Project 3: Report

Better Books Report Link

Market Analysis: Better Books' Stores

The first map displays the two potential locations for new Better Books' stores. The assessment was based on the existing Steiner and Bosworth store locations and comparisons of existing competitors, sales, and drive time.

The second map displays the actual areas of potential new Better Books' stores.

Map 2 shows the Better Books Store Locations and census blocks within a one mile area of each.

Project 3: Prepare

Map 1 shows four demographics for comparison to determine the best area for new Better Books store locations.

Project 2: Analyze

Project 2: Prepare

Project 2 deals with GIS and Landscape Design/Management for Marin City in Marin County, Califorinia located in the San Francisco Bay Urban Area. The first map is of Marin City using orthoimagery and various shapefiles. The second map is of Marin City with the reclassified land cover data protraying treesm, grass and impermeable areas such as roads and buildings. The metadata for the new reclassed land cover raster file is here. I have also provided a link to my process summary.

Part 4: Report-Public Health Analysis

As part of the first project in Special Topics in GIS we were to produce a health study based on air quality in the San Francisco bay area. The study is for local bay area county officials to determine if there is a correlation between asthma rates, air quality, and race. This included looking at asthma hospitalization rates, ozone and particular matter concentrations, and which hospitals to target for more resources. The study was broken into three parts: Demographics, A Closer Look at Asthma, and Targeted Hospitals.

Public Health Analysis: Part 1 Demographics

The first study was to look at the uninsured and poverty indicators in the nine counties and to determine the correlations with race, primarily Hispanic and African-American, and single mothers. The main goal was to determine which demographic needed to be targeted for the most help with health care. In order to accomplish this we used scatterplots to show the correlations between each demographic and the uninsured/unemployed. A series of comparison maps with scatterplots were used to determine that the African-American population appeared to be population which needed the funds the most.

Public Health Analysis: Part 2 A Closer Look at Asthma

The second part of the study was to see if there is a racial component to those that are being admitted to hospitals for asthma realted illnesses. By using a series of comparison maps with scatterplots to determine the racial correlation with asthma hospitalization rates we can determine where the funds need to be allocated. Once again the analysis showed the African-American population had the highest admittance rates and Alameda County was the target point.

Public Health Analysis: Part 3

Part 3 of the study included looking at where targeted asthma sufferers may suffer due to point sources of pollution and which hospitals are most likely to be utilized by the targeted population. The study mapped sources of pollution such as Toxic Release Index (TRI) point locations and roadways, hospitals and the distribution of the targeted population at the Census Tract level. These factors were compared by using weighted overlays to identify areas where the proximity to a hospital, the pollution factors and the targeted population could most likely lead to increased staffing and funding.

Results

The overall results found that the there is a clear correlation between poor communities with little to no health insurance. Demographics show that the African-American population has the highest number of asthma realted hospital admittance rates in the bay area and specifically in western Alameda County. After the weighted analysis based on TRI, asthma hospitalization rates, and the targeted population, the hospitals highlighted would need the most help in allocated funds to treat the people most affected. Overall I feel that this analysis was very effective in identifying the section of the bay area of San Francisco where the allocated funds are most necessary.

Part 3-Section 3-Public Health Analysis

Week 3 Part 2-Health Analysis

Week 3: Analysis and Report

Project 1: Air Pollution, Asthma, and Race in the San Francisco Bay Area

Prepare: I used the demographic data already provided to us in the US Counties shapefile therefore the metadata remained untouched. Here is the metadata for the three database files created for this project.



Asthma



Ozone



Particulate Matter (PM2.5)

Week 5: LiDAR Raster Image

This week's challenge focused on turning an las. file into a raster and interpreting the data in ArcMap or ERDAS Imagine Software. The area is of Pensacola Beach, Florida and depicts elevation in meters with the road, sand dunes, and water highlighted for reference. The image symbology had to be stretched in order to correctly identify these features. Overall the challenge was interesting and insightful and best of all not problematic.

Week 4 Classification

Lab 4 was the most difficult for me so far. The problem was trying to get the reclassified data to look like the original image but with all of the changes made during the classification process the colors of the map looked completely different. The legend also gave me fits even though I merged my data to make it easier to work with for some reason I could only get the Historgram data to display and not the class names. Very frustrating. Here is what I have though.

GermanTown Map

Here is the new and improved map as of 07/28/10 which is more like the challenge example should have been.

GermTown Map 2

Week 3: Orthorectification Deliverable

This is a link to the Week 3: Orthorectification challenge in Remote Sensing. I used the minimum of seven ground control points but could have used more although with the poor resolution on the aerial, finding good geographic control points was getting complicated. By using the obvious areas, i.e. airport runways, land points, and cross roads I was able to rectify the map of Pensacola, Florida. The overall result was not much different than the original, although it did shift it a little to the left. ERDAS software does not make it easy to create an appealing layout.

Orthorectification Map of Pensacola, FL

Module 2 Deliverables

Lab 2 delt with using the statistical data within the image software ERDAS in order to identify different features and how they are displayed. By using histograms and pixel value data, the user is able to identify peaks as well as low points within each layer of the image in which they are studying. There are three links to each individual map deliverable.
Lake.
Quarry.
Shallow Water.

ERDAS Bonus Map

Here is the link to my bonus challenge map. Was fairly easy to produce, but not very original.
http://students.uwf.edu/bos1/map1_challenge.xps

Final Project Map

A bivariate map using two thematic mapping styles to show two separate sets of data. The choropleth map illustrates the percentages of ACT composite scores within the United States while the proportional symbols (circles) portray the percentage of total graduates tested in each state.

Lab 10: Google Earth (Wind Farms)

I chose this area of Lake Huron based on local buoy information that gave adequate average wind speed for the site as well as depth of water for site location. It is more than six miles off shore, therefore the viewshed will not be impacted as well as the migratory bird patterns. I am guessing that Google Earth professional would make it a lot easier to export your final map into a JPEG and manipulate it, but for a quick and dirty one the free version of GE does just fine.

Week 10: Isramithic Map

Week 10's exercise was a bit more time consuming with trying to make the contour lines look right with the precipitation data. The most difficult part I had with this lab was trying to fill in each contour shape with a different gradient of color using the grayscale option. It is hard for me to tell the difference between shades unless I make them extreme and with seven or more shades it becomes trying to make them work.

Bonus Exercise

I decided to re-do the US Census exercise because I felt it was the weakest one to date that I have produced. More of an Illustrator exercise than ArcMap but I needed the practice either way. Hope it is acceptible.

Module 9: Flow Map

This map was pretty fun to make. After applying the same basic principles as that of the Proportional Symbols Map and watching the short tutorial on drawing arrows in Illustrator the rest of lab was really about picking a design to your liking. I chose to keep it simple due to time restraints, but believe this map still displays the information in a clear and concise manner. I am really enjoying learning Adobe Illustrator and look forward to learning how to use it for more interesting map designs in the future.

Module 8: Dot Maps

Week 8 lab assignment became challenging in the way that figuring how to apply each dot individually throughout the counties in order to properly represent the housing density was very time consuming. I ended up having to copy and paste each and every dot. I looked for multiple wasys in avoiding this very time consuming exercise to no avail. Maybe as I become more familiar with the ins and outs of the software another simpler way will become apparent.

Week 7: Map 1

The Week 7 assignment presented a couple of challenges for me. First trying to figure out the direct proportional circle symbols per each countries wine data proved difficult. Second I chose to represent the countries with little wine consumption the same as the rest as to keep the map easier to read.

Week 6: Map 2

Once again the hardest part of the lab for me the statistics. I also started out with a bad projection and did not have time to adjust so the states might look a little off. The grayscale also looked better in Illustrator than once posted to the blog.

Week 6: Map 1

Map 1 shows the percentage change in poulation using natural breaks data classification for the continental U.S., Alaska and Hawaii. Once again I am having problems with finding the right projection.

Percentage of Hispanics in Central and South Florida

This is a map showing the percentages of Hispanic Populations throughout Central and Southern Florida using various scaled geographic references. Another way to portray this would have been to use insert maps though I thought it would have gotten too cluttered. I do not know why the jpeg came out so small.

Florida Keys Map

This is a map of the Marathon area of the Florida Keys with several main features labeled using Adobe Illustrator CS4. I have used Illustrator 9.0 for the last few years drawing basic archaeological site maps so the only difficulty was getting used to the new version of the software. I thought using guide lines for the typography was the best way to go with the current scale of the map.

Week 3: Data Classifications (Map 2)

Map 2 shows the percentage of African Americans in Escambia County, Florida using the Natural Breaks (Jenks) method. I think this was the best classification to use for this particular data because it was easy to interpret, especially in the legend, although I did not decide to round out all of the numbers in the legend to keep the data more accurate. It also takes in to account the breaks within the data unlike the equal interval method. The color scheme also is more clustered in areas of the county making it easier to understand as a division within a population.

Week 3: Data Classification (Map 1)

Map 1 consists of four different data classifications showing the percentage of African Americans in Escambia County, Florida. The difficulty came with finding which normalization to use and how to interpret the data to make sure I was getting the right percentage and breaks.

Good Map

This is an example of a "good map". It clearly labels the areas and entrances in which visitors would frequent and along with its fine artistic value its functionality is clear and useful.

Bad Map

This is an example of what I would call a bad map. The map is a depiction of Six Flags Over Texas, and although it is nice to look at the functionality of the map is lost with confusion. There is no legend or clear labeling for the visitor to the park to use.