Abstract

Urban crowd-sourcing has become a popular paradigm to harvest spatial information about our evolving cities directly from citizens. OpenStreetMap (OSM) is an example of such paradigm, where citizens are turned into cartographers and engaged in building and maintaining a map of the world. Research has shown that OSM information is accurate, by comparing it with centrally maintained spatial information such as Ordnance Survey. However, we find that coverage is low and non uniformly distributed, thus challenging the long-term sustainability of urban crowd-sourcing as a mechanism to map the whole world. In this talk, we present recent work we have conducted to understand and model the sustainability of urban crowd-sourcing. Taking OpenStreetMap as a case study, we first investigate what contextual factors correlate with coverage of OSM information in urban settings. We find that population density, distance from the city centre, and socio-economic factors have a direct correlation with information coverage. Inspired by models of physical urban growth developed by urban planners, we then build a model of digital growth of crowd-sourced spatial information that takes into consideration such contextual factors. Using this model, we build a classification tool that predicts, at a fine level of spatial granularity, what areas of the city are likely to be neglected by OpenStreetMap contributors. These predictions can be used to organise events (e.g., OSM mapping parties) that direct crowd-sourcing towards areas in need. We conclude with an analysis of the direct and indirect impact of mapping parties on user contributions in both the short and long term.