Artificial Intelligence for Urban Planning in Latin America
DOI:
https://doi.org/10.70577/eag5rs05Keywords:
cluster, data, geospatial, planning, urbanism.Abstract
Urban planning in Latin America faces significant challenges due to rapid urban growth, socioeconomic inequality, and environmental vulnerability. With more than 80% of the population living in urban areas and a projected 90% by 2050, it is essential to optimize resource distribution and improve public services through data-driven approaches. This article proposes the use of clustering algorithms as key tools to identify homogeneous areas within cities, facilitating more equitable and sustainable planning. Using data science techniques such as K-means and DBSCAN, urban indicators are analyzed grouped into three dimensions: infrastructure (access to water, electricity, transportation), socioeconomic (income, education, health), and territorial (land use, green spaces). These methods allow the segmentation of critical areas, such as informal settlements or areas with infrastructure deficits, improving decision-making in public policies. The analysis is based on a synthetic dataset of 5,000 records, generated with realistic statistical distributions based on recent studies. Advanced techniques such as PCA are applied to reduce dimensionality, variable normalization, and validation metrics such as the Calinski-Harabasz index. The results show a bipolar urban structure with two well-defined clusters using K-means, while DBSCAN identifies multiple transition zones and spatial noise, typical of dynamic and informal urban contexts. It is therefore concluded that the combination of clustering, geospatial analysis, and data-driven strategies offers a robust methodology for guiding urban policies in Latin America, promoting equity and resilience to climate change.
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[1] Son, T. H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J. M., & Mehmood, R. (2023). Algorithmic urban planning for smart and sustainable development: Systematic review of the literature. Sustainable Cities and Society, 94, 104562. https://doi.org/10.1016/j.scs.2023.104562
[2] Sanchez, T. W., Shumway, H., Gordner, T., & Lim, T. (2023). The prospects of artificial intelligence in urban planning. International journal of urban sciences, 27(2), 179-194. http://dx.doi.org/10.1080/12265934.2022.2102538
[3] Do Nascimento, A. C. L., Galvani, E., Gobo, J. P. A., & Wollmann, C. A. (2022). Comparison between air temperature and land surface temperature for the city of São Paulo, Brazil. Atmosphere, 13(3), 491. https://doi.org/10.3390/atmos13030491
[4] Junta, U., Newiduom, L., Opuiyo, A., & Browndi, I. (2022). Predictive analysis of urban planning for through the operation of artificial cloud network. International Journal of Science and Advanced Technology, 62(2022), 622-627. https://isi.ac/storage/article-files/6LuYzVzjMZ0mxAUYgoDFjoH1wObqmvqsCFSuK1qz.pdf
[5] Ogas-Mendez, A. F., Pei, X., & Isoda, Y. (2022). Squatting behavior during the COVID-19 pandemic: The case of the informal settlement “Los Hornos” in Buenos Aires. Habitat International, 130, 102688. https://doi.org/10.1016/j.habitatint.2022.102688
[6] Gan, W., Zhao, Z., Wang, Y., Zou, Y., Zhou, S., & Wu, Z. (2024). UDGAN: A new urban design inspiration approach driven by using generative adversarial networks. Journal of Computational Design and Engineering, 11(1), 305-324. http://dx.doi.org/10.1093/jcde/qwae014
[7] Cook, M., & Karvonen, A. (2024). Urban planning and the knowledge politics of the smart city. Urban Studies, 61(2), 370-382. https://journals.sagepub.com/doi/10.1177/00420980231177688
[8] Cheng, W., Chu, Y., Xia, C., Zhang, B., Chen, J., Jia, M., & Wang, W. (2023). UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS. Frontiers in Environmental Science, 11, 1287858. http://dx.doi.org/10.3389/fenvs.2023.1287858
[9] Silva, C., & Vergara-Perucich, F. (2021). Determinants of urban sprawl in Latin America: evidence from Santiago de Chile. SN social sciences, 1(8), 202. https://link.springer.com/article/10.1007/s43545-021-00197-4
[10] Muñoz-Erickson, T. A., Meerow, S., Hobbins, R., Cook, E., Iwaniec, D. M., Berbés-Blázquez, M., ... & Robles-Morua, A. (2021). Beyond bouncing back? Comparing and contesting urban resilience frames in US and Latin American contexts. Landscape and Urban Planning, 214, 104173. https://doi.org/10.1016/j.landurbplan.2021.104173
[11] De Rosa, M., Flores, I., & Morgan, M. (2024). More unequal or not as rich? Revisiting the Latin American exception. World Development, 184, 106737. https://doi.org/10.1016/j.worlddev.2024.106737
[12] Velasco Arevalo, A., & Gerike, R. (2023). Sustainability evaluation methods for public transport with a focus on Latin American cities: A literature review. International Journal of Sustainable Transportation, 17(11), 1236-1253. https://doi.org/10.1080/15568318.2022.2163208
[13] Tiznado-Aitken, I., Vecchio, G., Guzman, L. A., Arellana, J., Humberto, M., Vasconcellos, E., & Muñoz, J. C. (2023). Unequal periurban mobility: Travel patterns, modal choices and urban core dependence in Latin America. Habitat International, 133, 102752. https://doi.org/10.1016/j.habitatint.2023.102752
[14] Ferrari, G., Guzmán-Habinger, J., Chávez, J. L., Werneck, A. O., Silva, D. R., Kovalskys, I., ... & Fisberg, M. (2021). Sociodemographic inequities and active transportation in adults from Latin America: an eight-country observational study. International journal for equity in health, 20(1), 190. https://equityhealthj.biomedcentral.com/articles/10.1186/s12939-021-01524-0
[15] Kundu, B., & Kumar, R. (2024). Enhancing crop resilience to climate change through biochar: a review. International Journal of Environment and Climate Change, 14(6), 170-184 https://doi.org/10.9734/ijecc/2024/v14i64219
[16] Khan, M. I., & Al‐Ghamdi, S. G. (2023). Enhancing Energy System Resilience: Navigating Climate Change and Security Challenges. Sustainable Cities in a Changing Climate: Enhancing Urban Resilience, 227-250. http://dx.doi.org/10.1002/9781394201532.ch14
[17] Asongu, S. A., Diop, S., & Addis, A. K. (2023). Governance, inequality and inclusive education in sub-saharan Africa. In Forum for Social Economics (Vol. 52, No. 1, pp. 43-68). Routledge. http://dx.doi.org/10.2139/ssrn.3734897
[18] Al-Sehrawy, R., Kumar, B., & Watson, R. (2023). The pluralism of digital twins for urban management: Bridging theory and practice. Journal of Urban Management, 12(1), 16-32. https://doi.org/10.1016/j.jum.2023.01.002
[19] Ibrahim, I. M., Radie, A. H., Jacksi, K., Zeebaree, S. R., Shukur, H. M., Rashid, Z. N., ... & Yasin, H. M. (2021). Task scheduling algorithms in cloud computing: A review. Turkish Journal of Computer and Mathematics Education, 12(4), 1041-1053. http://dx.doi.org/10.17762/turcomat.v12i4.612
[20] Ran, X., Xi, Y., Lu, Y., Wang, X., & Lu, Z. (2023). Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artificial Intelligence Review, 56(8), 8219-8264. http://dx.doi.org/10.1007/s10462-022-10366-3
[21] Zhou, Y., Zhang, X., & Ding, F. (2021). Hierarchical estimation approach for RBF-AR models with regression weights based on the increasing data length. IEEE Transactions on Circuits and Systems II: Express Briefs, 68(12), 3597-3601. http://dx.doi.org/10.1109/TCSII.2021.3076112
[22] Shalileh, S., & Mirkin, B. (2022). Community partitioning over feature-rich networks using an extended k-means method. Entropy, 24(5), 626. https://doi.org/10.3390/e24050626
[23] Vera, C., Lucchini, F., Bro, N., Mendoza, M., Löbel, H., Gutiérrez, F., ... & Toro, S. (2022). Learning to cluster urban areas: two competitive approaches and an empirical validation. EPJ Data Science, 11(1), 62. http://dx.doi.org/10.1140/epjds/s13688-022-00374-2
[24] Reyes, A., Mendoza, M., Vera, C., Lucchini, F., Dimter, J., Gutiérrez, F., ... & Reyes, A. (2024). SpatialCluster: A Python library for urban clustering. SoftwareX, 26, 101739. https://doi.org/10.1016/j.softx.2024.101739
[25] Casali, Y., Aydin, N. Y., & Comes, T. (2022). Machine learning for spatial analyses in urban areas: a scoping review. Sustainable cities and society, 85, 104050. https://doi.org/10.1016/j.scs.2022.104050
[26] Nagappan, S. D., & Daud, S. M. (2021). Machine learning predictors for sustainable urban planning. International Journal of Advanced Computer Science and Applications, 12(7). https://thesai.org/Publications/ViewPaper?Volume=12&Issue=7&Code=IJACSA&SerialNo=87
[27] Faraji, A., Homayoon Arya, S., Ghasemi, E., Soleimani, H., & Rahnamayiezekavat, P. (2023). A constructability assessment model based on BIM in urban renewal projects in limited lands. Buildings, 13(10), 2599. https://doi.org/10.3390/buildings13102599
[28] Chaturvedi, V., & de Vries, W. T. (2021). Machine learning algorithms for urban land use planning: A review. Urban Science, 5(3), 68. https://doi.org/10.3390/urbansci5030068
[29] Li, Y., Zhao, Q., & Zhong, C. (2022). GIS and urban data science. Annals of GIS, 28(2), 89-92. https://www.tandfonline.com/doi/pdf/10.1080/19475683.2022.2070969
[30] Sarker, I. H. (2022). Smart City Data Science: Towards data-driven smart cities with open research issues. Internet of Things, 19, 100528. https://doi.org/10.1016/j.iot.2022.100528
[31] Nel, E., MacLachlan, A., Ballinger, O., Cole, H., & Cole, M. (2023). Data-Driven Decision Making in Response to the COVID-19 Pandemic: A City of Cape Town Case Study. Sustainability, 15(3), 1853. http://dx.doi.org/10.3390/su15031853
[32] Schindler, S., & Kanai, J. M. (2021). Getting the territory right: Infrastructure-led development and the re-emergence of spatial planning strategies. Regional Studies, 55(1), 40-51. http://dx.doi.org/10.1080/00343404.2019.1661984
[33] Liu, K., Xu, X., Huang, W., Zhang, R., Kong, L., & Wang, X. (2023). A multi-objective optimization framework for designing urban block forms considering daylight, energy consumption, and photovoltaic energy potential. Building and Environment, 242, 110585. https://doi.org/10.1016/j.buildenv.2023.110585
[34] Martins, M. S., Kalil, R. M. L., & Dalla Rosa, F. (2021). Community participation in the identification of neighbourhood sustainability indicators in Brazil. Habitat International, 113, 102370. https://doi.org/10.1016/j.habitatint.2021.102370
[35] Vazquez, S. A., & Flores, C. C. (2022). The perception of public spaces in Mexico city, a governance approach. Journal of Urban Management, 11(1), 72-81. https://doi.org/10.1016/j.jum.2021.10.002
[36] Zamora-Moncayo, E. C., Herrera, B., Larrieta, J., DuBois, A., & Miguel Esponda, G. (2024). A participatory evaluation of an urban garden project in Ecuador: Exploring factors that impact the recovery of people with severe mental health problems. Qualitative Health Research, 34(14), 1472-1485. https://doi.org/10.1177/10497323241245867
[37] Peng, Z. R., Lu, K. F., Liu, Y., & Zhai, W. (2024). The pathway of urban planning AI: From planning support to plan-making. Journal of Planning Education and Research, 44(4), 2263-2279. http://dx.doi.org/10.1177/0739456X231180568
[38] Maebe, K., Hart, A. F., Marshall, L., Vandamme, P., Vereecken, N. J., Michez, D., & Smagghe, G. (2021). Bumblebee resilience to climate change, through plastic and adaptive responses. Global change biology, 27(18), 4223-4237. http://dx.doi.org/10.1111/gcb.15751
[39] Navarrete-Hernandez, P., Vetro, A., & Concha, P. (2021). Building safer public spaces: Exploring gender difference in the perception of safety in public space through urban design interventions. Landscape and Urban Planning, 214, 104180. https://doi.org/10.1016/j.landurbplan.2021.104180
[40] Kontokosta, C. E. (2021). Urban informatics in the science and practice of planning. Journal of Planning Education and Research, 41(4), 382-395. http://dx.doi.org/10.1177/0739456X18793716
[41] Michalina, D., Mederly, P., Diefenbacher, H., & Held, B. (2021). Sustainable urban development: A review of urban sustainability indicator frameworks. Sustainability, 13(16), 9348. https://doi.org/10.3390/su13169348
[42] Zhu, W., & Chen, J. (2022). The spatial analysis of digital economy and urban development: A case study in Hangzhou, China. Cities, 123, 103563. https://doi.org/10.1016/j.cities.2022.103563
[43] Peng, F. L., Dong, Y. H., Wang, W. X., & Ma, C. X. (2023). The next frontier: data-driven urban underground space planning orienting multiple development concepts. Smart Construction and Sustainable Cities, 1(1), 3. http://dx.doi.org/10.1007/s44268-023-00003-5
[44] Oti, E. U., Olusola, M. O., Eze, F. C., & Enogwe, S. U. (2021). Comprehensive review of K-Means clustering algorithms. criterion, 12, 22-23. http://dx.doi.org/10.31695/IJASRE.2021.34050
[45] Ran, X., Zhou, X., Lei, M., Tepsan, W., & Deng, W. (2021). A novel k-means clustering algorithm with a noise algorithm for capturing urban hotspots. Applied Sciences, 11(23), 11202. https://doi.org/10.3390/app112311202
[46] Mohsin, S. A., & Alfoudi, A. S. (2024). Internet Traffic Classification Model Based on A-DBSCAN Algorithm. International Journal of Intelligent Engineering & Systems, 17(5). https://oaji.net/articles/2023/3603-1723963830.pdf
[47] Tu, X., Fu, C., Huang, A., Chen, H., & Ding, X. (2022). DBSCAN spatial clustering analysis of urban “Production–Living–Ecological” space based on POI data: a case study of central urban Wuhan, China. International Journal of Environmental Research and Public Health, 19(9), 5153. https://doi.org/10.3390/ijerph19095153
[48] Jiang, Y., Liu, Q., Zhao, S., Zhang, T., Fan, X., Zhong, R. Y., & Huang, G. Q. (2024). Heterogeneous intensity-based DBSCAN (iDBSCAN) model for urban attention distribution in digital twin cities. Digital Engineering, 2, 100014. https://doi.org/10.1016/j.dte.2024.100014
[49] Cetin, Z., & Yastikli, N. (2025). Automatic Detection of Urban Trees from LiDAR Data Using DBSCAN and Mean Shift Clustering Methods in Fatih, Istanbul. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 95-102. http://dx.doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-95-2025
[50] Caudillo-Cos, C. A., Montejano-Escamilla, J. A., Tapia-McClung, R., Ávila-Jiménez, F. G., & Barrera-Alarcón, I. G. (2024). Defining urban boundaries through DBSCAN and Shannon's entropy: The case of the Mexican National Urban System. Cities, 149, 104969. https://doi.org/10.1016/j.cities.2024.104969
[51] Masum, M. H., Pal, S. K., Akhie, A. A., Ruva, I. J., Akter, N., & Nath, S. (2021). Spatiotemporal monitoring and assessment of noise pollution in an urban setting. Environmental Challenges, 5, 100218. https://doi.org/10.1016/j.envc.2021.100218
[52] Guida, C., Carpentieri, G., & Masoumi, H. (2022). Measuring spatial accessibility to urban services for older adults: an application to healthcare facilities in Milan. European transport research review, 14(1), 23. https://etrr.springeropen.com/articles/10.1186/s12544-022-00544-3#citeas:~:text=DOI-,https%3A//doi.org/10.1186/s12544%2D022%2D00544%2D3,-Share%20this%20article
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