On the Estimation of Smooth Maps from Regional Aggregates via Measurement Error Models: A Review

Ulrich Rendtel () and Timo Schmid ()
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Ulrich Rendtel: Freie Universität Berlin, Institute of Statistics and Econometrics
Timo Schmid: Otto-Friedrich-Universität Bamberg, Institute of Statistics

A chapter in Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science, 2024, pp 491-509 from Springer

Abstract: Abstract The contribution discusses the problems associated with generating maps that display local case numbers or local ratios for a system of areas, such as counties. Traditional maps, like choropleths, are discontinuous at borderlines of reference areas, making it difficult to identify local clusters. To overcome this issue, a two-dimensional kernel density estimator delivers a smooth regional distribution of the variable of interest without discontinuity. Due to confidentiality constraints, precise geo-coded information is not available, which ideally should be used for producing the density estimates. Therefore, we describe an algorithm used to generate a kernel density estimate from a set of area aggregates, which is used in several applications in the contribution. These applications include the construction of service maps for childcare, the transfer of student residences from ZIP-code aggregates to administrative area aggregates, the analysis of voting data, and the display of corona-incidence maps. Finally, we discuss the extension of this approach to other areas such as anonymization of geo-coded data and small area estimation.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-69111-9_24

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DOI: 10.1007/978-3-031-69111-9_24

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