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This paper introduces a spatiotemporal analysis framework for estimatinghourly changing population distribution in urban areas using geo-tagged tweets(the messages containing users' physical locations), land use data, anddasymetric maps. We collected geo-tagged social media (tweets) within theCounty of San Diego during one year (2015) by using Twitter's StreamingApplication Programming Interfaces (APIs). A semi-manual Twitter contentverification procedure for data cleaning was applied first to separate tweetscreated by humans and non-human users (bots). The next step is to calculate thenumber of unique Twitter users every hour with the two different geographicalunits: (1) census blocks, and (2) 1km by 1km resolution grids of LandScan. Thefinal step is to estimate actual dynamic population by transforming the numbersof unique Twitter users in each census block or grid into estimated populationdensities with spatial and temporal variation factors. A temporal factor wasbased on hourly frequency changes of unique Twitter users within San DiegoCounty, CA. A spatial factor was estimated by using the dasymetric method withland use maps and 2010 census data. Several comparison maps were created tovisualize the spatiotemporal pattern changes of dynamic populationdistribution.

Estimating hourly population distribution change at high spatiotemporal  resolution in urban areas using geo-tagged tweets, land use data, and  dasymetric maps