Abstract
The paper discusses the need of a high-level query language to allow analysts, geographers and, in general, non-programmers to easily cross-analyze multi-source VGI created by means of apps, crowd-sourced data from social networks and authoritative geo-referenced data, usually represented as JSON data sets (nowadays, the de facto standard for data exported by social networks). Since an easy to use high-level language for querying and manipulating collections of possibly geo-tagged JSON objects is still unavailable, we propose a truly declarative language, named J-CO-QL, that is based on a well-defined execution model. A plug-in for a GIS permits to visualize geo-tagged data sets stored in a NoSQL database such as MongoDB; furthermore, the same plug-in can be used to write and execute J-CO-QL queries on those databases. The paper introduces the language by exemplifying its operators within a real study case, the aim of which is to understand the mobility of people in the neighborhood of Bergamo city. Cross-analysis of data about transportation networks and VGI from travelers is performed, by means of J-CO-QL language, capable to manipulate and transform, combine and join possibly geo-tagged JSON objects, in order to produce new possibly geo-tagged JSON objects satisfying users’ needs.
Highlights
Volunteered Geographic Information (VGI), created by means of smart applications installed on mobile devices connected to the Internet, and geo-tagged crowdsourced information, created within social networks, named user-generated geo-tagged contents, are attracting more and more the interest of both scientific and business companies
The J-CO framework has been devised to enable non programmers to perform complex geo-spatial queries on geo-tagged JSON data-sets stored within a MongoDB database; we wanted users to be able to visualize such data-sets within classical GIS
The J-CO-QGIS Plug-in provides QGIS users with the possibility to explore collections in MongoDB databases and show geo-tagged JSON objects and their geometries as spatial layers, so that they can be overlaid with other information layers
Summary
Volunteered Geographic Information (VGI), created by means of smart applications installed on mobile devices connected to the Internet, and geo-tagged crowdsourced information, created within social networks, named user-generated geo-tagged contents, are attracting more and more the interest of both scientific and business companies. In the projects named iNaturalist and eBird (see Fitzpatrick et al 2002), flora and fauna observations are collected to study climate changes in relation to the geographic distribution of species’ habitats Such projects could not be carried out without cross-analyzing the distribution of VGI all over the world and information about geographic areas recognized to be the habitats of species. An example could be understanding how to improve public transportations, based on actual users needs, as well as understanding where to open new restaurants, discovering the areas of a city mostly visited by tourists, etc To ease such a kind of cross-analysis, a novel framework that permits to overlay, compare and correlate geo-tagged information from volunteers, crowdsourced and authoritative information within the same digital environment is needed by analysts.
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