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.

1. Using FME and Google Earth
to Dynamically Map Fish
Catch in Hawaii
Matthew Austin
NOAA Physical Scientist

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. Fishing Ecosystem Analysis Tool
(FEAT)

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. 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. My Office August 2009

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

9. 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

10. CML License Logbook Reporting Grids

11. 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?

12. 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?

13. 2005 Map
Oahu ZCTAs Compared to Census
Designated Places

14. 2005 Map

17. Generate Published Parameters
to Filter Source Data
 Dates of Catch
 Species of Fish
 Gear used
 Grid Area
 Port of Landing
 Fisherman’s residence

18. 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

19. 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

20. 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

21. FME Input
- Published Parameters

22. Google Earth Output

23. Static Map Examples
Generated with FME
 FEAT Workbench was run with output set to
shapefile
 PDF maps were generated using Arcmap

47. 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

48. 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

49. Thank You!
 Questions?
 For more information:
 Matt Austin matthew.austin@noaa.gov
 NOAA Coast Survey