  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.
