Final Poster & Reflection

My poster is finally complete and I am relieved to have completed all of the coursework for this semester!  In my previous course project, I had also wanted to compare a few different variables in a way where each layer could match up on one map, but unfamiliar with spatial analysis and weighted systems I instead created a poster with several different maps each only looking at the relationship between two variables.  I am glad that this course project gave me the opportunity to practice using my spatial analysis tools, and now feel more confident using ArcGIS and approaching different/more complicated methods necessary for the creation of effective and professional maps.  After looking at my analysis, I do not think that I have enough information to make a definitive conclusion about different variables that may impact people seeking asthma treatment, but I can say confidently that lower income areas that are further distances from hospitals are definitely at a greater risk than those living in more affluent and populated areas (even if there is more ozone that could irritate asthma symptoms!).  After completing two course projects over the course of a school year both looking at asthma within California, I would be interested to look in other areas of the United States to see if there are any similarities or surprises in the data.  I have attached the final version of my final undergraduate GIS map poster! (Sorry if it's blurry I had trouble uploading the PDF so I had to use a screenshot).

Update 5: Spatial Analysis & Weighted Overlay

Now that I have reclassified my rasters, I will assign each of them a weight based on how important I feel each category is for people who may suffer from asthma or may be at risk for not receiving treatment for their asthma.  I rated the distance to the hospital as the most important variable because if the condition is serious enough or life threatening, people are going to seek medical attention regardless of whether or not they have health insurance.  I weight I assigned to the hospitals was (0.35).  At a close second, I rated income as the second most important factor (0.3) because those who live below the poverty line may not be able to afford to seek medical help.  Rated third is ozone exceedance (0.25) because although ozone is an air pollutant that can aggravate asthma symptoms, I felt that income and hospitals have a greater impact on asthma at this time.  Lastly, is population density (0.1).  I chose to include population density because densely populated areas are more likely to have more hospitals, so although it is a piece of the puzzle, I did not feel that it was significant enough to give it a weight that more closely matched with the other three components.  After using the raster calculator to multiply the values,  I was able to produce a draft final output raster which I have attached below. 

Update 4: Reclassification of Rasters

Now that I have been able to convert all of my shapefiles to rasters, I will begin to reclassify each of the four variables so that I can assign each of them weights and eventually use a raster calculator to find areas within the state of California where asthma sufferers are at higher and lower risks of complications with their condition.  For ozone, areas that have lower numbers of ozone exceedance will be favored over those with more exceedances because higher levels of ozone may aggravate asthma symptoms due to poorer air quality.  Areas of higher income will be favored over areas of lower incomes because people with more money are more likely to be able to afford health insurances or the hospital bills incurred.  Counties with higher population densities are more likely to have more hospitals and because I did not see a correlation between higher population densities and ozone exceedances, I will not have to take the air quality into considereation. Before reclassifying hospitals, I will use the euclidean distance tool.  In studies, it has been shown that the closer the distance to the hospital, the less likely the patients condition will be fatal.  In the study that I gathered the information from, it was shown that hospitals within a distance of 6 miles have the lowest mortality rates with lower levels of survival at any distance larger than this.  Specifically, 12.5 miles was considered to be a far distance.  Therefore, any distance 6 miles and under was considered to be favorable.  The classifications were scaled from 1-3 with three being the most favorable and one being the least favorable.  Least favorable conditions are represented by lighter shades of blue while most favorable conditions are represented by darker shades of blue.  I have attached my initial reclassification results below.  The next step will be to assign each of the variables a weight and use the raster calculator to determine areas of high and low risk.
                                     

Ozone Reclassification

Population Density Reclassification

Income Reclassification 

         

                                    

Hospital Reclassification 

Update 3: Data & Initial Raster Results

I will now be looking at four different variables in order to determine the lowest and highest risk areas for asthma sufferers in the state of California: (1) Percentage of days ozone exceeded the national average, (2) Income, (3) Population Density, (4) Hospitals.  I will convert each of these shapefiles to rasters so that I can perform a spatial raster analysis using a weighted overlay.  After converting each shapefile to a raster, they will be reclassified for favorable criteria.  Because the ozone data that I had imported from excel are points rather than polygons, I performed a spatial join between ozone exceedances and counties and then converted the county polygon that now contains the ozone data to a raster.

 

Update #2 - Change of Plans & New Maps!

After seeing that there is not a visually significant relationship between ozone exceedance and population density, I began to think about the data that I was using and if it would  be more interesting to look at other variables.  When looking at the asthma data that I used in my previous course project, I realized that it was not looking at the number of cases of asthma within each county, but rather the number of people who were being hospitalized because of their asthma.  Therefore, the people who were being hospitalized may not even be from that particular county.  This made me think about what factors would influence people requiring hospitalization (other than the obvious severity of their asthma symptoms).  Thus, I decided that I would still look at ozone exceedance and population density, but I would also incorporate income and the number of hospitals within each county.  People who have lower incomes may be less likely to admit themselves to the hospital for their symptoms because they may not have health insurance or be able to afford the hospital bills.  Furthermore, in rural areas some hospitals may be too far away for people to be able to travel to.  The amount of hospitals in each county could also be related to population density, as more people would require more healthcare.  After importing data on medical facilities across the state of California, I narrowed down the facilities to general medical and surgical facilities to match the asthma hospitalization data I had also gathered online from the state of California.  I then used graduated colors to show the population density, and simple dots for the hospitals to show that areas of higher population densities are more likely to have a higher number of hospitals.  I also created a map showing the number of asthma hospitalization in relation to income to visually display how people in lower income areas may have less hospitalizations due to asthma.  I labeled major 4 major counties within California to serve as a location reference.