State Departments of Transportation (DOTs) in the U.S. have an increasing amount of digital data from various sources. One such set of data is structured unit price data collected from bid lettings. Such data contain unit prices of thousands of bid items from hundreds of projects every year. While state DOTs have such data from over a decade-long period, utilizing such data has been challenging because of the lack of automated analytical and visualization methodologies and tools to generate meaningful and actionable insights. This study develops an automated methodology to quickly and accurately generate color-coded visualization maps representing unit price variation across a geographical region. It uses Inverse Distance Weighted (IDW) technique that is based on the Toblers First Law of Geography. The law states that points closer together in space are more likely to have a similar value than points that are farther away. The methodology is automated using ArcPy site package in ArcGIS. It imports unit price data from preformatted spreadsheets and boundary maps from existing ArcGIS shape files to generate unit price maps. The tool and the visualizations are expected to aid state DOTs in generating and communicating meaningful insights for making data-driven decisions. It can be used to investigate areas with higher unit prices for various items which can aid state DOTs in identifying potential causes of higher unit prices such as lack of competition and lack of sources of materials (e.g. quarry) in nearby locations.