Web-based Implementation of Winter Maintenance Decision Support System Using GIS and Remote Sensing, May 2005

Winter maintenance, particularly snow removal and the stress of snow removal materials on public structures, is an enormous budgetary burden on municipalities and nongovernmental maintenance organizations in cold climates. Lately, geospatial technologies such as remote sensing, geographic information systems (GIS), and decision support tools are roviding a valuable tool for planning snow removal operations. A few researchers recently used geospatial technologies to develop winter maintenance tools. However, most of these winter maintenance tools, while having the potential to address some of these information needs, are not typically placed in the hands of planners and other interested stakeholders. Most tools are not constructed with a nontechnical user in mind and lack an easyto-use, easily understood interface. A major goal of this project was to implement a web-based Winter Maintenance Decision Support System (WMDSS) that enhances the capacity of stakeholders (city/county planners, resource managers, transportation personnel, citizens, and policy makers) to evaluate different procedures for managing snow removal assets optimally. This was accomplished by integrating geospatial analytical techniques (GIS and remote sensing), the existing snow removal asset management system, and webbased spatial decision support systems. The web-based system was implemented using the ESRI ArcIMS ActiveX Connector and related web technologies, such as Active Server Pages, JavaScript, HTML, and XML. The expert knowledge on snow removal procedures is gathered and integrated into the system in the form of encoded business rules using Visual Rule Studio. The system developed not only manages the resources but also provides expert advice to assist complex decision making, such as routing, optimal resource allocation, and monitoring live weather information. This system was developed in collaboration with Black Hawk County, IA, the city of Columbia, MO, and the Iowa Department of transportation. This product was also demonstrated for these agencies to improve the usability and applicability of the system.

Maximizing the Use of Roadway Weather Information Systems: Final Report, TR-404, 1998

This project was proposed as Phase I of a 2-phase program to evaluate the present use of weather information by Iowa Department of Transportation (IaDOT) personnel, recommend revised procedures, and then implement the resulting recommendations. Midway through Phase I (evaluation phase) the FORETELL project was funded. This project is a multi-state venture that engages the National Weather Service (NWS) and the Forecast Systems Laboratory of the National Oceanic and Atmospheric Administration and proposes to supplant the current weather information-generation and distribution system with an advanced system based on state-of-the-art technologies. The focus of the present project was therefore refined to consider use of weather data by IaDOT personnel, and the training programs needed to more effectively use these data. Results of the survey revealed that two major areas - training of personnel on use of data from whatever source and more precise information of frost formation - are not addressed in the FORETELL project. These aspects have been the focus of the present project.

Black Hawk County Criminal Justice Information Systems, Independent Auditor's Reports, Financial Statement and Required Supplementary Information, Schedule of Findings, June 30, 2004

Other Audit Reports - 28E Organizations

2008 Iowa Real Property Appraisal Manual, May 2008

********NOTE: There are nine sections to this manual, all in separate files.************* **********NOTE: Large files may take longer to open******** The basis of real property assessment in Iowa is market value as defined in Iowa Code §441.21. Iowa Code §§ 421.17(17) and 441.21(h) provide that assessment jurisdictions follow the guidelines and rules in this manual to help achieve uniformity in assessments. Assessors are encouraged to use the International Association of Assessing Officers’ Standard on Mass Appraisal of Real Property in their mass appraisal practices. Estimating market value in mass appraisal involves accurately listing properties, developing a sales file that includes the primary influences on market value, and developing models for subsets of properties that share common market influences using recognized mass appraisal techniques. The assessment of an individual property should not be based solely on the sale price. The Uniform Standards of Professional Appraisal Practice (USPAP) standard 6 says “In developing a mass appraisal, an appraiser must be aware of, understand, and correctly employ those recognized methods and techniques necessary to produce and communicate credible mass appraisals.” Accurate listing of property is the basis of a good mass appraisal program. On-site inspection and listing of property is essential in developing a good data base for revaluation. A physical review, including an on-site verification of property characteristics, should be conducted at least every four to six years. Land values should be reviewed every two years. Factors influencing the market of each property type should be identified and collected so that these factors can be considered in the mass appraisal model. It is equally important to maintain the data once it is collected. Accessing local government permit systems should be a part of a good data maintenance program along with an inspection program. Current cadastral maps and geographical information systems (GIS) are tools that are integral in checking accuracy of listings and maintaining a comprehensive data base.

IowAccess Project 8 : statewide geographic information systems (GIS) support, 1998

This is the final report of the of IowAccess Project 8, which designed and implemented a geospatial data infrastructure for Iowa, including a formalized coordination body, a coordination staff, and enhanced data clearing house, and a statewide GIS training and education effort.

Report on a special investigation of the University of Iowa Hospitals and Clinics, Health Care Information Systems Department, for the period January 1, 2005 through July 5, 2013

Report on a special investigation of the University of Iowa Hospitals and Clinics, Health Care Information Systems Department, for the period January 1, 2005 through July 5, 2013

Report on a special investigation of the Cerro Gordo County Management Information Systems (MIS) Department for the period January 1, 2008 through July 31, 2013

Report on a special investigation of the Cerro Gordo County Management Information Systems (MIS) Department for the period January 1, 2008 through July 31, 2013

Methods for Estimating Annual Exceedance-Probability Discharges for Streams in Iowa, Based on Data through Water Year 2010, TR-519, 2013

A statewide study was performed to develop regional regression equations for estimating selected annual exceedance- probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedanceprobability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage. Regional regression analysis, using generalized leastsquares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions. Average standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized leastsquares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations. These regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage.

An Analysis of Regional Economic Growth in the U.S. Midwest, April 2005

In this paper we examine some of the economic forces that underlie economic growth at the county level. In an effort to describe a much more comprehensive regional economic growth model, we address a variety of different growth hypotheses by introducing a large number of growth related variables. When formulating our hypotheses and specifying our growth model we make liberal use of GIS (geographical information systems) mapping software to “paint” a picture of where growth spots exist. Our empirical estimation indicates that amenities, state and local tax burdens, population, amount of primary agriculture activity, and demographics have important impacts on economic growth.