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Improving Large Area Population Mapping Using Geotweet Densities.

Nirav N Patel1, Forrest R Stevens2, Zhuojie Huang3

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Geo-located tweets from social media can improve population mapping accuracy. This study shows using geotweet density as a covariate enhances census disaggregation methods for creating finer-scale population datasets.

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Area of Science:

  • Geographic Information Systems (GIS)
  • Demography
  • Social Media Analytics

Background:

  • Traditional census data disaggregation methods rely on geospatial covariates that are not direct indicators of human presence.
  • Existing methods for creating fine-scale, gridded population datasets face limitations in accurately reflecting population densities.

Purpose of the Study:

  • To evaluate the effectiveness of geo-located tweets as a covariate for improving population mapping accuracy.
  • To assess the potential of social media data in enhancing census disaggregation models.

Main Methods:

  • Utilized geo-located tweets from Twitter as a covariate layer within a random forests-based census disaggregation model.
  • Analyzed tweet density in 1x1 km grid cells over a two-month period in Indonesia.
  • Compared population mapping accuracy between models with and without the geotweet covariate.

Main Results:

  • Incorporating geotweet densities as a covariate significantly increased the accuracy of population maps.
  • Models utilizing social media data demonstrated improved internal accuracy measures and external assessments.
  • Indonesia, with high Twitter penetration, served as an effective case study.

Conclusions:

  • Geo-located social media data, specifically geotweets, show significant potential for enhancing the accuracy of gridded population datasets.
  • This approach offers a promising avenue for more dynamic and accurate population distribution mapping.
  • The continuous and free availability of social media data makes it a valuable resource for demographic studies.