The ODOT Crash Inventory dataset, put together by Ohio's Department of Transportation (ODOT) and provided through their TIMS REST Services, gives a detailed look at specifically pedestrian and bicycle traffic accidents in Cuyahoga County. The dataset, originally organized by year, is merged into a single dataset by County Planning staff. This combination aids in better analyze and understand road safety trends in the county. It covers basics like when and where the crashes happened, as well as details like the type of road and weather conditions. This simplified dataset is useful for making informed decisions about safety improvements.Data Sources:ODOT (Ohio Department of Transportation), TIMS REST ServicesTemporal Coverage:Start Year: 2008End Year: 2023Update Frequency: AnnuallyLast Update: 10/03/2024

This data set depicts Oregon state highways that have been identified as tier 1, 2, or 3 seismic lifelines and identifies seismic lifelines for implementation of Policy 1E, Lifeline Routes, in the 1999 Oregon Highway Plan. This is the final work product for the Oregon Seismic Lifelines Identification project, completed by ODOT Transportation Development Division in May 2012. This shapefile was initially derived from the ODOT shapefiles "high_routes.shp."

ODOT 8-Year Work Plan Bridges 2020-2027

Oklahoma Department of Transportation Maintenance Facilities.

ODOT 8-Year Work Plan Bridges 2021-2028

OR-Trans is a GIS road centerline dataset compiled from numerous sources of data throughout the state. Each dataset is from the road authority responsible for (or assigned data maintenace for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best avaialble data from each road authority for their jurisdiction (or assigned data maintenance responsibility). Data is stored in a SQL database and exported in numerous formats.

ODOT 8-Year Work Plan Roads 2021-2028

Represents all ODOT roadways that are part of the 2020-2027 work plan. Identical to 'ODOT_Workplan_Roads_2020to2027' dataset but overlapping lines are not offset.

This data represents the ODOT State Railway System linework.

This theme delineates urban growth boundaries (UGBs) in the state of Oregon. The line work was created by various sources including the Oregon Department of Land Conservation and Development (DLCD), the Oregon Department of Transportation (ODOT), Metro Regional Council of Governments (Metro), county and city GIS departments, and the Oregon Department of Administrative Services - Geospatial Enterprise Office (DAS-GEO). UGB areas consist of unincorporated lands surrounding a city that show where the city plans to grow over the next 20 years. When a city needs to develop more residential, commercial, industrial, or public land, it annexes the needed area from its UGB. If a city runs out of needed land within the UGB, it can expand its UGB. Original UGBs were established under the Oregon Statewide Planning Goals in 1973 by the Oregon State Legislature (Senate Bill 100). Goal 14 of the statewide planning program is, "To provide for an orderly and efficient transition from rural to urban land use, to accommodate urban population and urban employment inside urban growth boundaries, to ensure efficient use of land, and to provide for livable communities." The process and requirements for designating and amending UGBs are in Oregon Administrative Rules, Chapter 660, Division 24 (OAR 660-024). Designating or amending a UGB requires a public process, as required by Planning Goal 1, followed by approval by both the city and county elected officials and acknowledgement by the DLCD. This process includes the city submitting a Post Acknowledgement Plan Amendment (PAPA) to DLCD to review for consistency with Goal 14. The PAPA submittal includes GIS files that delineate the changes to the UGB. DLCD aggregates the local GIS layers into the statewide UGB layer. UGB line work and attributes are verified with the city PAPA submittals entered in DLCD’s tabular database to ensure that all UGB updates reported to DLCD have been included in this dataset. UGBs that are currently in the appeal process at the time of publication of this layer are not included. The effDate attribute indicates the year in which the UGB amendment was acknowledged by DLCD. In 2022, DLCD acknowledged amendments to the following UGBs: Central Point, Dayton, Phoenix, and Turner. Corrections were also made to the Astoria and Condon UGBs to reflect the current acknowledged boundary.

Federal highway and transit statutes require, as a condition for spending federal highway or transit funds in urbanized areas, the designation of MPOs, which have responsibility for planning, programming and coordination of federal highway and transit investments. The federally designated MPOs are made up of large urban MPOs (population areas greater than 200,000): the Portland regional area, the Salem/Keizer area, and the Eugene/Springfield area; and small urban MPOs (population areas between 50,000 - 200,000): the Medford/Rogue Valley area, the Cities of Corvallis/Philomath, the City of Bend, Albany area, Middle Rogue, Longview/Kelso/Rainier and Walla Walla Valley.

City limits and city annexations for the State of Oregon. Source: ODOT, 1:24,000. Last updated: 2011.

Full metadata record can be found here: http://gis.oregon.gov/DAS/EISPD/GEO/docs/metadata/citylim_2011.xml

OR-Trans is a GIS road centerline dataset compiled from numerous sources of data throughout the state. Each dataset is from the road authority responsible for (or assigned data maintenance for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best available data from each road authority for their jurisdiction (or assigned data maintenance responsibility). Data is stored in a SQL database and exported in numerous formats.

BOC/Dev Serv.Version 20091030 (October 30, 2009). The Oregon Wetlands Cover is a compilation of polygon data from numerous sources, and represents the most comprehensive dataset available for the location and composition of the state's wetlands. It uses as a base all available digital data from the National Wetland Inventory (NWI; U.S. Fish and Wildlife Service, USFWS), to which has been added draft NWI mapping (Oregon Natural Heritage Information Center and The Wetlands Conservancy, ORNHIC and TWC), mapping from Local Wetland Inventories (LWIs; Department of State Lands, DSL), wetlands along state highways (Oregon Department of Transportation, ODOT), and mapping of individual sites by a variety of federal, state, academic, and nonprofit sources. Because the Oregon Wetlands Cover is based on the NWI framework, it represents all wetland categories in the Cowardin classification (Cowardin, L.M., V. Carter, F.C. Golet & E.T. LaRoe. 1979. Classification of wetlands and deepwater habitats of the United States. USDI Fish & Wildlfe Service, Biological Services Program. FWS/OBS-79/31. 103 pp. https://www.npwrc.usgs.gov/resource/wetlands/classwet/index.htm; https://el.erdc.usace.army.mil/emrrp/emris/emrishelp2/cowardin_report.htm) that are known to be present and mappable in Oregon. It includes perennially inundated "open water" aquatic habitats such as lacustrine limnetic, riverine (all subsystems), and marine and estuarine intertidal and subtidal. Man-made water features such as industrial detention ponds, log ponds, municipal sewage treatment lagoons, and flooded gravel pits are also included because they are often habitat for wetland biota. Portions of upland riparian areas may also be included where they are intermixed with riverine or palustrine wetlands and cannot be separated at the scale used in original mapping. Despite the inclusion of wetland spatial data from many different sources, a multitude of wetlands in Oregon have yet to be identified and mapped, and are not present in this coverage. This is especially true for seasonal, small, farmed, and forested wetlands that are difficult to detect with aerial photography.

This layer shows the high crash segments in Cleveland using crash data from 2015 to 2019. Crashes resulting in serious injuries or death are used to determine if a segment qualifies as a high-crash segment. In order to qualify, there must be 10 or more serious injury or fatal crashes in any 5-year period. Generally, one-mile segments with 10 or more serious or fatal crashes represent particularly dangerous road segments on the city's network.Crash data is obtained from ODOT: GCAT | ODOT TIMS (state.oh.us)Documentation and DefinitionsSee data table. Also, ODOT provides a data glossary: Data Glossary | ODOT TIMS (state.oh.us)Update FrequencyNot updated. See subsequent layers for more recent data (2016-2020, etc.)ContactsCity Planning Commission

This is a dataset download, not a document. The Open button will start the download.Digital Elevation Model. 10m pixels. Elevation values in feet. Elevation data assembled from merged 7.5-minute DEM blocks (10- by 10-m data spacing).

This dataset includes a multimodal assessment of the Cleveland Transportation Network, conducted as part of the Cleveland Moves initiative. It assesses need and comfort levels as we work to improve safety and mobility on Cleveland streets.The Pedestrian Crossing Level of Stress layer was created by our Cleveland Moves consultant, Toole Design. It uses information about the number of lanes, the speed limit, and the presence of a pedestrian island to calculate how stressful a crossing is for someone crossing. These attributes are provided by Ohio and City of Cleveland data about streets and intersections. This data was generated in 2024. The Bicycle Level of Traffic Stress layer was created by our Cleveland Moves consultant, Toole Design. It uses information about the number of lanes, the speed limit, the type of bikeway, and more to calculate the level of stress for someone riding a bicycle on a given street. These attributes are provided by Ohio and City of Cleveland data about streets and intersections. This data was generated in 2024. The ODOT Active Transportation Need layer was created by the Ohio Department of transportation, and uses several factors to determine need including access to a vehicle, poverty rates, and more.Update FrequencyThis dataset will be updated with additional analysis from the Cleveland Moves planning process by early 2025. After that point, it will be updated annually to reflect changes to Cleveland streets geared towards improving safety and mobility. Related ApplicationsA summary of this dataset can be found in the Cleveland Moves Network Assessment Dashboard.Data GlossaryThe ODOT Active Transportation Need dataset was developed by the Ohio Department of Transportation. More information about this dataset is available on their website: https://gis.dot.state.oh.us/tims_classic/Glossary ContactSarah Davis, Active Transportation Senior Plannersdavis2@clevelandohio.gov

This layer shows the high crash segments in Cleveland using crash data from 2013 to 2017. Crashes resulting in serious injuries or death are used to determine if a segment qualifies as a high-crash segment. In order to qualify, there must be 10 or more serious injury or fatal crashes in any 5-year period. Generally, one-mile segments with 10 or more serious or fatal crashes represent particularly dangerous road segments on the city's network.Crash data is obtained from ODOT: GCAT | ODOT TIMS (state.oh.us)Documentation and DefinitionsSee data table. Also, ODOT provides a data glossary: Data Glossary | ODOT TIMS (state.oh.us)Update FrequencyNot updated. See subsequent layers for more recent data (2014-2018, etc.)ContactsCity Planning Commission

Odot MilePosts 3/10/23

As part of the Great Lakes Restoration Initiative (GLRI) project template 774-18 entitled “Development of monitoring and response methodologies, and implementation of an Adaptive Management Framework to work towards Eradication of Grass Carp in Lake Erie” an integrated bathymetric/hydrodynamic/water-quality survey of the Maumee River (Ohio) was completed by the U.S. Geological Survey (USGS) in the summer of 2019. These data were collected to inform the development of a one-dimensional hydraulic model and associated Fluvial Egg Drift Simulator (FluEgg) model of the Maumee River downstream from Defiance, Ohio. The data contained in this data release were collected by the USGS Ohio-Kentucky-Indiana Water Science Center to inform the development of these models by the USGS Central Midwest Water Science Center. The survey was completed over two periods of time: June 24–28, 2019, and July 29 to August 1, 2019. The first survey period concentrated on the reach between Grand Rapids, Ohio, and Lake Erie, while the second period concentrated on the reach between Defiance, Ohio, and Grand Rapids, Ohio. Survey data include bathymetry (depth and bed elevation), three-dimensional water velocity, discharge, and basic water-quality properties. A total of 251 cross sections were surveyed (141 upstream from and 110 downstream from Grand Rapids Dam, respectively) and data were also collected along streamwise transits between sections. Due to rapids, high-water, access, and safety concerns, no data were collected in the 23.9-kilometer reach downstream from the dam at Grand Rapids, Ohio. The upstream-most cross section is 280 meters downstream from the low-head dam approximately 6.6 kilometers downstream from Defiance, Ohio. The downstream-most cross section is located 290 meters downstream from the U.S. Coast Guard Station at Toledo, Ohio (3900 N Summit St, Toledo, Ohio, 43611). All data were collected by a manned survey vessel with a two-person survey crew of trained hydrographers. All data were georeferenced using a Trimble R10 Global Navigation Satellite System (GNSS) receiver mounted on the survey vessel and connected to the Ohio Department of Transportation (ODOT) real-time virtual reference station (VRS) network. This component of the data release consists of water velocity and water-quality data measured in the Maumee River between Defiance, Ohio, and the river mouth at Lake Erie at Toledo, Ohio. Velocity data were collected using a 1200 kilohertz Teledyne RD Instruments RiverPro acoustic Doppler current profiler (ADCP) deployed on a fixed mount from the survey vessel. The GNSS receiver was mounted directly above the ADCP. The sampling frequency varied slightly with the dynamic configuration of the ADCP but was generally between 1 to 2 Hertz. Data have been post-processed using the Velocity Mapping Toolbox v4.09 (VMT; Parsons and others, 2013) and its GIS Table Creation Utility with temporal averaging of 5 seconds. Both layer- and depth-averaged velocities are included in the data files and files are included for both the depth from surface (DFS) reference and height above bottom (HAB) reference. Layers are defined in 1-meter intervals for both references across the full water column and 0.5-meter intervals for points within 2 meters of the water surface or bottom. Water-quality data include two-dimensional, near-surface point measurements of basic water-quality properties in the Maumee River between Defiance, Ohio, and the river mouth at Lake Erie at Toledo, Ohio. Water-quality properties include temperature, specific conductance, pH, dissolved oxygen, turbidity, total chlorophyll, and phycocyanin concentration (the latter two properties were only collected upstream of Grand Rapids, Ohio). These data were collected using a Xylem EXO2 sonde (SN 16J103377) equipped with a temperature/conductivity sensor (SN 17A103858), pH sensor (SN 18G103338), optical dissolved oxygen sensor (SN 17A103549), turbidity sensor (SN 16K102514), total algae phycocyanin smart sensor (SN 12M100504), and central wiper. The sonde was deployed off the side of a manned survey vessel using a fixed mount at a depth of approximately 0.3 meters below the water surface. All properties were sampled at 2-second intervals as the vessel completed the survey (for both cross sections and streamwise profiles) and a 6-second moving average was applied in post-processing. References: Parsons, D.R., Jackson, P.R., Czuba, J.A., Engel, F.L., Rhoads, B.L., Oberg, K.A., Best, J.L., Mueller, D.S., Johnson, K.K. and Riley, J.D., 2013, Velocity Mapping Toolbox (VMT): a processing and visualization suite for moving-vessel ADCP measurements. Earth Surface Processes and Landforms, v. 38, no. 11, p. 1244-1260. [Also available at https://doi.org/10.1002/esp.3367.]