This is a repository copy of Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm.
Several methods for modeling urban expansion are available. Most of them are based on a statistical, a cellular automaton (CA) and/or an agent-based (AB) approach. Statistical and CA approaches are based on the implicit assumption that people's behavior is not likely to change over the considered time horizon. Such assumption limits the ability to simulate long-term predictions as people's behavior changes over time. An approach to consider people's behavior is the use of an AB system, in which the decision-making process of agents needs to be parameterized. Most existing studies, which make use of empirical data to define the agents' decision-making criteria, rely on intensive data collection efforts. The considerable data requirements limit the AB-system's ability to model a large study area, as the number of agents for which data on decision-making criteria is required, increases with the size of the study area. This paper presents a hybrid urban expansion model (HUEM) that integrates logistic regression (Logit), CA and AB approaches to simulate future urban development. A key feature of HUEM lies in its ability to address various people behaviors that are variable over time through AB relying on a sample approach that combines Logit and CA. Three agent sets are defined; developer agents, farmer agents and planning permission authority agent. The agents' decision-making process is parameterized using CA and Logit models. The interactions of the agents are simulated through a series of rules. To assess HUEM performance, it is calibrated for Wallonia (Belgium) to simulate urban expansion between 1990 and 2000. Calibration results are then assessed by comparing the 2000 simulated map and the actual 2000 land-use map. Furthermore, the performance of HUEM is compared to a number of typical spatial urban expansion models, i.e. Logit model, CA model and CA-Logit to assess the added-value of HUEM. The comparison shows the performance of HUEM is better than other models in terms of allocation ability.
Micro-simulation travel demand and land use models require a synthetic population, which consists of a set of agents characterized by demographic and socio-economic attributes. Two main families of population synthesis techniques can be distinguished: (a) fitting methods (iterative proportional fitting, updating) and (b) combinatorial optimization methods. During the last few years, a third outperforming family of population synthesis procedures has emerged, i.e., Markov process-based methods such as Monte Carlo Markov Chain (MCMC) simulations. In this paper, an extended Hidden Markov model (HMM)-based approach is presented, which can serve as a better alternative than the existing methods. The approach is characterized by a great flexibility and efficiency in terms of data preparation and model training. The HMM is able to reproduce the structural configuration of a given population from an unlimited number of micro-samples and a marginal distribution. Only one marginal distribution of the considered population can be used as a boundary condition to "guide" the synthesis of the whole population. Model training and testing are performed using the Survey on the Workforce of 2013 and the Belgian National Household Travel Survey of 2010. Results indicate that the HMM method captures the complete heterogeneity of the micro-data contrary to standard fitting approaches. The method provides accurate results as it is able to reproduce the marginal distributions and their corresponding multivariate joint distributions with an acceptable error rate (i.e., SRSME=0.54 for 6 synthesized attributes). Furthermore, the HMM outperforms IPF for small sample sizes, even though the amount of input data is less than that for IPF. Finally, simulations show that the HMM can merge information provided by multiple data sources to allow good population estimates.
Abstract:An in-depth understanding of the main factors behind built-up development is a key prerequisite for designing policies dedicated to a more efficient land use. Infill development policies are essential to curb sprawl and allow a progressive recycling of low-density areas inherited from the past. This paper examines the controlling factors of built-up expansion and densification processes in Wallonia (Belgium). Unlike the usual urban/built-up expansion studies, our approach considers various levels of built-up densities to distinguish between different types of developments, ranging from low-density extensions (or sprawl) to high-density infill development. Belgian cadastral data for 1990, 2000, and 2010 were used to generate four classes of built-up areas, namely, non-, low-, medium-and high-density areas. A number of socioeconomic, geographic, and political factors related to built-up development were operationalized following the literature. We then used a multinomial logistic regression model to analyze the effects of these factors on the transitions between different densities in the two decades between 1990 and 2010. The findings indicate that all the controlling factors show distinctive variations based on density. More specifically, the centrality of zoning policies in explaining expansion processes is highlighted. This is especially 2 the case for high-density expansions. In contrast, physical and neighborhood factors play a larger role in infill development, especially for dense infill development.
Cools & Jacques Teller (2018) Comparing support vector machines with logistic regression for calibrating cellular automata land use change models,
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