Abstract

The dbmss package for R provides an easy-to-use toolbox to characterize the spatial structure of point patterns. Our contribution presents the state of the art of distance-based methods employed in economic geography and which are also used in ecology. Topographic functions such as Ripley's K, absolute functions such as Duranton and Overman's Kd and relative functions such as Marcon and Puech's M are implemented. Their confidence envelopes (including global ones) and tests against counterfactuals are included in the package.

Highlights

  • Numerous researchers in various fields concern themselves with characterizing spatial distributions of objects

  • In addition of ecologists analyzing the spatial distribution of plants, economists may be concerned with the location of new entrants (Duranton and Overman 2008) or with the location of shops according to the types of good sold (Picone, Ridley, and Zandbergen 2009)

  • Localization, i.e., the degree of dissimilarity between the geographical distribution of an industry and that of a reference (Hoover 1936), relies on discrete space and measures of inequality between zones, such as the classical Gini (1912) index or the more advanced Ellison and Glaeser (1997) index. This approach suffers from several limitations, mainly the modifiable areal unit problem (MAUP): Results depend on the way zones are delimited and on the scale of observation (Openshaw and Taylor 1979)

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Summary

Introduction

Numerous researchers in various fields concern themselves with characterizing spatial distributions of objects. The traditional approach to detect dbmss: Characterize Point Patterns in R localization, i.e., the degree of dissimilarity between the geographical distribution of an industry and that of a reference (Hoover 1936), relies on discrete space (a country is divided in regions for example) and measures of inequality between zones, such as the classical Gini (1912) index or the more advanced Ellison and Glaeser (1997) index. Distance-based methods have the advantage to consider space as continuous, i.e., without any zoning, allowing detecting spatial structures at all scales simultaneously and solving MAUP issues These methods estimate the value of a function of distance to each point calculated on a planar point pattern, typically objects on a map.

Rationale and statistical background
Absolute functions
Relative functions
Unification
Package content
Distance-based functions
Confidence envelopes
Examples
Goodness-of-fit test
Findings
Conclusion
Full Text
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