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
Se busca unir autómatas celulares con filtro inverso a una técnica llamada regresión geográficamente ponderada. Esta técnica matemática determina “potenciales de transición espacialmente diferenciados”, un insumo que se adhiere al modelo de autómatas celulares. Identifica pesos o influencia de los factores clave que inciden en la expansión de la ciudad en 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.
Article Details
Como citar
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: 20 jul. 2026
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
Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-
temporal urban growth trends using cellular automata models: A review. International Journal of
Applied Earth Observation and Geoinformation, 52, 380-389.
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.
ArcGis (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-imageryin-
arcgis-pro/
Batty, M. (2012). Building a science of cities. Cities, 29(1), S9-S16.
Berberoglu, 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.
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.
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.
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.
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.
Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-
temporal urban growth trends using cellular automata models: A review. International Journal of
Applied Earth Observation and Geoinformation, 52, 380-389.
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.
ArcGis (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-imageryin-
arcgis-pro/
Batty, M. (2012). Building a science of cities. Cities, 29(1), S9-S16.
Berberoglu, 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.
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.
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.
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.
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.
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, 2(2), 195-219.
Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2013). GWmodel: an R package for exploring
spatial heterogeneity using geographically weighted models. Journal of Statistical Software, 63(17),
1-50. https://doi.org/10.18637/jss.v063.i17
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. 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-Hernández, V., Pansza, E. M., & Daniel, D. Q. (2018). Geografía del robo a casa habitación
en Ciudad Juárez, Chihuahua (2007-2014). Investigaciones Geográficas, 96, 1-15. https://doi.
org/10.14350/rig.59545
INEGI (Instituto Nacional de Estadística y Geografía). (2021). En el Estado México somos 16 992 418
habitantes: Censo de Población y Vivienda 2020. Comunicado de prensa Núm. 55/21, 1-3. Toluca.
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.
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data-driven simulation of wildfire spreading. Journal of Computational Science, 21, 469-485.
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groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). Journal of Hydrology, 405
(1-2), 123-136.
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a latent class regression model. Transactions in GIS, 22(3), 789-805.
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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.
ArcGis (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-imageryin-
arcgis-pro/
Batty, M. (2012). Building a science of cities. Cities, 29(1), S9-S16.
Berberoglu, 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.
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.
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.
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.
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.
Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-
temporal urban growth trends using cellular automata models: A review. International Journal of
Applied Earth Observation and Geoinformation, 52, 380-389.
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.
ArcGis (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-imageryin-
arcgis-pro/
Batty, M. (2012). Building a science of cities. Cities, 29(1), S9-S16.
Berberoglu, 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.
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.
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.
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.
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.
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, 2(2), 195-219.
Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2013). GWmodel: an R package for exploring
spatial heterogeneity using geographically weighted models. Journal of Statistical Software, 63(17),
1-50. https://doi.org/10.18637/jss.v063.i17
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. 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-Hernández, V., Pansza, E. M., & Daniel, D. Q. (2018). Geografía del robo a casa habitación
en Ciudad Juárez, Chihuahua (2007-2014). Investigaciones Geográficas, 96, 1-15. https://doi.
org/10.14350/rig.59545
INEGI (Instituto Nacional de Estadística y Geografía). (2021). En el Estado México somos 16 992 418
habitantes: Censo de Población y Vivienda 2020. Comunicado de prensa Núm. 55/21, 1-3. Toluca.
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.
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.
Ozdemir, A. (2011). Using a binary logistic regression method and GIS for evaluating and mapping the
groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). Journal of Hydrology, 405
(1-2), 123-136.
Park, S., Lee, J. H., & Clarke, K. C. (2018). Capturing the heterogeneity of urban growth in South Korea using
a latent class regression model. Transactions in GIS, 22(3), 789-805.
Pellegrini, P. A., & Fotheringham, A. S. (2002). Modelling spatial choice: a review and synthesis in a migration
context. Progress in Human Geography, 26(4), 487-510.
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