Hear Ye! Hear Ye! Read all about it: Metro’s Westside Subway Extension Project moves forward
Although Los Angeles has a public transportation system that is currently lacking when compared with other major
cities, Angelenos can become enthusiastic about potential change as strides are taken to push Los Angeles toward
becoming a city more conducive to public transportation.
With the recent approval of the first phase of the long sought after “Subway-to-the-Sea,” Los Angeles is shifting towards
a more integrated transit system that not only would add efficiency, but also a number of other benefits to riders and
community members at multiple levels. The Los Angeles Metropolitan Transportation Agency, or Metro, recently
released the proposed alignment that would extend the Purple Line subway from its current terminus at Wilshire and
Western in mid-town Los Angeles approximately 9 miles westward along the Wilshire Corridor toward Beverly Hills,
Century City, Westwood and West Los Angeles (See Figure 1). The proposed subway would not preclude other transit
alternatives. Pedestrian and bicycle infrastructure, as well as rapid bus lanes, could be integrated into the project. Likely
to alleviate some of the epic traffic congestion that occurs during rush hour along that corridor, Angelenos can look
forward to 2022, the expected completion date at the earliest, to taking the subway as opposed to driving.
Figure 1: Metro’s proposed Westside Subway Extension Project
Source: Metro http://www.metro.net/projects/westside/#overview
Due to the subway project’s magnitude, it has the potential to transform surrounding neighborhoods and impact
multiple, overlapping populations. These neighborhood changes could affect health in a number of ways, one of them
being impacts on the food retail environment. Our transportation system plays a key role in how communities access
fresh nutritious food. Millions of Americans, especially people with low-incomes, the elderly, disabled, and other transitdependent populations, have difficulty accessing fresh, nutritious food. With diabetes, overweight and obesity rates
reaching epidemic proportions, this project can set an example for how development can be done with health in mind.
Barriers to Healthy Food:
These health concerns illustrate the importance of better understanding the complexities of our food environment
because there are a number of variables that come into play. Studies have proven that people with lower incomes are at
greater risk to be obese than those in the middle and upper class. As contradictory as it may seem, it is a harsh reality
that an increasing number of families and individuals are finding it difficult or impossible to maintain a healthy diet.
Much research has addressed the fact that healthy food is often more expensive and difficult to obtain compared to a
less healthy diet that tends to be high in calories and fat and low in quality (Furey et al., 2001). The result is that lower
income groups do not get adequate nutrient intake when compared to people in higher income brackets (Clifton, 2004;
Morland et al., 2008). Healthy food may also cost more for low income households because purchases are made in
smaller quantities (Morland et al., 2002). Many healthy foods, such as fruits and vegetables, are often more expensive
and are often viewed as luxury items by those with low-incomes. Morland et al. (2002) and Sharkey et al. (2009) report
that the price of healthy food and the variation between prices is a major concern for those living on low incomes.
Transportation and Food Access:
Full service supermarkets and farmers’ markets are scarce in low-income areas. Residents of areas poorly served by food
retail options are also more likely than the general public to be transit-dependent, so it can be difficult for them to travel
to food markets located outside of their immediate neighborhoods. According to data from the federal government’s
survey of personal transportation, a quarter of low-income households lack access to an automobile (reference). This
percentage is higher in some urban areas, leaving many residents dependent on walking, cabs and transit for food
shopping trips. For example in the Metro Service Planning Area of LA County, those who are not able to afford enough
food were less likely to rely on their personal vehicles than walking/biking or using public transportation for their food
shopping trips (See Figure 2).
Figure 2: Type of transportation used to access grocery stores by food security
Usual type of transportation to get to grocery store compared
by Food security (ability to afford enough food) , CHIS 2007
% type of transportation used
by ability to afford food
60
50
40
30
20
10
0
Personal vehicle
Public
Food delivered
as driver or transportation
by public
passenger
program
Walk or ride
bike
Someone else Taxicab/Other
does shopping
Type of transportation used to get to grocery stores
Able to afford enough food (food secure)
Not able to afford enough food (food insecure)
A poor transportation system cuts off access to many food outlets—especially for those who do not own a car or have
access to reliable and affordable public transportation. Residents of lower income and minority neighborhoods in most
urban areas face a double burden that severely limits their access to fresh, healthy food. Those food markets that are
located in low-income neighborhoods are often smaller, with less selection in general, and less and lower quality
produce. With fewer supermarkets and farmers’ markets, residents of low-income neighborhoods rely heavily on
convenience stores, liquor stores, and small ethnic grocers for groceries. These establishments stock packaged and
processed food items, but few, if any, fruits and vegetables. Fast food chains, serving high-fat items are often the only
source of prepared foods
Assessment of the Retail Food Environment:
In an effort to better understand the current food retail environment and the factors that may limit access, I decided to
explore the area around the proposed stations of the Westside Subway Extension because this project serves as an
opportunity to develop with the goal of designing healthy communities.
This analysis examines the coverage of food stores and key demographic patterns in the defined study area. Analyzing
the current distribution will help to understand what type of food the community has access to and where there are
clear gaps and a need for improving healthy food access.
With alignment information from Metro and census tract data from UCLA Mapshare, ArcGIS was used to create a layer
shapefile of the Purple Line extension. Census tracts within a ½ mile of the proposed route were selected to define the
study area (see Figure 3). The Los Angeles County GIS Portal provided a city layer that was overlaid on the study area for
better geographical orientation. For this assessment, census tracts were chosen as the unit of analysis. The
demographics of this area will be detailed later.
Figure 3: Defined study area
Data Source: Metro (route), UCLA Mapshare (census tract and local freeways), LA County GIS Portal (cities)
In order to assess the food environment from the perspective of transit-users, a ½ mile buffer radius was created around
each subway station (Figure 4). Several research studies have used either ¼ or ½ miles radii as walkable distances, but
1/2 mile radius was used in this analysis to get a greater sampling of the food options.
Figure 4: Study area with ½ mile buffer around proposed subway stations
Food store data:
Several levels of the food environment have been identified, with one being the “community environment.” This defines
the place where food can be obtained, including grocery stores, convenience stores, specialty stores, restaurants,
farmers’ markets that are generally open to the public. This analysis focused on supermarkets/grocery stores farmers’
markets, convenience stores and fast food establishments. The North American Industry Classification System (NAICS)
codes were used to select supermarkets/grocery stores and produce stores that were geocoded and mapped. Farmers’
markets locations were gathered from the California Farmers Market Association website and geocoded and spatially
joined. Figures 5 and 6 display the spatial distribution of the healthy and unhealthy food environment, respectively,
within the project area. While there are some clear clusters of supermarkets and grocery stores on the west side, there
are evident gaps in the availability of healthy food sources near certain locations in the project area. For example,
Figures 5a and 6a displays the same three stations, but it is clear to see that more fast food establishments and
convenience stores are readily available compared to supermarkets/small chain grocery stores and farmers’ markets.
Table 1 denotes the number of food establishments, by type of business, available within ½ mile radius of each Metro
subway station. Clearly, fast food establishments and convenience stores have a majority over establishments that offer
nutritious, healthful foods at affordable prices.
Figure 5: Healthy food Environment – Supermarkets/grocery stores and Farmers’ Markets
Data Source: Network for a Healthy California, California Farmers’ Market Association, UCLA Mapshare, Metro
Figure 5a: Healthy Food Environment – looking at Wilshire/La Brea, Wilshire/Fairfax and Wilshire/La Cienega stations
Data Source: Network for a Healthy California, California Farmers’ Market Association, UCLA Mapshare, Metro
Figure 6: Unhealthy food environment – Convenience stores and fast food
Data Source: Network for a Healthy California, UCLA Mapshare, Metro
Figure 6a: Unhealthy food environment: looking at Wilshire/La Brea, Wilshire/Fairfax and Wilshire/La Cienega stations
Data Source: Network for a Healthy California, UCLA Mapshare, Metro
Table 1: Distribution of Supermarkets, Farmers’ Markets, Convenience stores and fast food establishments within ½ mile
of the proposed subway stations
Type of Business within 1/2 mile of train station
Train Station
Farmers'
Convenience
Supermarket
Market
Store
Wilshire/Vermont
0
1
11
Wilshire /Normandie
1
0
6
Wilshire/Western
2
0
4
Wilshire /La Brea
2
0
3
Wilshire/Fairfax
1
0
5
Wilshire/La Cienega
0
0
4
Wilshire/Rodeo
2
0
2
Century City
2
1
0
Westwood/UCLA
5
1
2
Westwood VA
1
1
1
Data source: Network for a Healthy California, California Farmers’ Market Association.
Fast
food
11
11
9
7
8
8
5
5
16
2
Demographics of Study Area:
As shown below (Table 2), the demographic composition of neighborhoods along the Wilshire Corridor are highly varied,
generally following a gradient from predominantly low-income, Latino and Asian residents in Koreatown/Wilshire Center
on the eastern edge of the corridor to predominantly high-income, majority white neighborhoods to the West. Countywide the population is expected to be significantly older with fewer Whites and more Latinos by 2035.1 Similar
demographic trends could be expected for most of the project area. Except where noted, data are from Metro’s 2010
Westside Subway Extension DEIR/EIS, “Technical Report 08 – Community and Neighborhood.” For data not in the
DEIR/EIS, data from the Los Angeles Times’ “Mapping L.A. Project” were used. Neighborhood names and boundaries
varied somewhat between the two data sources.
Table 2: Demographic Characteristics of Neighborhoods along the Wilshire Corridor (displayed East to West)
1
% HH
earning
Transit
dependent g
Avg.
household
sizea
% of HH
rentinga
Violent crimes
/10,000/6 mo.
(May-Nov
2011)a
42.60%
28%
2.7
93.00%
20.3
23.30%
----
24%
----
----
-------
$44,647
20.20%
----
19%
----
----
-------
Windsor Square
$73,954
8.00%
23.60%
?
2.5
59.00%
17.8
Hancock Park
$90,246
7.00%
15.30%
?
2.1
52.70%
24.4
Picoc
$41,816
13.70%
----
12%
----
----
-------
Median
HH
income
% HH
below FPL
<$20,000/yr
Wilshire Ctr/
Koreatownf
$25,603
29.90%
Olympic Parkc
$33,306
Wilshire Parkc
Southern California Association of Governments. 2011. Draft Regional Transportation Plan: 2012-2035 Growth Forecast. Available
at http://rtpscs.scag.ca.gov/Documents/2012/draft/SR/2012dRTP_GrowthForecast.pdf
Miracle Milec
$46,538
8.40%
----
?
----
----
-------
Mid-city
West/Fairfaxb
$49,726
11.50%
21.90%
?
2.1
78.30%
17.4
Carthay
$54,112
12.40%
20.20%
?
2.1
64.70%
35.2
Beverly Hills
$97,726
6.00%
14.20%
?
2.2
56.50%
Not available
South
Robertsond
$49,294
12.80%
21.80%
?
2.1
73.10%
10.9
Century City
$93,353
8.70%
14.10%
8%
1.8
39.60%
1
Westwoode
$66,356
22.40%
25.60%
8%
2
64.10%
3.8
Total, Pop. wt’d
avgh
$54,688
17.60%
29.70%
?
2.3
76.70%
14
Notes for Table 2
a. From Los Angeles Times’ “Mapping L.A. Project” http://projects.latimes.com/mapping-la/neighborhoods/
b. L.A. Times did not have a neighborhood corresponding exactly to the “Mid-city/Fairfax” neighborhood listed in the Metro
documents. Data from the L.A. Times’ “Mid-Wilshire” neighborhood was used instead.
c. L.A. Times did not have a neighborhood whose boundaries corresponded to the neighborhoods that Metro defined as the
“Miracle Mile,” “Olympic Park,” “Pico,” and “Wilshire Park.” Portions of these neighborhoods are represented by other
neighborhoods defined by the L.A. Times.
d. L.A. Times data did not list a neighborhood corresponding exactly to the “South Robertson” neighborhood listed in Metro
documents. The overlapping “Pico/Robertson” neighborhood identified by the Times was used instead.
e. Some data from the Westwood neighborhood, especially income, may be skewed by the high proportion of university students
living in the neighborhood.
f. L.A. Times Mapping L.A. did not have a neighborhood whose boundaries corresponded exactly to the neighborhood that Metro
defined as the “Wilshire Center/Koreatown” neighborhood. Data from the L.A. Times’ “Koreatown” neighborhood was used
instead.
g. The Federal Transit Administration defines transit-dependent persons as those 1) without private transportation, 2) elderly (over
age 65), 3) youths (under age 18), and 4) persons below poverty or median income levels defined by the U.S. Census Bureau. The
presence of low income residents is discussed in detail in Sections 3.4 and 4.4 of the DEIR/EIS “Environmental Justice.”
h. Population-weighted averages for data from the L.A. Times are calculated using the Times’ population totals for neighborhoods,
not the totals from Metro’s Westside Subway Extension DEIR/EIS which are shown in this table.
There are also a number of schools in the project area. Schools located within 0.6 miles of the proposed subway
alignment include 27 primary schools with enrollment of 12,499 students and 8 secondary schools with an enrollment of
11,217 students. While not all are residents or walk to school, a large number can potentially walk to many of the fast
food establishments or convenience stores located nearby.
Each year, California students take part in physical fitness test that evaluate a child’s performance according to
established benchmarks. Students are placed into categories based on their performance and those who do not meet
the standard are said to fall outside the Healthy Fitness Zone (HFZ). These students are considered overweight and at
risk for diseases that result from sedentary living and unhealthy diet. For this study, I characterized childhood
overweight issues by analyzing the Body Composition test results available through the California Department of
Education’s Dataquest website. After determining the schools located within the project area, the fitness results were
aggregated for students in grades 5, 7, and 9 at all active public schools located within each zip code. 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]. These results were then mapped and are available in Figure 7.
While zip code level data may be too large to show neighborhood level effects, there has been research demonstrating
that health status of an individual may be linked to their zip code, indicating that one’s environment may be related to
health status. There is clearly more overweight students on the east end of the project area compared to the west.
Income is also a strong predictor of student weight status. Median household income data (obtained from Social
Explorer) showed a slight relationship between the student weight distribution and income levels in the area. However,
there was no method of assuming that the children who attended these schools also lived in the area, therefore, the
results were not substantive enough to include ,.
Figure 7: Percent of Students (Grade 5, 7 and 9) Not in the Healthy Fitness Zone for Body Composition (Overweight), by
school zip code, 2010-2011
Data source: CA Dept of Education – Physical Fitness test results, CA Dept of Education – School Directory, Metro, UCLA
Mapshare (zip code polygon and highways)
Creating a Food Insecurity Index:
Many researchers have studied how food environments vary across communities. Several characteristics that food
desert areas tend to have are: larger proportions of residents without high school degree, higher poverty rates, lower
median family incomes, greater fractions of families living in rural areas, a larger elderly population, higher amounts of
small grocery stores and convenience stores per capita, and a lack of access to fresh food (Larson, Story and Nelson,
2009; Morton and Blanchard, 2007). Considering these factors and understanding the demographics, I wondered
whether there was a measure that could help target areas where resources need to be focused. Recent research has
shown that food insecurity – the limited or uncertain availability of nutritionally sound, safe food – is common in the
recent recession and is positively associated with being overweight (Adams, Grammar-Strawn, and Chavez 2003). Foodinsecure youths may be particularly vulnerable to inhospitable food environments, as they turn to fast food more
frequently (Widome et al., 2009). Families that are limited by income and time also rely on more “convenient” meals
that are not always nutritious.
In order to understand where food insecurity can occur in the project area, 3 factors were analyzed:



The percentage of single female-headed households with kids
Households living in poverty (income below $20,000)
Populations poorly speaking English
Female-headed households with children tend to have limited income. These mothers are likely to work more hours to
provide a living for their families. With more hours away from the home, they may not always have the time to provide a
home-cooked meal, and therefore may rely on fast-food or restaurants. The next variable considered those living in
poverty, which according to the Health and Human Services Poverty Guidelines, is below $20,000 for a household of
four. Household with lower incomes are at a limited capacity to afford necessary daily needs. Higher cost of living and
transportation may outweigh the luxury of fresh fruits and produce, forcing people to choose between nutrition and
cheaper alternatives. Another factor that tends to indicate immigrant populations and lower socioeconomic status is the
ability to speak English. Research indicates that people with limited English proficiency may have greater language and
cultural barriers that are likely to contribute to poorer health outcomes.
Figure 8: Distribution of single female-headed households with kids
Data Source: Census, UCLA Mapshare, Metro
Figure 9: Distribution of Percent of Households with Incomes below $20,000
Data Source: Census, UCLA Mapshare, Metro
Figure 10: Distribution of Poor English-Speaking Population in the Study Area by census tract.
Data Source: Census, UCLA Mapshare, Metro
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 vectorbased 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.