This is my final project for Applications in GIS. I decided to do my project on the locations of crime density in Houston, Texas before and after Hurricane Katrina. I assumed I would find that the locations of crime rates would shift due to an influx of Katrina evacuees, but my results proved my assumptions wrong.
This map shows the 3 mile buffer around NORAD and the 500 feet security buffer around the airport.
This map shows the ingress and egress points into the 500 ft security buffer around the airport. I added an inset map to show the viewer where the airport is because of the map scale.
I had some problems getting the line of sight analysis to work. I eventually ended up getting it to work by using the elevation layer that was made after creating hillshade.
The first weighted overlay, weight each factor equally. In this situation proximity to the hospital and school carried the same importance as the home values and neighbors ages. When all values are weighted equally the choice places are not all near the school or hospital, the best location for this case is to the west of the hospital. The second weighted overlay takes into account the fact that the clients prefer to be close to work due to traffic congestion. The proximity to work locations now each carry a weight of 40% while the age and home values only carry a weight of 10% each. The result in this case are locations that are much closer to the work locations. My recommendation for the couple is to start looking in tract ID 11(just south of Paradise), this area is close to both work locations (about 2 mi. to each location), has a fairly high percentage of the population in the age range, and also carries above average median home values.
Geographic Information Systems play a large role in disaster response. GIS can help response agencies in the event of Earthquakes, Floods, Hurricanes, and most recently the Deepwater Horizon oil spill. The disasters exact location can be pinpointed with GIS to determine the extent of the damage and where the damage was the greatest. This will also allow the GIS users to determine where relief support is needed the most, what resources are available and their locations, and what infrastructure is available to supply the relief aid. Quickly locating people with high needs, such as elderly, physically sick, or mentally ill, can be done in efficiently in a GIS. Knowing the demographics of an affected area can help the responders deal with any language barriers. For example in Bayou la Batre, Alabama there is a large Vietnamese population. After hurricane Katrina help was slow to get to them because of the language barrier, had a translator gone in with the first responders the situation would have gone much smoother.
GIS is playing an important role in dealing with the Deepwater Horizon oil spill disaster. The mapping of the oil plume and fishery closures has become an everyday occurrence for us now. The fight against oil is also being done with GIS, booms are marked and recorded, and the agencies can tell how much boom is at each staging area. Sensitive areas are marked and protected with extra boom. Unfortunately this disaster isn’t even close to over, but I know GIS will play a large role throughout.
This is a map of the Perdido Bay Quad, with all the layers on it. This map isn't how I would like, but it was by far the most time consuming one to date. There were so many pitfalls along the way from projection issues to data issues.
This one wasn't too bad, it is a map of the closure area in the Gulf due to the Deepwater Horizon Rig. The hardest part was connecting all the points, then tracing the boundaries.
The first deliverable displays the elevation and hydrography of Mississippi's Gulf Coast. This went fairly smoothly, the one thing that tripped me up was symbolizing the water using unique values. Once I remembered where it was located I didn't have a problem.
The second deliverable shows a map of the flooded land and a chart of the percent of land types flooded in the area. I was having trouble resizing the chart the way I wanted, to include the land types at the bottom. Then I figured out to resize the actual chart window before adding it to the layout, lesson learned.
In this module the vegetation data was reclassified into 4 classifications that were included in a certain zone.
This is a model of the tools that were run.
This was probably the most time consuming module for this week, getting all the labels just so was a little tricky.
Q1: Which tool did you use? Was there any noticeable difference between its results and the results from the instructions? I used the intersect tool and did not notice and differences.
Q2: Which tool did you use here? Why? I used the erase tool, because I wanted to remove the conservation areas from the possible camp locations.
Q3: How many features are in this layer? What is the area of the largest feature? What is the area of the smallest feature? 79 features, largest is 1,918.8 acres or 7,765,034.5 sq m, the smallest is 0.185 acres or 748.1 sq m.
This is a map of hospitals around Port-Au-Prince. It shows that almost all of the hospitals have been damaged.
This map of Port-Au-Prince shows how many makeshift camps that have popped up since the earthquake. It also shows collapsed and damaged buildings, obstacles, and landslides.
In this lab I compared the size of four counties in Florida by using different map projections. It went fairly smoothly, the hardest part was getting the layout of the three maps just right. Also importing the table was a little aggravating, the numbers are pressed together.
This map shows elevations in Mexico using an elevation raster file with stretched symbology. I had hill shading turned on but with the different colors it didn't look right.
This map shows the urban areas as well as rivers, highways, and railroads. The hardest part of this exercise was getting all of the labels in the legend to look the right way.
In this map I used several different shapefiles to create a map of the Mexican states. 
World Population Map
Potential Youth Center Locations
Planning a Trip to San Diego
