A Spatial Analysis Model For Regional Industry, Yangtze...

LEAP 221, Professor John D. RADKE; Final Project, Student: Yujiang Mou, MCP 2012

A Spatial Analysis Model For Regional Industry,

Yangtze River Delta Region as a Case Study

Yujiang Mou, MCP 2012

Executive Summary:

Though 20 years’ efforts, China has blossomed into the world factory, the biggest export

country, and the second-largest economy. Manufacturing industry becomes the pillar

industry for China’s economy and makes up over half of nation’s total GDP. More

importantly, from the perspective of spatial location, manufacturing industries are

intensively concentrated in Yangtze River Delta, Pearl River Delta and Bohai Economic

Rim. So where do these manufacturing industries locate within the region? And how do

they change locations though time? To answer these questions, the author develops a GIS

model to identify spatial patterns and trace changes, which integrates some spatial

statistic methods based on GIS. The model is able to find the center of regional industry,

measure the compactness of regional industry, and locate the regional industry clusters.

The author takes Yangtze River Delta as a case and applies this model to measure the

spatial pattern for food manufacturing, textile manufacturing, and electronic

manufacturing.

Outline:

1. The Design of a Spatial Analysis Model

2. A Case Study of Yangtze River Delta


1. The Design of a Spatial Analysis Model

The model integrates four basic spatial statistic methods including mean center, central

feature, standard distance, and hot pot analysis to measure spatial patterns. By inputting

features and the associated attributes, this model is able to produce a set of new features

and indicators to describe the spatial distribution of regional industry, which can also be

used to trace changes through time. In this section, each method will be discussed in

detail, especially the definitions of key concepts and what spatial attributes can be

captured by each method.

Figure 1 Model Design


(1) The mean center of regional industry;

Definition: mean center is the location represented by the mean x-coordinate value and

the mean y-coordinate for all features in study area, which can be weighted by attribute

values.

Measurement: mean center represents the geographic center for all features in study area.

It is very useful for tracking changes in distribution or for comparing the distribution of

different types of features.

(2) The central feature (place) for regional industry;

Definition: central feature is the feature having the shortest distance to all features in the

study area.

Measurement: central feature is the most accessible feature.

(3) The standard distance;

Definition: standard distance or standard distance deviation is the average distance that

the feature vary from the mean center.

Measurement: it measures the degree to which features are concentrated or dispersed

around the geometric mean center. It can be weighted by certain attributes. The greater

the standard distance value, the more the distances vary from the average, and more

widely dispersed around the mean center. In addition, features inside the circle vary less

than the standard distance (the average distance from mean center to all the features).

Feature outside vary more. Two things are worth to mention: first, the standard distance

provides a better measure of compactness for features distributed regularly around the

mean, rather than clustered at opposite sides of the study area; second, standard distance

works best when there is no strong directional trend.


(4) Regional cluster/hot pot (Gi*: G statistics)

Definition: to calculate Gi* of feature i, GIS sums the values of i’s neighbors and divided

by the sum of the values of all the features in the study area. In GIS 10, the hot pot

analysis is an application of Gi*, but the statistic it reports is in fact a Z-score.

Measurement: by using Gi*, the hot pot and cold pot if existed can be found in the study

area. A group of features with high Gi* indicates a cluster or concentration of features

with high attribute values. Conversely, a group of features with low Gi* indicates a cold

pot. A Gi* value near 0 indicates there is no concentration of either high or low values

surrounding the targeted feature. For Z-score, the interpretation is the same: the higher

the Z-score indicates the cluster for high values, and the lower the Z-score indicates the

cluster of low values.

Figure 2 Illustrations of four methods in the model

Source: ArcGIS 10.0


2. Case Study

In this section, the author applies the spatial analysis model in Yangtze River Delta area

to study the spatial distribution of its regional manufacturing, as well as the changes of

industry location through time. Three industries have been analyzed including food

manufacuting , textile manufacuting, and electronic manufactuirng. Employment

data for each industry in 1998, 2002, and 2007 is used to indicat the distribution of

coresponding industry and trace spatial changes through time.

2.1 About the region

Yangtze River Delta region is the richest region in China, which occupies only 4% of

land but makes up one third of nation’s total GDP. Administratively speaking, it contains

three municipalities, including Shanghai, Jiangsu province, and Zhejiang province.

Shanghai acts as the center for the region financially and culturally. Nanjing, the capital

city for Jiangsu, and Hangzhou, the capital

city for Zhejiang province are all located in

the central area of Yangtze River Delta, and

connected to Shanghai through high-speed

rail. The total population accounts for 80

million by 2007, half of them are inhabited

in Shanghai (23 million), Nanjing (8

million) and Hangzhou (8 million). In

addition, Yangtze River Delta is famous for

its township entrepreneurship. Some

second-tier cities become the hub for

manufacturing such as Suzhou, Wenzhou,

Wuxi and Changzhou. As a better-off

region in China, regional and local

infrastructure including transport, electricity, and telecommunication are well established

which lay down the foundation for the development of manufacturing.


2.2 Preliminary analysis

(1) Spatial distribution

Figure 3 shows the spatial distribution of employment in each manufacturing industry in

2007, with each dot representing 100 employees. In terms of quantity of employment,

electronic manufacturing takes the lead, textile manufacturing follows as the second, and

food industry has the least regional employment. The significant presence of electronic

manufacturing in the region is because it has been set as one pillar industry by nation and

gained priority for further development. While textile manufacturing is labor-intensive

industry that China is planning to cut off or upgrade.

In addition, electronic manufacturing appears to be the most concentrated industry in the

region. It clustered in the central area and anchored by Shanghai which, as the financial

center of China, is able to offer large amount of capital investments and skilled workers

to fuel the growth of electronic manufacturing. Textile manufacturing industry is more

concentrated in the area surrounded by Shanghai where labor cost is relatively low. For

food industry, the employment is denser in Shanghai, but in general it spreads out across

the region.

Figure 3 The spatial distribution of employment in three manufacturing industries, 2007


(2) Spatial changes of regional industry through time

By comparing distribution of employment in 1998, 2002 and 2007, the author found that

different industries demonstrate different changes on spatial pattern over time. For food

industry, no significant concentration or decentralization happened from 1998 to 207,

employment distributed across the region with roughly the same density. For textile

industry, two poles, Suzhou and Shaoxing, have been reinforced over time. The most

dramatically spatial change took place in electronic industry, which had little appearance

in 1998 in Shanghai and surroundings, but formed a very strong spatial cluster since 2002.

Between 2002 and 2007, the employment in the central area became more dense, and

spread to the adjacent areas around Shanghai.

Figure 4 Spatial changes in employment, Food manufacturing


2.3 Model Analysis

(1) Mean center

The employment mean centers in 1998, 2002,

and 2007 for three industries have been

identified respectively and illustrated in figure

5. Blue cross represents employment mean

center in 1998, the yellow indicates the mean

center in 2002, and the red is for 2007.

For food industry, the mean center moved from

north to south following the movements of

population. The employment center of textile

industry is with food industry moving along the

same direction, but due to different reasons.

The movement of textile industry towards

Zhejiang province is largely due to the

emerging textile manufacturing factories in

southern Zhenjiang, which balances the textile

employment between two provinces and forms

two textile manufacturing poles on southern

side and northern side of Shanghai. For

electronic industry, the employment center was

moving towards Shanghai, particularly between

1998 and 2002. This indicates electric

manufacturing business is gravitating towards

Shanghai, which became the regional center of

electronic manufacturing.

Figure 5 The movements of employment mean center


(2) Central feature

The most accessible cities, central features, in terms of employment, have been identifeid

in the model. Three central places happened to locate all in central area. For food and

textile manufacuturing, the most accessible places is Suzhou which is a second-tire city in

Jiangsu province and known for its industry development. Regionally, Suzhou locates in

the heart of Yangtze River Delta and very close to Shanghai. For electronicy

manufactuirng, the central place is Kunshan which is the neighbor of Shanghai, also

serves as an important regoinal industry park spcialied in electronic manufacturing. The

IT manufacuture such as Dell, Accer, and Foxcon all set factories in Kunshan.

Figure 5 Central places for each industry, 2007

(3) Standard distance

The standard distance resonates the previous analysis: in 2007 food industry has the

largest standard distance as 244 kilometers; for textile manufacturing it is 167 kilometers,

and for electronic manufacturing it becomes 100 kilometers. Thus it suggests that

electronic industry is clustered around the mean center where most of manufactures are

concentrated. The reason why electronic manufactures tend to locate close to each other


has to do with the knowledge spillovers and other benefits generated from agglomeration

economies considering electronic manufacturing is one branch of high tech industry. For

textile, the average distance between individual producers is larger compared to

electronic manufacturing, and the regional industry forms a belt instead of a core. The

much larger standard distance for food industry indicates the industry is widely spread

over region.

Figure 6 Standard distance for each industry, 2007

(4) Hot pot analysis

The hot pot analysis can identify the clusters of high values (hot pot) and the cluster of

low values (cold pot), which pays special attention to unveil the relationships between

feature and its neighbors. In food manufacturing, two small clusters have been identified

as statistically significant. Textile industry presents three high value clusters with

relatively larger scale and lies in the central region to form a regional industry “belt”. For

electronic manufacturing, Shanghai, Suzhou and Kunshan form the regional cluster that

is also the center of regional electronic manufacturing.


Figure 7 Hot pot and cold pot, 2007

(5) Summary

By using the spatial analysis model, some conclusions can be reached:

(i) In Yangtze River Delta region, electronic manufacturing is the dominant

industry and highly concentrated in Shanghai with a radius as 100 kilometers.

The average distance between individual manufactures is short. In addition,

electronic manufacturing grew rapidly through time, and the employment

center kept moving towards Shanghai.

(ii) Textile manufacturing industry is concentrated in the central region, but does

not form a central core or cluster. Instead, the regional industry has three

clusters around Shanghai and forms an industry “belt”.

(iii) Food industry is widely spread over the region. Some small-scale clusters are

formed in Shanghai and off-shore islands, where food manufacture companies

are clustered together. Through time, the spatial pattern of food industry does

not change significantly. Only the employment center shifted from south

towards north, which has to do with population growth on northern region.


Reference

Mitchell A (1999) The ESRI guide to GIS analysis, volume 1: geographic patterns and

relationships. ESRI, Redlands [CA]

Mitchell A (2005) The ESRI guide to GIS analysis, volume 2: spatial measurements and

statistics. ESRI, Redlands [CA]

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