Iowa Prison Industries Five Year Transition and Business Plan 2011-2015, 2011

IPI is comprised of three divisions. Private Sector funds are handed over to the General Fund. Traditional Industries and Farms funds are managed by IPI. The auditor of the state provides oversight on policies, procedures, and compliance with state law. Each year, the auditor is responsible for providing the Governor, legislature, Director of Corrections, and the public the findings of their comprehensive audits. IPI has received a clean bill of health and has not been cited for any violations in ten (10) years. IPI operates under the guidance of an advisory board, comprised of seven members. The advisory board meets at least four (4) times per year at a location of the board‟s choice, generally at a different prison each quarter. The board reviews the financials, policies, approves any new private sector ventures and offers comprehensive guidance on issues that will impact correctional industries as well as the public and local businesses. Each member serves for two (2) years and may be re-appointed. IPI has found that retaining board members has helped immensely with the continuity of transition and has afforded IPI with superb leadership and guidance. IPI is 100% self-funding. We receive no appropriations from the general fund. We hire our staff, pay their salaries, and pay the stipend of the offenders. We pay for our raw materials, equipment, and construct our buildings all from the proceeds of our sales. We operate with a revolving fund and retain any earnings at year-ends. The retained earnings are used for expansion of our work programs.

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.