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Mapping the results of local statistics

Author

Listed:
  • Stephen Matthews

    (Pennsylvania State University)

  • Tse-Chuan Yang

    (State University of New York at Albany)

Abstract

Background: The application of geographically weighted regression (GWR) - a local spatial statistical technique used to test for spatial nonstationarity - has grown rapidly in the social, health, and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in the relationships between predictors and the outcome variable. Objective: A major challenge to users of GWR methods is how best to present and synthesize the large number of mappable results, specifically the local parameter parameter estimates and local t-values, generated from local GWR models. We offer an elegant solution. Methods: This paper introduces a mapping technique to simultaneously display local parameter estimates and local t-values on one map based on the use of data selection and transparency techniques. We integrate GWR software and GIS software package (ArcGIS) and adapt earlier work in cartography on bivariate mapping. We compare traditional mapping strategies (i.e., side-by-side comparison and isoline overlay maps) with our method using an illustration focusing on US county infant mortality data. Conclusions: The resultant map design is more elegant than methods used to date. This type of map presentation can facilitate the exploration and interpretation of nonstationarity, focusing map reader attention on the areas of primary interest.

Suggested Citation

  • Stephen Matthews & Tse-Chuan Yang, 2012. "Mapping the results of local statistics," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(6), pages 151-166.
    • Handle: RePEc:dem:demres:v:26:y:2012:i:6
    DOI: 10.4054/DemRes.2012.26.6
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    References listed on IDEAS

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    Cited by:

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    14. Khalid Al-Ahmadi & Ali Al-Zahrani, 2013. "NO 2 and Cancer Incidence in Saudi Arabia," IJERPH, MDPI, vol. 10(11), pages 1-19, November.
    15. Lewandowska-Gwarda Karolina, 2014. "Spatial Analysis Of Foreign Migration In Poland In 2012 Using Geographically Weighted Regression," Comparative Economic Research, Sciendo, vol. 17(4), pages 137-154, December.
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    18. Ingram, Matthew C. & Marchesini da Costa, Marcelo, 2019. "Political geography of violence: Municipal politics and homicide in Brazil," World Development, Elsevier, vol. 124(C), pages 1-1.
    19. Jinghu Pan & Weiguo Wang & Junfeng Li, 2016. "Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1879-1899, April.
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    More about this item

    Keywords

    mapping; geographically weighted regression; nonstationarity; local statistics;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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