ISCAP Proceedings: Abstract Presentation
Leveraging Location Analytics Methods and AI for Research Using the U.S. Census Data
Dan Farkas
Pace University
Namchul Shin
Pace University
Abstract
This research leverages location analytics methods for research exploring relationships between socio-economic and demographic factors, such as crime rates, education and income levels, and population density, across various geographic locations including counties, towns, and zip codes. The methods enable researchers to visualize such data on a map as educational attainment and household median income levels and analyze their patterns and impacts on target variables, such as crime rates. We employ the U.S. Census Bureau data and Tigerline geographic data, and the regression analysis techniques to provide quantitative insights into these relationships. While the primary emphasis is on the relationships between socio-economic and demographic factors utilizing the Census and Tigerline data, the use of artificial intelligence (AI) is discussed for generating research questions. This approach is particularly useful in Information Systems (IS) research classes, offering students the opportunity to develop novel research ideas grounded in robust data analysis. The paper also includes case studies to demonstrate practical applications and provides guidelines for integrating this methodology into academic settings.