The Urban Nexus Project: When Urban Mobility Analysis, VGI and Data Science Meet Together

The chapter discusses a multi-disciplinary research project called Urban Nexus, which aims at studying the role of big data in urban governance, along with the need of a high-level query language to allow analysts, geographers and, in general, non-programmers to easily cross-analyze multi-source big data produced by the inhabitants and coming also from other sources of information. The project focuses on the production of Volunteered Geographic Information by means of apps, crowd-sourced data from social networks and fosters an analysis based also on authoritative geo-referenced data coming from multiple institutional sources. The Urban Nexus project applies novel and multidisciplinary approaches to interpret social aspects of big data, in relation to three medium-sized cities and their surrounding areas: Bergamo in the wider context of Milan (Italy), Lausanne and the other cities of Switzerland and Cambridge and the metropolitan area of London (United Kingdom). Focused on urban regeneration and mobility, we want to devise and test methods to make such cities learning cities, i.e., territories where the inhabitants help to identify the design guidelines according to their competence on places that is their spatial capital [1].
In particular, we identified an analysis method of data sets represented by means of the JSON lightweight data-interchange format. Currently, JSON has become the de facto standard for exchanging VGI (Volunteered Geographic Information) and crowdsourced data created within social networks. Since an easy to use high-level language for querying and manipulating collections of possibly geo-tagged JSON objects is still unavailable, as well as an integrated framework for data management and visualization of geo-tagged JSON data sets, we devised a new framework named J-CO. In this framework, we propose a high-level language, called J-CO-QL (firstly introduced in [2]), which is based on a well-defined execution model. A plug-in for QGIS is provided as well: it 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.
By means of a practical example, we show how the J-CO framework is able to allow geographers and analysts to work on complex data sets coming from VGI sources, in order to study the mobility of city users who voluntarily participated to the gathering process of data for the city of Bergamo.