SCIRP Mobile Website

Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

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

has been cited by the following article:

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

    AUTHORS: Shawky Mansour

    KEYWORDS: Spatial Modelling, OLS, GWR, Residential Crowding, Alexandria Neighborhoods

    JOURNAL NAME: Journal of Geographic Information System, Vol.7 No.4, August 7, 2015

    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 spatialvariations of residential crowding determinates. The global ordinary least squares (OLS) modelassumes homogeneity of relationships between response variable and explanatory variablesacross 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 sameresponse variable and explanatory variables to capture spatial non-stationary of residentialcrowding. A comparison of the outputs of both models indicated that OLS explained 74 percent ofresidential 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.