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Issues and Challenges Pertaining to Large Scale Data Collection By Jeremy LaDart US Army Corps of Engineers – Mobile Dis

Issues and Challenges Pertaining to Large Scale Data Collection By Jeremy LaDart US Army Corps of Engineers – Mobile District. Mississippi Coastal Improvements Program. $10 Million Emergency Supplemental Appropriations (P.L. 109-359) 30 December 2005 Cost Effective Projects in lieu of

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Issues and Challenges Pertaining to Large Scale Data Collection By Jeremy LaDart US Army Corps of Engineers – Mobile Dis

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  1. Issues and Challenges Pertaining to Large Scale Data Collection By Jeremy LaDart US Army Corps of Engineers – Mobile District

  2. Mississippi Coastal Improvements Program • $10 Million Emergency Supplemental Appropriations (P.L. 109-359) 30 December 2005 • Cost Effective Projects in lieu of NED benefits • No Incremental Benefit-Cost Analysis • 6 month interim and 24 month comprehensive report requirements • Hurricane and Storm Damage Reduction • Salt Water Intrusion • Shoreline Erosion • Fish and Wildlife Preservation • Other Water Related Resource Projects Studies related to the consequences of the 2005 hurricanes * All efforts fully coordinated with the Louisiana Coastal Protection and Restoration Project (LACPR) team

  3. Six Step Planning Process

  4. Steps for Large Scale Data Collection • Determine Extent of Study Area • Define Data Needs • Determine if Data Currently Exists • Determine Validity of Existing Data • Identify the Collection Method and Acquire Data • Compile Data for Use • Create Metadata

  5. Major Issues • Monetary and Resource Limitation • Time Constraints • Scale/Scope Can be Overwhelming

  6. Major Challenges • Safety of Team Members • Cooperation of Agencies • Availability of Existing Data • Meets Desired Confidence Level • Data May Not Overlap Your Area

  7. Determine Study Area Extent • Maximum Area of Evaluation • Will Dictate Your Data Collection Methodology • Can be Subdivided into Multiple Levels • State, County, City, Census Tract, Census Block

  8. Examples of Economic Data Needs • Immediate Recovery Statistics • Socio-Economic Characteristics • Population • Income • Employment/Unemployment Rate • Structure Characteristics • Location • Occupancy Type • Extent of Damage • Value (Structure and Content) • First Floor Elevation

  9. Existing Data Sources • Sources of Existing Data: • Local, State, and Federal Government Agencies • Academia • Non-governmental Organizations (NGO’s) • Issues and Challenges: • May not Perfectly Overlap Your Study Area • Cooperation of Agencies • Verify Quality and Validity of Data • Meets Desired Confidence Level

  10. Data CollectionDetailed Field Analysis • Typically a Feasibility (Census) Level Analysis • Team Members Carefully Drive the Area and Collect Data at the Structure Level • Analyze Entire Population of Data • Data Should be Extremely Accurate • 95+ percent confidence level

  11. Data CollectionDetailed Field Analysis (Cont) • Pros: • More Accurate than Sampling • Often the Preferred Method of Data Collection • Issues and Challenges: • Extremely Time Consuming • Extensive Money and Resources • Safety of Team Members

  12. Data CollectionSampling Techniques • Random Sampling Techniques; • Simple Random • Stratified Random • Cluster • Multistage • Systematic

  13. Data CollectionSampling Techniques • Pros: • Faster than Detailed Field Analysis • Requires Less Money and Resources • Issues and Challenges: • Not as Accurate as Detailed Analysis • Utility and Confidence Level is Limited to Existing Data • Increased Accuracy = Increased Complexity

  14. Compiling Data for Use • Creation of a Database is CRUCIAL • Microsoft Excel and Access • Linux • GIS • Web Based • Issues and Challenges: • Quality Control is a MUST

  15. Create Metadata • Metadata is Data about Data • Important for You and Other Users • Examples of Metadata Include: • What is the Data? (structures, content, etc.) • Where Did the Data Come From? • What Was the Data intended to be Used For? • When Was the Data Collected? • Who Can Have the Data?

  16. MsCIP ExampleExtent of Study Area

  17. MsCIP ExampleTax Parcels in the MPI

  18. MsCIP ExampleMagnitude of Scope

  19. MsCIP ExampleMagnitude of Scope • 1,361 sqmi Area (100 sqmi Larger than Rhode Island) • Over 200,000 Parcels • 800 – 11X17 Parcel Maps • Over 3,000 man-hours

  20. Data Collection MsCIP Example • Cluster Sampling Technique with Vigorous Field Analysis • Team Members Drove Every Street within the MPI Area (1,361 sqmi) • Areas were grouped by Blocks, Neighborhoods, etc. • Field Collection was Conducted by Group

  21. Data Collection MsCIP Example • Pros: • More accurate than Sampling Alone • Faster than a Detailed Field Analysis • Issues and Challenges: • Not as Accurate as a Detailed Field Analysis • Safety of Team Members • All Team Members MUST be on the Same Page

  22. MsCIP Lessons Learned • Get Everyone on the Team Involved Early • You can NEVER over Verify Existing Data • Have a Defined QC Plan Up Front • Field Journal for Every Team Member

  23. QUESTIONS?

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