Using FME and Google Earth to Dynamically Map Fish Catch in Hawaii

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This presentation will discuss how FME Workbench was used to develop a translation that merges the State of Hawaii fish catch data with socioeconomic data from the Census Bureau to create Google Earth output for fisheries management in the Pacific Islands region using an ecosystem based approach.  This demonstrates how published parameters can turn FME into a powerful decision making tool for non-technical users.

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Using FME and Google Earth to Dynamically Map Fish Catch in Hawaii

  1. 1. Using FME and Google Earth to Dynamically Map Fish Catch in Hawaii Matthew Austin NOAA Physical Scientist
  2. 2. Abstract   This presentation will discuss how FME workbench was used to develop a translation that merges the State of Hawaii fish catch data with socioeconomic data from the Census Bureau to create Google Earth output for fisheries management in the Pacific Islands region using an ecosystem based approach. This demonstrates how published parameters can turn FME into a powerful decision making tool for non-technical users.
  3. 3. Fishing Ecosystem Analysis Tool (FEAT)
  4. 4. NOAA Fisheries Pacific Islands Fisheries Science Center, Honolulu HI Fisheries Monitoring and Socioeconomics Division - Provide data and research in support of Fisheries Management in the Pacific Region Human Dimension Research Program – Focus on studying the people side if fishing Collect and analyze data to build frameworks better understand fishermen and fishing communities and how they are impacted by fishing regulation and management Stewart Allen - Social Scientist, Program Manager
  5. 5. Background   NOAA rotational assignment with NOAA Fisheries Jan-April 2009 in Honolulu   Came back in August for two weeks   FME was used everyday for the project   The goal was to create a tool that could be used by non-technical users such as fisheries managers and analyst to generate map data from Hawaii’s commercial fish catch data
  6. 6. My Office August 2009
  7. 7. Data Sources   ZCTA shapefiles from Census   Socioeconomic data from Census SF-1 and SF-3   CML Logbooks 99-2008 from state of Hawaii Foxpro database in DBF format   Fishcatch Grid shapefile from State of Hawaii   Ports shapefile from State of Hawaii
  8. 8. Fishing for Data Sources Commercial Marine License databases –  CML required of all anglers selling fish –  License holder database updated annually –  Address and zip code available –  Logbook database describes port, fishing location, catch by species, pieces, and pounds –  sales and value available from dealer database –  Confidentiality issue; Data from three or more fishermen required
  9. 9. CML License Logbook Reporting Grids
  10. 10. Answering Questions About Fishing Communities… Spatially   Who   Commercial and recreational fishermen   What   What species of fish were caught?   What are the socioeconomic conditions of the fishermen’s communities?   Where   Where do fishermen live? (ZCTA/Socioecon. Zone)   Where fish are caught?   Where are the ports that fish are landed?   When   Days fished?
  11. 11. Answering Questions… Spatially (cont.)   Why   Profit?   Cover trip expense?   How   Gear type used to catch the fish?   How much   Sum of fish catch by port?   Sum of fish catch by areas fished?   Sum of fish caught by socioeconomic zones?
  12. 12. 2005 Map Oahu ZCTAs Compared to Census Designated Places
  13. 13. 2005 Map
  14. 14. Generate Published Parameters to Filter Source Data   Dates of Catch   Species of Fish   Gear used   Grid Area   Port of Landing   Fisherman’s residence
  15. 15. Calculate Fish Catch Statistics   Statistics Calculator Transformer   Sum pounds by feature type   Where fish was caught Fish Grid area   Where fish was landed- Port   Where the fishermen that caught the fish live- Island or ZCTA
  16. 16. Calculate Fish Catch Statistics (cont.)   Merge non-spatial Fish Catch with spatial feature types (Fish Grid Area, Port, ZCTA, Island) using the Feature Merger Transformer   Calculate percent of sum and total sum for all records of each feature type   Filter confidential data. If query returns fish catch of less than three fishermen
  17. 17. Set the Color Gradient for Output Features   Need to distinguish high medium and low values of pounds caught for each output feature   Since output is dynamic the gradient range needs to be dynamic   Accomplished through a custom transformer with the help of Mark Companas from Safe Software   KML Styler is used to easily style output features
  18. 18. FME Input - Published Parameters
  19. 19. Google Earth Output
  20. 20. Static Map Examples Generated with FME   FEAT Workbench was run with output set to shapefile   PDF maps were generated using Arcmap
  21. 21. Next Steps   Add more years of data   Move FEAT into production mode   Stakeholder Analysis   User Requirements   Implement at PIFSC   Could be easily web based FME Server   Could be implemented with other datasets (longline) and in other regions
  22. 22. Next Steps   Determine enhancement requirements   Take advantage of new features in FME 2010   PDF writer now supports layer order   Automate database update with FME. Add more years of data.   Publish FEAT FME workbench to FME Server   Configure web based integration with Google Maps or ArcGIS Server
  23. 23. Thank You!   Questions?   For more information:   Matt Austin matthew.austin@noaa.gov   NOAA Coast Survey

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