro

cess

ing

te

chni

que

s fo

r ag

gre

gat

ing

or

dis

agg

reg

atin

g r

aw d

ata

to d

esir

ed s

pat

ial u

nits

. F

or

bui

ldin

g le

vel a

naly

sis,

fo

r ex

amp

le, l

and

use

dat

a o

r ec

ono

mic

cen

sus

dat

a sh

oul

d b

e d

isag

gre

gat

ed f

rom

ori

gin

al t

ract

leve

ls t

o in

div

idua

l bui

ldin

g le

vels

(d

iscu

ssed

bel

ow

).

To

cla

ssify

sp

atia

l d

ata

in t

his

rep

ort

we

focu

s o

n th

e sp

atia

l un

it r

athe

r th

an

the

cont

ent

of

attr

ibut

e fie

lds.

Dat

aset

s w

ith

diff

eren

t sp

atia

l un

its

may

hav

e

Fig

ure

3. D

iffer

ent

spat

ial u

nits

of

anal

ysis

and

the

ir r

esp

ecti

ve

attr

ibut

es

9

sim

ilar

attr

ibut

es –

par

cels

, bui

ldin

gs,

cen

sus

blo

cks

and

vill

ages

can

all

cont

ain

info

rmat

ion

abo

ut t

he r

esid

enti

al p

op

ulat

ion,

the

bui

lt a

rea,

or

the

num

ber

of

bus

ines

s es

tab

lishm

ents

the

y ac

com

mo

dat

e (F

igur

e 3)

.

The

fo

llow

ing

list

of

dat

a is

see

n as

mo

st e

ssen

tial

fo

r C

PLs

to

co

llect

, in

ord

er

to c

arry

out

the

ana

lyti

cs p

rop

ose

d i

n th

e co

re m

od

ule.

Fo

r ea

ch d

atas

et,

des

ired

att

rib

utes

, req

uire

d r

aw d

ata,

and

po

tent

ial d

ata

sour

ces

are

exp

lain

ed

whe

re p

oss

ible

.

2.1.

1.1.

Bui

ldin

gs a

nd a

ddre

ss p

oint

s

Ind

ivid

ual

bui

ldin

gs

are

amo

ng t

he f

ines

t sp

atia

l un

it o

f an

alys

is c

om

mo

nly

used

in

citi

es.

A s

ubst

anti

al p

art

of

hum

an a

ctiv

itie

s ta

kes

pla

ce i

n b

uild

ing

s;

the

peo

ple

tha

t b

uild

ing

s ac

com

mo

dat

e an

d t

he a

ctiv

itie

s th

ey e

ngag

e in

, g

ener

ate

the

ori

gin

s an

d

des

tina

tio

ns

of

mo

st

ped

estr

ian

and

ve

hicu

lar

mo

vem

ent

on

city

str

eets

.

Bui

ldin

gs

are

typ

ical

ly r

epre

sent

ed a

s p

oly

go

n fe

atur

es (

bui

ldin

g f

oo

tpri

nts)

. P

oly

go

n fe

atur

es

illus

trat

e th

e re

alis

tic

geo

met

ry

of

the

actu

al

bui

ldin

g

foo

tpri

nts

and

al

low

va

luab

le

po

st-p

roce

ssin

g

anal

ysis

th

at

may

no

t b

e in

clud

ed i

n th

e o

rig

inal

att

rib

utes

(b

uild

ing

are

a o

r p

erim

eter

cal

cula

tio

ns,

3d

heig

ht

extr

usio

ns,

volu

me

calc

ulat

ions

).

Ho

wev

er,

bui

ldin

gs

can

also

b

e re

pre

sent

ed a

s p

oin

t fe

atur

es,

pla

ced

at

eith

er t

he c

entr

oid

of

the

actu

al

foo

tpri

nt o

r at

one

or

mo

re e

xter

ior

entr

ance

lo

cati

ons

of

the

bui

ldin

g.

Po

int

rep

rese

ntat

ion

of

bui

ldin

gs

can

be

usef

ul f

or

anal

yzin

g a

cces

sib

iliti

es b

etw

een

bui

ldin

gs

on

the

netw

ork

of

city

str

eets

tha

t re

qui

res

dis

cret

e lo

cati

ons

(F

igur

e 4

). P

oly

go

ns c

an e

asily

be

conv

erte

d t

o p

oin

ts,

but

no

t vi

ce v

ersa

. C

entr

oid

p

oin

ts,

may

fai

l ho

wev

er,

to c

aptu

re t

he a

ccur

ate

rela

tio

nshi

p b

etw

een

a b

uild

ing

and

its

stre

et(s

) o

r th

e vi

sual

sig

htlin

es a

vaila

ble

bet

wee

n b

uild

ing

s. It

is

the

refo

re p

refe

rab

le t

o r

epre

sent

bui

ldin

gs

wit

h re

alis

tic

po

lyg

ons

.

Ad

dre

ss p

oin

ts, d

iscu

ssed

her

eaft

er, c

an b

e p

lace

d a

t en

tran

ces

to a

ccur

atel

y ca

ptu

re

the

rela

tio

nshi

p

bet

wee

n b

uild

ing

s an

d

stre

ets.

T

he

geo

met

ry

of

bui

ldin

gs

(fo

otp

rint

s o

r 3D

vo

lum

es)

are

typ

ical

ly

ob

tain

ed

fro

m

sate

llite

im

ager

y o

r ae

rial

sca

ns (

e.g

. Li

DA

R,

ster

eo i

mag

ery)

. A

hig

h-re

solu

tio

n g

eo-

refe

renc

ed

sate

llite

im

age

can

be

used

as

a

bas

e fo

r d

raw

ing

b

uild

ing

Fig

ure

4.

Bui

ldin

g

foo

tpri

nts

and

th

eir

cent

roid

s,

Har

vard

Sq

uare

, Cam

brid

ge

MA

.

10

po

lyg

ons

w

ith

vect

or-

bas

ed

lines

in

d

raft

ing

so

ftw

are

like

Aut

oC

AD

, D

raft

Sig

ht

or

Rhi

noce

ros.

A

n ef

fort

sh

oul

d

be

mad

e to

d

isti

ngui

sh

two

au

tono

mo

usly

ow

ned

or

used

bui

ldin

gs

into

tw

o s

epar

ate

po

lyg

ons

whe

neve

r p

oss

ible

. Thi

s ca

n b

e ch

alle

ngin

g t

o d

o if

bui

ldin

gs

on

sate

llite

imag

ery

app

ear

to s

hare

wal

ls o

r ar

e o

ther

wis

e d

ense

ly s

pac

ed,

but

a c

aref

ul d

isti

ncti

on

of

ind

ivid

ual b

uild

ing

s ac

cord

ing

to

ad

dre

sses

can

gre

atly

ben

efit

late

r an

alys

is. A

p

hysi

cal g

roun

d c

heck

is u

sual

ly r

equi

red

to

co

ver

area

s th

at a

re p

oo

rly

visi

ble

in

the

sat

ellit

e im

age

(e.g

. cl

oud

y o

r o

bst

ruct

ed b

y tr

ees)

and

to

ens

ure

that

th

e d

raw

ing

s m

atch

rea

lity.

Gen

erat

ing

ad

dre

ss p

oin

t fe

atur

es t

ypic

ally

req

uire

s a

gro

und

sur

vey,

usi

ng

GP

S t

oo

ls f

or

reco

rdin

g t

he s

pat

ial p

osi

tio

n o

f ea

ch o

bse

rved

ad

dre

ss lo

cati

on

on

the

gro

und

(F

igur

e 5)

. If

accu

rate

str

eet

netw

ork

dat

a is

ava

ilab

le (

e.g

. fro

m

Nav

teq

, To

m T

om

) an

d t

he t

ota

l nu

mb

er o

f ad

dre

sses

on

each

str

eet

kno

wn,

th

en a

dd

ress

po

int

loca

tio

ns c

an a

lso

be

inte

rpo

late

d in

GIS

(w

ith

som

e sp

atia

l er

ror)

. T

oo

ls f

or

per

form

ing

suc

h ad

dre

ssin

g a

re o

ffer

ed i

n E

SR

I’s A

rcG

IS.

Man

agin

g s

tand

ard

ized

ad

dre

ssin

g d

ata

is u

sual

ly a

nat

iona

l lev

el a

ctiv

ity;

the

sy

stem

sh

oul

d

be

cons

iste

nt

thro

ugho

ut

the

coun

try.

E

ffo

rts

tow

ard

s a

nati

ona

l ad

dre

ss

dat

abas

e ap

pea

r to

b

e un

der

w

ay

at

the

Ind

one

sian

G

eosp

atia

l Inf

orm

atio

n A

gen

cy (

BIG

).

Des

ired

bui

ldin

g a

nd a

dd

ress

po

int

attr

ibut

es f

or

pro

po

sed

CP

L an

alyt

ics

incl

ude:

Vo

lum

e, t

ota

l flo

or

area

and

fo

otp

rint

are

a

Bui

ldin

g v

olu

me

and

flo

or

area

ind

icat

e th

e am

oun

t o

f av

aila

ble

sp

ace

for

hum

an a

ctiv

itie

s. A

ltho

ugh

floo

r ar

ea is

a m

ore

acc

urat

e in

dic

ato

r o

f sp

ace

for

livin

g, w

ork

ing

, stu

dyi

ng, i

t is

no

t al

way

s ea

sily

ob

tain

ed a

nd r

equi

res

det

aile

d

surv

eys

or

reg

istr

y re

cord

s.

Bui

ldin

g

volu

me,

ho

wev

er,

can

be

dir

ectl

y co

mp

uted

fro

m t

he b

asic

geo

met

ry i

nfo

rmat

ion

(fo

otp

rint

are

a an

d b

uild

ing

he

ight

). A

pp

roxi

mat

e b

uild

ing

flo

or

area

can

be

foun

d b

y d

ivin

g t

he b

uild

ing

he

ight

by

a ty

pic

al f

loo

r he

ight

(e.

g. 3

met

ers)

and

mul

tip

lyin

g it

by

the

area

of

the

foo

tpri

nt. T

ypic

al f

loo

r-to

-flo

or

heig

ht f

or

resi

den

tial

bui

ldin

gs

is 2

.8 m

eter

s

Fig

ure

5. P

arce

ls a

nd a

dd

ress

poi

nts,

Lo

s A

ngel

es.

11

for

off

ice

bui

ldin

g 3

.5 m

eter

s, f

or

inst

itut

iona

l b

uild

ing

s 4

met

ers

and

fo

r in

dus

tria

l bui

ldin

gs

5 m

eter

s.

Ad

dre

ss

Str

eet

add

ress

ing

is t

he p

ract

ice

of

assi

gni

ng u

niq

ue n

ames

to

sp

atia

l fea

ture

s,

typ

ical

ly b

uild

ing

s, p

lots

and

bus

ines

s lo

cati

ons

, usi

ng a

co

nsis

tent

hie

rarc

hica

l sy

stem

. A

str

eet

add

ress

ing

sys

tem

co

ntai

ns s

ever

al c

om

po

nent

s th

at a

re

cons

iste

nt a

cro

ss a

ll in

div

idua

l un

its.

The

mo

st t

ypic

al c

om

po

nent

s o

f an

ad

dre

ss a

re s

tree

t se

gm

ent

nam

e, s

tree

t ty

pe

(ro

ad, d

rive

, hig

hway

etc

.) p

lot

or

bui

ldin

g n

umb

er, u

nit

num

ber

(if

app

licab

le),

and

an

area

ID

suc

h as

ZIP

or

po

stal

co

de.

The

pur

po

se o

f us

ing

suc

h a

hier

arch

ical

nam

ing

sys

tem

is

to

allo

w u

sers

to

lo

cate

an

add

ress

eve

n w

hen

they

do

no

t ha

ve a

cces

s to

GIS

d

ata.

Str

eet

add

ress

ing

is

vita

l fo

r lo

cati

ng f

acili

ties

and

inf

rast

ruct

ure

(bus

ines

ses,

ho

spit

als,

sc

hoo

ls

etc.

),

and

d

eliv

ery

serv

ices

(e

.g.

po

stal

o

r em

erg

ency

se

rvic

es)

in a

n ur

ban

set

ting

. Dev

elo

pin

g a

sta

ndar

d s

tree

t ad

dre

ssin

g s

yste

m

as a

co

mm

on

pla

tfo

rm a

mo

ng a

ll p

ublic

and

pri

vate

ag

enci

es is

als

o c

ruci

al f

or

urb

an i

nfo

rmat

ion

man

agem

ent.

It

allo

ws

for

syst

emat

ical

ly s

tori

ng s

urve

yed

d

ata

at t

he h

ighe

st p

oss

ible

res

olu

tio

n (h

ous

eho

ld o

r b

usin

ess

leve

l).

It a

lso

al

low

s fo

r g

ener

atin

g a

gre

at d

eal

of

spat

ial

dat

a fr

om

reg

istr

y re

cod

es t

hat

cont

ain

add

ress

att

rib

utes

and

kee

p t

hem

co

ntin

uous

ly u

pd

ated

wit

h lit

tle

effo

rt a

nd c

ost

.

Dev

elo

pin

g a

str

eet

add

ress

ing

sys

tem

s is

oft

en a

nat

iona

l le

vel

effo

rt,

but

co

nduc

ted

at

a lo

cal l

evel

, whe

re C

PLs

can

pla

y a

sig

nific

ant

role

. The

re a

re a

nu

mb

er o

f ex

amp

les

of

such

eff

ort

s in

dev

elo

pin

g c

oun

trie

s, i

nclu

din

g i

n a

seri

es o

f S

ub-S

ahar

an c

oun

trie

s in

Afr

ica,

in c

olla

bo

rati

on

wit

h th

e W

orl

d B

ank

(see

Far

vacq

ue-V

itko

vic

et a

l 20

05)

.

Whi

le i

n th

e lo

ng-r

un,

pub

lic a

nd p

riva

te a

gen

cies

may

up

dat

e th

eir

add

ress

in

form

atio

n us

ing

a s

tand

ard

ad

dre

ssin

g s

yste

m, C

PLs

can

als

o h

elp

ass

emb

le

dat

a fr

om

reg

istr

y re

cord

s us

ing

ava

ilab

le s

tree

t in

form

atio

n. T

his

req

uire

s b

ring

ing

p

rese

ntly

av

aila

ble

ad

dre

sses

in

to

a un

iform

fo

rmat

. A

rcG

IS

geo

cod

ing

to

ols

, an

d P

ytho

n o

r V

B s

trin

g f

unct

ions

allo

w f

or

mat

chin

g t

he

Fig

ure

7. R

T-R

W i

s cu

rren

tly

the

smal

lest

ad

dre

ssin

g u

nit

in

Ind

ones

ia.

Eac

h R

T co

ntai

ns s

ever

al h

ouse

hold

s. S

ourc

e:Jo

hn

Tay

lor.

12

exis

ting

ad

dre

sses

tha

t co

me

in d

iffer

ent

form

ats:

“20

Do

ver

Dr.

” to

“20

Do

ver

Dri

ve”

or

“20

do

ver

dri

ve.”

Bus

ines

ses

esta

blis

hmen

ts a

nd E

mp

loym

ent

Bus

ines

ses

acti

viti

es o

ften

tak

e p

lace

in b

uild

ing

s; b

uild

ing

, thu

s, o

ffer

a n

atur

al

unit

of

rep

rese

ntat

ion

for

the

dis

trib

utio

n o

f b

usin

esse

s in

a c

ity.

Bus

ines

s es

tab

lishm

ent

and

em

plo

ymen

t d

ata

at a

n in

div

idua

l b

uild

ing

lev

el s

houl

d

idea

lly i

nclu

de

the

tota

l nu

mb

er o

f b

usin

esse

s an

d e

mp

loye

es c

lass

ified

by

diff

eren

t in

dus

try

cate

go

ries

(e.

g. r

etai

l es

tab

lishm

ents

or

serv

ices

), a

s sh

ow

n in

fig

ure

7.

Raw

b

usin

ess

loca

tio

n d

ata

typ

ical

ly

com

es

at

add

ress

o

r g

eog

rap

hic

coo

rdin

ate

(po

int)

lev

el,

as e

xpla

ined

fur

ther

bel

ow

. A

gg

reg

atin

g

such

po

int

dat

a to

bui

ldin

g f

oo

tpri

nts,

ho

wev

er,

pro

vid

es c

onv

enie

nt u

nits

of

anal

yses

and

allo

ws

bui

ldin

gs

to b

e us

ed a

s in

put

s in

mul

tip

le t

ypes

of

spat

ial

anal

yses

.

Bus

ines

s es

tab

lishm

ent

and

em

plo

ymen

t d

ata

are

usua

lly a

vaila

ble

in

two

d

iffer

ent

form

s. T

hey

may

be

avai

lab

le i

n ag

gre

gat

e ce

nsus

tra

ct l

evel

(o

r o

ther

sta

tist

ical

bo

und

arie

s),

ind

icat

ing

the

to

tal

num

ber

of

bus

ines

ses

wit

hin

each

ag

gre

gat

ed a

rea.

Sto

ring

det

aile

d b

usin

ess

clas

sific

atio

n in

form

atio

n is

no

t ty

pic

ally

fea

sib

le in

thi

s ca

se. S

eco

nd, e

very

bus

ines

s ca

n b

e sh

ow

n as

an

ind

ivid

ual

unit

, re

pre

sent

ed

by

po

ints

w

ith

attr

ibut

e in

form

atio

n (s

ee

2.1.1

.3.b

usin

ess

loca

tio

ns).

W

hen

bus

ines

s es

tab

lishm

ents

are

sho

wn

at t

he

bui

ldin

g le

vel,

the

bus

ines

s at

trib

utes

sho

uld

be

sum

mar

ized

, sho

win

g t

he s

um

tota

l of

all e

stab

lishm

ents

tha

t o

ccup

y ea

ch b

uild

ing

.

BP

S c

olle

cts

dat

a o

n m

ediu

m a

nd l

arg

e b

usin

ess

esta

blis

hmen

ts w

ith

mo

re

than

20

em

plo

yees

, w

hich

co

nsti

tute

s a

smal

l p

erce

ntag

e o

f al

l b

usin

esse

s in

In

do

nesi

a. B

PS

als

o s

urve

ys s

mal

l sa

mp

les

of

all

bus

ines

s ev

ery

year

, w

hich

ca

nno

t b

e d

isag

gre

gat

ed l

ow

er t

han

city

sca

le.

CP

Ls m

ay n

eed

to

co

nduc

t g

roun

d

surv

eys

to

colle

ct

mo

re

com

pre

hens

ive

dat

a o

n b

usin

ess

esta

blis

hmen

ts.

…

33 M

anuf

actu

ring

4

2 W

hole

sale

Tra

de

44

Ret

ail T

rad

e

44

1 M

oto

r V

ehic

le a

nd P

art

Dea

lers

44

11 A

uto

mo

bile

Dea

lers

…

.

44

12 O

ther

Mo

tor

Veh

icle

Dea

lers

4

412

1 R

ecre

atio

nal V

ehic

le D

eale

rs

4

412

10 R

ecre

atio

nal V

ehic

le D

eale

rs

44

122

Mo

torc

ycle

s, B

oat

, and

Oth

er M

oto

r V

ehic

le D

eale

rs

4

412

22 B

oat

Dea

lers

44

1228

Mo

torc

ycle

, AT

V a

nd a

ll o

ther

Mo

tor

Dea

lers

4

5 R

etai

l Tra

de

48

Tra

nsp

ort

atio

n an

d W

areh

ous

ing

…

Fig

ure

7:

NA

ICS

bus

ines

s cl

assi

ficat

ion;

b

usin

ess

esta

blis

hmen

ts s

houl

d b

e g

roup

ed a

nd c

ateg

oriz

ed b

ased

on

stan

dar

d

syst

ems

such

as

N

orth

A

mer

ican

In

dus

ial

Cla

ssifi

cati

on

Syst

ems

(NA

ICS)

, or

S

tand

ard

In

dus

tria

l C

lass

ifica

tion

Sys

tem

s (S

IC).

Dep

end

ing

on

the

anal

ytic

al t

ask

that

is

cond

ucte

d,

the

clas

sific

atio

n d

epth

wou

ld v

ary;

e.g

. in

N

AIC

S t

he s

ix-d

igit

lev

el i

s th

e m

ost

det

aile

d c

lass

ifica

tion,

ho

wev

er,

the

first

tw

o d

igits

are

eno

ugh

to d

istin

gui

sh r

etai

l tr

ade

bus

ines

s es

tab

lishm

ents

.

13

Num

ber

s o

f re

sid

ents

/ h

ous

eho

lds

Po

pul

atio

n is

the

key

det

erm

inan

t o

f d

eman

d f

or

a ci

ty’s

res

our

ces.

Det

aile

d

dat

a o

n sp

atia

l d

istr

ibut

ion

of

po

pul

atio

n –

and

dem

og

rap

hic

sub

-gro

ups

– al

low

s fo

r ef

ficie

nt

esti

mat

es

for

a ci

ty’s

re

sour

ce

need

s.

Po

pul

atio

n an

d

dem

og

rap

hic

dat

a ar

e no

t co

mm

onl

y d

isse

min

ated

at

the

bui

ldin

g l

evel

, b

ut

agg

reg

ated

to

ce

nsus

b

lock

o

r tr

act

leve

ls.

In

Ind

one

sia,

B

PS

co

nduc

ts

hous

eho

ld

surv

eys

and

p

rovi

des

ce

nsus

d

ata

at

the

villa

ge

(Des

a o

r K

elur

ahan

) le

vel.

If in

div

idua

l bui

ldin

gs’

typ

e (e

.g. r

esid

enti

al, c

om

mer

cial

etc

.),

and

flo

or

area

or

volu

me

are

kno

wn,

the

n p

op

ulat

ion

valu

es f

rom

hig

her-

leve

l sp

atia

l un

its

can

be

dis

agg

reg

ated

to

th

e b

uild

ing

le

vel

wit

h re

aso

nab

le

accu

racy

. T

he t

ota

l nu

mb

er o

f re

sid

ents

in

a ce

nsus

blo

ck c

an t

here

by

be

allo

cate

d b

etw

een

resi

den

tial

str

uctu

res,

wei

ghi

ng t

he a

lloca

tio

ns b

y th

e si

ze

of

each

bui

ldin

g (

Fig

ure

8).

Bui

ldin

g t

ype

and

sub

typ

e

Bui

ldin

g t

ype

des

crib

es t

he t

ypes

of

acti

viti

es t

hat

take

pla

ce i

n th

e b

uild

ing

(F

igur

e 9

). R

elia

ble

ass

essm

ent

of

the

real

est

ate

mar

ket

(ass

et a

nd u

se)

is n

ot

feas

ible

wit

hout

bui

ldin

g t

ype

and

sub

typ

e in

form

atio

n. B

uild

ing

typ

es o

r su

bty

pes

do

no

t ne

cess

ary

shar

e th

e sa

me

mar

ket.

Co

mm

erci

al a

nd r

esid

enti

al

spac

es

bel

ong

to

se

par

ate

mar

kets

an

d

sep

arat

e d

eman

d

seg

men

ts.

To

d

eter

min

e th

e su

pp

ly s

ide

of

each

mar

ket,

it

is e

ssen

tial

to

kee

p t

rack

of

bui

ldin

g s

tock

by

typ

e.

Bui

ldin

g s

ubty

pes

(e.

g.

hous

ing

) ca

n al

so h

ave

sep

arat

e m

arke

ts (

Fig

ure

10).

T

he

dem

and

fo

r la

rge

land

ed

hous

es

is

com

po

sed

o

f a

diff

eren

t so

cio

-ec

ono

mic

gro

up o

f b

uyer

s an

d r

ente

rs t

han

the

dem

and

fo

r sm

all

stud

ios

or

pub

lic h

ous

ing

uni

ts.

The

tw

o m

ain

sour

ces

for

bui

ldin

g t

ype

dat

a ar

e zo

ning

map

s, w

hich

usu

ally

d

o n

ot

cont

ain

sub

typ

e in

form

atio

n, a

nd g

roun

d s

urve

ys.

Dev

elo

pin

g a

nd

mai

ntai

ning

an

accu

rate

bui

ldin

g t

ype

dat

abas

e ca

n b

e ve

ry l

abo

r in

tens

ive,

b

ut t

he p

ay-o

ffs

are

also

hig

h si

nce

bui

ldin

g le

vel d

ata

allo

ws

for

man

y us

eful

an

alys

es a

bo

ut c

ity’

s re

al e

stat

e m

arke

t.

Fig

ure

8: D

isag

gre

gat

ing

pop

ulat

ion

info

rmat

ion

from

cen

sus

trac

t le

vel t

o b

uild

ing

s. T

he p

op

ulat

ion

of t

he c

ensu

s tr

act

is

allo

cate

d o

nly

amo

ng b

uild

ing

s th

at c

ont

ain

resi

den

tial

use

s,

wei

ghi

ng t

he a

lloca

tion

by

the

bui

ldin

g v

olum

es.

Nat

ural

ly

som

e sp

atia

l er

ror

is g

ener

ated

in

the

pro

cess

, b

ut s

tori

ng

pop

ulat

ion

esti

mat

es a

t an

ind

ivid

ual

bui

ldin

g l

evel

is

usef

ul

for

a nu

mb

er o

f hi

gh

-res

olu

tion

ana

lyse

s.

14

Onc

e a

relia

ble

bui

ldin

g t

ype

and

sub

typ

e d

atab

ase

is a

ssem

ble

d t

hro

ugh

gro

und

sur

veys

, it

is

pra

ctic

al t

o m

aint

ain

and

up

dat

e it

via

bui

ldin

g p

erm

it,

mo

difi

cati

on

per

mit

, dem

olit

ion

per

mit

and

cha

nge-

of-

use

per

mit

dat

abas

es. I

f a

new

p

erm

it

is

issu

ed,

the

finis

hed

b

uild

ing

o

ccup

ancy

p

erm

it

can

auto

mat

ical

ly s

igna

l to

the

bui

ldin

g d

atab

ase

man

ager

s th

at a

new

bui

ldin

g

has

bee

n ad

ded

to

the

sto

ck. T

he b

uild

ing

typ

e d

atab

ase

can

then

ver

ify t

he

dat

a an

d a

dd

the

new

bui

ldin

g t

o t

he r

epo

sito

ry.

A s

imila

r p

roce

dur

e ca

n fo

llow

oth

er t

ypes

of

bui

ldin

g p

erm

its.

2.1.

1.2.

Par

cels

Par

cel

geo

met

ry r

eco

rds

land

ow

ners

hip

bo

rder

s. T

he g

eom

etry

of

par

cel

bo

rder

s is

oft

en p

rovi

ded

by

nati

ona

l lan

d a

gen

cies

(e.

g. B

PN

in In

do

nesi

a). I

n In

do

nesi

a, T

ax D

irec

tora

te G

ener

al a

lso

pre

par

es p

arce

l p

oly

go

n d

atas

ets,

fo

r it

s o

wn

land

val

ue a

sses

smen

t p

urp

ose

s. T

he t

wo

par

cel

dat

abas

es c

urre

ntly

re

mai

n se

par

ate,

b

ut

BP

N

app

ears

to

b

e w

ork

ing

o

n a

join

t d

atab

ase1 .

Att

rib

utes

tha

t p

arce

ls s

houl

d c

ont

ain

incl

ude:

Ass

esse

d v

alue

and

tra

nsac

tio

n hi

sto

ry

Par

cel i

s an

intu

itiv

e un

it f

or

land

mar

ket

rela

ted

ana

lyse

s, a

s tr

ansa

ctio

ns a

nd

valu

e as

sess

men

ts

are

cond

ucte

d

at

the

par

cel

leve

l. In

In

do

nesi

a,

Tax

D

irec

tora

te G

ener

al k

eep

s tr

ack

of

land

tra

nsac

tio

ns, a

nd a

sses

ses

land

val

ues.

Zo

ning

Par

cel

is t

he a

pp

rop

riat

e un

it f

or

cont

aini

ng z

oni

ng a

ttri

but

es,

such

as

land

us

e, p

lot

rati

o,

heig

ht l

imit

and

set

bac

ks,

as b

uild

ing

per

mit

s ar

e is

sued

fo

r sp

ecifi

c p

arce

ls.

Zo

ning

inf

orm

atio

n p

rese

nted

in

mas

terp

lans

and

det

aile

d

pla

ns,

whi

ch a

re p

rep

ared

by

Bap

ped

a in

eac

h ci

ty i

n In

do

nesi

a, t

ypic

ally

sp

ecify

zo

ning

reg

ulat

ions

fo

r ea

ch p

arce

l.

1 S

our

ce: p

erso

nal c

om

mun

icat

ion

wit

h B

PN

Com

mer

cial

Bui

ldin

gs

(10

,912

) In

stit

utio

nal B

uild

ing

s (1

2,4

01)

In

dus

tria

l Bui

ldin

gs

(7,0

48)

R

esid

enti

al B

uild

ing

s (5

8,56

6)

Fig

ure

10: H

ous

ing

seg

men

tatio

n (s

ubty

pes

of

resi

den

tial

bui

ldin

g

cate

gor

ies)

in S

ing

apor

e.

Pub

lic H

ousi

ng (

916,

842

unit

s)

Con

do

min

ium

s (2

00

,00

0 u

nits

) La

nded

Hou

ses

(70

, 00

0 u

nits

)

Fig

ure

9: B

uild

ing

sto

ck b

y ty

pe

in S

ing

apor

e; m

arke

ts f

or d

iffer

ent

bui

ldin

g t

ypes

are

to

a la

rge

exte

nt in

dep

end

ent

of e

ach

othe

r.

15

Bui

ldin

g p

rop

erti

es

Mo

st

phy

sica

l b

uild

ing

st

ruct

ure

or

land

im

pro

vem

ent

dat

a ca

n b

e al

so

agg

reg

ated

to

the

par

cel

leve

l: e.

g.

tota

l flo

or

area

, to

tal

bui

ldin

g v

olu

me,

ad

dre

ss, t

ota

l num

ber

of

bus

ines

ses.

Fro

ntag

e

In a

dd

itio

n to

typ

ical

geo

met

rica

l p

rop

erti

es (

per

imet

er a

nd a

rea)

, it

is

usef

ul

for

par

cel

dat

aset

s to

co

ntai

n th

e le

ngth

of

stre

et f

ront

age:

the

len

gth

of

par

cel

per

imet

er t

hat

is d

irec

tly

conn

ecte

d t

o a

str

eet

(Fig

ure

11).

Fro

ntag

e is

an

imp

ort

ant

det

erm

inan

t fo

r la

nd v

alue

, and

ess

enti

al f

or

dev

elo

pin

g h

edo

nic

pri

cing

mo

del

s fo

r la

nd.

Par

cel t

ype

Par

cels

can

als

o o

pti

ona

lly b

e cl

assi

fied

bas

ed o

n th

e nu

mb

er o

f st

reet

s th

at a

p

arce

l is

dir

ectl

y co

nnec

ted

to

(F

igur

e 12

). A

“m

idd

le p

arce

l” h

as a

cces

s to

one

st

reet

, b

ut a

“co

rner

par

cel”

can

hav

e ac

cess

to

2,

3 o

r 4

str

eets

Sim

ilar

to

fro

ntag

e, p

arce

l ty

pe,

as

def

ined

ab

ove

, is

an

imp

ort

ant

det

erm

inan

t o

f la

nd

valu

e, a

nd u

sefu

l fo

r d

evel

op

ing

hed

oni

c p

rici

ng m

od

els.

2.1.

1.3.

Bus

ines

s lo

cati

ons

As

men

tio

ned

ab

ove

, ra

w b

usin

ess

esta

blis

hmen

ts a

nd e

mp

loym

ent

dat

a is

b

est

sto

red

at

an

in

div

idua

l b

usin

ess

esta

blis

hmen

t le

vel,

rep

rese

nted

as

p

oin

ts

(Fig

ure

13).

R

epre

sent

ing

se

par

ate

bus

ines

s es

tab

lishm

ents

w

ith

sep

arat

e p

oin

t fe

atur

es i

s th

e m

ost

ro

bus

t an

d u

sefu

l w

ay o

f st

ori

ng t

he

bus

ines

s es

tab

lishm

ents

’ d

ata.

Po

ints

can

alw

ays

be

agg

reg

ated

or

join

ed t

o

oth

er la

rger

uni

ts (

e.g

. bui

ldin

gs

or

par

cels

) if

need

ed.

The

att

rib

ute

info

rmat

ion

of

bus

ines

s lo

cati

ons

sho

uld

typ

ical

ly in

dic

ate:

- T

he le

gal

nam

e o

f th

e b

usin

ess

- T

he n

ame

of

a p

aren

t co

mp

any

(if

app

licab

le)

Fig

ure

11: P

arce

l str

eet

fron

tag

e

Fig

ure

12.

Par

cel

typ

e, i

ndic

atin

g t

he n

umb

er o

f st

reet

s th

at a

p

arce

l has

dir

ect

acce

ss t

o.

16

- D

etai

led

ind

ustr

y cl

assi

ficat

ion

cod

e (e

.g. N

AIC

S)

at a

s d

etai

led

leve

l as

avai

lab

le (

e.g

. 6 d

igit

s), s

ee F

igur

e 7.

-

Yea

r es

tab

lishe

d a

t th

e p

rese

nt lo

cati

on

- N

umb

er o

f em

plo

yees

-

Long

itud

e an

d la

titu

de

coo

rdin

ates

-

Ad

dre

ss

- Z

IP c

od

e -

To

wn,

Reg

ion

CP

Ls m

ay c

ond

uct

gro

und

sur

veys

to

co

llect

bus

ines

s lo

cati

on

dat

a as

BP

S

colle

cts

dat

a o

nly

on

med

ium

and

lar

ge

bus

ines

s es

tab

lishm

ents

wit

h m

ore

th

an 2

0 e

mp

loye

es.

In t

he l

ong

er r

un,

accu

rate

bus

ines

s lo

cati

on

dat

a ca

n b

e co

llect

ed f

rom

DG

T

ax r

eco

rds

that

sho

uld

acc

oun

t fo

r al

l b

usin

ess

loca

tio

ns f

or

inco

me

tax

and

sa

les

tax

reas

ons

. A g

oo

d t

ax s

yste

m c

an p

rod

uce

amp

le s

pat

ial d

ata

annu

ally

, at

alm

ost

no

ext

ra c

ost

.

2.1.

1.4

. Tra

nspo

rtat

ion

netw

ork

The

mo

vem

ent

of

go

od

s an

d p

eop

le in

cit

ies

take

s p

lace

thr

oug

h th

ree

laye

rs

of

netw

ork

s: v

ehic

ular

ro

ads,

ped

estr

ian

pat

hs,

and

pub

lic t

rans

it n

etw

ork

s.

The

lat

ter

is o

ften

use

d t

og

ethe

r w

ith

the

ped

estr

ian

netw

ork

, an

d f

orm

s a

mul

ti-m

od

al n

etw

ork

. A

naly

ses

that

hel

p u

s un

der

stan

d h

ow

res

our

ces

and

fa

cilit

ies

are

acce

ssib

le t

o u

sers

thr

oug

h th

e m

enti

one

d n

etw

ork

s re

qui

re d

ata.

M

ost

ci

ties

co

llect

an

d

pre

par

e ro

ad

cent

erlin

e d

atas

ets

(Fig

ure

14)

and

so

met

imes

pub

lic t

rans

it n

etw

ork

dat

aset

s, b

ut o

ften

ove

rlo

ok

the

ped

estr

ian

netw

ork

. R

oad

ce

nter

lines

ar

e th

e m

ost

co

mm

onl

y us

ed

GIS

d

ata

for

acce

ssib

ility

ana

lyse

s, n

ot

onl

y fo

r ve

hicu

lar

mo

vem

ent,

but

als

o f

or

ped

estr

ian

mo

vem

ent.

A la

rge

par

t o

f p

edes

tria

n flo

w t

akes

pla

ce a

long

str

eets

. Ho

wev

er,

stre

et c

ente

rlin

es d

o n

ot

cap

ture

ped

estr

ian

rout

es t

hat

are

not

alo

ng r

oad

s (e

.g.

thro

ugh

uno

ccup

ied

par

cels

in

info

rmal

set

tlem

ents

). P

urel

y ve

hicu

lar

rout

es, s

uch

as t

oll

road

s, d

o n

ot

have

sid

ewal

ks. I

t is

, thu

s, r

eco

mm

end

ed t

hat

the

CP

Ls p

rep

are

geo

spat

ial d

atas

ets

of

stre

et c

ente

rlin

es, p

ublic

tra

nsit

line

s,

as w

ell

as s

idew

alks

and

oth

er p

edes

tria

n p

aths

(F

igur

e 13

). A

gre

at d

eal

of

a

Fig

ure

13.

Ped

estr

ian

Net

wor

k an

d

bus

ines

s lo

cati

ons,

B

ugis

, Si

ngap

ore

. S

ourc

e: C

ity

Fo

rm L

ab.

Eac

h b

usin

ess

esta

blis

hmen

t p

oint

co

ntai

ns a

set

of

attr

ibut

es d

escr

ibin

g t

he b

usin

ess.

17

city

’s c

ircu

lati

on

in I

ndo

nesi

a o

ccur

s o

n fo

ot.

Bey

ond

acc

essi

bili

ty a

naly

ses,

si

dew

alk

dat

abas

es w

ill b

e al

so u

sefu

l fo

r si

dew

alk

imp

rove

men

t p

lans

.

Str

eet

netw

ork

cen

terl

ine

dat

a m

ay b

e av

aila

ble

in

Ind

one

sian

cit

ies

via

thir

d-

par

ty d

ata

pro

vid

ers,

suc

h as

NA

VT

EQ

.

Po

tent

ial a

ttri

but

es f

or

road

net

wo

rk d

atas

ets

incl

ude:

- W

idth

or

num

ber

of

lane

s -

Typ

e (e

.g. p

aved

or

unp

aved

) -

Str

eet

nam

e -

Ro

ad c

lass

ifica

tio

n -

Tra

ffic

dir

ecti

ona

lity

Des

ired

att

rib

utes

fo

r p

edes

tria

n ne

two

rk d

atas

ets

incl

ude:

- W

idth

-

Typ

e (e

.g. i

ndo

or,

out

do

or,

out

do

or

but

she

lter

ed)

Po

tent

ial a

ttri

but

es f

or

pub

lic t

rans

po

rt n

etw

ork

dat

aset

s in

clud

e:

- Li

st o

f b

uses

usi

ng t

he s

egm

ent

- A

vera

ge

tim

e co

nsum

ed o

n th

e se

gm

ent

- F

req

uenc

y (o

f b

us o

r tr

ain

on

the

seg

men

t)

- S

tart

and

end

sta

tio

ns o

f th

e se

gm

ent

2.1.

1.5.

Adm

inis

trat

ive

boun

dari

es

Ad

min

istr

ativ

e b

oun

dar

ies

are

abst

ract

ex

tent

s th

at

def

ine

the

spat

ial

auth

ori

ty o

f g

ove

rnan

ce o

f co

mm

unit

ies

in a

hie

rarc

hica

l o

rder

fro

m n

atio

nal

leve

l to

sm

alle

st g

roup

ing

s in

nei

ghb

orh

oo

ds

e.g

. R

T o

r R

W i

n In

do

nesi

an

citi

es).

Ad

min

istr

ativ

e b

oun

dar

ies

are

com

mo

n sp

atia

l un

its

for

sto

ring

so

cio

-ec

ono

mic

dat

a.

Fig

ure

14:

Stre

et

cent

erlin

es,

Los

Ang

eles

, C

A,

and

th

eir

attr

ibut

es: d

rive

dir

ectio

n, r

oad

typ

e, r

oad

sur

face

typ

e, a

nd r

oad

se

gm

ent

leng

th.

18

BP

S p

rovi

des

cen

sus

dat

a at

the

vill

age

leve

l, as

wel

l as

hig

her

agg

reg

atio

n le

vels

suc

h as

cit

y, d

istr

ict,

or

pro

vinc

e. M

icro

dat

a in

Ind

one

sian

cit

ies

is

typ

ical

ly c

olle

cted

by

the

head

of

villa

ge

at t

he R

T le

vel (

Fig

ure

15).

The

CP

Ls

sho

uld

dig

itiz

e an

d d

istr

ibut

e th

e fo

llow

ing

set

of

adm

inis

trat

ive

bo

und

arie

s:

- R

egen

cy (

Kab

upat

en)

or

Cit

y (K

ota

) -

Sub

-dis

tric

t (K

ecam

atan

) -

Nei

ghb

orh

oo

d/v

illag

e (D

esa

or

Kel

urah

an)

- B

lock

(R

T/R

W)

Eac

h ad

min

istr

ativ

e p

oly

go

n sh

oul

d c

arry

a u

niq

ue id

enti

fier

nam

e. L

ow

er le

vel

po

lyg

ons

sho

uld

als

o i

ndic

ate

the

nam

es o

r ID

s o

f th

e hi

ghe

r le

vel

po

lyg

ons

th

ey a

re p

art

of.

The

po

lyg

on

shap

efile

s ca

n b

e lik

ely

ob

tain

ed f

rom

the

loca

l B

PS

off

ice

up t

o t

he n

eig

hbo

rho

od

lev

el.

Map

pin

g t

he R

T-R

W b

oun

dar

ies

coul

d r

equi

re c

olla

bo

rati

on

wit

h th

e he

ads

of

villa

ges

. Suc

h m

app

ing

has

bee

n p

revi

ous

ly im

ple

men

ted

in S

olo

.

2.1.

1.6.

Oth

er s

pati

al d

ata

The

dat

aset

s d

iscu

ssed

ab

ove

co

nsti

tute

s o

nly

the

mo

st e

ssen

tial

dat

a th

at

can

be

used

in

the

core

mo

dul

e o

f C

PLs

. M

uch

of

the

dat

a is

als

o d

irec

tly

usef

ul f

or

oth

er o

pti

ona

l CP

L m

od

ules

. The

list

of

spat

ial d

ata

that

cit

ies

colle

ct

or

alre

ady

have

can

be

very

exh

aust

ive.

Man

y o

f th

ose

dat

a ca

n b

e as

soci

ated

to

one

or

seve

ral s

pat

ial u

nits

men

tio

ned

ab

ove

; e.g

. ene

rgy

cons

ump

tio

n ca

n b

e as

soci

ated

to

bui

ldin

gs

and

par

cels

, cr

ime

rate

s to

any

ad

min

istr

ativ

e b

oun

dar

ies.

Ho

wev

er, t

here

are

so

me

oth

er d

ata

that

req

uire

the

ir o

wn

spat

ial

unit

s: f

or

exam

ple

, d

ata

on

wat

er i

nfra

stru

ctur

e o

r W

i-F

i ho

tsp

ot.

A l

ist

of

spat

ial

dat

a th

at C

PLs

are

rec

om

men

ded

to

co

llect

is

pro

vid

ed i

n th

e ta

ble

b

elo

w.

Kot

a:

Cit

y

Kec

amat

an:

Dis

tric

t

Kel

urah

an o

r D

esa:

V

illag

e

RT

-RW

: S

mal

lest

ad

dre

ssin

g u

nit

Fig

ure

15: A

dm

inis

trat

ive

sub

div

isio

ns in

Ind

ones

ia.

Sour

ce: J

ohn

T

aylo

r.

19

bla

nk

No

t av

aila

ble

but

des

ired

for

pro

po

sed

ana

lyti

cs

!!

!!

!!

!!

! A

vaila

ble

!

!!

!!

!!

!"

Ava

ilab

le in

som

e of

the

par

tici

pat

ing

cit

ies

!!

!!

!!

!!

!!

!!

!!

!!

!!IN

DO

NES

IA U

RB

AN

DA

TA

BP

N

BP

S

Bap

ped

a D

G

Spat

ial

Pla

nnin

g

DG

T

ax

Oth

er

Po

tent

ial

Sour

ces

BIG

N

ote

s

!!D

AT

A

1!IM

AG

ER

Y

!!H

igh-

reso

luti

on s

atel

lite

imag

e !!

!!"

"

!!

"

!!A

eria

l pho

tog

rap

hy

!!!!

"

"

!!

"

!!D

igit

al E

leva

tion

Mo

del

(D

EM

) o

f ur

ban

top

ogra

phy

!!

!!!!

!!!!

!!O

vera

ll ur

ban

ext

ent

(bui

lt-u

p a

rea

in t

he m

etro

are

a)

!!!!

!!!!

!!W

orld

B

ank

"

2!P

LA

NN

ING

RE

GU

LA

TIO

NS

!!

!!!!

!!!!

!!Z

onin

g p

lans

!!

!!!

! !!

!!F

loo

r ar

ea r

atio

s (g

ross

plo

t ra

tios)

, as

spec

ified

in r

egul

atio

ns

!!!!

! !

!!

!!La

nd u

se a

s sh

ow

n in

city

mas

ter

pla

n

!!!!

! !

!!

!!B

uild

ing

hei

ght

s lim

it, a

s sp

ecifi

ed in

reg

ulat

ions

!!

!!!

! !!

20

3!S

TA

TIS

TIC

AL

BO

UN

DA

RIE

S

!!!!

!!!!

!!

!!P

rovi

ncia

l ad

min

istr

ativ

e b

ound

arie

s !!!

!!!!

!!

!!M

unic

ipal

ad

min

istr

ativ

e b

ound

arie

s

!!!

!!!!

!!

!!D

istr

ict

adm

inis

trat

ive

bou

ndar

ies

!!!

!!!!

!!

!!S

ub-d

istr

ict

adm

inis

trat

ive

bou

ndar

ies

!!!

!!!!

!!

!!Z

ip c

od

e ar

eas

!!!!

!!!!

!!

!!C

ensu

s tr

acts

(V

illag

e)

!!!

!!!!

!!

4!D

EM

OG

RA

PH

IC C

HA

RA

CT

ER

IST

ICS

!!!!

!!!!

!!

!!R

esid

enti

al p

op

ulat

ion

(cen

sus)

!!!

!!!!

!!

B

PS

dat

a ar

e ag

gre

gat

ed a

t vi

llag

e le

vel

!!R

esid

enti

al p

op

ulat

ion

by

sex

!!!

!!!!

!!

B

PS

dat

a ar

e ag

gre

gat

ed a

t vi

llag

e le

vel

!!R

esid

enti

al p

op

ulat

ion

by

age

gro

up

!!!

!!!!

!!

B

PS

dat

a ar

e ag

gre

gat

ed a

t vi

llag

e le

vel

!!R

esid

enti

al p

op

ulat

ion

by

resi

den

ce t

ype

!!!

!!!!

!!

B

PS

dat

a ar

e ag

gre

gat

ed a

t vi

llag

e le

vel

!!H

ous

ehol

d s

urve

ys: h

ouse

hold

inco

me

!!!

!!!!

!!

B

PS

dat

a ar

e ag

gre

gat

ed a

t vi

llag

e le

vel

!!H

ous

ehol

d s

urve

ys: f

amily

siz

e !!!

!!!!

!!

B

PS

dat

a ar

e ag

gre

gat

ed a

t vi

llag

e le

vel

21

5!U

RB

AN

FO

RM

!!

!!!!

!!!!

!!O

bse

rved

flo

or

area

rat

ios

!!!!

!!!!

!!

!!O

bse

rved

land

use

!!

!!

!!

!!O

ffic

ially

rec

og

nize

d in

form

al s

ettl

emen

ts

!!!!

"

"

!!W

orld

B

ank

!!B

uild

ing

flo

or a

reas

!!

!!"!

!!!!

!!C

ity b

lock

s !!

!!"

"

!!

!!P

arce

l bou

ndar

ies,

ow

ners

hip

!

!!"

"

!

!!B

uild

ing

foo

tpri

nts

!!!!

"

"

!!

!!B

uild

ing

hei

ght

s !!

!!!!

!!!!

!!B

uild

ing

ag

es

!!!!

!!!!

!!

!!B

uild

ing

use

s (e

.g. r

esid

entia

l, co

mm

erci

al, e

tc.)

!!

!!

!!

!!B

uild

ing

typ

es (

e.g

. wal

k-up

, con

do,

ro

w-h

ous

e, k

amp

ong

, in

form

al)*

!!

!!!!

!!!!

!!B

uild

ing

ad

dre

sses

/ZIP

Cod

es

!!!!

!!!!

!!

22

6!IN

FR

AS

TR

UC

TU

RE

!!

!!!!

!!!!

!!T

rans

por

tati

on in

fras

truc

ture

: ro

ads

by

cate

gor

y, #

lane

s,

dir

ecti

on, s

etb

acks

, cen

terl

ines

, int

erse

ctio

ns, t

raff

ic li

ght

s, t

oll

gat

es.

!!!!

"

"

!!

!!T

rans

por

tati

on in

fras

truc

ture

: rai

l *

!!!!

"

"

!!

!!T

rans

por

tati

on in

fras

truc

ture

: bus

line

s an

d s

tatio

ns*

!!!!

"

"

!!

!!T

rans

por

tati

on in

fras

truc

ture

: bic

ycle

ro

utes

!!

!!"

"

!!

!!T

rans

por

tati

on in

fras

truc

ture

: ped

estr

ian

sid

ewal

ks, c

ross

ing

s*

!!!!

"

"

!!

!!Se

rvic

e in

fras

truc

ture

: w

ater

, sew

age,

and

dra

inag

e*

!!!!

"

"

!!

!!Se

rvic

e in

fras

truc

ture

:ele

ctri

city

line

s, s

ubst

atio

ns

!!!!

"

"

!!

!!D

rink

ing

wat

er s

upp

ly n

etw

ork

s*

!!!!

"

"

!!

!!P

ota

ble

wat

er s

our

ce lo

cati

ons

(e.g

. wel

ls)*

!!

!!"

"

!!

7!E

CO

NO

MIC

CH

AR

AC

TE

RIS

TIC

S

!!!!

!!!!

!!

!!M

unic

ipal

/dis

tric

t ex

pen

dit

ure

by

econ

omic

cat

egor

ies*

!!

"

!!!!

!!

!!P

rovi

nce

exp

end

itur

e b

y e

cono

mic

cat

egor

ies*

!!

"

!!!!

!!

!!M

unic

ipal

/dis

tric

t re

venu

e b

y so

urce

s*

!!"

!!!!

!!

!!P

rovi

nce

reve

nue

by

sour

ces*

!!

"

!!!!

!!

23

8!E

ST

AB

LIS

HM

EN

TS

!!

!!!!

!!!!

!!F

irm d

istr

ibut

ion

(Ind

ivid

ual e

stab

lishm

ent

loca

tion

s)*

!!!!

!!!!

!!

!!Jo

b d

istr

ibut

ion

(job

s p

er a

rea/

typ

e)*

!!!!

!!!!

!!

!!P

oin

ts o

f in

tere

st (

mus

eum

s, in

stit

utio

ns)*

!!

!!"

"

!!

!!P

ublic

inst

itut

ions

(ho

spita

ls, p

olic

e st

atio

ns, l

ibra

ries

etc

.)*

!!!!

"

"

!!

!!P

ublic

inst

itut

ions

: sch

ools

(w

ith le

vels

, and

no.

stu

den

ts)*

!!

!!"

"

!!

!!P

ublic

inst

itut

ions

: ho

spit

als

(wit

h sp

ecia

ltie

s, a

nd c

apac

itie

s)*

!!!!

"

"

!!

!!P

ublic

inst

itut

ions

: oth

ers

(det

aile

d)*

!!

!!"

"

!!

9!N

AT

UR

AL

HA

ZA

RD

!!

!!!!

!!!!

!!Se

ism

ic h

azar

d z

ones

!!

!!

!!

Wor

ld

Ban

k

!!F

loo

d z

one

s !!

!!"

"

!!W

orld

B

ank

!!La

nd t

opog

rap

hy (

poi

nts

/ to

po

lines

) !!

!!"

"

!!W

orld

B

ank

24

10!

LA

ND

AN

D R

EA

L E

ST

AT

E M

AR

KE

T

!!!!

!!!!

!!

!!La

nd c

adas

ter:

par

cels

(ta

x, t

rans

acti

ons,

etc

.)*

! !!

!!!!

!

!!La

nd p

rice

s*

!!

!!!!

! D

evel

oper

s &

Bro

kers

DG

Tax

est

imat

es d

o no

t o

ften

ind

icat

e re

al m

arke

t va

lue

!!H

ous

ing

pri

ces*

!!!!

!!!

Dev

elop

ers

& B

roke

rs

D

G T

ax e

stim

ates

do

not

oft

en in

dic

ate

real

mar

ket

valu

e

!!C

omm

erci

al r

eal e

stat

e p

rice

s*

!!!!

!!!!

! D

evel

oper

s &

Bro

kers

DG

Tax

est

imat

es d

o no

t o

ften

ind

icat

e re

al m

arke

t va

lue

!!H

ous

ing

Ren

ts*

!!!!

!!!!

! D

evel

oper

s &

Bro

kers

DG

Tax

est

imat

es d

o no

t o

ften

ind

icat

e re

al m

arke

t va

lue

!!C

omm

erci

al r

ents

* !!

!!!!

!!!

Dev

elop

ers

& B

roke

rs

D

G T

ax e

stim

ates

do

not

oft

en in

dic

ate

real

mar

ket

valu

e

!!La

nd r

ents

* !!

!!!!

!!!

Dev

elop

ers

& B

roke

rs

D

G T

ax e

stim

ates

do

not

oft

en in

dic

ate

real

mar

ket

valu

e

!!H

ous

ing

ten

ure

(vac

ant,

ow

ner-

occu

pie

d, r

enta

l occ

upie

d)

!!!!

!!!!

Dev

elop

ers

& B

roke

rs

!!R

esid

enti

al u

nit

size

s/ n

o. o

f ro

om

s !!

!!!!

!!!!

Dev

elop

ers

& B

roke

rs

!!Is

sued

bui

ldin

g p

erm

its

wit

hin

the

pas

t x

per

iod

* !!

!!!!

! !!

!!Is

sued

dem

olit

ion

per

mit

s w

ithi

n th

e p

ast

x p

erio

d*

!!!!

!!!

!!

!!P

roje

cts

curr

ently

in c

onst

ruct

ion

(res

iden

tial

/com

mer

cial

/ind

ustr

ial/

off

ice/

oth

er)

!!!!

!!!

!!D

evel

oper

s &

Bro

kers

!!Sa

les

Pri

ces

/ R

ate

of

Sale

s at

eac

h ne

w d

evel

opm

ent

curr

entl

y on

the

mar

ket

!!!!

!!!!

!!D

evel

oper

s &

Bro

kers

!!Li

st o

f al

l per

mit

s re

qui

red

for

new

dev

elop

men

ts b

y la

nd u

se

typ

e an

d t

ypic

al d

urat

ions

. !!

!!!!

! !!

25

2.2.

DA

TA P

LATF

OR

M

To

ful

fill

thei

r p

rim

ary

go

al o

f as

sem

blin

g,

mai

ntai

ning

and

dis

trib

utin

g l

arg

e g

eosp

atia

l d

atab

ases

, th

e C

ity

Pla

nnin

g L

abs

need

a d

igit

al g

eosp

atia

l d

ata

pla

tfo

rm t

hat

sati

sfie

s fiv

e fu

ndam

enta

l req

uire

men

ts. T

he p

latf

orm

sho

uld

:

! A

llow

dat

a to

be

effic

ient

ly a

nd c

onv

enie

ntly

sto

red

and

man

aged

! A

llow

dat

a to

be

shar

ed a

cro

ss d

iffer

ent

city

dep

artm

ents

or

wit

h m

emb

ers

of

the

pub

lic o

ver

Inte

rnet

bro

wse

rs

! E

nab

le a

ll d

ata

man

agem

ent

op

erat

ions

to

be

per

form

ed f

rom

a l

oca

l ne

two

rked

co

mp

uter

! E

nab

le u

sers

to

do

wnl

oad

dat

a la

yers

tha

t th

ey h

ave

secu

rity

cle

aran

ce t

o

acce

ss

! E

nab

le t

he e

nd-u

sers

to

int

erac

t w

ith

the

dat

aset

s o

n a

web

-bro

wse

r, b

y q

uery

ing

the

ir a

ttri

but

es,

ove

rlay

ing

diff

eren

t d

ata

laye

rs,

usin

g s

imp

le b

ase-

map

s to

sit

uate

the

info

rmat

ion,

and

ove

rlay

ing

per

sona

l inf

orm

atio

n la

yers

on

pub

lishe

d m

aps.

The

ca

pac

ity

to

op

erat

e b

asic

sp

atia

l fu

ncti

ons

(e

.g.

spat

ial

sear

ch,

mea

sure

men

t o

r p

roxi

mit

y se

arch

, o

verl

ay f

unct

ion

etc.

) w

oul

d b

e d

esir

able

ad

dit

iona

l fun

ctio

ns f

or

the

end

use

rs, t

houg

h no

t a

first

-ord

er p

rio

rity

.

The

re

is

a co

nsid

erab

le

list

of

op

en

sour

ce

and

p

rop

riet

ary

GIS

se

rver

te

chno

log

ies

avai

lab

le

for

man

agin

g

spat

ial

dat

a.

Arc

GIS

S

erve

r,

Arc

GIS

O

nlin

e,

and

M

apIn

fo

Sp

atia

l S

erve

r ar

e am

ong

th

e m

ost

co

mm

onl

y us

ed

pro

pri

etar

y o

pti

ons

. O

n th

e o

ther

ha

nd,

Geo

Ser

ver,

O

pen

Geo

S

uite

o

r G

eoN

od

e ar

e so

me

exam

ple

s o

f w

idel

y us

ed

op

en

sour

ce

web

-bas

ed

geo

spat

ial

cont

ent

man

agem

ent

pla

tfo

rms.

The

Wo

rld

Ban

k’s

Pla

tfo

rm f

or

Urb

an M

anag

emen

t an

d A

naly

sis

(PU

MA

), c

urre

ntly

und

er d

evel

op

men

t, i

s al

so a

po

tent

ial o

pen

so

urce

op

tio

n fo

r th

e C

ity

Pla

nnin

g L

abs.

Ap

art

fro

m t

he

init

ial c

ost

diff

eren

ce, f

ast

setu

p t

ime,

and

off

-the

-she

lf av

aila

bili

ty o

f fu

ncti

ons

Fig

ure

16.

New

Yo

rk C

ity

Op

enD

ata.

Mor

e th

an 1

500

sp

atia

l d

atas

ets

are

pub

lic a

vaila

ble

thr

oug

h th

is g

eosp

atia

l p

latf

orm

, so

me

upd

ated

d

aily

. So

ftw

are:

O

pen

Geo

S

uite

E

nter

pri

se

Ed

itio

n.

26

are

the

mai

n ad

vant

ages

of

pro

pri

etar

y p

latf

orm

s. O

n th

e o

ther

han

d,

op

en

sour

ce p

latf

orm

s al

low

fo

r a

hig

her

leve

l of

cust

om

izat

ion.

So

me

Ind

one

sian

ci

ties

an

d

agen

cies

ha

ve

alre

ady

dev

elo

ped

th

eir

ow

n p

latf

orm

s (F

igur

e 17

) us

ing

bo

th p

rop

riet

ary

and

op

en s

our

ce o

pti

ons

. F

or

exam

ple

fo

r th

eir

per

mit

ting

sys

tem

s, S

urab

aya

has

utili

zed

Arc

GIS

Onl

ine,

w

hile

Bal

ikp

apan

has

dev

elo

ped

its

op

en s

our

ce p

latf

orm

. At

the

nati

ona

l lev

el,

BIG

, ha

s d

evel

op

ed a

n in

teg

rate

d o

pen

-so

urce

/pro

pri

etar

y (A

rcG

IS o

nlin

e)

dat

a-sh

arin

g p

latf

orm

. In

thei

r d

ecis

ion

for

dat

a p

latf

orm

op

tio

ns, C

PLs

’ sho

uld

co

nsid

er p

latf

orm

s th

e re

spec

tive

cit

ies

are

curr

entl

y us

ing

.

In a

dd

itio

n to

geo

spat

ial

cont

ent

man

agem

ent

soft

war

e, e

ach

CP

L re

qui

res

serv

er s

pac

e fo

r st

ori

ng it

s g

eosp

atia

l co

nten

t. C

PLs

can

ho

st t

heir

geo

spat

ial

dat

a o

n lo

cal

serv

ers

or

use

netw

ork

ed e

nter

pri

se s

tora

ge

syst

ems,

suc

h as

cl

oud

sto

rag

e o

n A

maz

on

Web

Ser

vice

s (A

WS

). W

hile

sto

ring

dat

a o

n a

clo

ud

can

be

mo

re e

xpen

sive

tha

n st

ora

ge

on

a lo

cal s

erve

r, o

utso

urci

ng c

oul

d a

lso

p

rovi

de

oth

er b

enef

its.

Clo

ud s

tora

ge

syst

ems

are

typ

ical

ly m

ore

sta

ble

tha

n lo

cal

host

s,

par

ticu

larl

y re

silie

nt

to

po

wer

sh

utd

ow

ns

and

hu

man

er

rors

, ke

epin

g

dat

a co

nsta

ntly

o

nlin

e.

Usi

ng

clo

ud

sto

rag

e al

so

shift

s th

e m

aint

enan

ce b

urd

en f

rom

CP

Ls t

o t

he s

ervi

ce p

rovi

der

. P

rofe

ssio

nal

dat

a st

ora

ge

syst

ems

off

er q

ualif

ied

tec

hnic

al a

ssis

tant

s w

ith

serv

ice

cont

ract

s.

Geo

spat

ial

cont

ent

man

agem

ent

soft

war

e ca

n b

e us

ed t

o s

et u

p d

iffer

ent

leve

ls o

f se

curi

ty a

cces

s to

diff

eren

t d

ata

user

s.

It is

po

ssib

le t

hat

CP

Ls u

se a

diff

eren

t p

latf

orm

s in

sho

rt a

nd lo

ng r

un. A

rcG

IS

onl

ine

in c

om

bin

atio

n w

ith

Am

azo

n cl

oud

sto

rag

e, f

or

inst

ance

, co

uld

be

an

op

tio

n fo

r sh

ort

ter

m, a

s it

is e

asy

and

fas

t to

set

up

wit

h lo

w in

itia

l co

st, w

hile

C

PLs

dev

elo

p t

heir

cus

tom

ized

op

en-s

our

ce p

erm

anen

t p

latf

orm

s.

Fig

ure

17. S

olo

Kit

a K

ota,

the

inte

ract

ive

web

-bas

ed m

ap f

or

the

city

of

Sura

kart

a (S

olo

). S

oftw

are:

Go

ogle

Map

s A

PI.

Fig

ure

18. M

IT G

eoW

eb d

ata

shar

ing

and

vis

ualiz

atio

n p

latf

orm

.

27

Fig

ure

19.

One

o

f th

e d

ata

pla

tfor

ms

of

the

Cit

y of

C

amb

rid

ge

– C

amb

rid

ge

City

Vie

wer

– t

hrou

gh

whi

ch a

la

rge

amou

nt

of

geo

spat

ial

dat

a in

clud

ing

b

uild

ing

s,

par

cels

, p

aved

su

rfac

es,

sid

ewal

ks,

stre

et

cent

erlin

es,

tree

s, a

nd in

fras

truc

ture

sys

tem

s is

pub

lishe

d.

Fig

ure

20.

Cen

sus

blo

ck

leve

l d

emo

gra

phi

c d

ata

of

Cam

bri

dg

e, B

osto

n an

d S

omer

ville

, fr

om C

ensu

s B

urea

u p

latf

orm

.

28

SPA

TIA

L G

RO

WTH

AN

D C

HA

NG

E A

NA

LYTI

CS

29

3.1.

SPA

TIA

L G

RO

WTH

AN

D C

HA

NG

E

Mo

nito

ring

tre

nds

in s

pat

ial d

ata

is a

ver

y us

eful

ana

lyti

cal t

echn

ique

tha

t ca

n p

rovi

de

valu

able

inf

orm

atio

n fo

r p

lann

ers.

Eff

ecti

ve p

lann

ing

dec

isio

ns a

re

bui

lt

upo

n sh

ort

an

d

long

-ter

m

pro

ject

ions

o

f th

e sp

atia

l d

istr

ibut

ion

of

reso

urce

s an

d d

eman

d.

The

se p

roje

ctio

ns a

re o

ften

mad

e b

y ex

trap

ola

ting

cu

rren

t tr

end

s in

sp

atia

l dat

a. F

ore

cast

ing

the

sp

atia

l dis

trib

utio

n o

f re

sour

ces

(e.g

. ho

usin

g,

job

s, a

gri

cult

ural

lan

d)

and

dem

and

(re

pre

sent

ed,

for

inst

ance

, b

y p

op

ulat

ion

and

inc

om

e) r

elie

s o

n an

und

erst

and

ing

of

curr

ent

and

pas

t tr

end

s.

Whi

le p

roje

cted

sp

atia

l va

lues

are

cru

cial

inp

uts

for

mo

re c

om

ple

x st

atis

tica

l m

od

els,

a s

imp

le c

om

par

iso

n o

f p

roje

cted

val

ues

wit

h b

ench

mar

ks f

rom

oth

er

citi

es

or

exis

ting

co

ndit

ions

ca

n p

rovi

de

a g

roun

d

for

evid

ence

-bas

ed

dec

isio

n-m

akin

g.

Bas

ed o

n p

roje

cted

val

ues,

pla

nner

s ca

n d

ecid

e w

heth

er

inte

rven

ing

act

ions

sho

uld

be

take

n to

str

eng

then

, sl

ow

do

wn

or

reve

rse

curr

ent

tren

ds.

Mo

nito

ring

the

shi

fts

in d

emo

gra

phi

c d

ata,

fo

r in

stan

ce, c

an r

evea

l a s

igni

fican

t in

crea

se i

n th

e nu

mb

er o

f ho

useh

old

s w

ith

youn

g c

hild

ren

in p

erip

hera

l ar

eas

of

a ci

ty,

whi

ch

can

incr

ease

th

e d

eman

d

for

scho

ols

, ho

spit

als,

o

r fo

od

re

sour

ces.

Evi

den

ce o

f su

ch a

gro

win

g d

eman

d a

llow

s p

lann

ers

to d

ecid

e if

new

fac

iliti

es a

re n

eed

ed t

o s

upp

ort

thi

s tr

end

or

if p

olic

ies

are

req

uire

d s

low

it

do

wn.

Und

erst

and

ing

the

rat

e o

f p

op

ulat

ion

gro

wth

sho

uld

fo

rm t

he b

asis

fo

r la

nd-u

se p

lann

ing

.

Alt

houg

h m

oni

tori

ng t

rend

s in

sp

atia

l d

ata

do

es n

ot

exp

lain

the

ir u

nder

lyin

g

caus

es, a

nd t

hus

cann

ot

sug

ges

t w

hat

inte

rven

tio

ns w

ill a

ffec

t th

em, i

t ca

n at

le

ast

fram

e th

e is

sues

tha

t p

lann

ers

sho

uld

fo

cus

on.

Fig

ure

21.

Ind

ones

ian

citi

es a

re f

acin

g r

apid

gro

wth

wit

h an

av

erag

e an

nual

urb

aniz

atio

n ra

te o

f 4

.2%

bet

wee

n 19

93 a

nd

200

7.

30

3.1.

1. M

appi

ng C

hang

e in

Spa

tial

Val

ues

A c

onv

enie

nt w

ay o

f m

oni

tori

ng,

sto

ring

, an

d r

epre

sent

ing

cha

nge

in s

pat

ial

pro

per

ties

is t

o in

clud

e ch

ang

e va

lues

– e

ithe

r ab

solu

te c

hang

e o

r it

s ra

te –

in

the

attr

ibut

es o

f ea

ch s

pat

ial

feat

ure

(Fig

ure

26).

Fo

r ex

amp

le, c

ensu

s b

lock

s ca

n co

ntai

n at

trib

utes

th

at

rep

rese

nt

the

chan

ge

in

thei

r b

uild

ing

st

ock

, p

arti

cula

r d

emo

gra

phi

c g

roup

, em

plo

ymen

t,

land

us

e co

vera

ge,

en

erg

y co

nsum

pti

on

or

per

cap

ita

area

of

gre

en s

pac

e in

a g

iven

per

iod

of

tim

e.

3.1.

2. S

pati

otem

pora

l dat

a If

eve

ry s

pat

ial

feat

ure

cont

ains

sta

rt a

nd e

nd t

ime

dat

a, w

e ca

n vi

sual

ize

snap

sho

ts o

f d

iffer

ent

po

ints

of

tim

e, u

sing

onl

y o

ne d

atas

et (

Fig

ure

22).

S

pat

iote

mp

ora

l d

atab

ases

are

use

ful

whe

n sp

atia

l un

its

them

selv

es c

hang

e o

ver

tim

e, n

ot

mer

ely

the

valu

e o

f th

eir

attr

ibut

es. C

hang

es in

bui

ldin

g s

tock

or

bus

ines

s es

tab

lishm

ents

can

be

bes

t st

ore

d b

y sp

atio

tem

po

ral

dat

abas

es,

as

the

spat

ial

feat

ures

cha

nge

ove

r ti

me

– so

me

bui

ldin

gs

are

dem

olis

hed

and

so

me

are

add

ed t

o t

he b

uild

ing

sto

ck o

ver

tim

e.

3.1.

3. T

he e

xpan

sion

of u

rban

ext

ent

The

mo

st f

und

amen

tal a

spec

t o

f g

row

th a

nd c

hang

e in

cit

ies

is h

ow

muc

h an

d

whe

re u

rban

are

as a

re e

mer

gin

g.

The

exp

ansi

on

in t

he b

oun

dar

y o

f ci

ties

re

pre

sent

s a

shift

in

land

use

co

vera

ge.

T

he m

ain

sour

ce o

f la

nd f

or

a ci

ty’s

g

row

th

is

usua

lly

agri

cult

ural

o

r fo

rest

la

nd.

Giv

en

the

sig

nific

ant

role

o

f ag

ricu

ltur

e se

cto

r in

Ind

one

sia’

s ec

ono

my,

ann

ual

loss

of

agri

cult

ural

lan

d c

an

sig

nific

antl

y re

duc

e it

s ci

ties

’ fo

od

sec

urit

y.

Fig

ure

22. S

pat

iote

mp

oral

dat

aset

s. E

ach

feat

ure

have

sta

rt

and

end

tim

e, a

llow

ing

for

mon

itor

ing

cha

nge

in s

pat

ial

feat

ures

: for

exa

mp

le in

the

ir si

ze, l

ocat

ion

or e

xist

ence

.

31

Ove

rlay

ing

sna

psh

ots

of

the

city

’s e

xten

t o

ver

tim

e ca

n ca

ptu

re t

rend

s in

p

hysi

cal

exp

ansi

on.

A f

utur

e ex

tent

can

be

pro

ject

ed b

ased

on

the

pas

t2 . H

ow

ever

, dis

ting

uish

ing

the

urb

an l

and

co

vera

ge

fro

m n

on-

urb

an l

and

ext

ent

of

a m

etro

po

litan

are

a is

no

t a

triv

ial

task

, an

d d

epen

ds

on

the

def

init

ion

of

“urb

an la

nd”.

Enh

ance

men

ts in

rem

ote

-sen

sing

tec

hno

log

ies

and

ava

ilab

ility

of

hig

h-re

solu

tio

n sa

telli

te im

ager

y ha

ve m

ade

accu

rate

geo

-ref

eren

ced

map

s o

f ur

ban

ext

ent

avai

lab

le f

or

man

y ci

ties

. Ext

ent

bo

und

arie

s fo

r m

ost

med

ium

to

la

rge

citi

es in

Asi

a ca

n b

e o

bta

ined

up

on

req

uest

fro

m A

nnem

arie

Sch

neid

er a

t th

e U

nive

rsit

y o

f W

isco

nsin

. By

ove

rlay

ing

diff

eren

t sn

apsh

ots

of

urb

an e

xten

t o

ver

tim

e ca

n no

t o

nly

cap

ture

the

gro

wth

rat

e, b

ut a

lso

its

char

acte

r. W

e ca

n d

isti

ngui

sh b

etw

een

leap

fro

g g

row

th, e

dg

e g

row

th o

r in

fill

dev

elo

pm

ents

jus

t b

y o

verl

ayin

g t

he e

xten

t m

aps

of

diff

eren

t ti

mes

(F

igur

e 23

).

Pro

ject

ing

the

fut

ure

exte

nt o

f a

city

als

o r

equi

res

info

rmat

ion

abo

ut b

uild

able

ar

ea a

roun

d it

. Geo

gra

phi

cal c

ons

trai

nts

such

as

wat

er b

od

ies,

ste

ep la

nds,

or

pro

tect

ed f

ore

sts

po

se a

bar

rier

to

gro

wth

and

sho

uld

be

acco

unte

d f

or

in

gro

wth

p

roje

ctio

ns.

The

av

aila

bili

ty

of

spac

e no

t o

nly

affe

cts

the

rate

o

f p

oss

ible

g

row

th

but

al

so

its

char

acte

r.

Cit

ies

that

ar

e co

nstr

aine

d

by

geo

gra

phi

c fe

atur

es,

such

as

wat

er b

od

ies

or

stee

ply

slo

ped

lan

d,

gro

w v

ery

diff

eren

tly

fro

m t

hose

wit

h no

bar

rier

s ar

oun

d t

hem

. The

fo

rmer

, fo

r in

stan

ce,

leav

e no

ro

om

fo

r le

apfr

og

dev

elo

pm

ent,

set

ser

ious

lim

its

on

spra

wl,

and

ten

d

to d

evel

op

at

hig

her

den

siti

es (

e.g

. H

ong

Ko

ng).

The

lat

ter

allo

w f

or

low

er

den

sity

dev

elo

pm

ent

and

fra

gm

ente

d g

row

th (

e.g

. Lo

s A

ngel

es).

No

te t

hat

The

gro

wth

and

cha

nge

map

pin

g w

e ha

ve d

iscu

ssed

so

far

, exa

min

ed t

rend

s in

a s

ing

le v

aria

ble

ove

r ti

me.

Ana

lyti

cal t

echn

ique

s th

at a

re p

rese

nted

in t

he -

follo

win

g, A

cces

sib

ility

Ana

lysi

s, Im

pac

t an

alys

is, a

nd s

pat

ial s

tati

stic

al m

od

els,

ca

n b

e us

ed t

o s

tud

y ch

ang

e am

ong

mul

tip

le s

ets

of

spat

ial d

ata.

2 A

m

ore

ac

cura

te

pro

ject

ion

of

bo

und

ary

req

uire

s co

ntro

lling

fo

r d

eter

min

ants

su

ch

as

po

pul

atio

n, a

nd e

cono

mic

per

form

ance

of

the

city

. T

his

req

uire

s re

gre

ssio

n m

od

els,

whi

ch a

re

exp

lain

ed la

ter

in t

his

rep

ort

.

Fig

ure

23:

Jaka

rta

200

0-2

010

: ur

ban

ex

pan

sion

, ca

teg

oriz

ed b

y ex

pan

sio

n ty

pe

(fro

m 1

,158k

m2

to 1,

520

km2)

Ed

ge

gro

wth

262

Sq

.Km

Le

apfr

og g

row

th 7

3 Sq

.Km

In

fill g

row

th 2

4 S

q.K

m

32

Fig

ure

26.

Pop

ulat

ion

gro

wth

bet

wee

n 19

90 a

nd 2

00

0 i

n C

amb

rid

ge,

Blo

cks

rep

rese

nted

in

whi

te e

xper

ienc

ed t

he lo

wes

t ch

ang

e (g

row

th o

r d

eclin

e) in

the

pop

ulat

ion

amo

ng a

ll ce

nsus

b

lock

s of

Cam

brid

ge.

Fig

ure

24. P

opul

atio

n of

cen

sus

blo

cks

in C

amb

ridg

e, 19

90.

Fig

ure

25. P

opul

atio

n of

cen

sus

blo

cks

in C

amb

ridg

e, 2

00

0.

33

3.2.

AC

CES

SIB

ILIT

Y A

NA

LYSI

S

Und

erst

and

ing

ho

w a

cces

sib

le t

he r

eso

urce

s o

f a

city

are

to

peo

ple

is

key

to

pla

nnin

g n

ew i

nfra

stru

ctur

es.

Acc

essi

bili

ty a

naly

ses

inve

stig

ate

how

lo

cati

ons

o

f o

ne

gro

up

of

phe

nom

ena

(ori

gin

s)

are

rela

ted

to

an

oth

er

gro

up

of

phe

nom

ena

(des

tina

tio

ns).

The

se s

pat

ial r

elat

ions

can

be

des

crib

ed in

var

ious

w

ays.

Acc

essi

bili

ty c

an b

e as

sess

ed a

long

tra

nsp

ort

atio

n ne

two

rks,

or

alo

ng

idea

lized

co

ntin

uous

sp

ace

that

sim

plif

ies

cons

trai

nts

to m

ove

men

t (F

igur

e 27

).

It

can

also

b

e d

escr

ibed

g

eom

etri

cally

(b

ased

o

n d

ista

nce)

o

r to

po

log

ical

ly (

e.g

. bas

ed o

n nu

mb

er o

f tu

rns

on

a ne

two

rk, o

r nu

mb

er o

f st

eps

in a

to

po

log

ical

gri

d).

In

this

rep

ort

we

focu

s o

n ex

amp

les

of

acce

ssib

ility

an

alys

es t

hat

are

cond

ucte

d o

ver

urb

an t

rans

po

rtat

ion

netw

ork

s. A

cces

sib

ility

m

easu

res

such

as

Gra

vity

and

Rea

ch a

re c

om

put

ed a

t a

fine

reso

luti

on

for

ind

ivid

ual b

uild

ing

s o

r ad

dre

ss p

oin

ts o

ver

a ne

two

rk o

f ci

ty s

tree

ts.

Acc

essi

bili

ty a

naly

sis

help

s us

id

enti

fy u

nder

serv

ed a

reas

, fo

r in

stan

ce,

area

s w

ith

low

p

edes

tria

n ac

cess

ibili

ty

to

scho

ols

(F

igur

e 31

).

Co

mp

arin

g

acce

ssib

ility

val

ues

of

ind

ivid

ual

ori

gin

s ac

ross

the

cit

y, o

r co

mp

arin

g t

heir

va

lues

to

b

ench

mar

ks

fro

m

oth

er

citi

es,

allo

ws

us

to

det

ect

area

s w

ith

pro

ble

mat

ic a

cces

sib

ility

val

ues.

Acc

essi

bili

ty a

naly

ses

also

pro

vid

e in

put

s to

oth

er a

naly

tics

pro

po

sed

in

this

re

po

rt.

The

y ar

e ke

y d

eter

min

ants

of

land

and

rea

l p

rop

erty

val

ues,

lan

d u

se

pat

tern

s, a

nd b

usin

ess

clus

teri

ng.

Acc

essi

bly

val

ues

can

be

used

in

hed

oni

c p

rici

ng m

od

els

for

land

and

rea

l pro

per

ties

. Po

tent

ial i

mp

acts

of

new

net

wo

rk

infr

astr

uctu

re,

such

as

b

rid

ges

, ro

ads,

b

us

rout

es,

or

sid

ewal

ks,

can

be

anal

yzed

usi

ng b

efo

re a

nd a

fter

acc

essi

bili

ty v

alue

s.

3.2.

1.A

cces

sibi

lity

Mea

sure

s A

mo

ng a

cces

sib

ility

met

rics

, R

each

and

Gra

vity

met

rics

are

sim

ple

to

sp

ecify

an

d c

an b

e in

terp

rete

d m

ost

int

uiti

vely

. R

each

and

Gra

vity

met

rics

can

be

com

put

ed o

ver

real

tra

nsp

ort

atio

n ne

two

rks,

and

be

imp

lem

ente

d f

or

vari

ous

sp

atia

l un

its,

in

clud

ing

b

uild

ing

s,

add

ress

p

oin

ts,

par

cels

o

r K

T

zone

s.

Fig

ure

27.

Acc

essi

bili

ty

typ

es.

Euc

lidea

n d

ista

nce,

ne

twor

k d

ista

nce,

num

ber

of

turn

s (t

opo

log

ical

), n

umb

er

of s

tep

s in

a g

rid

sp

ace

(top

olog

ical

)

34

Co

mp

utin

g a

cces

sib

ility

val

ues

ove

r ne

two

rk a

nd a

t b

uild

ing

or

add

ress

po

int

leve

l ca

n ca

ptu

re a

det

aile

d i

mag

e o

f p

roxi

mit

y to

a c

ity’

s re

sour

ces

at a

n in

div

idua

l ho

useh

old

leve

l.

Rea

ch

The

Rea

ch m

etri

c co

unts

the

num

ber

of

reso

urce

s th

at c

an b

e re

ache

d f

rom

an

ori

gin

wit

hin

a g

iven

sea

rch

rad

ius

ove

r a

netw

ork

of

pat

hs (

Fig

ures

28

, 31,

32).

Fo

r in

div

idua

l bui

ldin

gs,

fo

r in

stan

ce, i

t ca

n te

ll us

ho

w m

any

job

s, s

cho

ols

o

r w

ells

are

ava

ilab

le i

n a

10 m

inut

es w

alki

ng r

adiu

s ar

oun

d i

t. T

he m

etri

c d

oes

n’t

cap

ture

the

var

iati

on

in d

ista

nce

to d

iffer

ent

reac

hab

le r

eso

urce

s; i

t si

mp

ly

coun

ts

all

des

tina

tio

ns

wit

hin

the

giv

en

rad

ius.

If

w

e co

mp

ute

acce

ssib

ility

to

ret

ail

spac

es,

an e

stab

lishm

ent

loca

ted

60

0 m

eter

aw

ay f

rom

th

e o

rig

in i

s tr

eate

d t

he s

ame

as o

ne t

hat

is o

nly

50 m

eter

aw

ay f

rom

the

o

rig

in.

Thi

s d

raw

bac

k is

ad

dre

ssed

by

Gra

vity

met

ric.

The

ad

vant

age

of

the

Rea

ch m

etri

c is

tha

t is

int

uiti

ve t

o u

nder

stan

d a

nd c

om

mun

icat

e to

mul

tip

le

stak

eho

lder

s –

ever

yone

can

und

erst

and

wha

t it

mea

ns t

o h

ave

two

sch

oo

ls

wit

hin

10 m

inut

es w

alki

ng r

adiu

s as

op

po

sed

to

no

ne.

The

rea

ch c

entr

alit

y, R

r [i]

, of

a b

uild

ing

i, in

a s

tree

t ne

two

rk G

des

crib

es t

he

num

ber

of

oth

er b

uild

ing

s in

G t

hat

are

reac

hab

le f

rom

i a

t a

sho

rtes

t p

ath

dis

tanc

e o

f at

mo

st r

. It

is d

efin

ed a

s fo

llow

s:

Rr [i]

=!

!!∈!!

{!}:!![!,!]!!!

W[j]

whe

re d

[i,j]

is t

he s

hort

est

pat

h d

ista

nce

bet

wee

n no

des

i an

d j

in G

an

d W

[j]

is t

he w

eig

ht o

f d

esti

nati

on

nod

e j.

Fig

ure

32 il

lust

rate

s th

e im

ple

men

tati

on

of

Rea

ch t

o jo

bs

in C

amb

rid

ge

and

So

mer

ville

, MA

wit

hin

a 6

00

m w

alki

ng r

adiu

s al

ong

the

ava

ilab

le s

tree

t ne

two

rk.

Fig

ure

28.

The

R

each

m

etri

c is

a

netw

ork

an

alys

is

mea

sure

tha

t ca

ptu

res

the

num

ber

of

des

tina

tions

tha

t ca

n b

e re

ache

d

aro

und

a

pla

ce w

ithi

n a

giv

en

trav

el

rad

ius.

Rea

ch c

an b

e sp

ecifi

ed t

o su

mm

ariz

e ac

cess

ibili

ty

to a

ny k

ind

of

des

tinat

ion

– p

eop

le o

f a

cert

ain

typ

e,

bui

ldin

gs,

fir

ms,

tra

nsit

stat

ions

etc

.– a

nd t

he t

rave

l rad

ius

can

be

spec

ified

fo

r d

iffer

ent

trav

el

mod

es,

such

as

w

alki

ng, d

rivi

ng, b

ikin

g o

r p

ublic

tra

nsit.

35

Gra

vity

Sim

ilar

to R

each

, the

Gra

vity

met

ric

coun

ts t

he n

umb

er o

f re

sour

ces

that

can

b

e re

ache

d

fro

m

an

ori

gin

w

ithi

n a

sear

ch

rad

ius

ove

r a

netw

ork

, b

ut

add

itio

nally

acc

oun

ts f

or

thei

r d

ista

nce

fro

m t

he o

rig

in (

Fig

ure

30).

A b

uild

ing

w

ith

a fe

w s

hop

s lo

cate

d n

ext

do

or

will

get

a h

ighe

r ac

cess

ibili

ty v

alue

to

co

mm

erci

al

esta

blis

hmen

ts

than

a

bui

ldin

g

wit

h a

larg

e nu

mb

er

of

reta

il es

tab

lishm

ents

tha

t ar

e lo

cate

d f

ar a

way

.

The

att

ract

ion

of

des

tina

tio

ns d

oes

no

t d

rop

line

arly

whe

n th

eir

dis

tanc

e fr

om

th

e o

rig

in i

ncre

ases

, b

ut a

t an

exp

one

ntia

l ra

te,

and

it

vari

es f

or

diff

eren

t m

od

es o

f tr

ansp

ort

. The

inve

rse

exp

one

nt o

f d

ista

nce

is o

ften

use

d in

stea

d o

f si

mp

le in

vers

e d

ista

nce

for

wei

ghi

ng d

esti

nati

ons

, and

the

dis

tanc

e d

ecay

rat

e is

co

ntro

lled

by

a co

effic

ient

fo

r ea

ch m

od

e o

f tr

ansp

ort

. Gra

vity

of

po

int

i, in

g

rap

h G

, can

be

spec

ified

as:

Gra

vity

[i]r =

!![!]

!!.![!,!]

!!∈!!

{!}:!![!,!]!!!

whe

re G

ravi

ty[i

]r is

the

gra

vity

ind

ex a

t p

oin

t i

in n

etw

ork

G w

ithi

n se

arch

ra

diu

s r,

W[j

] is

the

wei

ght

of

des

tina

tio

n j,

d[i

,j] i

s th

e sh

ort

est

dis

tanc

e b

etw

een

i an

d j

, an

d b

is

the

exp

one

nt f

or

adju

stin

g t

he e

ffec

t o

f d

ista

nce

dec

ay.

The

se a

cces

sib

ility

mea

sure

s ca

n b

e sp

ecifi

ed i

n th

e U

rban

Net

wo

rk A

naly

sis

To

olb

ox

in A

rcG

IS.

A m

ore

exc

lusi

ve h

elp

do

cum

ent

is a

vaila

ble

wit

h th

e to

olb

ox

to e

xpla

in t

he s

pec

ifica

tio

ns i

n d

etai

l. In

ord

er t

o r

un t

he t

oo

lbo

x,

Arc

GIS

10

and

the

net

wo

rk A

naly

st e

xten

sio

n ar

e re

qui

red

.

Fig

ure

30. I

llust

ratio

n of

the

Gra

vity

met

ric.

Less

Gra

vity

Mo

re G

ravi

ty

36

Fig

ure

31.

Und

erse

rved

are

as;

the

area

s in

ora

nge

do

n’t

have

ac

cess

to

pub

lic s

choo

ls w

ithi

n a

120

0 m

eter

net

wor

k ra

diu

s.

Ove

rlay

ing

the

und

erse

rved

are

as a

nd c

ensu

s d

ata

show

s th

at

app

roxi

mat

ely

10,0

00

p

eop

le

do

n’t

have

w

alki

ng

acce

ss

to

pub

lic s

choo

ls

37

F

igur

e 32

. Rea

ch t

o jo

bs

loca

ted

with

in 6

00

met

er n

etw

ork

rad

ius

(ap

pro

xim

atel

y 10

min

utes

wal

k) in

Cam

bri

dg

e, M

A.

38

3.3.

SPA

TIA

L-ST

ATI

STIC

AL

MO

DEL

S

Unl

ike

acce

ssib

ility

an

d

gro

wth

an

alyt

ics,

sp

atia

l-st

atis

tica

l m

od

els

can

exam

ine

rela

tio

nshi

ps

bet

wee

n m

ore

tha

n tw

o s

pat

ial

valu

es.

Gro

wth

and

ch

ang

e an

alyt

ics

each

cap

ture

ove

r-ti

me

chan

ge

in o

nly

one

pro

per

ty o

f a

spat

ial

unit

. S

pat

ial-

stat

isti

cal

mo

del

s, h

ow

ever

, ca

n ex

amin

e th

e re

lati

ons

hip

o

f o

ne s

pat

ial

valu

e to

a n

umb

er o

f o

ther

var

iab

les,

and

are

the

reb

y b

ette

r su

ited

fo

r p

roje

ctin

g c

hang

es u

nder

mo

re r

ealis

tic

mul

tiva

riab

le s

cena

rio

s.

Sta

tist

ical

mo

del

s ca

n b

e d

evel

op

ed t

o e

xam

ine

the

rela

tio

nshi

p o

f la

nd p

rice

s to

va

rio

us

det

erm

inan

ts

incl

udin

g

acce

ssib

ility

(e

.g.

to

bus

st

op

s,

reta

il es

tab

lishm

ents

etc

.),

fro

ntag

e, a

rea,

or

par

cel

typ

e. H

avin

g s

uch

exp

lana

tory

m

od

els,

one

can

the

n p

red

ict

how

the

val

ue o

f ea

ch i

ndiv

idua

l p

arce

l is

lik

ely

to

chan

ge

whe

n,

for

exam

ple

, a

new

b

us

sto

p

or

road

is

co

nstr

ucte

d,

cont

rolli

ng f

or

cova

riat

es.

3.3.

1.R

egre

ssio

n A

naly

sis

In

st

atis

tics

, re

gre

ssio

n an

alys

is

exam

ines

w

heth

er

and

ho

w

a d

epen

den

t va

riab

le i

s re

late

d t

o o

ne o

r m

ore

ind

epen

den

t va

riab

les.

The

res

ults

of

a re

gre

ssio

n fu

ncti

on

gen

erat

e co

effic

ient

s fo

r th

e ef

fect

s th

at

each

o

f th

e in

dep

end

ent

vari

able

s ha

s o

n th

e d

epen

den

t va

riab

le a

nd a

n in

dic

atio

n o

f w

heth

er a

nd h

ow

sig

nific

ant

thes

e ef

fect

s ar

e. T

he m

od

els

also

tel

l us

ho

w

muc

h o

f th

e to

tal v

aria

tio

n in

the

dep

end

ent

vari

able

is e

xpla

ined

by

vari

atio

ns

in t

he g

iven

ind

epen

den

t va

riab

les.

Tho

se c

oef

ficie

nts

that

are

fo

und

to

be

sig

nific

ant

can

be

used

to

pre

dic

t fu

ture

cha

nges

und

er s

imila

r co

ndit

ions

.

Hed

oni

c p

rici

ng m

od

els,

whi

ch f

orm

one

typ

e o

f re

gre

ssio

n m

od

els,

are

wid

ely

used

fo

r p

roje

ctin

g la

nd o

r re

al e

stat

e va

lue.

In

thes

e m

od

els,

the

sel

ling

pri

ce

of

a re

al p

rop

erty

(e.

g. a

ho

usin

g u

nit)

is p

red

icte

d b

ased

on

a lin

ear

func

tio

n o

f th

e ch

arac

teri

stic

s o

f th

e un

it –

ag

e, s

ize,

num

ber

of

roo

ms,

str

uctu

ral

qua

lity,

acc

essi

bili

ty, o

wne

rshi

p s

truc

ture

, lo

t si

ze e

tc.

39

An

accu

rate

sp

ecifi

cati

on

of

reg

ress

ion

mo

del

s re

qui

res

the

use

of

spec

ializ

ed

soft

war

e lik

e S

AS

, S

tata

or

SP

SS

and

nec

essi

tate

s a

clea

r un

der

stan

din

g o

f co

ncep

ts

and

as

sum

pti

ons

th

at

reg

ress

ion

anal

ysis

is

g

roun

ded

o

n.

It

is

reco

mm

end

ed t

hat

thes

e m

od

els

be

spec

ified

by

onl

y st

aff

who

hav

e ha

d

pro

per

tr

aini

ng

in

reg

ress

ion

anal

ysis

an

d

und

erst

and

th

eir

foun

dat

ions

th

oro

ughl

y. S

imp

le m

ulti

ple

reg

ress

ions

and

biv

aria

te s

catt

er p

lots

can

als

o b

e sp

ecifi

ed in

MS

Exc

el, u

sing

the

ana

lysi

s to

olb

ox.

3.3.

2.Tr

end

Esti

mat

ion

and

Aut

oreg

ress

ive

Mod

els

Tre

nd e

stim

atio

n is

a f

orm

of

reg

ress

ion

anal

ysis

, whe

re t

ime

is t

he o

nly

linea

r p

red

icto

r o

f th

e d

epen

dab

le v

aria

ble

. Tre

nd e

stim

atio

n ex

amin

es a

co

rrel

atio

n b

etw

een

the

out

com

e va

lues

an

d

tim

e at

w

hich

th

ey

too

k p

lace

. T

rend

es

tim

atio

n is

sui

tab

le f

or

pro

ject

ing

the

long

-ter

m t

rend

in v

aria

ble

s w

hose

key

d

eter

min

ants

are

no

t fu

lly k

now

n b

ut a

pat

tern

in t

heir

val

ues

can

be

iden

tifie

d

ove

r ti

me.

We

may

no

t kn

ow

, fo

r in

stan

ce,

wha

t va

riab

les

can

pre

dic

t th

e in

crea

se o

f tr

avel

ers

to t

he c

ity

cent

er,

but

a t

rend

wit

h a

sig

nific

ant

year

ly

tim

e co

effic

ient

may

be

used

to

ob

serv

ed p

roje

ct t

he n

umb

er b

ased

on

pas

t o

bse

rvat

ions

. Eve

n if

the

dat

a o

scill

ated

up

and

do

wn,

a t

rend

reg

ress

ion

can

help

us

det

erm

ine

whe

ther

a s

igni

fican

t lo

ng-t

erm

inc

reas

e o

r d

ecre

ase

is

pre

sent

(F

igur

e 33

).

The

val

ue o

f va

riab

les

som

etim

es f

ollo

ws

a cy

clic

al p

atte

rn o

ver

tim

e, w

here

va

riab

les

at

one

o

bse

rvat

ion

per

iod

ar

e d

epen

den

t o

n va

lues

d

urin

g

the

pre

vio

us p

erio

d.

Ene

rgy

cons

ump

tio

n in

a c

ensu

s b

lock

, fo

r in

stan

ce,

may

fo

llow

a

cycl

ical

p

atte

rn,

follo

win

g

the

win

ter-

sum

mer

cy

cle

in

the

envi

ronm

ent.

Cyc

lical

pat

tern

s in

dat

a m

ay b

e in

dep

end

ent

of

the

ove

rall

long

-te

rm

tren

d.

Whi

le

ther

e m

ay

be

a w

inte

r-su

mm

er

cycl

e in

th

e en

erg

y co

nsum

pti

on

at t

he h

ous

eho

ld l

evel

, the

lo

ng-t

erm

tre

nd m

ay b

e in

sig

nific

ant

(the

to

tal

annu

al e

nerg

y co

nsum

pti

on

not

chan

gin

g),

eve

n w

hen

the

ener

gy

cons

ump

tio

n at

a p

arti

cula

r p

erio

d (

spri

ng)

may

exh

ibit

a c

yclic

al d

ecre

ase.

Aut

ore

gre

ssiv

e m

od

els

are

used

to

pre

dic

t o

ver-

tim

e ch

ang

es in

var

iab

les

wit

h cy

clic

al

pat

tern

s,

whe

re

ind

epen

den

t va

riab

les

incl

ude

the

valu

e o

f th

e

Fig

ure

33. T

rend

est

imat

ion

of g

row

th i

n to

tal

resi

den

tial

flo

or a

rea

in C

hina

4

0

dep

end

ent

vari

able

in

the

pre

vio

us m

easu

rem

ent

per

iod

, as

wel

l as

a l

inea

r ti

me

pre

dic

tor

that

may

or

may

no

t b

e si

gni

fican

t fo

r th

e lo

ng-t

erm

tre

nd

(Fig

ure

34).

Mo

re t

han

one

tim

e la

g v

aria

ble

can

be

used

to

cap

ture

lo

nger

cy

clic

al e

ffec

t an

d t

he li

near

tim

e va

riab

le c

an b

e sq

uare

d t

o c

aptu

re n

onl

inea

r ef

fect

s.

Bo

th l

inea

r tr

end

ana

lysi

s an

d a

uto

reg

ress

ive

anal

ysis

can

be

app

lied

to

a

num

ber

of

imp

ort

ant

pla

nnin

g p

rob

lem

s in

cit

ies.

Tre

nd a

naly

sis

can

cap

ture

th

e lo

ng t

erm

pat

tern

in

key

urb

an g

row

th i

ndic

ato

rs –

ann

ual

rura

l to

urb

an

land

co

nver

sio

n, i

ncre

ase

in r

esid

ents

or

job

s, c

ity

GD

P c

hang

e, g

row

th i

n tr

ansi

t ri

der

ship

, et

c. C

yclic

al t

rend

ana

lysi

s ca

n ca

ptu

re p

red

icte

d l

and

and

re

al

esta

te

valu

es,

seas

ona

l ch

ang

es

in

reso

urce

co

nsum

pti

on

or

cycl

ical

p

atte

rns

in c

ons

truc

tio

n p

erm

it a

pp

licat

ions

.

3.3.

3.Sp

atia

l Reg

ress

ion

Ana

lysi

s T

here

are

var

ious

tec

hniq

ues

for

carr

ying

out

reg

ress

ion

anal

ysis

, but

co

mm

on

assu

mp

tio

ns

und

erlie

m

ost

o

rdin

ary

leas

t sq

uare

s (O

LS)

reg

ress

ion

tech

niq

ues.

One

of

the

und

erly

ing

ass

ump

tio

ns is

tha

t th

e d

epen

den

t va

riab

le

on

the

left

-han

d s

ide

of

the

reg

ress

ion

equa

tio

n ca

n in

tera

ct w

ith

ind

epen

den

t va

riab

les

on

the

rig

ht-h

and

sid

e, b

ut s

epar

ate

ob

serv

atio

ns o

f th

e d

epen

den

t va

riab

le a

re i

ndep

end

ent

of

each

oth

er.

The

pri

ce o

f la

nd m

ay d

epen

d o

n se

vera

l ind

epen

den

t fa

cto

rs, s

uch

as lo

t si

ze, l

oca

tio

n an

d b

uild

ing

hei

ght

, but

it

sho

uld

no

t d

epen

d o

n th

e p

rice

of

land

of

the

neig

hbo

ring

par

cel.

In r

ealit

y th

is a

ssum

pti

on

may

no

t ho

ld;

land

val

ues

can

dep

end

on

neig

hbo

ring

lan

d

valu

es a

roun

d t

hem

.

Thi

s in

dep

end

ent

dis

trib

utio

n o

f th

e d

epen

den

t va

riab

le a

ssum

pti

on

of

OLS

re

gre

ssio

ns

is

rela

xed

in

sp

atia

l la

g

and

er

ror

typ

e m

od

els.

S

pat

ial

auto

corr

elat

ion

mo

del

s al

low

eit

her

the

dep

end

ent

vari

able

to

dep

end

on

adja

cent

dep

end

ent

vari

able

s o

r th

e er

ror

term

s o

f ad

jace

nt o

bse

rvat

ions

to

b

e co

rrel

ated

. T

he f

orm

er c

ase

can

be

mo

del

ed b

y th

e “s

pat

ial

lag

mo

del

s,”

and

the

latt

er b

y th

e “s

pat

ial e

rro

r m

od

els”

(Ans

elin

, 19

88

).

Fig

ure

34. T

he c

yclic

al p

atte

r in

the

med

ian

pri

ce o

f ho

uses

sol

d

in t

he U

S c

an b

e ex

pla

ined

by

an a

uto

reg

ress

ive

mod

el w

here

th

e p

red

icto

rs o

f th

e m

edia

n p

rice

of

hous

es a

re t

he p

ervi

ous

ob

serv

ed m

edia

n p

rice

s.

Sour

ce:

Eco

nom

agic

, re

pro

duc

ed

in

City

For

m L

ab.

Mill

ion

M2

Med

ian

Pric

e of

Ho

uses

Sol

d in

the

US

4

1

Sp

atia

l re

gre

ssio

ns

can

be

spec

ified

in

G

eoD

a o

r G

eoD

a S

pac

e so

ftw

are

pac

kag

es t

hat

are

free

ly d

istr

ibut

ed.

Man

y p

heno

men

a in

sp

atia

l an

alys

es

exhi

bit

sp

atia

l au

toco

rrel

atio

n w

hich

su

ch

mo

del

s ca

ptu

re.

If

spat

ial

auto

corr

elat

ion

is

pre

sent

th

en

OLS

au

tore

gre

ssiv

e m

od

els

yiel

d

a b

ette

r ex

pla

nati

on

to t

he v

aria

tio

ns in

ob

serv

ed d

ata

(Fig

ures

35,

36

& 3

7).

Fig

ures

35

and

36

pro

vid

e an

exa

mp

le o

f as

sed

lan

d v

alue

dis

trib

utio

n in

C

amb

rid

ge,

MA

. A s

pat

ial

lag

reg

ress

ion

is s

pec

ified

in

Geo

Da

to p

red

ict

how

th

e p

er-s

qua

re-f

oo

t va

lue

of

land

dep

end

s o

n fo

ur in

dep

end

ent

vari

able

s: p

lot

rati

on,

par

cel

size

, ac

cess

to

str

eets

and

dis

tanc

e fr

om

the

nea

rest

sub

way

st

atio

n. In

Fig

ure

35 (

top

) an

OLS

mo

del

is s

pec

ified

wit

h al

l fo

ur v

aria

ble

s, b

ut

wit

hout

allo

win

g f

or

auto

corr

elat

ion

bet

wee

n ne

ighb

ori

ng p

arce

ls.

Bel

ow

, a

spat

ial

lag

m

od

el

is

spec

ified

, w

hich

ad

ds

spat

ial

auto

corr

elat

ion

in

the

dep

end

ent

vari

able

(p

rice

per

sq

uare

fo

ot)

to

the

est

imat

ion

(“W

_LV

_PS

F”

in

the

mo

del

). T

he h

igh

z-va

lues

sug

ges

t th

at l

and

val

ues

in C

amb

rid

ge

are

ind

eed

str

ong

ly c

orr

elat

ed w

ith

neig

hbo

rs –

bea

utifu

l im

pro

vem

ents

in

the

neig

hbo

rs’

yard

can

sig

nific

antl

y in

crea

se s

urro

und

ing

lan

d v

alue

s. F

igur

e 36

p

lots

the

biv

aria

te e

ffec

t o

f p

roxi

mit

y to

sub

way

sta

tio

ns,

sho

win

g h

ow

lan

d

valu

es d

ecre

ase

as t

he d

ista

nce

to t

he n

eare

st s

ubw

ay s

tati

on

incr

ease

s,

cont

rolli

ng f

or

oth

er v

aria

ble

s.

Fig

ure

35.

Hed

onic

pric

ing

mod

el f

or L

and

val

ue i

n C

amb

ridg

e:.

The

d

epen

den

t va

riab

le i

s th

e as

sess

ed p

rice

of

land

is

US

$ p

er s

qua

re

foo

t, an

d p

red

icto

rs a

re p

lot

rati

o, la

nd a

rea,

par

cel t

ype

(nr.

Of

stre

ets

dir

ectl

y ac

cess

ed t

o fr

om p

arce

l),

and

dis

tanc

e to

sub

way

sta

tion

. U

nlik

e th

e sp

atia

l la

g

mod

el

(bot

tom

),

the

ord

inar

y le

ast

squa

re

reg

ress

ion

mo

del

(t

op)

do

es

not

acco

unt

for

the

spat

ial

auto

corr

elat

ion

amo

ng t

he la

nd v

alue

s of

nei

ghb

orin

g p

arce

ls.

4

2

Fig

ure

36.

The

rela

tion

ship

b

etw

een

the

land

va

lue

(US$

/Sq

.Ft)

and

dis

tanc

e to

sub

way

sta

tion

pre

dic

ted

by

the

spat

ial

lag

mod

el.

All

oth

er p

red

icto

rs o

f la

nd v

alue

ar

e ke

pt

cons

tant

.

010203040506070

050

010

0015

0020

0025

0030

0035

0040

00

Land value (US$/Sq.Ft)

Dis

tanc

e to

Sub

way

Sta

tion

(m)

Fig

ure

37.

Con

tain

ing

lan

d v

alue

(U

S$/S

q.F

t.),

land

are

a an

d a

gg

reg

ated

bui

ldin

g d

ata

(suc

h as

tot

al f

loor

are

a), t

he

par

cel

dat

aset

of

Cam

bri

dg

e, M

A,

pro

vid

es t

he b

asis

for

th

e he

don

ic p

rici

ng m

odel

fo

r la

nd.

Eac

h p

arce

ls’ d

ista

nce

to s

ubw

ay s

tati

ons,

is c

omp

uted

usi

ng t

he n

etw

ork

anal

yst

of A

rcG

IS, w

hich

req

uire

s st

reet

net

wor

k an

d t

rans

it st

atio

n lo

catio

ns d

atas

ets.

4

3

3.4

.IMPA

CT

AN

ALY

SIS

Eff

ecti

ve d

ecis

ions

in p

olic

y an

d s

pat

ial p

lann

ing

nee

d t

o b

e ev

alua

ted

bef

ore

th

eir

imp

lem

enta

tio

n.

Suc

h ev

alua

tio

ns

req

uire

an

un

der

stan

din

g

of

the

po

tent

ial i

mp

acts

of

the

dec

isio

ns. C

om

par

ing

the

pro

bab

le im

pac

ts o

f a

seri

es

of

alte

rnat

ive

dec

isio

ns t

o t

he e

xist

ing

co

ndit

ions

allo

ws

pla

nner

s to

est

ablis

h a

conc

rete

bas

e fo

r in

form

ed d

ecis

ion-

mak

ing

.

Imp

act

anal

ysis

in

clud

es

two

b

road

g

roup

s o

f an

alyt

ics.

T

he

first

g

roup

in

clud

es a

naly

tics

and

sta

tist

ical

mo

del

s th

at c

an p

red

ict

the

imp

act

of

a p

rop

ose

d

po

licy

(e.g

. zo

ning

) o

r sp

atia

l in

terv

enti

on

(e.g

. si

dew

alk

imp

rove

men

t) o

n an

out

com

e va

riab

le, s

uch

as la

nd v

alue

, acc

essi

bili

ty, c

rim

e ra

te o

r em

plo

ymen

t. T

he s

eco

nd t

ype

of

anal

ysis

kee

ps

trac

k o

f ch

ang

es i

n q

uest

ion

vari

able

s (e

.g.

land

val

ue,

or

ped

estr

ian

mo

vem

ent)

bef

ore

, d

urin

g

and

aft

er a

n in

terv

enti

on.

The

lat

ter

pro

vid

es u

sefu

l em

pir

ical

evi

den

ce u

po

n w

hich

fut

ure

pla

nnin

g d

ecis

ions

can

be

mad

e.

Acc

essi

bili

ty

anal

yses

an

d

reg

ress

ions

ca

n b

e us

ed

as

inp

uts

to

imp

act

anal

ysis

, sin

ce t

hey

can

cap

ture

cha

nges

in a

var

iab

le w

hen

spat

ial c

ond

itio

ns

chan

ge.

To

ana

lyze

the

im

pac

t o

f a

new

bri

dg

e o

r b

us r

out

e, f

or

inst

ance

, ac

cess

ibili

ty a

naly

ses

can

be

used

to

mea

sure

the

cha

nge

in a

cces

sib

ility

va

lues

– h

ow

muc

h th

e ci

tize

ns’

acce

ssib

ility

to

jo

bs

chan

ges

whe

n a

new

b

rid

ge

is b

uilt

ove

r th

e ri

ver.

Uti

lizin

g a

hed

oni

c p

rici

ng m

od

el a

llow

s us

to

ass

ess

the

imp

act

of

the

sam

e b

rid

ge

on

land

val

ues

acro

ss t

he c

ity.

The

out

put

of

acce

ssib

ility

ana

lysi

s,

whi

ch

com

put

es

the

chan

ges

in

ac

cess

ibili

ty

valu

es,

can

be

used

in

th

e he

do

nic

pri

cing

m

od

el

for

land

, in

w

hich

ac

cess

ibili

ty

valu

es

form

ke

y in

dep

end

ent

vari

able

s. I

f o

ther

var

iab

les

are

kep

t co

nsta

nt,

the

refle

cted

lan

d

valu

e ch

ang

e re

veal

s th

e p

ure

imp

act

of

the

new

bri

dg

e o

n la

nd v

alue

s.

Fig

ure

38 i

llust

rate

s th

e im

pac

t o

f ho

w a

hyp

oth

etic

al h

ighw

ay t

hat

cuts

th

roug

h th

e G

eyla

ng

neig

hbo

rho

od

in

S

ing

apo

re,

on

the

acce

ssib

ility

o

f b

uild

ing

s to

b

usin

esse

s.

Co

mp

arin

g

the

gra

vity

in

dex

fr

om

b

uild

ing

s to

b

usin

esse

s b

efo

re a

nd a

fter

the

pro

po

sed

hig

hway

in

a lo

cal

1km

wal

king

4

4

rang

e sh

ow

s a

56%

d

eclin

e,

on

aver

age,

in

ac

cess

ibili

ty

to

bus

ines

s es

tab

lishm

ents

. T

he i

mp

act

of

chan

ge

in a

cces

sib

ility

on

oth

er k

ey v

aria

ble

s su

ch a

s la

nd v

alue

can

be

then

ana

lyze

d b

y a

spat

ial s

tati

stic

al m

od

el (

Fig

ure

39).

Fig

ures

39

and

40

bel

ow

mo

del

the

po

tent

ial i

mp

act

of

a ne

w s

ubw

ay s

tati

on

on

land

val

ues

in C

amb

rid

ge,

MA

, usi

ng t

he h

edo

nic

pri

cing

mo

del

fo

r la

nd i

n C

amb

rid

ge,

bas

ed o

n th

e p

rese

nt la

nd v

alue

s (S

ee F

igur

e 35

). T

he e

xam

ple

in

Fig

ure

39 d

emo

nstr

ates

the

est

imat

ed d

iffer

ence

in

land

val

ues

bef

ore

and

af

ter

the

pro

po

sed

sub

way

sta

tio

n. U

sing

co

effic

ient

est

imat

ed i

n th

e sp

atia

l la

g m

od

el o

f F

igur

e 35

, the

to

tal h

ypo

thet

ical

cha

nge

in la

nd v

alue

s th

at c

oul

d

resu

lt

fro

m

add

ing

th

e ne

w

sub

way

st

op

is

ar

oun

d

$20

m

illio

n.

The

d

istr

ibut

ion

of

the

new

per

sq

uare

fo

ot

pri

ces

is s

how

n in

Fig

ure

40

.

4

5

F

igur

e 38

. Com

par

ison

of

the

acce

ssib

ility

val

ues

bef

ore

(lef

t) a

nd a

fter

a h

ighw

ay c

ut t

hrou

gh

Gey

lang

, Sin

gap

ore

show

s a

sig

nific

ant

dro

p i

n th

e lo

cal

gra

vity

to

bus

ines

s es

tab

lishm

ents

(ce

nter

). S

uch

com

par

ison

s in

acc

essi

bili

ty v

alue

s ca

n b

e us

ed a

s in

put

to

reg

ress

ion

mod

els

for

pre

dic

ting

the

oth

er i

mp

acts

of

a sp

atia

l int

erve

ntio

n (s

ee F

igur

e 39

and

40

). T

he

per

cent

age

chan

ge

in a

cces

sib

ility

to

bus

ines

ses

as a

res

ult

of t

he p

rop

ose

d h

ighw

ay is

sho

wn

on t

he r

ight

.

Per

cent

age

ch

ang

e

Gra

vity

in

dex

4

6

`

Fig

ure

39. A

naly

zing

the

imp

act

of a

pro

po

sed

sub

way

sta

tion

in n

orth

Cam

bri

dg

e on

the

val

ue (

US

$/S

q.F

t)

of l

and

s w

ithi

n 10

-min

ute

wal

k ar

ound

the

pro

pos

ed s

tati

on,

usin

g t

he h

edon

ic p

rici

ng m

odel

for

lan

d

dev

elop

ed in

the

pre

viou

s se

ctio

n (s

ee F

igur

e 35

). T

he f

igur

e sh

ows

a co

mp

aris

on b

etw

een

the

pre

sent

val

ues

(lef

t) a

nd t

he p

red

icte

d v

alue

s (r

ight

).

4

7

Fig

ure

40

. As

a re

sult

of

the

new

sub

way

, lan

d v

alue

s in

crea

se 8

% in

dol

lars

per

sq

uare

fo

ot o

n av

erag

e, w

hich

is

ap

pro

xim

atel

y $2

0,0

00

,00

0 i

n to

tal

for

all

par

cels

loc

ated

wit

hin

600

met

ers

from

the

pro

po

sed

sub

way

st

atio

n.

4

8

PLA

NN

ING

DEC

ISIO

NS

SUPP

OR

T

4

9

4.P

LAN

NIN

G D

ECIS

ION

SU

PPO

RT

The

ult

imat

e g

oal

of

CP

L d

ata

colle

ctio

n an

d s

pat

ial

gro

wth

ana

lyse

s is

to

su

pp

ort

cit

ies

in t

heir

pla

nnin

g d

ecis

ions

wit

h co

ncre

te e

vid

ence

. In

form

ing

p

lann

ing

dec

isio

ns b

y m

easu

rab

le e

vid

ence

do

es n

ot

alw

ays

req

uire

co

mp

lex

anal

ytic

s; p

lann

ing

evi

den

ce c

an s

om

etim

es b

e d

irec

tly

extr

acte

d f

orm

raw

sp

atia

l dat

a.

In t

he p

revi

ous

sec

tio

ns w

e d

iscu

ssed

geo

spat

ial

dat

a, a

num

ber

of

anal

ytic

al

acti

viti

es a

nd t

heir

po

tent

ial

app

licat

ions

fo

r ur

ban

pla

nnin

g.

The

way

the

se

anal

ytic

al t

echn

ique

s ca

n in

form

pla

nnin

g d

ecis

ions

can

be

sum

mar

ized

as

follo

ws:

1)

By

des

crib

ing

qua

litie

s o

f sp

ace

in m

easu

reab

le t

erm

s, a

naly

sis

of

spat

ial

dat

a m

akes

it

po

ssib

le t

o c

om

par

e ex

isti

ng c

ond

itio

n to

ce

rtai

n b

ench

mar

ks,

and

to

the

reb

y in

form

pla

nner

s o

f p

rese

nt

chal

leng

es.

Sp

atia

l d

ata

and

ana

lyti

cs h

elp

cit

ies

iden

tify

pro

ble

ms

and

fra

me

que

stio

ns t

hey

sho

uld

fo

cus

on.

Ben

chm

arks

can

be

cho

sen

to m

eet

a ci

ty’s

go

als

and

id

eals

bas

ed o

n na

tio

nal

or

inte

rnat

iona

l ex

amp

les,

o

r b

ased

o

n m

ore

co

mp

lex

und

erly

ing

ca

uses

of

the

ob

serv

ed p

atte

rns.

F

or

exam

ple

, by

loo

king

at

rent

al p

aym

ents

as

a sh

are

of

hous

eho

ld

inco

me

in c

ensu

s tr

acts

and

co

mp

arin

g t

hat

to d

esir

ed r

atio

s, o

ne

can

dir

ectl

y as

sess

whe

ther

so

me

hous

eho

ld i

nco

me

gro

ups

are

pay

ing

to

o l

arg

e a

po

rtio

n o

f th

eir

mo

nthl

y in

com

e o

n ho

usin

g.

In

oth

er c

ases

, th

e as

sess

men

t m

ay r

equi

re s

ever

al a

naly

tica

l st

eps.

A

cces

sib

ility

ana

lysi

s, f

or

inst

ance

, ca

n in

form

pla

nner

s w

heth

er

acce

ss t

o k

ey i

nfra

stru

ctur

es o

r fa

cilit

ies

is u

nder

serv

ed i

n ce

rtai

n ar

ea, a

nd if

so

, inf

orm

s p

lann

ers

whe

re s

uch

area

s ar

e lo

cate

d.

2)

In

ad

dit

ion

to

iden

tify

ing

ex

isti

ng

chal

leng

es,

spat

ial

dat

a an

d

anal

ytic

s ca

n b

e us

ed t

o i

den

tify

fo

rthc

om

ing

cha

lleng

es.

Tre

nd

esti

mat

ion

anal

yses

can

des

crib

e p

rob

able

fo

rthc

om

ing

iss

ues

by

com

par

ing

th

e p

red

icte

d

valu

e o

f a

vari

able

to

it

s d

esir

ed

50

ben

chm

ark

valu

e. K

eep

ing

tra

ck o

f tr

end

s in

the

num

ber

of

mul

ti-

fam

ily

bui

ldin

g

per

mit

s th

at

are

annu

ally

is

sued

, an

d

tren

ds

in

dem

og

rap

hic

gro

ups

that

fo

rm t

ypic

al o

ccup

ants

fo

r su

ch u

nits

, p

lann

ers

can

pre

dic

t w

heth

er t

he c

ity

is h

ead

ed t

ow

ard

sho

rtag

es

or

ove

rsup

ply

in t

he m

arke

t fo

r m

ulti

-fam

ily h

ous

ing

.

3)

Geo

spat

ial

dat

a an

d a

naly

tics

can

inf

orm

pla

nner

s o

f un

der

lyin

g

inte

ract

ions

an

d

corr

elat

ions

b

etw

een

spat

ial

vari

able

s.

Rep

rese

ntin

g s

pat

ial q

ualit

ies

wit

h nu

mer

ic d

ata

allo

ws

us t

o u

tiliz

e st

atis

tica

l re

gre

ssio

n an

alys

is t

o e

xpla

in r

elat

ions

hip

bet

wee

n su

ch

vari

able

s.

Sp

atia

l-st

atis

tica

l m

od

els

can

be

used

to

id

enti

fy

the

det

erm

inan

ts

of

ob

serv

ed

soci

o-e

cono

mic

va

riab

les.

S

tati

stic

al

mo

del

s ca

n o

utlin

e sp

atia

l co

ndit

ions

tha

t re

qui

re c

hang

e in

ord

er

to i

mp

rove

so

cio

-eco

nom

ic i

ndic

ato

rs.

If a

mo

del

sho

ws

a st

rong

ne

gat

ive

corr

elat

ion

bet

wee

n th

e ex

iste

nce

of

com

mer

cial

es

tab

lishm

ents

tha

t fa

ce d

irec

tly

to s

tree

ts a

nd c

rim

e ra

tes

on

thes

e st

reet

s, p

lann

er m

ay u

se t

his

evid

ence

fo

r d

ecid

ing

whe

re t

o

allo

cate

co

mm

erci

al s

pac

e in

zo

ning

pla

ns.

A

sta

tist

ical

mo

del

tha

t an

alyz

es p

revi

ous

sid

ewal

k im

pro

vem

ent

out

com

es

may

re

veal

a

po

siti

ve

corr

elat

ion

bet

wee

n si

dew

alk

qua

lity

and

bus

ines

s re

venu

e al

ong

sid

ewal

ks. O

ne in

terp

reta

tio

n o

f th

is c

orr

elat

ion

is t

hat

sid

ewal

k im

pro

vem

ent

can

be

an e

ffec

tive

to

ol

for

incr

easi

ng

bus

ines

s re

venu

e in

ar

eas

whe

re

pro

per

si

dew

alks

do

no

t ex

ist

but

oth

er p

reco

ndit

ions

fo

r co

mm

erce

are

in

pla

ce.

The

co

rrel

atio

n co

effic

ient

of

the

mo

del

can

be

used

to

es

tim

ate

how

m

uch

bus

ines

s o

wne

rs

coul

d

ben

efit

fr

om

su

ch

pub

lic in

vest

men

t, a

nd w

heth

er t

hey

coul

d b

e in

volv

ed in

fin

anci

ng

sid

ewal

ks t

hro

ugh

taxa

tio

n.

4)

Imp

act

anal

yses

al

low

p

lann

ers

to

asse

ss

diff

eren

t fu

ture

in

vest

men

t o

r im

pro

vem

ent

scen

ario

s b

ased

on

a ke

y o

utco

me

vari

able

. A

co

mp

aris

on

of

diff

eren

t al

tern

ativ

es c

an a

llow

one

to

id

enti

fy t

he m

ost

imp

actf

ul s

cena

rio

.

51

The

sp

atia

l an

alys

is t

echn

ique

s th

at f

orm

the

fo

cus

of

the

CP

Ls’ c

ore

mo

dul

e in

clud

e a)

sp

atio

-tem

po

ral

chan

ge

map

pin

g,

b)

acce

ssib

ility

ass

essm

ent,

c)

tren

d a

naly

sis,

d)

spat

ial

reg

ress

ion

anal

ysis

and

e)

imp

act

anal

ysis

. R

athe

r th

an e

lab

ora

ting

on

any

one

ap

plic

atio

n o

f th

ese

tech

niq

ues

at g

reat

er le

ngth

, w

e ha

ve t

ried

to

pro

vid

e a

bri

ef o

verv

iew

of

the

natu

re a

nd u

tilit

y o

f ea

ch

tech

niq

ue,

po

inti

ng

tow

ard

s ap

plic

atio

ns

for

vari

ous

ur

ban

p

lann

ing

an

d

man

agem

ent

task

s. T

he e

xact

ap

plic

atio

n fo

cus

of

the

tech

niq

ues

in t

he f

our

p

arti

cip

atin

g C

PL

citi

es –

Sur

abay

a, D

enp

asar

, P

alem

ban

g a

nd B

alik

pap

an –

sho

uld

be

iden

tifie

d t

og

ethe

r w

ith

the

loca

l g

ove

rnm

ent

rep

rese

ntat

ives

and

C

PL

staf

f. T

he a

naly

sis

sho

uld

be

cho

sen

to a

dd

ress

the

mo

st i

mp

ort

ant

spat

ial a

naly

sis

and

pla

nnin

g q

uest

ions

sp

ecifi

c to

eac

h ci

ty.

5.R

efre

nces

Ans

elin

, L.

(19

98

). E

xplo

rato

ry s

pat

ial

dat

a an

alys

is i

n a

geo

com

put

atio

nal

envi

ronm

ent.

In

P.

Long

ley,

S.

Bro

oks

, B

. M

acm

illan

and

R.

McD

onn

ell

(Ed

s.),

G

eoC

om

put

atio

n, a

Pri

mer

, 77-

94

. New

Yo

rk: W

iley.

Far

vacq

ue-V

itko

vic,

C.,

Go

din

, L.

, Le

roux

, H

., V

erd

et,

F.,

& C

have

z, R

. (2

00

5).

Str

eet

Ad

dre

ssin

g

and

th

e M

anag

emen

t o

f C

itie

s.

The

W

orl

d

Ban

k,

Was

hing

ton,

D.C

.

Sch

neid

er,

A,

Fri

edl,

M.

A.,

& P

ote

re,

D.

(20

09

). A

new

map

of

glo

bal

urb

an

exte

nt

fro

m

MO

DIS

sa

telli

te

dat

a.

Env

iro

nmen

tal

Res

earc

h Le

tter

s,

4(4

),

44

00

3.

Sch

neid

er,

A.,

Fri

edl,

M.

A.,

& P

ote

re,

D.

(20

10).

Map

pin

g g

lob

al u

rban

are

as

usin

g

MO

DIS

50

0-m

d

ata:

N

ew

met

hod

s an

d

dat

aset

s b

ased

o

n “u

rban

ec

ore

gio

ns”.

Rem

ote

Sen

sing

of

Env

iro

nmen

t, 1

14(8

), 1

733–

174

6.

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