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

  • 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 date: 2011-12-27

  Revised date: 2012-02-20

  Online published: 2012-10-20

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


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.

Cite this article

QI Lili, BO Yanchen . MAUP Effects on the Detection of Spatial Hot Spots in Socio-economic Statistical Data[J]. Acta Geographica Sinica, 2012 , 67(10) : 1317 -1326 . DOI: 10.11821/xb201210003


[1] Openshaw S. Concepts and Tecniques in Modern Geography. Norwich: Geobooks, 1984

[2] Butkiewicz T, Meentemeyer R K, Shoemaker D A et al. Alleviating the modifiable areal unit problem withinprobe-based geospatial analyses. Computer Graphics Forum, 2010, 29(3): 923-932.

[3] Meng Bin, Wang Jinfeng. A review on the methodology of scaling with geo-data. Acta Geographica Sinica, 2005, 60(2): 277-288. [孟斌, 王劲峰. 地理数据尺度转换方法研究进展. 地理学报, 2005, 60(2): 277-288.]

[4] Robinson W S. Ecological correlations and the behavior of individuals. International Journal of Epidemiology, 2009, 40(5): 1-5.

[5] Swift A, Liu L, Uber J. Reducing MAUP bias of correlation statistics between water quality and GI illness. Computers,Environment and Urban Systems, 2008, 32(2): 134-148.

[6] Parenteau M P,Sawada M, The modifiable areal unit problem (MAUP) in the relationship between exposure to NO2 andrespiratory health. International Journal of Health Geographics, 2011, 10(58), doi: 10.1186/1476-072X-10-58.

[7] Arbia G, Petrarca F. Effects of MAUP on spatial econometric models. Letters in Spatial and Resource Sciences, 2011, 4(3): 173-185.

[8] Fotheringham A S, Wong D W S. The modifiable areal unit problem in multivariate statistical analysis. Environmentand Planning A, 1991, 23(7): 1025-1044.

[9] Amrhein C G, Flowerdew R. The effect of data aggregation on a Poisson regression model of Canadian migration.Environment and Planning A, 1992, 24(10): 1381-1391.

[10] Wang F. Quantitative Methods and Applications in GIS. Boca Raton, FL: Taylor & Francis, 2006

[11] Haining R. Spatial Data Analysis: Theory and Practice. New York: Cambridge University Press, 2003

[12] Wang Jinfeng, Wu Jilei, Sun Yingjun et al. Techniques of spatial data analysis. Geographical Research, 2005, 24(3):464-472. [王劲峰, 武继磊, 孙英君等. 空间信息分析技术. 地理研究, 2005, 24(3): 464-472.]

[13] Cliff A D, Ord. J K. Spatial Process:Models and Applications. London: Pion, 1981

[14] Ord J K, Getis A. Testing for local spatial autocorrelation in the presence of global autocorrelation. Journal ofRegional Science, 2001, 41(3): 411-432.

[15] Anselin L. Local indicators of spatial association: LISA. Geographical Analysis, 1995, 27(2): 93-115.

[16] Fotheringham A S. Trends in quantitative methods I: Stressing the local. Progress in Human Geography, 1997, 21(1):88-96.

[17] Fotheringham A S. Trends in quantitative methods III: Stressing the visual. Progress in Human Geography, 1999, 23(4): 597-606.

[18] Meng Bin, Wang Jinfeng, Zhang Wenzhong et al. Evaluation of regional disparity in China based on spatial analysis.Scientia Geographica Sinica, 2005, 25(4): 393-400. [孟斌, 王劲峰, 张文忠等. 基于空间分析方法的中国区域差异研究. 地理科学, 2005, 25(4): 393-400.]

[19] Meng Bin, Zhang Jingqiu, Wang Jinfeng et al. Application of spatial analysis to the research of real estate: TakingBeijing as a case. Geographical Research, 2005, 24(6): 956-965. [孟斌, 张景秋, 王劲峰等. 空间分析方法在房地产市场研究中的应用: 以北京市为例. 地理研究, 2005, 24(6): 956-965.]

[20] Cao Zhidong, Wang Jinfeng, Gao Yige et al. Risk factors and autocorrelation characteristics on severe acuterespiratory syndrome in Guangzhou. Acta Geographica Sinica, 2008, 63(9): 981-993. [曹志冬, 王劲峰, 高一鸽等. 广州SARS流行的空间风险因子与空间相关性特征. 地理学报, 2008, 63(9): 981-993.]

[21] Wang Jinfeng, Bo Yanchen, Zhu Caiying et al. Research and development of spatial analysis functions in GIS. Journalof Image and Graphics, 2001, 6(9): 849-853. [王劲峰, 柏延臣, 朱彩英等. 地理信息系统空间分析能力探讨. 中国图像图形学报, 2001, 6(9): 849-853.]

[22] Wang Jinfeng, Meng Bin, Zheng Xiaoying et al. Analysis on the multi-distribution and the major influencing factorson severe acute respiratory syndrome. Chin. J. Epidemiol, 2005, 26(3): 164-168. [王劲峰, 孟斌, 郑晓瑛等. 北京市2003 年SARS疫情的多维分布及其影响因素分析. 中华流行病学杂志, 2005, 26(3): 164-168.]

[23] Wu Jilei, Wang Jinfeng, Zheng Xiaoying et al. A review on application of spatial data analysis technology in publichealth. Progress in Geography, 2003, 22(3): 219-228. [武继磊, 王劲峰, 郑晓瑛等. 空间数据分析技术在公共卫生领域的应用. 地理科学进展, 2003, 22(3): 219-228.]

[24] Reynolds H D. The modifiable area unit problem: Empirical analysis by statistical simulation [D]. Ontario:Department of Geography University of Toronto, 1998.

[25] Chen Jiangping, Zhang Yao, Yu Yuanjian. Effect of MAUP in spatial autocorrelation. Acta Geographica Sinica, 2011,66(12): 1597-1606. [陈江平, 张瑶, 余远剑. 空间自相关的可塑性面积单元问题效应. 地理学报, 2011. 66(12):1597-1606.]

[26] Wang Jichuan, Guo Zhigang. Logistic Regression Model: Methods and Application. Beijing: Higher Education Press,2001. [王济川, 郭志刚. Logistic回归模型: 方法与应用. 北京: 高等教育出版社, 2001.]