GeoAI y complejidad urbana: revisión sistemática según las directrices PRISMA

  • Guillermo Geovanny Guzmán Chávez
  • Alexandra Patricia Ortiz Almeida
Keywords: Urban computing, geospatial intelligenc, data-driven planning

Abstract

This study examines the role of GeoAI in understanding urban complexity, with the aim of identifying its main contributions to contemporary territorial planning. The research was conducted through a systematic literature review based on PRISMA guidelines, employing a structured search strategy using Boolean operators across academic databases. From an initial set of records, inclusion and exclusion criteria were applied, resulting in the selection of eighteen scientific articles relevant to the analysis. The findings demonstrate that GeoAI has the capacity to transform traditional urban planning processes through the integration of multi-source data, automated territorial analysis, and the modeling of complex spatial dynamics. 

References

Referencias

Aidaoui, A., Dechaicha, A., Alkama, D., Menai, I., y Salah Salah, H. (2024). Mapping tomorrow’s cities: GeoAI strategies for sustainable urban planning and land use optimization. Journal of Contemporary Urban Affairs, 8(1), 158–176. https://doi.org/10.25034/ijcua.2024.v8n1-9

Chen, X., Li, Y., Li, X., y Huang, Z. (2026).Nonlinear and congestion-dependent effects of transport and built-environment factors on urban CO₂ emissions: A GeoAI-based analysis of 50 Chinese cities. Buildings, 16(2), 297. https://doi.org/10.3390/buildings16020297

De La Cruz, M., Martínez-Cuevas, S., García-Aranda, C., y Morillo, M. C. (2025). GeoAI-driven building facade classification for urban pattern analysis: A case study in Murcia, Spain. Journal of Urban Management. Advance online publication. https://doi.org/10.1016/j.jum.2025.11.005
Published
2026-08-10
How to Cite
Guzmán Chávez, G. G., & Ortiz Almeida, A. P. (2026). GeoAI y complejidad urbana: revisión sistemática según las directrices PRISMA. Cuadernos Del Centro De Estudios De Diseño Y Comunicación, (330). https://doi.org/10.18682/cdc.vi330.14110