Week 7_Location Decisions

I live in Seminole County and decided to use my home county for the project. I like living here and was interested in the data I might find to replicate the Alachua assignment. Seminole County is quite small in comparison to a lot of Florida counties so it is relatively easy to be close to almost any criteria that might be set. I searched the layers available on the Seminole County government site and FGDL. Seminole County has quite a few choices for layers and I didn't have to go far to find what I needed. I decided I would like to live in a more rural setting (low density) and acreage, near the trail system, and in an area with the 40 - 49 age group.

The basemap includes a county boundary, cities, roads, natural lands, water bodies and parks. The second map shows the trails and a distance of approximately one mile increments. The towns and parks are there as a reference. The third map shows tracts of land in green corresponding to the choice of ages 40 - 49. The darker the color the more ideal the area will be. Low density areas of 50 acres or more were figured in and the higher the percentage the larger the tract of land. The trails are shown in black as a reference. There are several areas that look potentially ideal preferably in the northwest area to also be closer to the water.

Tools and model builder would not work, error message of "no license.

Wk6_Urban Planning

This week's activity involved answering "where" for the potential home purchase for a professor and doctor moving to Alachua County and working at the University of Florida and North Florida Regional Medical Center. Criteria was based on closeness to both work places, a neighborhood with a high percentage of people ranging in ages from 40 - 49, and a neighborhood with high home values.

The first map is a base map providing an overview of the area with cities, roads, public lands, and their places of employment. The next map used numerous spatial analysis tools to refine the criteria and produce information for distance from places of employment in bands of approximately 3 miles, and predominate areas of age and home value with graduated color schemes. Data was downloaded from the US Census Bureau and joined to a Census Tract layer for median home values. The third map used information from the four maps above to create a weighted overlay using Model Builder based on importance. The first overlay gave each of the four criteria equal importance of 25%. The second model gave weight to close distance from work. Each produced areas of importance using graduated color scheme.

The lab was straight forward and very helpful in the details and repetitiveness of steps. In the step to export median home values the data in the attribute table that had been joined would not show up in the new layer. I repeated it numerous times and it finally showed up. I don't know why it finally showed up or why it didn't in the first place. This is one of those steps I've used numerous times through the months and it is always quick and easy. Therefore my presentations are very basic in the interest of time. Living in GIS world "101" was so much nicer!

The three maps were created from the ESRI Urban Planning & Environmental Impact studies. The first map is assessing the traffic impact from a new building on the university grounds. Buffers were used & a bar graph to show traffic volume. The study shows that the impact is in the local area of the proposed building.

The second map is a study based on parcels and housing types for student occupancy in areas around Pewter University. Fields were added to attributes, quieries and calculations done. It highlights areas of student concentration.

The third map is an economic based analysis to present a location quotient (LQ) showing industry in each of 19 local government authorities. Tables were joined, fields added, calculations done, and graduated colors to symbolize values for agr., forestry, and fishing in the area.

All were easily accomplished. I had no problems mentioned in the tips. Although these exercises are very detailed, it reminds me of small things easily forgotten like labeling specifics in the legend.

GIS summary

            The role of GIS has become incorporated into disaster response enabling damage assessment and is capable of quickly delivering large amounts of information to large numbers of people. GIS can answer such questions as location and size of a natural disaster, value of assets, locations of hazards, or ranking vulnerability of certain areas as shown with the ESI layers in our assignment. Custom maps, graphs, or animations for special needs can be generated and easily communicated to the public like the current trajectory maps on the NOAA site. The trajectories exemplify the capability of GIS for complex calculations and probability based on numerous data including weather and software models already in place like ACP or CATS.

            Software such as ACP enables GIS responders to access large amounts of data like positioning of oil containment booms or information for local salvage companies. It allows a GIS team with no knowledge of the area or experience with an oil spill to work with minimal outside direction and represents the collaborative efforts of government agencies for GIS disaster response. Geographic data in place allows the GIS team to respond quickly and customize information for specific needs and requests. It also enables decision makers and the community to have up to date information for focused awareness and preparedness. Disaster relief organizations can use the information to determine where best to allocate resources for cleanup and recovery. NOAA's response team produces daily trajectories with field verification of surface oil, fishery closures, and accounts of/inventories of rescued wildlife. A history of the Deepwater Horizon Incident and the GIS response will create important data for future events and response teams.

Wk4_Animation

This is the link to my Deepwater Horizon Oil Extent animation of the oil spill over the last month. After reconciling the projection issues (trying several times) everything seemed to work fine.
Deepwater Horizon Animation

Wk 4_Oil Spill Assignment

I chose area Long Point/Index 4945. This area is in Bay County just south of Panama Beach and includes Tyndall AFB which is managed by the Department of Defense. In the event of a hurricane, Bay County's reservoir could be at risk for saltwater intrusion and pollutants from the oil spill. Although not contained within the study site, existing reefs are in close proximity and at grave risk.

Currently there are no fishery closures, beaches and parks are open, and the winds are expected to be more westerly this week. Reconnaissance has confirmed sporadic reports of tarballs/areas of light sheen to Bay County but officials say there are no signs of oil in Bay County now. There are no booms deployed in this area.

The most at risk feature in my area was the ESIL_10A+, Salt and brackish-water marshes (most sensitive). This feature was 149,851 linear feet. The state oversees the managed lands in this area.

Invertebrates include: Endemic species of crayfish, the Purple Skimmer, and Atlantic Geoduck. Bay scallops are already dramatically reduced.

Fish: Gulf Sturgeon. Fringed Pipefish.

Reptiles: American Alligator (I include this one because who wants to clean a gator!), Atlantic Loggerhead Turtle, Atlantic Green Turtle, Leatherback Turtle, Kemp's Ridley, Gulf Salt Marsh Snake.

Birds: Roseate Spoonbill, Limpkin, Great Egret, Snowy Plover, Wood Stork, Pelican, and numerous herons, egrets, terns.

Mammal: Beach mice & manatee.

Sea grass and numerous protected plants in the bay ecosystem are at risk.

Week3_Hurricanes

This project looked at the effects of flooding on the Mississippi coast from Hurricane Katrina. Data was examined, organized, and work documented. A process summary was created and environments set for each map. Analysis was done looking at elevation, bathymetry, and hydrography for map 1 deliverable. Flooded land was reclassified, calculated and graphed for map 2&3 deliverable (data values were switched barren/developed). Infrastructure, health facilities, and churches are shown in relation to flooded land cover for map 4 deliverable.