Food Insecurity Project¶

I want to evaluate food insecurity in the United States, and the correlation between diabetes, obesity, and food insecurity. According to the USDA, food insecurity is the " limited or uncertain availability of nutritionally adequate and safe foods, or limited or uncertain ability to acquire acceptable foods in socially acceptable ways." Many Americans face food insecurity for a multitude of reasons such as income, employment, disabilities, etc. You can read more about it here: https://www.feedingamerica.org/hunger-in-america.

In [1]:
#food insecurity data
import pandas as pd

df_food_insecurity = pd.read_csv('Feeding_America_Food_Insecurity_2018.csv')
df_food_insecurity.head()
Out[1]:
OBJECTID_1 FIPS STATE_FIPS CNTY_FIPS NAME STATE_NAME POPULATION State County__State Food_Insecurity_Rate_2018 ... Pct__FI_Btwn_Thresholds Pct__FI___High_Threshold ChildFoodInsecurityRate_2018 Food_Insecure_Children_2018 FoodInsecureChildrenHH_1_2018 FoodInsecureChildrenHH_2_2018 Cost_Per_Meal_2018 W_AnnFoodBudgetShortfall_2018 Shape__Area Shape__Length
0 1 1001.0 1 1 Autauga Alabama 56903 AL Autauga County, Alabama 0.156 ... 0.132 0.405 0.214 2870.0 0.81 0.19 3.33 4857000.0 1.565568e+09 213533.767840
1 2 1003.0 1 3 Baldwin Alabama 214651 AL Baldwin County, Alabama 0.129 ... 0.187 0.442 0.169 7710.0 0.84 0.16 3.58 16274000.0 4.248941e+09 635766.429870
2 3 1005.0 1 5 Barbour Alabama 26585 AL Barbour County, Alabama 0.219 ... 0.108 0.241 0.320 1740.0 0.94 0.06 3.12 2988000.0 2.342322e+09 257811.107201
3 4 1007.0 1 7 Bibb Alabama 23003 AL Bibb County, Alabama 0.151 ... 0.212 0.319 0.209 970.0 1.00 0.00 2.94 1690000.0 1.621299e+09 191315.886193
4 5 1009.0 1 9 Blount Alabama 57971 AL Blount County, Alabama 0.136 ... 0.279 0.292 0.191 2580.0 1.00 0.00 3.14 4149000.0 1.684210e+09 238847.459010

5 rows × 26 columns

Note on Data¶

I'm getting in contact with Feeding America to get 2020 data so that the year matches the diabetes and obesity data.

Data Key¶

  • Objectid through CNTY_FIPS are all city and observation identifiers that I won't be using -Federal Information Processing Standards - numeric codes assigned to various geographies
  • Name: county name
  • State Name : state name
  • Population : population of the county
  • State: state abbreviation
  • County_State : the county and state together
  • Food_Insecurity_Rate_2018 : the percentage of the county that faces food insecurity
  • Food_Insecure_Persons_2018 : the actual number of food insecure people in the county
  • Low_Threshold_in_state : lowest gross income limit as a percent of the federal poverty guidelines for any federal nutrition program
  • Low_Threshold_Type : type of program low threshold applies to
  • High_Threshold_in_state : highest gross income limit as a percent of the federal poverty guidelines for any federal nutrition program
  • High_Threshold_Type : type of program high threshold applies to
  • Pct_FI≤_Low_Threshold : the percentage of food insecure individuals who live in households with income at or below low threshold in a state
  • Pct__FI_Btwn_Thresholds : the percentage of food insecure individuals who live in households with income above the low threshold and below or equal to the high threshold in the county
  • PctFI_High_Threshold : the percentage of food insecure individuals who live in households with income above high threshold in the county
  • ChildFoodInsecurityRate_2018 : percent of children that are food insecure in the county
  • Food_Insecure_Children_2018 : the actual number of food insecure children in the county
  • FoodInsecureChildrenHH_1_2018 : percent of food insecure children who live in households with income below 185% of the federal poverty line
  • FoodInsecureChildrenHH_2_2018 : percent of food insecure children who live in households with income above 185% of the federal poverty line
  • Cost_Per_Meal_2018 : average meal cost
  • W_AnnFoodBudgetShortfall_2018 : for all people that are food insecure, this measures the gap between food prices and what they can afford
In [2]:
#obesity data
df_obesity = pd.read_csv('percentage-of-obese-us-adults-by-state-2021.csv')
df_obesity.head()
Out[2]:
State Obesity Percentage Unnamed: 2
0 West Virginia 40.6 in %
1 Kentucky 40.3 in %
2 Alabama 39.9 in %
3 Oklahoma 39.4 in %
4 Mississippi 39.1 in %

Data Key¶

  • State : the state
  • Obesity Percentage : the percent of that state's population that is obese
In [3]:
df_diabetes = pd.read_csv('percentage-of-us-adults-with-diabetes-as-of-2020-by-state.csv')
df_diabetes.head()
Out[3]:
State Diabetes Percentage Unnamed: 2
0 Mississippi 17.2 in %
1 West Virginia 17.1 in %
2 Alabama 16.7 in %
3 Louisiana 16.0 in %
4 Arkansas 15.6 in %

Data Key¶

  • State : the state
  • Diabetes Percentage : the percent of that state's population that is diabetic

Links for Data

  • Food Insecurity Data: https://map.feedingamerica.org/
  • Obesity Data: https://www.statista.com/statistics/378988/us-obesity-rate-by-state/
  • Diabetes Data: https://www.statista.com/statistics/240875/leading-states-based-on-percentage-of-adults-with-diabetes-in-the-us/

I'm thinking of clustering states and regions based on similar food insecurity data and evaluate if food insecurity is directly correlated with diabetes and obesity. I also want to see if states with severe food insecurity are in certain regions of the US.

In [ ]: