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dc.contributor.authorZhou, Xiao
dc.contributor.authorNoulas, Anastasios
dc.contributor.authorMascolo, Cecilia
dc.contributor.authorZhao, Zhongxiang
dc.date.accessioned2018-10-10T05:16:19Z
dc.date.available2018-10-10T05:16:19Z
dc.date.issued2018
dc.identifier.isbn9781450355520
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/283335
dc.description.abstractCultural activity is an inherent aspect of urban life and the success of a modern city is largely determined by its capacity to o er gen- erous cultural entertainment to its citizens. To this end, the optimal allocation of cultural establishments and related resources across urban regions becomes of vital importance, as it can reduce nan- cial costs in terms of planning and improve quality of life in the city, more generally. In this paper, we make use of a large longitudinal dataset of user location check-ins from the online social network WeChat to develop a data-driven framework for culture planning in the city of Beijing. We exploit rich spatio-temporal representations on user activity at cultural venues and use a novel extended version of the traditional latent Dirichlet allocation model that incorporates temporal information to identify latent patterns of urban cultural interactions. Using the characteristic typologies of mobile user cul- tural activities emitted by the model, we determine the levels of demand for di erent types of cultural resources across urban areas. We then compare those with the corresponding levels of supply as driven by the presence and spatial reach of cultural venues in local areas to obtain high resolution maps that indicate urban re- gions with lack or oversupply of cultural resources, and thus give evidence and suggestions for further urban cultural planning and investment optimisation.
dc.description.sponsorshipCambridge Trust
dc.publisherAssociation for Computing Machinery
dc.titleDiscovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning
dc.typeConference Object
prism.endingPage1078
prism.publicationNameKDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
prism.startingPage1069
dc.identifier.doi10.17863/CAM.30703
dcterms.dateAccepted2018-05-05
rioxxterms.versionofrecord10.1145/3219819.3219929
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-05-05
dc.contributor.orcidZhou, Xiao [0000-0002-0868-764X]
dc.contributor.orcidMascolo, Cecilia [0000-0001-9614-4380]
dc.publisher.urlhttp://dx.doi.org/10.1145/3219819.3219929
rioxxterms.typeConference Paper/Proceeding/Abstract
cam.issuedOnline2018-07-19
pubs.conference-nameKDD 2018: 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
pubs.conference-start-date2018-08-19
pubs.conference-finish-date2018-08-25


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