Spatial Modeling of Residential Crowding in Alexandria Governorate, Egypt: A Geographically Weighted Regression (GWR) Technique

Abstract

Despite growing research for residential crowding effects on housing market and public health perspectives, relatively little attention has been paid to explore and model spatial patterns of residential crowding over space. This paper focuses upon analyzing the spatial relationships between residential crowding and socio-demographic variables in Alexandria neighborhoods, Egypt. Global and local geo-statistical techniques were employed within GIS-based platform to identify spatial variations of residential crowding determinates. The global ordinary least squares (OLS) model assumes homogeneity of relationships between response variable and explanatory variables across the study area. Consequently, it fails to account for heterogeneity of spatial relationships. Local model known as a geographically weighted regression (GWR) was also employed using the same response variable and explanatory variables to capture spatial non-stationary of residential crowding. A comparison of the outputs of both models indicated that OLS explained 74 percent of residential crowding variations while GWR model explained 79 percent. The GWR improvedstrength of the model and provided a better goodness of fit than OLS. In addition, the findings of this analysis revealed that residential crowding was significantly associated with different structural measures particularly social characteristics of household such as higher education and illiteracy. Similarly, population size of neighborhood and number of dwelling rooms were found to have direct impacts on residential crowding rate. The spatial relationship of these measures distinctly varies over the study area.

Share and Cite:

Mansour, S. (2015) Spatial Modeling of Residential Crowding in Alexandria Governorate, Egypt: A Geographically Weighted Regression (GWR) Technique. Journal of Geographic Information System, 7, 369-383. doi: 10.4236/jgis.2015.74029.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Brueckner, J.K. (2000) Urban Sprawl: Diagnosis and Remedies. International Regional Science Review, 23, 160-171. http://dx.doi.org/10.1177/016001700761012710
[2] Sudhira, H., Ramachandra, T. and Jagadish, K. (2004) Urban Sprawl: Metrics, Dynamics and Modelling Using GIS. International Journal of Applied Earth Observation and Geoinformation, 5, 29-39.
http://dx.doi.org/10.1016/j.jag.2003.08.002
[3] Lata, K.M., Sankar Rao, C., Krishna Prasad, V., Badrinath, K. and Raghavaswamy, V. (2001) Measuring Urban Sprawl: A Case Study of Hyderabad. GIS Development, 5, 26-29.
[4] Yeh, A.G.-O. and Xia, L. (2001) Measurement and Monitoring of Urban Sprawl in a Rapidly Growing Region Using Entropy. Photogrammetric Engineering and Remote Sensing, 67, 83-90.
[5] Chen, J., Guo, F. and Wu, Y. (2011) One Decade of Urban Housing Reform in China: Urban Housing Price Dynamics and the Role of Migration and Urbanization, 1995-2005. Habitat International, 35, 1-8. http://dx.doi.org/10.1016/j.habitatint.2010.02.003
[6] Yu, Z. (2006) Heterogeneity and Dynamics in China’s Emerging Urban Housing Market: Two Sides of a Success Story from the Late 1990s. Habitat International, 30, 277-304.
http://dx.doi.org/10.1016/j.habitatint.2004.02.010
[7] Li, S.-M. and Huang, Y. (2006) Urban Housing in China: Market Transition, Housing Mobility and Neighborhoods Change. Housing Studies, 21, 613-623. http://dx.doi.org/10.1080/02673030600807043
[8] Clark, W.A., Deurloo, M.C. and Dieleman, F.M. (2000) Housing Consumption and Residential Crowding in US Housing Markets. Journal of Urban Affairs, 22, 49-63. http://dx.doi.org/10.1111/0735-2166.00039
[9] Clark, W. and Drever, A.I. (2000) Residential Mobility in a Constrained Housing Market: Implications for Ethnic Populations in Germany. Environment and Planning A, 32, 833-846.
http://dx.doi.org/10.1068/a3222
[10] Myers, D. and Lee, S.W. (1996) Immigration Cohorts and Residential Overcrowding in Southern California. Demography, 33, 51-65. http://dx.doi.org/10.2307/2061713
[11] The Official Home of UK Legislation (2015) 1985UK Housing Act, Definition of Overcrowding. http://www.legislation.gov.uk/
[12] UN Habitat (2015) Housing & Slum Upgrading.
http://unhabitat.org/urban-themes/housing-slum-upgrading/
[13] Acevedo-Garcia, D. (2000) Residential Segregation and the Epidemiology of Infectious Diseases. Social Science & Medicine, 51, 1143-1161. http://dx.doi.org/10.1016/S0277-9536(00)00016-2
[14] King, N.B. (2003) Immigration, Race, and Geographies of Difference in the Tuberculosis Pandemic. In: Gandy M. and Zumla, A. Eds., Return of the White Plague: Global Poverty and the New Tuberculosis, Verso, London, 39-54.
[15] Williams, D.R. and Collins, C. (2001) Racial Residential Segregation: A Fundamental Cause of Racial Disparities in Health. Public Health Reports, 116, 404-416.
http://dx.doi.org/10.1016/S0033-3549(04)50068-7
[16] Reitmanova, S., and Gustafson, D.L. (2012) Coloring the White Plague: A Syndemic Approach to Immigrant Tuberculosis in Canada. Ethnicity & Health, 17, 403-418.
http://dx.doi.org/10.1080/13557858.2011.645156
[17] Schluter, P., Ford, R., Mitchell, E. and Taylor, B. (1997) Housing and Sudden Infant Death Syndrome. The New Zealand Cot Death Study Group. The New Zealand Medical Journal, 110, 243-246.
[18] Torrey, E.F. and Yolken, R.H. (1998) Is Household Crowding a Risk Factor for Schizophrenia and Bipolar Disorder? Schizophrenia Bulletin, 24, 321.
[19] Shmool, J.L., Kubzansky, L.D., Newman, O.D., Spengler, J., Shepard, P. and Clougherty, J.E. (2014) Social Stressors and Air Pollution across New York City Communities: A Spatial Approach for Assessing Correlations among Multiple Exposures. Environmental Health, 13, 91. http://dx.doi.org/10.1186/1476-069X-13-91
[20] Tunstall, H., Mitchell, R., Gibbs, J., Platt, S. and Dorling, D. (2011) Socio-Demographic Diversity and Unexplained Variation in Death Rates among the Most Deprived Parliamentary Constituencies in Britain. Journal of Public Health, 34, 296-304.
[21] Adeboyejo, A.T. and Onyeonoru, I. (2003) Residential Density and Adolescent Reproductive Health Problems in Ibadan, Nigeria. African Population Studies, 18, 81-95.
[22] Dhonte, P., Bhattacharya, R. and Yousef, T. (2000) Demographic Transition in the Middle East-Implications for Growth, Employment, and Housing. International Monetary Fund, No. 0-41.
[23] Al-Habees, M.A. (2012) Determination of the Residential Housing Needs Expected for Cities of Jordan Within the Period of (2014-2024). Management Science and Engineering, 6, 130-139.
[24] Baker, M.G., Goodyear, R., Telfar Barnard, L. and Howden-Chapman, P. (2006) The Distribution of Household Crowding in New Zealand: An Analysis Based on 1991 to 2006 Census Data: Wellington.
[25] Olmos, J.C.C. and Garrido, á.A. (2007) African immigrants in Almeria (Spain): Spatial Segregation, Residential Conditions and Housing Segmentation. Sociologia, 39, 535-559.
[26] Haan, M. (2011) The Residential Crowding of Immigrants in Canada, 1971-2001. Journal of Ethnic and Migration Studies, 37, 443-465. http://dx.doi.org/10.1080/1369183X.2011.526772
[27] Khalifa, M.A. (2011) Redefining Slums in Egypt: Unplanned versus Unsafe Areas. Habitat International, 35, 40-49. http://dx.doi.org/10.1016/j.habitatint.2010.03.004
[28] Mohamed, N.S., Nofal, L.M., Hassan, M. and Elkaffas, S. (2003) Geographic Information Systems (GIS) Analysis of under Five Mortality in Alexandria. The Journal of the Egyptian Public Health Association, 79, 243-262.
[29] Ryan, T.P. (2008) Modern Regression Methods. Vol. 655, John Wiley & Sons, Hoboken.
[30] Chatterjee, S. and Hadi, A.S. (2013) Regression Analysis by Example. John Wiley & Sons, Hoboken.
[31] Montgomery, D.C., Peck, E.A. and Vining, G.G. (2012) Introduction to Linear Regression Analysis. Vol. 821, John Wiley & Sons, Hoboken.
[32] Fotheringham, S., Charlton, M. and Brunsdon, C. (1998) Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis. Environment and Planning A, 30, 1905-1927. http://dx.doi.org/10.1068/a301905
[33] Brunsdon, C., Fotheringham, A.S. and Charlton, M. (2008) Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. In: Kemp, K., Ed., Encyclopedia of Geographic Information Science, Sage Publication, California, 558.
[34] Charlton, M., Fotheringham, S. and Brunsdon, C. (2009) Geographically Weighted Regression. White Paper. National Centre for Geocomputation, National University of Ireland Maynooth, 1-14.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.