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| Id | 638 | |
| Author | Zhou X., Hristova D., Noulas A., Mascolo C., Sklar M. | |
| Title | Cultural investment and urban socio-economic development: A geosocial network approach | |
| Reference | Zhou X., Hristova D., Noulas A., Mascolo C., Sklar M.; Cultural investment and urban socio-economic development: A geosocial network approach ;Royal Society Open Science vol:4 issue: 9.0 page: |
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| Link to article | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030211974&doi=10.1098%2frsos.170413&partnerID=40&md5=aecd72aac1382644dea243e6604ae7c4 |
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| 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, owing 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 3 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 geosocial 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 policymaking and investment optimization. © 2017 The Authors. |
Cultural investment and urban socio-economic development: A geosocial network approach. In line with this stream of research we take advantage of the geosocial network data from Foursquare and show its success in capturing and predicting the socio-economic change related to government cultural expenditure which has vital implications for culture-led urban regeneration in neighbourhoods. This IMD is the index we particularly focus on in the study to assess socio-economic status of London areas. These signals suggest that compared to prosperous areas deprived neighbourhoods in London spent more money on culture and had larger number of venues created and higher in-degree centrality and out-degree centrality showed from to. Finding evidence of the regenerative effects of such investment in local areas we take a step further by trying to predict socioeconomic impact based on geographical network and cultural expenditure features.
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