[en] 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.
Research center :
LEMA
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
El Saeid Mustafa, Ahmed Mohamed ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)
Cools, Mario ; Université de Liège - ULiège > Département ArGEnCo > Transports et mobilité
Saadi, Ismaïl ; Université de Liège - ULiège > Département ArGEnCo > Transports et mobilité
Teller, Jacques ; Université de Liège - ULiège > Département ArGEnCo > Urbanisme et aménagement du territoire
Language :
English
Title :
Coupling agent-based, cellular automata and logistic regression into a hybrid urban expansion model (HUEM)
Alternative titles :
[en] HUEM
Publication date :
2017
Journal title :
Land Use Policy
ISSN :
0264-8377
eISSN :
1873-5754
Publisher :
Pergamon Press - An Imprint of Elsevier Science, Oxford, United Kingdom
Volume :
69C
Pages :
529-540
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
The research was funded through the ARC grant for Concerted Research Actions and through the Special Fund for Research, both financed by the Wallonia-Brussels Federation.
Al-Ahmadi, K., See, L., Heppenstall, A., Hogg, J., Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia (2009) Ecol. Complex., 6, pp. 80-101
Allison, P.D., Logistic Regression Using the SAS System: Theory and Application (1999)
Bürgi, M., Hersperger, A.M., Schneeberger, N., Driving forces of landscape change − current and new directions (2005) Landsc. Ecol., 19, pp. 857-868
Batty, M., Cities and Complexity: Understanding Cities with Cellular Automata, Agent-based Models, and Fractals (2007), MIT Press
Batty, M., The size, scale, and shape of cities (2008) Science, 319, pp. 769-771
Beckers, A., Dewals, B., Erpicum, S., Dujardin, S., Detrembleur, S., Teller, J., Pirotton, M., Archambeau, P., Contribution of land use changes to future flood damage along the river Meuse in the Walloon region (2013) Nat. Hazards Earth Syst. Sci., 13, pp. 2301-2318
Belgian Federal Government, Population (2015), http://statbel.fgov.be/fr/modules/publications/statistiques/population/population_-_chiffres_population_1990-2010.jsp, [WWW Document]. Stat. Belg. URL (accessed 4.19.15)
Bert, F.E., Podestá, G.P., Rovere, S.L., Menéndez, Á.N., North, M., Tatara, E., Laciana, C.E., Toranzo, F.R., An agent based model to simulate structural and land use changes in agricultural systems of the argentine pampas (2011) Ecol. Model., 222, pp. 3486-3499
Bičı́k, I., Jeleček, L., Štěpánek, V., Land-use changes and their social driving forces in Czechia in the 19th and 20th centuries (2001) Land Use Policy, 18, pp. 65-73
Cammerer, H., Thieken, A.H., Verburg, P.H., Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria) (2013) Nat. Hazards, 68, pp. 1243-1270
Cary, W.A., Antrop. M., Landscape change and the urbanization process in Europe. Landsc. Urban Plan (2004) Dev. Eur. Landsc., 67, pp. 9-26
Clarke, K.C., Gaydos, L.J., Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore (1998) Int. J. Geogr. Inf. Sci., 12, pp. 699-714
Crk, T., Uriarte, M., Corsi, F., Flynn, D., Forest recovery in a tropical landscape: what is the relative importance of biophysical, socioeconomic, and landscape variables? (2009) Landsc Ecol., 24, pp. 629-642
Dujardin, S., Boussauw, K., Brévers, F., Lambotte, J.-M., Teller, J., Witlox, F., Sustainability and change in the institutionalized commute in Belgium: exploring regional differences (2012) Appl. Geogr., 35, pp. 95-103
Engelen, G., Poelmans, L., Uljee, I., Van der Meulen, M., A cellular automata based spatial optimisation model to delineate new locations for economic activity in Limburg Province, Belgium (2016) Proceedings of CAMUSS 2016. Presented at the Second International Symposium On Cellular Automata Modeling For Urban And Spatial Systems, Québec, Canada
García, A.M., Santé, I., Crecente, R., Miranda, D., An analysis of the effect of the stochastic component of urban cellular automata models (2011) Comput. Environ. Urban Syst., 35, pp. 289-296
García, A.M., Santé, I., Boullón, M., Crecente, R., Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm. Application to a small urban settlement of NW Spain (2013) Int. J. Geogr. Inf. Sci., 27, pp. 1593-1611
Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., Hokao, K., Modeling urban land use change by the integration of cellular automaton and Markov model (2011) Ecol. Model., 222, pp. 3761-3772
Haggert, B.A., Review of land use and the causes of global warming (1995) Trans. Inst. Br. Geogr., 20, pp. 518-520
He, C., Okada, N., Zhang, Q., Shi, P., Li, J., Modelling dynamic urban expansion processes incorporating a potential model with cellular automata (2008) Landsc. Urban Plan., 86, pp. 79-91
Holland, J.H., Adaptation in Natural and Artificial Systems (1975), U Michigan Press Oxford, England
Hosme, D.W., Jr., Lemeshow, S., Applied Logistic Regression (2004), John Wiley & Sons
Hosseinali, F., Alesheikh, A.A., Nourian, F., Agent-based modeling of urban land-use development, case study: simulating future scenarios of Qazvin city (2013) Cities, 31, pp. 105-113
Hu, Z., Lo, C.P., Modeling urban growth in Atlanta using logistic regression (2007) Comput. Environ. Urban Syst., 31, pp. 667-688
Jantz, C.A., Goetz, S.J., Shelley, M.K., Using the sleuth urban growth model to simulate the impacts of future policy scenarios on urban land use in the baltimore-Washington metropolitan area (2003) Environ. Plan. B Plan. Des., 31, pp. 251-271
Li, X., Zhou, W., Ouyang, Z., Forty years of urban expansion in Beijing: what is the relative importance of physical, socioeconomic, and neighborhood factors? (2013) Appl. Geogr., 38, pp. 1-10
Lin, Y., Deng, X., Li, X., Ma, E., Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? (2014) Front. Earth Sci., 1-12
Liu, X., Li, X., Shi, X., Wu, S., Liu, T., Simulating complex urban development using kernel-based non-linear cellular automata (2008) Ecol. Model., 211, pp. 169-181
Matthews, R.B., Gilbert, N.G., Roach, A., Polhill, J.G., Gotts, N.M., Agent-based land-use models: a review of applications (2007) Landsc. Ecol., 22, pp. 1447-1459
Miller, B.L., Miller, B.L., Goldberg, D.E., Goldberg, D.E., Genetic algorithms, tournament selection, and the effects of noise (1995) Complex Syst., 9, pp. 193-212
Mitsova, D., Shuster, W., Wang, X., A cellular automata model of land cover change to integrate urban growth with open space conservation (2011) Landsc. Urban Plan., 99, pp. 141-153
Montgomery, D.C., Runger, G.C., Applied Statistics and Probability for Engineers (2003), fourth. ed. John Wiley & Sons New York
Mustafa, A., Saadi, I., Cools, M., Teller, J., Measuring the effect of stochastic perturbation component in cellular automata urban growth model. procedia environ. sci (2014) 12th International Conference on Design and Decision Support Systems in Architecture and Urban Planning, DDSS 2014 22, pp. 156-168
Mustafa, A., Cools, M., Saadi, I., Teller, J., Urban development as a continuum: a multinomial logistic regression aroach (2015) Computational Science and Its Applications–ICCSA 2015, Lecture Notes in Computer Science, pp. 729-744. , O. Gervasi B. Murgante S. Misra M.L. Gavrilova A.M.A.C. Rocha C. Torre D. Taniar B.O. Apduhan Springer International Publishing
Mustafa, A., Bruwier, M., Teller, J., Archambeau, P., Erpicum, S., Pirotton, M., Dewals, B., Impacts of urban expansion on future flood damage: a case study in the River Meuse basin, Belgium (2016) Sustainable Hydraulics in the Era of Global Change, , Taylor & Francis Group
Pérez, E., Herrera, F., Hernández, C., Finding multiple solutions in job shop scheduling by niching genetic algorithms (2003) J. Intell. Manuf., 14, pp. 323-339
Parker, D.C., Meretsky, V., Measuring pattern outcomes in an agent-based model of edge-effect externalities using spatial metrics (2004) Agric. Ecosyst. Environ., 101, pp. 233-250
Poelmans, L., Van Rompaey, A., Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas: a case study in the Flanders-Brussels region (2009) Landsc. Urban Plan., 93, pp. 10-19
Poelmans, L., Van Rompaey, A., Complexity and performance of urban expansion models (2010) Comput. Environ. Urban Syst., 34, pp. 17-27
Poelmans, L., Van Rompaey, A., Batelaan, O., Coupling urban expansion models and hydrological models: how important are spatial patterns? (2010) Land Use Policy, 27, pp. 965-975
Poelmans, L., Engelen, G., Uljee, I., van der Meulen, M., RuBeLim −Ruimte Voor Bedrijvigheid in Limburg (2013), (No. 2013/RMA/R/0255)
Puertas, O.L., Henríquez, C., Meza, F.J., Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago Metropolitan Area, 2010–2045 (2014) Land Use Policy, 38, pp. 415-425
Ralha, C.G., Abreu, C.G., Coelho, C.G.C., Zaghetto, A., Macchiavello, B., Machado, R.B., A multi-agent model system for land-use change simulation (2013) Environ. Model. Softw., 42, pp. 30-46
Sang, L., Zhang, C., Yang, J., Zhu, D., Yun, W., Simulation of land use spatial pattern of towns and villages based on CA-Markov model (2011) Math. Comput. Model Agric. (CCTA 2010), 54, pp. 938-943
Shafizadeh Moghadam, H., Helbich, M., Spatiotemporal urbanization processes in the megacity of Mumbai, India: a Markov chains-cellular automata urban growth model (2013) Appl. Geogr., 40, pp. 140-149
Shan, J., Alkheder, S., Wang, J., Genetic algorithms for the calibration of cellular automata urban growth modeling (2008) Photogramm. Eng. Remote Sens., 74, pp. 1267-1277
Valbuena, D., Verburg, P.H., Bregt, A.K., A method to define a typology for agent-based analysis in regional land-use research (2008) Agric. Ecosyst. Environ., 128, pp. 27-36
Verburg, P.H., Overmars, K.P., Dynamic simulation of land-Use change trajectories with the clue-S model (2007) Modelling Land-Use Change, The GeoJournal Library., pp. 321-337. , E. Koomen J. Stillwell A. Bakema H.J. Scholten Springer Netherlands
Verburg, P.H., Schot, P.P., Dijst, M.J., Veldkamp, A., Land use change modelling: current practice and research priorities (2004) GeoJournal, 61, pp. 309-324
Verburg, P.H., van Eck, J.R.R., de Nijs, T.C.M., Dijst, M.J., Schot, P., Determinants of land-Use change patterns in the Netherlands (2004) Environ. Plan. B Plan. Des., 31, pp. 125-150
Verhetsel, A., Thomas, I., Beelen, M., Commuting in Belgian metropolitan areas: the power of the Alonso-Muth model (2010) J. Transp. Land Use, 2
Vermeiren, K., Van Rompaey, A., Loopmans, M., Serwajja, E., Mukwaya, P., Urban growth of Kampala, Uganda: pattern analysis and scenario development (2012) Landsc. Urban Plan., 106, pp. 199-206
Wang, H., He, S., Liu, X., Dai, L., Pan, P., Hong, S., Zhang, W., Simulating urban expansion using a cloud-based cellular automata model: a case study of Jiangxia, Wuhan China (2013) Landsc. Urban Plan., 110, pp. 99-112
White, R., Engelen, G., High-resolution integrated modelling of the spatial dynamics of urban and regional systems (2000) Comput. Environ. Urban Syst., 24, pp. 383-400
White, R., Engelen, G., Uljee, I., The cellular automaton eats the regions: unified modeling of activities and land use in a variable grid cellular automaton (2015) Modeling Cities and Regions As Complex Systems: From Theory to Planning Applications, , The MIT Press Cambridge, Massachusetts
Wu, F., Calibration of stochastic cellular automata: the application to rural-urban land conversions (2002) Int. J. Geogr. Inf Sci., 16, pp. 795-818
Yang, Q., Li, X., Shi, X., Cellular automata for simulating land use changes based on support vector machines (2008) Comput. Geosci., 34, pp. 592-602
Yang, X., Zheng, X.-Q., Chen, R., A land use change model: integrating landscape pattern indexes and Markov-CA (2014) Ecol. Model., 283, pp. 1-7
Zhang, H., Zeng, Y., Bian, L., Yu, X., Modelling urban expansion using a multi agent-based model in the city of Changsha (2010) J. Geogr. Sci., 20, pp. 540-556