Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data
| Autor: | Irene Meta, Fernando M. Cucchietti, Patricio Reyes, Feliu Serra-Buriel, Eduardo Graells-Garrido |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa, Barcelona Supercomputing Center |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science media_common.quotation_subject Population 0211 other engineering and technologies 02 engineering and technology Social group Computer Science - Computers and Society 020204 information systems Computers and Society (cs.CY) 11. Sustainability 0202 electrical engineering electronic engineering information engineering Floating population Regional science education Subdivision media_common Urban mobility Social and Information Networks (cs.SI) education.field_of_study Mobile computing business.industry Sustainable urban development 1. No poverty Spatial analysis Mobile phone data 021107 urban & regional planning Computer Science - Social and Information Networks World population Informàtica mòbil Mobile phone Sustainability Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils [Àrees temàtiques de la UPC] Desenvolupament urbà sostenible business Diversity (politics) |
| Zdroj: | Companion Proceedings of the Web Conference 2020 WWW (Companion Volume) UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| DOI: | 10.1145/3366424.3384370 |
| Popis: | Urban population grows constantly. By 2050 two thirds of the world population will reside in urban areas. This growth is faster and more complex than the ability of cities to measure and plan for their sustainability. To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions: areas with over- and under-representation of specific population groups, named hot and cold spots respectively. Using aggregated mobile phone data, we apply this methodology to the city of Barcelona to assess the mobility of three groups of people: women, elders, and tourists. We find that, within the three groups, cold spots have a lower diversity of amenities and services than hot spots. Also, cold spots of women and tourists tend to have lower population income. These insights apply to the floating population of Barcelona, thus augmenting the scope of how inclusiveness can be analyzed in the city. 10 pages, 10 figures. To be presented at the Data Science for Social Good workshop at The Web Conference 2020 |
| Databáze: | OpenAIRE |
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