Impacts of Rural Development on Iowa’s Secondary Road Systems, TR-548, September 2010

Today, many of Iowa’s counties are experiencing an increase in rural development. Two specific types of development were focused on for this research: rural residential subdivisions and livestock production operations. Rural residential developments are primarily year round single-family homes, though some are vacation homes. Livestock production in Iowa includes hog, beef, and poultry facilities. These two types of rural development, while obviously very different in nature and incompatible with each other, share one important characteristic: They each generate substantial amounts of new traffic for Iowa’s extensive secondary road system. This research brings together economic, spatial, and legal analysis methods to address the impacts of rural development on the secondary road system and provide county engineers, county supervisors, and state legislators with guidance in addressing the challenges associated with this development.

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.