IDEAS home Printed from https://ideas.repec.org/a/kap/jgeosy/v21y2019i2d10.1007_s10109-019-00299-x.html
   My bibliography  Save this article

Operational local join count statistics for cluster detection

Author

Listed:
  • Luc Anselin

    (The University of Chicago)

  • Xun Li

    (The University of Chicago)

Abstract

This paper operationalizes the idea of a local indicator of spatial association for the situation where the variables of interest are binary. This yields a conditional version of a local join count statistic. The statistic is extended to a bivariate and multivariate context, with an explicit treatment of co-location. The approach provides an alternative to point pattern-based statistics for situations where all potential locations of an event are available (e.g., all parcels in a city). The statistics are implemented in the open-source GeoDa software and yield maps of local clusters of binary variables, as well as co-location clusters of two (or more) binary variables. Empirical illustrations investigate local clusters of house sales in Detroit in 2013 and 2014, and urban design characteristics of Chicago census blocks in 2017.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:jgeosy:v:21:y:2019:i:2:d:10.1007_s10109-019-00299-x
    DOI: 10.1007/s10109-019-00299-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10109-019-00299-x
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10109-019-00299-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Manuel Ruiz & Fernando López & Antonio Páez, 2010. "Testing for spatial association of qualitative data using symbolic dynamics," Journal of Geographical Systems, Springer, vol. 12(3), pages 281-309, September.
    2. A Getis, 1984. "Interaction Modeling Using Second-Order Analysis," Environment and Planning A, , vol. 16(2), pages 173-183, February.
    3. Luc Anselin & Sergio J. Rey, 2010. "Perspectives on Spatial Data Analysis," Advances in Spatial Science, in: Luc Anselin & Sergio J. Rey (ed.), Perspectives on Spatial Data Analysis, chapter 0, pages 1-20, Springer.
    4. López, Fernando & Matilla-García, Mariano & Mur, Jesús & Marín, Manuel Ruiz, 2010. "A non-parametric spatial independence test using symbolic entropy," Regional Science and Urban Economics, Elsevier, vol. 40(2-3), pages 106-115, May.
    5. 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.
    6. Barry Boots, 2003. "Developing local measures of spatial association for categorical data," Journal of Geographical Systems, Springer, vol. 5(2), pages 139-160, August.
    7. George A. Akerlof, 1997. "Social Distance and Social Decisions," Econometrica, Econometric Society, vol. 65(5), pages 1005-1028, September.
    8. 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.
    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. Jirjies, Saman & Wallstrom, Garrick & Halden, Rolf U. & Scotch, Matthew, 2016. "pyJacqQ: Python Implementation of Jacquez's Q-Statistics for Space-Time Clustering of Disease Exposure in Case-Control Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i06).
    11. Elizabeth A. Mack & Kevin Credit & Matthew Suandi, 2018. "A comparative analysis of firm co-location behaviour in the Detroit metropolitan area," Industry and Innovation, Taylor & Francis Journals, vol. 25(3), pages 264-281, March.
    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. Wang, Liye & Zhang, Siyu & Tang, Lanping & Lu, Yanchi & Liu, Yanfang & Liu, Yaolin, 2022. "Optimizing distribution of urban land on the basis of urban land use intensity at prefectural city scale in mainland China," Land Use Policy, Elsevier, vol. 115(C).
    2. Camille Laville, 2021. "Keep Off the Grass : Grassland Scarcity and the Security Implications of Cross-Border Transhumance Between Niger and Nigeria," Working Papers hal-03350202, HAL.
    3. Alfonso Gallego-Valadés & Francisco Ródenas-Rigla & Jorge Garcés-Ferrer, 2021. "Spatial Distribution of Public Housing and Urban Socio-Spatial Inequalities: An Exploratory Analysis of the Valencia Case," Sustainability, MDPI, vol. 13(20), pages 1-17, October.
    4. Camille Laville, 2021. "Keep Off the Grass : Grassland Scarcity and the Security Implications of Cross-Border Transhumance Between Niger and Nigeria," CERDI Working papers hal-03350202, HAL.
    5. Greg Rybarczyk & Richard R. Shaker, 2021. "Predicting Bicycle-on-Board Transit Choice in a University Environment," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    6. Arnaud Bataille & Habib Salami & Ismaila Seck & Modou Moustapha Lo & Aminata Ba & Mariame Diop & Baba Sall & Coumba Faye & Mbargou Lo & Lanceï Kaba & Youssouf Sidime & Mohamed Keyra & Alpha Oumar Sily, 2021. "Combining viral genetic and animal mobility network data to unravel peste des petits ruminants transmission dynamics in West Africa," PLOS Pathogens, Public Library of Science, vol. 17(3), pages 1-22, March.
    7. 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).
    8. Xinyu Shi & Xiaoqing Zhao & Pei Huang & Zexian Gu & Junwei Pu & Shijie Zhou & Guoxun Qu & Qiaoqiao Zhao & Yan Feng & Yanjun Chen & Aimeng Xiang, 2023. "Rural Development under Poverty Governance: The Relationship between Rural Income and Land Use Transformation in Yunnan Province," Land, MDPI, vol. 12(2), pages 1-21, January.
    9. Luc Anselin, 2019. "Quantile local spatial autocorrelation," Letters in Spatial and Resource Sciences, Springer, vol. 12(2), pages 155-166, August.
    10. Xufeng Cui & Sisi Huang & Cuicui Liu & Tingting Zhou & Ling Shan & Fengyuan Zhang & Min Chen & Fei Li & Walter T. de Vries, 2021. "Applying SBM-GPA Model to Explore Urban Land Use Efficiency Considering Ecological Development in China," Land, MDPI, vol. 10(9), pages 1-15, August.
    11. Juan Andrés Cabral, 2021. "Understanding unsolved crimes hotspots: a spatial approach," Asociación Argentina de Economía Política: Working Papers 4445, Asociación Argentina de Economía Política.

    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. Luc Anselin, 2019. "Quantile local spatial autocorrelation," Letters in Spatial and Resource Sciences, Springer, vol. 12(2), pages 155-166, August.
    2. Herrera Gómez, Marcos & Ruiz Marín, Manuel & Mur Lacambra, Jesús, 2014. "Testing Spatial Causality in Cross-section Data," MPRA Paper 56678, University Library of Munich, Germany.
    3. Fernando A. López & Mariano Matilla-García & Jesús Mur & Antonio Páez & Manuel Ruiz, 2016. "A note on the SG(m) test," Journal of Geographical Systems, Springer, vol. 18(1), pages 87-96, January.
    4. Marcos Herrera & Manuel Ruiz & Jesús Mur, 2013. "Detecting Dependence Between Spatial Processes," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(4), pages 469-497, February.
    5. López-Hernández , Fernando A. & Artal-Tur, Andrés & Maté-Sánchez-Val, M. Luz, 2011. "Identifying nonlinear spatial dependence patterns by using non-parametric tests: Evidence for the European Union," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 21, pages 19-36.
    6. Herrera Gómez, Marcos, 2013. "Análisis de Estructuras Espaciales Persistentes. Desempleo Departamental en Argentina [Persistent Spatial Structure Analysis. Regional Unemployment in Argentina]," MPRA Paper 49407, University Library of Munich, Germany.
    7. Páez, Antonio & López, Fernando A. & Ruiz, Manuel & Morency, Catherine, 2013. "Development of an indicator to assess the spatial fit of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 217-233.
    8. Jaya Jumrani & P. S. Birthal, 2017. "Does consumption of tobacco and alcohol affect household food security? Evidence from rural India," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(2), pages 255-279, April.
    9. Michele Cincera & Lydia Greunz & Jean-Luc Guyot & Olivier Lohest, 2006. "Capital humain et processus de création d'entreprise: le cas des primo-créateurs wallons," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 49(2).
    10. Entorf, Horst & Spengler, Hannes, 2000. "Socioeconomic and demographic factors of crime in Germany: Evidence from panel data of the German states," International Review of Law and Economics, Elsevier, vol. 20(1), pages 75-106, March.
    11. Iryna Hayduk & Maude Toussaint‐Comeau, 2022. "Determinants of noncognitive skills: Mediating effects of siblings' interaction and parenting quality," Contemporary Economic Policy, Western Economic Association International, vol. 40(4), pages 677-694, October.
    12. Stracke, Rudi & Hörtnagl, Tanja & Kerschbamer, Rudolf, 2016. "Competing for Market Shares: Why the Order of Moves Matters Even When It Shouldn't," VfS Annual Conference 2016 (Augsburg): Demographic Change 145532, Verein für Socialpolitik / German Economic Association.
    13. Thomas Aronsson & Olof Johansson-Stenman, 2014. "When Samuelson Met Veblen Abroad: National and Global Public Good Provision when Social Comparisons Matter," Economica, London School of Economics and Political Science, vol. 81(322), pages 224-243, April.
    14. Finneran, Lisa & Kelly, Morgan, 2003. "Social networks and inequality," Journal of Urban Economics, Elsevier, vol. 53(2), pages 282-299, March.
    15. Tanya Araújo & Miguel St. Aubyn, 2008. "Education, Neighborhood Effects And Growth: An Agent-Based Model Approach," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 99-117.
    16. Montolio, Daniel & Planells-Struse, Simón, 2015. "When police patrols matter. The effect of police proximity on citizens’ crime risk perception," Journal of Economic Psychology, Elsevier, vol. 50(C), pages 73-93.
    17. John I. Carruthers & Selma Hepp & Gerrit-Jan Knaap & Robert N. Renner, 2012. "The American Way of Land Use," International Regional Science Review, , vol. 35(3), pages 267-302, July.
    18. Giulio Zanella, 2004. "Discrete Choice with Social Interactions and Endogenous Memberships," Department of Economics University of Siena 442, Department of Economics, University of Siena.
    19. Pritha Dev & Blessing U. Mberu & Roland Pongou, 2016. "Ethnic Inequality: Theory and Evidence from Formal Education in Nigeria," Economic Development and Cultural Change, University of Chicago Press, vol. 64(4), pages 603-660.
    20. Joshua M. Epstein, 2007. "Agent-Based Computational Models and Generative Social Science," Introductory Chapters, in: Generative Social Science Studies in Agent-Based Computational Modeling, Princeton University Press.

    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
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

    Statistics

    Access and download statistics

    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:kap:jgeosy:v:21:y:2019:i:2:d:10.1007_s10109-019-00299-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.