A class of spatial econometric methods in the empirical analysis of clusters of firms in the space

Arbia, Giuseppe; Espa, Giuseppe; Quah, Danny

doi:10.1007/s00181-007-0154-1

Cited by 83 publications

(43 citation statements)

References 39 publications

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“…Therefore we restrict the maximum radius by one fourth of the maximum pairwise distances between locations of plants (cf. Duranton and Overman, 2005;Arbia et al, 2008).…”

Section: Testing For Unconditional Concentration and Dispersionmentioning

confidence: 99%

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Spatial point pattern analysis and industry concentration

2010

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in MAGKS Göttingen Spatial Point Pattern Analysis and Industry ConcentrationReinhold Kosfeld · Hans-Friedrich Eckey · Jørgen Lauridsen Abstract. Traditional measures of spatial industry concentration are restricted to given areal units. They do not make allowance for the fact that concentration may be differently pronounced at various geographical levels. Methods of spatial point pattern analysis allow to measure industry concentration at a continuum of spatial scales. While common distancebased methods are well applicable for sub-national study areas, they become inefficient in measuring concentration at various levels within industrial countries. This particularly applies in testing for conditional concentration where overall manufacturing is used as a reference population. Using Ripley's K function approach to second-order analysis, we propose a subsample similarity test as a feasible testing approach for establishing conditional clustering or dispersion at different spatial scales. For measuring the extent of clustering and dispersion, we introduce a concentration index of the style of Besag's (1977) L function. By contrast to Besag's L function, the new index can be employed to measure deviations of observed from general spatial point patterns. The K function approach is illustratively applied to measuring and testing industry concentration in Germany.

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“…Therefore we restrict the maximum radius by one fourth of the maximum pairwise distances between locations of plants (cf. Duranton and Overman, 2005;Arbia et al, 2008).…”

Section: Testing For Unconditional Concentration and Dispersionmentioning

confidence: 99%

“…In particular clustering and dispersion can only be detected but not quantified. Arbia et al (2008) use the bivariate K function approach to identify co-location across different industries. 2 The contribution of this paper to the literature is threefold.…”

Section: Introductionmentioning

confidence: 99%

Spatial point pattern analysis and industry concentration

2010

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“…Applications of this field of study include monitoring disequilibrium and domination during economic development, cultivating chances of cooperation and rivalry, creating milieu for shaping industrial competitiveness, and stimulating technological innovation (Belussi & Caldari, 2009;Funderburg & Boarnet, 2008;Mota & de Castro, 2004;Porter, 1998a;Rigby and Essletzbichler, 2002;Steinle and Schiele, 2002). While it is has been acknowledged that a group of related industries are co-located or geographically proximate (LaFountain, 2005), few studies have examined the spatial pattern of the co-agglomeration of industries to inform the above applications (Arbia, Espa, Giuliani, & Mazzitelli, 2010;Arbia, Espa, & Quah, 2008). As urban and regional economies grow, the spatial distribution and geographical co-agglomeration of industries may not necessarily follow the same patterns.…”

Section: Introductionmentioning

confidence: 99%

Spatial representations and policy implications of industrial co-agglomerations, a case study of Beijing

Yang

Tao

Chahine

2016

Habitat International

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a b s t r a c tIndustrial parks have been tested in various regions around the world, in attempt to foster innovation and fuel economic growth. Despite the importance of industry co-agglomerations in regional growth, few studies examine them in regional geographic space. This paper combines exploratory spatial data analysis and inputeoutput method to explore the spatial pattern of key industrial co-agglomerations in Beijing, which is illustrated by location, function, frequency, spatial hierarchy and spacing. The results contribute to linking abstract economic and actual geographical spaces in urban and regional growth, enabling urban and regional planners to judge and evaluate planning initiatives before and after implementation. The failure of sub-center plans and risks of industrial parks schemes in Beijing are addressed. By considering industrial inputeoutput relations including environmental and human resources, urban planners can optimize the development of such co-agglomerations to foster sustainable urban development.

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“…The bibliographic survey reveals that in recent years, some attempts have been made to simulate the basic parameters of clusters [1], processes of their formation [2][3][4][5][6][7][8][9], including the problems of their self-organization [10][11][12], intracluster interactions [13][14][15][16], cluster functioning [17], the systems of intracluster objectives [6], their life cycle [18][19][20][21][22][23], entropy processes, degradation and collapse of cluster structures [7,21,24], etc.…”

Section: Introductionmentioning

confidence: 99%