Year: 2018
Type: article
Abstract: A large share of human activity is now routinely captured and stored by commercial organisations as consumer data. These new forms of data have provided many new and exciting opportunities for understanding population trends at fine spatial scales. In many settings, they have reduced our dependence on theory and traditional modelling approaches and fundamentally changed how geographers approach producing representations of the population. However, consumer data are typically created for commercial benefit and do not have academic standard data quality controls, and so, in repurposing the data for social research, we encounter inherent issues of veracity. Without an understanding of uncertainty, social scientists can risk overstretching consumer datasets beyond the specific populations or phenomena they directly pertain to. Moreover, the technical characteristics of large and complex datasets also make it challenging to generate valid information efficiently. Therefore, this paper reviews the major challenges to harnessing consumer data to produce valid spatial representations of the population at large.
Source: Geography Compass
DOI: 10.1111/gec3.12374
Authors: Guy Lansley, James Cheshire
Institution: University College London
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,700
OpenAlex ID: W2803382601