Spatial point pattern analysis and industry concentration

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 one to measure industry concentration a...

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Bibliographic Details
Published in:The Annals of regional science Vol. 47; no. 2; pp. 311 - 328
Main Authors: Kosfeld, Reinhold, Eckey, Hans-Friedrich, Lauridsen, Jørgen
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.10.2011
Springer
Springer Nature B.V
Subjects:
Allowances
Analysis
Cluster analysis
Clustering
Concentration
Continuums
Deutschland
Dispersions
Distribution
Economic concentration
Economics
Economics and Finance
Environmental Economics
Externality
Function
Geography
Germany
Indexes
Industrial concentration
Industrialized nations
Industrie
Industry
Innovations
Landscape/Regional and Urban Planning
Localization
Location of industry
Measures
Messung
Microeconomics
Pattern analysis
Productivity
Regional
Regional science
Regional studies
Regional/Spatial Science
Regionales Cluster
Regions
Similarity
Spatial analysis
Special Issue Paper
Studies
Tests
Theorie
R12
C46
L70
L60
ISSN:0570-1864, 1432-0592
Online Access:Get full text
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Description
Summary: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 one to measure industry concentration at a continuum of spatial scales. While common distance-based 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 (J R Stat Soc B, 25:294, 1977) L function. The new index can be employed to measure the extent of substantial clustering and dispersion. The K function approach is employed to identifying measuring industry concentration in Germany.
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ISSN:0570-1864
1432-0592
DOI:10.1007/s00168-010-0385-5