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The amount of user-generated geospatial content on the Web is constantly increasing, making it a valuable source of information for enabling, enriching and enhancing geospatial applications and services. However, this content is highly heterogeneous and diverse, varying significantly in quality and accuracy. Extracting, integrating, and mining these crowdsourced geospatial data from the Web is far from trivial. Among the main challenges are to retrieve data from multiple sources, each one providing its own access methods and restrictions, to deal with different schemas and taxonomies, and to find matching entities across multiple sources. In this paper, we present our work for retrieving and integrating crowdsourced Points of Interest (POIs) from popular Web sources. We retrieve POIs from different Web sources and we describe the steps taken for mapping the source categories to a common schema, detecting duplicate POIs, and eventually clustering them to identify hotspots. We present the results of this process applied to six major Web sources to retrieve and integrate POIs located in the metropolitan areas of three European capital cities.

Catchment area and reachability analysis, i.e., the area from which a location attracts visitors and the minimum distance to a target location, respectively, are interesting problems when studied in the context of time-parameterized networks, such as road networks affected by traffic. This work utilizes live-traffic assessment results produced by Floating Car Data and their application to such crucial geomarketing test cases. We combine state-of-the-art isochrone computation utilizing live-traffic and demographics data to provide efficient catchment area and reachability calculations. The online demo presented here, showcases the critical impact of live-traffic assessment on business intelligence decisions related to space.

The ever-increasing stream of Web and mobile applications addressing geospatial data creation has been producing a large number of user-contributed geospatial datasets. This work proposes a means to query such data using a collaborative Web-based approach. We employ crowdsourcing to the fullest in that used-generated point-cloud data will be mined by the crowd not only by providing feature names, but also by contributing computing resources. We employ browser-based collaborative search for deriving the extents of geospatial objects (Points of Interest) from point-cloud data such as Flickr image locations and tags. The data is aggregated by means of a hierarchical grid in connection with an exploratory and a refinement search phase. A performance study establishes the effectiveness of our approach with respect to the amount of data that needs to be retrieved from the sources and the quality of the derived spatial features.

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