Name
Abstract | ||
|---|---|---|
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 `bioindicatorsu0027, borrowed from ecology, to the urban context. The objective is to use an `indicator groupu0027 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-profileu0027 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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Physics and Society | Population,Regional science,Artificial intelligence,Economic rent,Phenomenon,Mathematics,Machine learning,Socioeconomic status |
DocType | Volume | Citations |
Journal | abs/1712.08924 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aike Alexander Steentoft | 1 | 0 | 0.34 |
| Ate Poorthuis | 2 | 10 | 2.45 |
| Bu Sung Lee | 3 | 452 | 35.22 |
| Markus Schläpfer | 4 | 74 | 8.64 |