Person:
Moya Gómez, Borja

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

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Search Results

Now showing 1 - 10 of 28

The pulse of the cycling city: visualising Madrid bike share system GPS routes and cycling flow
(Journal of Maps, 2018) Romanillos Arroyo, Gustavo; Moya Gómez, Borja; Zaltz-Austwick, Martin; Lamíquiz-Daudén, Patxi J.
With the aim of shifting towards a more sustainable urban transport model, cycling mobility isbeing promoted in many cities and, in consequence, Bike Share Systems have been the focus ofattention in an increasing number of studies over the past years. However, we know very littleabout the impact of these BSS in cities beyond the station level. What paths do cyclists follow?What are the most important urban arteries in terms of cycling flow? These are importantquestions to be addressed in order to implement policies and infrastructure where they arereally needed. The main goal of this study is to visualise the cycling flow derived fromMadrid BSS activity, obtained by processing over 250,000 GPS routes, and to provide ananalysis of how this flow is distributed across the urban street network at different moments.We explore the diverse levels of use over the course of the day, and during the weekdays,weekends or holidays, as well as the different cycling patterns of frequent and occasional users.
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.
The daily dynamic potential accessibility by car in London on Wednesdays
(Journal of Maps, 2017) Moya Gómez, Borja; García Palomares, Juan Carlos
The map presented in this paper shows the effect of congestion on daily accessibility in the London metropolitan area on Wednesdays. Because of its dynamic nature, it is challenging to both calculate the effects of this phenomenon and to represent it clearly on simple maps. Although we can use many traditional techniques for this purpose, they are usually static, and they may lose some essential information on the effects studied. In this paper, we used two cartographic techniques rarely used in accessibility studies – cartograms and 3D maps, which we believe can achieve a more striking representation in static and animations of both the traffic-induced spatial distortion and the accessibility levels obtained. The results are presented in two animated maps and some snapshots of them – static maps. Both types of maps reinforce each other: Together, they can properly show the direct space–time link between congestion and accessibility, and can, therefore, give a more detailed overview of the consequences of this phenomenon.
Project number: 79
SIGUE - Materiales para actividades no presenciales en asignaturas vinculadas a los Sistemas de Información Geográfica (SIG): recopilación, preparación y evaluación
(2017) García Palomares, Juan Carlos; Gutiérrez Puebla, Javier; Rodríguez Moya, Juana María; Mínguez García, Carmen; de Andrés de Pablo, Nuria; Tanarro García, Luis Miguel; Vía García, Miguel; Salas Olmedo, María Henar; Moya Gómez, Borja; Romanillos Arroyo, Gustavo; Osorio Arjona, Joaquín; Fernández Fernández, José María
City dynamics through Twitter: Relationships between land use and spatiotemporal demographics
(Cities, 2018) García Palomares, Juan Carlos; Moya Gómez, Borja; Condeço Melhorado, Ana Margarida; Gutiérrez Puebla, Javier; Salas Olmedo, María Henar
Social network data offer interesting opportunities in urban studies. In this study, we used Twitter data to analyse city dynamics over the course of the day. Users of this social network were grouped according to city zone and time slot in order to analyse the daily dynamics of the city and the relationship between this and land use. First, daytime activity in each zone was compared with activity at night in order to determine which zones showed increased activity in each of the time slots. Then, typical Twitter activity profiles were obtained based on the predominant land use in each zone, indicating how land uses linked to activities were activated during the day, but at different rates depending on the type of land use. Lastly, a multiple regression analysis was performed to determine the influence of the different land uses on each of the major time slots (morning, afternoon, evening and night) through their changing coefficients. Activity tended to decrease throughout the day for most land uses (e.g. offices, education, health and transport), but remained constant in parks and increased in retail and residential zones. Our results show that social network data can be used to improve our understanding of the link between land use and urban dynamics.
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.
Identifying Temporal Patterns of Visitors to National Parks through Geotagged Photographs
(Sustainability, 2019) Barros Sulca, Diana Carolina; Moya Gómez, Borja; García Palomares, Juan Carlos
Visitor data is essential for decision-making, policy formulation, and monitoring of protected areas. In this context, the data on the temporal distribution of visitors is essential to characterize influx and seasonality, and even to measure the carrying capacity of a site. However, obtaining information from visitors often involves high costs and long production times. Moreover, traditional visitor data has a limited level of detail. New sources of data can provide valuable information regarding the timing of visits. In this study, we tested the use of geotagged data to infer the temporal distribution of visitors to 15 Spanish national parks, and we identified temporal patterns of the visits at three levels: monthly, weekly, and daily. By comparing official monthly visitor counts and geotagged photographs from Flickr, we observed that the number of monthly users who upload photos significantly reflects the number of monthly visitors. Furthermore, the weekly and daily distributions of the Flickr data provided additional information that could contribute to identifying the periods of highest visitor pressure, design measures to manage the concentration of visitors, and improve the overall visitor experience. The results obtained indicate the potential of new data sources for visitor monitoring in protected areas and to open opportunities for future research. Moreover, monitoring tourism in protected areas is crucial to ensure the sustainability of their resources and to protect their biodiversity.
Project number: 230
BDinnova – Formación del profesorado en herramientas de manejo, análisis y visualización de BigData geolocalizado
(2019) García Palomares, Juan Carlos; Gutiérrez Puebla, Javier; Rodríguez Moya, Juana María; Mínguez García, María del Carmen; Michelini, Juan José; García Ruiz, Yolanda; Condeço Melhorado, Ana; Romanillos Arroyo, Gustavo; Vía García, Miguel; Jiménez Gómez, Isidro; Stepniak, Marcin; Moya Gómez, Borja; Osorio Arjona, Joaquín
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.
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.