Land Use Detection & Identification using Geo-tagged Tweets

TL;DR

This paper demonstrates that geo-tagged tweets can be effectively used with supervised learning to identify land use patterns in cities, aiding urban planning by matching predicted land use with official zoning data.

Contribution

It introduces a novel approach using Twitter activity signatures and supervised learning to detect land use, validated across three Australian cities with promising results.

Findings

Predicted land use closely matches city zoning data.

Geo-tagged tweets contain useful features for land use detection.

Method successfully applied to multiple cities.

Abstract

Geo-tagged tweets can potentially help with sensing the interaction of people with their surrounding environment. Based on this hypothesis, this paper makes use of geotagged tweets in order to ascertain various land uses with a broader goal to help with urban/city planning. The proposed method utilises supervised learning to reveal spatial land use within cities with the help of Twitter activity signatures. Specifically, the technique involves using tweets from three cities of Australia namely Brisbane, Melbourne and Sydney. Analytical results are checked against the zoning data provided by respective city councils and a good match is observed between the predicted land use and existing land zoning by the city councils. We show that geo-tagged tweets contain features that can be useful for land use identification.