Acta Geographica Sinica ›› 2012, Vol. 67 ›› Issue (10): 1317-1326.doi: 10.11821/xb201210003

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MAUP Effects on the Detection of Spatial Hot Spots in Socio-economic Statistical Data

QI Lili1,2,3, BO Yanchen1,2,3   

  1. 1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Application, CAS, Beijing 100875, China;
    2. School of Geography, Beijing Normal University, Beijing 100875, China;
    3. Beijing Key Laboratory of Environmental Remote Sensing and Digital City by Beijing Normal University, Beijing 100875, China
  • Received:2011-12-27 Revised:2012-02-20 Online:2012-10-20 Published:2012-12-19
  • Supported by:

    National High Technology Research and Development Program of China (863 Program), No.2006AA120106; Natural Science Foundation of China Project, No.40971237; Special Program for Prevention and Control of Infectious Diseases, No.2008ZX10004-012

Abstract: The study of spatial distribution of population and economic situations is important for government policy making. County-level agriculture statistical data in 2000 and Beijing's second economic census data in 2008 were collected in order to explore the hot spots' scale effects. First, China's county-level agriculture statistical data and Beijing's second economic census data were aggregated to different scales based on certain aggregation rules. Second, hot spots detection was implemented based on G value at each scale respectively. Third, the changes of hot spots at different scales were analyzed. Fourth, factors affecting the changes were identified by employing Logistic Regression Model and a prediction model was built. Results show that, space hot spots explored by G value have significant MAUP effects. The higher the aggregation level, the greater the spatial scale, the less the number of hot spots. The number of units in a hot spot on the confidence level of 99.9% has a significant effect on the changes of hot spots. The mean G value of a hot spot on the confidence level of 98% has a significant effect on the changes of hot spots. Hot spots will become less susceptible to MAUP when they have more units and a larger G value. When the hot spot distribution is already known in the fine scale, changes of a hot spot can be predicted based on the model we built, which depends on unit number that the hot spot contains and mean G value of the hot spot. The prediction accuracy of China's county-level agriculture statistical data can reach 93.8% and that of Beijing's second economic census data can reach 94.2%. The consistent conclusion of the two datasets shows that scale effects on the detection of spatial hot spots have nothing to do with variables and study areas.

Key words: local G index, hot spot, MAUP, scale effect, Logistic regression