La ciudad no es un árbol estático: comprender las áreas urbanas a través de la óptica de los datos de comportamiento en tiempo real

• Esteban Moro ^[1]
  1. [1] Universidad Carlos III de Madrid

     Universidad Carlos III de Madrid

     Madrid, España

     
• Localización: ZARCH: Journal of interdisciplinary studies in Architecture and Urbanism, ISSN 2341-0531, Nº. 19, 2022, págs. 28-39
• Idioma: español
• Títulos paralelos:
  • A city is not a static tree: understanding urban areas through the lens of real-time behavioral data
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Las ciudades son el principal terreno sobre el que se desarrollan —y se desarrollarán— nuestra sociedad y cultura. Frente a la concepción tradicional de las ciudades como espacio físico, en torno a nuestros barrios, el uso reciente de grandes conjuntos de datos de movilidad ha permitido estudiar el comportamiento humano a escalas espaciales y temporales sin precedentes, más allá de nuestros espacios residenciales. Este artículo muestra cómo es posible utilizar estos conjuntos de datos para investigar el papel que desempeña el comportamiento humano en problemas urbanos tradicionales como la segregación, la salud pública o las epidemias. Además de medir o monitorizar estos problemas de forma exhaustiva, el análisis de estos grandes conjuntos de datos mediante técnicas de aprendizaje automático o detección de causalidad permite desvelar raíces conductuales detrás de esos problemas. Como resultado, solo incorporando datos de comportamiento en tiempo real podemos diseñar políticas o intervenciones más eficientes que contribuyan a mejorar estos problemas sociales críticos en nuestras áreas urbanas.

  • English

    Cities are the main ground on which our society and culture develop today and will evolve in the future. Against the traditional understanding of cities as physical spaces mostly around our neighborhoods, recent use of large-scale mobility datasets has enabled the study of our behavior at unprecedented spatial and temporal scales, much beyond our static residential spaces. Here we show how it is possible to use these datasets to investigate the role that human behavior plays in traditional urban problems like segregation, public health, or epidemics. Apart from measuring or monitoring such problems in a more comprehensive way, the analysis of those large datasets using modern machine learning techniques or causality detection permits to unveil of the behavioral roots behind them. As a result, only by incorporating real-time behavioral data can we design more efficient policies or interventions to improve such critical societal issues in our urban areas.

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