Since the tables could not be uploaded onto the blog, I have attached the final report in .doc format
While the mapping results of the above factors demonstrate that there are higher concentrations of the low income, single-headed households with kids, and or limited English proficiency, it is difficult to discern whether this distribution is important. In order to determine whether these results are statistically significant, hotspot analysis (spatial analyst) was conducted to determine the significance of these distribution clusters. Doing this exercise will help determine locations where there are elevated levels of food insecurity. Before the data could be analyzed using Hotspot Analysis, the vector-based data had to be rasterized and then reclassified. Even though this process can be tedious, the modeling capability of ArcGIS helped to streamline the procedures. The results of the modeling process is provided below in Figure 11.
Figure 11a: Example of Modeling Used in Spatial Analyst – Converting feature class to raster
Figure 11b: Example of Modeling Used in Spatial Analyst – Reclassifying rasters
This process provides the following output (Figure 12) that shows the areas where food insecurity exists based on the factors inputted into the model. From this map at county level, we can see that there are high levels of food insecurity in the expected areas of South LA, East LA and Wilmington. There are also some major pockets in the San Fernando Valley. This data also highlighted the San Gabriel Valley, but that could be a result of the higher number of immigrant populations. Based on this map, the most urban, populated and low-income areas of Los Angeles County have high levels of food insecurity. The project area is also visible in this map, and it demonstrates that the clusters shown in the three separate risk factor maps were statistically significant, and confirms the conclusion that the East side of the project areas is more food insecure and thus will require a greater amount of resources. Having improved transit service can improve access to healthier food options. Although the mid-Wilshire area has a plethora of restaurants, sections of this area near the subway alignment are “food swamps” because of the relative lack of full service markets where residents can by fresh produce.
Figure 12: Output of Hotspot Analysis, applied to Los Angeles County
Economic factors such as poverty and low car ownership, as well as other personal factors such as level of education and English speaking ability can have a significant impact on what opportunities people have for healthy living. In the research arena, there has been a shift from a focus on individual and behavioral factors that influence food choices, to examining the physical or environmental factors and the geographical distribution of affordable healthy food (Morland et al., 2002; Sharkey et al., 2009).
Several approaches to increasing access to healthy, fresh foods have gained traction in recent years. In Pennsylvania, the Fresh Food Financing Initiative has invested almost $60 million in supermarket development in underserved areas. Other programs include the Fair Food Program, which supports local farmers in their production and marketing of fresh food; and Common Market, a non-profit that connects farmers and urban consumers. Other programs target development of community gardens in underserved areas in order to make healthy, safe, affordable food available in disadvantaged communities. Preliminary evidence suggests that involvement in community gardening may be associated with higher fruit and vegetable intake (Alaimo et al. 2008) as well as improve relations with neighbors (Teig et al. 2009).
Transportation and land use policies, programs and projects attuned to the communities food security needs can build bridges between local fresh produce, food retailers and consumers. Transportation programs and projects can make it easier for low-income families, the aged, and other with mobility challenges and particular nutrition needs to access supermarkets, farmers’ markets and other sources of affordable, healthful foods. Transportation improvements may include increasing bus routes to food retailers and supermarket-sponsored shuttle services.
Where poor access to healthy food options exists or is exacerbated by project activities, possible mitigation measures include allowing farmers markets on Metro property near stations, assuring good bus connectivity between stations and supermarkets, and coordinating efforts with local planning and redevelopment agencies that are working to improve the availability of healthy food options.
Improving transportation options to and from such food sources as supermarkets and farmers’ markets increases a community’s access to healthy foods. The area between Crenshaw and La Brea is particularly bereft of full service markets selling fresh fruit and vegetables. Through improved mobility for residents in food deserts such as this, and by making areas near the alignment more attractive to food retailers selling healthier foods, the proposed subway can play a valuable role in helping attain the goals set out in the Los Angeles Food Policy Task Force’s recently released “Good Food for All Agenda.”
Summary of Skills Used:
- Modeling: used to create raster files of data and then to reclassify the raster files
- Measurement/Analysis: created ½ mile buffer around transit stations
- Original data: Food environment data was collected from various sources. Also used the CA Physical Fitness Test to determine students who are overweight.
Optional requirements (7):
- Hotspot Analysis/Spatial Analsyst: prepared and created raster dataset that was used to assess food insecurity.
- Create Indices: created a food security index by combining several census data
- Charts/Images: Used CHIS data to determine what type of transportation people use to get groceries, based on their ability to obtain food.
- Aggregating attributable fields: Attribute fields were aggregated in ArcMap to create a new attribute, percent Grade 5, 7 and 9 students who were overweight [(students not in HFZ/total students taking test )x 100].
- Boundary sub-set selection: created smaller shapefiles by selecting features that were located within a distance (0.5 miles) of the proposed route (study area), or those features that were contained within the study area (food establishments).
- Geoprocessing: Used clipping tool to modify LA County layer.
- Geocoding: Geocoded locations of supermartkets/grocery stores, farmers’ markets, convenience stores and fast food establishments.
- Buffering: created ½ mile buffer around subway stations to denote walking distance.
- Custom shapefile creation: Used Metro route information to create a shapefile of the proposed route and station locations.
- Inset Map: created inset map showing LA County on California map, and project area on LA County map. Included both set of maps in two separate data frames to be displayed on the final layout.
Emergency Planning for the City of Beverly Hills:
“Where should our containers go?” – An Initial Analysis of
Brief Introduction of Planning Issue
As an intern for the City of Beverly Hills Office of Emergency Management, I am often required to order, reorder, and track various supplies that the City has set aside for its populace in case of any local or large scale disaster. These supplies are located throughout the City in 8 different storage containers. The addresses for each of these are as follows:
- 241 Moreno Drive, Beverly Hills, CA 90210
- 605 N. Whittier Drive, Beverly Hills, CA 90210
- 624 N. Rexford Drive, Beverly Hills, CA 90210
- 200 S. Elm Drive, Beverly Hills, CA 90210
- 8701 Charleville Blvd. Beverly Hills, CA 90210
- 8400 Gregory Way, Beverly Hills, CA 90210
- 471 S. Roxbury Drive, Beverly Hills, CA 90210
- 1100 Coldwater Canyon, Beverly Hills, CA 90210
These 8 different containers all store equal amounts of water, food, first aid supplies, over the counter medications, blankets, cots, and various other valuable materials that can be crucial for the survival of our community during any prolonged emergency. However, these containers are all located in very “convenient” locations, by which I mean City owned property, such as parks and public school campuses. The problem with this is that these containers have no real strategic placement within the City, especially when considering vast variations in demographic characteristics, such as age or population density. Thus, it may be possible that the City can better place these containers in such a way that they are closer to higher-risk groups, such as the elderly, who are more prone to chronic diseases, disability, and the effects of disasters in general.
Therefore, the planning question I am addressing is, can we strategically place some of these containers near high risk groups, based on GIS techniques and Census data?
Layout Descriptions, Methods Used, and Limitations
This is my introductory map. It is meant to orient the viewer to the location and boundaries of the City of Beverly Hills. As explained in the presentation, the City of Beverly Hills is rather small when compared to other cities in Southern California, or Los Angeles County for that matter. The City has a total land area of approximately 5.7 sq. miles and a population of 34,109. The data for this map was gathered from Census Tiger Data, which was found online. This map features an inset map, a smaller shapefile that was created through an attribute sub-sets selection (Beverly Hills boundary clipped from CA Places shapefile), and an extent field (red) that was added to show the area shown on the larger window.
This is the second map. It shows the relative locations of all the emergency conatiners. The emergency container locations were geocoded (by using the addresses above) and added as a separate shapefile to the layer. However, some of these locations did not display correctly, so a couple of them were added directly as a custom shapefile to the layer. Once all locations were verified to be accurate, the locations were aggregated into one layer and displayed as a separate shapefile (pictured). As one can begin to see, there is a cluster of emergency containers in the Southern portion of the City, especially in the Southeastern sector.
This is the third map. The Census Tract population data was gathered from the Census American FactFinder website. The map was constructed using the Boundary sub-set selection method in order to make the smaller Shapefile from the larger Los Angeles County census tract file. This map is meant to show the size of the population in each census tract relative to the position of each container. Graduated colors were used to ease the interpretation of this data, in which darker colors represent larger population totals and lighter colors lower population totals.
This is the fourth map. The Census Block Group population data was gathered from the Census American FactFinder website. This map displays the same type of data (population) as the map above, except its focus is the block group level, instead of census tract level. Therefore, this map is a bit more detailed for the purposes of this analysis than the previous map. Thus, one can begin to distinguish smaller geographic areas within the City with higher populations. From personal knowledge, the areas that are darker tend to be areas of the City that have a higher proportion of apartments and other multi-family housing units. It is important to locate such sectors of the City since more people within a small geographic boundary means more people may be in need of services during an emergency, especially if those people live in apartments (smaller living areas means less space to store personal emergency supplies). A similar graduated color symbology was used as before.
This map shows the total housing units by the Block Group Level. The Census Block Group population data was gathered from the Census American FactFinder website. This map is meant to complement the conclusion reached above, in which I postulate that the darker areas of the map (above) represented sectors of the City with more housing units. Again, from an emergency planning perspective, placing emergency containers closer to these high population areas may be beneficial to both the City and the recipients of such services. A similar graduated color symbology was used as before.
This map was created through the generation of my own data. I was able to take the total land area, given by the attribute table for the Census Block Group level, convert this figure from Sq. Meters to Sq. Miles, and then take the Population Totals for each Census Block Group (in the population attribute table) and divide this figure by the Sq. Miles number. These new figures were then added to a new field within the attribute table for this layer. The same graduated symbology (colors) were used to represent the data. We can now see the population density of each Census Block Group within the City of Beverly Hills. By doing this, we can see that only one container is located within an area of one of the highest population densities.
With this map, we turn our focus from high population areas which may need more services and supplies from the City simply because they have more people per square mile, to areas with a higher risk group, the elderly (defined as those >65 years). This map shows the areas with high owner occupied units by the elderly. The elderly tend to live by themselves or with their significant others, and because of their age, may need more medical attention or supplies. Because of mobility issues that arise with advancing age, they may also need distribution centers to be closer to their homes for them to use these services.
This map shows the areas with a high proportion of renter occupied units by the elderly. This specific population may be of greater concern to us since elderly that are renting may have even fewer resources than their home-owning counterparts.
This is a spatial analysis (distance) in which the emergency container locations are the centroids, and areas of darker color represent areas within the City that lie furthest away from emergency containers. As one can see, the Southern section of the City contains no dark-red colors, whereas the North and Northwestern sections of the City do contain substantial areas of red. These areas may be of concern since its inhabitants may be reluctant to travel far distances to gather supplies in the event of an emergency.
This map is similar to the one above, with the exception that it contains 0.5 mile buffers around the emergency containers in order to better understand the distances we are seeing with the spatial analysis.
In order to perform a weighted analysis in which I gave different weights to different characteristics we have observed thus far, I needed to convert some of these layers to rasters and then converting them into indices. I was able to reclassify these rasters (using the model above) and give certain characteristics appropriate weights.
In this index, I was able to combine the raster for total population and distance from containers (Total Pop + Distance). Both were weighted in a similar manner, in which 5 classifications were given (1 for lowest [distance or population] to 5 for highest [distance or population]). The resulting analysis revealed a couple areas, some extremely near containers, that can be considered high risk. This is maily due to the fact that those areas contain extreme numbers of people (when compared to other sectors of the City). One limitation with this analysis is that I incorrectly set the cell size analysis (it was way too big), and thus a better map was possible that could have revealed a better interpretation of this data.
This was yet another weighted analysis consisting of Total Elderly Owners + Total Elderly Renters = Total Elderly Population, which was then added to the Distance Raster. The weights were highest for greater distance and greater numbers of elderly in each block group, and lowest for less distance and less elderly people. Again, the limitation with this analysis is the mistake I made in setting the cell size.
This is the last analysis. I added the population raster to the distance raster by using the map algebra function (same as before). I gave greater weights to areas with higher population densities and greater distances from emergency containers, and less weight to areas of low population density and lesser distances from emergency containers. The same limitation appears here as before.
Even though my analysis was compromised by the mistake in cell size, I can undoubtably say that the City of Beverly Hills may be able to relocate emergency containers in such a way that they can better address issues of need and risk. Most of the analyses above point to a specific location that is lacking containers in both distance and all characteristics (population density and elderly population) included in the weighted analysis. This area of the City lies in the Eastern boundary of the City near the center. If at least one container is relocated from the clusters found in the Southwestern sector, it may be able to diminish the theoretically high-risk profile of this Eastern border.
Yet, I cannot help but to think whether a clearer picture could have been achieved if my mistake in cell size was corrected. Moreover, I believe the next step for the City to take is to use GIS technology to determine the best way to distribute supplies within each container (use unequal distribution as opposed to equal distribution). For instance, containers within sectors of the City with a high population of toddlers should have higher supplies of baby formula and pampers, wheres sectors of the City with more elderly populations should contain less of these items.
I believe GIS is extremely useful under these conditions, and the topic of emergency management planning in general. Despite my mistake, I was still able to locate sectors of the City that may be in greater need for a closer emergency container location. I was also able to influence my boss, Pamela Mottice-Muller, to consider relocating one of the emergency containers, and to begin plans for unequal distribution of supplies based on these slides.
Sources of Data
US Census Bureau – Tiger Data
US Census Bureau – American FactFinder
Study of Gold Line in Los Angeles Based on GIS
The Gold Line is a light rail system that starts from East Los Angeles, running through downtown of LA until arrive to Pasadena. It opened on June 23th 2003 with a daily ridership projection of 26,000 to 32,000 people per day. Until 2012, the whole line is 19.7 miles long and has 21 stops.
Compare travel behavior changes since the opening of Gold Line.
- Study the potential layouts of bus routes which connect critical transit nodes along Gold Line and surrounding areas, compare service efficiency of these different routes.
Travel Behavior Change from 2000 to 2010
I selected two variables to measure the change of travel behavior in 2000 and 2010, Commuting Time and Percentage of Auto Dependence(people who travel by driving) respectively, which can be easily acquired and calculated from 2000 SF3 Sample Data and 2010 ACS 5-year estimates provided by U.S. Census Bureau.
Find three important transit nodes along Gold Line
Firstly, I made a Service Area Analysis to find areas which are within 15 minutes walking distance(walking speed is 80 meters per minute) from each Gold Line station based on street network of Los Angeles.
Then, I calculated population of each station service area to decide which stations should be strengthened as transit nodes by connecting more bus routes. As a result, I select Union Station, Highland Park and Lake as three transit nodes, which served relatively more population(136138,51319 and 72710 respectively) and are located evenly along Gold Line.
Calculate Transit Need Index in surrounding areas
Transit Need Index is the extent to which bus service should be provided. Generally speaking, there are four factors which might influence whether the transit need is higher or not, Population Density, Household Income, Percentage of Population Who are Under 18 and Above 65 Years, Percentage of No Car Family. The following figure is the analysis of these four factors.
On the base of converting four factors from feature files to raster files, I reclassified these determinants according to the following rules:
(1)Population Density(Person/Sq miles): <10000=1, 10000~15000=2, 15000~20000=3, 20000~30000=4, >30000=5
(2)Household Income: <30000=4, 30000~50000=3, 50000~100000=2, >100000=1
(3)Percentage of Population Who are Under 18 and Above 65 Years: <15=1, 15~25=2, 25~40=3, >40=4
(4)Percentage of No Car Family: <5=1, 5~15=2, >15=3
After reclassification of these factors, Transit Need Index was calculated by the formula——Transit Need Index=Population Density + Household Income + Percentage of Population Who are Under 18 and Above 65 Years + Percentage of No Car Family.
Through locating five addresses on the areas with higher Transit Need Index, I acquired an original point shape file by Geocoding these addresses.
Give suggestions on bus routes and compare them
Firstly, I made OD Cost Matrix to analyze connection between the five addresses and the three transit nodes.
Secondly, I gave a suggestion of three bus routes which connect the three transit nodes and the five transit need address by using Closest Facility Analysis function in GIS.
Finally, I made a Buffer Analysis of 500 meters with each bus routes and compared the three buffer areas by person/length of route. Obviously, bus route1(from west of LA downtown to Gold Line Union Station) is the transportation corridor with highest service efficiency among the three routes.
(2)Three transit nodes along Gold Line, Union Station, Highland Park and Lake should be strengthened by adding more bus routes connecting five surrounding transit need areas with them. Of the three suggested bus routes, the most important one is from west of LA downtown to Union Station.
GIS Skills Used
(1)Inset Map, (2)Point graduated symbol, (3)Creating indices, (4)Attribute sub-sets selection, (5)Boundary sub-sets selection, (6)Geoprocessing, (7)Buffering, (8)Custom shapefile creation, (9)Charts, (10)Hotspot analysis, (11)Geocoding, (12)Network Analysis
(1)U.S. Census Bureau:2000 SF3 Sample Data, 2010 ACS 5-year estimates.
For the last 17 years, the most popular professional sports league in the United States has been absent from the nation’s second largest city and media market. Since the Rams departure to St. Louis and the Raiders return to Oakland (both in 1995), Los Angeles has been without a franchise in the National Football League (NFL). Since the Rams and Raiders both played their last games in Southern California, 21 new stadiums have been built around the league; and every other city which had lost a team in the modern era has seen a new franchise come in. At current, two stadium proposals are vying for the right to become the home of the NFL’s triumphant return to the City of Angeles. Both privately financed, the 68,000 seat Farmers Field is slated to be built on the current site of the West Hall of the Los Angeles Convention Center next to the STAPLES Center, while the 75,000 seat Los Angeles Stadium’s proposed location is in a vacant field 20 miles to the east of downtown in the City of Industry. &  This GIS report analyzes the two competing stadium bids in terms of site suitability, access, and residential noise exposure.
Spatial Analysis of Sites
To explore the suitability of the two sites for a future NFL stadium I decided to explore four variables related to access and community resistance spatially: proximity to existing football stadium capacity, proximity to Metrolink commuter rail stations, proximity to Metro Rail lines, and distance from populations over the age of 67. The following model was then used to reclassify the rasters on a scale of 1-5 to achieve an output of low-high (proximity / or suitability) depending on the variable.
To achieve the layout (below) kernel density was performed on geocoded points of the current football stadiums in Los Angeles which contained a population field for stadium capacity.
Access to Metro Rail (below) was achieved by performing the Euclidean Distance to Metro Rail lines at a cell size of 25. Metro Rail in this analysis is comprised of the Blue, Gold, and Green light rail lines as well as the Purple and Red subway lines. It should be noted that the addition of the Expo Line will bring even more Metro Rail capacity in close proximity to the Farmers Field site. Metro Developer was used to source the data for the lines.
The layout analyzing Metrolink station proximity (below) was derived from the kernel density of Metrolink stations. As both stadiums would draw fans from not just L.A. County but the entire Southern California region, commuter rail would provide a valuable option for those from further afield. Metrolink has already shown its commitment to reducing gameday congestion and traffic with its “Ducks Express” and “Angels Express” trains to Anaheim. The data for stations and lines was sourced from Metrolink directly.
From the three rasters displayed above and a raster of populations over 67 (with the lowest receiving the highest desirability) derived from using feature to raster under conversion tools, a hotspot analysis (below) was performed to show locations in L.A. County most suitable for a new NFL stadium under the given criteria. Using the raster calculator the variables were given the following weights to create an index: proximity to Metro Rail 40%, proximity to Metrolink stations 30%, proximity to existing stadium capacity 20%, and populations over 67 years old 10%. As a transportation planner I instinctively placed a higher weight on the transit variables but there is also reasoning behind this. Both of these variables carry a far greater scope and scale in terms of creating a successful stadium project and a surrounding vibrant space. While the variables “proximity to existing capacity” and “populations over 67” voice concerns over the rationality and disturbances of the projects, in reality these are likely to carry little weight in the long-run as neither project is utilizing public funds.
Network Analysis of Sites
In order to perform a network analysis of the sites a network dataset was built from a detailed L.A. County streets shapefile sourced from the UCLA Mapshare. By using “Calculate Geometry” and the Field Calculator, driving times were computed by dividing the distance of the streets by their respective speed limits (provided in the shapefile). To assess the driving times to the sites from across L.A. County the service area feature of the network analyst was used. Each site was loaded as a “facility” by drawing each as a point in a new shapefile. The two resulting layouts (below) represent the range of driving times to each site from across L.A. County.
The results for Farmers Field were impressive, with most of the major population centers of L.A. County within only a half an hour drive of the site.
The results for Los Angeles Stadium were far more skewed to the east, with major population centers such as the Westside, San Fernando Valley, South Bay, and Long Beach up to an hours drive from the site.
Noise Exposure Buffers
The final analysis conducted was to assess the amount of residents that would be subjected to disruptive and in extreme cases hazardous levels of sound. The base maximum noise level was set at 140dB. This was derived from the highest noise recording in the NFL’s loudest stadium (Seattle), taken during the 2006 NFC Championship game when the crowd reached 137dB. A calculator was used to measure the attenuation of noise levels over distance, from which the buffer distances and decibel levels for the layouts were derived. A shapefile provided by the UCLA Mapshare allowed for population by block group from the 2000 Census to be merged into the attribute table of each buffer. The resulting layouts (below) display the dispersion of maximum noise levels expected by the stadiums (drawn as polygons from a new shapefile) and the populations within each buffer that will be exposed. For reference, sustained exposure at 85dB+ can result in permanent hearing loss, while at 100dB serious damage can occur in 15 minutes. 
Findings and Conclusions
This initial analysis showed that both potential sites have advantages and drawbacks. In terms of rail access Farmers Field was in very high proximity to Metro Rail lines while Los Angeles stadium was in little proximity. The kernel density of Metrolink stations suggested that both sites had “moderate” access to the commuter rail service. This does not take into account that on a much smaller scale the Los Angeles Stadium site would be far better suited for Metrolink as it contains a station adjacent to the parcel. Under the criteria of the hotspot analysis other areas such as Montebello (roughly between the two sites) and parts of the San Fernando Valley were suggested as far better suited for the stadium.
In terms of driving times from the network analysis Farmers Field was the clear winner with most of L.A. County’s major population points within a half hour drive. Of course any analysis of traffic in Los Angeles should mention that the service area is calculated without the impedance of traffic jams. It should also be mentioned that while Los Angeles Stadium did not perform as well in terms of driving times the analysis was only conducted on the scale of L.A. County. If performed for a wider area of Southern California by including San Bernardino and Orange Counties, the Los Angeles Stadium site may prove to actually be better suited for serving drivers than Farmers Field.
In regard to the noise exposure analysis a far greater number of people would be exposed to disruptive levels of sound near Farmers Field. It should be noted however that in terms of both disruptive sound and public health the extreme proximity to the interchange of the 10 and 110 Freeways should be of far greater concern to the residents. While GIS is very useful in analyzing specific variables and gaining a visual perspective of a topic this is a prime example of the danger of ignoring variables if they are not included in a map. It should also be mentioned that the scale at which we set our analysis (in this case L.A. County) should not limit our capacity to understand an issue. In further exploration of this topic the inclusion of Orange, Riverside, San Bernardino, and Ventura Counties would be beneficial.
In terms of a recommendation for the optimal site for an NFL stadium in Los Angeles this simple analysis is not enough. Without the use of public funds either one of these projects would provide a fantastic addition to Greater Los Angeles. While Farmers Field clearly provides the more transit-friendly option, self-selection theory suggests that most gameday travelers will choose to drive. Concerns over externalities such as traffic and noise may also be overstated as the usage of an NFL stadium is limited to only a few (often weekends) days a year.
 Markazi, Arash. “A 16-year Rocky Relationship: Why No NFL Team in L.A.? It’s a Study of Power Brokers and Bureaucratic Morass.” ESPN, 5 Feb. 2011. Web. 22 Mar. 2012
 “Our Plan.” Farmers Field, 2012. Web. 23 Mar. 2012
 “LOS ANGELES STADIUM.” Los Angeles Football Stadium at Grand Crossing. 2010. Web. 23 Mar. 2012
 Hobson, Katherine. “USA! USA! Nascar, NFL May Be Louder Than the Vuvuzela!” The Wall Street Journal. 16 June 2010. Web. 23 Mar. 2012
Lab 1, 2 Prepared Files, LA Locator from Lab 4
Air Monitoring Stations Near Los Angeles Highways
This GIS mapping project explores the policy problem of particulate matter exposure around major highways in Los Angeles County. Children are at an increased risk of harm from particulate matter exposure. Recognizing this concern, in 2003 the California State Legislature enacted a law that no new schools be built less than 500 feet from very busy roadways. However, there are currently 41 schools in Los Angeles County that are less than 500 feet (or approximately 275 meters) away from a major highway, and these schools are predominately in lower income census tracts. The elementary school data came from the UCLA Mapshare Los Angeles Schools shapefile, where I selected only elementary schools to include in my study.
I mapped these 41 schools as a part of my midterm project, demonstrated in the image above.
Air Monitoring Stations and Distance from Elementary Schools
An important impetus for change regarding particulate matter exposure for these children is to ensure accurate measurements of air quality take place in order to set and enforce air quality standards. Currently, there are 19 air monitoring stations in Los Angeles County, which are mapped above, alongside a map of the 41 elementary schools that are 150 meters or closer to a major highway. The air monitoring stations for the county come from original data I collected from the South Coast Air Quality Management District website. I input the address data for each of the air monitoring stations, and geocoded the addresses in order to map the locations.
The above map demonstrates the distance of the closest air monitoring station to each of the 41 elementary schools that are within 150 meters of a major highway. To map these distances I used the network analysis tool of closest facility, and was able to determine which air monitoring station was closest to each of the 41 elementary schools.
As this chart demonstrates, 16 schools are 4,000 meters or less from an air monitoring station, 8 schools are between 4,000 meters and 6,000 meters from an air monitoring station, and 17 schools are more than 6,000 meters away from an air monitoring station. These schools are all within 150 meters of a major highway, so the schools also serve as indicators of how close air monitoring stations are to major highways.
This map lays out the various air monitoring stations within Los Angeles County along with the major LA County highways. The ‘major highways’ were derived from the LA County Highway shapefile from the UCLA Mapshare through selection by attributes (where FCC=A15). The highways are mapped with 150 meter buffers, and the air monitoring stations are mapped with 275 meter buffers. These buffers were created through the ArcGIS buffer tool. The Natural Resources Defense Council has sued the South Coast Air Quality Management District for not accurately measuring air quality because the air monitoring stations are too far away from highways to measure the dense particulate matter exposure near highways. NRDC believes air monitoring stations should be around 900 feet (approximately 275 meters) from a major highway, thus my decision to map a 275 meter buffer around each of the air monitoring stations.
By extracting information from my buffer, I discovered there is only one air monitoring station in LA County that is within 275 meters of a major highway. This station is mapped in the layout above. This reveals there is a need for air monitoring stations in LA County closer to highways, which have higher levels of particulate matter.
Variables to Determine Ideal Locations for New Air Monitoring Stations
There are three variables I used to determine what areas new air monitoring stations should be located. The first variable is the distance from elementary schools that are within the 150 meter buffer of a major highway. It is more desirable for new monitoring stations to be close to these schools. The second variable is the distance of new air monitoring stations from a major highway. In this instance, it is more desirable for new monitoring stations to be close to highways. The third variable is the distance from existing air monitoring stations. In this instance, is it more desirable for new monitoring stations to be further away from other air monitoring stations to maximize efficiency.
The next step in determining where new air monitoring stations would be ideally located is a hot spot analysis. A hot spot analysis provides information on where there are clusters of my three values spatially. The first step was to rasterize my three variables: distance from elementary schools that are within 150 meters of a major highway, distance from a major highway, and distance from existing air monitoring stations. After rasterization, I reclassified my variables to be broken down into numeric categories 1 through 5, where 1 is the most desirable scenario and 5 is the least desirable scenario. I used the modeling tool to reclassify:
Reclassification of Variables
The above map demonstrates the reclassification of the distance from elementary schools that are within 150 meters of a major highway. The areas with the lightest color green are the most desirable locations for new air monitoring stations depending on their distance from the aforementioned elementary schools. The darker the gradation of green, the less desirable the location is for new air monitoring stations in terms of this particular variable.
This map is the reclassification of the distance from major highways variable. The lightest color, which represents 1, is the most desirable location for new air monitoring stations if this is the only variable to consider, and the darker the gradation, the less desirable the location. As is evident, the most desirable locations surround the major highways.
This map is the reclassification of the distance from existing air monitoring stations. Again, 1 is the most desirable location for a new monitoring station if distance from existing air monitoring stations is the only variable to consider. However, in this instance the most desirable regions are on the outskirts of the county, because the majority of existing air monitoring stations are clustered more toward the center of the county.
This map compares all three previous maps side by side. However, none of the variables alone really creates the analysis necessary to determine where new air monitoring stations should be located. For example, if distance from existing air monitoring stations was the only factor, a new air monitoring station would be built in the northeast corner of the county, which is far away from the targeted population of children in schools near major highways. Therefore, an index is necessary of all three variables.
Index of the Variables
After rasterizing and reclassifying each variable as was demonstrated in the previous maps, I created an index of the three variables in order to conduct a hot spot analysis of the areas where new air monitoring stations would be most beneficial. Because I do not believe the three variables are equal, I gave weights to each. The distance from a major highway is the most predominant variable, I believe, so I gave it a weight of 5. While schools are the driving variable, I am most worried about schools by highways, and making distance from highway the most heavily weighted variable will also encompass other important populations beyond schools, including low income populations that live near the major highways. I gave the distance from elementary schools that are within 150 meters of a major freeway a weight of 3, because I find it to be the second most important factor being that these schools are the target population. I gave distance from existing air monitoring stations a weight of 2, because I believe that it is important in terms of efficiency, but my greatest concern is equity, which is why it is given the lowest weight, but a weight greater than 1. I then used the Map Algebra Raster Calculator to implement my weights using the equation: [(“h_reclass”*5)+ (“s_reclass”*3) + (“a_reclass”*2)]/3.
This map shows the result of the index. This map does not include highways, schools, or air monitoring stations to get a clearer view of the best places for new air monitoring stations. Again, the areas with the lightest color are the most ideal locations for new air monitoring stations, and the desirability reduces as the gradation becomes darker. Because I gave weights to my variables, the scale is no longer from 1-5, but it represents the same five classifications of data.
Adding back in the schools, air monitoring stations, and highways to the layout, my suggestion would be to place the air monitoring station(s) in one of the more congested areas where there are large groupings of schools, and a conversion of the lightest color from the hot spot analysis. The particular area I chose to highlight also has a conversion of major highways.
Conclusions and Policy Implications
GIS provided an effective platform to conduct an analysis determining the locations in which new air monitoring stations would ideally be located. GIS also allowed for the visual representation of air monitoring stations and revealed the large gaps in air quality monitoring. These gaps likely originate from budgetary issues. However, due to whatever reason from which the lack of air monitoring stations stems, the extent of the air quality problems that face those who live, work, and go to school near major highways is largely unrecorded. Mandating air quality monitoring stations to be near highways protects the children who are at the greatest risk of exposure to particulate matter, because these children not only go to school near major highways, but likely live near a major highway as well.
 Beate Ritz, MD, PhD, and Michelle Wilhelm, PhD. “Air Pollution Impacts on Infants and Children.” Accessed February 1, 2012. http://www.environment.ucla.edu/reportcard/article.asp?parentid=1700
 Southern California Particle Center and Supersite. Accessed, February 1, 2012. http://www.scpcs.ucla.edu/news/Freeway.pdf
 “Annual Air Quality Monitoring Network Plan.” South Coast Air Quality Management District. http://www.aqmd.gov/tao/AQ-Reports/AQMonitoringNetworkPlan/AQnetworkplan.htm
 “Lawsuit Seeks Justice for 1.2 Million Residents Living Near SoCal Freeways: Lack of Air Monitors Fails to Capture Full Extent of Preventable Pollution.” Natural Resources Defense Council. Accessed March 10, 2012. http://www.nrdc.org/media/2012/120103.asp.
“Lawsuit Seeks Justice for 1.2 Million Residents Living Near SoCal Freeways: Lack of Air Monitors Fails to Capture Full Extent of Preventable Pollution.” Natural Resources Defense Council. Accessed March 10, 2012. http://www.nrdc.org/media/2012/120103.asp.
 “How Hot Spot Analysis: Getis-Ord Gi (Spatial Statistics) Works,” ArcGis Desktop 9.3 Help. January 2009. Accessed March 14, 2012. http://webhelp.esri.com/ArcGISdesktop/9.3/index.cfm?TopicName=How%20Hot%20Spot%20Analysis:%20Getis-Ord%20Gi*%20(Spatial%20Statistics)%20works