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Creating Structured, Linked Geographic Data from Historical Maps: Challenges and Trends

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Using Historical Maps in Scientific Studies

Part of the book series: SpringerBriefs in Geography ((BRIEFSGEOGRAPHY))

Abstract

Historical geographic data are essential for a variety of studies of cancer and environmental epidemiology, urbanization, and landscape ecology. However, existing data sources typically contain only contemporary information. Historical maps hold a great deal of detailed geographic information at various times in the past. Yet, finding relevant maps is difficult, and the map content is not machine-readable. This chapter presents the challenges and trends in building a map processing, modeling, linking, and publishing framework. The framework will enable querying historical map collections as a unified and structured spatiotemporal source in which individual geographic phenomena (extracted from maps) are modeled (described) with semantic descriptions and linked to other data sources (e.g., DBpedia). This framework will allow making use of historical geographic datasets from a variety of maps, efficiently, over large geographic extents. Realizing such a framework poses significant research challenges in multiple fields in computer science including digital map processing, data integration, and the Semantic Web technologies, and other disciplines such as spatial, social, and health sciences. Tackling these challenges will not only advance research in computer science and geographic information science but also present a unique opportunity for interdisciplinary research.

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Notes

  1. 1.

    http://ngmdb.usgs.gov/ngmdb/ngmdbhome.html.

  2. 2.

    http://ngmdb.usgs.gov/maps/TopoView/.

  3. 3.

    http://www.davidrumsey.com/.

  4. 4.

    http://OldMapsOnline.org/.

  5. 5.

    http://maps.nls.uk/.

  6. 6.

    This chapter is based on a previous vision paper presented at the 2015 ACM SIGSPATIAL Conference [Chi15] and the First Place of the Best Vision Paper Award sponsored by the Computing Research Association’s Computing Community Consortium under the CCC Blue Sky initiative.

  7. 7.

    https://www.geonames.org/.

  8. 8.

    http://wiki.dbpedia.org/.

  9. 9.

    This is just an example. By no means the author is an expert of soil contamination or growing grapefruits.

  10. 10.

    To appreciate this difficulty from experience, the reader is encouraged to explore how long it would take to find a large-scale map of 1941 Budapest.

  11. 11.

    http://ablesw.com/r2v/download.html.

  12. 12.

    https://github.com/NYPL/map-vectorizer/.

  13. 13.

    http://commons.pelagios.org/.

  14. 14.

    http://www.visionofbritain.org.uk/data/#tabgb1900.

  15. 15.

    A USGS historical topographic map with the 600 DPI (dots-per-inch) scan resolution is about 12, 000 × 12, 000 pixels.

  16. 16.

    National Science Foundation (United States).

  17. 17.

    The reader is referred to [Cla10] for a detailed introduction to GISs and GIS data formats.

  18. 18.

    https://geojson.org/.

  19. 19.

    http://www.opengeospatial.org/.

  20. 20.

    https://www.nhgis.org.

  21. 21.

    http://geovocab.org/doc/neogeo.

  22. 22.

    https://www.w3.org/RDF/.

  23. 23.

    For example, the URI, https://www.geonames.org/3020251/embrun.html, refer to the town Embrun in France. The reader is referred to the GeoNames Ontology website (http://www.geonames.org/ontology/documentation.html) for more examples about URIs and the Geo Semantic Web.

  24. 24.

    http://usc-isi-i2.github.io/karma/.

  25. 25.

    https://www.w3.org/2005/Incubator/geo/XGR-geo-ont-20071023/.

  26. 26.

    See the full tutorial here: https://github.com/usc-isi-i2/Web-Karma/wiki/Working-with-geospatial-data/.

  27. 27.

    https://recogito.pelagios.org/.

  28. 28.

    http://commons.pelagios.org/.

  29. 29.

    The reader is referred to [Tam18] for an overview of ontologies and the ontologies that describes geographic data.

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Authors and Affiliations

  1. Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA

    Yao-Yi Chiang

  2. Department of Computer Science, University of Southern California, Los Angeles, CA, USA

    Weiwei Duan

  3. Department of Geography, University of Colorado, Boulder, CO, USA

    Stefan Leyk & Johannes H. Uhl

  4. Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA

    Craig A. Knoblock

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Chiang, YY., Duan, W., Leyk, S., Uhl, J.H., Knoblock, C.A. (2020). Creating Structured, Linked Geographic Data from Historical Maps: Challenges and Trends. In: Using Historical Maps in Scientific Studies. SpringerBriefs in Geography. Springer, Cham. https://doi.org/10.1007/978-3-319-66908-3_3

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