Week 10 Homeland Security

Creating buffers around critical infrastructure that will require additional security under threat; NORAD and Cheyenne Mountain Heliport. A summary of features within the NORAD buffer identified the heliport. Ingress and egress points were created along the perimeter of the buffer where roads intersect to mark as surveillance locations. A shapefile of surveillance points around NORAD was created and a surface analysis done to show areas of obstruction and a line of sight profile generated.

Everything with the lab worked well until the last steps; 3D line of sight. I checked projections and redid several time to no avail.

Week 4_Supervised Classification

Land use map created by supervised classification showing histogram and land class names.
Week4 Land Class Map

Week9 Homeland Security Assgn Part 1

Identifying, defining, and compiling a comprehensive geospatial database for homeland security planning and operations at the North American Aerospace Defense Command (NORAD) in Colorado. The only problems with this weeks lab were the same as most people. Everything had to be deleted from my H drive prior to this semester... bummer, and trying to find the correct files to either repair or to complete/follow the lab. Projecting, modifying, exporting files, etc. was not a problem. Everything worked, at least as I interpreted the instructions, without any errors or crashes (a first).

Week 8_Crime Activity

This map shows Washington DC county police stations and the total crime from each precinct. Police stations recording the highest crime are the Sixth, Seventh, and Third Districts. A graph was created to show the crime totals for each station with a legend of station names and totals of crime.

The second map focused on auto theft and times of day; night, morning, afternoon, and evening. The overall rate of crime and auto theft occurs in the evening period of 6 p.m. to midnight. The density map shows two areas of high activity suggesting the need for more police sub stations.

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.