Final Blog

My final GIS project is finally complete, and I am relieved that I have finished it.  Overall, I think that I was able to form some good statistical analysis, however it is evident that ozone if probably not the only/most determining factor when it comes to the number of asthma hospitalizations in California.  I think it would be interesting to continue this data in the future by exploring other variables within California that may result in more hospitalizations.  I have attached a screenshot of my final poster so that you can get a look at the final product! (I apologize for it being so blurry, my blog would not let me upload a PDF file as an image).

12/15 Update

I have made two new maps.  One with just the counties that were considered to be hot spots through my hot spot analysis, and a second map looking at the five counties in California that had the highest levels of both ozone exceedance and asthma hospitalizations.  Now that all of my maps are done I have started to begin the analysis of my data and looking to see if there are any significant relationships between the variables that I looked at.  In order to test the significance, I used a paired two-sample t-Test through excel.  I tried doing more analysis through GIS but was experiencing a lot of difficulties with it so I decided to use my original excel sheet data to do the analysis myself.  I was not surprised that when comparing the data of all of the counties the p-value showed that there is a significant relationship.  However, I was surprised that there was not a significant relationship between both the Hotspots and the top 5 counties.  My goal for next week is to have my poster completed.

12/6 Update

In order to try and connect my data I have decided to perform a hot spot analysis based off of my asthma and ozone data within the counties.  This analysis detects locations that are statistically significant in regards to their spatial clusters.  There are only four counties within California that were found to be statistically significant hotspots within my data so my next step is going to be to create a new map with only these hotspots to show the counties in California with a significant relationship between the two.  However, I also want to perform a statistical analysis in excel between ozone and asthma for both every county and these four counties to see if the results are consistent or if they vary. (Also, sorry the screenshot looks blurry, it looks much more clear on my exported pdf)

11/29 Update

I have successfully downloaded my asthma data from the California Environmental Health Tracking Program website.  The data that I selected was also from 2015 and included all races, genders, and races.  My asthma data is not based off of the number of cases (which was surprisingly difficult to find usable data for) but is instead based off of the number of hospitalizations per county in 2015 that were because of asthma.  For the symbology I considered doing graduated symbols, but decided to go with proportional symbols because the purpose of my map is to look at the connection between the amount of ozone pollution and the number of asthma hospitalizations.  Therefore, when you look at the map with proportional symbols it is much easier to point out areas that are more effected rather than just looking at 3 symbols of different sizes that represent ranges of values.  I also considered using a special symbol, but I was unable to find one that would display clearly on the map in relation to the values.  It was difficult to find a color that I felt was appropriate, I did not want the color to distract from my ozone percentages and likewise.  Therefore, I decided on a gray color.  My next step is going to be to use a GIS analysis tool in order to determine if there is a correlation between these two variables and if the results that I am seeing are significant or due to chance.  I am interested to see what the test will reveal because the highest number of asthma hospitalization occurs in a county that has one of the highest percentages of days that went above the ozone pollution standards.

11/22 Update

After reviewing my population density map and realizing that I actually just made a map showing the populations per county, I began to reevaluate my project.  I have decided that if I were to make the same map using the graduated colors, but based off of the ozone pollution that exceeded standards instead, I would  be able to more effectively be able to determine/show if there is a connection between the number of asthma cases and the amount of ozone pollution.  In order to get the ozone pollution data, I went to the "California Environment Health Tracking" website where I was able to enter the criteria that I was looking for.  My ozone data is based off of the most recent data that they had on the site (2015) which is also the data year that I now plan to use for my asthma data to keep everything consistent.  After downloading the data from the website, I entered it into an excel sheet based off of the county.  Then, I exported the data into GIS where I expressed the xy coordinates and then joined the table with my county data so that I could display it on the map.  Below is my map for the percentage of days that California exceeded the US standard for ozone pollution (0.070 ppm).  I also took Maggie's advice and went for a blue color! My next step will be to download my asthma data and import it into excel.