This is a repository copy of Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm.

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The objective of this paper is to investigate the respective influence of various urban pattern characteristics on inundation flow. A set of 2000 synthetic urban patterns were generated using an urban procedural model providing locations and shapes of streets and buildings over a square domain of 1×1km. Steady two-dimensional hydraulic computations were performed over the 2000 urban patterns with identical hydraulic boundary conditions. To run such a large amount of simulations, the computational efficiency of the hydraulic model was improved by using an anisotropic porosity model. This model computes on relatively coarse computational cells, but preserves information from the detailed topographic data through porosity parameters. Relationships between urban characteristics and the computed inundation water depths have been based on multiple linear regressions. Finally, a simple mechanistic model based on two district-scale porosity parameters, combining several urban characteristics, is shown to capture satisfactorily the influence of urban characteristics on inundation water depths. The findings of this study give guidelines for more flood-resilient urban planning.

HIGHLIGHTS Seven approaches present solutions for urban densification  Successful roof stacking requires integrating urban, engineering and architectural aspects  European cities have a great potential to be densified through roof stacking  30% of the population increase could be covered by roof stacking in Brussels ABSTRACTFacing the need to accommodate a growing number of inhabitants in major European cities, this research aimed to establish a methodology that facilitates decision making on urban densification through roof stacking.The methodology adopts a systematic approach on three consecutive levels: urban, engineering, and social.Multiple criteria are identified to assess and map the roof stacking potential in terms of location and number of added floors. The Brussels Capital Region was chosen as a case study to experiment with the developed workflow chart and validate the proposed approach, using ArcGIS software, by creating a map of the urban densification potential through roof stacking of Brussels at the city scale. The results show a realistic potential of accommodating 30% of the expected population increase in Brussels by the year 2040 using only roof stacking, provided that the current urban regulations are respected. In addition, a theoretical potential to accommodate more than the expected population increase by the same year is proposed provided that urban planning regulations are relaxed in relation to the height of buildings. Further applications to other cities in 2 Europe would help create additional opportunities to develop an automated tool for estimating such potentials on a wider scope.

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.

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.

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,