Int. J. of Business Intelligence and Data Mining   »   2018 Vol.13, No.1/2/3

 

 

Title: Understanding urban development types and drivers in Wallonia: a multi-density approach

 

Authors: Ahmed Mustafa; Ismaïl Saadi; Mario Cools; Jacques Teller

 

Addresses:
ArGEnCo Department, University of Liège, Allée de la Découverte 9, Quartier Polytech 1, 4000 Liège, Belgium; Department of Computer Science, Purdue University, 305 N University St, West Lafayette, 47907 IN, USA
ArGEnCo Department, University of Liège, Belgium
ArGEnCo Department, University of Liège, Belgium
ArGEnCo Department, University of Liège, Belgium

 

Abstract: In this study, urban development process in the Walloon region (Belgium) has been analysed. Two main aspects of development are quantitatively measured: the development type and the definition of the main drivers of the urbanisation process. Unlike most existing studies that consider the urban development as a binary process, this research considers the urban development as a continuous process, characterised by different levels of urban density. Eight urban classes are defined based on the Belgian cadastral data for years 2000 and 2010. A multinomial logistic regression model is employed to examine the main driving forces of the different densities. Sixteen drivers were selected, including accessibility, geo-physical features, policies and socio-economic factors. Finally, the changes from the non-urban to one of the urban density classes are detected and classified into different development types. The results indicate that zoning status (political factor), slope, distance to roads, population densities and mean land price, respectively, have impact on the urbanisation process whatever maybe the density. The results also show that the impact of these factors highly varies from one density to another.

 

Keywords: urban development; urban density; development type; driving forces; multinomial logistic regression model; cadastral data.

 

DOI: 10.1504/IJBIDM.2017.10004788

 

Int. J. of Business Intelligence and Data Mining, 2018 Vol.13, No.1/2/3, pp.309 - 330

 

Available online: 03 Nov 2017

 

 

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