Joint Doctorate in Geoinformatics: Enabling Open Cities
Short Description
GEOTEC is one of three partners organising the Joint Doctorate “Geoinformatics: Enabling Open Cities (GEO-C)”, funded under the EU Marie Curie International Training Networks (ITN) program, European Joint Doctorates (EJD). GEO-C aims to contribute methods and tools to realise smart and open cities, in which all groups of society can participate on all levels and benefit in many ways. Complementary strands of research in GEO-C (participation, data analysis & fusion, services) will lead to an improved understanding of how to build open cities and will produce a prototypical open city toolkit. With a budget of over 3’5 million EURO, Geo-C provides 15 Phd students (5 in Spain, 5 in Portugal, 5 in Germany) the opportunity to do research and advance the state of the art in smart and open cities.
GEOTEC’s contribution
The main contribution is the Open City Toolkit (OCT), that it is envisioned as an integrated, open source software empowering citizens, providing them with analytical tools and citizen-centric services in the context of a smart city. It is incorporating the results of the various research lines within the GEO-C phd students. It is designed to keep all the resulting resources (i.e., data, processes, services, guidelines, standards, ontologies, and models) along with utilities, tools and applications that make use of these resources
Publications
Santa, Fernando; Henriques, Roberto; Torres-Sospedra, Joaquín; Pebesma, Edzer A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments Journal Article In: Sustainability , 11 (3), pp. 595, 2019, (IF:). @article{Santa2019, title = {A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments}, author = {Fernando Santa and Roberto Henriques and Joaquín Torres-Sospedra and Edzer Pebesma}, doi = {10.3390/su11030595}, year = {2019}, date = {2019-03-13}, journal = {Sustainability }, volume = {11}, number = {3}, pages = {595}, abstract = {An in-depth descriptive approach to the dynamics of the urban population is fundamental as a first step towards promoting effective planning and designing processes in cities. Understanding the behavioral aspects of human activities can contribute to their effective management and control. We present a framework, based on statistical methods, for studying the spatio-temporal distribution of geolocated tweets as a proxy for where and when people carry out their activities. We have evaluated our proposal by analyzing the distribution of collected geolocated tweets over a two-week period in the summer of 2017 in Lisbon, London, and Manhattan. Our proposal considers a negative binomial regression analysis for the time series of counts of tweets as a first step. We further estimate a functional principal component analysis of second-order summary statistics of the hourly spatial point patterns formed by the locations of the tweets. Finally, we find groups of hours with a similar spatial arrangement of places where humans develop their activities through hierarchical clustering over the principal scores. Social media events are found to show strong temporal trends such as seasonal variation due to the hour of the day and the day of the week in addition to autoregressive schemas. We have also identified spatio-temporal patterns of clustering, i.e., groups of hours of the day that present a similar spatial distribution of human activities.}, note = {IF:}, keywords = {}, pubstate = {published}, tppubtype = {article} } An in-depth descriptive approach to the dynamics of the urban population is fundamental as a first step towards promoting effective planning and designing processes in cities. Understanding the behavioral aspects of human activities can contribute to their effective management and control. We present a framework, based on statistical methods, for studying the spatio-temporal distribution of geolocated tweets as a proxy for where and when people carry out their activities. We have evaluated our proposal by analyzing the distribution of collected geolocated tweets over a two-week period in the summer of 2017 in Lisbon, London, and Manhattan. Our proposal considers a negative binomial regression analysis for the time series of counts of tweets as a first step. We further estimate a functional principal component analysis of second-order summary statistics of the hourly spatial point patterns formed by the locations of the tweets. Finally, we find groups of hours with a similar spatial arrangement of places where humans develop their activities through hierarchical clustering over the principal scores. Social media events are found to show strong temporal trends such as seasonal variation due to the hour of the day and the day of the week in addition to autoregressive schemas. We have also identified spatio-temporal patterns of clustering, i.e., groups of hours of the day that present a similar spatial distribution of human activities. |
Akande, Adelouwa; Cabral, Pedro; Gomes, P; Casteleyn, Sven The Lisbon ranking for smart sustainable cities in Europe Journal Article In: Sustainable cities and society, 44 , pp. 475-487, 2019, (IF). @article{Akande2019, title = {The Lisbon ranking for smart sustainable cities in Europe}, author = {Adelouwa Akande and Pedro Cabral and P. Gomes and Sven Casteleyn}, year = {2019}, date = {2019-02-01}, journal = {Sustainable cities and society}, volume = {44}, pages = {475-487}, abstract = {There has recently been a conscious push for cities in Europe to be smarter and more sustainable, leading to the need to benchmark these cities’ efforts using robust assessment frameworks. This paper ranks 28 European capital cities based on how smart and sustainable they are. Using hierarchical clustering and principal component analysis (PCA), we synthesized 32 indicators into 4 components and computed rank scores. The ranking of European capital cities was based on this rank score. Our results show that Berlin and other Nordic capital cities lead the ranking, while Sofia and Bucharest obtained the lowest rank scores, and are thus not yet on the path of being smart and sustainable. While our city rank scores show little correlation with city size and city population, there is a significant positive correlation with the cities’ GDP per inhabitant, which is an indicator for wealth. Lastly, we detect a geographical divide: 12 of the top 14 cities are Western European; 11 of the bottom 14 cities are Eastern European. These results will help cities understand where they stand vis-à-vis other cities, giving policy makers an opportunity to identify areas for improvement while leveraging areas of strength.}, note = {IF}, keywords = {}, pubstate = {published}, tppubtype = {article} } There has recently been a conscious push for cities in Europe to be smarter and more sustainable, leading to the need to benchmark these cities’ efforts using robust assessment frameworks. This paper ranks 28 European capital cities based on how smart and sustainable they are. Using hierarchical clustering and principal component analysis (PCA), we synthesized 32 indicators into 4 components and computed rank scores. The ranking of European capital cities was based on this rank score. Our results show that Berlin and other Nordic capital cities lead the ranking, while Sofia and Bucharest obtained the lowest rank scores, and are thus not yet on the path of being smart and sustainable. While our city rank scores show little correlation with city size and city population, there is a significant positive correlation with the cities’ GDP per inhabitant, which is an indicator for wealth. Lastly, we detect a geographical divide: 12 of the top 14 cities are Western European; 11 of the bottom 14 cities are Eastern European. These results will help cities understand where they stand vis-à-vis other cities, giving policy makers an opportunity to identify areas for improvement while leveraging areas of strength. |
Technical contact: Sergi Trilles (strilles@uji.es)
IP: Joaquín Huerta (huerta@uji.es)
Website: http://geo-c.eu/