Challenge background
Food deserts can impact the overall health of a community, in 2009, as part of a year-long congressionally mandated study coordinated by the Economic Research Service (ERS) of the U.S. Department of Agriculture, the Institute of Medicine (IOM) and the National Research Council (NRC) were asked to convene a two-day workshop to understand the public health effects of food deserts. On January 26-27, 2009, workshop speakers provided presentations on how to measure and understand the extent of food deserts, their impact on individual behaviors and health outcomes in various populations, and effective ways to increase the availability of fruits and vegetables and to improve the food environment.
The problem
Using AI/Data Science to evaluate solutions that were implemented in order to ameliorate the impact of Food Deserts in Brooklyn. The impact we're seeking to make is to have an AI platform that can be used as part of a longitudinal study to understand the long term health effects of Food Deserts and to see what works and what can be improved upon.
Goal of the project
- Source data from 2009 to the present to see the impact of policies in hopes of having an open-source platform that will be incorporated in a longitudinal study.
- Process data and analyze the impact of solutions that have been implemented.
- Apply Data Science, Data Engineering, and Machine Learning to understand and make recommendations to strengthen what is in place.
- Develop an open-source platform.
Project timeline
- 1
Week 1
Week 1: Data Collection
- 2
Week 2
Week 2: Data Cleaning
- 3
Week 3
Week 3: Data Analysis
- 4
Week 4
Week 4: Data Analysis
- 5
Week 5
Week 5: Build Machine Learning Models
- 6
Week 6
Week 6: Build Machine Learning Models
- 7
Week 7
Week 7: Build API if necessary
- 8
Week 8
Week 8: Build a Platform is feasible
What you'll learn
- Data Collection
- Data Cleaning
- Data Analysis
- Build Machine Learning Models
- Build API if Necessary
- Build a Platform is feasible