Introduction

Centered around Raleigh, NC, Wake County is the second most populous county in North Carolina. With a population of a little over a million people, it’s a very concentrated county and therefore very useful to study when it comes to food scarcity.

According to the Wake County Government Site about 13% of the population was considered food scarce I 2014, which comes down to adequate access to nutritious foods. This is based on multiple variables, but primarily low income and low access communities.

Food scarcity has become such an issue in Wake County, that there’s even a Health Assessment, the 2016 Wake County Community Health Needs Assessmentcomprehensive guide. According to this guide, Wake County performs poorly when it comes to “fast food consumption and frequency, exercise opportunities, adult obesity, food environment index, fruit and vegetable consumption, physical inactivity, and obesity among children ages 2-4”. In other words, with many socially vulnerable regions of Wake County, there are higher frequencies of food scarcity.

The last area included within this study are food deserts, which are similar to areas that are food scarce with low access to nutritious foods, but these are more urban areas with limited access to fresh, high quality healthy foods that lie in typically impoverished areas. This helpful geo spatial map portrays exactly where the food deserts are within Wake County. They are primarily in the surrounding areas in Raleigh, with the largest food desert existing just west of the city. This report will target a number of variables that feed into food scarcity and the presence of food deserts.

Hypothesis

For this project, the focus will be on studying restaurants, farmer’s market, population, open space, and the social vulnerability index to confirm a correlation between areas without significant amounts of farmer’s markets and restaurants and substantial existing populations to have increased levels of social vulnerability. Social vulnerability involves those in lower socio-economic brackets who are in impoverished areas with lack of resources. These groups tend to be the largest inhabitants in food deserts.

Method

Packages that I downloaded:

  1. sf
  2. tigris
  3. mapview
  4. tidyverse
  5. maps
  6. hexbin

I first extracted Wake County Open Data for restaurant, farmers markets, population, open space, and social vulnerability GeoJSON data. While social vulnerability doesn’t necessarily directly target food scarcity, it checks off one of the requirements for a food scarce region (low income/low access). Furthermore, this data is a useful indicator for the kinds of people living in a certain area, rather than just the quality and quantity of food in the region. I then downloaded Wake tracts for a map of the county to facilitate my geo spatial maps for other variables:

Code for the Wake County tract for the maps: {r}wake2 <- read_sf("https://opendata.arcgis.com/datasets/21d4ff44498a4007beefdcbde6fcd2a9_0.geojson"){r}

library(sf)
## Linking to GEOS 3.6.1, GDAL 2.1.3, PROJ 4.9.3
wake2 <- read_sf("https://opendata.arcgis.com/datasets/21d4ff44498a4007beefdcbde6fcd2a9_0.geojson")

Code for downloading farmers markets GeoJSON data: {r}fm <- read_sf("https://opendata.arcgis.com/datasets/cafa31e25919412fb0733b4a55240237_0.geojson"){r}

fm <- read_sf("https://opendata.arcgis.com/datasets/cafa31e25919412fb0733b4a55240237_0.geojson")
library(maps)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
## ✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
## ✔ tidyr   0.8.3       ✔ stringr 1.4.0  
## ✔ readr   1.3.1       ✔ forcats 0.4.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ✖ purrr::map()    masks maps::map()
library(mapview)
library(hexbin)
fmData<- fm %>%
  st_coordinates() %>%
  as_tibble()

Code for restaurant GeoJSON data: {r}restaurants <- read_sf("https://opendata.arcgis.com/datasets/5c68797ce230422d92a9edf72193a04e_0.geojson"){r}

restaurants <- read_sf("https://opendata.arcgis.com/datasets/5c68797ce230422d92a9edf72193a04e_0.geojson")
rData <- restaurants %>%
  st_coordinates() %>%
  as_tibble()

Code for population GeoJSON data: {r}population <- read_sf("https://opendata.arcgis.com/datasets/21d4ff44498a4007beefdcbde6fcd2a9_0.geojson"){r}

population <- read_sf("https://opendata.arcgis.com/datasets/21d4ff44498a4007beefdcbde6fcd2a9_0.geojson")
popData <- population %>%
  st_coordinates() %>%
  as_tibble()

Code for open space GeoJSON data: {r}openSpace <- read_sf("https://opendata.arcgis.com/datasets/035a5a811a4c41669501cbfd82ef82f6_0.geojson"){r}

openSpace <- read_sf("https://opendata.arcgis.com/datasets/035a5a811a4c41669501cbfd82ef82f6_0.geojson")

osData <- openSpace %>%
  st_coordinates() %>%
  as_tibble()

Code for social vulnerability index GeoJSON data: {r}svi <- read_sf("https://opendata.arcgis.com/datasets/2fee65cd8160450d8e0b1665b8ea38ba_1.geojson"){r}

library(sf)
library(tidyverse)
svi <- read_sf("https://opendata.arcgis.com/datasets/2fee65cd8160450d8e0b1665b8ea38ba_1.geojson")
sviNC <- svi %>%
  select(COUNTY, ST_ABBR)
sviWake1 <- sviNC %>%
  filter(COUNTY %in% " Wake" & ST_ABBR %in% "NC")
sviWakedata <- sviWake1 %>%
  st_coordinates() %>%
  as_tibble()

After downloading the data, I proceeded to scrape them for their geographic coordinates. Beginning with farmers markets, I made a new variable for each GeoJSON data set: {r}fmData <- restaurants %>% st_coordinates() %>% as_tibble(){r}

I did the same thing for restaurants, population, open space, and social vulnerability data, that way I could start plotting them after. In the next section of the report, Findings, I have each of my variables plotted using the geographic coordintes to see if my hypothesis stands.

Findings

Farmer’s Market Data

library(tidyverse)
fmData %>%
  ggplot() +
  geom_sf(data = wake2, color = "grey", fill = NA) +
  geom_hex(aes(X, Y), alpha = 0.6) +
  ggtitle("Farmers Markets in Wake County") +
  theme(plot.title = element_text(hjust = 0.5))

{r} fmData %>% ggplot() + geom_sf(data = wake2, color = "grey", fill = NA) + geom_hex(aes(X, Y), alpha = 0.6) {r}

The hexbin points on the plot pertain to farmers markets in Wake County. There are a handful of farmers markets in the middle of the county in Raleigh, and very few scattered east and west. North of Raleign, there are also extremely few farmers markets with very large pockets of empty space just south of Raleign and north east and west. This visually shows the infrequency of local, fresh produce outside Raleigh.

mapview(fm, popup = popupTable(fm, zcol = c("MARKET_NAME", "SEASONS_OF_OPERATION", "DAYS_OF_OPERATION")))

{r}mapview(fm, popup = popupTable(fm, zcol = c("MARKET_NAME", "SEASONS_OF_OPERATION", "DAYS_OF_OPERATION"))){r} As a more interactive view of the first farmers market map, this includes the name of the market, the seasons that its open, and the days its open. It’s useful because it demonstrates the accessibility of farmers markets. While one market downtown Raleigh may be open all year round once a week, another one 30 minutes away might be open only half the year. Accessibility is important when it comes to food scarcity, because having a market nearby is only as good as when its open. If there are 6 months of the year when the market is closed, there might not be another good option to access healthy, nutritious food. For the other half of the year, these local populations may suffer from not having these resources.

rData %>%
  ggplot() +
  geom_sf(data = wake2, color = "grey", fill = NA) + 
  stat_density_2d(aes(X, Y, fill = ..level..), geom = "polygon", alpha = .4) +
  scale_fill_viridis_c("Density", option = "D", direction = -1) +
  ggtitle("Restaurants in Wake County") +
  theme(plot.title = element_text(hjust = 0.5))
## Warning: Removed 142 rows containing non-finite values (stat_density2d).

{r}rData %>% ggplot() + geom_sf(data = wake2, color = "grey", fill = NA) + stat_density_2d(aes(X, Y, fill = ..level..), geom = "polygon", alpha = .4) + scale_fill_viridis_c("Density", option = "D", direction = -1) + ggtitle("Restaurants in Wake County") + theme(plot.title = element_text(hjust = 0.5)){r} The map above pertains to the density of restaurants in Wake County, and as expected, there is a very high concentratin in Raleigh, to the west, north, and far south. North east of Raleigh there aren’t many restaurants. This data set it useful is showing resources, but also challenging as it also portrays fast food in the area. As seen in Wake County’s Health Assessment PDF, fast food and obesity are big issues in Wake County, and without healthy alternatives, this feeds into low access areas. A high quantity of restaurants therefore doensn’t necessarily mean healthy options and can be deeply connected to high quantities of fast food chains in socially vulnerable areas.

popData %>%
  ggplot() +
  geom_sf(data = wake2, color = "grey", fill = NA) + 
  stat_density_2d(aes(X, Y, fill = ..level..), geom = "polygon", alpha = .4) +
  scale_fill_viridis_c("Density", option = "D", direction = -1) +
  ggtitle("Population Density in Wake County") +
  theme(plot.title = element_text(hjust = 0.5))

{r}popData %>% ggplot() + geom_sf(data = wake2, color = "grey", fill = NA) + stat_density_2d(aes(X, Y, fill = ..level..), geom = "polygon", alpha = .4) + scale_fill_viridis_c("Density", option = "D", direction = -1) + ggtitle("Population Density in Wake County") + theme(plot.title = element_text(hjust = 0.5)){r} The map above is important because it includes population densities within Wake County. Population is probably one of the most crucial variables to keep in mind when studying food scarcity, because it serves as a useful comparison between where people live and where the most resources lie. According to the map, populations are very dense almost everywhere except the outlying areas of Wake County. To reference previous maps, there are not many farmers markets, and therefore availablility of fresh and nutritious produce in many areas south of Raleigh where there are still relatively dense populations. This begins to shed light on food scarce regions in Wake County.

osData %>%
  ggplot() +
  geom_sf(data = wake2, color = "grey", fill = NA) + 
  stat_density_2d(aes(X, Y, fill = ..level..), geom = "polygon", alpha = .4) +
  scale_fill_viridis_c("Density", option = "D", direction = -1) +
  ggtitle("Open Space in Wake County") +
  theme(plot.title = element_text(hjust = 0.5))

{r}osData %>% ggplot() + geom_sf(data = wake2, color = "grey", fill = NA) + stat_density_2d(aes(X, Y, fill = ..level..), geom = "polygon", alpha = .4) + scale_fill_viridis_c("Density", option = "D", direction = -1) + ggtitle("Open Space in Wake County") + theme(plot.title = element_text(hjust = 0.5)){r} When comparing popuation in Wake County to available resources, it’s crucial to also have open space as an element, becasuse those regions are designated green spaces abundant with trees and grass - offset for nature and biodiversity conservation. Ruling these regions out as possible areas of food scarce in the county helps narrow down possible regions and eliminates empty, extraneous space for this study. As the color progressing into greens and blues, it means more dense areas of open space. Surprisingly, many areas in the north east and south where it seems like there should be open space (that has less restaurants/farmer’s markets) it actually doesn’t exist. This brings forth regions that are now more obviously desolte when it comes to nutritious foods and resources.

sviWakedata %>%
  ggplot() +
  geom_sf(data = wake2, color = "grey", fill = NA) + 
  stat_density_2d(aes(X, Y, fill = ..level..), geom = "polygon", alpha = .4) +
  scale_fill_viridis_c("Density", option = "D", direction = -1) +
  ggtitle("Social Vulnerability Index") +
  theme(plot.title = element_text(hjust = 0.5))

{r}sviWakeData %>% ggplot() + geom_sf(data = wake2, color = "grey", fill = NA) + stat_density_2d(aes(X, Y, fill = ..level..), geom = "polygon", alpha = .4) + scale_fill_viridis_c("Density", option = "D", direction = -1){r} The map above is the most eye opening of any of the data collected for this report, because it shows the social vulnerability index for Wake County. This index uses U.S. Census data to show the social vulnerability for each tract. This index is measured on a different variables: “poverty, lack of vehicle access, and crowded housing. It then”groups them into four related themes: Socioeconomic Housing, Composition, Disability Minority Status, Language Housing, and Transportation Variables“. Food scarcity really boils down to limited to no access to nutritious foods, which can be drawn from low access and low income communities. Furthermore, the social vulnerability index is useful to answer the question,”how are these people?“. What’s shocking is that the map reveals almost all of Wake County as socially vulnerable. The big towns/cities have higher densities as seen with the blue color coding density system, which can be inferred as they have larger population.

Analysis

The data collected confirm parts of my hypothesis, but definitely not all of it. Yes, areas that have limited acccess to nutritious produce from farmers markets, or restaurants with large, dense populations without open space are more socially vulnerable, and therefore more food scarce. After referencing the geosppatial data online for food deserts, that can definitely be confirmed. What is more gray/hard to concretely quantify with data in a report is the fact that practically ALL of Wake County is socially vulnerable. Especially by Raleigh, abundant with markets and restaurants open all the time, there are people from the census tract who are socially vulnerable. Another piece of data that was surprising was how concentrated the county is. In the center of Wake County closest to the major cities on where there are the most resources, and also the most people with lack thereof. So yes, there may be people with a farmers market a couple of yards from them, but they are still unable to afford expensive produce.

Conclusion

This report centered on studying Wake County and its food scarcity, and in doing so many different variables were put front and center. Farmers Market, restaurant, open space, population, and social vulnerability data were all pooled in an attempt to justify my hypothesis:

There is a correlation between areas without significant amounts of farmer’s markets and restaurants and substantial existing populations to have increased levels of social vulnerability, and therefore immense food scarcity in the region.

Like my analysis references, the quantity of social vulnerability is strong enough to justify this hypothesis only to the extent to where I can say yes, but also, even urban, resource rich populations are struggling. Through this report, and important conclusion to draw is that there is poverty everywhere, all around even without realizing it. Even in the richest cities of the world like Paris, New York, San Francisco, and more there are struggles with food scarcity within communities. If anything, it demonstrates the need to take action, wherever and whenever becasue there will always be a community that needs it.