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6 – GEOSMART CITIES
MANUEL GARCIA ALVAREZ MSC
Sensor networks are becoming common infrastructures in cities, they provide situational
awareness of the urban environment. Sensors provide spatial and temporal data that can
be turned into information for building understating of the dynamics of a city, e. g. mobility
patterns and energy consumption behaviors, and for making decisions. Today’s sensor
networks produce massive amounts of data which through an exhaustive analysis can
enable smart city applications. A current example of the used of sensor networks is
Santander. Santander is a city in Spain, it counts with a very well developed network with
over 2500 sensor nodes and it provides real time observations of traffic, weather and public
transport. Though the availability of data is not a problem for some cities, looking on how
to use data to provide solutions to city problems is big challenge. In this session, students
will work around the case of Santander to identify urban problems and propose solutions
from a data-driven perspective. Furthermore, students will be provided with the
opportunity to get familiar with geographic data, apply geovisual analytics, and create
innovative applications.
AFTER THIS COURSE THE STUDENT WILL BE ABLE TO:
Identify urban problems in which location and time play a main role.
Propose solutions using a data-driven approaches.
Execute spatio-temporal analytical tasks on sensor data to support decision making.
Apply geo-visual analytics on the exploitation of geographic information.
ABOUT THE AUTHOR
Manuel Garcia pursuits a PhD in the department of Geo-Information Processing, ITC Faculty.
He has a background in geo-information processing. His work focuses on the exploitation
data from sensor networks using a ‘complex event processing’ paradigm. Another of his
interests is on uncertainty propagation on spatio-temporal datasets. In a previous work, he
developed a method to represent spatial uncertainty of moving objects. He holds a
bachelor degree on Agriculture Science, and his work experience include: lecturing
Geographic Information Systems in the national university of Guatemala, and being a
consultant and GIS Analyst.

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geosmart_2016

  • 1. 6 – GEOSMART CITIES MANUEL GARCIA ALVAREZ MSC Sensor networks are becoming common infrastructures in cities, they provide situational awareness of the urban environment. Sensors provide spatial and temporal data that can be turned into information for building understating of the dynamics of a city, e. g. mobility patterns and energy consumption behaviors, and for making decisions. Today’s sensor networks produce massive amounts of data which through an exhaustive analysis can enable smart city applications. A current example of the used of sensor networks is Santander. Santander is a city in Spain, it counts with a very well developed network with over 2500 sensor nodes and it provides real time observations of traffic, weather and public transport. Though the availability of data is not a problem for some cities, looking on how to use data to provide solutions to city problems is big challenge. In this session, students will work around the case of Santander to identify urban problems and propose solutions from a data-driven perspective. Furthermore, students will be provided with the opportunity to get familiar with geographic data, apply geovisual analytics, and create innovative applications. AFTER THIS COURSE THE STUDENT WILL BE ABLE TO: Identify urban problems in which location and time play a main role. Propose solutions using a data-driven approaches. Execute spatio-temporal analytical tasks on sensor data to support decision making. Apply geo-visual analytics on the exploitation of geographic information. ABOUT THE AUTHOR Manuel Garcia pursuits a PhD in the department of Geo-Information Processing, ITC Faculty. He has a background in geo-information processing. His work focuses on the exploitation data from sensor networks using a ‘complex event processing’ paradigm. Another of his interests is on uncertainty propagation on spatio-temporal datasets. In a previous work, he developed a method to represent spatial uncertainty of moving objects. He holds a bachelor degree on Agriculture Science, and his work experience include: lecturing Geographic Information Systems in the national university of Guatemala, and being a consultant and GIS Analyst.