The idea is to create standards and essential data sets for urbanized and large areas in order to create a foundation necessary for the U.S. security community to properly respond to an emergency situation. MEDS is created using data from various internet sources and are prepared in a large rang of formats, including shapefiles, rasters, tables, and geodatabases. The data collected conforms to specific themes and must be current to at least two years for urban area and current to five years for larger areas. The data collected falls under these categories:
Orthoimagery: High resolution aerial imagery.
Elevation: Digital Elevation Models, usually from LIDAR imagery, showing terrain elevation.
Hydrology: Lakes, rivers, spillways, dams, etc.
Transportation: Streets, railways, highway infrastructure, airlines, etc.
Boundaries: Political boundaries like State, County, Township, and possibly tax parcel information.
Structures: Important critical infrastructure
Geographic Names:Information about the physical and cultural geographic features, collected by GNIS
The data themes make up the backbone of the nations ability to respond to effectively to natural disasters, terrorist attacks, crisis, or any emergency involving federal or local government involvement.
This goal of this weeks lab was to prepare our data as it would be prepared if it were part of a DHS MEDS dataset. This is what my final geodatabase looked like.
To start we'll clarify that this database was prepared for a specific study area inside of Boston Massachusetts, specifically preparing for the response to the 2013 Boston Marathon bombing. Let's take this data layer at a time. I'll only be discussing layers that I made changes too, some were simply imported like the Hydrography, Orthoimagery and Elevation layers.
Boundaries: This data layer includes the Massachusetts county layer, the city of Boston and surrounding cities as well as a 10 mile buffer around those cities to create the study area. Aside from checking projections nothing was done to these layers.
Transportation: Initially the transportation layer contained a single feature containing all the roadways in the study area. To prepare it for my study the roads were joined to the CFCC code table, adding the CFCC road codes to each feature. I then separated out the roads I wanted by their CFCC codes and created separate feature classes for each one. As in the image above, the Local, Secondary and Primary road layers were pulled out of the original feature then symbolized and set to only display at a relevant extent to avoid clutter on the map.
Landcover: To symbolize the landcover raster appropriately I used a colormap issued by the National Land Cover Database, this gave me the appropriate coloration and labels for the raster.
Geographic Names: This feature was given to me as a text file containing the locations names and XY coordinate data. I created a point layer from the XY data, projected it to match the rest of the geodatabase and then selected and exported the points inside the study area for our map.