Methods for neighbourhood Mapping, boundary agreement

Nicholas S Dalton and Mark Hurrell

Environment and Planning B, 2023, vol. 50, issue 2, 401-415

Abstract: Any analytical study of a neighbourhood must begin with an accurate definition of the geographic region that contains it. For a long time, there has been an interest in taking surveys of neighbourhood extents, but this can generate numerous haphazardly sketched polygons. Researchers typically face the challenges of using boundary polygons reported by each participant and unifying these polygons into one representative boundary. Over the years, several researchers have reported their findings on methods for unifying these boundaries. We present and compare the following five methods (two existing, one modified and two new): Dalton radial average, Bae–Montello average, a vectorised version of the Bae–Montello raster grid overlay, a vectorised derivative inspired by the Wenhao kernel density axis method maximum kernel density axis and a new k-medians clustering method. A crowd-sourced evaluation method is presented. N =42 raters ranked the five methods of aggregating real boundary data based on the results from three study areas. We found that the boundary aggregation method derived from the Bae–Montello grid, closely followed by the Dalton radial average method, provided the most reasonable results. This paper outlines the reasons for these results and illustrates how this knowledge may point to the ability of future algorithms to improve the presented methods. The paper ends with a recommendation that neighbourhood boundaries should utilise boundaries derived from the Bae–Montello raster grid overlay method and/or the Dalton radial average method to facilitate comparisons in the field.

Keywords: Spatial analysis; spatial interaction; spatial interaction models; spatial modelling; spatial representation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:50:y:2023:i:2:p:401-415

DOI: 10.1177/23998083221115195

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