Estimating micro-populations through social media analytics

Abstract

Estimation of crowd sizes or the occupancy of buildings and skyscrapers can often be essential. However, traditional ways of estimation through manual counting, image processing or in the case of skyscrapers, through total water usage are awkward, inefficient and often inaccurate. Social media has developed rapidly in the last decade. In this work, we provide novel solutions to estimate the population of suburbs and skyscrapers—so-called micro-populations, through the use of social media. We develop a big data solution leveraging large-scale harvesting and analysis of Twitter data. By harvesting real-time tweets and clustering tweets within suburbs and skyscrapers, we show how micro-populations can be calculated. To validate this, we construct linear and spatial models for the suburbs in four cities of Australia using census data and geospatial data models (shapefiles). Our prediction of micro-population shows that Twitter can indeed be used for population prediction with a high degree of accuracy.