The top 200 locations where reported collisions occurred at intersections have been identified. The crash cluster analysis methodology for the top intersection clusters uses a fixed meter search distance of 25 meters (82 ft.) to merge crash clusters together. This analysis was based on crashes where a police officer specified one of the following junction types: Four way intersection, T-intersection, Y-intersection, five point or more. Furthermore, the methodology uses the Equivalent Property Damage Only (EPDO) weighting to rank the clusters. EPDO is based any type of injury crash (including fatal, incapacitating, non-incapacitating and possible) having a weighting of 21 compared to a property damage only crash (which has weighting of 1). The clusters were reviewed in descending EPDO order until 200 locations were obtained. The clustering analysis used crashes from the three year period from 2016-2018. The area encompassing the crash cluster may cover a larger area than just the intersection so it is critical to view these spatially.

Point geometry with attributes displaying street intersections of all public and private named roads in East Baton Rouge Parish, Louisiana.

This intersection points feature class represents current intersections in the City of Los Angeles. Few intersection points, named pseudo nodes, are used to split the street centerline at a point that is not a true intersection at the ground level. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way. The right of way information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Intersection layer was created in geographical information systems (GIS) software to display intersection points. Intersection points are placed where street line features join or cross each other and where freeway off- and on-ramp line features join street line features. The intersection points layer is a feature class in the LACityCenterlineData.gdb Geodatabase dataset. The layer consists of spatial data as a point feature class and attribute data for the features. The intersection points relates to the intersection attribute table, which contains data describing the limits of the street segment, by the CL_NODE_ID field. The layer shows the location of the intersection points on map products and web mapping applications, and the Department of Transportation, LADOT, uses the intersection points in their GIS system. The intersection attributes are used in the Intersection search function on BOE's web mapping application NavigateLA. The intersection spatial data and related attribute data are maintained in the Intersection layer using Street Centerline Editing application. The City of Los Angeles Municipal code states, all public right-of-ways (roads, alleys, etc) are streets, thus all of them have intersections. List of Fields:Y: This field captures the georeferenced location along the vertical plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, Y = in the record of a point, while the X = .CL_NODE_ID: This field value is entered as new point features are added to the edit layer, during Street Centerline application editing process. The values are assigned automatically and consecutively by the ArcGIS software first to the street centerline spatial data layer, then the intersections point spatial data layer, and then the intersections point attribute data during the creation of new intersection points. Each intersection identification number is a unique value. The value relates to the street centerline layer attributes, to the INT_ID_FROM and INT_ID_TO fields. One or more street centerline features intersect the intersection point feature. For example, if a street centerline segment ends at a cul-de-sac, then the point feature intersects only one street centerline segment.X: This field captures the georeferenced location along the horizontal plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, X = in the record of a point, while the Y = .ASSETID: User-defined feature autonumber.USER_ID: The name of the user carrying out the edits.SHAPE: Feature geometry.LST_MODF_DT: Last modification date of the polygon feature.LAT: This field captures the Latitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.OBJECTID: Internal feature number.CRTN_DT: Creation date of the polygon feature.TYPE: This field captures a value for intersection point features that are psuedo nodes or outside of the City. A pseudo node, or point, does not signify a true intersection of two or more different street centerline features. The point is there to split the line feature into two segments. A pseudo node may be needed if for example, the Bureau of Street Services (BSS) has assigned different SECT_ID values for those segments. Values: • S - Feature is a pseudo node and not a true intersection. • null - Feature is an intersection point. • O - Intersection point is outside of the City of LA boundary.LON: This field captures the Longitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.

The FDOT GIS Intersection feature class provides spatial information on Florida intersections. This information includes intersection direction and surface type. This direction data is required for all roads. The surface type is required for all functionally classified roadways on the SHS and major roadway intersections on HPMS standard sample sections, including Active Off the SHS. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 06/28/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/intersection.zip

Roads and Highways manages intersections, however they are not singular points; RH creates a series of points - one for each intersecting road at that intersection. For DDOT, it is more useful to have a single intersection point representing the intersection. Through a custom DDOT script,the series of intersection points is reduced into a single representative point.For more information please visit DDOT's wiki page

The Statistics Canada street network for 2016 was used to derive street intersection counts within buffers of 100, 250, 300, 500, 750 and 1000 meters of each DMTI Spatial single link postal code for the year 2019. Only street intersections with more than one street segment joining were counted - no dead ends were included. A higher value indicates more intersections and a greater degree of connectivity enabling more direct travel between two points using existing streets. CANUE staff used ArcGIS and the Line and Junction Connectivity Toolbox (see supporting documentation) to create intersection counts and PostGres SQL to produce buffer counts.

Attention new flow URL carto.ain.fr becomes geodata.ain.fr

Last modification of the graph Nov 2023: decommissioning of the RD979 from Boulevard Charles de Gaulle to the roundabout of the Alagnier (urban part in the town of bourg) avenue Amédée Mercier

Roads are all represented by a single axis even in the case of separate pavements. At junctions / intersections, sections not participating in the main road are cut off at the outer edge of the junction (stop / cede the passage). To ensure the topology of the road graph pseudo roads have been added (categorie_admin=intersection) but do not participate in the linear of the RD.

For roundabouts, the calculation of the PR+abs follows the axis of the roundabout in the direction of the route (meaning of the PR) and thus travels part of the roundabout virtually while physically there is a discontinuity (PV_beginning / PV_end).

The field 'categorie_admin' allows to know its use. rd / intersection / junction_pn / roundabout. The total length of the network coming together as the sum of the cumulf-cumuld of the rd and giratoire.

Field determination rule: axis

the wording of the route is in the form of Dxx (e.g. D23)

For roads with name extensions (letter after the number of the RD), this extension is added afterwards with a minus (indent of 6) in separator (e.g.: D23-C).

For roundabouts an extension 'GIRxxx' is added next with an underscore (tiret of 8) as a separator.

ex D72-A_GIR_02_0262.

PR of the entry of the roundabout on 2 characters with 0 in prefix to complete 4-character abscissa of the roundabout entry with 0 prefixes to complete

Field determination rule: axis_cd01

Axes of the Departmental Routes of Ain (e.g. 01 D0117A).

Rules for naming roundabouts (in the process of being entered):

E.g.: GIR0117A_09_0611 GIR route type prefix RD number on 4 characters with 0 in prefix to complete Letter from RD with _ if no letter Separator _ PR of the entry of the roundabout on 2 characters with 0 in prefix to complete 4-character abscissa of the roundabout entry with 0 prefixes to complete

Intersection management scheme:

https://geodata.ain.fr/apps/external/img/GPR_schema_intersection.png" alt="Intersection Management Scheme">

This dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high pedestrian crash counts observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018, 2019, and 2021. To enhance its utility, the dataset meticulously colour-codes each month associated with elevated crash occurrences, providing a nuanced perspective. Furthermore, the dataset categorises road intersections based on their placement within "high-high" clusters (marked with pink tabs) or "high-low" outlier cells (indicated by red tabs). For ease of navigation, the intersections are further organised alphabetically by suburb name, ensuring accessibility and clarity.Data SpecificsData Type: Geospatial-temporal categorical data with numeric attributesFile Format: Word document (.docx)Size: 255 KBNumber of Files: The dataset contains a total of 264 road intersection records (68 "high-high" clusters and 196 "high-low" outliers)Date Created: 21st May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, Open Refine, Python, SQLProcessing Steps: The raw road traffic crash data underwent a comprehensive refining process using Python software to ensure its accuracy and consistency. Following this, duplicates were eliminated to retain only one entry per crash incident. Subsequently, the data underwent further refinement with Open Refine software, focusing specifically on isolating unique crash descriptions for subsequent geocoding in ArcGIS Pro. Notably, during this process, only the road intersection crashes were retained, as they were the only incidents with spatial definitions.Once geocoded, road intersection crashes that involved a pedestrian were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which the pedestrian crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections involving pedestrian crashes identified as either "high-high" clusters or "high-low" outliers were extracted for inclusion in the dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

This dataset represents the entire road intersections within the Region including traffic lights that have no road intersect. The data has been mapped as points.

Intersections Crashes - Crashes that occurred at an intersection or are intersection-related.Code value document click HEREThis is a geographical representation of the data available in the CTCDR. Data set represents all MMUCC Crashes from January, 2015 to crashes reported to the DOT and processed within the last 30 - 60 days

GIS layer composed of Lorain County Township, Tract and Original Lot numbers within each township. Not all townships have Tracts and/or Original Lots. *Each layer has a different scale visibility. You may have to zoom in to a certain extent for the layer to turn on.

Components Unknown: A connector that staff cannot define because the structure is buried, paved over, flooded, etc. The location is often inferred. Unknown is for use in the field.Intersection: A connector point whose location is inferred as the convergence between one or more stormwater conveyance structures (e.g. a pipe) where there is believed to be no catch basin or other structure that serves as a connector.

Plow Routes

2015 - 2017 Top 200 Intersection Crash Cluster Locations.The top 200 locations where reported collisions occurred at intersections have been identified. The crash cluster analysis methodology for the top intersection clusters uses a fixed meter search distance of 25 meters (82 ft.) to merge crash clusters together. This analysis was based on crashes where a police officer specified one of the following junction types: Four way intersection, T-intersection, Y-intersection, five point or more. Furthermore, the methodology uses the Equivalent Property Damage Only (EPDO) weighting to rank the clusters. EPDO is based any type of injury crash (including fatal, incapacitating, non-incapacitating and possible) having a weighting of 21 compared to a property damage only crash (which has weighting of 1). The clusters were reviewed in descending EPDO order until 200 locations were obtained. The clustering analysis used crashes from the three year period from 2015-2017. The area encompassing the crash cluster may cover a larger area than just the intersection so it is critical to view these spatially.

View metadata for key information about this dataset.This layer was developed to aid the Street Lighting Division in planning, referencing and maintaining the active intersection controls within the City of Philadelphia. Examples include: providing information regarding group replacement projects and any individual edits, using tables from layer for billing, and aiding cityworks.For questions about this dataset, contact dominick.cassise@phila.gov. For technical assistance, email maps@phila.gov.

The Los Angeles Department of Transportation developed a transportation and health database that includes all collisions in the most recently available five-year period, as well as key environmental variables. These data, currently available on the City’s GeoHub, will be continually be updated as new information becomes available. The purpose for developing it was 1. to help the City identify a list of prioritized locations along the High Injury Network (HIN) for the development of safety projects and 2. to develop "countermeasure pairing," the process of identifying the physical design and engineering countermeasures that would most effectively address each "collision profile," a group of collisions with similar contributing factors. The Vision Zero Los Angeles initiative used a hierarchical clustering to develop these LA-specific collision profiles, intersection profile counts, and collision intersection scores. The data found here is a result of that work and will be used in the development of our Action Plan. Please reference the Vision Zero GIS Data Dictionary.pdf for key field names and descriptions.

To view metadata and source links for data download, see the Gallatin Sensitive Lands Protection Plan Report Appendix C Part 2 Descriptions of Model Inputs and Processing. To view the item details for a specific model input or map layer, scroll down to find the name of the layer.Layers labeled as "old" are no longer maintained in this geodatabase and are not used in any Plan reports, maps, or other materials.

The dataset contains locations and attributes of intersections created as part of the Master Address Repository (MAR) for the Office of the Chief Technology Officer (OCTO) and participating DC government agencies. The intersection points - MAR is primarily derived from DC Department of Transportation's (DDOT) intersection table in their Street Spatial Database (SSD). Regular at-grade intersections (TYPE = 'REGULAR') as well as overpasses (TYPE = 'OVERPASS'), underpasses (TYPE = 'UNDERPASS') and ramps (TYPE = 'RAMP') are included. The overpasses, underpasses, and ramps are based on street centerlines from DDOT. More information on the MAR can be found at https://opendata.dc.gov/pages/addressing-in-dc. The data dictionary is available: https://opendata.dc.gov/documents/2a4b3d59aade43188b6d18e3811f4fd3/explore. In the MAR 2, the IntersectionPt is called INTERSECTIONS_PT and is primarily based off of street data from DC Department of Transportation's Roads & Highways database. It also features additional useful information such as created date, last edited date, begin date, and more.

Polygon areas that capture the intersection of road right of ways. Used to relate other traffic features to the intersection areas such as traffic poles, pedestrian controls, traffic control cabinets, etc.