Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87802
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dc.contributor.authorPoorthuis, Ateen
dc.contributor.authorLee, Bu-Sungen
dc.contributor.authorSchläpfer, Markusen
dc.contributor.authorSteentoft, Aike Alexanderen
dc.date.accessioned2018-08-08T04:08:24Zen
dc.date.accessioned2019-12-06T16:49:46Z-
dc.date.available2018-08-08T04:08:24Zen
dc.date.available2019-12-06T16:49:46Z-
dc.date.issued2018en
dc.identifier.citationSteentoft, A. A., Poorthuis, A., Lee, B.-S., & Schläpfer, M. (2018). The canary in the city : indicator groups as predictors of local rent increases. EPJ Data Science, 7(1), 21-.en
dc.identifier.issn2193-1127en
dc.identifier.urihttps://hdl.handle.net/10356/87802-
dc.description.abstractAs cities grow, certain neighborhoods experience a particularly high demand for housing, resulting in escalating rents. Despite far-reaching socioeconomic consequences, it remains difficult to predict when and where urban neighborhoods will face such changes. To tackle this challenge, we adapt the concept of ‘bioindicators’, borrowed from ecology, to the urban context. The objective is to use an ‘indicator group’ of people to assess the quality of a complex environment and its changes over time. Specifically, we analyze 92 million geolocated Twitter records across five US cities, allowing us to derive socio-economic user profiles based on individual movement patterns. As a proof-of-concept, we define users with a ‘high-income-profile’ as an indicator group and show that their visitation patterns are a suitable indicator for expected future rent increases in different neighborhoods. The concept of indicator groups highlights the potential of closely monitoring only a specific subset of the population, rather than the population as a whole. If the indicator group is defined appropriately for the phenomenon of interest, this approach can yield early predictions while simultaneously reducing the amount of data that needs to be collected and analyzed.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.format.extent15 p.en
dc.language.isoenen
dc.relation.ispartofseriesEPJ Data Scienceen
dc.rights© 2018 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.subjectIndicator Groupen
dc.subjectSocial Sensingen
dc.titleThe canary in the city : indicator groups as predictors of local rent increasesen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.identifier.doi10.1140/epjds/s13688-018-0151-yen
dc.description.versionPublished versionen
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