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Quantile local spatial autocorrelation

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  • Luc Anselin

    (The University of Chicago)

Abstract

This note introduces the concept of quantile local spatial autocorrelation as a special case of a local indicator of spatial association (LISA) for the situation where the variables of interest are binary. This provides additional insight into the spatial distribution of observations at the extremes of the distribution. The concept is illustrated with an analysis of local spatial clusters and outliers for health outcomes using data for 791 Chicago census tracts in 2014.

Suggested Citation

  • Luc Anselin, 2019. "Quantile local spatial autocorrelation," Letters in Spatial and Resource Sciences, Springer, vol. 12(2), pages 155-166, August.
    • Handle: RePEc:spr:lsprsc:v:12:y:2019:i:2:d:10.1007_s12076-019-00234-0
    DOI: 10.1007/s12076-019-00234-0
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    References listed on IDEAS

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    1. Peter Congdon, 2016. "A local join counts methodology for spatial clustering in disease from relative risk models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(10), pages 3059-3075, May.
    2. Barry Boots, 2003. "Developing local measures of spatial association for categorical data," Journal of Geographical Systems, Springer, vol. 5(2), pages 139-160, August.
    3. Philip Kostov, 2009. "A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(1), pages 53-72.
    4. Zhang, Lei & Leonard, Tammy, 2014. "Neighborhood impact of foreclosure: A quantile regression approach," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 133-143.
    5. Luc Anselin & Xun Li, 2019. "Operational local join count statistics for cluster detection," Journal of Geographical Systems, Springer, vol. 21(2), pages 189-210, June.
    6. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    7. Barry Boots, 2006. "Local configuration measures for categorical spatial data: binary regular lattices," Journal of Geographical Systems, Springer, vol. 8(1), pages 1-24, March.
    8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    9. J. Keith Ord & Arthur Getis, 2001. "Testing for Local Spatial Autocorrelation in the Presence of Global Autocorrelation," Journal of Regional Science, Wiley Blackwell, vol. 41(3), pages 411-432, August.
    10. Daniel P. McMillen, 2013. "Quantile Regression for Spatial Data," SpringerBriefs in Regional Science, Springer, edition 127, number 978-3-642-31815-3, March.
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    Cited by:

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    2. Liye Wang & Jiwei Xu & Yaolin Liu & Siyu Zhang, 2023. "Spatial Characteristics of the Non-Grain Production Rate of Cropland and Its Driving Factors in Major Grain-Producing Area: Evidence from Shandong Province, China," Land, MDPI, vol. 13(1), pages 1-22, December.
    3. Tianyi Zhao & Yuning Cheng & Yiyang Fan & Xiangnan Fan, 2022. "Functional Tradeoffs and Feature Recognition of Rural Production–Living–Ecological Spaces," Land, MDPI, vol. 11(7), pages 1-27, July.
    4. Wang, Liye & Zhang, Siyu & Xiong, Qiangqiang & Liu, Yu & Liu, Yanfang & Liu, Yaolin, 2022. "Spatiotemporal dynamics of cropland expansion and its driving factors in the Yangtze River Economic Belt: A nuanced analysis at the county scale," Land Use Policy, Elsevier, vol. 119(C).

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    More about this item

    Keywords

    Spatial clusters; LISA; Join count statistic; Multivariate spatial association; Spatial data science;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality

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