Cultural Investment and Urban Socio-Economic Development: A Geo-Social Network Approach

TL;DR

This study leverages geo-social network data from Foursquare to assess and predict the socio-economic impact of cultural investments in London's neighborhoods, aiding urban planning and policy decisions.

Contribution

It introduces a novel approach using geo-social network indicators and supervised learning to track and predict socio-economic changes due to cultural expenditure.

Findings

Network indicators correlate with local growth likelihood.

Supervised models accurately predict deprivation changes.

Geo-social data can serve as a proxy for socio-economic assessment.

Abstract

Being able to assess the impact of government-led investment onto socio-economic indicators in cities has long been an important target of urban planning. However, due to the lack of large-scale data with a fine spatio-temporal resolution, there have been limitations in terms of how planners can track the impact and measure the effectiveness of cultural investment in small urban areas. Taking advantage of nearly 4 million transition records for three years in London from a popular location-based social network service, Foursquare, we study how the socio-economic impact of government cultural expenditure can be detected and predicted. Our analysis shows that network indicators such as average clustering coefficient or centrality can be exploited to estimate the likelihood of local growth in response to cultural investment. We subsequently integrate these features in supervised learning models to infer socio-economic deprivation changes for London's neighbourhoods. This research presents how geo-social and mobile services can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing urban areas and thus gives evidence and suggestions for further policy-making and investment optimisation.