The Canary in the City: Indicator Groups as Predictors of Urban Change

TL;DR

This paper introduces a novel approach using socio-economic indicator groups derived from geolocated social media data to predict urban neighborhood changes, such as rent increases, with early warning capabilities.

Contribution

It adapts the ecological concept of bioindicators to urban environments, demonstrating that specific social media user groups can serve as effective predictors of urban change.

Findings

High-income-profile users predict rent increases

Indicator groups enable early detection of urban change

Monitoring specific social groups reduces data requirements

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 `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.