Lab 06 - MEDS Prepare

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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.

Lab 5 - DC Crime Mapping

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For this week's lab we took a bite out of crime in the Washington, D.C. area.  My first task was establishing a quality workspace by modifying the environment settings.  Once this was accomplished I saved the map twice in order to preserve my settings for the second map.

In the above map, I utilized a Microsoft Excel Spreadsheet and the Display XY data tool to identify and display multiple crimes that were committed in Washington, D.C. on January 2011.  The analysis conducted was to locate where a new proposed police substation should be.  Using the street addresses provided from a spreadsheet that contained a list of all Washington, D.C. Police Stations, I was able to Geocode their precise locations using a Locator Tool and the Roadway layer provided by Tele Atlas.  Then I was able to mark those locations with unique symbology on the map.  Using a multi ring buffer tool I was able to determine how many crimes were committed within .5, 1, and 2 miles of a police station.  Those findings are in the map.  I was also able to use a spatial join to tie each crime to the closest police station.  With this link I was able to calculate a percentage that each police station covers, compared to the other departments.  With the analysis completed I identified a location on the map that I felt would be the best location for an additional Police Substation.  The location of the substation is ideal to fight the two highest crimes in the city, Theft and Theft from Auto.

The second map that I created was focused on specific crimes.  The Kernel Density tool was used to display these crimes in a "Hot Spot" style of mapping.  Areas where the specific crime occurred in greater frequency showed up on the map in a darker shade.  These areas were compared to the population density using the Swipe tool, and the results were somewhat surprising.  Homicides occurred in low to medium population density areas.  Sex Abuse was more predominant in medium population density areas.  Burglaries, although wide spread throughout the city, were more concentrated on heavily populated areas.  

Overall the project was a great learning experience.  I had some trouble with selecting the proper size of my maps so that there was not too much unused space.  I believe in the end I have a well balanced map and clearly provide the readers with the information I want to present.

Lab 4 - Hurricanes

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In this week's lab we continued with Natural Hazards with the emphasis being on hurricanes and their destructive power.  To begin the lab I was asked to generate the path of Super Storm Sandy which struck the East Coast on October 29, 2012 just north of Atlantic City, New Jersey.  The map points were generated using xy data from a pre-existing spreadsheet.  When the 31 points were generated I was able to plot the path of the storm utilizing the Point to Line tool in ArcToolbox.  I also created a unique symbol to represent the storm's symbology.  Color coding this symbology allowed me the ability to show the intensity of the storm along its track.  Each symbol was then labeled to identify both the wind speed and barometric pressure.  The last thing to do was to add my graticules to identify the longitude and latitude, as well as the basic map essentials.  With this done I was able to produce the map shown below.

The second deliverable in this lab is a Before and After map of a location devastated by Hurricane Sandy's landfall, Toms River, New Jersey.  This part of the lab struck home to me as I am a New Jersey resident.  The towns and shoreline impacted by the storm are locations I frequent often. 

To begin this lab I first consolidated the data I needed into a geodatabase that would be used to work with the data directly.  I created a Mosaic of Before and After images of the area affected by Sandy.  Then I added a new Feature Dataset to the Geodatabase that contained New Jersey Counties, Municipalities, State and Road feature classes.  Adding the Study area I was able to identify the area that I would be studying for structural damage.  With the creation of a new point feature class I was able to add a domain that would allow anyone adding points to this feature class a limited selection on coding the attribute data.  Using effect tools such as swipe and flicker, I was then able to compare the before and after aerial images to determine how structures were affected.  I utilized the parcel layer to identify where a marker should be placed, and  I rated how bad the Structural Damage was, as well as the Wind Damage sustained.  I also was able to identify the property type and if the house was inundated.  All these attributes were added to the Structure Damage feature class.  With all the data gathered I then symbolized the damage and prepared a table showing the results.

I identified 9th Street as my primary focus, and began to develop my map with the data provided.  I added the basic map elements and produced the following map.

As I stated above, this lab and the Storm affected me personally.  A week prior to the storm I was at a MACUrisa conference with several GIS Professionals and ESRI representatives in Atlantic City.  At the time, the storm was still in the Caribbean and I sat with one of the top Federal USGS GIS staff in the Northeast.  I asked him directly, "What do you think about this storm in the Caribbean?  You think it will hit here?"  He smiled and said, "No, tho

se storms always come up here and make a right turn.  There's no way it will turn left."  A week later, it turned left.  Hopefully what we learned here in New Jersey will mitigate the hazards we face in the future.