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2020
- handle: 10670/1.78e6tg
- hal: hal-01082178
- doi: 10.1007/s00168-019-00946-7
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info:eu-repo/semantics/altIdentifier/doi/10.1007/s00168-019-00946-7
info:eu-repo/semantics/OpenAccess
Mots-clés
Agglomeration Aggregation Spatial Concentration Point Patterns Economic Geography C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C60 - General R - Urban, Rural, Regional, Real Estate, and Transportation Economics/R.R1 - General Regional Economics/R.R1.R12 - Size and Spatial Distributions of Regional Economic ActivitySujets proches
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Gabriel Lang et al., « Distance-based measures of spatial concentration: Introducing a relative density function », HAL-SHS : économie et finance, ID : 10.1007/s00168-019-00946-7
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Résumé
For a decade, distance-based methods have been widely employed and constantly improved in the field of spatial economics. These methods are a very useful tool for accurately evaluating the spatial distribution of plants or retail stores, for example (Duranton and Overman, 2008). In this paper, we introduce a new distance-based statistical measure for evaluating the spatial concentration of economic activities. To our knowledge, the m function is the first relative density function to be proposed in the economics literature. This tool supplements the typology of distance-based methods recently drawn up by Marcon and Puech (2012). By considering several theoretical and empirical examples, we show the advantages and the limits of the m function for detecting spatial structures in economics.