Exploring spatial evolution of economic clusters: A case study of Beijing

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International Journal of Applied Earth Observation and Geoinformation 19 (2012) 252–265

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International Journal of Applied Earth Observation andGeoinformation

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xploring spatial evolution of economic clusters: A case study of Beijing

henshan Yanga,∗, Richard Sliuzasb, Jianming Caia, Henk F.L. Ottensc

Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Anwai, Chaoyao District, Beijing 100101, ChinaFaculty of Geo-Information Science and Earth Observation, Twente University (UT), Hengelostraat 99, P.O. Box 6, 7500 AA Enschede, The NetherlandsDepartment of Human Geography & Urban and Regional Planning, Utrecht University (UU), Waldeck Pyrmontkade 9, 3583 TW Utrecht, The Netherlands

r t i c l e i n f o

rticle history:eceived 30 June 2011ccepted 30 May 2012

eywords:conomic clusterpatial cluster

a b s t r a c t

An identification of economic clusters and analysing their changing spatial patterns is important forunderstanding urban economic space dynamics. Previous studies, however, suffer from limitations asa consequence of using fixed geographically areas and not combining functional and spatial dynamics.The paper presents an approach, based on local spatial statistics and the case of Beijing to understandthe spatial clustering of industries that are functionally interconnected by common or complementarypatterns of demand or supply relations. Using register data of business establishments, it identifies eco-

patial statisticsoran’s I

eijing

nomic clusters and analyses their pattern based on postcodes at different time slices during the period1983–2002. The study shows how the advanced services occupy the urban centre and key sub centres.The Information and Communication Technology (ICT) cluster is mainly concentrated in the north partof the city and circles the urban centre, and the main manufacturing clusters are evolved in the key subcenters. This type of outcomes improves understanding of urban-economic dynamics, which can supportspatial and economic planning.

. Introduction

Identifying the clustering of economic activities and the changesf the spatial representation of these clusters is a fundamen-al but challenging issue for understanding urban and regionalevelopment, particularly when these changes occur rapidly dueo intense urban and economic growth and restructuring. Con-entional understanding relies heavily on expert opinion and/orxperiences highlighted in case studies (Piore and Sabel, 1984). Thisay lead to lack of relevant knowledge to support policy decisions.ith the increasing availability of geo-referenced data, there is a

reat potential to investigate the real distribution and spatial char-cteristics of economic activities. The paper presents an approach,ased on the statistics of global and local Moran I, to investigateconomic clusters and their evolution in space in a case study ofeijing.

The term economic cluster is used here to describe a relativelyeographically concentrated group of related or complementary

∗ Corresponding author. Tel.: +86 10 64889035; fax: +86 10 64889279.E-mail addresses: yangzs@igsnrr.ac.cn, yangzspat@gmail.com (Z. Yang),

liuzas@itc.nl (R. Sliuzas), caijm@igsnrr.ac.cn (J. Cai), h.ottens@geo.uu.nlH.F.L. Ottens).

upon the model of industrial districts or industrial complexes,1

which take the business concentration as a group of geographicallybounded firms (Piore and Sabel, 1984). However, with traditionalfactors of production becoming increasingly ubiquitous across geo-graphical space and the increasing importance of capital, talent andknowledge, which are more mobile, in location decisions, currenteconomic clusters are likely to be foot-loose (Porter, 1990; Maskelland Lorenzen, 2004). It is entirely possible that several locationsexist for the same or similar cluster activities to form a united powerfor the competitiveness of a metropolis (Mills, 1992; Ketels, 2003).These new factors require the rethinking of the expression of spatialrelationship in the forming of economic clusters and an approachto identify economic spatial clusters accordingly.

Traditionally, identification of spatial concentration is basedon measures based on comparison of distributions. For exam-ple, the Gini coefficient compares industrial concentration wherespace plays not an important rule (Arbia, 2001; Duranton andOverman, 2005), while the entropy index largely considers geo-graphic concentration without concerning the characteristics ofindustrial development (Garrison and Paulson, 1973). As one of

1 A counterpart in Soviet and Chinese scholarships is territorial productionsystems.

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alues. In order to avoid of this problem, O’Donoghue and Gleave2004) amends the standard LQ and demonstrate its application ofelimiting industrial agglomerations for business services sectors

n the United Kingdom. However, the LQ values are dependent onhe size of the firms. For example, a high value of the LQ couldappen if a place is dominated by only one large firm. Besides,he standard LQ assumes that values have a normal distribution,hich often is not the case. All these methods actually measurenevenness in the distribution of economic activities in industrialnd/or geographical spaces and do not address either economic oreographical relations.

In recent research spatial statistics, attention has been given topatial relations in devising clusters, for example the application of

and D functions (Marcon and Puech, 2003). Particularly, Durantonnd Overman (2008) have attempted to extend the geographicalnalysis of industries that cluster based on vertical linkages usingoint analysis. They distinguish this phenomenon as co-localisationf establishments in the different industries driven by the sameocal choices as industries of the same type. However, the verticalinkages are quite arbitrary since only paired industries are takennto consideration. Relationships between industries can alter overime at different stages of development. There is also a significantontribution to the development of the application of spatial staticsn attempts to uncover the process of firm demography by study-ng the dynamics of localisation through space–time K-functionsfor example, Arbia et al., 2010; Kang, 2010). However, K-functionsdentify clusters based on the density or number of firms in a par-icular area, hardly concerning the functional relationships and thepatial spillovers of the area where the cluster is located (Marconnd Puech, 2010).

Given this discussion, we decided to apply an approach basedn spatial associations, and specifically Local Indicators of Spatialutocorrelations (LISA), as a new way to explore spatial clustersnd their evolution in Beijing during 1983–2002. The next sectionummarizes the understanding of spatial clusters in the new erand shows the relevance of the LISA method for investigating spa-ial clusters. Section 3 describes the methodology based on thelobal and local Moran’s I. Section 4 presents the results of theluster analysis for the main spatial clusters generated. A discus-ion of the value of this approach to investigate spatial clusters andhe requirements for a data infrastructure to support such researchonclude the paper.

. Understanding and measuring economic spatial clusters

Traditionally, the forming of industrial districts was attributedo an individual plant or firms sharing common productive fac-ors like labour, land, capital, energy, sewage and transportationMarshall, 1890). In particularly, the saving of transportation andabour costs was a major reason that business establishmentsocated each other in proximity (Weber, 1965). Recently, tra-itional transaction costs are gradually being replaced by costselated to using information, technology and skilled labour in

mode of flexible specialisation in production systems for com-eting or sharing contract and market opportunities (Harrison,007).

able 1ummary of data of registered enterprise establishments and employment.

Period of establishment 1983–1987

Enterprises 3032

Employment (,000 persons) 589

Enterprises (with employment registration) 2933

Average size (persons) 201

ervation and Geoinformation 19 (2012) 252–265 253

These changes require the rethinking of spatially articulatedeconomic clusters, which may or may not be geographicallybounded in a city or region but with strong functional relations.Attributed to the advance of information and communication tech-nologies, the clustering of productive activities may arise fromdifferent sources and may involve firms belonging to the sameindustry as well as firms from different sectors (Amiti, 2005; Arbiaet al., 2008). With the rapid changing of industrial structures, someclusters prosperous today may dwindle tomorrow, and thereforeeconomic clusters could be mobile in geographic space (Dumaiset al., 2002; Duranton and Overman, 2008). Their growth anddecline is highly relevant, not only for the area where they arelocated but also for neighbouring places, particularly with respectto wealth generation, employment and population distribution(Baumont et al., 2004). As clusters are the nexus of organisingurban-spatial social and economic activities (Portnov, 2006), thedevelopment paths of clusters should be carefully monitored fortheir roles in city and region development.

Economic clusters, therefore, have a two-fold meaning in thegeographical space. First, they denote the locales where firms,plants and business activities are highly concentrated, creatingopportunities and generating demands on transportation and pub-lic facilities. Clusters may function in areas in a city such as theCentral Business District (CBD), office parks or economic zones. Toidentify such locations is a way to better know and understandthe internal structure of urban development so as to enable or bal-ance local development by calibrating the function of places. Sinceexisting places enjoy better facilities and infrastructure for sim-ilar economic activities, it is also a means to prepare candidateplaces for new investments and spatial projects in urban plan-ning. As Webber (1963) contends, the economic cluster is a peculiarresource of urban space associated with the economies of localisa-tion and agglomeration.

Second, economic clusters explicitly reveal spatial interactionsamong places through their intricate complexes of social andeconomic relationships. Although social ties and economic trans-actions are largely invisible, they constitute basic drivers thatshape urban space. Changes of social and economic relationshipsprofoundly affect spatial constructs (Healey, 1997; Ward, 2002;Healey, 2006). Elements of urban structure, such as the supplyand demand of housing, transportation networks and commut-ing patterns, may be tuned to the internal structure of firms, aswell as being influenced by their production process and locationbehaviour (Næss, 2006; Rossi-Hansberg et al., 2009).

The economic spatial cluster (spatial clusters for short there-after) is in this research therefore defined as a significantgeographical concentration of economic activities and its asso-ciated pattern in space. The spatial pattern is an importantperspective to understand urban development, as it describesthe non-random distribution of economic activities (Unwin,1996). Spatial interaction reflects the cooperation and competi-tion between urban districts and their developmental strategies.Although urban growth is a result of both clustered and dispersedeconomic development, economic clusters to a large extent consti-

1988–1992 1993–1997 1998–2002

9661 119,918 330,866786 4029 8250

7729 78,852 328,844102 51 25

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Compared to the approaches mentioned above, spatial asso-iation analysis provides a particular way to understand andnvestigate spatial clusters. This method addresses the phe-omenon that events in geographical space are often dependentpon each other. In other words, there is a propensity for nearby

ocations to possess similar attributes (Goodchild, 1992, p. 33).vents in one location are (partially) affected by ones at other loca-ions through interaction, exchange and transfer processes thatiminish over distance. Examples are population diffusion and eco-omic growth. Some processes may be unobservable, for instancehe knowledge and information exchange, though they greatlynfluence spillover effects in spatial processes. These spatial pro-esses are evident in local and regional statistics; for examplemployment increases in the form of spatial association.

Spatial association analysis provides a way to investigate spa-ially conditioned urban structure and growth (Páez and Scott,004). More specifically, local statistics can reveal the inner struc-ure of economic variables across city or regional space. They areeveloped because global values will not be universally applicablehroughout a study region and may not reflect subtle differences at

finer level of investigation (Anselin, 1995; Unwin, 1996; Unwinnd Unwin, 1998; Fotheringham and Brunsdon, 1999; Boots andkabe, 2007).

As a measurement of spatial associations, Local Indicators ofpatial Autocorrelations (LISA) statistics suggest clusters based onhe assumption that a spatial pattern is a non-random distribu-ion of economic activities. Firms that are located close to onenother are more likely to have similar characteristics than thosearther away. LISA statistics can be aggregated in a certain way toroduce a global autocorrelation measure, and therefore are use-ul to identify influential location patterns (Páez and Scott, 2004).

Fig. 2. Postcode m

Fig. 1. Workflow to prepare business creation dataset for exploring spatial clusters.

market coverage, acting as an incentive for private and publicagents to choose locations that maximise their potential markets

ap of Beijing.

th Observation and Geoinformation 19 (2012) 252–265 255

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patial units. Among other LISA statistics, Anselin’s local Moran’s Ias proven to be of special interest in helping to understand spa-ial patterns and processes. Therefore also this research adopts thetatistics of the global and local Moran’s I to investigate the spatiallustering of key economic clusters in Beijing.

. Methodology

.1. Functional relationships tested

A central challenge for identifying spatial clusters is to makexplicit the geographic proximity that facilitates economic coop-ration and competition (Porter, 1996, 1998; Martin and Sunley,003). This can be realised via an assumption that certain func-ional relationships reflect theoretical constructs in reality. In thisesearch, complementary and horizontal relationships betweenector-based Input–Output (I–O) accounts were used to representpatial clusters. This is based upon the assumption that supplyhains are firm or plant specific. Vertical relationships, on the oneand are more dependent on the market, and therefore more dif-cult to maintain themselves and to be established in analysis. Onhe other hand, horizontal relationships are complementary andelatively stable. Through an analysis of horizontal relationshipsolicy makers and planners can be informed about the particularlaces for specific types of economic activities. Since innovation

s largely incubated within complementary and horizontal inter-ctions (Hoover and Giarratani, 1999), the places where thesenteractions occur are also of interest for indicating the areas withevelopmental opportunities. A spatial cluster in this research isherefore considered to be a particular area with a concentrationf a group of firms with common and complementary economicinkages that share common facilities and supportive agencies.

From the complete I–O accounts of Beijing in the years of 1987,992, 1997 and 2002 obtained from Beijing Statistical Bureau, all

ndustries were grouped at the 2- or 3-digit level of the indus-rial classification based upon the International Standard Industriallassification (SIC) of the United Nations. The procedure followedhe work of Czamanski (1971) and Feser and Bergman (2000) in

easuring industrial relationships and applying the principle com-onent factor analysis to identify the latent structure of demandnd supply between industries. This method has been proved effi-ient in detecting complementary and horizontal relationships inndustrial subsystems (O hUallachain, 1984). The spatial manifes-ation of the derived key clusters, including the Finance, Insurance,usiness and Real Estate (FIRE), Information and Communicationechnology (ICT), Education and Sciences (ES), Manufacture ofachinery and Metalworking (MMM), petroleum and chemicals,as then examined using LISA statistics.

.2. Data description

The main data used was the register of business establishmentsrom the Beijing Industrial and Commercial Registration System.his is an obligatory registration system of all starting businesses byhe Beijing Municipal Bureau for Industrial and Commercial Admin-stration. It records basic information of each company’s name,ddress, postcode, employment, establishing date, and businesscope at the time of the businesses establishment.

A fuzzy logic approach according to Chinese characters wasmployed to code each firm’s economic activity with the SIC. Inotal, 463,477 valid records were processed covering the period

an’s

–198

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–199

–200

256 Z. Yang et al. / International Journal of Applied Earth Observation and Geoinformation 19 (2012) 252–265

afipmhboootete

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Fig. 3. Spatial evolution of the

3%. The employment of each business in each year was thenggregated for each sector representing the gross increase ofmployment in each postcode area. Fig. 1 illustrates the workflowf dealing with this dataset to make it suitable for spatial clusternalysis.

Corresponding to the I–O survey, the data was grouped with 5-year span since 1983 when a market style economy wasrst encouraged. Table 1 summarises the dataset after the codingrocess in different periods. It shows that after the establish-ent of the market economy the average size of new enterprises

as decreased from 201 employees in middle of 1980s to 25y 2002. This corresponds to the transformation from a state-wned economy to a private economy in which many privatelywned, small and medium size companies started and big state-wned companies were restructured. The aggregate data smoothedhe instability of the business establishment which might oth-rwise be abnormally high or low in a specific year attributedo the economic transformation and uncertainty of the marketconomy.

Some caution should be noted for this data. The datasetocuments the business establishment including the firms thatere established before the liberalisation and still maintained

fterwards. As the system is a compulsory registration whenrms start, it in principle records all start-ups of both pub-

ic and private businesses. However, it hardly provides accurateumbers of employment of firms because a large number of

nformal employment exists, which may not be counted. Also, asart-time and temporary employment is very common the data

cluster at the municipal level.

may over-estimate actual employment in full-time equivalents.Nor does the current employment figure in each cluster derivedfrom this dataset fully reflect reality. There were no records aboutthe closing of or changes in companies so that the actual employ-ment is unavailable. Despite these shortcomings, it is assumed thatthe gross increase of employment reflects the strength of the clus-ters for any period by showing the capacity to create new business,so that it can be used as a proxy to measure the vitality of economicactivities in each area over time. Moreover, employment is also agood indicator for both production and demand on facilities andservices of any given area.

3.3. Global and local Moran’s I

The global and local Moran’s I were used to examine spatial clus-tering of firms in the predefined functional relationship, measuredby their initial employment. The global Moran’s I describes over-all spatial autocorrelation across all geographical units. As one ofcommonly used LISA, the local Moran’s I allows for decompositionof global indicators and assessment of significant local spatial clus-tering around an individual location (Anselin, 1995, p. 94). It canthus reflect the spatial effects and relations of economic activitiesamong the geographical units.

Local Moran’s I has been widely used for exploring and analysing

Z. Yang et al. / International Journal of Applied Earth Observation and Geoinformation 19 (2012) 252–265 257

e ICT c

astaps

splcdafiams

Fig. 4. Spatial evolution of th

nd Wei, 2008). Further, some policies can be tested using Moran’statistics in a policy context such as the relationship between spa-ial distributions of regional income and regional development fundllocations in Europe (Dall’erba, 2005), and the spatial clustering ofroperty values for decentralisation policy to redistribute urbanocial and economic activities (Han, 2005).

In this research, the global and local associations were mea-ured at the level of postcode tracts of Beijing municipality (220ostcodes in the municipality, Fig. 2). Currently, the business estab-

ishments were recorded based on postcodes for reasons such asonfidentiality and practicality. Due to data availability, postcodeata is perhaps at the finest spatial level to perform spatial arealnalysis. At this spatial level, to our knowledge, there are veryew studies of economic activities in China. Postcodes can be read-

patial division, which cannot fully reflect the actual boundary of

luster at the municipal level.

spatial clusters. This must be considered when the results producedby this approach are compared with the classic model of industrialdistricts.

Formally, for each cluster of interest across n spatial units, theglobal Moran’s I is:

I = n∑ni=1

∑nj=1wij

×∑n

∑nj=1wij(xi − x̄)(xj − x̄)∑n

i=1(xi − x̄)2

where x1 is the observation x in area i, x̄ is the average across theunits, and wij is the spatial weight (with j /= i). The range of Moran’s I

e ES c

Fig. 5. Spatial evolution of th

lose to zero means random scattering (Anselin, 1995, 2005).Localoran’s I is

i = n × (xi − x̄)∑i(xi − x̄)2

wij(xi − x̄) = zi

m0×∑

wijzj, with m0 =∑

here zi and zj are in deviations from the mean. In making these cal-ulations, the spatial weights matrix W (wij) was row standardizedn order to avoid scale dependence and facilitate the interpretationf the statistics (Anselin, 1995).

A crucial issue in calculating Moran’s I is to determine the exam-nation and representation of the spatial linkage of spatial unitsAnselin and Getis, 1992; Anselin, 1995; Rey and Montouri, 1999;nselin et al., 2006; Yu and Wei, 2008). This is formally expressed

n a spatial weight:{= 1 if i, j are adjacent neighbours

ij = = otherwise

There are a number of ways to define ‘neighbourhoods’O’Sullivan and Unwin, 2002, pp. 201–202). Assuming the

luster at the municipal level.

conditions within a particular area and its neighbours contributedto the forming of geographical concentrations of businesses andindustries, featured by the growth and diffusion of similar eco-nomic activities, the ‘neighbourhood’ is defined by the existenceof shared borders i.e., rook contiguity weights. More specifically, ifpostcode areas share a border, wij = 1, or otherwise wij = 0. There isa big difference in the size of postcode areas in the urban and peri-urban areas (Fig. 2), ranging from 1.4 km2 to 59.7 km2 within the5th ring road and up to 473 km2 for the municipality as a whole.Therefore the analysis was performed at both the municipal and theurban level (i.e. only for the postcode areas with centroids withinthe 5th ring road).

The statistical inference of the Global and local Moran’s I wasbased on a permutation approach to yield pseudo-significance level(p value), which indicates the extremeness (or lack of extremeness)of the observed statistic, relative to (and conditional on) the values

Z. Yang et al. / International Journal of Applied Earth Observation and Geoinformation 19 (2012) 252–265 259

mfi(HewS

Fig. 6. Spatial evolution of the

t p = 0.05 level (Anselin, 1995; Anselin et al., 1996). The processas conducted with row standardisation of spatial weights in theeoDa software environment (Anselin et al., 2006).

By referring to the Local Moran’s I cluster map and significanceap, four types of spatial patterns can be obtained at the speci-

ed significance level: High–High (HH), Low–Low (LL), Low–HighLH) and High–Low (HL) (Anselin, 1995; Anselin et al., 2006). TheH and HL patterns can be seen as locales of spatial clusters in an

pecifically, these four patterns are:

Fig. 7. Spatial evolution of the petroleu

cluster at the municipal level.

• Hotspots (HH): the postcode and its surrounding postcodes havea concentration of a type of economic activity (i.e. significantlyhigher than the average level) suggesting spatial spillovers fromthe central place to its periphery.

• Islands (HL): the postcode itself shows a significantly high con-centration of a type of economic activity but in its periphery thepresence is lower than average, implying few spatial spilloversfrom the central place to its periphery.

• Atoll (LH): the postcode has a significantly low level of a type ofeconomic activity while in its neighbouring postcodes this typeof activity is significantly high.

m cluster at the municipal level.

netFnsf

Fig. 8. Spatial evolution of the c

Cold spots (LL): both the postcode and its peripheral areas arerelatively inactive with respect to a type of economic activity.

. Results and interpretation

Owing to the different spatial size of postcode areas and theharacteristics of different economic activities, the spatial clustersor FIRE, ICT and ES were examined at the urban level and MMM,etroleum and Chemicals were also explored at the municipal level,s these activities are currently found in the peri-urban districtsutside of the 5th ring road.

.1. Evolution of spatial clusters at the municipal level

According to the functional analysis, not all clusters show sig-ificant expansion (startups) in all periods. Particularly at thearly stage of their forming, clusters have few interactions amonghe sectors, perhaps embodied as several key relevant sectors.

al cluster at the municipal level.

like industrial specialisation, in 1988–1992 the Petroleum clustercould be decomposed into organic chemicals and chemical mate-rials. In such cases, the spatial association was examined throughthe key sector of the cluster or its sub-clusters.

There were many changes of spatial associations of examinedkey clusters for the period of 1983–2002 (Table 2). In the period of1983–1987, the city’s economy was still dominated by manufac-turing and government policy orders largely determined its spatialdistribution. In particularly, the industries of machinery manufac-turing and petroleum gained much momentum in this period withthe strong support of government for building some super-largecompanies in some outer districts, for example the Capital Ironin Shijingshan District, and Yanshan Petrochemicals in Changping(Yang et al., 2010). As a result, the clusters of MMM and Petroleumshowed spatial associations at the municipal level as indicated bysignificant global Moran’s I (the value of Moran’s I was not high).However, the global Moran’s I for the cluster of Chemicals was closeto 0 and insignificant (p > 0.05), largely due to the development of

th Obs

eriod, following government plans finance services and educationere highly concentred in several areas which also showed sig-ificant global associations, while business activities and Research

Development (R&D) were very few, with no significant globalssociations.

With the transitioning to a market economy taking form byhe end of the 1980s, the FIRE cluster emerged and showedignificant global Moran’s I from 1988 to 2002. The ICT clus-

Fig. 9. Spatial evolution of the FIRE

distributed in the urban centre so that the global spatial associa-tion was not significant during 1998–2002 (p = 0.93). The ES clustermaintained a significant spatial association 1983–2002, perhapsattributable to knowledge spillovers. In the period of 1988–1992,the global spatial association was insignificant for the clustersof MMM, Petroleum and Chemicals, reflecting that these used tobe government-supported industries strongly influenced by themarket forces. With the development of the market, these clus-

cluster in the urban centre.

he ICT

optmttcii

Fig. 10. Spatial evolution of t

ndustrial restructuring, the clusters of MMM and Chemicals hado significant global spatial associations though Petroleum wastill significant during 1988–2002, perhaps due to the technologicalpgrading of oil industries.

Using the local Moran’s I map, the locale and spatial evolutionf the clusters can be clearly seen. Fig. 3 shows that the majorlaces for finance and business were in the key northern towns ofhe municipality, rather than in the urban centre. Since 1992, the

ain location of finance moved to the urban centre, and since then,he business and finance activities have been functionally groupedogether. The urban centre has become a major place for the FIREluster. Particularly after 1998 FIRE activities were dominant andn almost all districts of the urban centre, though some key townsn the northern municipality also remained FIRE islands.

Fig. 4 shows that during 1983–1987 only 3 ICT cluster islandsn the north of the urban centre. These islands and their peripheralreas were however the seeds of hotspots that quickly grew during988–1992. By 1993–1997, these ICT hotspots of information and

communication facilities expanded rapidly forming a belt circlingthe urban centre, while the other ICT sub-cluster, manufacture ofcomputers, concentrated to the north of the urban centre. By 2002these two ICT-related sub-clusters were combined and showed upas two significant hotspots.

The ES cluster emerged in 1998. Before that year, the R&D activ-ity was mainly concentrated in a hotspot formed by the location ofbranches of the Chinese Academy Sciences, mainly in the east partof the urban centre. After 1988, the ES concentration was grow-ing mainly in the northern part of the urban centre, but after 1998further growth was located in the city’s southern districts (Fig. 5).

The hotspots for the MMM cluster barely changed betweenthe periods 1983–1987 and 1988–1992; they were in the northof the municipality outside the urban centre. During 1993–1997,

th Obs

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As regards the Petroleum cluster, Fig. 7 indicates that during982–1987 most key towns in the sub-urban area had the islandattern and the hotspots were along Badaling Motorway. In theext period, some petroleum islands in the south disappeared asid the hotspots, though the latter re-emerged in the southwestf the municipality during 1993–1997. During 1998–2002, thereere two regions of petroleum hotspots respectively in the south

nd north.During 1983–1987, the islands of the chemical cluster were in

he key towns of the south and northeast, with one hotspot locatedlong Badaling Motorway. Another two hotspots of chemical mate-ials emerged in the Beijing–Tianjin corridor during 1988–1992.he area containing the Yanshan petrochemical complex took on

Fig. 11. Spatial evolution of the ES

4.2. Evolution of spatial clusters at the urban level

The global analysis showed that the FIRE, ICT and ES were heav-ily concentrated in the urban centre and this section examines thesein more detail.

For the FIRE cluster business and financial activities were sig-nificant in the CBD area and the Financial Street (where theheadquarters of national banks are concentrated, Fig. 9). However,from 1993 to 2002 employment growth in the FIRE cluster was notrestricted to the CBD and the Financial Street. Rather, the location ofFIRE related jobs was seemingly quite flexible and dispersed withinthe urban centre.

The growth of employment in the ICT sector shows a different

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64 Z. Yang et al. / International Journal of Applied Ear

f information and communication facilities was concentred in theast part of the urban centre, with two important locations: theeijing Electronic and Fibre Manufacturing (BEFM) business parknd the Beijing Development Area (BDA).

Several of Beijing’s leading tertiary education and researchgencies, Peking and Tsinghua Universities and the Institutes of thehinese Academic Sciences (CAS) form a strong ES cluster (Fig. 11).o a large extent, these locations overlapped or were close to theCT cluster (see Fig. 10), reflecting also the policy to generate high-ech spin-off companies from these leading research institutions.y 1998–2002, there was no significant spatial association for theS cluster in the urban centre, implying the growth of ES activitiesad become more dispersed over the urban centre.

. Discussion and conclusion

This study presents an approach to examine the patterns ofpatial clusters via the techniques of spatial statistics. Specifically,ocal statistics or LISA, through local Moran’s I, explicitly empha-izes geographic proximity in the notion of economic clusters. LISAllows a more objective analysis of the location of spatial clusters,hich can complement experts’ opinions related to cluster forma-

ion and management. More importantly, the approach enables thepatial spillover effects of economic activities to be quantified to

large extent. By comparison to conventional economic studiesn industrial districts, this approach investigates economic spa-ial clusters citywide, thereby disclosing different agglomeration orispersal forms of economic activities in different types of indus-rial chains. It therefore adjusts the perception and understandingf spatial clusters from being geographically bounded districts tounctionally related locales. The analysis results better approximatehe actual cluster development processes and locations and allow aapid and timely assessment of the spatial effect of economic activ-ties in geographical space, though clearly the availability of theecessary data is also an issue to consider.

The study has revealed the spatial patterns and temporalhanges of places of job growth in Beijing’s main economic clus-ers. The FIRE and ES clusters concentrate in the urban centre andn the sub-urban towns. The ICT cluster is mainly concentrated inhe north and encircling the urban centre. The MMM cluster has, byontrast, undergone a process of convergence and diffusion in theub-urban of the city, while the spatial patterns of the petroleumnd chemical clusters are very similar to one another, reflectingheir strong linkages in both production and economic processes.

Both the Badaling motorway and Beijing-Tianjin corridor formmportant geographic axes for facilitating spatial interactions forhe manufacturing clusters including the ICT, MMM, petroleum andhemicals clusters. These results corroborate our local knowledgebout the distribution of the main economic activities; yet moremportantly, they drive our understanding about the interactions ofpatial clusters and their mutual relationships with urban structuresing the powerful tool of spatial statistics.

Although the research approach is sound and useful, the analysisould be further improved if more detailed multi-temporal data onusiness activities were available. The availability of data such ashe characteristics of lifecycle of the establishment, business cat-gories, and type of ownership, employment and a more specificddress would almost certainly reveal additional patterns of clusterevelopment and lifecycles. More meaningful outcomes, particu-

arly for urban planning, can also be derived from smaller spatial

ervation and Geoinformation 19 (2012) 252–265

survey data and the spatial information system is therefore desir-able.

Acknowledgements

The authors would like to especially thank Prof. Xiaolu Gao inthe Institute of Geographic Resources Research, Chinese Academyof Sciences for providing the register of business establishmentsof Beijing. The research is sponsored by National Natural SciencesFoundation of China (Grant no. 41001101) and ITC research fund.

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