The National Preparedness Goal is a result of Homeland Security Presidential Directive-8 (HSPD-8) and is designed “to achieve and sustain risk-based target levels of capability to prevent, protect against, respond to, and recover from major events, and to minimize their impact on lives, property, and the economy through systematic and prioritized efforts by Federal, State, local and Tribal entities, their private and nongovernmental partners, and the general public.” This information and more is available at: National Preparedness Guidelines
The idea is that GIS can be very helpful in the event of a Natural or Man-made disaster and having the right types of information can save lives. The datasets we are talking about are:
DHS is a huge Federal Agency composed of a number of Agencies:
The idea is that GIS can be very helpful in the event of a Natural or Man-made disaster and having the right types of information can save lives. The datasets we are talking about are:
DHS Minimum Essential Data Sets (MEDS)
-Orthoimagery -Boundaries
-Elevation -Structures
-Hydrography -Land Cover
-Transportation -Geographic Names
An individual's safety has always depended on how well that person is prepared. It is no different when we are discussing municipalities. As we discuss GIS information, we are concerned about:
1. The quality, accuracy and currency of all types of data about a particular location;
2. Efficient and effective methods to optimize data sharing and interoperability between agencies and jurisdictions;
3. Geospatial analysis to provide situational awareness at all stages of a homeland security operation.
Therefore, DHS has promulgated guidelines that a comprehensive geospatial database be prepared, ready, available, and accessible to a community’s needs for the prevention, preparation, response, and recovery relating to any catastrophic event. This is one of the most important components of a successful homeland security operation.
Our task is to compile a MEDS for the Boston area prior to the running of the Marathon - yes, we are traveling back in time. We are tasked to assemble the data to Prepare our map for use in a Homeland Security planning and operations analysis. I collected and re-named the layers indicated above (DHS MEDS) and ensured the projections were consistent. DHS requires that the MEDS data be in the North America Datum of 1983.
There was a lot going on in this MEDS Prepare exercise, and while I don't want to leave anything out, I'll just hit the highlights. I started the project by preparing my environment and setting the default Geodatabase to my BostonData.gdb. This is a good way to start any project and a habit I am glad to be acquiring. After adding data to my map, I needed to Join a table to my Transportation layer. Working with Tabular data is interesting because I normally think of GIS as strictly images that comprise my map. But the tabular data can be highly useful in describing or grouping data to make the presentation more meaningful. I took the Census Feature Class Code (CFCC) table and Joined it to my Transportation layer to better describe the types of roads in the Boston area and then group many of those roads together to streamline my legend. Another "very cool" task here was that I used the Scale Range to hide or not display certain labels at Small Scales so that when you are "zoomed out" the map doesn't appear cluttered. Then, when you "zoom in" or select a much Larger Scale, say Street Level, the labels magically appear. I thought this was brilliant.
Another interesting task was using the Extract by Mask Tool. This tool is found at: Spatial Analyst Tools > Extraction > Extract by Mask. Essentially, you use one layer (extent) to extract features from another layer. I also learned that to create a color map, the input raster dataset must be a single band raster with integer values and a pixel depth of 16-bit unsigned or fewer.
One last area that was interesting to me was, converting the schema.ini file that contained all the GNIS information. The Geographic Names Information System (GNIS), was developed by the U.S. Geological Survey in corporation with the U.S. Board on Geographic Names. GNIS data contains information about physical and cultural geographic features in the U.S. and associated areas, both current and historical (not including roads and highways). The database holds the federally recognized names of each feature and defines the location of the feature by state, county, USGS topographic map and geographic coordinates. The GNIS is the official vehicle for geographic names and it is used by the Federal Government as the source for applying geographic names to Federal maps & other printed and electronic products. The GNIS is also used to provide names data to government agencies and to the public, and provide the Geographic Names data layers to The National Map. In other words, GNIS is a very important system in the GIS world. The problem with the schema.ini file was that the information was not in rows and columns that made sense. When I opened the schema.ini file the first time I could not recognize headings, names, etc. the way it was formatted. So, I had to change the format from CSV Delimited to Delimited and add a parenthetical "pipe." This is the how I made the change in Notepad: Format=Delimited(|). The symbol in the parenthesis is a "pipe"; this character is found on the backslash key and to access it you use "shift" , and then hit the backslash key.
But that's not all folks. For some reason, after I made the change to the .ini file, my data still wasn't neatly formatted as it should have been. I tried closing and opening the file a few times and finally had to exit my remote session (disconnect from the remote server) and walk away for about 30 minutes. On a positive note, I had time to grab another cup of coffee and stretch my legs. When I returned and re-connected to the remote server, "abracadabra" the file opened in perfect form. This allowed me to use ArcCatalog, to right-click MA_FEATURES_20130404.txt and select the Create Feature Class and From XY Table to create my Geographic Names layer.
One final note. I also took a look at the USGS: The National Map website to get a better idea of what is involved if I had to obtain all the MEDS information (the 7 datasets listed above) completely on my own. Wow! That would indeed be quite a time consuming task. I downloaded some of the data, just for fun and it took me quite a while to get to the data; select the appropriate data; request the data be sent to my email account; then download and extract the files on my computer. My map is not yet complete as we will continue to work on the MEDS process next week. But, here's a look at my MEDS layers as depicted in the Table of Contents (TOC). Remember, this is not yet complete, so please don't judge me.
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