APA Digital Coast Needs Assessment Survey
The total data are summarized below. Click on a region for specific regional data:
Respondents' data needs for planning practice/analysis/research (Question #12)a
|DATA SET||None||Current Data||Time-Series Data||Data Projections||TOTAL|
|Bathymetric Elevation (depth of lake or ocean floors)||300||44||277||40||69||10||41||6||687||100|
|Benthic Environmental Data (bottom of rivers, lakes, or oceans)||268||39||297||43||88||13||34||5||687||100|
|Building Quantities & Types (commercial, industrial, residential)||58||8||318||46||222||32||89||13||687||100|
|Economic Output by Business Sector||112||16||236||34||179||26||160||23||687||100|
|Employment by Business Sector||108||16||241||35||174||25||164||24||687||100|
|Land Cover and Land Cover Change||39||6||208||30||332||48||108||16||687||100|
|Marine Jurisdictional Boundaries||259||38||371||54||34||5||23||3||687||100|
|Population Attributes (age, race, education, etc.)||79||11||220||32||177||26||211||31||687||100|
|Relative Sea Level Rise||154||22||114||17||158||23||261||38||687||100|
|Risk Management Data (e.g. storm surge, floodplain, etc.)||69||10||250||36||140||20||228||33||687||100|
|Wages/Earnings by Business Sector||156||23||248||36||151||22||132||19||687||100|
a. Responses to Question #13 (What other data needs do you have to best support your planning practice/ analysis/research?) are provided at the end of this page.
Table 4a lists 21 possible data sets for which respondents were asked to assess their needs to support their planning practice, analysis, and research.
The five data sets with the highest percentage of respondents indicating they had no need are:
- Navigational (48%);
- Bathymetric Elevation (44%);
- Benthic Environmental Data (39%);
- Marine Jurisdictional Boundaries (38%); and
- Wages/Earnings by Business Sector (23%).
The seven data sets (three tied at #5) with the highest percentage of respondents indicating they needed current data are:
- Land Elevation (68%);
- Archaeological/Cultural Resources (63%);
- Marine Jurisdictional Boundaries (54%);
- Aerial/Satellite Imagery (52%); and
- Building Quantities & Types (46%);
- Coastal Habitat (46%); and
- Water Quality (46%).
The five data sets with the highest percentage of respondents indicating they needed time-series data are:
- Land Cover and Land Cover Change (48%);
- Land Use (36%);
- Aerial/Satellite Imagery (35%);
- Building Quantities & Types (32%); and
- Water Quality (32% each).
The five data sets with the highest percentage of respondents indicating they needed data projections are:
- Relative Sea Level Rise (38%);
- Risk Management Data (33%);
- Population Attributes (31%);
- Population Counts (31% each); and
- Shoreline Erosion (30%).
Table 4b below shows the top five needs in each of the three data types (current, time series, and projections). Only two data sets show up in more than one category: aerial/satellite imagery, and building quantities and types, both ranking highly for both current and time series data.
What Planners Need: Data Top Five in each Category Ranked by % of Respondents
What other data needs do you have to best support your planning practice / analysis / research? (Question #13 — 100 free responses)
- Aerial imagery
- Maryland DNR coast line change vector file generated from the MDDNR air photos....pretty rudimentary, but evocative to many.
- google earth style aerial oblique photography "birds-eye" fashion, showing existing conditions in 3-d, and google street view type photography.
- IR Images showing flora. Temperature images showing heat islands. Having these two images in time series for both winter and summer would be very helpful for research on micro climates that reduce energy use.
- Agricultural Best Management Practices, time series.
- bathymetric survey data for near shore arctic navigation and channel discovery
- Wisdom from Native American and Pacific Island elders about how land forms have occurred or coastal waters behaved.
- records of storm events w loss of life & property damage, flood hazard maps, high/low tides
- Complete sets of flood maps and historical atlases dating back to 1840s.
- Demographic/ environmental research which accurately captures past conditions is most useful- those records are the most scattered
- wages per sector or per job, time series
- Probable import / export loads, especially as a result of Panama Canal expansion
- fragility of near shore environments as it relates to urbanization (for example, Land Use change and its impact (projections) on water quality (marine) and near shore environmental degradation.
- Critical habitat
- accurate location of sensitive marine environments like eelgrass, feeder bluffs, etc.
- Brownfields and environmentally damaged areas
- priority terrestrial and aquatic habitat; green infrastructure
- The health of an artificial reef project off the Southern California coast
- Up to date information on sub aquatic vegetation (SAVs) is also critical.
- Protected Species
- habitat location, point source pollution, change in coastlines
- Current and projects for riparian and coastal vegetation
- Greenhouse gas emission projections
- Current and transport of oil and other pollutants by currents
- wetland boundaries; hazardous waste
- Predicted habitat distributions under sea level rise scenarios (SLAMM analysis)
- RTE species habitat/locations, time-series. Tree/vegetation species distributions, time-series and projections.
- Conservation easements/restrictive covenants, current data or time series.
- LAND USE/POPULATION/INFRASTRUCTURE
- Settlement patterns
- Residential units and jobs by parcel
- Land Use Projections Land Development
- projected population growth
- housing quality, utility lines.
- Amount of land/water area devoted to particular land use categories based upon SIC or Agricultural SIC manual in square feet/acres
- Infrastructure (Capital Improvement Projects, ie roads, wastewater/sewer/stormwater)
- Housing stock, location, condition, land ownership
- Change of use (ie; from industrial to mixed use, or habitat reclamation)
- Condition of infrastructure- streets, lines, etc.
- Municipal service district boundaries
- power and utility transmissions impacts on environment and residences
- location of state facilities
- Development capacity, current data
- Government land with managing agency, current data.
- The military is currently developing studies on the future of compatibility between the military mission and the needs of the civilian. The military uses the coastal datasets to help determine the future US Fleet operations in the coastal areas to the Operation areas off-shore.
- PARCEL INFORMATION
- Up to date parcel information is important, including the location and ownership of active/abandoned bridges, and any submerged ship wrecks, or buried utilities.
- Property ownership, easements, right-of-way, utility billing, tax assessed/collected, lease data, assets
- property ownership, assessment and transaction history
- hunting mgt
- Recreational capacity/use
- earthquake zones
- geologic hazard
- land subsidence projections
- Potential landslide areas- soils, geology
- Elevation data needs to be uniform in resolution for statewide projects. Shoreline erosion data and projections are needed for the mid-Atlantic, especially in estuarine areas.
- High resolution elevation data along the coastline (Lidar) to project sea level rise impacts.
- superposition of future changes in sea level and shoreline on current coastal bridge and roadway network geographical layout
- Transportation data, traffic counts
- Transportation infrastructure
- transportation infrastructure current and projected
- Transportation counts
- Transportation related data
- traffic flows
- river channel migration - time series
- Tsunami inundation areas. Maybe current. Tides can be pretty extreme and I wonder if at the highest tide of the year would a tsunami be even worse than at average or a low tide. Inundation lines are needed and probably should show worse case scenario.
- flood insurance maps; tsunami inundation maps; long-term sea level rise maps
- It would be nice for FEMA to add sea level ries projections to their flood maps so that we can better manage development in those areas.
- Detailed high/low tide (and average) coastline data
- Floodplain, flooding and lake/ocean level data
- Storm runoff into subwatersheds.
- hydrological change over time; wetland boundaries; wetland quality; TMDL allocations
- flood plains for small streams
- drinking water aquifer supply and demand
- River flows, time series
- Storm water flooding
- sea level rise – projected
- vulnerability, where would sea level rise first have an effect, cost-effectiveness of different options related to seal level rise
- Groundwater recharge and water use
- Storm surge projections for current and future conditions.
- Stormwater infrastructure, time series.
- Wind forces
- tidal and off-shore wind resources
- zoning in the coastal zone
- NONE/INCLUDED ABOVE (17)
- NOT APPLICABLE (4)
- NON-TOPICAL COMMENTARY
- For many practice areas, we really need both the current data and the time series to place information in context for future planning.
- I don't know what time-series or projections mean, so I selected 2 for all.
- All listed above saying "none"
- Answers of "none" above indicate data our agency collects and maintains. We need it, but not from an outside source.
- Many of the items I marked as projections would also be very useful as current and time series data.
- For most items marked "time series," projections are also needed.
- Margin of error analyses
- Any data would be useful. If not time series data, the most current available is critical.
- Primary need is to make sure available data can be meaningfully used at the local level.
- LIDAR is needed
- Most of the current data is available in some form, but not always easy to use if you are not a DIT person or have it in a form the fits with the system being used. As a planner, projections are helpful to plan for the future. Most current data is easily available on-line.
- In each time series data selected, I would also want current data first; so it's really 2 and 3
- SURVEY CRITIQUE
- #12 ov poorly worded and formatted.
- #12 was hard to answer - not sure I understood the 3 categories
- In this section I would have chosen multiple types of data sets for different categories.