Charting the geographies of crowdsourced information in Greater London

Abstract

Crowdsourcing platforms and social media produce distinctive geographies of informational content. The production process is enabled and influenced by a variety of socio-economic and demographic factors, shaping the place representation, i.e., the amount and type of information available in an area. In this study, we explore and explain the geographies of Twitter and Wikipedia in Greater London, highlighting the relationships between the crowdsourced data and the local geo-demographic characteristics of the areas where they are located. Through a set of robust regression models on a sample of 1.6M tweets and about 22,000 Wikipedia articles, we identify level of education, presence of people aged 30–44, and property prices as the most important explanatory factors for place representation at the urban scale. To some extent, this confirms the received knowledge of such data being created primarily by relatively wealthy, young, and educated users. However, about half of the variability is left unexplained, suggesting that a broader inclusion of potential factors is necessary.