using fme and google earth to dynamically map fish catch in hawaii
DESCRIPTION
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.TRANSCRIPT
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Using FME and Google Earth to Dynamically Map Fish Catch in Hawaii
Matthew Austin NOAA Physical Scientist
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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.
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Fishing Ecosystem Analysis Tool (FEAT)
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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
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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
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My Office August 2009
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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
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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
Fishing for Data Sources
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CML License Logbook Reporting Grids
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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?
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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?
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Oahu ZCTAs Compared to Census Designated Places
2005 Map
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2005 Map
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Generate Published Parameters to Filter Source Data
Dates of Catch Species of Fish Gear used Grid Area Port of Landing Fisherman’s residence
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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
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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
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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
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FME Input - Published Parameters
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Google Earth Output
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Static Map Examples Generated with FME
FEAT Workbench was run with output set to shapefile
PDF maps were generated using Arcmap
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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
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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