Project 1 Report

Matt Van Singel

A Plan for the Allocation of Funds to Accommodate Increasing Incidence of Asthma in the Bay Area.

Introduction

Figure 1: Base map showing annual hospitalization rates for asthma related illnesses per 10,000 people in the Bay area

California is operating in a financial crisis that is currently being exacerbated by increasing environmental stressors. Over the last decade the San Francisco Bay area has seen a steady rise in hospital admittance rates due to asthma related illnesses, most likely due to pollution. The two primary causes of poor air quality in the Bay area are particulate matter (PM) and ozone. By tracking these two parameters we would theoretically be able to predict where asthma suffers are likely to experience pollution-induced flare-ups. Once we calculate these areas of concern we can derive which hospitals would be in the target area and thus provide the necessary staff and supplies to treat the increasing amounts of asthma patients.

Identifying a Target Area

It is important to asses where the funds will be directed to help assist people in need. Analyzing areas of uninsured and other poverty indicators will allow for agencies to allocate money to specific areas. As seen in Figure 1 on the top image the map shows areas of uninsured in dark blue and dots to indicate high areas of unemployment. In Alameda County the percent uninsured is 13%-15% and also houses a high unemployment rate at 45%-60% of the total population. This county would be the area of greatest need based on these indicators alone and would thus be a prime benefactor of government aid.

Figure 1: Shows the relationship between several variables including insured vs. uninsured and on the lower maps with uninsured vs. Hispanic populations and uninsured vs. African American populations.

The next area of study to determine which area would be in greatest need would be that of ozone and particulate matter. Determining which areas are at highest exposure would allow for further analysis of a specific area and demographic. In Figure 2 the top image displays the total number of individuals per 10,000 people hospitalized for an asthma related illness as a percent of total annual hospitalizations. Through this data it is clear to see Alameda County displays the highest percentage of hospitalized individuals at 17%-21% which Solano, Contra Costa and San Francisco counties hospitalizing 13%-17%. The other two maps in Figure 2 both illustrate the percent of asthma related hospitalizations compared to Hispanic and African American Populations. The map on the lower left shows the correlation between asthma hospitalizations and Hispanic population which shows a weak correlation. The map on the lower right shows a similar illustration with African American populations showing a very strong correlation. Through this analysis it was determined that the African American population in Alameda County would be the most 'at risk' population to particulate matter from surrounding pollution sites.

Figure 2: Shows the percentage of individuals hospitalized due to asthma related illness and also correlates this data to Hispanic and African American populations.

The final aspect that needs to be considered in the search for the target area is where the particulate matter and ozone concentrations are highest. This data was obtained from air quality monitoring stations located throughout the San Francisco metro area. Monthly maximum and averages values are recorded through these sites and listed online for public access. Figure 3 shows the base map of hospitalization incidence per 10,000 individuals on the top map. The lower left illustration shows the very strong correlation between atmospheric particulate matter and incidence of hospitalizations due to asthma related illnesses. This correlation is centered in Alameda County with high particulate matter counts in the surrounding counties as well. The lower right illustration of Figure 3shows the concentration of ozone compared to hospitalization rates. While the correlation is not as strong there is still evidence to support such a claim.

 

Figure 3: Shows the rate of hospitalized individuals per 10,000 compared to ozone and particulate matter concentrations from data obtained through the air quality monitoring stations in the Bay area.

 

 

 

 

 

 

 

 

 

 

 

 

Examining the Target Area: Alameda County

Figure 4: shows the Euclidian distance from each hospital with the highest African American populations in yellow along with each pollution site.

After it was determined that African Americans in Alameda County were the most at risk demographic in the Bay area further analysis was necessary to determine what hospitals would need increases in staffing and supplies. Through this section ArcGis was utilized to help locate potential areas asthma sufferers would be most likely to experience symptoms. This was done by mapping all area hospitals and creating a Euclidian distance around each location, then separating the highest concentrations of African Americans for comparison. Figure 1 shows the Euclidian distance from each hospital with the highest African American populations in yellow and each pollution site in black circles along with major roads in the area.

Figure 5

Once all data was input into ArcGis further analysis could be performed. All main data layers were reclassified to best portray the information then ArcMap was used to create a new program for which to analyze the data. Each layer was put into a weighted overlay and a map product was output from the created program (Figure 5). Figure 5 shows the areas of overlap for pollution sites and hospital locations along with major roads and highest concentrations of African Americans. The areas in red and blue are the area of highest concern while purple is of lesser concern. Through this map product it can be determined that Alameda Hospital, Highland General Hospital and Booth Memorial Hospital are the three hospitals in highest need of staffing and supplies to treat increasing rates of hospitalization due to asthma related illnesses.

 

 

Unit 10: Analyze Data for Homeland Security

This week I was asked to analyze data that was created during the previous weeks assignment. This was a very difficult assignment complicated further by frequent server crashes, program time outs and long tedious hours of analyzing. The first road block I came to was the time out and ultimately closing of ArcMap after trying to save edits to the GNIS layer. Not only did this absorb 3-4 hours of time it was very frustrating. The next problem I encountered was creating a buffer for this point. After using the buffer tool ArcMap would create a buffer but the layer would not display on the map even after using the "zoom to layer" function. I pressed on simply by displaying the buffers for the airports and then focused on the nearest airport to the NORAD point. I was then able to produce the map of the ingress and egress routes in the second deliverable without much problem although I was unsure if I was meant to display the routes for just this location or multiple sites. The next problem I encountered was creating surveillance points. When I loaded the coordinates of COUA_DEM_UTM13 the shapefile produced no coordinates. After working on the project for nearly 14 hours I did my best to assemble a map and turn in at least a partial final deliverable.

Unit 8: Crime Analysis

As shown above, crime is not deterred at all by the 1,000 foot drug free zone. In fact, juvenile crime is at it's highest around schools according to data. Additionally there were no schools without a crime committed within 1,000 feet of the school. Thomson Elementary school was the highest incidence reported with 169 criminal reports.

Unit 7: Location Decisions- on your own

This week I was presented with the opportunity to create a location decision based on parameters I chose. I chose to use a similar output to last weeks guided exercise with some variations on the data used. The variables I chose were age 18-21, rent $500-549, distance form psych clinic and distance from community college. Overall I feel that I benefited more from doing a similar output to the previous week in that I was able to perfect the same skills instead of trying to learn and recall old tasks which I did not have the time to do.

Week 6: Urban Planning: Alachua County Location Decisions

This week I was asked to created a series of maps based on parameters requested from a couple looking to move close to their grandchildren at UF. Overall, the exercise provided very clear helpful instructions that allowed me to complete the somewhat difficult tasks. Few problems occured from a few areas where detail was left out but the discussion board provided the neccessary feedback from such outcomes.

Oil Spill Activity

A disaster of any magnitude can be a devastating event; however, GIS can help mitigate such events. In any, disaster the first task at hand is to understand the event that has occurred and in the case of a deep water oil spill the first step is no different. Using GIS can help show the location of an event and help orientate agencies to the disasters location. Communication is an immense aspect of disaster coordination and this is where GIS visualizations are invaluable. When speaking in terms of the deep water oil spill in the Gulf of Mexico, GIS has produced many maps and animations for many different audiences. For many, a map of the exact location of the oil rig where the event occurred was the starting visual aid. Once the location of this event was established GIS can then help further explain the dynamics of the situation.

GIS work is indispensable in today's world, especially in disaster relief coordination. Its ability to consolidate huge amounts of data into easy to understand visual aids allows for many agencies to work together to minimize the effects of these events. With the current oil spill, there are many agencies both state, federal and public that are working congruently to both stop and alleviate the effects of one of the worst disasters in history. Information needs to be portrayed regarding jurisdiction to the many agencies that will help with oil mitigation both on the open ocean and soon, all along the gulf coast. To do this, federal agencies such as FEMA, individual state agencies and public companies (BP) need personalized visual data to help them accomplish their goals. GIS accomplishes these goals by allowing for fast transfers of information in to customized maps and projections.

Perhaps greater prevention will come from this tragic event. Many agencies in current times are utilizing GIS to help plan and prevent for disasters. If a plan of action had been put in place for the oil rig to help better contain such an event there might have been a possibility that the wide spread environmental impact could have been reduced. Implementing boom locations, promoting interagency course of action plans to allow for faster response times and having access to other environmental data may have saved billions of dollars and hopefully one day such plans will be mandated accordingly.

Week 4- Natural Hazards: Oil Spill

The above maps are presented to help show the many aspects at which GIS can help in a disaster. The ability to map hazards, environmentally important lands, areas of highest concern and also to plan a course of action is invaluable to many relief efforts. These maps show the socioeconomic impacts of an oil spill by mapping areas of importance- such as marinas, the ecological impact of an oil spill by showing how many and what type of creatures will be affected and also what areas of management each location falls under to better plan a relief effort.