Artificial neural networks in the application of the growth of the urban sprawl

Eduardo Jiménez-López; Luis Antonio López-Rivera

doi:10.29057/icbi.v11i21.10565

Eduardo Jiménez-López El Colegio Mexiquense A.C. https://orcid.org/0000-0002-1883-3890
Luis Antonio López-Rivera Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-0204-0500

DOI: https://doi.org/10.29057/icbi.v11i21.10565

Palabras clave: Redes Neuronales Artificiales, Autómatas Celulares, Crecimiento urbano vertical

Resumen

El objetivo de este trabajo es el uso de redes neuronales artificiales y autómatas celulares que apoyen las decisiones de planeación urbana en México. Proponemos modelo automatizado que predice el crecimiento urbano vertical, utilizando factores socioeconómicos y geográficos. Se presenta un modelo multidisciplinario que maneja redes neuronales artificiales, autómatas celulares, métodos de análisis espacial, procesamiento de imágenes que permiten proyectar y simular diferentes escenarios de crecimiento urbano. Todo esto está integrado en QGIS a través del lenguaje de programación Python. El modelo se prueba en ciudades mexicanas como Ciudad de México, Guadalajara y Monterrey durante los años 2015-2020. Se obtuvieron rangos de confiabilidad de 72% a 76%, validados por: i) el número promedio de rascacielos proyectados, ii) Posición usando el índice Kappa, y iii) Valor en la imagen usando el índice Jaccard. Con esto proponemos una técnica que permite tomar decisiones mejor informadas para la planificación urbana y anticipar nuevas necesidades de infraestructura, proyecciones y normativas.

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Abhishek N., Jenamani M., Mahanty B., (2017) Urban growth in Indian cities: Are the driving forces really changing?. Habitat International, 69,48-57.

Adamatzky A., (2018). Cellular Automata: A Volume in the Encyclopedia of Complexity and Systems Science. Springer Publishing Company, Incorporated, EUA.

Agatonovic-Kustrin S., Beresford R., (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of pharmaceutical and biomedical analysis, 22(5), 717-727.

Agyemang F., Silva E., Anokye P., (2018). Towards sustainable urban development: the social acceptability of high-rise buildings in a Ghanaian city. GeoJournal, 83(6), 1317-1329.

Almeida C., Gleriani J., Castejon E., Soares‐Filho B., (2008). Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics. International Journal of Geographical Information Science, 22(9), 943-963.

Anderson T., Darling D., (1954). A test of goodness of fit. Journal of the American statistical association, 49(268), 765-769.

Bhatti S., Tripathi N., (2014). Built-up area extraction using Landsat 8 OLI imagery. GIScience & remote sensing, 51(4), 445-467.

Bose B., (1994). Expert system, fuzzy logic, and neural network applications in power electronics and motion control. Proceedings of the IEEE, 82(8), 1303-1323.

Bouchard M., Jousselme A., Doré P., (2013). A proof for the positive definiteness of the Jaccard index matrix. International Journal of Approximate Reasoning, 54(5), 615-626.

Burton-Johnson A., Black M., Fretwell P., Kaluza-Gilbert J., (2016). An automated methodology for differentiating rock from snow, clous EMPORIS (2020). Locate buildings in every country around the world, 13 December 2020. https://www.emporis.com/buildingsds and sea in Antarctica from Landsat 8 imagery: a new rock outcrop map and area estimation for the entire Antarctic continent. The Cryosphere, 10(4), 1665-1677.

CTBUH (2020). CTBUH Height criteria, 26 June 2020. https://www.ctbuh.org

De Sa J., (2012). Pattern recognition: concepts, methods and applications, Springer Science & Business Media, Germany.

Duque J., Lozano N., Patino J., Restrepo P., Velasquez W., (2019). Spatio-Temporal Dynamics of Urban Growth in Latin American Cities: An Analysis Using Nighttime Lights Imagery. World Bank Policy Research Working Paper, (8702).

ESRI (2016). Fundamentals of panchromatic sharpening, 21 june 2020. https://desktop.arcgis.com/es/arcmap/10.3/manage-data/raster-and-images/fundamentals-of-panchromatic-sharpening.htm.

EMPORIS (2020). Locate buildings in every country around the world, 13 December 2020. https://www.emporis.com/buildings

Felix A., (2015). Impactos del crecimiento vertical en la expansión de la zona conurbada de Querétaro, Universidad Autónoma de Nuevo León.

Gómez E., Obregón N., Rocha D., (2013). Cloud segmentation methods applied to satellite images. Tecnura, 17(36), 96-110.

Haykin S., Network N., (2004). A comprehensive foundation. Neural networks, 2, 41.

He Q., Liu Y., Zeng C., Chaohui Y., Tan R., (2017). Simultaneously simulate vertical and horizontal expansions of a future urban landscape: A case study in Wuhan, Central China. International Journal of Geographical Information Science, 31(10), 1907-1928.

Huang Z., Lu Y., Wong N., Poh C., (2019). The true cost of “greening” a building: Life cycle cost analysis of vertical greenery systems (VGS) in tropical climate. Journal of Cleaner Production, 228, 437-454.

INAFED (2015). Socioeconomic data by municipality, www.inafed.gob.mx, 18 June 2019.

INEGI (2015a). Economically Active popullation, sc.inegi.org.mx/cobdem, 17 June 2019.

INEGI (2015b). Housing, 17 June 2019. https://www.inegi.org.mx/programas/intercensal/0A2015/default.html{#}Tabulados{%}0A.

INEGI (2015c). About INEGI, 17 December 2020. https://www.inegi.org.mx/default.html.

INEGI (2019). Mapas geográficos, 15 December 2020. https://www.inegi.org.mx/app/mapas/.

INEGI (2020). Edafología, 7 November 2020. https://www.inegi.org.mx/temas/edafologia/.

Irons J., Dwyer J., Barsi J., (2012). The next Landsat satellite: The Landsat data continuity mission. Remote Sensing of Environment, 122, 11-21.

Jiménez, E., Chávez, T., Garrocho, C. (2018). Modelando la expansión urbana con autómatas celulares: aplicación de la Estación de Inteligencia Territorial (Christaller). Geografía y Sistemas de Información Geográfica, Geosig, 12, 1-26.

Jiménez 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 E., (2022). 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.

Kaviari F., Mesgari M., Seidi E., Motieyan H., (2019). Simulation of urban growth using agent-based modeling and game theory with different temporal resolutions. Cities, 95, 102387.

Kotkar S., Jadhav B., (2015). Analysis of various change detection techniques using satellite images. In 2015 International Conference on Information Processing (ICIP), IEEE, 664-668.

Koziatek O., Dragićević S., (2019). A local and regional spatial index for measuring three-dimensional urban compactness growth. Environment and Planning B: Urban Analytics and City Science, 46(1), 143-164.

Kristollari V., Karathanassi V., (2020). Artificial neural networks for cloud masking of Sentinel-2 Ocean images with noise and sunglint. International Journal of Remote Sensing, 41(11), 4102-4135.

Lampe O., Hauser H., (2011). Interactive visualization of streaming data with kernel density estimation. In 2011 IEEE pacific visualization symposium, 171-178.

Massey F., (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association, 46(253), 68-78.

Montgomery D., (2017). Design and analysis of experiments. John wiley & sons, EUA.

Mualam N., Salinger E., Max D., (2019). Increasing the urban mix through vertical allocations: Public floorspace in mixed use development. Cities, 87, 131-141.

Mustafa A., Cools M., Saadi I., Teller J., (2017). Coupling agent-based, cellular automata and logistic regression into a hybrid urban expansion model (HUEM). Land Use Policy, 69, 529-540.

Openshaw S., Taylor P., (1984). The modifiable unit areal problem, Norwich: Geo books, England.

Ou C., Yang J., Du Z., Li P., Zhu D., (2018). Simulating Multiple Land Use Changes by Incorporating Deep Belief Network into Cellular Automata: A Case Study in BEIJING-TIANJINHEBEI Region, China. China. Lund, 12-15.

Palma, J., Morales, R., (2008). Inteligencia artificial. Técnicas, métodos y aplicaciones. Mc Graw

Parker M., (2014). Skyscrapers: The city and the megacity. Theory, culture & society, 31(7-8) 267-271.

Rajasekaran S., Pai G.V., (2003). Neural networks, fuzzy logic and genetic algorithm: synthesis and applications. PHI Learning Pvt. Ltd, New Delhi.

Richardson D., Van Oosterom P., (2002). Advances in Spatial Data Handling: 10th International Symposium on Spatial Data Handling. Springer Science & Business Media.

Sabater, N., Ruiz-Verdú, A., Delegido, J., Fernández-Beltrán, R., Latorre-Carmona, P., Pla, F., ... & Moreno, J. (2016). Development of advanced products for the SEOSAT/Ingenio mission. Revista de Teledetección, (47), 23-40.

Shim J., Park S., Park E., (2004). Public space planning of mixed–use high–rise buildings–focusing on the use and impact of deck structure in an urban development in Seoul. Tall buildings in historical cities–culture and technology for sustainable cities, Seoul, South Korea, 13, 764-771.

Silva E., (2004). The DNA of our regions: artificial intelligence in regional planning. Futures, 36(10). 1077-1094.

SKYCPRAPER (2020), Metropolitian Areas, 12 October 2020. https://skyscraperpage.com/database/metro/, skyscraperpage.com/database/metro/

Tallarida R., Murray R., (1987). Chi-square test. In Manual of pharmacologic calculations (pp. 140-142). Springer, EUA.

Tobler W., (1970). A computer movie simulating urban growth in the Detroit region. Economic geography, 46(sup1).

USGS (2020), EarthExplorer, earthexplorer.usgs.gov, 04 April 2021.

Wang L., Zeng Y., Chen T., (2015). Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Systems with Applications, 42(2) 855-863.

Wu N., Silva E., (2010). Artificial intelligence solutions for urban land dynamics: a review. Journal of Planning Literature, 24(3), 246-265.

Zhang W., Li W., Zhang C., Hanink D., Liu Y., Zhai R., (2018). Analyzing horizontal and vertical urban expansions in three East Asian megacities with the SS-coMCRF model. Landscape and urban planning, 177, 114-127.

Zhao C., Jensen J., Zhan B., (2017). A comparison of urban growth and their influencing factors of two border cities: Laredo in the US and Nuevo Laredo in Mexico. Applied geography, 79, 223-234.