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Global Roads Data: A Strategy for Development

Global Roads Data: A Strategy for Development. Results of the Global Roads Workshop, 1-3 October 2007 Lamont Campus of Columbia University, Palisades, NY Alex de Sherbinin Center for International Earth Science Information Network (CIESIN) The Earth Institute at Columbia University. Outline.

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Global Roads Data: A Strategy for Development

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  1. Global Roads Data: A Strategy for Development Results of the Global Roads Workshop, 1-3 October 2007 Lamont Campus of Columbia University, Palisades, NY Alex de Sherbinin Center for International Earth Science Information Network (CIESIN) The Earth Institute at Columbia University

  2. Outline • The goal • The need • Approaches to data development • A new global product • CODATA working group • Questions for APAN participants

  3. 1. The goal • A global roads data base that has: • improved geographic and temporal coverage, • consistent coding of road types, and • good documentation of sources • Available free-of-charge on an “attribution only” basis (i.e., public commons) • Nominal scale of 1:250,000

  4. Example of a 1:250k product Source: ViaMichelin at http://www.viamichelin.com/viamichelin/int/dyn/controller/Maps

  5. The “ideal” product 1:250k scale Major travel routes only. Not urban streets Attributes for road type, surface, and road use Metadata on sources, purpose, timeliness and restrictions Verification of accuracy Consistent classes between countries Connectivity between borders/tiles Update frequency at least every 5 years, and preferably on rolling basis

  6. 2. The need • There is currently no globally consistent, reasonably complete, roads data product available to the development, disaster response, health, conservation, and research communities • Best available is VMAP, produced by the U.S. National Geospatial Intelligence Agency (NGA) • Covers only 25-30% of the global roads network • Little documentation of sources or verification of spatial accuracy • More data exist – but no release is anticipated

  7. 20% coverage globally VMAP0 vs. Int’l Road Statistics / road network (IRF) = % coverage, km per country VMAP 0 Source: Andy Nelson, JRC

  8. < 20% 20-40 40-60 60-80 >80% 20% coverage globally VMAP0 vs. Int’l Road Statistics / road network (IRF) = % coverage, km per country VMAP 0 Source: Andy Nelson, JRC

  9. VMAP1 publicly released data - complete or almost complete countries VMAP 1 22 / 4% Source: Andy Nelson, JRC

  10. Primary Secondary Tracks/Unpaved International Steering Committee for Global Mapping (ISCGM) has completed the following Source: Andy Nelson, JRC

  11. User needs • Pre- and post-disaster planning • Economic development • Environment and land use • Research community • Private sector

  12. Disaster response The map at left of travel time costs owing to a major flood in 2006 in the Horn of Africa region shows the value of combining road network data with digital elevation models (DEMs), flood remote sensing or meteorological data in order to plan for flood response, or to allocate additional travel time in the event of floods coupled with some other emergency. Bad road data will affect the validity of the results. Produced by Paul Bartel, HIU, US State Dept.

  13. Development policy The map at left represents an accessibility map based on low resolution/poor quality roads data, and the map at right represents an accessibility map for the same region based on high resolution/high quality roads data. Allocation of development resources based on the roads data at left would not yield optimal results, since some of the apparently most inaccessible regions actually have dense road networks. Source: Glenn Hyman, CIAT

  14. Biodiversity conservation Road expansion and improvement increases the farm gate price of commodities such as beef, soybeans and palm oil, and is a powerful economic incentive for the expansion of plantations on the forest frontier. These products are also under increasing global demand as food products and biofuel feed stocks. Conservation planning with better knowledge of road networks can diminish the cost of trade-offs between biodiversity conservation and the expansion of livelihood opportunities in agriculture and forestry. Source: Vera-Diaz et al. (forthcoming).

  15. 3. Approaches to developing the data • “Mix-and-match” approach • Buy from private sector • Military sources (e.g., NIMA) • “Crowd sourcing”, e.g. OpenStreetMap • Create a new data product

  16. Mix-and-match approach • Pros: • Can be accomplished quickly at relatively little cost • Cons: • Lack of consistent coverage among countries • Problems with matching networks at borders • CIESIN’s SEDAC plans to develop a catalog and to carry out preliminary evaluation of available data

  17. Mix and match approachCombining available national-level data* Total – 105 countries, 67% by area, 59% by population * Multiple sources including VMAP1, Global Map, CGIAR, World Bank DEC-RC, FAO Geonetwork, and others found by Andy Nelson 105/59% Source: Andy Nelson, JRC

  18. Buy from private sector • Held conversations with Teleatlas • Willing to engage in data development partnerships with a limited number of users • Unwilling to sell a “skeletal” map or earlier version without restrictions on further use • Data for developing countries are still relatively sparse • Economist*: “risk of digital map monopoly…” by Navteq & Teleatlas in the navigation map arena * Economist, “Location, Location, Location”, October 4, 2007

  19. Military sources • Main obstacle is military/intelligence community is not committed to public commons approach • Portions of their data which are available (e.g. VMAP1 tiles) may be useful for validation

  20. Crowd sourcing • Pros • Many hands make light work • Openstreetmap a successful model of this approach • Cons • Poor quality control • Fewer inputs in low income countries www.openstreetmap.org

  21. Create a new product • Pros • Develop a consistent, well documented product • Methods for integrating multiple source data have been developed by Georigin for data-poor Africa • Build on top of this for future updates • Cons • The cost will likely be > US$1million • Bringing the approach to scale

  22. Georigin approach Example form Nigeria: A Russian 1:200 000 topographic map at left (georeferenced, cropped, datum shifted to WGS84) can be integrated with data from Landsat 7 (geometrically enhanced with GPS ground control points) and GPS tracks at right to produce a road map. Source: John Dann, Georigin, Ltd.

  23. 4. Specifications for a new global product • Terminology and Classification • Data model building on on UN Spatial Data Infrastructure specifications. • Each road segment will include information on its provider, its collection date as well as an indication of data quality and reliability. • Initially, only information on primary, secondary and tertiary roads will be collected. • Database structure and functionality • The database would be structured so as to allow basic network analysis and routing functions in addition to cartographic representation.* • This implies ensuring topological consistency in the data, as well as the ability to establish connectivity with external data layers such as settlements and other transportation networks. • The database would be structured in order to allow versioning and maintenance of a historical archive of the evolution of global road networks. * These would include deriving macro and meso-level transport costs, optimal routes between population centers, contingency plans in case of shocks to the network and optimized road rehabilitation investment decisions.

  24. Data model

  25. Approach proposed at first Global Roads Workshop – October 2007 • A combination of the following data sets will be used to manually digitize roads and attributes according to the data model described above. • Scanned 1:200,000 paper maps developed by the Russian military (ranging in dates from the late 1960s to the early 1980s) and the US Joint Operations Graphic (JOG) navigation maps. • Geocover Landsat pansharpened 15m imagery baselined to the year 2000, which are orthorectified and are available free of charge. • GPS tracks wherever available to add the most recent routes. • Roads will be manually digitized and attributes assigned according to a data model. • Digitization could occur anywhere assuming a suitable tool and management structure is developed

  26. 5. CODATA working group • A working group has been proposed under ICSU’s Committee on Data (CODATA) • This group will oversee quality control and move the process forward • Representatives of CIESIN (myself) and UN Joint Logistics Centre (Olivier Cottray) serve as co-chairs

  27. CODATA working group members

  28. 6. Questions for the APAN participants • Let us know how you might be able to contribute: • Improved methodologies • National roads data sets for inclusion in the catalog • Funding opportunities • Suggestions regarding low cost but reliable/well managed “click worker” shops in Asia

  29. For more information on the Global Roads Data workshop and the overall strategy, visit: http://www.ciesin.columbia.edu/confluence/display/roads

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