Do low income areas have a correlation to food deserts?
A food desert is an area that has limited access to affordable and nutritious food
Personal Interest and Why:
I have been very interested in the topic of food deserts since coming to USC. All my life, I have been fortunate to grow up on a farm and have fresh food on my table, not just food but organic, healthy greens. There is another aspect of food deserts that are so intriguing to me. The lack of knowledge people have in low income areas on which foods are good for you and which are not.
Since coming to USC, I have noticed this major issue in south central and all over Los Angeles county. Los Angeles County is my spatial location.
Again, a food desert is a geographic area where residents lack access to healthy food options. Lack of access could be a product of many things: distance, convenience, or low-income. All these things affect a resident's ability to acquire healthy food options.
New York Times Article on Fast Food in Los Angeles:
To the right is an image of Figueroa Street by USC. This was one of the first images that pops up when you do a google search on food deserts. There are numerous areas in Los Angeles County that look like this.
With my data, I found some answers with where these locations are, what are the important locations that stand out on the map, where can we update and design for the good of the people, and which locations should the government pay most attention too. This is mainly downtown LA and looking out for the lower income areas when it comes to their health and safety.
This leads into Geodesign with the question and process of how we can change these impoverished areas, for the people in the place, to add more an accessibility grocery stores and organic foods to these lower income households. How can we bring education and community together when it comes to food and how to take care of the planet. Do we design more transportation access, more community garden access, more organic grocery store access. All big picture ideas, but to keep in mind.
Low Income Housing Data
Metadata:
Los Angeles County Percentage of Low Income Housing Unit. Source Data: ACS 2015 5 Year Estimate, U.S. Department of Housing and Urban Developments.
Created: April 4, 2017
ARCGIS Online Source, Polygon Data
Higher percentage downtown in blue, northern rural area blue as well
Fast Food Restaurants Los Angeles County (added onto Low Income Data Map)
Metadata:
Fast food, pizza, and sandwich restaurant rate per 10,000 residents 2012 from the Health Atlas for the City of Los Angeles July 2013
ARCGIS Online Source, Point Data
Restaurant symbology
The center and cities mainly effected. Downtown LA fast food restaurants are stacked on each top of each other. Less coastal fast food locations because there is higher income, a higher knowledge of healthy food, a higher accessibility to organic foods and grocery stores.
Hot Spot Analysis
When creating this hot spot, I linked fast food and low income housing together to gather information. I zoomed in Los Angeles County, there is a huge divide showing the hot spot (red) downtown and more lower income neighborhoods / communities in comparison to the cold spot (blue) in higher income neighborhoods / communities. This makes sense with Malibu (coastal), Brentwood, Santa Monica, Beverly Hills, etc. in the cold spot.
*I did have some issues with making the hot spot smaller with the band and had lots of failures but I did try!*
Los Angeles Poverty Data
Number of individuals under 130% FPL for Los Angeles County from the 2015 ACS (American Community Survey)
Polygon Data
FPL = Federal Poverty Level
The darker the blue, the more impoverished areas in Los Angeles County. This is in relation to the Low Income Housing Map. I wanted to show another source. Again, the center and main cities of LA County are darkest. Downtown LA is hit hard with poverty meaning more obesity, more homelessness, low income neighborhoods, unsafer neighborhoods, etc. Long beach area is also mainly effected and dark blue.
ARCGIS Online Source
Los Angeles Obesity Hot Spots
From Obesity_Diabetes_Data_CDC points used
Hot Spot (red) is highest obesity rates, Cold Spot (blue) is lowest obesity from data set. Red is more centralized, blue is on the outer ring of the red and lower income areas. Blue is also more coastal locations, as we talk about coastal areas having a reputation for higher income, safety, and better, healthier, and more options for food.
Not as much of a divide compared to the other hot spot analysis. Shows a clear indicator of where the obesity problem is in the city. This map allows us to start the process of better design, with this map as proof that these food deserts exist and are hurting low income families.
ARCGIS Online Data
Map 1: Fast Food, Map 2: Poverty, Map 3: Obesity Hot Spot
Comparing poverty and obesity hot spot maps: it is clear that where it is a dark blue it is the most poverty and when there is a dark red hot spot, there is a correlation. Highest poverty locations have the highest obesity rates and hot spot locations.
Comparing the red hot spot to the fast food locations: there is another clear correlation. High obesity with fast food since this is cheap food for families to buy and eat every day. Many families can not afford to go to the grocery store everyday or at all. Families also struggle to get to the grocery store when they are farther away from their homes and there is a poor public transportation system like LA has. What needs to be taken into account along with everything else...there is not always time for parents and families to cook every day and fast food restaurants are simple and easy options on the go when you work day long shifts and work pay check to pay check in these low income areas darkest on Map 2.
Spatial Autocorrelation Report for Low Income Data (Global Morans I/Incremental Spatial Autocorrelation tools)
z-score: 35.016, there is a less than 1% likelihood that this cluster pattern could be the result of random chance.
- Red = clustered
- Shows that this data is clustered spatially. As distance increases, the z-score increases; making it clear there is a strong clustering when it comes to low income housing.
*Side note to Professor: I worked tirelessly with the data and GWR kept failing for this data aspect, therefore could not show in map form.
GWR (Geographically Weighted Regression) definition: a spatial analysis technique that takes non-stationary variables into consideration (aka low income housing and fast food locations) and models the local relationships between these predictors and an outcome of interest.
USDA: United States Department of Agriculture Research Atlas
Official counted food deserts mapped in the Los Angeles Area
- Green - Low Income and Low Access census tracts where a significant number of people in this radius is more than a mile and 10 miles from the closest supermarket
- Orange - Low Income and Low Access at 1/2 and 10 miles
- Purple - Low Income locations (census tracts with a poverty rate of 20% or higher)
This map proves my hot spots and distance locations correct -- main hit is center of the city, Downtown Los Angeles one of the main areas.
Acknowledgment: This Story Map is created to satisfy final research project requirement for SSCI 381: Statistics for Spatial Sciences taught by Prof. Orhun Aydin.
