urban development; urban density; development type; driving forces; multinomial logistic regression model; cadastral data
Abstract :
[en] 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.
Research Center/Unit :
LEMA
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
El Saeid Mustafa, Ahmed Mohamed ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)
Saadi, Ismaïl ; Université de Liège - ULiège > Département ArGEnCo > Transports et mobilité
Cools, Mario ; Université de Liège - ULiège > Département ArGEnCo > Transports et mobilité
Teller, Jacques ; Université de Liège - ULiège > Département ArGEnCo > Urbanisme et aménagement du territoire
Language :
English
Title :
Understanding urban development types and drivers in Wallonia. A multi-density approach
Publication date :
2018
Journal title :
International Journal of Business Intelligence and Data Mining
ISSN :
1743-8187
eISSN :
1743-8195
Publisher :
Inderscience Enterprises Ltd.
Volume :
13
Issue :
Nos. 1/2/3
Pages :
309–330
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
ARCFloodLand
Funders :
The research was funded through the ARC grant for Concerted Research Actions, financed by the WalloniaBrussels Federation
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