This summer, I’ve embarked on an interesting journey to learn the true extent to which data drives community outreach and intervention. This tool embodies the intersection of politics and data, and represents the coordinated efforts of many prominent community stakeholders. Throughout the process of developing and eventually launching this new version of the Food Abundance Index, I will have gained extremely valuable experience in a multitude of different subject areas. The most prominent of these being data analytics, a subject I’m extremely passionate about and involved with on Pitt campus. I have participated in multiple case competitions hosted by companies like PricewaterhouseCoopers and Dick’s Sporting Goods where I used data analysis techniques like predictive analytics and dashboard visualization to address a real-world issue the company was facing. This demonstrated that data plays a significant role in decision making for companies and governments alike. I was also elected as a Vice President of the League of Emerging Analytics Professionals with a specific focus on leveraging data for community good and exploring how non-profit and government entities use data in decision making. It is evident that data is extremely powerful, which increases the importance of the tool that we are developing. During my initial research, I learned that accessibility is one of the most prominent issues in the perpetuation of the data gap that exists between companies and communities. Oftentimes community institutions like local governments and nonprofits do not have the fiscal ability to acquire the data they need to make important and influential decisions. Over the summer, I will not only learn about how this decision making process works, but also effective ways to make this process more efficient. For companies, an underdeveloped, under researched decision could lead to significant financial loss. However, for local governments and nonprofits, similar caliber decisions have the potential to leave people without access to basic human needs, like food.
I have had the opportunity to meet with a diverse group of people experienced in the issue of food insecurity. During her visit to Pitt, I had the amazing opportunity to speak with Amanda Little, author of The Fate of Food, to discuss her global food system experience and her predictions about the future of food systems. This was truly eye-opening, as not only did her book provide several deep dives into obscure but vital aspects of food systems, but after speaking with her it was evident that her experience supported the importance of building resilient food systems. I also had the opportunity to meet with Dean Murrell in the fall to discuss the Food Abundance Index and Pitt’s new Food Scholars Community, which marked the beginning of this project. Dean Murrell has been a vital resource to me throughout this process, and her knowledge about this issue is of extreme depth, and I consider myself extremely lucky to have her as my mentor as we continue to develop this application. In addition, my early experiences in research contributed significantly to my understanding of the community impact of academic research. In high school, I developed a comprehensive community outreach model to combat youth gang recruitment in Montgomery County, Maryland. Dubbed the “Mentor Organizations for Community Outreach” Model, or the M.O.C.O Model, this project created a framework for intervention that used community data to found anti-gang initiatives in different Montgomery County communities. Focusing specifically on youth gang recruitment, the methodology I employed was a two prong content and meta analysis that focused on crime data with a high propensity to be gang related as a primary indicator for at risk areas. I constructed data visualizations to depict three at risk areas, and used the content analysis of national intervention models to construct a framework for youth programs in each area. This research effectively demonstrated the necessity of data in policy implementation, and reinforced that policy can only be as reliable as the data it is based on. As a result, equitable access to reliable, informative data for government entities is absolutely necessary to maximize the impact of their decision making.
The lack of this equitable access to powerful data is what the methodology of this project addresses. The Food Abundance Index, developed in 2011 by researchers at Pitt, sought to address this gap by providing a means for communities to collect valuable data in an efficient and inexpensive manner. Over the past decade, there have been numerous advancements in data collection software and methodologies. The original Food Abundance Index was entirely paper-based, with a series of scorecards that correspond to different parameters of food insecurity within a community. For the project this summer, the question that I’ve been focusing on is how can we combine new data and modern analytics techniques with the precedent of accessible data set by the Food Abundance Index to make data collection more efficient. From the beginning, the purpose of this project has been to reinvent the Food Abundance Index with modern data and analytics. During the early stages of this research, we determined that some form of data warehousing was necessary to house the Food Abundance Index collection done by communities. After an in-depth literature review, I discovered a data visualization method that can not only warehouse the data but also visualize it. This data mapping software was employed in a tool developed through collaborative effort between the Montgomery County Department of Health and Human Services, and a local nonprofit called Manna Food. This tool incorporates over 60 different dimensions of data through various layers and visualizations. I elected to use a similar model as the foundation for the reinvented Food Abundance Index, with modifications made to incorporate new dimensions.
This tool is based on the five core parameters of the Food Abundance Index: Accessibility, Affordability, Diversity, Density, and Quality. Each of these five parameters has a series of dimensions that contribute to a holistic Food Abundance Index score. For the data collection tool, the interactive map is categorized into each of these five parameters, and the user can navigate through the tool and select which parameters they wish to view. Upon selection of one of the categories, a data visualization with multiple selective layers are presented, which the user can toggle to display and hide different dimensions. Each of these parameters consists of two types of data: external and internal. The external data is data that has been sourced from pre-existing databases and queried into the visualization software. This type of data includes things like food outlet locations, public transportation locations, and other general census data. The internal data is derived from a survey component that I developed complementary to the data visualization tool. This survey is embedded in the tool, and respondents can access it via web browser or SMS. Its purpose is to collect qualitative data about the community, allowing for the voices of community members to be heard as this data collection progresses. This survey aspect also covers elements of the Food Abundance Index that cannot be automated with external data. Using both this internal survey data, collected during the beta launch, and the external data we’re working on incorporating, we’ll be able to produce a Food Abundance Index score that accurately reflects the level of food insecurity within the beta test regions. However, this score is only the foundation, broadly assessing the region’s level of food insecurity to support community decision making. As I progress with this research through the summer, it is my goal to expand and refine the framework which will allow the platform to incorporate new types of data and expand on a national level.