Fine-grained population estimation

Bast, Hannah; Storandt, Sabine; Weidner, Simon

Fine-grained population estimation

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2015

Autor:innen

Bast, Hannah

Storandt, Sabine

Weidner, Simon

DOI (zitierfähiger Link)

https://doi.org/10.1145/2820783.2820828

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

ALI, Mohamed, ed.. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY: ACM, 2015, 17. ISBN 978-1-4503-3967-4. Available under: doi: 10.1145/2820783.2820828

Zusammenfassung

We show how to estimate population numbers for arbitrary user-defined regions, down to the level of individual buildings. This is important for various applications like evacuation planning, facility placement, or traffic estimation. However, census data with precise population numbers is typically only available at the level of cities, villages, or districts, if at all.
Previous approaches either rely on available census data for already small areas or on sophisticated input data like high resolution aerial images. Our framework uses only freely available data, in particular, OpenStreetMap data. In the OpenStreetMap project, crowd-sourced data is collected about street networks, buildings, places of interest as well as all kind of regions and natural structures world-wide. We use this data to learn three classifiers that are relevant for the population distribution inside an area: residential vs. industrial vs. commercial landuse, inhabited vs. uninhabited buildings, and single-family vs. multi-family houses. Once learned, we can use these classifiers for population estimation even in areas without any census data at all.
Our experiments show good average accuracy (measured as the deviation from actual census data) for rural areas (25%), metropolitan areas (10%), and cities in countries other than that containing the training data (12%).

Fachgebiet (DDC)

004 Informatik

Konferenz

23rd SIGSPATIAL, 3. Nov. 2015 - 6. Nov. 2015, Seattle, Washington, USA

Zitieren

ISO 690

BAST, Hannah, Sabine STORANDT, Simon WEIDNER, 2015. Fine-grained population estimation. 23rd SIGSPATIAL. Seattle, Washington, USA, 3. Nov. 2015 - 6. Nov. 2015. In: ALI, Mohamed, ed.. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY: ACM, 2015, 17. ISBN 978-1-4503-3967-4. Available under: doi: 10.1145/2820783.2820828

BibTex

@inproceedings{Bast2015Fineg-43821,
  year={2015},
  doi={10.1145/2820783.2820828},
  title={Fine-grained population estimation},
  isbn={978-1-4503-3967-4},
  publisher={ACM},
  address={New York, NY},
  booktitle={Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems},
  editor={Ali, Mohamed},
  author={Bast, Hannah and Storandt, Sabine and Weidner, Simon},
  note={Article Number: 17}
}

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