Data and Methods
COGS data selection was a multi-part process where data was sought to inform one of the five COGS themes, vetted against a series of feasibility questions, and selected for prototype 1.0 or not. The five COGS themes that each data layer had to fall under were: Economic Social and Political Conservation Network Landscape Values Climate Change Adaptation The feasibility questions against which each data source was vetted were: Did the data source answer a COGS question/theme? And/or did it serve as a metric for a larger theme (e.g., environmental justice) Could the data source help differentiate between multiple project proposals? Was the data source available, and consistent, at a national scale? Was the data source available at a fine enough grain to include in Marxan? Did the data tell a story (i.e., we were not just using it because it was available)?
Selected Data Sets by Theme
Presented below are the seven data sets that have been selected for inclusion in COGS prototype 1.0. Each dataset is grouped by the COGS theme it falls under and is further sorted by sub-theme. Under each data source is an example of the type of question the data can help answer/identify for the acquisition decision-makers as well as a description of the data source and any caveats that exist.
Land Value – Average value of land and buildings per acre Question – How does the project proposal compare to others in terms of cost per acre? Description – Dataset released by the USDA Census of Agriculture. This is the only full-coverage freely available estimate of land value that the team found. Land value is extremely variable within counties, so this dataset is only appropriate for national-level comparisons.
Social & Political
Environmental Justice/Underserved Populations – GINI Coefficient
Question – How does the project proposal compare to others in terms of ensuring environmental justice for current and future generations of Americans?Description – A measure of income inequality at the county level. The GINI index is a single variable that ranges from 0 to 1, with 0 indicating perfect income equality (i.e. everyone’s income is equal) and 1 indication total income inequality (where only one person makes all the income). In practice the GINI index variable ranges between the two extremes and allows for comparative analysis of areas based on income inequality.
Landscape connectivity – Areas from the USGS Protected Area Database (PAD) and the National Conservation Easement Database (NCED) with Gap Status 1 or 2 Question – How does the project proposal compare to others in terms of supporting development of an ecologically-connected network of lands and waters as a response to climate change? Description – The GAP Status Code is a measure of management intent to conserve biodiversity defined as: Status 1: An area having permanent protection from conversion of natural land cover and a mandated management plan in operation to maintain a natural state within which disturbance events (of natural type, frequency, intensity, and legacy) are allowed to proceed without interference or are mimicked through management. Status 2: An area having permanent protection from conversion of natural land cover and a mandated management plan in operation to maintain a primarily natural state, but which may receive uses or management practices that degrade the quality of existing natural communities, including suppression of natural disturbance. Both the PAD and the NCED use this GAP status code; we pulled out all polygons that had a GAP status of 1 or 2 in the conterminous US. Using these polygons, we developed a GAP status index that combines two metrics: 1) the percent of each planning unit that has GAP 1 or 2 protection, and 2) the mean distance in each planning unit from an area with GAP 1 or 2 protection. In the tool, we can use this “protected status index” as either an opportunity layer (to build off the existing estate) or a constraint layer (to favor currently under-protected areas).
Anthropocentric-derived – David Theobald housing density data Question – How does the project proposal compare to others in terms of site-based vulnerability due to anthropocentric drivers of change? Description – A set of raster layers that show current and projected housing data for the lower 48 states at ten year intervals from 1970 to 2100. Anthropocentric-derived – David Theobald human modification layer Question – How does the project proposal compare to others in terms of site-based vulnerability due to anthropocentric drivers of change. Description – Index of human modification based on land cover type, road layers, energy extraction infrastructure, and other data.
Climate Change Adaptation
Climate-derived – USGS Coastal Vulnerability to Sea-Level Rise Question – How does the project proposal compare to others in terms of vulnerability to sea-level rise? Description – The Coastal Vulnerability Index (CVI) provides a preliminary overview, at a national scale, of the relative susceptibility of the nation's coast to sea-level rise. This initial classification is based upon variables including geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise, and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of coastal regions where physical changes are likely to occur due to sea-level rise.