Office Automation with GIS in

Urban Planning and Management

Anthony Yeh

Centre of Urban Planning and

Environmental Management

GIS Research Centre

University of Hong Kong

University of Hong Kong

GIS and Urban Planning

Urban

Planning

Spatial

Analysis &

Modelling

Non-GIS

Database/Data

data

data

Spatial Query &

Mapping

Geo-Processing

Functions

GIS

GIS

database

-GIS

commands

-macros

-GIS

commands

-macros

Data

import

export

-modelling

programs

-statistical

analysis

package

-compiled

modelling

program

GIS

database

GIS

database

-GIS commands

with modelling

functions

-macors

(a)

Loosely-Coupled Architecture

for Integrating Models with GIS

(b)

Tightly-Coupled Architecture

for Integrating Models with GIS

(c)

Fully Integrated Architecture

for Integrating Models with GIS

Integration of Models with GIS

GIS GIS GIS

Use of GIS in Urban Planning

CUPEM, The University of Hong Kong

General Administration

Development Control

Plan Making

Use of GIS in Urban Planning

General Administration

CUPEM, The University of Hong Kong

Office Automation – mapping, data retrieval

Public Enquiry – internet map, 3-D map

Use of GIS in Urban Planning

Development Control

CUPEM, The University of Hong Kong

Office Automation

Land Suitability Analysis

3-D Analysis

Use of GIS in Urban Planning

Plan Making

CUPEM, The University of Hong Kong

Office Automation - mapping

Land Suitability Analysis

Location of Facilities and Activities

Plan Generation

Plan Evaluation

Office Work

Computer Network

Human Interactions：

Not working at the same time

Not working in the same place

Multimedia information

Office Automation

System Design

Integration of Texts, Images and Maps

Textual

Databases

Graphics

Databases

ARC/INFO

001036589SQL Server

052-01

001036589

052-01

Related

Information

MapObjects

SQL Server

ARC/INFO

Dynamic Updating

Transport DB

Survey

Construction Dept

GZ

Urban Planning

Database

Construction

Database

Query

UpdatingQuery

Updating

Query

Updating

Office Automation

System

Planning

Land Use

Application

Inspection

Public Enquiry

Work Flow Management

in Development Control

Pr i v aCi t i z e Pu b l Ag e n D e p t

S u b m i

S i t

L o c a t i n

L a n d

P e r m i t t

T i t

R e g i s t e

)

L o c a

( S

R e g i s t

P r o p e

( S

L a n d

B o u n d

( S

B u i l A p p l i c a

L e g a l P e r m i

D e p t

D e p t

Priva te Cit izen or Pub lic Agen cy

Dep t. A

Subm ittin g

Sit e Lo catin g (E1 )

L and usePermit ting (E2 )

TitleRe gistering (E 3)

Loca tion (S1 )

Regist ered Prop erty (S3 )

L and useBoun dary

Build ing App licatio n(s)

Le gal Permit or No tice

Age nts Even ts Sta tes

Ur ba n Pla nning a nd La nd Management Organiza ti onAppli cant

Be haviora l Aspects Structur al Aspec ts

De pt. C

Dep t. B

Work Flow Management

Public Enquiry - Internet

Office Automation with

Case-Based Reasoning and GIS

CUPEM, The University of Hong Kong

Introduction (1)

Assumptions of KBS (rule-based or model-based)

(Han and Kim, 1990):

• the problem is clearly specifiable and well-

bounded;

• the ‘deep knowledge’, i.e. the relations between

the factors or elements of the problem, are

known and can be expressed explicitly;

• problem solving methods can be articulated; and

• experts agree on solutions, i.e. the cause-effect

relationships are definitely defined.

Introduction (2)

• Unfortunately, these requirements are seldom

met in planning.

“It is very difficult to find an urban planning problem

which is well-bound, well understood, and takes a few

minutes to a few hours to solve” (Han and Kim, 1990).

CBR and Its Advantages to

Urban Planning

Case-Based Reasoning (CBR) is a method or

technique of KBS which uses the previous

similar case(s) to help to solve a current new

problem (Kolodner, 1993, pp.4).

CBR can be typically represented by a schematic

cycle comprising four REs :

RETRIEVE the most similar case(s);

REUSE the case(s) to attempt to solve the problem;

REVISE the proposed solution if necessary, and

RETAIN the new solution as a part of a new case.

CUPEM, The University of Hong Kong

The CBR Cycle

RETRIEVE

RETAIN

REVISE

REUSE

Problem

Case-Base

Library

Confirmed

Solution

Proposed

Solution

Advantages of Case-Based

Systems CBS (1) store knowledge in the form of concrete stories

(cases) rather than abstract rules or models.

CBR provides a good method to involve

‘informal’ information’, e.g. personal judgements,

hunches, intuition, hearsay and personal

experiences (Han and Kim, 1990).

CBR can provide raw but comprehensive and

original information to the planner, and leave

space for the intelligence of planners to

recognize and understand the problem.

Advantages of Case-Based

Systems CBS (2)

solution to a problem can be conveniently saved

in the case-base and the system’s knowledge

increases.

Learning method is very simple and can be

performed by experts themselves during working

process without knowledge engineers’ help.

CBR will not just tell the planner what he or she

should do but present previously handled similar

problems to the planner

Applying CBR in Processing

Planning Applications• Processing planning applications is a daily work

in the Planning Department of the Hong Kong

Government.

• Make recommendations to the Town Planning

Board

The Integration of CBS

(ESTEEM) and GIS (ArcView)• CBS software - ESTEEM

• GIS software - ArcView

integrated through DDE of Windows

Representation of a

Planning Application Case

A planning application case contains three parts :

graphics (map, picture, charts) (handled by

ArcView)

feature values (dbf file, handled by ESTEEM)

comments (txt file)

– valuable tips about this case

– list of cases which are relevant to this case

CUPEM, The University of Hong Kong

Integration of CBS and GIS

User Interface

GIS(ArcView)

CBS(ESTEEM)

Graphics CommentsDescribing

Features

User Inputs System Outputs

CBR Result(Information about the Retrieved Cases)

Descriptions of

the New Application

Identifying

Codes

Contents of

the Specified CasesMatching

DirectoriesInformation about

the Retrieved Cases

Shape Files Text FilesFeature

Values

Case-Base Library

Main Retrieval Functions of

the System (Office Automation)

• Spatial Retrieval

• Nonspatial Retrieval

CUPEM, The University of Hong Kong

An Example of a Planning Application Case

An Example of Index Map for Spatial Retrieval

Nearest Neighbour Algorithm

• assess similarity between stored cases and the new

problem based on matching a weighted sum of

features (Kolodner, 1993, pp. 354; Watson, 1995).

Case Retrieval : Similarity Index

Nearest Neighbour Algorithm

assess similarity between stored cases and the new

problem based on matching a weighted sum of features

(Kolodner, 1993, pp. 354; Watson, 1995). The algorithm

is represented as

wi = importance of dimension i

sim = similarity function for comparing

feature fiR and fi

I

fiR and fi

I = values for feature fi in the input (I) and

retrieved cases (R), respectively

(Kolodner, 1993, pp. 355).

w sim f f

w

i iI

iR

i

n

ii

n

( , )1

1

Non-Spatial Retrieval

Similarity-Based Retrieval

If more than one similar case are found, the result

will be listed in a table of ArcView:

• Identification Code (Appnum)

• Approval Probability (Approval)

• Similarity Score (Similarity)

Approval Probability = 0 to 1

0 = not approved

1 = approved

List of Similar Cases

• It will calculate the approval probability of the new

application based on previous similar cases.

• The New Approval (NA) probability is calculated with the

following formula:

Evaluation of a New Case

NA = proposed probability of approving the new application

ai = approval probability of each retrieved case

si = similarity of each retrieved case

NAa s s 2

s2

i i i

i

- +

((( ) ) )0 5 0 5. .

Evaluation of a Planning Application

Case Library

Adjustment by

the Experts

Proposed Approval Score

of the new application

Result of the

application

Evaluation Documents

Application

Documents

Deliberation of

Town Planning Board

Proposed Approval Score

of the new application

Operations of

CBR Engine

Information for matching

Features of the

new application

Modified Approval Scoreof the new application

... ...

Data of the new case

Similar Case1

(Approval Score1)

Similar Case2

(Approval Score2)

Similar Casen

(Approval Scoren)New Case

RETRIEVE

REVISE

REUSE

RETAIN

Case-Based Reasoning and GIS

An Office Automation System

(text-graphics integration)

An Expert System for Experts

Corporate Memory System

Ensure Consistent Decision

CUPEM, The University of Hong Kong

Discussion

• The integration of CBR with other reasoning

engine to deal with situation without cases.

CBR can be integrated with other reasoning

engines, e.g. rule-based reasoning (RBR) engine

and model-based reasoning (MBR) engine, to build

a more powerful KBS (Bartsch-Sporl, 1995;

Burstein, etc., 1996; Kolodner, 1993, pp.92-96).

• Maintenance of cases - time decay function