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Street-Weighted Interpolation Techniques for Demographic Count Estimation in Incompatible Zone Systems

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  • Michael Reibel

    (Department of Geography and Anthropology, California State Polytechnic University, Pomona, CA 91768, USA)

  • Michael E Bufalino

    (Center for Geographic Information Science Research, California State Polytechnic University, Pomona, CA 91768, USA)

Abstract

Data processing for the spatial analysis of small-area social, demographic, and economic data often requires the combination of data spatially aggregated to two or more incompatible zone systems in a region, such as a set of enumeration districts that changes over time. Such situations can be addressed by areal interpolation—the transfer of data between zonal systems according to spatial algorithms. The authors test a technique of areal interpolation using geographic information systems (GIS) that employs a digital map layer representing streets and roads to derive varying density weights for small areas within aggregation zones. The technique reduces errors in estimation compared with estimates derived using the commonly applied area-weighting technique, with its assumption of uniform density. The street-weighting technique is much easier to use than other interpolation techniques that have also been shown to reduce error compared with area-based weighting.

Suggested Citation

  • Michael Reibel & Michael E Bufalino, 2005. "Street-Weighted Interpolation Techniques for Demographic Count Estimation in Incompatible Zone Systems," Environment and Planning A, , vol. 37(1), pages 127-139, January.
    • Handle: RePEc:sae:envira:v:37:y:2005:i:1:p:127-139
    DOI: 10.1068/a36202
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    Cited by:

    1. Kiatkulchai Jitt-Aer & Graham Wall & Dylan Jones & Richard Teeuw, 2022. "Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand," 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. 113(1), pages 185-211, August.
    2. Renaud Le Goix & Elena Vesselinov, 2015. "Inequality shaping processes and gated communities in US western metropolitan areas," Urban Studies, Urban Studies Journal Limited, vol. 52(4), pages 619-638, March.
    3. Dmowska Anna, 2019. "Dasymetric modelling of population distribution – large data approach," Quaestiones Geographicae, Sciendo, vol. 38(1), pages 15-27, March.
    4. David Briggs & Daniela Fecht & Kees De Hoogh, 2007. "Census data issues for epidemiology and health risk assessment: experiences from the Small Area Health Statistics Unit," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 355-378, March.
    5. Guangqing Chi, 2009. "Can knowledge improve population forecasts at subcounty levels?," Demography, Springer;Population Association of America (PAA), vol. 46(2), pages 405-427, May.
    6. Vignes, Céline & Rimbourg, Sarah & Ruiz-Gazen, Anne & Thomas-Agnan, Christine, 2013. "Fiches méthodologiques, méthodes statistiques d’allocation spatiale : interpolation de données surfaciques," TSE Working Papers 13-446, Toulouse School of Economics (TSE).

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