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.

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.  

 

Final Deliverables

Final Write Up
Final PPT

Yeah!

Week11_3D Analyst

Navigating and tools in ArcScene. I think I'll stay away from the "fly" tool. The map shows target points and observations and managing viewers.

Week11_Reclassify Data to Common Scale

Data reclassified according to suitability of study to show sensitivity to drought in vegetated areas. No data was assigned to developed areas. Liked the exercise.

Week11_Spatial Analyst Model Builder

Model building performing multiple geoprocessing tasks. How great is that for saving time!

Week11_Adding Custom Text

This exercise was working with label classes and customizing, SQL, label priority, and creating callouts and when to use them. Informative and fun; easy to follow exercise.

Week11_Manage Labels with Class Exercise

This exercise was managing label classes in multiple layers for clarity, adding symbols, orientation, and learning about visible scale range using SQL. The exercise was great info and tips.

Week10_Working with Raw Data & Tables

This exercise joined raw tax roll data and shapefiles to find the 4 largest tract landowners in Gulf County, Florida.

Week9_Buffering & Overlay

This map utilized buffering and overlay to determine potential campsite areas in a state forest. It was a fun lab using the different tools. Straight forward as always. Usually get sidestepped by "user error"!

Q1. I used Intersect tool and found no differences in the results.

Q2. I used Erase to remove the conservation areas falling within the buffers_union.

Q3. There are 79 features in the possible campsite areas layer. Square meters created huge numbers and who can relate? In kilometers the largest feature is 7.765 km2 and the smallest is .00075 km2 (also hard to relate). So, I also chose to look at the areas in acres. The largest feature is 1918.774 acres and the smallest is 0.185 acres.

Week7_Editing Features

This lab used editing tools to modify and add new features to the UWF campus, building on last week's lab. The ESRI course was very detailed, as always, and easy to follow. It was all very good practice.

Week 6_Georeferencing

Georeferencing the UWF campus in two parts. The north campus was done through 10 control points, 1st Order transformation, returning a total RMS of 6.61793. The south campus was achieved using 20 control points, 2nd Order transformation, returning a total RMS of 4.68964. On both transformations, adding additional points did not return a significant or better result. On the south campus transformation, the 2nd Order transformation appeared to be the better visually compared to the 3rd Order.

Week5_Data Search

Manatee County was my subject area. On the first map I chose to show public lands as points so as not to conflict with all of the other info. Also, most of the public lands (parks, memorials, reserves, etc.) relate to the strategic habitats and wetlands of the second map. The third map consists of satellite imagery. Fortunately, FGDL had all of the info I needed.

Haiti Map

I chose this map because it was one that I was using with friends who have friends/family in Haiti. It is one of the interactive ones with layers and images and it was easy to use for moving around and viewing different areas. User friendly. As a static map, not very good because there is no information/legend to tell what we are looking at but I liked this screen because it shows the multiple faults, red lines, in Haiti (I had no idea).

Week 4_Projections

Week 4 maps; using ArcToolbox to project shapefiles into three coordinate systems highlighting the four counties to display the slight differences. The instructions, as always, are concise and easy to follow. The exercise was easy; hopefully some of the information will will stick for future recall! The only problem I had was not with this portion of the lab but the UWF cell size I had was not one of the choices on the quiz.

Wk3_Map3_Elevation

This map is using a color ramp for elevation. I chose this because it seemed logical and familiar; green lower to brown or arid at higher elevations. Still trying to understand the options in the stretched scenario and how to determine which is more applicable. Hopefully as time goes on it will become more clear.

Wk3_Map2_Central Mexico

This map worked with manipulating information for the layout view, labels, layers, legend, etc. I like the annotation for the labels. No surprises.

Wk3_Map1_Population

For the population map I chose this color ramp because I thought it contrasted well between breaks. This assignment was pretty painless. Learning things I don't remember from other classes; that always makes it fun.

Week2_Lab1_World Population Map

This map displays world population by country. I chose this color ramp because I felt there was good visual contrast between breaks, easily understood order, and the highest and lowest values stood out. I rounded the numbers to fit better and because it made no difference in what the map is meant to display. The lab was easy to understand, no problems.

Module 1 Youth Center Map

This map shows 6 potential youth center locations through use of attributes and spatial relationships. The ESRI exercise was very descriptive and easy to follow, no problems, even when playing with alternate scenarios. Easy to get lost in computerland playing around with the options and information available on ESRI site.

Module 1 Tourist Map

This map shows points of interest (highlighted) described in the ESRI exercise in the San Diego area, easy walking distance to trolley stops. The exercises were easily accomplished and fun. It was a good refresher. No trouble or difficulty with any of the tools. The only problems were with the maps refreshing and opening. I'm guessing because of using TS?