Land Use Detection & Identification using Geo-tagged Tweets
This research offers an incremental method for urban planners to potentially identify land use patterns using social media data.
This paper explores the use of geo-tagged tweets to identify land use patterns in urban areas. By applying supervised learning to Twitter activity signatures from Brisbane, Melbourne, and Sydney, the study found a good match between predicted land use and official city council zoning data.
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.