This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement Nº 847635.
Department of Geography
Faculty of Geography and History
The Research Group Transport, Infrastructure and Territory – tGIS was established in 2005 joining the interests of researchers in transport geography and economics mainly from Universidad Complutense Madrid (UCM). The group gained strength with the incorporation of researchers from relates disciplines like architecture and civil engineering. In 2012 the group joined TRANSyT, the Transport Research Centre at Universidad Politécnica of Madrid, which favours further collaboration in research projects, knowledge transfer and networking opportunities. In 2017, tGIS has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (https://cordis.europa.eu/project/rcn/208383_en.html).
The group has a broad experience in the study of metropolitan processes from the point of view of transport and mobility and their relations with the territory and urban economy. Moreover, the team accumulates a wealth of experience and expertise in quantitative techniques and in GIS analysis applied to the study of metropolitan areas in the European framework. During the last years, the group has launched a new research line related to the use of Big Data in the fields of mobility, transport and urban dynamics.
tGIS has obtained a rating of “Excellent” (96 point over 100) in the external evaluation of the UCM Research Groups, realized by the State Research Agency (Agencia Estatal de Investigación – AEI).
The Transport, Infrastructure and Territory Research Group makes use of different software licenses to carry out its research projects, such as ESRI licenses for ArcGIS 10.6, ArcGIS Pro and ArcGIS Online, with continues updates and with licenses for the necessary extensions to conduct accessibility analyses: network analysis, spatial and 3D analysis and geostatistical analysis. In addition, we have licenses to software oriented to statistically process large datasets, such as SPSS (22), Stata (9.2) and MatLab (2013) and R. To improve data visualisation, we have licenses to graphic design software, such as Adobe Photoshop (Extended 11), Adobe Creative Suite (6) or graphic-based research software such as DepthMap.
Thanks to our collaboration with other research groups of the Complutense University, we also have access to a developer account with Google and iOS and an Amazon Web Services (AWS) account, where we can hire servers and cloud computing services with EC2 and EBS.
The research topic is essentially to explore the opportunities that new data sources offer to analyze and understand different urban and territorial dynamics. The three main aspects:
1. What kind of dynamics? The focus will be mainly directed to: 1) the study of urban transport and mobility, with a particular interest in the emergent forms of transport and what is known as Mobility as a Service. 2) The research on accessibility and its relation to economic activity and land use distribution. 3) The analysis of tourism. 4) Modelling of land use and transport. The project is open to study other urban and territorial dynamics related to the previous ones.
2. What kind of data? The project will have a particular interest in the use of new data sources, with the aim of not substituting but complementing more conventional sources. It will have a special focus on Big Data, with specific insights into social networks, mobile data, credit and travel card records, GPS data collected from different apps, etc. In addition, the research will be based on the use of geolocated data, especially on the use of data with high spatio-temporal resolution.
3. What kind of analyses will be carried out? We will mostly focus on the spatial analysis of the dynamics previously described. Network analysis and spatial statistics will play a major role. In addition, the project aims to deliver more “dynamic” analyses, improving traditional “static” analyses that look at particular moments in time. The use of Big Data offers the opportunity of monitoring different dynamics over time, opening the possibility of analyzing temporal patterns or the impact that particular events may have in the regular performance of different urban systems.