Distance-based measures of spatial concentration: Introducing a relative density function

Gabriel Lang (), Eric Marcon () and Florence Puech ()
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Gabriel Lang: MIA-Paris - Mathématiques et Informatique Appliquées - INRA - Institut National de la Recherche Agronomique - AgroParisTech
Eric Marcon: UMR ECOFOG - Ecologie des forêts de Guyane - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - INRA - Institut National de la Recherche Agronomique - AgroParisTech - UG - Université de Guyane - CNRS - Centre National de la Recherche Scientifique - UA - Université des Antilles
Florence Puech: RITM - Réseaux Innovation Territoires et Mondialisation - UP11 - Université Paris-Sud - Paris 11

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Abstract: 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.

Keywords: Agglomeration; Aggregation; Spatial Concentration; Point Patterns; Economic Geography (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
Note: View the original document on HAL open archive server: https://hal.science/hal-01082178v4
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Published in The Annals of Regional Science, 2020, 64 (2), pp.243-265. ⟨10.1007/s00168-019-00946-7⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01082178

DOI: 10.1007/s00168-019-00946-7

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