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An Adaptive Filter for Estimating Spatially-Varying Parameters: Application to Modeling Police Hours Spent in Response to Calls for Service

Author

Listed:
  • Stuart A. Foster

    (Department of Geography, The Ohio State University, Columbus, Ohio 43210)

  • Wilpen L. Gorr

    (School of Urban and Public Affairs, Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

The Spatial Adaptive Filter (SAF), introduced in this paper, uses generalized damped negative feedback to estimate spatially-varying parameters for multivariate models. Previous adaptive filters have been designed to estimate time-varying parameters and process data recursively in time sequence. SAF processes all data simultaneously in an iterative algorithm. Monte Carlo studies show that SAF is successful in automatically identifying and estimating step-jump and continuous spatial variation in the parameters of causal variables. A case study on census-tract data from Columbus, Ohio, relating police-vehicle hours spent in responding to calls to socio-economic indicators, has systematic spatial variation in estimated parameters. Independent variables that are significant in inner-city areas of Columbus become progressively less significant in moving to outlying areas.

Suggested Citation

  • Stuart A. Foster & Wilpen L. Gorr, 1986. "An Adaptive Filter for Estimating Spatially-Varying Parameters: Application to Modeling Police Hours Spent in Response to Calls for Service," Management Science, INFORMS, vol. 32(7), pages 878-889, July.
    • Handle: RePEc:inm:ormnsc:v:32:y:1986:i:7:p:878-889
    DOI: 10.1287/mnsc.32.7.878
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    Citations

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    Cited by:

    1. Sven Müller, 2012. "Identifying spatial nonstationarity in German regional firm start-up data," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 32(2), pages 113-132, September.
    2. Wei, Chuan-Hua & Qi, Fei, 2012. "On the estimation and testing of mixed geographically weighted regression models," Economic Modelling, Elsevier, vol. 29(6), pages 2615-2620.
    3. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Testing for Spatial Autocorrelation among the Residuals of the Geographically Weighted Regression," Environment and Planning A, , vol. 32(5), pages 871-890, May.
    4. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    5. Xu, Wan & Lambert, Dayton M., 2011. "Business Establishment Growth in the Appalachian Region, 2000-2007: An Application of Smooth Transition Spatial Process Models," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(3), pages 1-16, August.
    6. Yonghua Zou, 2015. "Re-examining the Neighborhood Distribution of Higher Priced Mortgage Lending: Global versus Local Methods," Growth and Change, Wiley Blackwell, vol. 46(4), pages 654-674, December.
    7. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model," Environment and Planning A, , vol. 32(1), pages 9-32, January.
    8. Dayton M. Lambert & Wan Xu & Raymond J. G. M. Florax, 2014. "Partial Adjustment Analysis of Income and Jobs, and Growth Regimes in the Appalachian Region with Smooth Transition Spatial Process Models," International Regional Science Review, , vol. 37(3), pages 328-364, July.

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