Ismaïl Saadi

Addressing the determinants of built-up expansion and densification processes at the regional scale

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 operationalised following the literature. We then used a multinomial logistic regression model to analyse 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 the case for high-density expansions. In contrast, physical and neighbourhood factors play a larger role in infill development, especially for dense infill development.

Urban Development as a Continuum: A Multinomial Logistic Regression Approach

Urban development is a complex process influenced by a number of driving forces, including spatial planning, topography and urban economics. Identifying these drivers is crucial for the regulation of urban development and the calibration of predictive models. Existing land-use models generally consider urban development as a binary process, through the identification of built versus non-built areas. This study considers urban development as a continuum, characterized by different level of densities, which can be related to different driving forces.

A Time Monte Carlo method for addressing uncertainty in land-use change models

ABSTRACT One of the main objectives of land-use change models is to explore future land-use patterns. Therefore, the issue of addressing uncertainty in land-use forecasting has received an increasing attention in recent years. Many current models consider uncertainty by including a randomness component in their structure. In this paper, we present a novel approach for tuning uncertainty over time, which we refer to as the Time Monte Carlo (TMC) method. The TMC uses a specific range of randomness to allocate new land uses. This range is associated with the transition probabilities from one land use to another. The range of randomness is increased over time so that the degree of uncertainty increases over time. We compare the TMC to the randomness components used in previous models, through a coupled logistic regression-cellular automata model applied for Wallonia (Belgium) as a case study. Our analysis reveals that the TMC produces results comparable with existing methods over the short-term validation period (2000–2010). Furthermore, the TMC can tune uncertainty on longer-term time horizons, which is an essential feature of our method to account for greater uncertainty in the distant future.

Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

ABSTRACT Land use change models enable the exploration of the drivers and consequences of land use dynamics. A broad array of modeling approaches are available and each type has certain advantages and disadvantages depending on the objective of the research. This paper presents an approach combining cellular automata (CA) model and support vector machines (SVMs) for modeling urban land use change in Wallonia (Belgium) between 2000 and 2010. The main objective of this study is to compare the accuracy of allocating new land use transitions based on CA-SVMs approach with conventional coupled logistic regression method (logit) and CA (CA-logit). Both approaches are used to calibrate the CA transition rules. Various geophysical and proximity factors are considered as urban expansion driving forces. Relative operating characteristic and a fuzzy map comparison are employed to evaluate the performance of the model. The evaluation processes highlight that the allocation ability of CA-SVMs slightly outperforms CA-logit approach. The result also reveals that the major urban expansion determinant is urban road infrastructure.

Understanding urban development types and drivers in Wallonia. A multi-density approach

In this study, urban development process in the Walloon region (Belgium) has been analysed. Two main aspects of development are quantitatively measured: the development type and the definition of the main drivers of the urbanisation process. Unlike most existing studies that consider the urban development as a binary process, this research considers the urban development as a continuous process, characterised by different levels of urban density. Eight urban classes are defined based on the Belgian cadastral data for years 2000 and 2010. A multinomial logistic regression model is employed to examine the main driving forces of the different densities. Sixteen drivers were selected, including accessibility, geo-physical features, policies and socio-economic factors. Finally, the changes from the non-urban to one of the urban density classes are detected and classified into different development types. The results indicate that zoning status (political factor), slope, distance to roads, population densities and mean land price, respectively, have impact on the urbanisation process whatever maybe the density. The results also show that the impact of these factors highly varies from one density to another.

Development trajectory of an integrated framework for the mitigation of future flood risk: results from the FloodLand project

ABSTRACT In this paper, the development trajectory of an integrated framework for the mitigation of future flood risk of the Ourthe river basin in Belgium is discussed. The paper contributes to the state-of-the-art by presenting an integrated multidisciplinary framework capable of making long-term projections (time horizon 2050 and 2100) with the objective of mitigating future flood risk by proposing alternative land-use scenarios. It bridges numerous different fields, including urban planning, transport engineering, hydrology, geology, environmental engineering, and economics. The overall design and validation results of the different sub-modules of the framework are presented, and ongoing and future enhancements are highlighted.

Investigating scalability in population synthesis: a comparative approach

ABSTRACT In this paper, we investigate the influence of scalability on the accuracy of different synthetic populations using both fitting and generation-based approaches. Most activity-based models need a base-year synthetic population of agents with various attributes. However, when several attributes need to be synthesized, the accuracy of the synthetic population may decrease due to the mixed effects of scalability and dimensionality. We analyze two population synthesis methods for different levels of scalability, i.e. two to five attributes and different sample sizes – 10%, 25% and 50%. Results reveal that the simulation-based approach is more stable than Iterative Proportional Fitting (IPF) when the number of attributes increases. However, IPF is less sensitive to changes in sample size when compared to the simulation-based approach. We also demonstrate the importance of choosing the appropriate metric to validate the synthetic populations as the trends in terms of RMSE/MAE are different from those of SRMSE.

An efficient hierarchical model for multi-source information fusion

Abstract In urban and transportation research, important information is often scattered over a wide variety of independent datasets which vary in terms of described variables and sampling rates. As activity-travel behavior of people depends particularly on socio-demographics and transport/urban-related variables, there is an increasing need for advanced methods to merge information provided by multiple urban/transport household surveys. In this paper, we propose a hierarchical algorithm based on a Hidden Markov Model (HMM) and an Iterative Proportional Fitting (IPF) procedure to obtain quasi-perfect marginal distributions and accurate multi-variate joint distributions. The model allows for the combination of an unlimited number of datasets. The model is validated on the basis of a synthetic dataset with 1,000,000 observations and 8 categorical variables. The results reveal that the hierarchical model is particularly robust as the deviation between the simulated and observed multivariate joint distributions is extremely small and constant, regardless of the sampling rates and the composition of the datasets in terms of variables included in those datasets. Besides, the presented methodological framework allows for an intelligent merging of multiple data sources. Furthermore, heterogeneity is smoothly incorporated into micro-samples with small sampling rates subjected to potential sampling bias. These aspects are handled simultaneously to build a generalized probabilistic structure from which new observations can be inferred. A major impact in term of expert systems is that the outputs of the hierarchical model (HM) model serve as a basis for a qualitative and quantitative analyses of integrated datasets.

Calibration of MATSim in the context of natural hazards in Belgium

In Belgium, river floods are among the most frequent natural disasters and they may cause important changes on travel demand. In this regard, we propose to set up a large scale scenario using MATSim for guarantying an accurate assessment of the river floods impact on the transportation systems. In terms of inputs, agent-based models require a base year population. In this context, a synthetic population with a respective set of attributes is generated as a key input. Afterwards, agents are assigned activity chains through an activity-based generation process. Finally, the synthetic population and the transportation network are integrated into the dynamic traffic assignment simulator, i.e. MATSim. With respect to data, households travel surveys are the main inputs for synthesizing the populations. Besides, a steady-state inundation map is integrated within MATSim for simulating river floods. To our knowledge, very few studies have focused on how river floods affect transportation systems. In this regard, this research will undoubtedly provide new insights in term of methodology and traffic pattern analysis under disruptions, especially with regard to spatial scale effects. The results indicate that at the municipality level, it is possible to capture the effects of disruptions on travel behavior. In this context, further disaggregation is needed in future studies for identifying to what extent results are sensitive to disaggregation. In addition, results also suggest that the target sub-population exposed to flood risk should be isolated from the rest of the travel demand to reach have more sensitive effects. DOI: http://dx.doi.org/10.4995/CIT2016.2016.4098