A cross-analysis framework for multi-source volunteered, crowdsourced, and authoritative geographic information: the case study of volunteered personal traces analysis against transport network data

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.