24 resultados para Business Intelligence, ETL, Data Warehouse, Metadati, Reporting

em Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States


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Funds for this report and grant were provided to the Iowa Division of Criminal and Juvenile Justice Planning (CJJP) and Statistical Analysis Center, by the Justice Research and Statistics Association (JRSA) through a cooperative agreement entitled “Juvenile Justice Evaluation Resource Center” with the Office of Juvenile Justice and Delinquency Prevention (OJJDP), U.S. Department of Justice (DOJ).

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A review of the Iowa Department of Transportation's field data collection and reporting system has been performed. Included were several systems used by the Office of Construction and Local Jurisdictions. The entire field data collection and reporting systems for asphalt cement concrete (ACC) paving, portland cement concrete (PCC) paving, and PCC structures were streamlined and computerized. The field procedures for materials acceptance were also reviewed. Best practices were identified and a method was developed to prioritize materials so transportation agencies could focus their efforts on high priority materials. Iowa State University researchers facilitated a discussion about Equal Employment Opportunity (EEO) and Affirmative Action (AA) procedures between the Office of Construction field staff and the Office of Contracts. A set of alternative procedures was developed. Later the Office of Contracts considered these alternatives as they developed new procedures that are currently being implemented. The job close-out package was reviewed and two unnecessary procedures were eliminated. Numerous other procedures were reviewed and flowcharted. Several changes have been recommended that will increase efficiency and allow staff time to be devoted to higher priority activities. It is estimated the improvements in ACC paving, PCC paving and structural concrete will by similar to three full time equivalent (FTE) positions to field construction, field materials and Office of Materials. Elimination of EEO interviews will be equivalent to one FTE position. It is estimated that other miscellaneous changes will be equivalent to at least one other FTE person. This is a total five FTEs. These are conservative estimates based on savings that are easily quantified. It is likely that total positive effect is greater when items that are difficult to quantify are considered.

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This project is part of an effort conducted by the Justice Research and Statistics Association (JRSA) under a grant whose objective is to provide states with descriptions of existing methodologies to collect Domestic Violence (DV) and Sexual Assault (SA) data. JRSA has identified three different methodologies to collect such data: · Incident-based reporting as part of the Uniform Crime Reports · Specialized data collection from law enforcement through a separate data collection system · Specialized data collection coming directly from service providers. One state has been selected as an example of each type of data collection above, with Iowa selected as a representative of states with incident based reporting (IBR) as part of the UCR system.

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Welcome to the first issue of the ICON Data Download, a periodic report intended to communicate findings relevant to those who work directly with offenders, as well as those involved in planning, policy and budgeting. This issue highlights work conducted by research partner Christopher Lowenkamp, Ph.D., of the University of Cincinnati and his research associate, Kristin Bechtel, M.S. Data for this analysis was provided from the Iowa Justice Data Warehouse – and takes advantage of the link between ICON and ICIS (the court database) to readily track offender recidivism.

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This report describes the results of the research project investigating the use of advanced field data acquisition technologies for lowa transponation agencies. The objectives of the research project were to (1) research and evaluate current data acquisition technologies for field data collection, manipulation, and reporting; (2) identify the current field data collection approach and the interest level in applying current technologies within Iowa transportation agencies; and (3) summarize findings, prioritize technology needs, and provide recommendations regarding suitable applications for future development. A steering committee consisting oretate, city, and county transportation officials provided guidance during this project. Technologies considered in this study included (1) data storage (bar coding, radio frequency identification, touch buttons, magnetic stripes, and video logging); (2) data recognition (voice recognition and optical character recognition); (3) field referencing systems (global positioning systems [GPS] and geographic information systems [GIs]); (4) data transmission (radio frequency data communications and electronic data interchange); and (5) portable computers (pen-based computers). The literature review revealed that many of these technologies could have useful applications in the transponation industry. A survey was developed to explain current data collection methods and identify the interest in using advanced field data collection technologies. Surveys were sent out to county and city engineers and state representatives responsible for certain programs (e.g., maintenance management and construction management). Results showed that almost all field data are collected using manual approaches and are hand-carried to the office where they are either entered into a computer or manually stored. A lack of standardization was apparent for the type of software applications used by each agency--even the types of forms used to manually collect data differed by agency. Furthermore, interest in using advanced field data collection technologies depended upon the technology, program (e.g.. pavement or sign management), and agency type (e.g., state, city, or county). The state and larger cities and counties seemed to be interested in using several of the technologies, whereas smaller agencies appeared to have very little interest in using advanced techniques to capture data. A more thorough analysis of the survey results is provided in the report. Recommendations are made to enhance the use of advanced field data acquisition technologies in Iowa transportation agencies: (1) Appoint a statewide task group to coordinate the effort to automate field data collection and reporting within the Iowa transportation agencies. Subgroups representing the cities, counties, and state should be formed with oversight provided by the statewide task group. (2) Educate employees so that they become familiar with the various field data acquisition technologies.

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This report goes beyond documentation of the population growth and needs of women offenders to describe how the Iowa Department of Corrections is addressing treatment needs of the female offender population. Except where noted, information was obtained from the Iowa Corrections Offender Network (ICON) with many of the reports obtained via the Iowa Justice Data Warehouse.

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This report begins with the prevalence and projected growth of offenders convicted of sex crimes within Iowa’s community-based corrections and prison populations, then describes how the Iowa Department of Corrections is addressing sex offenders’ issues through the provision of treatment and management strategies. Except where noted, information was obtained from the Iowa Corrections Offender Network (ICON) with many of the reports obtained via the Iowa Justice Data Warehouse.

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This report goes beyond mere documentation of the problem. It describes how the Iowa Department of Corrections is addressing mental health issues among the offender population through the provision of treatment. All data was obtained from Iowa Corrections Offender Network (ICON) information residing in the Iowa Justice Data Warehouse, and the ICON-Medical module. 1

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This report, however, goes beyond documentation of the problem. It describes how the Iowa Department of Corrections is addressing substance abuse among the offender population through the provision of treatment, and monitoring for current drug and alcohol usage. All information was obtained from the Iowa Corrections Offender Network (ICON) with many of the reports obtained via the Iowa Justice Data Warehouse.

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The Iowa Juvenile Court Services Offices are issuing their fourth annual statewide report. The the Iowa Division of Criminal and Juvenile Justice Planning (CJJP). This report would not be possible without the dedication of, and assistance from, all of the above-mentioned people. The eight Chief Juvenile Court Officers would like to take this opportunity to thank their staff for their dedication and their ability to enter accurate information on every youth referred to Juvenile Court Services; the staff at the Iowa Court Information System, without whom this report would not be possible; and CJJP for their maintenance of the Iowa Justice Data Warehouse and their support in preparing this document.

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The Iowa Juvenile Court Services Offices are issuing their fourth annual statewide report. The the Iowa Division of Criminal and Juvenile Justice Planning (CJJP). This report would not be possible without the dedication of, and assistance from, all of the above-mentioned people. The eight Chief Juvenile Court Officers would like to take this opportunity to thank their staff for their dedication and their ability to enter accurate information on every youth referred to Juvenile Court Services; the staff at the Iowa Court Information System, without whom this report would not be possible; and CJJP for their maintenance of the Iowa Justice Data Warehouse and their support in preparing this document.

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The Data Warehouse Replacement Project includes updated County Health Snapshot reports. The snapshots provide an overview of key health indicators for local communities. They contain county-level measures that are organized into eight categories. The categories are asthma, cancer, health behaviors and outcomes, heart disease and other chronic conditions, infectious disease, mortality and injury prevention, population statistics and reproductive outcomes. The updated county health snapshots include almost forty new chronic disease indicators in addition to all of the indicators in the existing snapshots.There will be two different reports available in the Data Warehouse replacement system. One of the reports is a multi-year county health snapshot. This report has a similar format to the previous county health snapshot report in the current Data Warehouse. It will display multiple years of data for a single county. The data will be for the most current year available and the two years prior. The state values for the current year will also be included.

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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.

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The NRS state data quality standards identify the policies, processes and materials that states and local programs should have in place to collect valid and reliable data for the National Reporting System (NRS). The Division of Adult Education (DAEL) within the Office of Vocational and Adult Education developed the standards to define the characteristics of high quality state and local data collection systems for the NRS. The standards provide an organized way for DAEL to understand the quality of NRS data collection within the states and also provide guidance to states on how to improve their systems. States are to complete this checklist, which incorporates the standards, with their annual NRS data submission to rate their level of implementation of the standards. The accompanying policy document describes DAEL’s requirements for state conformance to the standards and explains the use of the information from this checklist.

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The purpose of the Iowa TOPSpro Data Dictionary is to provide a statewide-standardized set of instructions and definitions for coding Tracking Of Programs And Students (TOPSpro) forms and effectively utilizing the TOPSpro software. This document is designed to serve as a companion document to the TOPS Technical Manual produced by the Comprehensive Adult Student Assessment System (CASAS). The data dictionary integrates information from various data systems to provide uniform data sets and definitions that meet local, state and federal reporting mandates. The sources for the data dictionary are: (1) the National Reporting System (NRS) Guidelines, (2) standard practices utilized in Iowa’s adult literacy program, (3) selected definitions from the Workforce Investment Act of 1998, (4) input from the state level Management Information System (MIS) personnel, and (5) selected definitions from other Iowa state agencies.