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Developing Conservation Data Sharing Tools for the Island of Maui, Hawai‘i

Developing Conservation Data Sharing Tools for the Island of Maui, Hawai‘i. Samuel N.R. Aruch MGIS- Capstone. Background Partner relationships and needs Objectives Current Infrastructure Implementation and timeline Products and examples Challenges Measures of success. Background

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Developing Conservation Data Sharing Tools for the Island of Maui, Hawai‘i

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  1. Developing Conservation Data Sharing Tools for the Island of Maui, Hawai‘i Samuel N.R. Aruch MGIS- Capstone

  2. Background • Partner relationships and needs • Objectives • Current Infrastructure • Implementation and timeline • Products and examples • Challenges • Measures of success

  3. Background • Terrestrial conservation implementation in Hawai'i • Challenges • Logistics • Costs

  4. Partner Relationships The Nature Conservancy Maui Program West Maui Mountains Watershed Partnership East Maui Watershed Partnership Goals Logistics Logistics Maui Invasive Species Committee

  5. Partner Relationships

  6. Needs – Shared logistics in remote and rugged areas

  7. Needs - Track fence work and measure goals

  8. Needs - Track fence work and measure goals

  9. Needs- Share weed control data and measure goals

  10. Project objective: • Design an enterprise level data sharing structure for conservation partners on the island of Maui. • Perform a case study with two examples of shared data sets • Track fence work and measure goals • Share weed control data and measure goals ool

  11. Current Infrastructure • Local “real time” data: MS Access & ArcGIS Desktop • Voluntary collaborative work on fence and weed standards • Variable technical capacity

  12. Current infrastructure - “Real time” Management Data

  13. Current infrastructure - “Real time” Management Data

  14. Current infrastructure - “Real time” Management Data

  15. Collaborative Standards- Fences • Fence Name • Fence Section • Status (complete, partial, proposed, unmaintained, removed) • Source (hand-drawn, GPS) • Agency Built (TNC, EMWP, NPS, etc…) • Agency Managed (TNC, EMWP, NPS, etc…) • Purpose (pig, deer, dirt bike, cattle, etc…) • Material (hogwire, mesh, barb, hog panels, etc…) • Apron (yes, no, partial) • Height ( # in feet) • Length (from GIS) • Last Check (from database) • Condition (from database)

  16. Implementation – Data sharing 3 different scenarios Program –Local user interface and data tables Check in/ Out Shared Data Server Hosted Program - Local user interface only Aggregated/ Filtered Export Program –Local user interface and data tables Aggregated/ filtered data

  17. Implementation – Timeline Phase 1 – Sharing structure Future Phases November 2011 Assessment July – August 2011 Plan – meetings with partners and collaborators September - October 2011 Implement- Build tools and scripts Develop products Long term goals Web portals Mobile tools Permanent support and hosting

  18. Implementation – Hardware and software • Explore existing infrastructure and what works with collaborators • Open source vs. proprietary • Off the shelf vs. roll your own • MS SQL, MySQL, PostgreSQL, PHP, Python etc.

  19. Products and Benefits • Visualizations for managers, administrators, and partners • Status of resources and management • Enhanced collaboration • Accountability • Continuity in perpetuity

  20. Examples - Fenced Unit Management Status * Sample data may not be current or accurate

  21. Examples – Analysis by watershed

  22. Challenges • Building momentum, collaboration, and relationships • Work from the bottom up and top down • Data compatibility and quality • Information security and trust • Don’t reinvent the wheel • Scalability • Usability • Costs • Time

  23. Measures of Success • Data shared by 2 or more projects • Products are used by 1 or more administrators • Products are adopted by 1 or more new agencies

  24. Summary • Ecosystem threats • Conservation Partnerships and shared logistics • Objective- Design an enterprise level data sharing structure • Perform a case study with two examples of shared data sets • Aggregate and share data using different scenarios • Measure success by use

  25. Through information collaboration we are empowered to look across entire conservation landscapes with the ability to make the best possible decisions when protecting our natural resources for future generations.

  26. Questions? • Sam Aruch • samaruch@gmail.com

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