IDEAS home Printed from https://ideas.repec.org/a/kap/jgeosy/v8y2006i4p335-355.html
   My bibliography  Save this article

Hidden negative spatial autocorrelation

Author

Listed:
  • Daniel Griffith

Abstract

No abstract is available for this item.

Suggested Citation

  • Daniel Griffith, 2006. "Hidden negative spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 8(4), pages 335-355, October.
    • Handle: RePEc:kap:jgeosy:v:8:y:2006:i:4:p:335-355
    DOI: 10.1007/s10109-006-0034-9
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10109-006-0034-9
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10109-006-0034-9?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. Stirböck, Claudia, 2002. "Explaining the Level of Relative Investment Specialisation: A Spatial Econometric Analysis of EU Regions," ZEW Discussion Papers 02-49, ZEW - Leibniz Centre for European Economic Research.
    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.
    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. 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. Rene Westerholt & Enrico Steiger & Bernd Resch & Alexander Zipf, 2016. "Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-31, September.
    3. Millo, Giovanni, 2017. "A simple randomization test for spatial correlation in the presence of common factors and serial correlation," Regional Science and Urban Economics, Elsevier, vol. 66(C), pages 28-38.
    4. Fernando A. López & Pedro J. Martínez-Ortiz & Juan-Gabriel Cegarra-Navarro, 2017. "Spatial spillovers in public expenditure on a municipal level in Spain," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 39-65, January.
    5. Bivand, Roger & Müller, Werner G. & Reder, Markus, 2009. "Power calculations for global and local Moran's," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2859-2872, June.
    6. Kirillov, Andrew, 2021. "A study on spatial autocorrelation: Case of Russian regional inflation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 5-22.
    7. 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.
    8. Andrea Vaona, 2010. "Spatial autocorrelation and the sensitivity of RESET: a simulation study," Journal of Geographical Systems, Springer, vol. 12(1), pages 89-103, March.
    9. 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.
    10. Sauquet, Alexandre & Marchand, Sébastien & Féres, José Gustavo, 2014. "Protected areas, local governments, and strategic interactions: The case of the ICMS-Ecológico in the Brazilian state of Paraná," Ecological Economics, Elsevier, vol. 107(C), pages 249-258.
    11. 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.
    12. 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.
    13. 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.
    14. Jørgen Lauridsen & Reinhold Kosfeld, 2007. "Spatial cointegration and heteroscedasticity," Journal of Geographical Systems, Springer, vol. 9(3), pages 253-265, September.

    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. Behr, Andreas & Schiwy, Christoph & Hong, Lucy, 2022. "Impact of Agglomeration Economies on Regional Performance in Germany," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 52(1), May.
    2. Daniel Griffith & David Wong, 2007. "Modeling population density across major US cities: a polycentric spatial regression approach," Journal of Geographical Systems, Springer, vol. 9(1), pages 53-75, April.
    3. Małkowska, Agnieszka & Telega, Agnieszka & Głuszak, Michał & Marona, Bartłomiej, 2021. "Spatial diversification of property tax policy – Searching for yardstick competition in Polish metropolitan areas," Land Use Policy, Elsevier, vol. 109(C).
    4. Giuseppe Arbia & Francesca Petrarca, 2011. "Effects of MAUP on spatial econometric models," Letters in Spatial and Resource Sciences, Springer, vol. 4(3), pages 173-185, October.
    5. Wasantha Athukorala & Wade Martin & Prasad Neelawala & Darshana Rajapaksa & Clevo Wilson, 2016. "Impact Of Wildfires And Floods On Property Values: A Before And After Analysis," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 61(01), pages 1-23, March.
    6. Stirböck, Claudia, 2004. "Comparing Investment and Employment Specialisation Patterns of EU Regions," ZEW Discussion Papers 04-43, ZEW - Leibniz Centre for European Economic Research.
    7. Joseph Karanja & Lawrence M. Kiage, 2022. "Scale implications and evolution of a social vulnerability index in Atlanta, Georgia, USA," 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 789-812, August.
    8. Athukorala, Wasantha & Martin, Wade & Wilson, Clevo & Rajapaksa, Darshana, 2019. "Valuing bushfire risk to homeowners: Hedonic property values study in Queensland, Australia," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 44-56.
    9. H. S. Chang & T. L. Chen & H. T. Cheng, 2018. "Comparing the spatial patterns of earthquake disaster probability and individual risk perception: a case study of Yongkang Township in Tainan, Taiwan," 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. 93(3), pages 1589-1610, September.
    10. Stirböck, Claudia, 2004. "A Spatial Econometric Analysis of Regional Specialisation Patterns Across EU Regions," ZEW Discussion Papers 04-44, ZEW - Leibniz Centre for European Economic Research.
    11. Richard Henry Rijnks & Sierdjan Koster & Philip McCann, 2019. "The Neighbour’s Effect on well‐Being: How Local Relative Income Differentials Affect Resident's Subjective Well‐Being," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 110(5), pages 605-621, December.
    12. 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.

    More about this item

    Keywords

    Eigenvector; Hidden negative spatial autocorrelation; Negative spatial autocorrelation; Spatial autoregressive model; Spatial filter model; C21; R23;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

    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:8:y:2006:i:4:p:335-355. 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.