Predictive modeling of urbanization in Oran, Algeria: a spatial analysis of driving factors and land cover change using land change modeler and logistic regression

Rabia Samah Choukri , Tarik Ghodbani , María Teresa Camacho Olmedo , Walid Al-Shaar

Computational Urban Science ›› 2026, Vol. 6 ›› Issue (1) : 10

PDF
Computational Urban Science ›› 2026, Vol. 6 ›› Issue (1) :10 DOI: 10.1007/s43762-026-00238-9
Original Paper
research-article

Predictive modeling of urbanization in Oran, Algeria: a spatial analysis of driving factors and land cover change using land change modeler and logistic regression

Author information +
History +
PDF

Abstract

Urbanization is a global phenomenon with critical challenges for land use management and ecosystem conservation. This work examines urbanization dynamics and their effects on land cover categories, emphasizing the importance of understanding these processes for effective urban management and planning. The study focuses on the peri-urban areas of the second largest Algerian metropolis Oran, specifically examining the south and west peripheries along two major road axes, which have witnessed significant land use changes over the past three decades. With an AUC accuracy of 0.9, the application of integrated Land Change Modeler (LCM) and Logistic Regression analysis revealed a notable increase in urban areas in the periphery of Oran between 1998 and 2019, resulting in a 4340.97-hectare expansion of the urban area. Modeling projections indicate that this trend will continue with the urban area more than doubling from 2019 to 2030, with an expected 11,031-hectare for the urban category by 2030. These results highlight a growing urban sprawl, accompanied by a decline in agricultural areas, forest cover, and Ramsar-designated wetlands, which play a critical role in sustaining regional biodiversity. Furthermore, the analysis identifies distance to roads and proximity to urban settlements as key drivers influencing urban growth in the study area. These findings emphasize the need for a more effective urban planning policy and enforcement mechanisms to balance urban growth and with ecological preservation and agricultural sustainability. This research contributes to a better understanding of the complex land use and land cover patterns in North African contexts and offers insights to local and regional stakeholders developing reliable land management strategies in rapidly urbanizing regions.

Keywords

Land use policy / Land Change Modeler / Urban planning / Land use Land cover / Algeria

Cite this article

Download citation ▾
Rabia Samah Choukri, Tarik Ghodbani, María Teresa Camacho Olmedo, Walid Al-Shaar. Predictive modeling of urbanization in Oran, Algeria: a spatial analysis of driving factors and land cover change using land change modeler and logistic regression. Computational Urban Science, 2026, 6(1): 10 DOI:10.1007/s43762-026-00238-9

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Abd EL-kawy OR, Ismail HA, Yehia HM, Allam MA. Temporal detection and prediction of agricultural land consumption by urbanization using remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 2019, 22(3): 237-246

[2]

Aguejdad R, Houet T, Hubert-Moy L. Spatial validation of land use change models using multiple assessment techniques: A case study of transition potential models. Environmental Modeling & Assessment, 2017, 22: 591-606

[3]

Aksoy H, Kaptan S. Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model. Geocarto International, 2022, 37(4): 1183-1202

[4]

Alphan H. Analysis of road development and associated agricultural land use change. Environmental Monitoring and Assessment, 2018, 190(1 5

[5]

Alphan H, Doygun H, Unlukaplan YI. Post-classification comparison of land cover using multitemporal Landsat and ASTER imagery: The case of Kahramanmaraş, Turkey. Environmental Monitoring and Assessment, 2009, 1511–4): 327-336

[6]

AL-Rousan N, Mat Isa NA, Mat Desa MK, AL-Najjar H. Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems. International Journal of Intelligent Systems, 2021, 36(10): 5605-5669

[7]

Al-Shaar W. Analyzing the effect size of urban growth driving factors: Application of multilayer-perceptron Markov-chain model for the Riyadh city. Modeling Earth Systems and Environment, 2024, 10(1): 303-312

[8]

Al-Shaar W, Bonin O, de Gouvello B. Scenario-based predictions of urban dynamics in Île-de-France region: A new combinatory methodologic approach of variance analysis and frequency ratio. Sustainability, 2022, 14(11 6806

[9]

Araya YH, Cabral P. Analysis and modeling of urban land cover change in Set\’ ubal and Sesimbra, Portugal. Remote Sensing, 2010, 26): 1549-1563

[10]

Atef I, Ahmed W, Abdel-Maguid RH. Future land use land cover changes in El-Fayoum governorate: A simulation study using satellite data and CA-Markov model. Stochastic Environmental Research and Risk Assessment, 2024, 38(2): 651-664

[11]

Balk D, Leyk S, Jones B, Montgomery MR, Clark A. Understanding urbanization: A study of census and satellite-derived urban classes in the United States, 1990–2010. PLoS ONE, 2018, 13(12 e0208487

[12]

Barreira-González P, Barros J. Configuring the neighbourhood effect in irregular cellular automata based models. International Journal of Geographical Information Science, 2017, 313): 617-636

[13]

Bendjelid A. La fragmentation de l’espace urbain d’Oran (Algérie). Mécanismes, acteurs et aménagement urbain. Insaniyat/انسانيات, 1998, 5: 61-84

[14]

Bendjelid A. Oran face aux actions d’aménagement urbain d’Alger: Similitudes, modulations et effets de l’image de la capitale sur les pouvoirs locaux. Insaniyat / إنسانيات, 2004, 23: 91-110

[15]

Bendjelid A. Oran face aux actions d’aménagement urbain d’Alger: Similitudes, modulations et effets de l’image de la capitale sur les pouvoirs locaux. Insaniyat / إنسانيات, 2004, 23: 91-110

[16]

Bounoua L, Bachir N, Souidi H, Bahi H, Lagmiri S, Khebiza MY, Nigro J, Thome K. Sustainable development in Algeria’s urban areas: Population growth and land consumption. Urban Science, 2023, 7(1 29

[17]

Breiman L. Random forests. Machine Learning, 2001, 451): 5-32

[18]

Brown DG, Verburg PH, Pontius RG, Lange MD. Opportunities to improve impact, integration, and evaluation of land change models. Current Opinion in Environmental Sustainability, 2013, 5(5): 452-457

[19]

Çakir G, Ün C, Baskent EZ, Köse S, Sivrikaya F, Keleş S. Evaluating urbanization, fragmentation and land use/land cover change pattern in Istanbul city, Turkey from 1971 to 2002. Land Degradation & Development, 2008, 19(6): 663-675

[20]

Caldeira TP. Peripheral urbanization: Autoconstruction, transversal logics, and politics in cities of the global south. Environment and Planning d: Society & Space, 2017, 35(1): 3-20

[21]

Camacho Olmedo MT, Paegelow M, Mas JF. Interest in intermediate soft-classified maps in land change model validation: Suitability versus transition potential. International Journal of Geographical Information Science, 2013, 27(12): 2343-2361

[22]

Camacho Olmedo, M. T., Paegelow, M., Mas, J.-F., & Escobar, F. (Eds.). (2018). Geomatic Approaches for Modeling Land Change Scenarios. Springer International Publishing. https://doi.org/10.1007/978-3-319-60801-3

[23]

Chen Y, Chang K, Han F, Karacsonyi D, Qian Q. Investigating urbanization and its spatial determinants in the central districts of Guangzhou, China. Habitat International, 2016, 51: 59-69

[24]

City-Facts. (2024). Oran, Algeria—Population. https://www.city-facts.com/oran-algeria/population

[25]

Cohen B. Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technology in Society, 2006, 28(1–2): 63-80

[26]

Cotella, G. (2023). Sustainable Urbanisation for Territorial Cohesion. A Multi-scalar Perspective. In E. Medeiros (Ed.), Public Policies for Territorial Cohesion (pp. 105–125). Springer International Publishing. https://doi.org/10.1007/978-3-031-26228-9_6

[27]

Council, N. R.. Advancing Land Change Modeling: Opportunities and Research Requirements, 2014The National Academies Press

[28]

Cramp S, Simmons KEL. Handbook of the birds of Europe, the Middle East, and North Africa: The birds of the Western Palearctic, 1977Oxford University Press

[29]

Dai E, Wang Y, Ma L, Yin L, Wu Z. ‘Urban-rural’ gradient analysis of landscape changes around cities in mountainous regions: A case study of the Hengduan Mountain Region in Southwest China. Sustainability, 2018, 10(4 1019

[30]

Díaz-Pacheco J, García-Palomares JC. Urban sprawl in the Mediterranean urban regions in Europe and the crisis effect on the urban land development: Madrid as study case. Urban Studies Research, 2014, 2014: 1-13

[31]

(DPSB). (2023). Dataset on housing and infrastructure [Unpublished dataset].

[32]

Eastman, J. R. (2006). IDRISI Andes Guide to GIS and Image Processing. Clark Labs-Clark University, Worcester, 45. web: http://www.clarklabs.org

[33]

Eastman JR. IDRISI Selva Tutorial (IDRISI Production), 2012Clark Labs-Clark University45

[34]

Eastman JR, He J. A regression-based procedure for Markov transition probability estimation in land change modeling. Land, 2020, 911 407

[35]

Eastman, J. R., & Toledano, J. (2018). A Short Presentation of the Land Change Modeler (LCM). In M. T. Camacho Olmedo, M. Paegelow, J.-F. Mas, & F. Escobar (Eds.), Geomatic Approaches for Modeling Land Change Scenarios (pp. 499–505). Springer International Publishing. https://doi.org/10.1007/978-3-319-60801-3_36

[36]

El Mjiri I, Rahimi A, Bouasria A, Bounif M, Boulanouar W. Long-term LULC monitoring in El Jadida, Morocco (1985–2020): A machine learning-based comparative analysis. ISPRS International Journal of Geo-Information, 2025, 1411 445

[37]

Fang Z, Ding T, Chen J, Xue S, Zhou Q, Wang Y, Wang Y, Huang Z, Yang S. Impacts of land use/land cover changes on ecosystem services in ecologically fragile regions. Science of the Total Environment, 2022, 831 154967

[38]

Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters, 2006, 278): 861-874

[39]

Galewski T, Collen B, McRae L, Loh J, Grillas P, Gauthier-Clerc M, Devictor V. Long-term trends in the abundance of Mediterranean wetland vertebrates: From global recovery to localized declines. Biological Conservation, 2011, 144(5): 1392-1399

[40]

García-Álvarez D, Camacho Olmedo MT, Paegelow M, Mas JF. Land Use Cover Datasets and Validation Tools: Validation Practices with QGIS, 2022Springer International Publishing

[41]

Gemitzi A. Predicting land cover changes using a CA Markov model under different shared socioeconomic pathways in Greece. Giscience & Remote Sensing, 2021, 58(3): 425-441

[42]

Gharaibeh A, Shaamala A, Obeidat R, Al-Kofahi S. Improving land-use change modeling by integrating ANN with cellular automata-Markov chain model. Heliyon, 2020, 6(9 e05092

[43]

Girma R, Fürst C, Moges A. Land use land cover change modeling by integrating artificial neural network with cellular automata-Markov chain model in Gidabo river basin, main Ethiopian rift. Environmental Challenges, 2022, 6 100419

[44]

Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 2017, 202: 18-27

[45]

Gregorich M, Strohmaier S, Dunkler D, Heinze G. Regression with highly correlated predictors: Variable omission is not the solution. International Journal of Environmental Research and Public Health, 2021, 188 4259

[46]

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. Ecological Modelling, 2011, 22220–22): 3761-3772

[47]

Guisan A, Zimmermann NE. Predictive habitat distribution models in ecology. Ecological Modelling, 2000, 135: 147-186

[48]

Han H, Yang C, Song J. Scenario simulation and the prediction of land use and land cover change in Beijing, China. Sustainability, 2015, 7(4): 4260-4279

[49]

Hasan S, Shi W, Zhu X, Abbas S, Khan HUA. Future simulation of land use changes in rapidly urbanizing South China based on Land Change Modeler and remote sensing data. Sustainability, 2020, 12(11 4350

[50]

Hellwig N, Walz A, Markovic D. Climatic and socioeconomic effects on land cover changes across Europe: Does protected area designation matter?. PLoS ONE, 2019, 147 e0219374

[51]

Hietel E, Waldhardt R, Otte A. Analysing land-cover changes in relation to environmental variables in Hesse, Germany. Landscape Ecology, 2004, 195): 473-489

[52]

Japan Space Systems, METI/NASA. (2019). ASTER GDEM Download Portal [Japan Space Systems]. https://gdemdl.aster.jspacesystems.or.jp/index_en.html

[53]

Jiang S, Zhang Z, Ren H, Wei G, Xu M, Liu B. Spatiotemporal characteristics of urban land expansion and population growth in Africa from 2001 to 2019: Evidence from population density data. ISPRS International Journal of Geo-Information, 2021, 10(9 584

[54]

Junk WJ. Long-term environmental trends and the future of tropical wetlands. Environmental Conservation, 2002, 294): 414-435

[55]

Kentor J. Structural determinants of peripheral urbanization: The effects of international dependence. American Sociological Review, 1981, 46(2): 201

[56]

Kim Y, Newman G, Güneralp B. A review of driving factors, scenarios, and topics in urban land change models. Land, 2020, 98 246

[57]

Kingsford RT, Bino G, Finlayson CM, Falster D, Fitzsimons JA, Gawlik DE, Murray NJ, Grillas P, Gardner RC, Regan TJ, Roux DJ, Thomas RF. Ramsar wetlands of International Importance–Improving conservation outcomes. Frontiers in Environmental Science, 2021, 9 643367

[58]

Klosterman RE. The what if? Collaborative planning support system. Environment and Planning B, Planning & Design, 1999, 263393-408

[59]

Lakjâa A. Les périphéries oranaises: Urbanité en émergence et refondation du lien social. Les Cahiers d'EMAM, 2009, 18: 29-44

[60]

Lekka C, Petropoulos GP, Detsikas SE. Appraisal of EnMAP hyperspectral imagery use in LULC mapping when combined with machine learning pixel-based classifiers. Environmental Modelling & Software, 2024, 173 105956

[61]

Leta MK, Demissie TA, Tr\"anckner J. Modeling and prediction of land use land cover change dynamics based on land change modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia. Sustainability, 2021, 13(7 3740

[62]

Li X, Gong P. Urban growth models: Progress and perspective. Science Bulletin, 2016, 6121): 1637-1650

[63]

Li T, Shilling F, Thorne J, Li F, Schott H, Boynton R, Berry AM. Fragmentation of China’s landscape by roads and urban areas. Landscape Ecology, 2010, 25(6): 839-853

[64]

Li K, Feng M, Biswas A, Su H, Niu Y, Cao J. Driving factors and future prediction of land use and cover change based on satellite remote sensing data by the LCM model: A case study from Gansu Province, China. Sensors, 2020, 20(10 2757

[65]

Liu J, Deng X. Progress of the research methodologies on the temporal and spatial process of LUCC. Chinese Science Bulletin, 2010, 5514): 1354-1362

[66]

López E, Bocco G, Mendoza M, Duhau E. Predicting land-cover and land-use change in the urban fringe. Landscape and Urban Planning, 2001, 55(4): 271-285

[67]

Luo T, Zhang T, Wang Z, Gan Y. Driving forces of landscape fragmentation due to urban transportation networks: Lessons from Fujian, China. Journal of Urban Planning and Development, 2016, 142(2 04015013

[68]

Mahapatra A, Hore U, Singh A, Kumari M. The effect of urbanization on the shrinkage of wetlands in the Noida-Greater Noida region and its surrounding sub-urban areas. Ecological Frontiers, 2024, 441): 96-104

[69]

Mas J-F, Kolb M, Paegelow M, Camacho Olmedo MT, Houet T. Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software, 2014, 51: 94-111

[70]

Masek JG, Lindsay FE, Goward SN. Dynamics of urban growth in the Washington DC metropolitan area, 1973–1996, from Landsat observations. International Journal of Remote Sensing, 2000, 21(18): 3473-3486

[71]

Mediterranean Wetlands Observatory. (2012). Mediterranean wetlands: Outlook. First Mediterranean wetlands observatory report - Technical report. Tour du Valat.

[72]

Megahed Y, Cabral P, Silva J, Caetano M. Land cover mapping analysis and urban growth modelling using remote sensing techniques in Greater Cairo Region—Egypt. ISPRS International Journal of Geo-Information, 2015, 4(3): 1750-1769

[73]

Ministère de l’Aménagement du Territoire et de l’Environnement. (2010). Schéma National d’Aménagement du Territoire 2030 (SNAT 2030). République Algérienne Démocratique et Populaire, Ministère de l’Intérieur, des Collectivités Locales et de l’Aménagement du Territoire.

[74]

MohanRajan SN, Loganathan A, Manoharan P. Survey on land use/land cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and challenges. Environmental Science and Pollution Research, 2020, 27(24): 29900-29926

[75]

Mounia B, Foued B. Logic of urban planning in mastering of the growing growth of urban spaces. Case of the city of Ain Mlila (Algeria). International Journal of Innovative Studies in Sociology and Humanities, 2022, 7(9): 147-158

[76]

Müller K, Steinmeier C, Küchler M. Urban growth along motorways in Switzerland. Landscape and Urban Planning, 2010, 98(1): 3-12

[77]

Mun J, Lee JS, Kim S. Effects of urban sprawl on regional disparity and quality of life: A case of South Korea. Cities, 2024, 151 105125

[78]

Musa SI, Hashim M, Reba MNM. A review of geospatial-based urban growth models and modelling initiatives. Geocarto International, 2016, 32(8): 813-833

[79]

Musa SI, Hashim M, Reba MNM. A review of geospatial-based urban growth models and modelling initiatives. Geocarto International, 2017, 32(8): 813-833

[80]

Mustard, J. F., Defries, R. S., Fisher, T., & Moran, E. (2012). Land-Use and Land-Cover Change Pathways and Impacts. In G. Gutman, A. C. Janetos, C. O. Justice, E. F. Moran, J. F. Mustard, R. R. Rindfuss, D. Skole, B. L. Turner, & M. A. Cochrane (Eds.), Land Change Science (Vol. 6, pp. 411–429). Springer Netherlands. https://doi.org/10.1007/978-1-4020-2562-4_24

[81]

Naghibi SA, Pourghasemi HR, Pourtaghi ZS, Rezaei A. Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Science Informatics, 2015, 8(1): 171-186

[82]

National Research Council. (2014). Findings, Conclusions, and Implications. In J. Travis, B. Western, & S. Redburn (Eds.), The Growth of Incarceration in the United States: Exploring Causes and Consequences (pp. 334–357). The National Academies Press.

[83]

National Water Resources Agency. (2013). Lithology map of Algeria. National Water Resources Agency.

[84]

Noszczyk T. A review of approaches to land use changes modeling. Human and Ecological Risk Assessment, 2019, 25(6): 1377-1405

[85]

Office National des Statistiques (ONS). (2011). Collections Statistiques N° 163/2011, Série S : Statistiques Sociales, V° Recensement Général de la Population et de l’Habitat – 2008 – Armature Urbaine (No. 163/2011). Office National des Statistiques (ONS).

[86]

OpenStreetMap. (2022). OpenStreetMap. https://www.openstreetmap.org/#map=5/28.41/1.65

[87]

Ouchra H, Belangour A, Erraissi A. Supervised machine learning algorithms for land cover classification in Casablanca, Morocco. Ingénierie des Systèmes d'Information, 2024, 29(1): 377-387

[88]

Paegelow M, Camacho Olmedo MT. Modelling environmental dynamics: Advances in geomatic solutions, 2008Springer

[89]

Plieninger T, Draux H, Fagerholm N, Bieling C, Bürgi M, Kizos T, Kuemmerle T, Primdahl J, Verburg PH. The driving forces of landscape change in Europe: A systematic review of the evidence. Land Use Policy, 2016, 57: 204-214

[90]

Poelmans L, Van Rompaey A. Complexity and performance of urban expansion models. Computers, Environment and Urban Systems, 2010, 34(1): 17-27

[91]

Pontius GR, Malanson J. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 2005, 19(2): 243-265

[92]

Pontius RG, Petrova SH. Assessing a predictive model of land change using uncertain data. Environmental Modelling & Software, 2010, 25(3): 299-309

[93]

Pontius RG, Schneider LC. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 2001, 85(1–3): 239-248

[94]

Pontius RG, Spencer J. Uncertainty in extrapolations of predictive land-change models. Environment and Planning B, Planning & Design, 2005, 32(2): 211-230

[95]

Pugh C. “Urban bias”, the Political Economy of Development and Urban Policies for Developing Countries. Urban Studies, 1996, 33(7): 1045-1060

[96]

Ramsar. (2019). Ramsar sites information service: Grande sebkha d’Oran.https://rsis.ramsar.org/ris/1055?language=en.

[97]

Rebouha, F. (2010). Transport, mobilité, et accès aux services des populations défavorisées: Le cas des habitants des grandes périphéries d’Oran [Géographie]. Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf.

[98]

Ren Y, Y, Fu B, Comber A, Li T, Hu J. Driving factors of land change in China’s Loess Plateau: Quantification using geographically weighted regression and management implications. Remote Sensing, 2020, 12(3): 453

[99]

Roy, H. G., Fox, D. M., & Emsellem, K. (2014). Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales. In B. Murgante, S. Misra, A. M. A. C. Rocha, C. Torre, J. G. Rocha, M. I. Falcão, D. Taniar, B. O. Apduhan, & O. Gervasi (Eds.), Computational Science and Its Applications – ICCSA 2014 (Vol. 8582, pp. 315–330). Springer International Publishing. https://doi.org/10.1007/978-3-319-09147-1_23

[100]

Salem M, Tsurusaki N, Divigalpitiya P. Analyzing the driving factors causing urban expansion in the peri-urban areas using logistic regression: A case study of the Greater Cairo Region. Infrastructures, 2019, 41 4

[101]

Sargent, R. G. (2009). Verification and validation of simulation models. Proceedings of the 2009 Winter Simulation Conference (WSC), 162–176. https://doi.org/10.1109/WSC.2009.5429327

[102]

Seto KC, Fragkias M, Güneralp B, Reilly MK. A meta-analysis of global urban land expansion. PLoS ONE, 2011, 68 e23777

[103]

Shaw SK, Sravani N, Sharma A, Anand J. Assessment of probable zones of agricultural land suitability based on MCDM, probabilistic, and data-driven approach in Krishna District, India. Environmental Monitoring and Assessment, 2025, 1973 339

[104]

Shrestha N. Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 2020, 8239-42

[105]

Shrestha MK, York AM, Boone CG, Zhang S. Land fragmentation due to rapid urbanization in the Phoenix Metropolitan Area: Analyzing the spatiotemporal patterns and drivers. Applied Geography, 2012, 32(2): 522-531

[106]

Smith Guerra, P., & Romero Aravena, H. (2009). Efectos del crecimiento urbano del Área Metropolitana de Concepción sobre los humedales de Rocuant-Andalién, Los Batros y Lenga. Revista de geografía Norte Grande, 43. https://doi.org/10.4067/S0718-34022009000200005

[107]

Somvanshi SS, Bhalla O, Kunwar P, Singh M, Singh P. Monitoring spatial LULC changes and its growth prediction based on statistical models and earth observation datasets of Gautam Budh Nagar, Uttar Pradesh, India. Environment, Development and Sustainability, 2020, 22(2): 1073-1091

[108]

Tian G, Liu J, Xie Y, Yang Z, Zhuang D, Niu Z. Analysis of spatio-temporal dynamic pattern and driving forces of urban land in China in 1990s using TM images and GIS. Cities, 2005, 22(6): 400-410

[109]

Trexler JC, Travis J. Nontraditional Regression Analyses. Ecology, 1993, 74(6): 1629-1637

[110]

Turner B, Meyfroidt P, Kuemmerle T, Müller D, Roy Chowdhury R. Framing the search for a theory of land use. Journal of Land Use Science, 2020, 15(4): 489-508

[111]

UNEP/MAP. State of the Mediterranean Marine and Coastal Environment, 2012UNEP/MAP Barcelona Convention

[112]

United Nations. World urbanization prospects: The 2018 revision, 2018United Nations

[113]

Van Vliet J, Bregt AK, Hagen-Zanker A. Revisiting kappa to account for change in the accuracy assessment of land-use change models. Ecological Modelling, 2011, 222(8): 1367-1375

[114]

Verburg PH, Schot PP, Dijst MJ, Veldkamp A. Land use change modelling: Current practice and research priorities. GeoJournal, 2004, 61(4): 309-324

[115]

Verburg PH, Crossman N, Ellis EC, Heinimann A, Hostert P, Mertz O, Nagendra H, Sikor T, Erb K-H, Golubiewski N, Grau R, Grove M, Konaté S, Meyfroidt P, Parker DC, Chowdhury RR, Shibata H, Thomson A, Zhen L. Land system science and sustainable development of the earth system: A global land project perspective. Anthropocene, 2015, 12: 29-41

[116]

Wang J, Maduako IN. Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on hybrid methods for LULC modeling and prediction. European Journal of Remote Sensing, 2018, 51(1): 251-265

[117]

Wang F, Hasbani J-G, Wang X, Marceau DJ. Identifying dominant factors for the calibration of a land-use cellular automata model using Rough Set Theory. Computers, Environment and Urban Systems, 2011, 35(2): 116-125

[118]

Wang Z, Li L, Li Y. From super block to small block: Urban form transformation and its road network impacts in Chenggong, China. Mitigation and Adaptation Strategies for Global Change, 2015, 20(5): 683-699

[119]

Wang J, Huang B, Fu D, Atkinson PM, Zhang X. Response of urban heat island to future urban expansion over the Beijing–Tianjin–Hebei metropolitan area. Applied Geography, 2016, 70: 26-36

[120]

Wang L, Omrani H, Zhao Z, Francomano D, Li K, Pijanowski B. Analysis on urban densification dynamics and future modes in southeastern Wisconsin, USA. PLoS ONE, 2019, 14(3 e0211964

[121]

Wang R, Feng Y, Wei Y, Tong X, Zhai S, Zhou Y, Wu P. A comparison of proximity and accessibility drivers in simulating dynamic urban growth. Transactions in GIS, 2021, 252): 923-947

[122]

Wei YD, Ewing R. Urban expansion, sprawl and inequality. Landscape and Urban Planning, 2018, 177: 259-265

[123]

Williams WD. Salinisation: A major threat to water resources in the arid and semi-arid regions of the world. Lakes & Reservoirs: Science, Policy and Management for Sustainable Use, 1999, 4(3–4): 85-91

[124]

Yuan Af., Sawaya KE, Loeffelholz BC, Bauer ME. Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 2005, 98(2–3): 317-328

[125]

Zhai R, Zhang C, Li W, Zhang X, Li X. Evaluation of driving forces of land use and land cover change in New England area by a mixed method. ISPRS International Journal of Geo-Information, 2020, 96 350

[126]

Zhang Y, Lu X, Liu B, Wu D. Impacts of urbanization and associated factors on ecosystem services in the Beijing-Tianjin-Hebei urban agglomeration, China: Implications for land use policy. Sustainability, 2018, 10114334

[127]

Zheng Q, He S, Huang L, Zheng X, Pan Y, Shahtahmassebi A, Shen Z, Yu Z, Wang K. Assessing the impacts of Chinese Sustainable Ground Transportation on the dynamics of urban growth: A case study of the Hangzhou Bay Bridge. Sustainability, 2016, 87666

RIGHTS & PERMISSIONS

The Author(s)

PDF

4

Accesses

0

Citation

Detail

Sections
Recommended

/