Microsimulation of Residential Activity for Alternative Urban Development Scenarios: A Case Study on Brussels and Flemish Brabant

Abstract

The historically rooted suburbanization of Flanders and the Brussels Capital Region (BCR) in Belgium has resulted in severe urban sprawl, traffic congestion, natural land degradation and many related problems. Recent policy proposals put forward by the two regions aim for more compact urban development in well-serviced areas. Yet, it is unclear how these proposed policies may impact residential dynamics over the coming decades. To address this issue, we developed a Residential Microsimulation (RM) framework that spatially refines coarse-scale demographic projections at the district level to the level of census tracts. The validation of simulated changes from 2001 to 2011 reveals that the proposed framework succeeds in modelling historic trends and clearly outperforms a random model. To support simulation from 2011 to 2040, two alternative urban development scenarios are defined. The Business As Usual (BAU) scenario essentially represents a continuation of urban sprawl development, whereas the Sustainable Development (SUS) scenario strives for higher-density development around strategic well-serviced nodes in line with proposed policies. This study demonstrates how residential microsimulation supported by scenario analysis can play a constructive role in urban policy design and evaluation.Sustainability 2020, 12, 2370 2 of 28 considerable importance for regional and municipal governance, to better plan its infrastructure and services, or to adjust strategies should they potentially yield undesirable results. In this research, we address this topic by means of Residential Microsimulation (RM) and scenario analysis.RM is a modelling approach that considers residential activity of individuals and households [16]. If the microsimulation has a path-dependent temporal dimension and covers geographic units, it is said to be dynamic and spatial [17,18]. The idea of microsimulation has been around since the 1950s [19], yet it expanded into a research field mainly from the 1970s onward, driven by developments in computational capacity [16,18,20]. RM draws heavily on residential location choice modelling, which can be described as a behavioral modelling approach that attempts to quantify the process of deciding one's place of residence. Although several methods are suited to performing this type of analysis [21], discrete choice modelling based on the random utility framework is widely used [22,23]. Important examples of spatial dynamic RM applications include UrbanSim [24][25][26], ILUTE (Integrated Land Use Transport Environment) [27,28] and ILUMASS (Integrated Land Use Modelling and Transportation System Simulation) [29]. RM can be used to spatially disaggregate regional demographic projections to smaller geographic units [30,31]. Another feature of RM is its ability to spatiotemporally assess changing relations between various aspects of the urban environment. By doing so, it can yield emerging behavior, i.e., outcomes that are difficult to predict based solely on an understanding of the underlying system at one point in...