Homeland Security - Prepare Minimum Essential Data Sets

Applications in GIS - Mod 6

The Department of Homeland Security, recognizing the importance of geospatial information and technology in securing our nation, has developed the DHS Geospatial Data Model (DHS GDM) in support of urgent DHS mission requirements. This standards-based logical data model is to be used to collect, discover, store and share homeland security geospatial data.

During a crisis, emergency operations personnel are dependent on a comprehensive geospatial database that is prepared, ready and available for immediate use.  The MEDS layer configuration is a standardized file and directory structure that enables inter-operable and efficient data analysis in times of crisis.  To this end, a series of Minimum Essential Data Sets (MEDS) for urban areas and large area spatial extents was established as vital to successful homeland security operations.  There are eight MEDS. 

Boundaries are used to define the geographic area of interest.  Urban areas are defined as Tier 1 and Tier 2. Tier 1 Urban Areas are the largest, most populated metropolitan centers in the country.  These boundaries are set by mapping the extent of the combined entities in the metropolitan centers and a 10 mile buffer beyond. 

Elevation is represented by digital elevation models (DEMS), or digital  terrain models (DTM).  They are representations of continuous elevation values over a topographic surface typically used to present terrain relief.  They are used to make 3D graphics that display terrain slope, aspect, and profiles between selected points.

Geographic Names from the Geographic Names Information System (GNIS), developed by the US Geological Survey (USGS) contains information about physical and cultural geographic features in the US and associated areas.  This database contain federally recognized names of features and defines the location by state, county, USGS topographic map and geographic coordinates.

Hydrology includes information about surface water features such as lakes, ponds, streams, rivers, springs and wells and other hydrologic areas that have great significance to homeland security such as spillways and dam weirs.  These critical infrastructures that control the flow and direction of flood waters require close surveillance and protection from possible attack.

Land Cover data sets are important to homeland security planning and operations.  They provide detailed information about land uses and ground conditions which assist in preventing and mitigating the impacts of a catastrophic event.

Orthoimagery is aerial photography that has had distortion and relief displacement removed so ground features are displayed in their true position.  This type of imagery allows direct measurements to be made while providing a realistic visualization of the landscape.  This ability for first responders to view geographic features that may not be mapped can reduce response times considerably and save lives.

Transportation includes roads, railroads, airports, ferry crossings and more.  Each plays a critical role in our lives and as such are attractive targets to terrorist.  Transportation systems are used not only to transport people from place to place, and consumer goods to distributions centers, but also to transport highly toxic and volatile substances.  For this reason they pose a significant public safety and security threat that must constantly be guarded against.

Structures consists of significant buildings and facilities that are focal meeting places for large groups of people or house critical infrastructure heavily dependent on for everyday operations.  This could include government offices, police stations, power lines and substations, sports arenas and entertainment centers, houses of worship, schools, hospital, and more.

This weeks lesson was to put together a MEDS package.  While we didn't have the time to download all the data ourselves, we did have to organize the data by creating group layers and manipulate some of the data so it could be analyzed.

We started by creating group layers for each of the MEDS except Structures and adding to them the datasets that had been downloaded for us.  Next we manipulated the transportation data by joining a table of Census Feature Class Codes (CFCC) to our roads layer.  We then used this code to create separate local, secondary and primary roads we could then symbolize individually.  The Land Cover needed to be clipped to the boundary area and symbolized according to the National Land Cover Database standards.  The Geographic Names file had all the data jumbled into one column so we had to adjust that before we could even add that to the map and define the projection.  Once that was done we were able to do a Select by Location to create a dataset of just those points that fell within the boundary of our study area.  None of the other data sets required any manipulation other than putting them into a layer group.  With all that done we were then able to save all the layer groups to layer files that could be shared.