ARTICLE KNOWLEDGE GRAPH

Analysis of interlinked descriptions of entities - objects, events, situations or abstract concepts – while also encoding the semantics





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:

Keywords Cultural investment; Culture-led regeneration; Deprivation prediction; Geosocial network
Link to article https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030211974&doi=10.1098%2frsos.170413&partnerID=40&md5=aecd72aac1382644dea243e6604ae7c4
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.

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