67 resultados para GIS (geographic information system)


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Audit report on the Black Hawk County Criminal Justice Information System for the year ended June 30, 2016

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Stream channel erosion in the deep loess soils region of western Iowa causes severe damage along hundreds of miles of streams in twenty-two counties. The goal of this project was to develop information, systems, and procedures for use in making resource allocation decisions related to the protection of transportation facilities and farmland from damages caused by stream channel erosion. Section one of this report provides an introduction. Section two presents an assessment of stream channel conditions from aerial and field reconnaissance conducted in 1993 and 1994 and a classification of the streams based on a six stage model of stream channel evolution. A Geographic Information System is discussed that has been developed to store and analyze data on the stream conditions and affected infrastructure and assist in the planning of stabilization measures. Section three presents an evaluation of two methods for predicting the extent of channel degradation. Section four presents an estimate of costs associated with damages from stream channel erosion since the time of channelization until 1992. Damage to highway bridges represent the highest costs associated with channel erosion, followed by railroad bridges and right-of-way; loss of agricultural land represents the third highest cost. An estimate of costs associated with future channel erosion on western Iowa streams is also presented in section four. Section four also presents a procedure to estimate the benefits and costs of implementing stream stabilization measures. The final section of this report, section five, presents information on the development of the organizational structure and administrative procedures which are being used to plan, coordinate, and implement stream stabilization projects and programs in western Iowa.

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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.

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The purpose of the proposed Highway Location Reference Procedure is stated in the contract as follows: "Establishment of a highway network locational reference process that will primarily allow for the proper correlation of pavement management data, and secondarily provide the basis for other existing and future data base integration and for the planned Iowa DOT Geographic Information System. In addition, the locational reference process will be able to correlate network applications with a statewide spatial location method to facilitate the relationship of Iowa DOT data to that of other agencies and to allow for the graphic display of the network in map form." The Design Specifications and Implementation Plan, included in this Final Report, are intended to provide the basis for proceeding with immediate development and implementation of the pavement management system. These specifications will also support the future Iowa DOT implementation of other integrated data bases and/or the planned Geographic Information System.

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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.

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This research involved two studies: one to determine the local geoid to obtain mean sea level elevation from a global positioning system (GPS) to an accuracy of ±2 cm, and the other to determine the location of roadside features such as mile posts and stop signs for safety studies, geographic information systems (GIS), and maintenance applications, from video imageries collected by a van traveling at traffic speed.

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Global positioning systems (GPS) offer a cost-effective and efficient method to input and update transportation data. The spatial location of objects provided by GPS is easily integrated into geographic information systems (GIS). The storage, manipulation, and analysis of spatial data are also relatively simple in a GIS. However, many data storage and reporting methods at transportation agencies rely on linear referencing methods (LRMs); consequently, GPS data must be able to link with linear referencing. Unfortunately, the two systems are fundamentally incompatible in the way data are collected, integrated, and manipulated. In order for the spatial data collected using GPS to be integrated into a linear referencing system or shared among LRMs, a number of issues need to be addressed. This report documents and evaluates several of those issues and offers recommendations. In order to evaluate the issues associated with integrating GPS data with a LRM, a pilot study was created. To perform the pilot study, point features, a linear datum, and a spatial representation of a LRM were created for six test roadway segments that were located within the boundaries of the pilot study conducted by the Iowa Department of Transportation linear referencing system project team. Various issues in integrating point features with a LRM or between LRMs are discussed and recommendations provided. The accuracy of the GPS is discussed, including issues such as point features mapping to the wrong segment. Another topic is the loss of spatial information that occurs when a three-dimensional or two-dimensional spatial point feature is converted to a one-dimensional representation on a LRM. Recommendations such as storing point features as spatial objects if necessary or preserving information such as coordinates and elevation are suggested. The lack of spatial accuracy characteristic of most cartography, on which LRM are often based, is another topic discussed. The associated issues include linear and horizontal offset error. The final topic discussed is some of the issues in transferring point feature data between LRMs.