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NCHS - Drug Poisoning Mortality by County: United States
healthdata.gov | Last Updated 2023-07-25T17:57:16.000ZThis dataset contains model-based county estimates for drug-poisoning mortality. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8). REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. 2. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html. 3. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013. 4. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014. 5. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf. 6. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e. 7. National Center for Health Statistics. County geog
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Dallas Animals Field Data Fiscal Year 2016 - 2017
www.dallasopendata.com | Last Updated 2021-08-17T14:48:04.000ZDallas Animal Services data that pertains to operations by Animal Services Officers (ASO) who respond to calls in the field throughout the City of Dallas. ASO’s document their work using Chameleon software, an animal shelter software program. The document will be updated on a daily basis, so that citizens have a greater understanding of what ASO’s are doing in the neighborhoods of Dallas. “Helping Dallas be a safe, compassionate, and healthy place for people and animals”. Start date is October 01, 2016
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Dallas Animal Medical Record Fiscal Year 2017 -2018
www.dallasopendata.com | Last Updated 2021-08-17T14:46:16.000ZDallas Animal Medical Data pertains to operational processes carried out by medical personnel who provide care and medical attention to the animals received at Dallas Animal Services. Medical personnel document their work using Chameleon software, an animal shelter management program. The Dallas Animal Medical Data is updated daily to help citizens better understand the operational processes that the medical personnel perform daily for the animals and citizens of the City of Dallas. “Helping Dallas be a safe, compassionate, and healthy place for people and animals”. The period covered by this dataset goes from October 01, 2017 - September 30, 2018.
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Bureau of Street Lighting (BSL) - Cumulative # of street lights converted to LED (Monthly)
data.lacity.org | Last Updated 2020-11-30T17:02:37.000ZThis dataset contains metrics that measure the operational performance of the Bureau of Street Lighting. These metrics are used on a regular basis by the department and the Mayor to evaluate progress and inform decision making. Performance management forms the foundation of a data-driven culture of innovation and excellence in the City of Los Angeles.
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NEW HORIZONS SDC PLUTO CRUISE RAW V2.0
data.nasa.gov | Last Updated 2023-01-26T20:54:05.000ZThis data set contains Raw data taken by the New Horizons Student Dust Counter instrument during the pluto cruise mission phase. This is VERSION 2.0 of this data set. SDC collected science data intermittently during the hibernation years following the Jupiter encounter, designated as the PLUTOCRUISE phase. There were also Annual Checkouts (ACOs), STIM calibrations, Noise calibrations, and an anomaly in November, 2007. SDC's main science data collection periods were during hibernation. During ACOs, science data are taken intermittently but the user must be careful in analyzing these data since there is usually more activity on the spacecraft during hibernation. STIM and Noise refer to scheduled calibrations and are done with a regular cadence of one per year after the Jupiter encounter; they occurred sporadically in the early years of the mission. Note that some SDC data files have the same stop and start time and a zero exposure time. The reason for this is that the start and stop time for SDC data files are the event times for the first and last events in the files, so for files that contain a single event, these two values are the same. The changes in Version 2.0 were re-running of the ancillary data in the data product, updated geometry from newer SPICE kernels, minor editing of the documentation, catalogs, etc., and resolution of liens from the December, 2014 review, plus those from the May, 2016 review of the Pluto Encounter data sets. New observations added with this version (V2.0) include ongoing cruise observations from August, 2014 through January, 2015.
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Workforce Innovation Opportunity Act (WIOA) Title III Performance Accountability Metrics PY 2017-2018 - Current Annual Labor and Industry
data.pa.gov | Last Updated 2022-06-09T15:50:25.000ZA comprehensive collection of data that assesses the effectiveness of Pennsylvania in achieving positive outcomes for individuals served by the workforce development system’s Title III Wagner-Peyser (Labor Exchange) program. Data is compiled in compliance with US Department of Labor’s Employment and Training Administration guidance on Workforce Innovation and Opportunity Act (WIOA) Performance Accountability. Data is available for the state and each of the CareerLink® offices in the commonwealth.
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Case Closing without a New Offense CY 2018 By County as Reported by the Juvenile Court Judges' Commission
data.pa.gov | Last Updated 2022-10-17T20:21:58.000ZThe juvenile justice system has a responsibility to protect the community from known juvenile offenders. Juveniles who do not commit a new offense while under court supervision have a higher probability of remaining crime free. This data represents the number and percentage of juveniles who successfully completed supervision without a new offense resulting in a Consent Decree, Adjudication of Delinquency, ARD, Nolo Contendere, or finding of guilt in a criminal procedure. <br/> CY - Calendar Year This dataset is contained within the Juvenile Court Judges' Commission’s 2018 Statewide Juvenile Justice Outcome Measure report: this report reflects outcomes of juvenile offenders whose cases were closed during the report period and who have received a period of supervision from a county juvenile probation department. These reported outcomes are associated with community protection, accountability, and competency development; three core goals of Pennsylvania's juvenile justice system. Since 2009, county juvenile probation departments have supervised and closed 126,006 cases. The proportion of cases closed successfully (without a new offense) during this time is 84.1%. For the year 2018, the proportion of cases closed successfully was 85.4%.
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CPD Arrest Seasonality
internal.chattadata.org | Last Updated 2024-06-06T22:37:16.000ZThis dataset represents charges that have been grouped by case numbers. For individual charges please visit: https://internal.chattadata.org/Public-Safety/Public-CPD-Arrest-Charges/v9y9-uavb The data provided in this public portal/website represents general data of incidents based on the Tennessee Incident Based Reporting System (TIBRS). Incidents involving protected classes (juveniles, domestic abuse victims) by Tennessee law have been removed. Additionally, some incident addresses have been generalized to block level and randomly offset to protect the privacy of victims of crime. All crime data posted is preliminary and may or may not have been reviewed and approved by the Chattanooga Police Department’s (CPD) quality control process; therefore, the data may change upon further investigation. The City of Chattanooga and the Chattanooga Police Department caution against using crime data provided in this public portal to make decisions regarding the safety of, amount of or type of crime occurring in a particular area. Users should not make decisions as it relates to safety solely based on the data provided on this website, but should seek independent verification directly through CPD’s Crime Analyst Unit. The information in this portal is provided strictly as a courtesy to the public.
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Net Zero Porter Ranch: Solar PV Permits by Year
data.lacity.org | Last Updated 2023-05-22T09:33:24.000ZThe Department of Building and Safety issues permits for the construction, remodeling, and repair of buildings and structures in the City of Los Angeles. Permits are categorized into building permits, electrical permits, and mechanical permits (which include plumbing, HVAC systems, fire sprinklers, elevators, and pressure vessels). Depending on the complexity of a project, a permit may be issued the same day with Express Permit or e-Permit ("No Plan Check" category), or a permit may require that the plans be reviewed ("Plan Check" category) by a Building and Safety Plan Check personnel.
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EMS INcidents Chloropleth Map Fall Injury Counts by Zip Code Current Calendar Year
data.marincounty.org | Last Updated 2024-04-05T13:30:40.000ZEmergency Medical Service ambulance dispatch incidents in Marin County, CA, for the period beginning March, 2013 through September, 2019. Data is updated every three to six months. Data includes time stamps of events for each dispatch, nature of injury, and location of injury. Data also includes geocoding of most incident locations, however, specific street address locations are "obfuscated" and are generally shown within a block and are not, therefore, exact locations. Geocoding results are also based on the quality of the address information provided, and should therefore not be considered 100% accurate. Some of the data may be interpreted incorrectly without adequate knowledge of the clinical context. Please contact EMS@marincounty.org if you have any questions about the interpretation of fields in this dataset.