GIS 4102/5103: Week 7 - Working spatial data

Unfortunately, I'm 1,000% positive I failed this assignment.  It's been an extremely hectic week, both with school and work.  I've been working 12 hour days every day this week and then I come home and work for hours on my schoolwork.  And having ADD doesn't help much, either, because my brain can only focus until around 8/8:30 at night.  But with the hours I've been putting in this week, it's a lot earlier.  
I unfortunately got a late start to this assignment and that turned out to bite me in the ass.  
I came across issues earlier tonight with step 7, and using a for loop to populate the empty dictionary I created.  I ran across these issues around 6:00, in which I assumed I wouldn't get a response in time, if I were to post to the forum.  I read through all of the posts and comments, word for word, a million times each.  I researched how to complete this task, I successfully completed all of the exercises, I also read the book.  Nothing seemed to help me in any which way.  I did try different scripts, and codes, all of which proved to be ineffective.  
I also didn't quite understand where to place the print methods.  The image below shows what I ended up with.  Towards the end of this assignment, I started to give up.  Everything I tried would either produce an error, or not give me the result I wanted.  
I am really hoping to get some feedback on this assignment.  More than anything, I want to learn how to run the script correctly, how to complete the assignment.  That's what's so frustrating to me, because I'm an extremely hard worker, and a very hard critic towards myself.  I feel like I'm letting myself down because I didn't have enough time to complete the assignment.  I promise this won't happen again, it's just been an extremely stressful week. 
And again, if you could please help me try to understand this better, or even at all, that would be greatly appreciated.       

GIS 4048: Week 6 - Prepare MEDS

The minimal essential data sets (MEDS) is a standards-based geospatial model used by local, state and federal governments for the collection, discovery, storing and sharing of data for large urban areas.  The MEDS provide the foundation necessary for Homeland Security to carry out the key national security strategy objectives: preventing terrorist attacks within the U.S., reducing the country's vulnerability to terrorism, and minimizing damage and accelerating recovery from natural or man-made disasters.

The analysis of MEDS allow for identification of features that maybe vulnerable and shows possible means to protect them.  The data sets could also be used following an incident to provide information on certain areas, such as the best route in and out of an area to provide a response to thee incident. 

MEDS consists of: hydrography, orthoimagery, land cover, elevation, geographic names, transportation, boundaries, and structures. 

The National Hydrography Data (NHD) provides data regarding lakes, ponds, streams, rivers, springs and wells.  The data set also includes the location of dam spillovers weirs to control the direction and flow of floodwater.  The structure data set identifies structures of importance that may be targets for terrorist attacks such as: areas of large congregation of people, government buildings, office buildings, research centers, large sports venues, churches, military installations and historical structures. 

Orthoimagery is a data set of aerial photographs that have been corrected to remove distortion and relief displacement so that direct measurements of locations, distances, and directions can be made.  Ortho corrected images also provide a realistic view of the landscapes. 

The land cover data set provides information of tree canopy, percent impervious surface and 16 classifications of land cover shown at 30 meter resolution.  This allows homeland security personnel to get a layout of the land. 

The elevation data set consists of digital elevation models (DEM) generated by the USGS. DEMs contain information regarding the elevation and terrain of a certain area.  The data can be used to generate 3D models of the terrain.

The geographic names data set contains the federally recognized names for physical and cultural geographic features in U.S. both current and historical locations.

Finally, the transportation data set classifies roads as local, primary, and secondary roads. They are defined using the Census Feature Classification Code (CFCC), which ranges from A00 to A75. The classification allows security analysts to identify roads for evacuations and the security risks if compromised. 

To prepare the MEDS for the Boston Metropolitan Statistical Area (BMSA), the data was organized into group layers according to the type of data.  Prior to grouping the transportation layers together, the road classifications had to be identified and were selected from the original file of Massachusetts Roads.  The roads were then symbolized and set to be shown at a scale of 1:24,000 or larger so the roads could easily be seen, while still making the map readable.  The land cover data was extracted as a mask to lie within the BMSA boundary and converted and shown as a color map.  The symbology was labeled with descriptions.  The geographic names were projected to the appropriate state plane for Massachusetts and then the features were named according to the Feature_Name.  The names were set to appear at a scale of 1:24,000 or larger.  Lastly, the group layers were saved as layer files (as shown below), which preserves the symbologies, labels zoom settings, and other manipulations of the data.  The layer files allow for the sharing of data especially in a crisis situation. 

GIS 4102/5103: Week 6 - Python Geoprocessing

This past week we learned how to manipulate tools in ArcMap by using Python.  Shown below are the results of using three different tools: AddXY, Buffer, and Dissolve.  I was able to add XY coordinates to the attribute table of the hospitals.shp layer by using the AddXY tool.  I also created a 1000 meter buffer around the hospital features using the buffer tool.  And finally, I learned how to use the dissolve tool which dissolves the hospital buffers into a separate, single feature.  I also managed to enable overwrite output so can continue to run the script, even if the file exists.  Last but not least, I printed the messages using the GetMessages() tool, which is what's shown below.  
I enjoyed this assignment because it was pretty easy to comprehend and it wasn't that difficult.  I feel like I'm beginning to understand Programming more and more as the class goes on.  I'm looking forward to learning more tools and different scripts. 

GIS 4048: Week 5 - HLS DC Crime Mapping

This week's assignment was like a dream come true.  I have always loved law enforcement with a huge passion and have always been so fascinated and curious by everything they do. When it comes time for me to find a new job, I'm strongly considering becoming a crime analyst for a local police department.  The assignment this past week was to create a map showing the crime occurrences in relation to local police departments within Washington DC.  We were also asked to create a map showing the crime density for burglaries, sexual abuse, and homicides in the DC area. 

The map shown below portrays the total crime in relation to local police departments.  The different types of crime are listed and color coded based on their severity.  Theft, being the most common, is also the least violent.  You could also find out that information by referring to the graph in the lower left of the map.  The graph shows the number of crimes committed and the severity of those crimes.  The police stations are shown using graduated symbols, which represent the percent of crimes by those departments.  I displayed the block groups by use of graduated colors and a Natural Breaks classification method.  The different colors represent the population density throughout Washington DC. 
I strongly debated on showing the different crimes on the map because I felt it looked too cluttered. However, since I was asked to provide the map with a proposed police station, I thought it best to include the crime layer.  That way, you're able to see the severity of crimes in relation to the proposed police stations. 

The map shown below represents crime density showing burglary, sexual abuse, and homicide within Washington DC.  These maps were created using a search radius of 1500 in the Kernel Density tool.  The density analysis portrayed are based on the following counts of each offense.  Burglary: 339 cases, Sexual Abuse: 15 cases, and Homicide: 9 cases. The density shading is also overlaid over a population density by square mile.  As you can see, burglaries are very much higher in concentration around the more densely populated area. This is still true for sex crimes, however these increase to the mid and mid east DC areas. Homicides, on the other hand, are less frequent and are less centrally located.

Overall, I really enjoyed this assignment.  It gave me an idea as to what I might accomplish as a Crime Analyst.  I found this assignment to be one of the most fascinating ones, yet.  On top of it all, I got to learn how to use a new tool.  I don't think there were any huge challenges for me this past week.  However, the map displaying the crimes in relation to the police departments took a lot longer than expected.  I'm really looking forward to seeing the rest of the Homeland Security assignments, and seeing what else I can do. 

GIS 4102/5103: Week 5 - Geoprocessing in Arc

This past week I have been learning how to use geoprocessing within ArcMap.  I learned how to create a new toolbox and I was able to better my skills within the ModelBuilder. Using the ModelBuilder, I was able to create a new output shapefile, as shown below.  I also became acquainted with converting the model into a script.  Overall, this assignment really wasn't too hard.  I feel like I'm finally starting to understand the language.  I'm looking forward to learning more throughout the rest of thee semester.  

GIS 4048: Week 4 - Hurricanes

In the immediate hours after a hurricane's landfall, there is a need for damage assessment. This past week I got to work as a GIS technician for a major university, in which I have been contracted by a private company to digitize/assess damage caused by Hurricane Sandy at the site level.  In doing so, I created a map showing Hurricane Sandy's track history during the month of October in 2012.  Looking at the map, you'll notice that the states that are most impacted are shown in yellow with a green diagonal line.  The rest of the states are shown in grey.  By tracking Hurricane Sandy, you can see the mile per hour winds and the Bar. Pressure.  

I also created a map showing the property damage caused by the tropical storm.  The image at the top of the map, located below, shows the imagery before the storm hit. Looking at the bottom imagery, you'll notice how much damage was caused by the storm.  You'll also see that there are different points in the bottom imagery.  Those points represent the property damage.  You'll notice that the buildings that are closest to the water have more of an impact than those further away from the water.  I also provided two inset maps showing the location of the Damage Assessment Area.  

Overall, I really enjoyed creating these two maps.  I learned multiple new tools this past week, and I'm looking forward to learning more.  The creation of these two maps went pretty smoothly for the most part.  I did have one challenge, however.  Upon the creation of my first map, I think I messed up the projections somehow, which caused me to start from scratch.  Fortunately, it was only a small hiccup and I was able to complete everything in a day's work. 

GIS 4102/5103: Week 4 - Debugging and Errors

This past week focused on Debugging and Errors.  We had to ensure that three different scripts would run properly, without any errors.  I think it's fairly easy to be able to find and locate different errors and be able to correct them.  In the first image located below, we were given instructions to correct the two errors/exceptions located within the script.  After doing so, the script should successfully print the names of all of the fields in the parks.shp attribute table.  In the second script provided, there were eight errors/exceptions which needed to be fixed.  As seen in the second image below, the script successfully prints out the names of all the layers within the data frame.  Finally, the third script provided contained errors that were preventing it from running.  Rather than fixing those errors, we were asked to use a try-except statement.  I struggled with this part of the assignment, a little bit.  I was unsure where to place the try-except statements.  But fortunately, I was able to figure it out and run the script without getting any error messages.  In the third image below, Part A prints an error statement stating the problem.  Whereas Part B encountered no errors and successfully printed out the Name, Spatial Reference, and Scale of the data frame. 

GIS 4048: Week 3 - Tsunami

This past week we focused on tsunami evacuation zones in Japan.  In the image below, you'll notice a multiple ring buffer which shows the nuclear evacuation zones.  The enlarged inset map provides a look at the coastline as well as the tsunami evacuation zones.  I also provided another inset map which shows the vicinity of Japan.  You'll also notice that on the lower right side of the map, there's a table explaining the radiation effects. 
I became reacquainted with the Multiple Ring Buffer when creating the Nuclear Evacuation Zones. I was also able to get more practice with the model tool to create the three tsunami evacuation zones.  
The only challenge I really faced during this assignment was converting the .xls file into a shapefile.  My mind went completely blank when I tried to complete that task.  Fortunately, I was able to figure it out.  Overall, I really enjoyed this assignment.  I love putting maps together and seeing the end result. 

GIS 4102/5103: Python Fundamentals Part II

This assignment took a lot longer than expected.  This past week, we practiced using the while loop, the if statement, along with some other functions.  We were asked to create a list of 20 random integers between 0-10, which you'll find in the image below.  That list was created using the while loop and the .append function.  And then we were asked to remove an unlucky number from that previously created list.  I was unable to figure out the .remove function, so instead I used the if and else statements followed by the while loop.  

This assignment was definitely a challenge for me.  The most difficult part for me is understanding the language.  But I'm hoping to learn more and be able to understand everything as the semester goes on.