Research
I do not have a lot of publications yet, but I hope that the ones I have right now are of interest for you. If you have any questions or are interested in commenting my work I will be happy to receive any comments.
1.
Contreras-Ochando, Lidia; Font-Julian, Cristina I.; Nieves, David; Martínez-Plumed, Fernando
How Data Science helps to build Smart Cities: València as a use case Book Chapter
In: Colomina, Begoña Cantó; Ferreira, Vanesa G. Lo Iacono (Ed.): Universitat Politècnica de València, 2018, ISBN: 978-84-09-02970-9.
Abstract | Links | BibTeX | Tags: Data Science, Machine Learning, Open Data, Smart Cities
@inbook{nokey,
title = {How Data Science helps to build Smart Cities: València as a use case},
author = {Lidia Contreras-Ochando and Cristina I. Font-Julian and David Nieves and Fernando Martínez-Plumed},
editor = {Begoña Cantó Colomina and Vanesa G. Lo Iacono Ferreira},
doi = {http://hdl.handle.net/10251/104672},
isbn = {978-84-09-02970-9},
year = {2018},
date = {2018-06-07},
urldate = {2018-06-07},
publisher = {Universitat Politècnica de València},
abstract = {The high degree of datification and connectivity embedded in a Smart City demands tools and mechanisms for data manipulation, knowledge extraction and representation that facilitate the extraction of meaningful insights. Clearly, Data Science can make enormous contributions to the development of Smart Cities, especially when it comes to gather and process information, combined with the capabilities of machine learning. In this regard, this paper discusses the use of Data Science methodologies and machine learning techniques to Smart City management aspects such as infrastructures, public safety and health, citizens' empowerment, transportation, etc. and presents a number of practical cases in the context of Smart Cities in València, Spain.},
keywords = {Data Science, Machine Learning, Open Data, Smart Cities},
pubstate = {published},
tppubtype = {inbook}
}
The high degree of datification and connectivity embedded in a Smart City demands tools and mechanisms for data manipulation, knowledge extraction and representation that facilitate the extraction of meaningful insights. Clearly, Data Science can make enormous contributions to the development of Smart Cities, especially when it comes to gather and process information, combined with the capabilities of machine learning. In this regard, this paper discusses the use of Data Science methodologies and machine learning techniques to Smart City management aspects such as infrastructures, public safety and health, citizens' empowerment, transportation, etc. and presents a number of practical cases in the context of Smart Cities in València, Spain.
2.
Contreras-Ochando, Lidia; Font-Julian, Cristina I.; Contreras-Ochando, Francisco; Ferri, Cèsar
AirVLC: An application for real-time forecasting urban air pollution Proceedings
Proceedings of the 2nd International Workshop of Mining Urban Data, 2015.
Abstract | BibTeX | Tags: Data Science, Machine Learning, Open Data, Smart Cities
@proceedings{nokey,
title = {AirVLC: An application for real-time forecasting urban air pollution},
author = {Lidia Contreras-Ochando and Cristina I. Font-Julian and Francisco Contreras-Ochando and Cèsar Ferri},
year = {2015},
date = {2015-07-11},
abstract = {This paper presents Airvlc, an application for producing real-time urban air pollution forecasts for the city of Valencia in Spain. Although many cities provide air quality data, in many cases, this information is presented with significant delays (three hours for the city of Valencia) and it is lim- ited to the area where the measurement stations are located. The application employs regression models able to predict the levels of four differ- ent pollutants (CO, NO, PM2.5, NO2) in three different locations of the city. These models are trained using features that represent traffic inten- sity, persistence of pollutants and meteorological parameters such as wind speed and temperature. We compare different learning techniques to get the better performance in the prediction of pollu- tants. According to our experiments, ensembles of decision trees (Random Forest) outperforms the rest of methods in almost all of our tests. Airvlc incorporates the best regression models and, by a distance-weighted combination of the predictions, is able to generate a real-time pollu- tion map of the city of Valencia. The application also includes a warning system for sending no- tifications to users when a nearby risk pollution concentration is detected. },
howpublished = {Proceedings of the 2nd International Workshop of Mining Urban Data},
keywords = {Data Science, Machine Learning, Open Data, Smart Cities},
pubstate = {published},
tppubtype = {proceedings}
}
This paper presents Airvlc, an application for producing real-time urban air pollution forecasts for the city of Valencia in Spain. Although many cities provide air quality data, in many cases, this information is presented with significant delays (three hours for the city of Valencia) and it is lim- ited to the area where the measurement stations are located. The application employs regression models able to predict the levels of four differ- ent pollutants (CO, NO, PM2.5, NO2) in three different locations of the city. These models are trained using features that represent traffic inten- sity, persistence of pollutants and meteorological parameters such as wind speed and temperature. We compare different learning techniques to get the better performance in the prediction of pollu- tants. According to our experiments, ensembles of decision trees (Random Forest) outperforms the rest of methods in almost all of our tests. Airvlc incorporates the best regression models and, by a distance-weighted combination of the predictions, is able to generate a real-time pollu- tion map of the city of Valencia. The application also includes a warning system for sending no- tifications to users when a nearby risk pollution concentration is detected.
3.
Contreras-Ochando, Lidia; Font-Julian, Cristina I.; Morillo, Paulina; Vallejo, Diego
TransparencyScience. Return on research investment, where do the funds go? Proceedings
iConference 2015 Proceedings, 2015.
Abstract | BibTeX | Tags: Citizen Participation, Crowdfunding, Data mining, Data Visualization, Open Data, Open Government, Public Investment, Social Network, Transparency
@proceedings{nokey,
title = {TransparencyScience. Return on research investment, where do the funds go?},
author = {Lidia Contreras-Ochando and Cristina I. Font-Julian and Paulina Morillo and Diego Vallejo},
year = {2015},
date = {2015-06-17},
urldate = {2015-06-17},
abstract = {The web application www.transparencyscience.es has been created in order to provide reliable information about public investment in science, in order to allow citizens to exercise their rights: to be informed in a transparent way, to control their government’s actions and to bring their ideas to guide the country’s policies on public investment in science. To achieve these goals, www.transparencyscience.es collects and process data from several open sources of the Spanish government. It uses different kinds of content and visualizations to facilitate the understanding of the Spanish public investment in science. It encourages citizen participation in three ways: a voting system; commenting system for collecting citizens’ opinion in natural language; and finally, acrowdfunding system for proposed actions/petitions/etc.
The purposes of this paper are both to explain why we have designed and created the web application www.transparencyscience.es, and to describe how it works. It also reveals some added value in comparison with other projects in Spain.},
howpublished = {iConference 2015 Proceedings},
keywords = {Citizen Participation, Crowdfunding, Data mining, Data Visualization, Open Data, Open Government, Public Investment, Social Network, Transparency},
pubstate = {published},
tppubtype = {proceedings}
}
The web application www.transparencyscience.es has been created in order to provide reliable information about public investment in science, in order to allow citizens to exercise their rights: to be informed in a transparent way, to control their government’s actions and to bring their ideas to guide the country’s policies on public investment in science. To achieve these goals, www.transparencyscience.es collects and process data from several open sources of the Spanish government. It uses different kinds of content and visualizations to facilitate the understanding of the Spanish public investment in science. It encourages citizen participation in three ways: a voting system; commenting system for collecting citizens’ opinion in natural language; and finally, acrowdfunding system for proposed actions/petitions/etc.
The purposes of this paper are both to explain why we have designed and created the web application www.transparencyscience.es, and to describe how it works. It also reveals some added value in comparison with other projects in Spain.
The purposes of this paper are both to explain why we have designed and created the web application www.transparencyscience.es, and to describe how it works. It also reveals some added value in comparison with other projects in Spain.