[en] Abstract. Our studies aim at modelling and simulating urban expansion scenarios for Brussels capital region, Brabant of Flanders and Wallonia. Thereby we use a non-ordered multinomial logistic regression (MLR) coupled with cellular automata. Our model helps to study the probability for built-up development based on a) impact of different causative factors on expansion process and b) effect of the neighbouring cells on future built-up development. In our study, we have used 100×100 m raster data representing cadastral built-up of our study area. The model is then calibrated using the maps for years 2000–2010 and to simulate 2020. Thereto, simulated 2020 maps has been validated with observed 2020 built-up maps using fuzzy set theory. Our results show that all through our study, zoning or land use policies play an important role for expansion along all the built-up density classes. Besides, slope distance to highways and major cities encourages new urban development commonly known as ‘Urban sprawls’. Distance to main roads, employment opportunities aids to further development from low density to medium density areas. While most of the factors impact negatively to further high density development showing that our area is highly governed by the zoning status in case of densification.
Precision for document type :
Analysis of case law/Statutory reports
Disciplines :
Architecture Civil engineering Human geography & demography
Author, co-author :
Chakraborty, Anasua ; Université de Liège - ULiège > Urban and Environmental Engineering
Mustafa, Ahmed Mohamed El Saeid ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)
Teller, Jacques ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)
Language :
English
Title :
Modelling multi-density urban expansion using Cellular Automata for Brussels Metropolitan Development Area
Publication date :
31 May 2024
Journal title :
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Ahmed, B., Ahmed, R., Zhu, X., 2013. Evaluation of Model Validation Techniques in Land Cover Dynamics. ISPRS Int. J. Geo Inf., 2, 577-577.
Al-Ahmadi, K., See, L., Heppenstall, A., Hogg, J., 2009. Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia. Ecological Complexity, 6, 80-101.
Cammerer, H., Thieken, A., Verburg, P., 2013. Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria). Natural Hazards, 68, 1243-1243.
Chakraborty, A., Mustafa, A., Omrani, H., Teller, J., 2023. A Framework to Probe Uncertainties in Urban Cellular Automata Modelling Using a Novel Framework of Multilevel Density Approach: A Case Study for Wallonia Region, Belgium. Springer, Urban Book Series, Cham, Switzerland.
Chakraborty, A., Omrani, H., Teller, J., 2022. A Comparative Analysis of Drivers Impacting Urban Densification for Cross Regional Scenarios in Brussels Metropolitan Area. Land 2022, 11, 2291.
Chen, Y., Li, X., Liu, X., Ai, B., 2014. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. International Journal of Geographical Information Science, 28, 234-255.
Hagen, A., 2003. Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographical Information Science, 17(3), 235-249.
Han, Y., Jia, H., 2017. Simulating the spatial dynamics of urban growth with an integrated modeling approach: A case study of Foshan, China. Ecological Modelling, 353, 107-116.
Hu, Z., Lo, C., 2007. Modeling urban growth in Atlanta using logistic regression. Comput. Environ. Urban Syst., 31, 667-688.
Liao, J., Tang, L., Shao, G., Qiu, Q., Wang, C., Zheng, S., Su, X., 2014. A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence. International Journal of Geographical Information Science, 28, 720-738.
Loibl, W., Tötzer, T., 2003. Modeling growth and densification processes in suburban regions-simulation of landscape transition with spatial agents. Environ. Model. Softw., 18, 553-563.
Montgomery, D. C., Runger, G. C., 2010. Applied statistics and probability for engineers. John Wiley & Sons.
Mustafa, A., Cools, M., Saadi, I., Teller, J., 2015. Urban Development as a Continuum: A Multinomial Logistic Regression Approach. Springer, Computational Science and Its Applications - ICCSA 2015, Cham, Switzerland.
Mustafa, A., Heppenstall, A., Omrani, H., Saadi, I., Cools, M., Teller, J., 2018a. Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm. Comput. Environ. Urban Syst., 67, 147-156.
Mustafa, A., Saadi, I., Cools, M., Teller, J., 2014. Measuring the Effect of Stochastic Perturbation Component in Cellular Automata Urban Growth Model. Procedia environmental sciences, 22, 156-168.
Mustafa, A., van Rompaey, A., Cools, M., Saadi, I., Teller, J., 2018b. Addressing the determinants of built-up expansion and densification processes at the regional scale. Urban Studies, 55, 3279-3298.
Poelmans, L., Rompaey, A., 2009. Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flanders-Brussels region. Landscape and Urban Planning, 93, 10-19.
Poelmans, L., Rompaey, A., 2010. Complexity and performance of urban expansion models. Comput. Environ. Urban Syst., 34, 17-27.
Tannier, C., Thomas, I., 2013. Defining and characterizing urban boundaries: A fractal analysis of theoretical cities and Belgian cities. Comput. Environ. Urban Syst., 41, 234-248.
Verburg, P., van Eck, J., de Nijs, T., Dijst, M., Schot, P., 2004. A review of current calibration and validation practices in landchange modeling. Environ. Model. Softw., 82, 174-182.
Vliet, J., Bregt, A., Brown, D., Delden, H., Heckbert, S., Verburg, P., 2016. A review of current calibration and validation practices in land-change modeling. Environ. Model. Softw., 82, 174-182.
White, R., Uljee, I., Engelen, G., 2012. Integrated modelling of population, employment and land-use change with a multiple activity-based variable grid cellular automaton. International Journal of Geographical Information Science, 26, 1251-1280.
Zhang, Q., Ban, Y., Liu, J., Hu, Y., 2011. Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China. Comput. Environ. Urban Syst., 35, 126-139.
