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
Pajarito-Grajales, Diego; Maas, Suzanne; Attard, Maria; Gould, Michael
Path of least resistance: using geo-games and crowdsourced data to map cycling frictions Book Chapter
In: Skarlatidou, Artemis; (eds.) Geographic Citizen Science Design: No one left behind., Muki Haklay (Ed.): Chapter 8, pp. 165-185, UCL press, 2021, ISBN: 978-1-78735-614-6.
@inbook{Pajarito-Grajales2021,
title = {Path of least resistance: using geo-games and crowdsourced data to map cycling frictions},
author = {Diego Pajarito-Grajales and Suzanne Maas and Maria Attard and Michael Gould},
editor = {Artemis Skarlatidou and Muki Haklay (eds.) Geographic Citizen Science Design: No one left behind. },
doi = {https://doi.org/10.14324/111.9781787356122 },
isbn = {978-1-78735-614-6},
year = {2021},
date = {2021-01-03},
pages = {165-185},
publisher = {UCL press},
chapter = {8},
abstract = {Urban cycling is an alternative mode of transport promoted by cities worldwide to reduce congestion and pollution and to increase citizens’ physical activity (Oldenziel et al. 2015). Cycling data, such as information about the cycling modal share, preferred routes and the main constraints or frictions faced during cycling, can be used as an evidence base for urban planning, cycling infrastructure design, cycling advocacy campaigns, promotion of alternative commuting and the assessment of impacts and benefits of cycling planning and promotion (Gossling 2018). The same data also have wider applicability in planning cycling policies, for instance to evaluate the impact of...},
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pubstate = {published},
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Urban cycling is an alternative mode of transport promoted by cities worldwide to reduce congestion and pollution and to increase citizens’ physical activity (Oldenziel et al. 2015). Cycling data, such as information about the cycling modal share, preferred routes and the main constraints or frictions faced during cycling, can be used as an evidence base for urban planning, cycling infrastructure design, cycling advocacy campaigns, promotion of alternative commuting and the assessment of impacts and benefits of cycling planning and promotion (Gossling 2018). The same data also have wider applicability in planning cycling policies, for instance to evaluate the impact of...
Akande, Adelouwa; Cabral, Pedro; Casteleyn, Sven
Understanding the sharing economy and its implication on sustainability in smart cities Journal Article
In: Journal of Cleaner Production, vol. 277, pp. 124077, 2020, ISBN: 0959-6526, (IF).
@article{Akande2020b,
title = {Understanding the sharing economy and its implication on sustainability in smart cities},
author = {Adelouwa Akande and Pedro Cabral and Sven Casteleyn },
doi = {https://doi.org/10.1016/j.jclepro.2020.124077 },
isbn = {0959-6526},
year = {2020},
date = {2020-12-30},
journal = {Journal of Cleaner Production},
volume = {277},
pages = {124077},
abstract = {The purpose of this article is to evaluate the main drivers of the sharing economy through an exhaustive weighting and meta-analysis of previous relevant quantitative research articles, obtained using a systematic literature review methodology. The authors analysed 22 quantitative studies from 2008 through. Out of the 249 extracted relationships (independent – dependent variable), the paper identifies the “best” predictors used in theoretical models to study the sharing economy. These include: attitude on intention to share, perceived behavioural control on intention to share, subjective norm on intention to share, economic benefit on attitude, and perceived risk on attitude. Geographically, Germany and the United States of America were found to be the nations with the highest number of respondents. Temporally, an increasing trend in the number of articles on the sharing economy and respondents was observed. The consolidation of the drivers of the sharing economy provides a solid theoretical foundation for the research community to explore existing hypotheses further and test new hypotheses in emerging contexts of the sharing economy. Given the different conceptual theories that have been used to study the sharing economy and their application to different contexts, this study presents the first attempt at advancing knowledge by quantitatively synthesizing findings presented in previous literature},
note = {IF},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The purpose of this article is to evaluate the main drivers of the sharing economy through an exhaustive weighting and meta-analysis of previous relevant quantitative research articles, obtained using a systematic literature review methodology. The authors analysed 22 quantitative studies from 2008 through. Out of the 249 extracted relationships (independent – dependent variable), the paper identifies the “best” predictors used in theoretical models to study the sharing economy. These include: attitude on intention to share, perceived behavioural control on intention to share, subjective norm on intention to share, economic benefit on attitude, and perceived risk on attitude. Geographically, Germany and the United States of America were found to be the nations with the highest number of respondents. Temporally, an increasing trend in the number of articles on the sharing economy and respondents was observed. The consolidation of the drivers of the sharing economy provides a solid theoretical foundation for the research community to explore existing hypotheses further and test new hypotheses in emerging contexts of the sharing economy. Given the different conceptual theories that have been used to study the sharing economy and their application to different contexts, this study presents the first attempt at advancing knowledge by quantitatively synthesizing findings presented in previous literature
Technical contact: Sergi Trilles (strilles@uji.es)
IP: Joaquín Huerta (huerta@uji.es)
Website: http://geo-c.eu/
