Constructing the Basic Urban Study Units Based on MAUP (A Case Study of Demographic Zoning of Zanjan)

Subject Areas :

Hassanali Faraji Sabokbar ¹ ( Associate Professor, Faculty of Geography, University of Tehran, Tehran, Iran )
marzieh sedaghatkish ² ( PhD student in Geography and Urban Planning, University of Isfahan, Isfahan, Iran )
Alireza Rahmati ³ ( M.Sc of Remote Sensing and Geographic Information Systems University of Tehran, Tehran, Iran )

Received: 2018-10-23 Accepted : 2019-02-04 Published : 2021-11-22

Keywords: Demographic Indicators, base unit of study, modifiable areal unit problem, rectangular patter,

Abstract :

In any type of geographic data analysis defining the primary unit has direct effect on the results. The key point in spatial studies is the exact definition of areal units. The necessity of defining the basic unit of study is that for instance if once we examine the relationship between income and crime rate in the neighborhood unit and again in the level of urban area, different results will be obtained. This problem originates from the fact that raw data (e.g. census) are shown in the census block format. So we have to aggregate this data into neighborhood units and urban areas or any other basic unit for our study. Since the choice of these basic units does not have any rule and is arbitrary, different results will be obtained. In fact results of statistical analysis are not independent of the scale at which analysis has been done. This problem was first identified by Gehlke and Biehlin as modifiable areal unit problem (MAUP).                                                                                                The purpose of this study is to identify the basic areal units which make the least bias in raw data after data aggregation in the unit. We have applied a comparative research method and layers of census blocks are utilized for statistical analysis. In this article, we have selected three basic units based on urban network, urban locality and fishnet as well as using twenty demographic indicators and measures of central dispersion and correlation to examine the effect of MAUP. The results indicate that rectangular base unit is less affected by the MAUP.   

References:

 

1. انصاری، م.، شریعت­پناهی، م.، ملک حسینی، ع.، مدیری، م. 1397. تحلیل الگوی گسترش شهری در شهرهای میانه­اندام با استفاده از مدل­های کمی (مطالعه موردی: شهر ملایر). آمایش محیط، 11(43): 147-182.


2. پورمحمدی، م.، جمالی، ف.، اصغری زمانی، ا. 1387. ارزیابی گسترش فضایی-کالبدی شهر زنجان باتأکید برتغییر کاربری زمین طی دوره 1384-1355. پژوهش­های جغرافیای انسانی، 40(63): 46-29.


3. حبیبی ک.، پوراحمد، ا.، مشکینی، ا. 1387. از زنگان تا زنجان سیری بر تحولات کالبدی-فضایی بافت کهن شهر. انتشارات دانشگاه زنجان.


4. سجادی، ژ.، اسکندپور، م.، رستمی، ه. 1394. تحلیل تغییرات شبکه شهری استان همدان در یک دوره 50 ساله (1385-1335). فصلنامه آمایش محیط، 8(30): 38-19.


5. شیعه، 1. 1390. با شهر و منطقه در ایران. انتشارات دانشگاه علم و صنعت ایران.


6. مرکز آمار ایران. نتایج تفصیلی سرشماریهای عمومی نفوس و مسکن سالهای 1390-1335. شهرستان زنجان.


7. Butkiewicz, T., Meentemeyer, R. K., Shoemaker, D. A., Chang, R., Wartell, Z., Ribarsky, W. Alleviating the Modifiable Areal Unit Problem within Probe-Based Geospatial Analyses. Computer Graphics Forum, 29(3): 923-932.


8. Flowerdew, M., Green, R. 1996. New evidence on the modifiable areal unit problem. In P. Longley (Ed.). Spatial analysis : modelling in a GIS environment Cambridge: GeoInformation Internat.


9. Flowerdew, R. 2011. How serious is the Modifiable Areal Unit Problem for analysis of English census data?. Popul Trends, 145(1): 106-118.


10. Fotheringham, A. S., Brunsdon, C., Charlton, M. Geographically Weighted Regression the Analysis of Spatially Varying Relationships. from


11. http://public.eblib.com/EBLPublic/PublicView.do?ptiID=141619userid=^u


12. Fotheringham, A. S., Wong, D. W. S. 1991. The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A, 23(7): 1025-1044.


13. Gehlke, C. E., Biehl, Katherine. 1934. Certain effects of grouping upon the size of the correlation coefficient in census tract material. Journal of the American Statistical Association, 29(185): 169-170.


14. Goodchild, M. F. 1979. The Aggregation Problem in Location-Allocation. Geographical Analysis, 11(3): 240-255.


15. Goodchild, M. F. 2011. Scale in GIS: An overview. Geomorphology, 130(1–2): 5-9.


16. Guo, J. Y., Bhat, C. R. 2004. Modifiable areal units: problem or perception in modeling of residential location choice?. Transportation Research Record, 1898(1): 138-147.‏


17. Haining, R. 2012. Ecological Analysis of Urban Offence and Offender Data. In V. Ceccato (Ed.), The Urban Fabric of Crime and Fear, 141-163.


18. He, Z., Zhao, W., Chang, X. 2007. The modifiable areal unit problem of spatial heterogeneity of plant community in the transitional zone between oasis and desert using semivariance analysis. Landscape Ecology, 22(1): 95-104.


19. Hui, C. 2009. A Bayesian Solution to the Modifiable Areal Unit Problem. In A.-E. Hassanien, A. Abraham & F. Herrera (Eds.), Foundations of Computational Intelligence Volume 2 (Vol. 202, pp. 175-196): Springer Berlin Heidelberg.


20. Jelinski, D.E, Wu, J. 1996. The modifiable areal unit problem and implications for landscape ecology. Landscape Ecology, 11(3): 129-140.


21. Lechner, A. M., Langford, W. T., Jones, S. D., Bekessy, S. A., Gordon, A. 2012. Investigating species–environment relationships at multiple scales: Differentiating between intrinsic scale and the modifiable areal unit problem. Ecological Complexity, 11: 91-102.‏


22. Lembo Jr, A.J., Lew, M.Young., Laba, M., Baveye, P. 2006. Use of spatial SQL to assess the practical significance of the Modifiable Areal Unit Problem. Computers & Geosciences, 32(2): 270-274.


23. Martin, D. 2003. Extending the automated zoning procedure to reconcile incompatible zoning systems. International Journal of Geographic Information Science, 17(2): 181-196.


24. Mitra, R., Buliung, R. N. 2012. Built environment correlates of active school transportation: neighborhood and the modifiable areal unit problem. Journal of Transport Geography, 20(1): 51-61.


25. Openshaw, S. 1977. Optimal zoning systems for spatial interaction models. Environment and Planning A, 9(2): 169-184.


26. Openshaw, S. 1984. THE MODIFIABLE AREAL UNIT PROBLEM (Vol. 38). CATMOG: GeoBooks.


27. O'Sullivan, D., Unwin, D. 2003. Geographic information analysis. Hoboken, NJ: Wiley.


28. Páez, A., Scott, D. 2005. Spatial statistics for urban analysis: A review of techniques with examples. GeoJournal, 61(1): 53-67.


29. Schuurman, N., Bell, N., Dunn, J. R., Oliver, L. 2007. Deprivation indices, population health and geography: an evaluation of the spatial effectiveness of indices at multiple scales. Journal of urban health, 84(4): 591-603.‏


30. Secco, G., Zulian, G. 2008. Modeling the Social Benefits of Urban Parks for Users. In M. Carreiro, Y.-C. Song & J. Wu (Eds.), Ecology, Planning, and Management of Urban Forests (pp. 312-335): Springer New York.


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


32. Tranmer, M., Steel, D. G. 1998. Using census data to investigate the causes of the ecological fallacy. Environment & planning A, 30(5): 817-831.


33. Tu, J. 2010. Exploring the Spatially Varying Impact of Urbanization on Water Quality in Eastern Massachusetts Using Geographically Weighted Regression. In N. Hoalst-Pullen & M. W. Patterson (Eds.). Geospatial Technologies in Environmental Management (Vol. 3, pp. 143-162): Springer Netherlands.


34. Wheeler, D., Páez, A. 2010. Geographically Weighted Regression. In M. M. Fischer & A. Getis (Eds.). Handbook of Applied Spatial Analysis (pp. 461-486): Springer Berlin Heidelberg.


35. Zandbergen, P. A., Chakraborty, J. 2006. Improving environmental exposure analysis using cumulative distribution functions and individual geocoding. Int J Health Geogr, 5, 23.