IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v2y2019i3p27-415d258088.html
   My bibliography  Save this article

Negative Spatial Autocorrelation: One of the Most Neglected Concepts in Spatial Statistics

Author

Listed:
  • Daniel A. Griffith

    (School of Economic, Political, and Policy Sciences, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, USA)

Abstract

Negative spatial autocorrelation is one of the most neglected concepts in quantitative geography, regional science, and spatial statistics/econometrics in general. This paper focuses on and contributes to the literature in terms of the following three reasons why this neglect exists: Existing spatial autocorrelation quantification, the popular form of georeferenced variables studied, and the presence of both hidden negative spatial autocorrelation, and mixtures of positive and negative spatial autocorrelation in georeferenced variables. This paper also presents details and insights by furnishing concrete empirical examples of negative spatial autocorrelation. These examples include: Multi-locational chain store market areas, the shrinking city of Detroit, Dallas-Fort Worth journey-to-work flows, and county crime data. This paper concludes by enumerating a number of future research topics that would help increase the literature profile of negative spatial autocorrelation.

Suggested Citation

  • Daniel A. Griffith, 2019. "Negative Spatial Autocorrelation: One of the Most Neglected Concepts in Spatial Statistics," Stats, MDPI, vol. 2(3), pages 1-28, August.
    • Handle: RePEc:gam:jstats:v:2:y:2019:i:3:p:27-415:d:258088
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/2/3/27/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/2/3/27/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel A. Griffith, 2013. "Better Articulating Normal Curve Theory for Introductory Mathematical Statistics Students: Power Transformations and Their Back-Transformations," The American Statistician, Taylor & Francis Journals, vol. 67(3), pages 157-169, August.
    2. Daniel A. Griffith & David W. S. Wong & Thomas Whitfield, 2003. "Exploring Relationships Between the Global and Regional Measures of Spatial Autocorrelation," Journal of Regional Science, Wiley Blackwell, vol. 43(4), pages 683-710, November.
    3. Hodges, James S. & Reich, Brian J., 2010. "Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love," The American Statistician, American Statistical Association, vol. 64(4), pages 325-334.
    4. E Saliby & R J Paul, 2009. "A farewell to the use of antithetic variates in Monte Carlo simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(7), pages 1026-1035, July.
    5. Daniel Griffith, 2006. "Hidden negative spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 8(4), pages 335-355, October.
    6. Yongwan Chun & Daniel A. Griffith & Monghyeon Lee & Parmanand Sinha, 2016. "Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters," Journal of Geographical Systems, Springer, vol. 18(1), pages 67-85, January.
    7. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    8. John Hughes & Murali Haran, 2013. "Dimension reduction and alleviation of confounding for spatial generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 139-159, January.
    9. Schelling, Thomas C, 1969. "Models of Segregation," American Economic Review, American Economic Association, vol. 59(2), pages 488-493, May.
    10. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4.
    11. Yongwan Chun & Daniel Griffith & Monghyeon Lee & Parmanand Sinha, 2016. "Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters," Journal of Geographical Systems, Springer, vol. 18(1), pages 67-85, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel A. Griffith & Yongwan Chun, 2022. "Some useful details about the Moran coefficient, the Geary ratio, and the join count indices of spatial autocorrelation," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-30, December.
    2. Pronti, A. & Zoboli, R., 2024. "Something new under the sun. A spatial econometric analysis of the adoption of photovoltaic systems in Italy," Energy Economics, Elsevier, vol. 134(C).
    3. Uğur Ursavaş & Carlos Mendez, 2023. "Regional income convergence and conditioning factors in Turkey: revisiting the role of spatial dependence and neighbor effects," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 71(2), pages 363-389, October.
    4. Daniel A. Griffith, 2020. "A Family of Correlated Observations: From Independent to Strongly Interrelated Ones," Stats, MDPI, vol. 3(3), pages 1-19, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lan Hu & Yongwan Chun & Daniel A. Griffith, 2020. "Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000–2010," Journal of Geographical Systems, Springer, vol. 22(3), pages 291-308, July.
    2. Lan Hu & Daniel A. Griffith & Yongwan Chun, 2018. "Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates: Florida, USA, 2000–2011," IJERPH, MDPI, vol. 15(11), pages 1-18, October.
    3. Daniel A. Griffith & Yongwan Chun & Jan Hauke, 2022. "A Moran eigenvector spatial filtering specification of entropy measures," Papers in Regional Science, Wiley Blackwell, vol. 101(1), pages 259-279, February.
    4. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
    5. Donegan, Connor & Chun, Yongwan & Hughes, Amy E., 2020. "Bayesian estimation of spatial filters with Moran's eigenvectors and hierarchical shrinkage priors," OSF Preprints fah3z, Center for Open Science.
    6. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    7. Rodolfo Metulini & Roberto Patuelli & Daniel A. Griffith, 2018. "A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade," Econometrics, MDPI, vol. 6(1), pages 1-15, February.
    8. Duncan Lee & Alastair Rushworth & Sujit K. Sahu, 2014. "A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution," Biometrics, The International Biometric Society, vol. 70(2), pages 419-429, June.
    9. Philip A. White & Durban G. Keeler & Daniel Sheanshang & Summer Rupper, 2022. "Improving piecewise linear snow density models through hierarchical spatial and orthogonal functional smoothing," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.
    10. Cici Bauer & Jon Wakefield, 2018. "Stratified space–time infectious disease modelling, with an application to hand, foot and mouth disease in China," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1379-1398, November.
    11. Duncan Lee & Richard Mitchell, 2013. "Locally adaptive spatial smoothing using conditional auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 593-608, August.
    12. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
    13. Joshua P. Keller & Adam A. Szpiro, 2020. "Selecting a scale for spatial confounding adjustment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1121-1143, June.
    14. Duncan Lee & Craig Anderson, 2023. "Delivering spatially comparable inference on the risks of multiple severities of respiratory disease from spatially misaligned disease count data," Biometrics, The International Biometric Society, vol. 79(3), pages 2691-2704, September.
    15. Wenwen Sun & Daisuke Murakami & Xin Hu & Zhuoran Li & Akari Nakai Kidd & Chunlu Liu, 2023. "Supply–Demand Imbalance in School Land: An Eigenvector Spatial Filtering Approach," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
    16. Jingyi Zhang & Bin Li & Yumin Chen & Meijie Chen & Tao Fang & Yongfeng Liu, 2018. "Eigenvector Spatial Filtering Regression Modeling of Ground PM 2.5 Concentrations Using Remotely Sensed Data," IJERPH, MDPI, vol. 15(6), pages 1-24, June.
    17. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
    18. Jennifer F. Bobb & Maricela F. Cruz & Stephen J. Mooney & Adam Drewnowski & David Arterburn & Andrea J. Cook, 2022. "Accounting for spatial confounding in epidemiological studies with individual‐level exposures: An exposure‐penalized spline approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1271-1293, July.
    19. Daniel A. Griffith & Richard E. Plant, 2022. "Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects," Stats, MDPI, vol. 5(4), pages 1-20, December.
    20. Yongwan Chun & Daniel A. Griffith & Monghyeon Lee & Parmanand Sinha, 2016. "Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters," Journal of Geographical Systems, Springer, vol. 18(1), pages 67-85, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:2:y:2019:i:3:p:27-415:d:258088. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.