Vital Signs selected to participate in 2017 Data Science for Social Good Program

  • May 19, 2017
  • Posted by: Matt Cooper

Vital Signs is excited to announce that we will be participating in the 2017 Data Science for Social Good (DSSG) program, hosted by the eScience Institute at the University of Washington.  The mission of the Institute is to engage researchers across disciplines in developing and applying advanced computational methods and tools to real-world problems in data-intensive discovery. The program is inspired by similar endeavors at the University of Chicago and Georgia Tech, and is delivered as part of the Data Science Incubator program. The eScience institute has already had a number of projects from previous DSSG programs lead to data driven real world solutions.

Through the DSSG program, four tech-savvy graduate students and two data scientisits from a variety of backgrounds will work as volunteers to analyze and visualize Vital Signs data over the course of ten intensive weeks.  At the same time, two members of the Vital Signs team, Matt Cooper,VS Data Manager and Tabby Njung'e,VS Technical Operations Manager will travel to Seattle.  They will work directly with the data scientistis and graduate students to answer critical questions about agricultural sustainability, environmental health, human wellbeing and equitable development by doing a deep dive into Vital Signs data.

The Vital Signs team has prepared a number of key questions in partnership with stakeholders to answer these questions.  The first few weeks of the program, the volunteers will generate summary statistics along metrics of agricultural intensification, food security, education, income, and environmental health.  The summary statistics will then feed into regressions to answer key questions, such as:

  1. Does Agricultural Commercialization enhance nutrition and food security?
  2. Is Agricultural Intensification associated with equitable outcomes in terms of increases in education, income, production and land area for all households?
  3. How does the education level obtained and gender of the farmer relate to:
    1. Nutrition status of the family
    2. The farmer’s productivity
    3. Crops grown by the farmer
    4. Inputs used by the farmer
    5. The technologies the farmer adopts
  4. What role does nature play in improving and supporting livelihoods?
  5. What areas have enough water to meet the demand for human and agricultural use, and where are the biggest gaps?
  6. Where is agricultural intensification most significantly associated with degradation?  What agricultural practices are most closely related to more or less degradation?

Answering these questions using advances regressions and machine learning techniques will allow the Vital Signs to speak to bigger policy relevant issues, such as assessing the sustainability of agricultural systems across different criteria including profitability, yields food security (peoples’ access and quality of the food), human health and economic and social well being.  Additionally, these results shoudl inform how the massive land restoration planned under the AFR100 and the Bonn Challenge will improve land for agriculture and livelihoods.

Stay tuned for more blog posts on the results of these analyses !