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

Matthew Austin NOAA Physical Scientist

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.

Fishing Ecosystem Analysis Tool (FEAT)

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

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

My Office August 2009

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

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

CML License Logbook Reporting Grids

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?

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?

Oahu ZCTAs Compared to Census Designated Places

2005 Map

2005 Map

Generate Published Parameters to Filter Source Data

  Dates of Catch   Species of Fish   Gear used   Grid Area   Port of Landing   Fisherman’s residence

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

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

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

FME Input - Published Parameters

Google Earth Output

Static Map Examples Generated with FME

  FEAT Workbench was run with output set to shapefile

  PDF maps were generated using Arcmap

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

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

Thank You!

  Questions?

  For more information:   Matt Austin matthew.austin@noaa.gov   NOAA Coast Survey