• DocumentCode
    2713418
  • Title

    The spatial heterogeneity and correlation of population distribution in Xinjiang Uygur Autonomous Region

  • Author

    Zhu, Yuxin ; Zhang, Jinzong

  • Author_Institution
    Sch. of Geogr. & Remote Sensing, Beijing Normal Univ., Beijing, China
  • fYear
    2011
  • fDate
    24-26 June 2011
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Dispersion index and Gini coefficient can show the differences between areas. Spatial autocorrelation analysis is a way that analyzed data´s correlation based on the spatial correlation analysis by studying correlation between one polygon and the nearest neighboring polygon through recognizing similar degree of major spatial object and other spatial objects. This paper studies the heterogeneity and correlation of population spatial distribution of Xinjiang Uygur Autonomous Region by using the spatial autocorrelation statistical techniques, Geography Information System (GIS) analysis, Gini coefficient and dispersion index based on county scale by population statistics data in 2009. There is obvious character of correlation between heterogeneity and correlation of population spatial distribution. The heterogeneity of population distribution is higher on the county scale than that on city scale, and the difference between two scales is high. Moran´s I and Local Moran´s I show that spatial concentration characteristic of population density is obvious.
  • Keywords
    geographic information systems; public administration; statistical analysis; Gini coefficient; Xinjiang Uygur autonomous region; dispersion index; geography information system analysis; population distribution; spatial autocorrelation statistical techniques; Cities and towns; Correlation; Dispersion; Geographic Information Systems; Indexes; Standardization; dispersion index; heterogeneity Index; spatial statistic; spatialautocorrelation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoinformatics, 2011 19th International Conference on
  • Conference_Location
    Shanghai
  • ISSN
    2161-024X
  • Print_ISBN
    978-1-61284-849-5
  • Type

    conf

  • DOI
    10.1109/GeoInformatics.2011.5981116
  • Filename
    5981116