Asthma is a chronic disease that affects 8 million people in the United States, or 8% of the population (Center for Disease Control and Prevention). Involving the airway of the lungs, people who experience asthma have swelling and inflammation of the airways, making it difficult for air to navigate throughout the lungs and resulting in patient symptoms such as tightness of the chest, wheezing, coughing, shortness of breath, and difficulty breathing. Asthma attack triggers can be separated into two categories; allergies such as pet dander and pollen, and non-allergic triggers that include environmental factors such as changes in the weather and air quality (AAAAI). Poorer air quality may be associated with ozone and airborne particles, both of which are known to be triggers of asthma and are found in higher populated areas. Ozone contributes to smog which can be irritating to the lungs, reduce lung function, and make it more difficult to breathe while airborne particles found in smog, dust, and smoke can enter the lungs and cause long-term and short-term problems such as reduced lung function, and more frequent asthma attacks (Air Pollution and Asthma). Using the findings of my previous project, this project will aim to look at population density within each county, and whether that influences air quality. It will then aim to look at the relationship between these three variables in order to determine if higher levels of each are found in clusters throughout California.
Objectives:
This project is set to use the data and conclusions drawn from my previous course project in order to determine if there is a relationship between population density of each county in California and air quality of each county. I would expect for the more densely populated counties to have poorer air quality due to the assumption that more people would equate to more pollution being produced. Furthermore, rather than just basing my air quality data off of my previous citations, I may choose to look at the number of factories within each county, as I presume that a higher number of factories would result in more air pollution and thus lower air quality. With that being said, as I have seen from my previous project, results do not always follow with what is expected. I would also be interested in overlapping the air quality, population density, and asthma hospitalization data in order to see if there is a “hotspot” within California where these three variables correspond.
Methodology:
I will use ArcMap and online GIS resources in order to display the following information:
• Create a new layer of data showing the population densities
• Determine if higher population densities have lower air quality
• Determine if there is overlap within California counties where population density, low air quality, and asthma hospitalizations correspond.
• Analysis Used:
• Geoprocessing to intersect desired variables
• Hot-spot analysis to detect locations that are statistically significant in regards to their spatial clusters
• Correlation tools to detect relationships between the variables
Data Sources:
Current data sources that will be used for this project are as follows:
• Asthma ED Visit Rates by County 2012 [Download ArcGIS online]. (2012) California Department Visit Rates by County in 2012, CA: CDPH-DATA [October, 2017]
• Emergency Department Visits Due to Asthma, Both Sexes, All Ages, All Races/Ethnicities, Spatially Modeled, Age-Adjusted Rates Per 10,000, 2010, Counties [Download]. (2010) California: Office of Statewide Health Planning and Development [October, 2017].
• CA Counties 2012 [Download ArcGIS online]. (2012) California US Census Bureau, FracTrackerAlliance [October, 2017]
• County Population [Download ArcGIS online]. U.S. Bureau of Reclamation (USBR), charles.schafer_CDPHDATA [October, 2017]
• EPA AirData Air Quality Monitors [Download]. CA, ESRI [October, 2017]
(https://epa.maps.arcgis.com/apps/webappviewer/index.html?id=5f239fd3e72f424f98ef3d5def547eb5&extent=-146.2334,13.1913,-46.3896,56.5319)
Work Plan:
Week of January 26th: Draft GIS Proposal
Week of February 2nd: Finalize draft proposal
Week of February 9th: Collect data (search for more in-depth air quality data)
Week of February 16th: Take a look at previous data/maps in order to determine county focus. Should I include all counties of California or focus on the top counties that I had focused in on in my other project?
Week of February 23: Take new data and organize it into an excel sheet that can be imported into a GIS Map
Week of March 2nd: Import new layer data into GIS / select correct coordinate system/projections
Week of March 9th: Organize layers using correct symbology/appropriate color scheme
Week of March 16th: Begin to visually analyze layers to see if there are any obvious overlapping/possible relationships
Week of March 23rd: Begin searching for spatial analysis tools that could accurately determine if there is a relationship between the data
Week of March 30th: Analyze data
Week of April 6th: Finalize maps/add final details (scale bar, legend, titles, north arrow etc.)
Week of April 13th: Work on final poster
Week of April 20th: Start finalizing project poster
Week of April 27th: Continue to work on finalizing the final poster
Week of April 30th: Turn in Final Project
Deliverables:
A comprehensive map will be created illustrating if there is a connection between population density and air quality. Another layer will be used in order to display if there is a relationship between all three variables (population density, asthma, air quality). Analysis tools will also be used in order to support claims as well as show the data more clearly on the map.
Sources:
"AAFA." Air Pollution and Asthma | AAFA.org. AAFA, n.d. Web.
"Asthma | AAAAI." The American Academy of Allergy, Asthma & Immunology. AAAAI Foundation, n.d. Web.
"Vital Signs." Centers for Disease Control and Prevention. Centers for Disease Control and Prevention, 03 May 2011. Web.
