A Comparative Analysis of Drivers Impacting Urban Densification for Cross Regional Scenarios in Brussels Metropolitan Area

Abstract

Our research aims at unveiling the various drivers that can have an impact on urban densification. Unlike the usual logistic modelling techniques, our study considers multi-level built-up densities ranging from low to high built-up density. The commonly used dataset for a number of present studies is based on raster images. Our study uses vector-based cadastral data to create maps for the years 2000, 2010 and 2020 in order to better trace densification. Furthermore, our study addresses the situation of a metropolitan area, Brussels, that spreads over three different regions that are developing independent land-planning policies. Since the state reform of 1993, Belgium has undergone a significant political transformation with a decentralization of land-planning policies from the state level to regional authorities. This reform allowed a progressive divergence of planning policies between the three regions, i.e., Flanders, the Brussels Capital Region and Wallonia. According to our findings, all the controlling factors exhibit distinct variation over all their density classes for the three regions. This may be due to differences in socioeconomic, territorial, and regulatory factors. For Flanders and the Brussels Capital Region, slope and distance to roadways are the most significant drivers explaining densification, whereas densification in Wallonia is predominantly influenced by land-use policies, especially the zoning regime. These results highlight the impact of considering cross-regional divergences in the implementation of planning policies at the metropolitan level, especially in those metropolitan areas that are expanding into different regions with divergent planning policies.

Modelling multi-density urban expansion using Cellular Automata for Brussels Metropolitan Development Area

“…where (Pc)ij, Y = kn is the probability of change from the reference class to class kn occurring in cell ij (Mustafa et al, 2015;Chakraborty et al, 2022). Our MLR model uses a maximum likelihood estimation method to achieve the best set of coefficients for each X variable.…”

“…However, they often blur the diversity of land use within each cell. On the other hand, smaller scales down to 10×10 m offer enhanced details but require abundant computational power, particularly when analysing large geographical areas (Mustafa et al, 2014;Chakraborty et al, 2022). Examining several built-up densities rather than using a binary categorization (i.e., non-built-up/built-up) is one way to handle the trade-off between heterogeneity and coarse regular cell spaces.…”