Probabilidad de Crecimiento de la Mancha Urbana de Toluca con Autómatas Celulares
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ago 22, 2024
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https://doi.org/10.30878/ces.v32n0a16
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Eduardo Jiménez López
http://orcid.org/0000-0002-1883-3890
Edel Gilberto Cadena Vargas
http://orcid.org/0000-0002-3131-9597
http://orcid.org/0000-0002-1883-3890
Edel Gilberto Cadena Vargas
http://orcid.org/0000-0002-3131-9597
Resumen
El objetivo de este trabajo es unir Autómatas Celulares con Filtro Inverso a una técnica llamada Regresión Geográficamente Ponderada. Esta técnica matemática determina potenciales de transición espacialmente diferenciados que es un insumo que se adhiere al modelo de Autómatas Celulares. Determina pesos o influencia de los factores clave que inciden en la expansión de la ciudad a escala de pixel en imágenes satelitales procesadas. Se encuentran reglas de vecindad más realistas que registran un buen nivel de bondad de ajuste a la simulación de la expansión de la mancha urbana. Todo este modelo de expansión de la mancha urbana es aplicado al Área Metropolitana de Toluca.
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JIMÉNEZ LÓPEZ, Eduardo; CADENA VARGAS, Edel Gilberto.
Probabilidad de Crecimiento de la Mancha Urbana de Toluca con Autómatas Celulares.
CIENCIA ergo-sum, [S.l.], v. 32, ago. 2024.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/21517>. Fecha de acceso: 25 jun. 2025
doi: https://doi.org/10.30878/ces.v32n0a16.
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Ciencias exactas y aplicadas
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
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Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2017). Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. International Journal of Applied Earth Observation and Geoinformation, 59, 65-78.
Almeida, C. D., Gleriani, J. M., Castejon, E. F., & Soares Filho, B. S. (2008). Using neural networks and cellular automata for modelling intra urban land use dynamics. International Journal of Geographical Information Science, 22(9), 943-963.
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Du, S., Wang, Q., & Guo, L. (2014). Spatially varying relationships between land-cover change and driving factors at multiple sampling scales. Journal of Environmental Management, 137, 101-110.
Feng, Y., Liu, Y., & Tong, X. (2018). Comparison of metaheuristic cellular automata models: A case study of dynamic land use simulation in the Yangtze River Delta. Computers, Environment and Urban Systems, 70, 138-150.
Feng, Y., & Tong, X. (2018). Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules. GIScience & Remote Sensing, 55(5), 678-698.
Feng, Y., & Tong, X. (2020). A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods. International Journal of Geographical Information Science, 34(1), 74-97.
Duque, J. C., Velásquez, H., & Agudelo, J. (2011). Infraestructura pública y precios de vivienda: una aplicación de regresión geográficamente ponderada en el contexto de precios hedónicos. Ecos de Economía, 15(33), 95-122.
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Guanglong, D., Erqi, X., & Hongqi, Z. (2017). Urban expansion and spatiotemporal relationships with driving factors revealed by geographically weighted logistic regression. Journal of Resources and Ecology, 8(3), 277-286.
Gutiérrez-Puebla, J., García-Palomares, J. C., & Daniel-Cardozo, O. (2012, September). Regresión Geográficamente Ponderada (GWR) y estimación de la demanda de las estaciones del Metro de Madrid. In XV Congreso Nacional de Tecnologías de la Información Geográfica (pp. 1-13).
Harris, R., Singleton, A., Grose, D., Brunsdon, C., & Longley, P. (2010). Grid‐enabling geographically weighted regression: a case study of participation in higher education in England. Transactions in GIS, 14(1), 43-61.
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Kamusoko, C., Aniya, M., Adi, B., & Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435-447.
Leao, S., Bishop, I., & Evans, D. (2004). Simulating urban growth in a developing nation’s region using a cellular automata-based model. Journal of urban planning and development, 130(3), 145-158.
Leiva, R. A., & Herrera, M. (1999). Generalización de la distancia de Mahalanobis para el análisis discriminante lineal en poblaciones con matrices de covarianza desiguales. Revista de la Sociedad Argentina de Estadística, 3.
Li, X., & Yeh, A. G. O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4), 323-343.
Li, G., Sun, S., & Fang, C. (2018). The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis. Landscape and Urban Planning, 174, 63-77.
Liang, X., Liu, X., Li, D., Zhao, H., & Chen, G. (2018). Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model. International Journal of Geographical Information Science, 32(11), 2294-2316.
Liu, X., Li, X., Shi, X., Zhang, X., & Chen, Y. (2010). Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. International Journal of Geographical Information Science, 24(5), 783-802.
Liu, D., Clarke, K. C., & Chen, N. (2020). Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area. Computers, Environment and Urban Systems, 84, 101545.
Lu, B., Brunsdon, C., Charlton, M., & Harris, P. (2017). Geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 31(5), 982-998.
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Maimaitijiang, M., Ghulam, A., Sandoval, J. O., & Maimaitiyiming, M. (2015). Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data. International Journal of Applied Earth Observation and Geoinformation, 35, 161-174.
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CC BY-NC-ND
Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2017). Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. International Journal of Applied Earth Observation and Geoinformation, 59, 65-78.
Almeida, C. D., Gleriani, J. M., Castejon, E. F., & Soares Filho, B. S. (2008). Using neural networks and cellular automata for modelling intra urban land use dynamics. International Journal of Geographical Information Science, 22(9), 943-963.
Argis (03 de agosto de 2022). Introducción a las imágenes multiespectrales multidimensionales. Usar datos ráster e imágenes en ArcGIS Pro. https://learn.arcgis.com/es/paths/using-raster-data-and-imagery-in-arcgis-pro/
Batty, M. (2012). Building a science of cities. Cities, 29, S9-S16.
Berberoğlu, S., Akın, A., & Clarke, K. C. (2016). Cellular automata modeling approaches to forecast urban growth for adana, Turkey: A comparative approach. Landscape and urban planning, 153, 11-27.
Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298.
Brunsdon, C., Fotheringham, A. S., & Charlton, M. (2002). Geographically weighted summary statistics—a framework for localised exploratory data analysis. Computers, Environment and Urban Systems, 26(6), 501-524.
Cao, M., Bennett, S. J., Shen, Q., & Xu, R. (2016). A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion. International Journal of Geographical Information Science, 30(10), 1961-1979.
Cao, Y., Zhang, X., Fu, Y., Lu, Z., & Shen, X. (2020). Urban spatial growth modeling using logistic regression and cellular automata: A case study of Hangzhou. Ecological Indicators, 113, 106200.
Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and planning B: Planning and design, 24(2), 247-261.
Dietzel, C., & Clarke, K. C. (2007). Toward optimal calibration of the SLEUTH land use change model. Transactions in GIS, 11(1), 29-45.
Du, S., Wang, Q., & Guo, L. (2014). Spatially varying relationships between land-cover change and driving factors at multiple sampling scales. Journal of Environmental Management, 137, 101-110.
Feng, Y., Liu, Y., & Tong, X. (2018). Comparison of metaheuristic cellular automata models: A case study of dynamic land use simulation in the Yangtze River Delta. Computers, Environment and Urban Systems, 70, 138-150.
Feng, Y., & Tong, X. (2018). Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules. GIScience & Remote Sensing, 55(5), 678-698.
Feng, Y., & Tong, X. (2020). A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods. International Journal of Geographical Information Science, 34(1), 74-97.
Duque, J. C., Velásquez, H., & Agudelo, J. (2011). Infraestructura pública y precios de vivienda: una aplicación de regresión geográficamente ponderada en el contexto de precios hedónicos. Ecos de Economía, 15(33), 95-122.
Gao, C., Feng, Y., Tong, X., Lei, Z., Chen, S., & Zhai, S. (2020). Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR. Computers, Environment and Urban Systems, 81, 101459.
Garrocho, C., Jiménez, E., & Chávez-Soto, T. (2020). Expansión de la ciudad: un instrumento de simulación de escenarios para los sectores público y privado. La situación demográfica de México 2020, 2(2).
Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2013). GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models. arXiv preprint arXiv:1306.0413.
Gounaridis, D., Chorianopoulos, I., Symeonakis, E., & Koukoulas, S. (2019). A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales. Science of the Total Environment, 646, 320-335.
Grün, D. (2020). Revealing dynamics of gene expression variability in cell state space. Nature methods, 17(1), 45-49.
Guanglong, D., Erqi, X., & Hongqi, Z. (2017). Urban expansion and spatiotemporal relationships with driving factors revealed by geographically weighted logistic regression. Journal of Resources and Ecology, 8(3), 277-286.
Gutiérrez-Puebla, J., García-Palomares, J. C., & Daniel-Cardozo, O. (2012, September). Regresión Geográficamente Ponderada (GWR) y estimación de la demanda de las estaciones del Metro de Madrid. In XV Congreso Nacional de Tecnologías de la Información Geográfica (pp. 1-13).
Harris, R., Singleton, A., Grose, D., Brunsdon, C., & Longley, P. (2010). Grid‐enabling geographically weighted regression: a case study of participation in higher education in England. Transactions in GIS, 14(1), 43-61.
Hernández, V. H., Pansza, E. M., & Daniel, D. Q. (2018). Geografía del robo a casa habitación en Ciudad Juárez, Chihuahua (2007-2014). Investigaciones Geográficas (Mx), (96), 01-15.
INEGI (2021), Dirección Regional Centro Sur / Coordinación Estatal México COMUNICADO DE PRENSA NÚM. 55/21, COMUNICADO DE PRENSA NÚM. 55/21 26 DE ENERO DE 2021 TOLUCA, ESTADO DE MÉXICO
Jafari, M., Majedi, H., Monavari, S. M., Alesheikh, A. A., & Kheirkhah Zarkesh, M. (2016). Dynamic simulation of urban expansion based on cellular automata and logistic regression model: Case study of the Hyrcanian Region of Iran. Sustainability, 8(8), 810.
Jardón, E., Jiménez, E., & Romero, M. (2018, December). Spatial Markov chains implemented in GIS. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 361-367). IEEE.
Jiménez López, E. (2019). Cadenas de Markov espaciales para simular el crecimiento del Área Metropolitana de Toluca, 2017-2031. Economía, sociedad y territorio, 19(60), 109-140.
Jiménez-López, E. (2022, June). Inverse Filter in the Growth of Urban Sprawl with Cellular Automata Model. In Complex Systems and Their Applications: Second International Conference (EDIESCA 2021) (pp. 231-247). Cham: Springer International Publishing.
Kamusoko, C., Aniya, M., Adi, B., & Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435-447.
Leao, S., Bishop, I., & Evans, D. (2004). Simulating urban growth in a developing nation’s region using a cellular automata-based model. Journal of urban planning and development, 130(3), 145-158.
Leiva, R. A., & Herrera, M. (1999). Generalización de la distancia de Mahalanobis para el análisis discriminante lineal en poblaciones con matrices de covarianza desiguales. Revista de la Sociedad Argentina de Estadística, 3.
Li, X., & Yeh, A. G. O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4), 323-343.
Li, G., Sun, S., & Fang, C. (2018). The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis. Landscape and Urban Planning, 174, 63-77.
Liang, X., Liu, X., Li, D., Zhao, H., & Chen, G. (2018). Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model. International Journal of Geographical Information Science, 32(11), 2294-2316.
Liu, X., Li, X., Shi, X., Zhang, X., & Chen, Y. (2010). Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. International Journal of Geographical Information Science, 24(5), 783-802.
Liu, D., Clarke, K. C., & Chen, N. (2020). Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area. Computers, Environment and Urban Systems, 84, 101545.
Lu, B., Brunsdon, C., Charlton, M., & Harris, P. (2017). Geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 31(5), 982-998.
Mahiny, A. S., & Clarke, K. C. (2012). Guiding SLEUTH land-use/land-cover change modeling using multicriteria evaluation: towards dynamic sustainable land-use planning. Environment and planning B: planning and design, 39(5), 925-944.
Maimaitijiang, M., Ghulam, A., Sandoval, J. O., & Maimaitiyiming, M. (2015). Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data. International Journal of Applied Earth Observation and Geoinformation, 35, 161-174.
Martellozzo, F., Amato, F., Murgante, B., & Clarke, K. C. (2018). Modelling the impact of urban growth on agriculture and natural land in Italy to 2030. Applied Geography, 91, 156-167.
Martín-Rodríguez, Ó., Fernández-Molina, J. C., Montero-Alonso, M. Á., & González-Gómez, F. (2015). The main components of satisfaction with e-learning. Technology, Pedagogy and Education, 24(2), 267-277.
Mayfield, H. J., Lowry, J. H., Watson, C. H., Kama, M., Nilles, E. J., & Lau, C. L. (2018). Use of geographically weighted logistic regression to quantify spatial variation in theenvironmental and sociodemographic drivers of leptospirosis in Fiji: A modelling study. The Lancet Planetary Health, 2(5), e223–e232.
Mennis, J. (2006). Mapping the results of geographically weighted regression. The Cartographic Journal, 43(2), 171-179.
Mirbagheri, B., & Alimohammadi, A. (2017). Improving urban cellular automata performance by integrating global and geographically weighted logistic regression models. Transactions in GIS, 21(6), 1280-1297.
Mohamed, A., & Worku, H. (2020). Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Climate, 31, 100545.
Molinero-Parejo, R., Aguilera-Benavente, F., & Gómez-Delgado, M. (2021). Regresión Logística Geográficamente Ponderada para identificar los factores explicativos de la distribución de usos de suelo en escenarios futuros de crecimiento urbano. Boletín de la Asociación de Geógrafos Españoles, (88).
Mustafa, A., Heppenstall, A., Omrani, H., Saadi, I., Cools, M., & Teller, J. (2018). Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm. Computers, Environment and Urban Systems, 67, 147-156.
Newland, C. P., Maier, H. R., Zecchin, A. C., Newman, J. P., & van Delden, H. (2018). Multi-objective optimisation framework for calibration of Cellular Automata land-use models. Environmental modelling & software, 100, 175-200.
Ntinas, V. G., Moutafis, B. E., Trunfio, G. A., & Sirakoulis, G. C. (2017). Parallel fuzzy cellular automata for data-driven simulation of wildfire spreading. Journal of computational science, 21, 469-485.
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