The final presentations for the 2023 Iowa State University Data Science for the Public Good (DSPG) program took place on July 14th from 8:30am to 12pm at the Student Innovation Center. Presentations recordings and links to project files are available below. Questions regarding DSPG can be sent to firstname.lastname@example.org.
8:30 Welcome and 'Coffee Talk' with Dr. Shawn Dorius, ISU Department of Sociology and Criminal Justice
9:00 Data-Driven Insights for Local Food Markets: AI, Pricing, and Crop Flow
10:00 Using Data to Inform Decision Making for Rural Grocery Stores
11:00 WINVEST (Walking Infrastructure Investigation)
11:10 AI-Driven Housing Evaluation for Rural Community Development
DSPG Disclaimer: Please note that the data and results presented are student work. While the students strive to be as complete and accurate as they can, this is also a learning internship and therefore errors may exist. Following these student presentations, faculty and staff review the data, scripts and results before use in final publications.
Data-Driven Insights for Local Food Markets: AI, Pricing, and Crop Flow
This project focuses to enhance local food markets by providing valuable insights and optimizing the crop flow. It involves creating a comprehensive map showcasing the prices of eggs and bacon across counties, developing web-scraping spiders for data collection, and demonstrating their effectiveness with a specific crop example. Additionally, the project also aims on optimizing the crop flow from supply to demand, maximizing overall profit through AI algorithms that consider factors like transportation costs.
AI-Driven Housing Evaluation for Rural Community Development
The absence of a comprehensive assessment of housing quality in rural areas of Iowa hinders resource allocation and negatively impacts residents' well-being and economic growth. Evaluating existing housing conditions is subjective and lacks thorough investigations due to limited resources. To address these challenges, an AI-driven approach is proposed. By utilizing web scraping and AI models, housing features can be categorized as good or poor quality, enabling targeted investment strategies, and directing financial resources where they are most needed. This project aims to eliminate biases, streamline housing evaluations, and inform decision-making for rural housing investment and development initiatives.
Using Data to Inform Decision Making for Rural Grocery Stores
This DSPG project aims to develop a tool that will help provide users information on opening, inheriting, and operating grocery store in their preferred rural location. The DSPGGrocery package, which hosts the calculations for the project is hosted on an R Shiny application to perform dynamic calculations based on user-given input to estimate revenues and costs with opening a grocery store in a particular location.
WINVEST (Walking Infrastructure Investigation)
The DSPG Summer 2023 students worked on a collaborative project this summer as part of the education and outreach component of DSPG. This project partnered with Council of Governments (CoGs) serving selected communities to provide on the ground data in neighborhoods to accurately assess and map community infrastructure features in order to identify multiple projects appropriate for CDBG infrastructure funding. DSPG students utilized map collection technologies (smart phones and Global Positioning Systems app) to inventory neighborhoods infrastructure and overlay this site level data with other community attributes as part of the deliverable for this project.