No abstract

The creation of an accurate simulation of future urban growth is considered to be one of the most important challenges of the last five decades that involves spatial modeling within a GIS environment. Even though built-up densification processes, or transitions from low to high density, are critical for policymakers concerned with limiting sprawl, the literature on models for urban study reveals that most of them focus solely on the expansion process. Although the majority of these models have similar goals, they differ in terms of implementation and theoretical assumptions. Cellular automata (CA) models have been proven to be successful at simulating urban growth dynamics and projecting future scenarios at multiple scales. This paper aims to revisit urban CA models to determine the various approaches for a realistic simulation and prediction of urban densification. The general characteristics of CA models are described with respect to analysis of various driving factors that influence urban scenarios. This paper also critically analyzes various hybrid models based on CA such as the Markov chain, artificial neural network (ANN), and logistic regression (LR). Limitation and uncertainties of CA models, namely, neighborhood cell size, may be minimized when integrated with empirical and statistical models. The result of this review suggests that it is useful to use CA models with multinomial logistic regression (MLR) in order to analyze and model the effects of various driving factors related to urban densification. Realistic simulations can be achieved when multidensity class labels are integrated in the modeling process.

Urban expansion models are widely used to understand, analyze and predict any peculiar scenario based on input probabilities. Modelling and uncertainty are concomitant, and can occur due to reasons ranging from-discrepancies in input variables, unpredictable model parameters, spatio-temporal variability between observations, or malfunction in linking model variables under two different spatiotemporal scenarios. However, uncertainties often occur because of the interplay of model elements, structures, and the quality of data sources employed; as input parameters influence the behavior of cellular automaton (CA) models. Our study aims to address these uncertainties. While most studies consider neighborhood effects, timestep and spatial resolution, our study uniquely focuses on the susceptibility of multi density classes and varying cell size on uncertainty. Hence this chapter offers a theoretical elucidation of the concepts, sources, and strategies for managing uncertainty under various criteria as well as an algorithm for enumerating the model's accuracy for Wallonia, Belgium.

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.