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CitySense: Retrieving, Visualizing and Combining Datasets on Urban Areas

Authors:
Danae Pla Karidi
Harry Nakos
Alexandros Efentakis
Yannis Stavrakas

Keywords: Social networks; Crowdsourcing; Open data; Geographic visualization.

Abstract:
Social networks, available open data and massive online APIs provide huge amounts of data about our surrounding location, especially for cities and urban areas. Unfortunately, most previous applications and research usually focused on one kind of data over the other, thus presenting a biased and partial view of each location in question, hence partially negating the benefits of such approaches. To remedy this, this work presents the CitySense framework that simultaneously combines data from administrative sources (e.g., public agencies), massive Point of Interest APIs (Google Places, Foursquare) and social microblogs (Twitter) to provide a unified view of all available information about an urban area, in an intuitive and easy to use web-application platform. This work describes the engineering and design challenges of such an effort and how these different and divergent sources of information may be combined to provide an accurate and diverse visualization for our use-case, the urban area of Chicago, USA.

Pages: 61 to 69

Copyright: Copyright (c) IARIA, 2017

Publication date: May 21, 2017

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-558-6

Location: Barcelona, Spain

Dates: from May 21, 2017 to May 25, 2017