Context

COVID 19 Testing summary data from Ohio

Content

From the website: *"The COVID-19 diagnostic testing data, including total daily tests performed and daily percentage of positive tests, as reported to the Ohio Department of Health (ODH). This data includes laboratory testing from hospitals, private labs and the ODH lab. Data is updated daily.

For more information, visit: https://coronavirus.ohio.gov/wps/portal/gov/covid-19/dashboards/key-metrics/testing"*

Acknowledgements

The State of Ohio

Inspiration

When exploring this data, suggested focus is visualization, trends.

Data Description: This data set provides all public datasets, links, documents and community created filters hosted on the City of Cincinnati's Open Data Portal.

Data Creation: This data set is maintained by the City of Cincinnati's Open Data host, Socrata.

Data Created By: Socrata

Refresh Frequency: Daily

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this data set.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

AbstractThis dataset comprises detailed records of motor vehicle crashes occurring in Ohio, USA, from January 1, 2017, to December 31, 2023. Collected by law enforcement agencies using standardized OH-1 crash reporting forms and centralized by the Ohio Department of Public Safety, the dataset captures detailed information on 1,679,019 crashes involving 2,656,086 vehicles and 3,577,822 occupants. Structured across three levels—crash, vehicle, and occupant—the dataset includes attributes such as crash timing and location, environmental and road conditions, vehicle specifications, operational factors, occupant demographics, injury severity, safety equipment usage, and behavioral indicators like alcohol or drug involvement. Severity information is documented at both the crash and individual occupant levels, covering outcomes ranging from no injury to fatal incidents. The dataset features a total of 119 systematically named variables at the crash, vehicle, and occupant levels. A complete list of features, along with categorical value mappings, is provided in the accompanying documentation.Description of the data and file structureThis dataset contains comprehensive records of motor vehicle crashes reported across the state of Ohio, USA, from January 1, 2017, to December 31, 2023. The data were collected by law enforcement agencies using standardized crash reporting forms (OH-1) and centralized through the Ohio Department of Public Safety’s data systems.It captures detailed, structured information related to crash events, vehicles involved, and individuals affected. Each data sample corresponds to an occupant of a vehicle. There are unique identifiers for each crash and involved vehicle. Hence, the dataset is organized into three primary levels:Crash-Level Data: Includes unique identifiers for each of the 1,679,019 reported crashes, along with temporal details (date, time), location attributes, environmental conditions (e.g., weather, light, road surface), and overall crash characteristics (e.g., number of units involved, severity classification, work zone presence). The identifier for the crash is the feature “DocumentNumber”.Vehicle-Level Data: Comprises identifiers for each of the 2,656,086 vehicles (units) involved in a crash. Attributes include vehicle type, make, model, year of manufacture, vehicle defects, and operational details such as posted speed, traffic control devices, and pre-crash actions. Interacting vehicle types and hazardous material indicators are also documented. Vehicle-Level features are identified by the prefix ”Units.” in the feature name.Occupant-Level Data: Contains 3,577,822 records detailing individuals involved in crashes. This includes demographic information (age, gender), seating position, person injury severity, use of safety equipment (e.g., seat belts, airbags, helmets), and behavioral factors such as alcohol or drug involvement, distraction status, and test results where applicable. Occupant-Level features are identified by the prefix “Units.People.” in the feature name.The severity of the accident is also documented. The “CrashSeverity” feature document the severity of the crash in the following levels: Fatal (15021), Suspected Serious Injury (83764), Suspected Minor Injury (483026), Possible Injury (461019), and No Apparent Injury (2440823). Similarly, also individual people injury levels are recorded in the feature “Units.People.Injury”. The file "summary_2023_new.pdf" is a summary file that contains data analysis of the dataset (statistics and plots).There are 119 unique features in the data, and their complete list of name and type is reported below. Their categorical levels in case of integer-encoding is found in the file “mapping.yaml”.Access informationOther publicly accessible locations of the data:The full dataset submitted to figshare is not available elsewhere in its complete and curated form. However, data covering the most recent five years, including the current year, are publicly accessible through the following sources:Ohio Department of Public Safety Crash Retrieval Portal: https://ohtrafficdata.dps.ohio.gov/crashretrievalOhio Statistics and Analytics for Traffic Safety (OSTATS): https://statepatrol.ohio.gov/dashboards-statistics/ostats-dashboardsThese public portals provide access to selected crash data but do not include the full historical dataset or the cleaned, integrated, and reformatted version provided through this submission.Data was derived from the following sources:Ohio Department of Public SafetyHuman subjects dataThis dataset was derived entirely from publicly available traffic crash reports collected and disseminated by the Ohio Department of Public Safety through the Ohio Statistics and Analytics for Traffic Safety (OSTATS) platform.To ensure compliance with ethical standards for data sharing, this dataset contains no direct identifiers (e.g., names, addresses, license plate numbers, or VINs linked to individuals). All personal identifiers have been removed or were not included in the public dataset. Furthermore, the dataset contains no more than three indirect identifiers per record. These indirect identifiers (e.g., crash year, crash county, and age group) were selected based on their relevance to the study while minimizing re-identification risk.Where possible, continuous variables were converted to categories (e.g., age groups instead of exact age), and geographic detail was limited to broader regional indicators rather than precise location data. Data cleaning and aggregation procedures were conducted to further reduce identifiability while retaining the analytic value of the dataset for modeling injury risk across system domains.As described in the associated manuscript, all analyses were conducted on this de-identified dataset, and no additional linkage to identifiable information was performed. As such, this dataset does not require IRB oversight or data use agreements and is suitable for open-access publication under CC-BY licence.No direct interaction or intervention with human participants occurred during the creation of this dataset, and no personally identifiable information (PII) is included.Given the publicly available nature of the source data and the absence of PII, explicit participant consent was not required. However, by relying exclusively on open-access government data and following de-identification protocols aligned with the Common Rule (45 CFR 46), this dataset meets ethical standards for public data sharing.

Data Description: This dataset captures confirmed shooting events in the City of Cincinnati. Shootings events are captured in the Computer Aided Dispatch System (CAD), and are ultimately stored in the City's Records Management System (RMS).

No personal or identifying (or otherwise sensitive) victim or suspect information is included in this data set.

Data Creation: This data is created through the City’s computer-aided dispatch (CAD) system.

Data Created By: The source of this data is the Cincinnati Police Department.

Refresh Frequency: This data is updated daily.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/xw7t-5phj

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

ML3DBOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information.The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

This dataset is updated more frequently and can be visualized on NCWQR's data portal.

If you have any questions, please contact Dr. Laura Johnson or Dr. Nathan Manning.

The National Center for Water Quality Research (NCWQR) is a research laboratory at Heidelberg University in Tiffin, Ohio, USA. Our primary research program is the Heidelberg Tributary Loading Program (HTLP), where we currently monitor water quality at 22 river locations throughout Ohio and Michigan, effectively covering ~half of the land area of Ohio. The goal of the program is to accurately measure the total amounts (loads) of pollutants exported from watersheds by rivers and streams. Thus these data are used to assess different sources (nonpoint vs point), forms, and timing of pollutant export from watersheds. The HTLP officially began with high-frequency monitoring for sediment and nutrients from the Sandusky and Maumee rivers in 1974, and has continually expanded since then.

Each station where samples are collected for water quality is paired with a US Geological Survey gage for quantifying discharge (http://waterdata.usgs.gov/usa/nwis/rt). Our stations cover a wide range of watershed areas upstream of the sampling point from 11.0 km2 for the unnamed tributary to Lost Creek to 19,215 km2 for the Muskingum River. These rivers also drain a variety of land uses, though a majority of the stations drain over 50% row-crop agriculture.

At most sampling stations, submersible pumps located on the stream bottom continuously pump water into sampling wells inside heated buildings where automatic samplers collect discrete samples (4 unrefrigerated samples/d at 6-h intervals, 1974–1987; 3 refrigerated samples/d at 8-h intervals, 1988-current). At weekly intervals the samples are returned to the NCWQR laboratories for analysis. When samples either have high turbidity from suspended solids or are collected during high flow conditions, all samples for each day are analyzed. As stream flows and/or turbidity decreases, analysis frequency shifts to one sample per day. At the River Raisin and Muskingum River, a cooperator collects a grab sample from a bridge at or near the USGS station approximately daily and all samples are analyzed. Each sample bottle contains sufficient volume to support analyses of total phosphorus (TP), dissolved reactive phosphorus (DRP), suspended solids (SS), total Kjeldahl nitrogen (TKN), ammonium-N (NH4), nitrate-N and nitrite-N (NO2+3), chloride, fluoride, and sulfate. Nitrate and nitrite are commonly added together when presented; henceforth we refer to the sum as nitrate.

Upon return to the laboratory, all water samples are analyzed within 72h for the nutrients listed below using standard EPA methods. For dissolved nutrients, samples are filtered through a 0.45 um membrane filter prior to analysis. We currently use a Seal AutoAnalyzer 3 for DRP, silica, NH4, TP, and TKN colorimetry, and a DIONEX Ion Chromatograph with AG18 and AS18 columns for anions. Prior to 2014, we used a Seal TRAACs for all colorimetry.

2017 Ohio EPA Project Study Plan and Quality Assurance Plan

Project Study Plan

Quality Assurance Plan

Data quality control and data screening

The data provided in the River Data files have all been screened by NCWQR staff. The purpose of the screening is to remove outliers that staff deem likely to reflect sampling or analytical errors rather than outliers that reflect the real variability in stream chemistry. Often, in the screening process, the causes of the outlier values can be determined and appropriate corrective actions taken. These may involve correction of sample concentrations or deletion of those data points.

This micro-site contains data for approximately 126,000 water samples collected beginning in 1974. We cannot guarantee that each data point is free from sampling bias/error, analytical errors, or transcription errors. However, since its beginnings, the NCWQR has operated a substantial internal quality control program and has participated in numerous external quality control reviews and sample exchange programs. These programs have consistently demonstrated that data produced by the NCWQR is of high quality.

A note on detection limits and zero and negative concentrations

It is routine practice in analytical chemistry to determine method detection limits and/or limits of quantitation, below which analytical results are considered less reliable or unreliable. This is something that we also do as part of our standard procedures. Many laboratories, especially those associated with agencies such as the U.S. EPA, do not report individual values that are less than the detection limit, even if the analytical equipment returns such values. This is in part because as individual measurements they may not be considered valid under litigation.

The measured concentration consists of the true but unknown concentration plus random instrument error, which is usually small compared to the range of expected environmental values. In a sample for which the true concentration is very small, perhaps even essentially zero, it is possible to obtain an analytical result of 0 or even a small negative concentration. Results of this sort are often “censored” and replaced with the statement “

Censoring these low values creates a number of problems for data analysis. How do you take an average? If you leave out these numbers, you get a biased result because you did not toss out any other (higher) values. Even if you replace negative concentrations with 0, a bias ensues, because you’ve chopped off some portion of the lower end of the distribution of random instrument error.

For these reasons, we do not censor our data. Values of -9 and -1 are used as missing value codes, but all other negative and zero concentrations are actual, valid results. Negative concentrations make no physical sense, but they make analytical and statistical sense. Users should be aware of this, and if necessary make their own decisions about how to use these values. Particularly if log transformations are to be used, some decision on the part of the user will be required.

Analyte Detection Limits

https://ncwqr.files.wordpress.com/2021/12/mdl-june-2019-epa-methods.jpg?w=1024

For more information, please visit https://ncwqr.org/

This information will not be updated while the Cincinnati Police Department undergoes transfer to a new data management system.

Data Description: This data represents officer involved shooting incidents by the Cincinnati Police Department. An officer involved shooting (OIS) may be defined as the discharge of a firearm, which may include accidental and intentional discharges, by a police officer, whether on or off duty.

Data Creation: This data is created through reporting by the Cincinnati Police Department.

Data Created By: The source of this data is the Cincinnati Police Department.

Refresh Frequency: This information will not be updated while the Cincinnati Police Department undergoes transfer to a new data management system.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/c64e-ybfz/

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

These orthophotos and digital surface model (DSM) were derived from low-altitude (approximately 92-m above ground surface) images collected from Unmanned Aerial System (UAS) flights over edge-of-field sites that are part of U.S. Geological Survey (USGS) Great Lakes Restoration Initiative (GLRI) monitoring. The objective of this UAS photogrammetry data collection was to provide information on the tile-drain network in individual fields with the goal of understanding already observed patterns in runoff amount and water quality from these sites. A 3DR Solo quadcopter served as the flight vehicle, flights were pre-planned using Mission Planner, and flights were flown using Tower. Geospatial data were originally in WGS84 and projected to a local coordinate system for each site. Visible color (Vis-C) imagery was collected with a Ricoh GRII as a single band. Multispectral (MS) imagery was collected with a MicaSense RedEdge 3 as five co-located bands: blue (B; approximately 475-500 nanometers [nm]), green (G; 550-560 nm), red (R; 660-670 nm), red-edge (710-720 nm), and near infrared (NIR; 820-860 nm). Images were collected at 2-second intervals, with a flight speed of 9 meters per second (m/s) or 7 m/s (visible and multispectral, respectively) with approximately 75% overlap between sequential images and 70% sidelap between adjacent flight lines. Cameras used local time for visible and thermal imagery collection but Coordinated Universal Time (UTC) for multispectral imagery collection. Photogrammetry to integrate the individual images into an orthophoto and digital surface model (for visible imagery) was done using Agisoft Metashape.

Comprehensive dataset containing 15 verified Archaeological site businesses in Ohio, United States with complete contact information, ratings, reviews, and location data.

Site-specific multiple linear regression models were developed for one beach in Ohio (three discrete sampling sites) and one beach in Pennsylvania to estimate concentrations of Escherichia coli (E. coli) or the probability of exceeding the bathing-water standard for E. coli in recreational waters used by the public. Traditional culture-based methods are commonly used to estimate concentrations of fecal indicator bacteria, such as E. coli; however, results are obtained 18 to 24 hours post sampling and do not accurately reflect current water-quality conditions. Beach-specific mathematical models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts”. Software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, water temperature, change in lake level over 24 hours, and antecedent rainfall. Model results are used by managers to report water-quality conditions to the public through the Great Lakes NowCast in 2019 (https://pa.water.usgs.gov/apps/nowcast/). Model performance in 2019 (sensitivity, specificity, and accuracy) was compared to using the previous day's E. coli concentration (persistence method).

DescriptionCrime incidents starting with those reported in 2016. The data provided is the latest available information and is updated regularly as statistics change. For access to comprehensive reports, kindly submit a public record request here.Note: Crimes that occurred before 2016 are included if the date reported was in 2016 or later.Disclaimer: The City strives to provide the highest-quality information on this platform. The content on this website is provided as a public service, on an ‘as is’ basis. The City makes no warranty, representation, or guarantee of any type as to the content, accuracy, timeliness, completeness, or fitness for any particular purpose or use of any public data provided on this portal; nor shall any such warranty be implied, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose. The City assumes no liability by making data available to the public or other departments.This dataset is featured in the following app(s):Cleveland Division of Police Crime DashboardCrime Incidents MapData GlossarySee the Attributes section below for details about each column in this dataset.Update FrequencyDaily around 8 AM ESTContactsCity of Cleveland, Division of Police

This dataset provides information about the number of properties, residents, and average property values for Sites Road cross streets in Bay Village, OH.

ML2OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroxyl derived from radiances measured by the THz radiometer. The data version is 5.0. Data coverage is continuous from August 8, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML2OH data product should read section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information.The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

The TROPESS Chemical Reanalysis OH Monthly 3-dimensional Product contains vertical concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.

This dataset contains a tabular file of phytoplankton abundance and community composition analysis in samples collected from two sites in the Western Lake Erie Basin and one inland lake site in northeast Ohio. Samples were processed by the Ohio Water Microbiology Lab of the U.S. Geological Survey (USGS) and analyzed by BSA Environmental Inc. and during federal fiscal years 2016-2018. The dataset includes phytoplankton taxa (genus and species), division, tally (number of cells counted for each taxa present), density (cells per liter), and total biovolume (cubic micrometers per liter) for each sample collected. These data can be used to assess the community composition of phytoplankton at the sites, identify cyanobacteria species, and determine abundance and biovolume of any known toxin-producing cyanobacteria. The data were part of a larger study, "Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio", in which site-specific multiple linear regression models were developed for eight sites in Ohio−six in the Western Lake Erie Basin and two in northeast Ohio on inland reservoirs−to quickly predict action-level exceedances for microcystin, a cyanotoxin commonly found in freshwaters, in recreational and drinking waters used by the public.

This information will not be updated while the Cincinnati Police Department undergoes transfer to a new data management system.

Data Description: This data represents documented assaults on officers. Assaults on Officers may be defined as the assault of duly sworn city, university and college, county, state, tribal, and federal law enforcement officers. Incidents that are identified as an assault on an officer can include but are not limited to crimes such as aggravated assault, robbery, theft, vandalism, targeted assault (knowingly harming and officer), and recklessly harming an officer.

Data Creation: This data is recorded using the City's Record Management System (RMS) that stores agency-wide data about law enforcement operations.

Data Created By: The source of this data is the Cincinnati Police Department.

Refresh Frequency: This information will not be updated while the Cincinnati Police Department undergoes transfer to a new data management system.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/mrju-z9ui

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

DISCLAIMER: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

Railway centerlines within Stark County, Ohio. This layer was created by Digital Data Technologies, Inc. in 2003 and provided to the county. It is not actively updated.