Review of Inconsistencies Between SUDAS and Iowa DOT Specifications Phase III: Continued Implementation of Recommendations into SUDAS Specifications, TR-607, 2010

The objective of the Phase 3 project was to re-write the identified sections of the SUDAS specifications into the imperative mood, consistent with the format utilized during the Phase 2 project and other work completed by SUDAS staff. Figures for the identified sections were updated to match the new SUDAS format, similar to the Iowa DOT Standard Road Plans. While the Iowa DOT does not intend to incorporate all of the following sections into their specification book, consistency with the Iowa DOT specifications was strived for wherever possible. Maintaining consistency between the specifications simplifies design, bidding, and construction.

Just the Facts for 2012

There are 20,552 full-time employees who work for the State of Iowa Executive Branch(excluding Fair Authority, Community-Based Corrections, and the Regents employees). These employees are undoubtedly the most valuable resource for providing timely and quality services to Iowans. To strategically manage this resource, state departments and policymakers must have thorough and accurate information. The information in “Just the Facts for 2008” is a snapshot of the workforce, collected,compiled, and presented in a format that will aid agencies and decision makers in strategic planning. In many cases, data cover a number of years and are presented to give the reader a sense of trends. While the Department of Administrative Services, Human Resource Enterprise (DAS/HRE)wants to present data in its purest form so readers can draw their own conclusions, we also have a responsibility to clarify anything that may be confusing or misleading. It is important to highlight workforce trends and explain their significance to the work of Iowa state government. The following chapter summaries are intended to do that.

Management Strategy for Iowa’s Water Resources, December 2007

In Iowa, the Department of Natural Resources (DNR)is responsible for regulating water allocation and use through the issuance of water use permits, but improvements are necessary in this process to assure sustainable supplies into the future. In recent years, there have not been resources dedicated at the state level to properly track and assess water quantity issues. Resources for water use and water quantity monitoring (groundwater level and surface gauges) have continued to decline and have resulted in data becoming outdated and in a format that is difficult to analyze in order to make good decisions.

Grade and Cross Slope Estimation from LIDAR-based Surface Models, 2003

Many transportation agencies maintain grade as an attribute in roadway inventory databases; however, the information is often in an aggregated format. Cross slope is rarely included in large roadway inventories. Accurate methods available to collect grade and cross slope include global positioning systems, traditional surveying, and mobile mapping systems. However, most agencies do not have the resources to utilize these methods to collect grade and cross slope on a large scale. This report discusses the use of LIDAR to extract roadway grade and cross slope for large-scale inventories. Current data collection methods and their advantages and disadvantages are discussed. A pilot study to extract grade and cross slope from a LIDAR data set, including methodology, results, and conclusions, is presented. This report describes the regression methodology used to extract and evaluate the accuracy of grade and cross slope from three dimensional surfaces created from LIDAR data. The use of LIDAR data to extract grade and cross slope on tangent highway segments was evaluated and compared against grade and cross slope collected using an automatic level for 10 test segments along Iowa Highway 1. Grade and cross slope were measured from a surface model created from LIDAR data points collected for the study area. While grade could be estimated to within 1%, study results indicate that cross slope cannot practically be estimated using a LIDAR derived surface model.