Person:
Moya Gómez, Borja

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First Name
Borja
Last Name
Moya Gómez
URI
https://hdl.handle.net/20.500.14352/80746
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Geografía e Historia
Department
Geografía
Area
Geografía Humana
Identifiers
UCM identifierORCIDScopus Author IDWeb of Science ResearcherIDDialnet IDGoogle Scholar ID

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2020 - 202411
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    Cómo aplicar Big Data en la planificación del transporte: El uso de datos de GPS en el análisis de la movilidad urbana
    (2020) Gutiérrez Puebla, Javier; Benítez, Carolina; García Palomares, Juan Carlos; Romanillos Arroyo, Gustavo; Rubinstein da Silva, Elías; Leaño, Juan Manuel; Ribeiro, Karisa Maia; Scholl, Lynn; Moya Gómez, Borja; Condeço Melhorado, Ana Margarida; Benítez, Carolina
    La presente nota técnica explica de qué manera el análisis de grandes volúmenes de datos de la telefonía móvil puede aplicarse a la planificación del transporte y la infraestructura urbana. La actividad de los usuarios permite conocer su huella digital y, por lo tanto, entender sus trayectorias espacio-temporales de manera desagregada y extrapolada, estableciendo matrices de origen destino. La producción de grandes volúmenes de datos masivos, Big Data, abre interesantes posibilidades para entender los flujos de movilidad de nuestras ciudades de la región y su complementariedad con los métodos tradicionales de recolección de datos, como los son las encuestas de movilidad domiciliarias de origen destino de viajes, permitiendo disponer de información siempre actualizada y en menor tiempo. La nota técnica agrupa y sistematiza los conocimientos generados por especialistas e investigadores de distintos países del mundo; y analiza 6 casos de éxito realizados de implementación en distintas ciudades latinoamericanas y en otros ámbitos internacionales, donde se indica cómo se han obtenido esas matrices de viajes y cuáles han sido sus aplicaciones.
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    The city turned off: Urban dynamics during the COVID-19 pandemic based on mobile phone data
    (Applied Geography, 2021) Romanillos Arroyo, Gustavo; García Palomares, Juan Carlos; Moya Gómez, Borja; Gutiérrez Puebla, Javier; Torres, Javier; López, Mario; Cantú-Ros, Oliva G.; Herranz, Ricardo
    Due to the rapid expansion of the COVID-19 pandemic, many countries ordained lockdowns, establishing different restrictions on people’s mobility. Exploring to what extent these measures have been effective is critical in order to better respond to similar future scenarios. This article uses anonymous mobile phone data to study the impact of the Spanish lockdown on the daily dynamics of the Madrid metropolitan area (Spain). The analysis has been carried out for a reference week prior to the lockdown and during several weeks of the lockdown in which different restrictions were in place. During these weeks, population distribution is compared during the day and at night and presence profiles are obtained throughout the day for each type of land use. In addition, a spatial multiple regression analysis is carried out to determine the impact of the different land uses on the local population. The results in the reference week, pre-COVID-19, show how the population in activity areas increases in each time slot on a specific day and how in residential areas it decreases. However, during the lockdown, activity areas cease to attract population during the day and the residential areas therefore no longer show a decrease. Only basic essential commercial activities, or others that require the presence of workers (industrial or logistics) maintain some activity during lockdown.
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    New Data and Computational Methods Opportunities to Enhance the Knowledge Base of Tourism
    (Handbook of Computational Social Science for Policy, 2023) Romanillos Arroyo, Gustavo; Moya Gómez, Borja; Bertolini, Eleonora; Fontana, Matteo; Gabrielli, Lorenzo; Signorelli, Serena; Vespe, Michele
    Tourism is becoming increasingly relevant at different levels, intensifying its impact on the environmental, the economic and the social spheres. For this reason, the study of this rapidly evolving sector is important for many disciplines and requires to be quickly updated. This chapter provides an overview and general guidelines on the potential use of new data and computational methods to enhance tourism’s knowledge base, encourage their institutional adoption and, ultimately, foster a more sustainable tourism.First, the chapter delivers a brief review of the literature on new data sources and innovative computational methods that can significantly improve our understanding of tourism, addressing the big data revolution and the emergence of new analytic tools, such as artificial intelligence (AI) or machine learning (ML). Then, the chapter provides some guidelines and applications of these new datasets and methods, articulated around three topics: (1) measuring the environmental impacts of tourism, (2) assessing the socio-economic resilience of the tourism sector and (3) uncovering new tourists’ preferences, facilitating the digital transition and fostering innovation in the tourism sector.
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    Exploring night and day socio-spatial segregation based on mobile phone data: The case of Medellin (Colombia)
    (Computers, Environment and Urban Systems, 2021) Moya Gómez, Borja; Stępniak, Marcin; García Palomares, Juan Carlos; Frías Martínez, Enrique; Gutiérrez Puebla, Javier
    Social segregation research has a long tradition in urban studies. Usually, these studies focus on the residential dimension, using official registries (e.g., census data), which show population distribution at night. Nevertheless, these studies disregard the fact that the population in cities is highly mobile, and its spatial distribution dramatically changes between night and day. The emergence of new data sources (Big Data) creates perfect conditions to consider segregation as a process, by providing the opportunity to dynamically analyse temporal changes in social segregation. This study uses mobile phone data to analyse changes in social segregation between night and day. Our case study is Medellin (Colombia), a highly socially-segregated, South American city, where social integration policies are being developed, targeting the population in the most disadvantaged neighbourhoods. We use several complementary indicators of social segregation, supplementing them with mobility indicators that help explain changes in spatial segregation between night and day. The main conclusion is that daily mobility reduces the concentration of a particular group within neighbourhoods and increases the degree of social mixing (exposure) in local settings. This greater social exposure softens local contrasts (outliers) and increases the extension of spatial clusters (positive spatial autocorrelation), so general clustering trends emerge more clearly. The study also makes clear that increased exposure during the day mainly occurs due to the mobility of the low-income population, who are the most likely to leave their neighbourhood during the day and who travel the greatest distances to the most diverse set of destinations.
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    Project number: 165
    COLABORA – Aprendizaje colaborativo en geografía para la generación y uso de datos geolocalizados
    (2020) Condeço-Melhorado, Ana; García Palomares, Juan Carlos; Gutiérrez Puebla, Javier; López López, María Victoria; Michelini, Juan José; Moya Gómez, Borja; Osorio Arjona, Joaquín; Pérez Campaña, Rocío; Rodríguez Moya, Juana María; Romanillos Arroyo, Gustavo; Stepniak, Marcín; Talavera García, Rubén
    Este proyecto de innovación docente ha tenido como objetivo general emplear diferentes técnicas y actividades que fomenten el aprendizaje colaborativo en la generación, tratamiento y uso de los datos geolocalizados. Además, ha permitido la formación del profesorado en metodologías de aprendizaje colaborativo centradas en el uso de datos geolocalizados en relación con las tecnologías de la información geográfica (TIG), con el fin de incorporar este tipo de herramientas en sus asignaturas.
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    The Rio Olympic Games: A Look into City Dynamics through the Lens of Twitter Data
    (Sustainability, 2020) Condeço Melhorado, Ana Margarida; Mohino, Inmaculada; Moya Gómez, Borja; García Palomares, Juan Carlos
    The Olympic Games have a huge impact on the cities where they are held, both during the actual celebration of the event, and before and after it. This study presents a new approach based on spatial analysis, GIS, and data coming from Location-Based Social Networks to model the spatiotemporal dimension of impacts associated with the Rio 2016 Olympic Games. Geolocalized data from Twitter are used to analyze the activity pattern of users from two different viewpoints. The first monitors the activity of Twitter users during the event—The arrival of visitors, where they came from, and the use which residents and tourists made of different areas of the city. The second assesses the spatiotemporal use of the city by Twitter users before the event, compared to the use during and after the event. The results not only reveal which spaces were the most used while the Games were being held but also changes in the urban dynamics after the Games. Both approaches can be used to assess the impacts of mega-events and to improve the management and allocation of urban resources such as transport and public services infrastructure.
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    Project number: 424
    Realidad Aumentada para el aprendizaje en asignaturas vinculadas a las Tecnologías de la Información Geográfica (TIG-RA)
    (2023) Talavera García, Rubén; Condeço Melhorado, Ana Margarida; García Palomares, Juan Carlos; Gutiérrez Puebla, Javier; Michelini, Juan José; Moya Gómez, Borja; Pérez Campaña, Rocío; Romanillos Arroyo, Gustavo; Hewitt, Richard James; Santiago Iglesias, Enrique; Cara Santana, Yeray; Rodríguez Pacheco, Farid Leonardo
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    Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods
    (Transportation, 2021) Moyano, Amparo; Stepniak, Marcin; Moya Gómez, Borja; García Palomares, Juan Carlos
    This paper aims to evaluate the impacts of the economic context on traffic congestion and its consequential effects on private vehicle accessibility. We conduct a long-term analysis of spatiotemporal traffic congestion patterns in Madrid (Spain), comparing two urban realms: the 2008 economic crisis and the following post-crisis situation. We apply TomTom Speed Profiles data to assess daily variations in traffic congestion and their changes between both periods, and Twitter data to capture spatial patterns of the daily pulse of the city. Increased traffic, a by-product of economic recovery, resulted in higher congestion, particularly during peak hours. Nevertheless, these changes are spatially uneven. In the city core, an increase in congestion is relatively temporally homogeneous, while in the peripheral suburban zones, there has been only a marginal increase in travel times. On the other hand, in the urban outskirts, increased traffic congestion is more severe but visibly different between north and south. These differences have strong social connotations: over 40% of the population experienced a dramatic increase in travel times (more than 25%) during peak hours. Moreover, low-income groups are more likely to live in the more affected southern districts, suffering most the negative consequences of increased congestion.
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    El sistema médico de emergencias de Madrid a prueba: análisis del rendimiento espaciotemporal del SAMUR-PC en los primeros meses de la nueva normalidad postCOVID-19
    (Boletín de la Asociación Española de Geografía (BAGE), 2023) Pérez-Fernández, Onel; Moya Gómez, Borja
    Quienes requieren de atención sanitaria de emergencia no pueden esperar. Las ambulancias deben llegar al lugar del suceso lo más rápido posible. Las ambulancias suelen estar asignadas a bases, que se distribuyen por toda la ciudad para minimizar el tiempo de llegada al suceso. Sin embargo, la distribución espacial de los sucesos cambia a lo largo del día, debido al ritmo y uso que las personas hacen de la ciudad. Este artículo evalúa, por medio de modelos de localización-asignación, el desempeño espaciotemporal del SAMUR-PC, el Servicio Médico de Emergencias de Madrid (España) en dos escenarios diferenciados, antes de la pandemia de la COVID-19 y durante los primeros meses de la nueva normalidad. Los resultados muestran que el sistema respondió relativamente bien al cambio de los patrones de los sucesos debidos a la pandemia, aunque hubiese sido necesario hacer algunas intervenciones para garantizar el mismo servicio que antes de la crisis epidemiológica.
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    Combining high-resolution tessellations and detailed transport networks for accessibility analysis in large areas: indicators of human pressure on coastal areas.
    (GeoFocus, Revista Internacional de Ciencia y Tecnología de la Información Geográfica, 2024) Moya Gómez, Borja; Ojeda Zújar, José; García Palomares, Juan Carlos; Pérez Alcántara, Juan Pedro; Gutiérrez Puebla, Javier; Sánchez Rodríguez, Esperanza
    Human pressure on coastal areas poses a serious threat to their conservation. This pressure can be measured using accessibility indicators. However, a detailed accessibility analysis, with highly spatially disaggregated information and complex transport networks, requires millions of optimal routes to be obtained. To reduce processing times, this paper uses the centroid of a regular tessellation representing trip origins (square tiles with population and average income) and a layer of points associated with the coastline to represent destinations. Route calculations between all origins and all destinations are performed once, and then, depending on the objective of the study (accessibility to ports, beaches, lighthouses, or other points of interest), optimal route selections to the corresponding destination type can be made. The study results show the different degrees of human pressure on Andalusian beaches using different accessibility indicators