Lab 06 - MEDS Prepare
GIS 4048
The Minimum Essential Data Set (MEDS) was born from Homeland Security Strategy Objectives as compiled by the White House on July 16, 2002. These objectives were passed down to the Department of Homeland Security as Presidential Directive 8 which 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." The MEDS is made up of data from multiple internet sources. The minimal data for a MEDS dataset may include, Shapefiles, Raster Dataset, Tables, and GeoDatabases that will assist in times of emergency.
The data within MEDS is comprised of shapefiles from a variety of useful datasets which are grouped together based on their types. The following is a list of some of the recommended datasets that are included in the MEDS.
Orthoimagery provides aerial imagery of the area, and requires a higher resolution. A resolution of 1 foot is typical for this Raster Dataset. This imagery assists responders in giving them a bird's eye view of the area.
Elevation is typically provided by a Digital Elevation Model (DEM). These DEMS can be obtained from the USGS or if a larger area is needed NMSS can provide DEM datatsets from the National Elevation Dataset (NED).
Hydrography can be obtained nationally from the USGS and U.S. Environmental Protection Agency (EPA) from their National Hydrography Database, which is also a component of the National Map.
Transportation data can be obtained from the National Map, and can also be updated from Federal, State, and local entities. The transportation layer helps responders with egress planning and identifies what roads are considered Primary, Secondary or Local in nature.
Boundaries are typically identified and obtained from the National Map. As locations become more defined a more accurate projection can be used to make the data more precise as State Plane projections can be used.
Structures, although not obtained in our prepare section, could be obtained from local and state government and can be seen utilizing the aerial imagery to assist in creating a new layer if needed.
Land Cover can be obtained from the National Land Cover Database of 2006. This detailed database provides users 30 meter cell resolution and 16 different classes of land cover. This information is important to homeland security planning and operations, giving the readers a "lay of the land" and helps mitigate the impacts of a catastrophic event.
Geographic Names or Geographic Names Information System (GNIS) was developed by the USGS in cooperation with the U.S. Board on Geographic Names, and contains information about physical and cultural geographic features in the U.S. and associated areas. These records contain both current and historical data.
In this week's lab we prepared the MEDS geodatabase for use in the future. Having information up to date and readily available is key when dealing with MEDS as you never know when a disaster may strike and the data will be needed. Although some of the data was provided to us from UWF, it's important to know where to go to keep this information up to date, and to obtain it for myself in the future.
For this lab we utilized a variety of techniques we have learned in the past to massage the data and make it display the information that is key for the study area, in this case the greater Boston, Massachusetts area. Our first objective was to set up the environment that we would use. To do this we created multiple Group Layers to help organize the data we would be using. In the Transportation Group, I categorized the feature class based off of the CFCC ranges that identified Primary, Secondary, and Local Roads in the area. This was accomplished using spatial join and grouping the roadways based off of the CFCC code. With the new road layers identified and created independently, I next adjusted symbology and scale ranges so that the data was easier to see on the map and clear when looking at different scales. For Hydrography, we worked with adding data to Group Layers, but made no major modifications to the data as it was presented. Land Cover was modified slightly. I used the Extract by Mask tool from Spatial Analysis to extract the Classifications found within the Boston Study Area. Then I imported a Color Map to identify those classifications and assigned them a uniform symbology. After applying the new labels to the features, I saved my copy of the layer. Orthoimagery and Elevation layers, like Hydrography, were added to their corresponding Group Layer with no major changes needed. The final group, Geographic Names, was next and allowed me to work with manipulating formats and schema to prepare data for import. Here we had to modify the schema.ini file to make it a pipe delimited format. This allowed me the capability to import the XY data from the table and create a point feature class of locations and points of interest. This data covered a wide area that we needed to narrow down in our study area. To do this, I first used an attribute search to identify the features that fell within the counties of interest. I then narrowed my results further by taking those features that I selected and identified which were completely within the study area boundaries. The remaining features were saved and added to the Geographic Names group layer. With this completed, I was able to modify the labels and symbology to appear aesthetically pleasing when viewed at different scale ranges. My final task was to make it easier to assign these same settings in the future. To do this, I saved each group layer as a layer (.lyr) file so I could bring up this data in the future and not have to rebuild all the data, label scales, or symbology.
Although this lab seemed short, I believe what we learned this week is vital to being prepared to handle emergencies in the future. It gave me a good understanding of key essential layers, where to find them, and how to prepare the data for use in a specific area. As we never know when or where a disaster might strike, it's good to know that the data is available, and with some help, ready for immediate use.
In this week's lab we prepared the MEDS geodatabase for use in the future. Having information up to date and readily available is key when dealing with MEDS as you never know when a disaster may strike and the data will be needed. Although some of the data was provided to us from UWF, it's important to know where to go to keep this information up to date, and to obtain it for myself in the future.
For this lab we utilized a variety of techniques we have learned in the past to massage the data and make it display the information that is key for the study area, in this case the greater Boston, Massachusetts area. Our first objective was to set up the environment that we would use. To do this we created multiple Group Layers to help organize the data we would be using. In the Transportation Group, I categorized the feature class based off of the CFCC ranges that identified Primary, Secondary, and Local Roads in the area. This was accomplished using spatial join and grouping the roadways based off of the CFCC code. With the new road layers identified and created independently, I next adjusted symbology and scale ranges so that the data was easier to see on the map and clear when looking at different scales. For Hydrography, we worked with adding data to Group Layers, but made no major modifications to the data as it was presented. Land Cover was modified slightly. I used the Extract by Mask tool from Spatial Analysis to extract the Classifications found within the Boston Study Area. Then I imported a Color Map to identify those classifications and assigned them a uniform symbology. After applying the new labels to the features, I saved my copy of the layer. Orthoimagery and Elevation layers, like Hydrography, were added to their corresponding Group Layer with no major changes needed. The final group, Geographic Names, was next and allowed me to work with manipulating formats and schema to prepare data for import. Here we had to modify the schema.ini file to make it a pipe delimited format. This allowed me the capability to import the XY data from the table and create a point feature class of locations and points of interest. This data covered a wide area that we needed to narrow down in our study area. To do this, I first used an attribute search to identify the features that fell within the counties of interest. I then narrowed my results further by taking those features that I selected and identified which were completely within the study area boundaries. The remaining features were saved and added to the Geographic Names group layer. With this completed, I was able to modify the labels and symbology to appear aesthetically pleasing when viewed at different scale ranges. My final task was to make it easier to assign these same settings in the future. To do this, I saved each group layer as a layer (.lyr) file so I could bring up this data in the future and not have to rebuild all the data, label scales, or symbology.
Although this lab seemed short, I believe what we learned this week is vital to being prepared to handle emergencies in the future. It gave me a good understanding of key essential layers, where to find them, and how to prepare the data for use in a specific area. As we never know when or where a disaster might strike, it's good to know that the data is available, and with some help, ready for immediate use.
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