The canary in the city : indicator groups as predictors of local rent increases
As 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 c...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/10356/87802 http://hdl.handle.net/10220/45545 |
| _version_ | 1826110099323617280 |
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| author | Poorthuis, Ate Lee, Bu-Sung Schläpfer, Markus Steentoft, Aike Alexander |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Poorthuis, Ate Lee, Bu-Sung Schläpfer, Markus Steentoft, Aike Alexander |
| author_sort | Poorthuis, Ate |
| collection | NTU |
| description | As 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. |
| first_indexed | 2024-10-01T02:28:56Z |
| format | Journal Article |
| id | ntu-10356/87802 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:28:56Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/878022020-03-07T11:48:59Z The canary in the city : indicator groups as predictors of local rent increases Poorthuis, Ate Lee, Bu-Sung Schläpfer, Markus Steentoft, Aike Alexander School of Computer Science and Engineering Indicator Group Social Sensing As 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. NRF (Natl Research Foundation, S’pore) Published version 2018-08-08T04:08:24Z 2019-12-06T16:49:46Z 2018-08-08T04:08:24Z 2019-12-06T16:49:46Z 2018 Journal Article Steentoft, 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-. 2193-1127 https://hdl.handle.net/10356/87802 http://hdl.handle.net/10220/45545 10.1140/epjds/s13688-018-0151-y en EPJ Data Science © 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. 15 p. application/pdf |
| spellingShingle | Indicator Group Social Sensing Poorthuis, Ate Lee, Bu-Sung Schläpfer, Markus Steentoft, Aike Alexander The canary in the city : indicator groups as predictors of local rent increases |
| title | The canary in the city : indicator groups as predictors of local rent increases |
| title_full | The canary in the city : indicator groups as predictors of local rent increases |
| title_fullStr | The canary in the city : indicator groups as predictors of local rent increases |
| title_full_unstemmed | The canary in the city : indicator groups as predictors of local rent increases |
| title_short | The canary in the city : indicator groups as predictors of local rent increases |
| title_sort | canary in the city indicator groups as predictors of local rent increases |
| topic | Indicator Group Social Sensing |
| url | https://hdl.handle.net/10356/87802 http://hdl.handle.net/10220/45545 |
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