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SCA Tool Suite
  • Strategic Conservation Assessment (SCA) of Gulf Coast Landscapes - Project Tool Suite
  • PREFACE
  • Introduction
    • Purpose of the SCA Project
    • Scope
    • Definitions, Acronyms and Abbreviations
    • Overview of SCA Region
    • Overview of SCA Tools
    • Considerations for the Best Use of the SCA Tools
  • Conservation Planning Inventory Tool
    • Purpose
    • Scope
    • Site Map
    • Design Method
      • Inventory Search
      • Stakeholder Engagement
      • Analysis
      • Potential Biases and Limitations
    • Software Development Tools
  • Conservation Prioritization Tool
    • Purpose
    • Scope
    • Site Map
    • Design Method
      • Stakeholder Engagement
      • Design Decisions
      • Measures Development
      • Multicriteria Decision Analysis (MCDA)
      • Analysis
    • Analysis Modes
      • Single Project Mode
      • Multi-Project Mode
    • Software Development Tools
  • Conservation Visualization Tool
    • Purpose
    • Scope
    • Site Map
    • Design Method
    • Code Repository
    • Software Development Tools
  • Support - Learn More Pages
    • Data Measures for Habitat
    • Data Measures for Water Quality & Quantity
    • Data Measures for Living Coastal & Marine Resources
    • Data Measures for Community Resilience
    • Data Measures for Gulf Economy
    • Frequently Asked Questions
    • Known Issues and Reporting
  • Document Control
  • Appendix
    • Appendix A
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  1. Conservation Prioritization Tool
  2. Design Method

Analysis

PreviousMulticriteria Decision Analysis (MCDA)NextAnalysis Modes

Last updated 4 years ago

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During the analysis, users are expected to provide one or more AoIs directly drawn or an Esri shapefile containing the AoIs or watersheds. The tool will then subset and aggregate the data measures for the provided AoIs from the SCA measures database.

Figure 20. Conservation Prioritization Tool (CPT) multi-project mode flow.
Figure 21. Data aggregation from user provided areas of interest (AoIs).

Figure 22. Hex aggregation.
Figure 23. Data scaling.
Figure 24. Data weighting and normalization.