Final – Tamanna Rahman – Part 2

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

Conclusion/Recommendations:

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:

Required (3):

  • 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.