GIS 4048 Module 6: Homeland Security - Prepare MEDS

This week was our second module within the broader category of GIS use in homeland security. Last week the focus was localized and specific to crime in Washington, D.C whereas this module expands our focus to consider the data needs in order to prepare for and respond to national security incidents. To do this effectively, cities, states and the nation need to have data on hand, organized, processed, and compatible with other datasets.

While we may expect a certain degree of preparedness in geospatial data on the parts of various governmental entities, the critical importance of these data for efficient and effective response to security incidents (whether human or naturally caused) requires coordination to ensure different datasets work together and are available for all users who may need them. This geospatial data coordination forms a small part of the larger aim of the Homeland Security Presidential Directive-8 to guide governmental entities at all levels to prepare for emergencies. This Directive outlines scenarios to which governmental entities must be prepared to respond (National Planning Scenarios); a list of approximately 1600 tasks for entities to incorporate in their preparedness planning (Universal Task List); and a list of 37 capabilities necessary for effective preparedness and response (Target Capabilities List).

Foundational for geospatial preparedness is the concept of Minimum Essential Datasets (MEDS). As the name indicates, MEDS defines the types of data needed to maintain the required level of preparedness for emergencies. This is applied primarily to areas deemed to be most at risk (e.g., Tier 1 urban areas). The data needed to meet the MEDS requirement are as follows: orthoimagery, elevation, hydrography, transportation, boundaries, structures, land cover, and geographic names. The table below goes into detail on the requirements for each dataset.

Table 1: From Fiscal Year 2009 Homeland Security Grant Program Supplemental Resource: Geospatial Guidance, available at http://www.fema.gov/pdf/government/grant/hsgp/fy09_hsgp_geospatial.pdf

This week's exercise guided us in importing, processing, and organizing the required data outlined in MEDS for Boston in preparation for our analysis next week of the 2013 Boston Marathon bombing. Some of the data needed no processing (such as the boundary of the Boston urban area) while others needed to be masked, clipped, or selected to cover only the needed extent. The transportation data, originally in a single file, were separated into three layers, each representing a road category and symbolized accordingly. The geographic name data were imported in table form and added as XY data in ArcMap. All layers were projected into the Massachusetts Stateplane coordinate system. After all data layers were organized in a geodatabase and symbolized accordingly, they were each saved as a layer file. This ensures the layers retain their symbology when they are added to additional map documents. 

The result is a well-organized geodatabase and layer files with data processed and ready for further analysis. Such analysis will be the task in the next module.

GIS 4048 Module 5: DC Crime Mapping

Figure 1: Map of police stations categorized by number of crimes nearby; depicts the site of a proposed new police station.

Figure 2: Kernel density maps of three offence categories.

Our task this week was to make use of a variety of tools to analyze crime that occurred in Washington, D.C., in January of 2011. We began by geocoding police station locations using a CSV file. Only one of the stations needed to be manually located on the map. We then imported crime data from another CSV file. In order to analyze the crime and police station data, we performed two spatial joins. The first join involved a multiple ring buffer of half a mile, a mile, and two miles. This gave us an idea of the number of crimes that occurred at different distances from police stations. The second join was to police stations themselves, letting us know the relative number of crimes that occurred closest to each station.

The second layout depicts three kernel density maps, one for each of three crime categories. Kernel density maps work by using a user-provided radius (1500 square kilometers in the above case) and summing the values of each crime instance. The kernel density analysis assigns the highest value to the location of the incident; the value then decreases out to the radius. This gives us an idea of where potential crime hotspots may lie. Assuming the analysis is sound, it also helps mitigate the contingency of the exact locations of each crime in our interpretation. In other words, the specific location of a crime may be arbitrary, but the larger hotspots seen in a kernel density analysis may tell us more about areas of high crime potential and that need attention.

GIS 4048 Module 4: Natural Hazards - Hurricanes

Figure 1: Map layout depicting hurricane Sandy's path with storm categorizations and data readings.

Figure 2: Side by side comparison of one street in Toms River Township showing the visible damage extent of the storm.

Continuing the previous modules' focus on GIS applications to natural disasters, this week we focused on some of the GIS tasks that may be utilized in response to hurricanes. Specifically, we looked at Hurricane Sandy and the damage sustained by Toms River Township, New Jersey in October of 2012.

Compared to the previous module, our task this week involved more work in organizing data into two geodatabases. Raster datasets (seen in Figure 2 as the before and after imagery) were imported into two newly created mosaic datasets within one geodatabase. New Jersey reference data layers, such as roads and townships, were imported into one feature dataset. Keeping data well-organized may take time, but doing so will more than likely save time for the original map creator and especially anyone else working on the project. In my internship I have seen examples of poorly organized data that have forced me to spend time looking for data in obscure places. Had some time been invested in keeping data organized and well-labeled, my task would often have been easier.

Figure 1 depicts the path of Hurricane Sandy along with data collected at various points. The points were imported from Excel spreadsheet data and symbolized based on storm category. This layer was used as input for the Points to Line tool to create the polyline depicting Sandy's path.

Figure 2 symbolizes parcel damage based on a visual assessment of the imagery. The structure damage layer was created manually and symbolized according to damage level. A major part of the lab was the creation of attribute domains for the geodatabase in order to restrict the possible values of the structure damage layer's attribute table to a set of coded values. Not only does this make the completion of the attribute table more efficient as each point is digitized, it also helps minimize error when multiple people are entering data.