OSU Basemap

OSU Transportation

Vector polygon map data of mileposts from the state of Ohio containing 19608 features.

Milepost GIS data consists of points along a linear feature, such as roads or railways. They serve as reference points to measure distances along these features. Mile markers are often labeled with numbers indicating their distance from a starting point, such as a highway's origin or a railway station.

These mileposts are invaluable for navigation, route planning, emergency response, and data collection. For example, they help drivers and emergency services identify their location precisely on a road. In transportation planning, mile markers aid in analyzing traffic patterns, determining optimal routes, and estimating travel times. Additionally, they facilitate maintenance activities by providing clear reference points for inspecting and repairing infrastructure.

This data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

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The United States Public Land Survey (PLS) divided land into one square mile units, termed sections. Surveyors used trees to locate section corners and other locations of interest (witness trees). As a result, a systematic ecological dataset was produced with regular sampling over a large region of the United States, beginning in Ohio in 1786 and continuing westward.
We digitized and georeferenced archival hand drawn maps of these witness trees for 27 counties in Ohio. This dataset consists of a GIS point shapefile with 11,925 points located at section corners, recording 26,028 trees (up to four trees could be recorded at each corner). We retain species names given on each archival map key, resulting in 70 unique species common names. PLS records were obtained from hand-drawn archival maps of original witness trees produced by researchers at The Ohio State University in the 1960’s. Scans of these maps are archived as “The Edgar Nelson Transeau Ohio Vegetation Survey” at The Ohio State University: http://hdl.handle.net/1811/64106.
The 27 counties are: Adams, Allen, Auglaize, Belmont, Brown, Darke, Defiance, Gallia, Guernsey, Hancock, Lawrence, Lucas, Mercer, Miami, Monroe, Montgomery, Morgan, Noble, Ottawa, Paulding, Pike, Putnam, Scioto, Seneca, Shelby, Williams, Wyandot. Coordinate Reference System: North American Datum 1983 (NAD83). This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.

Download .zipTo show the location and spatial extent of State of Ohio lands managed by the Ohio Department of Natural Resources 2016Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesOffice of Information TechnologyGIS Records2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Polygons that represent the boundary or management area for significant facilities in a community. Examples include schools, government facilities, stadiums, casinos, and fire stations. Additional information about each Facility can be organized in to seperate tables and related to these locations using the FacilityID key. The feature classification and type schema for the facility sites grew out of work with the Department of Homeland Security's HSIP Program and has evolved to support a diverse set of facilities inventoried for a variety of uses by a local government.

The Kirwan Institute for the Study of Race and Ethnicity at Ohio State University developed the Detroit Regional Opportunity Index to compare levels of opportunity for people growing up in different parts of a region. The Index was developed by combining many different data indicators for opportunity into a single score. More information on the Detroit methodology and composite data can be found here: http://kirwaninstitute.osu.edu/wp-content/uploads/2014/08/20131211neighborhood.pdf

The full report from Kirwan on the Detroit Opportunity project can be found here: http://kirwaninstitute.osu.edu/?my-product=opportunity-for-all-inequity-linked-fate-and-social-justice-in-detroit-and-michigan/

Download .zipThis theme shows the county boundaries for the State of Ohio. Boundaries were assembled from individual USGS Quadrangle digital line graph files (dlg) of the boundary layer (bd). These files were obtained from the GIS Support Center, Ohio Department of Administrative Services and were originally prepared by the Ohio State University Center for Mapping in cooperation with the United States Geological Survey with additional funding from several state agencies and other groups. These files were augmented in a few instances with hand digitizing where small missing line segments were found. The Lake Erie shoreline and islands in Lake Erie were added from the Hydrography layer (hy). It should be noted that because the quadrangles used varied over a wide rangle of years this line does not represent a particular water level and is for illustrative purposes only. For example there is a wide offset between the shoreline on the Metzger Marsh Quadrangle and that of the Oak Harbor Quadrangle. This distance is so large that no attempt was made to edgematch the two quadrangles. The line connecting the two shorelines represents the quadrangle boundary. For the purposes of this map Sandusky Bay and Muddy Creek Bay were included as a part of Lake Erie. The Lake Erie shoreline is not meant to represent any Lake Erie shoreline which may be used for regulatory puposes. An updated boundary for Fairfield-Licking-Perry Counties within Buckeye Lake was included by digitizing the boundary from a more recently updated quadrangle map. Conversion from the dlg format to ArcInfo was accomplished using ArcInfo software in conjuction with an AML program to adjust the quadrangles so that their corners fall on the exact coordinates of the quadrangle corners. Due to the way in wihich coordinates are stored in the dlg's there is some variation in the quadrangle corner coordinates.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesOffice of Information TechnologyGIS Records2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

This dataset gathers total species richness, total abundance and total biomass of fishes recorded in six Mediterranean Marine Protected Areas in summer by underwater visual censuses performed on rocky areas at varying distances from the core of the MPA. Belt transects (25 x 5 m) were run parallel to the coast between 6 and 12 m depth, except in Tabarca Posidonia beds (50 x 5 m). Additionally, reduced fish species richness, abundance and biomass excluding zooplanktivorous fish species (Atherinidae, Clupeidae, Centracanthidae, Engraulidae, Pomacentridae, and the Sparidae Boops boops) were given as their fluctuating abundance and aggregative behavior may mask the effect of protection. Location of the 6 Mediterranean Marine Protected Areas

The Transboundary Freshwater Dispute Database, developed and maintained by the OSU College of Earth, Ocean, and Atmospheric Sciences, in collaboration with the Northwest Alliance for Computational Science and Engineering. There are six main components of the database: Data contains both global and regional information in searchable tabular and spatial datasets, treaty and compact libraries, and GIS shapefiles available for download. Research and Projects includes completed and ongoing projects and links to projects either conducted at or collaborated with Oregon State University faculty and students. Bibliographies and Digital Collections contains the Water Conflict and Cooperation Bibliography and the Middle East Water Collection of scholarly papers ranging from the historic perspective to the present day. Publications includes papers and books related to water conflict and/or cooperation, with links to download. Map and Image Gallery features maps and images for download created by current and former students and faculty, as well as collaborating partners. Useful Resources and External Links is a list of related water conflict and cooperation websites. Wide use of electronic and hardcopy versions of data, GIS coverages, and findings produced by the Transboundary Freshwater Dispute Database (TFDD) project is encouraged. License information: Product of the Transboundary Freshwater Dispute Database, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University. Additional information about the TFDD can be found at: http://transboundarywaters.science.oregonstate.edu.

C. shasta (Ceratonova shasta) is a parasite that can adversely affect salmonids and in some instances maybe fatal. This parasite completes its life cycle by infecting polychaete worms (Manayunkia speciosa) in their myxospore stage; then is released as a actinospore which infects salmonids. Steelhead (Oncorhynchus mykiss), Chinook (Oncorhynchus tshawytscha) and Coho (Oncorhynchus kisutch) are species of concern regarding C. shasta infection in the Klamath River.U.S. Fish and Wildlife Service Fish and Aquatic Conservation program's Arcata office in coordination with Oregon State University's John L. Fryer Aquatic Animal Health Laboratory annually collects information on abundance of polychaete worms and infection rates of C. shasta for three reaches on the Klamath River from below the Shasta River confluence to the Scott River confluence.

Wetland Priority Sites for the Willamette Valley Basin, Version 20090812 (Aug 12, 2009) Oregon Natural Heritage Information Center and The Wetlands Conservancy (TWC) have created a GIS layer for the Willamette Valley that identifies areas with concentrations of important wetland habitats and opportunities for successful wetland restoration. The map is intended to assist conservationists, private landowners, and policymakers in choosing where to site projects for wetland conservation, restoration, mitigation, and enhancement. It will help focus wetland work in the most important places, support no net less of wetland values or acres, and build on past or ongoing project locations. The data is a component of the Oregon Wetlands Explorer website, a collaborative project funded by EPA.Rev 20090812. Synchronization with near-finalized Willamette Valley Synthesis coverage by The Nature Conservancy (TNC). Modified Site Names, attempting to be consistent with TNC's Willamette Valley Synthesis naming. Rev 20090715. Incorporated feedback from provisional version sent out for review in March 2009. The map is based on The Nature Conservancy (TNC) Willamette Synthesis project, with subsequent adjustments and additions made by OSU and The Wetlands Conservancy. The Willamette Synthesis represents a two-year effort that integrates (1) TNC's portfolio sites identified by ecoregional planning (2), ODFW's Conservation Opportunity Areas from their Oregon Conservation Strategy, (3) NRCS hydric soils mapping, (4) FEMA floodplain mapping, (5) Army Corps of Engineers historical floodway maps, and (6) Oregon's Greatest Wetlands as identified by The Wetlands Conservancy and OSU, discussed further below; and a number of other sources detailed in http://oregonstate.edu/ornhic/transfer/wv_synthesis_draft_methods.zip. The Wetlands Conservancy (TWC) and Oregon Natural Heritage Information Center (ORNHIC) developed an "Oregon's Greatest Wetlands" layer, identifying areas in the state having wetlands of significant conservation interest. The "Oregon's Greatest Wetlands" areas were included in the initial Synthesis Site layer. In 2008, TWC and ORNHIC analyzed historic (pre-settlement) vegetation reconstructions, hydric soil densities, and current wetland densities (using National Wetland Inventory and Local Wetland Inventory data where available) that were within the Willamette Valley Ecoregion synthesis sites identified by The Nature Conservancy. The sites were further filtered with information obtained from various Agency and NGO conservation plans. We then reduced in size, or eliminated, WVER synthesis sites based on this analysis. Brief reasoning for the site selection is provided in the Motiv attribute. To improve the focus on wetlands, OSU and TWC then removed the larger upland portions (e.g., oak savanna and woodland, upland prairie) from the Synthesis map, and included additional wetland information based on conservation data, restoration opportunities, and cluster analysis of USFWS National Wetlands Inventory mapping. The lower portion of the Sandy River watershed is located in the Level III Willamette Valley Ecoregion. As such, it was included in the TNC Willamette Synthesis project, even though it is not strictly part of the Willamette Basin. We thus include wetland priority sites for the Sandy River watershed in this dataset.

Note: Due to constraints in ArcGIS Pro, the data variables are truncated in the provided dataset. For a clear understanding of the variables, please refer to the excel table provided below : https://docs.google.com/spreadsheets/d/1MdtiiOTUFrptmqFxWx3lR_o37Ju-lZi3/edit?usp=sharing&ouid=100146291201047562152&rtpof=true&sd=true

This specific shapefile likely includes information on various geographic entities within Oklahoma, such as counties, cities, roads, water bodies, and other relevant boundaries. It can be used for a wide range of applications including mapping, spatial analysis, urban planning, environmental assessment, and more.With the 2019 timestamp, this shapefile captures the geographical layout of Oklahoma as it was in that year, offering insights into its administrative divisions, land use patterns, and infrastructure. Analysts, researchers, planners, and policymakers can leverage this data to better understand the spatial dynamics and make informed decisions related to the state's development and management.

For a comprehensive understanding of all the data variables, please refer to the following link: Google Sheets.

Note: Due to the large file size of the shapefile, the data has been uploaded as a feature layer containing only latitude and longitude information. The geometry data is not included in this upload. For a comprehensive understanding of the variables, please refer to the detailed description of excel table provided here, https://docs.google.com/spreadsheets/d/1S9QoW6M-_c1GpfLb0SpG89UOtChVmIqn/edit?usp=sharing&ouid=100146291201047562152&rtpof=true&sd=true

Note:Due to restrictions within ArcGIS Pro, the data variable names in the dataset may be truncated. For a comprehensive understanding of the variables, please refer to the detailed description provided In the Excel file here. https://docs.google.com/spreadsheets/d/1t2Mfnvyr3sZOAaAx5C0o9IbgM4BHzNmJ/edit?usp=sharing&ouid=100146291201047562152&rtpof=true&sd=trueThese variables provide insights into the distribution and administration of COVID-19 vaccines in Oklahoma, allowing for analysis of vaccination rates, coverage disparities, and the impact of vaccination efforts on different age groups within the state.

Drawing from multiple sources, including the Behavioral Risk Factor Surveillance System (BRFSS) data from 2021 and 2020, Census Bureau's ZIP Code population estimates for 2021 and 2020, and American Community Survey (ACS) estimates covering the periods of 2017–2021 and 2016–2020, this dataset provides a robust foundation for analyzing and understanding health trends at the ZIP code level. It serves as a valuable resource for public health officials, researchers, and policymakers seeking to address and mitigate health disparities and challenges within Oklahoma's communities.For a comprehensive understanding of the data variables, please consult the following link: Google Sheets.

Field Name

Description

StateName

Name of the state (Oklahoma)

date

Date of the data point (YYYY-MM-DD)

covid-19_OK

The search interest in the term "COVID-19" in Oklahoma on the given date

sars-cov-2_OK

The search interest in the term "SARS-CoV-2" in Oklahoma on the given date

coronavirus_OK

The search interest in the term "coronavirus" in Oklahoma on the given date

Omicron_OK

The search interest in the term "Omicron" in Oklahoma on the given date

Delta_OK

The search interest in the term "Delta" in Oklahoma on the given date

Fever_OK

The search interest in the term "fever" in Oklahoma on the given date

fatigue_OK

The search interest in the term "fatigue" in Oklahoma on the given date

diarrhea_OK

The search interest in the term "diarrhea" in Oklahoma on the given date

pneumonia_OK

The search interest in the term "pneumonia" in Oklahoma on the given date

sore throat_OK

The search interest in the term "sore throat" in Oklahoma on the given date

loss of smell_OK

The search interest in the term "loss of smell" in Oklahoma on the given date

loss smell_OK

Another variation for tracking the search interest in "loss of smell" in Oklahoma on the given date

loss taste_OK

The search interest in the term "loss of taste" in Oklahoma on the given date

cough_OK

The search interest in the term "cough" in Oklahoma on the given date

nasal congestion_OK

The search interest in the term "nasal congestion" in Oklahoma on the given date

Pytrends is an unofficial Google Trends API for Python. It enables users to programmatically fetch Google Trends data, which can be useful for various applications such as market research, academic studies, and tracking public interest in specific topics over time. Benefits of Using Pytrends: Automated Data Collection: Pytrends allows for automated and repeatable data collection from Google Trends, saving time and effort compared to manual extraction.

Customizable Queries: Users can specify keywords, timeframes, geographic locations, and other parameters to tailor the data to their specific needs.

Integration with Data Analysis Tools: Pytrends data can be easily integrated with tools like pandas for further analysis, visualization, and reporting.

Real-Time Insights: By regularly updating and analyzing Google Trends data, users can gain real-time insights into public interest and behavior, which is valuable for decision-making and research.

This map highlights 8962 stations with monthly discharge data, including data derived daily up to 20 December 2013. The GRDB (Global Runoff DataBase) is built on an initial dataset collected in the early 1980s from the responses to WMO (World Meteorological Organization request to its member countries to provide a global hydrological data set to complement a specific set of atmospheric data in the framework of the First Global GARP Experiment (FCGE). The initial dataset of monthly river discharge data over a period of several years around 1980 was supplemented with the UNESCO monthly river discharge data collection 1965-85. Today the database comprises discharge data of nearly 9.000 gauging stations from all over the world. Since 1993 the total number of station-years has increased by a factor of around 10.Credits and partnerships:OSU - College of Earth, Ocean and Atmospheric SciencesCarniege Corporation of New YGloabl orkNASCE - Northwest Alliance for Computational Science & EngineeringInternational Water Management InstituteUNESCO - United Nations Educational, Scientific and Cultural OrganisationUSGS - United States Geological Survey