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 2014-2016. The area encompassing the crash cluster may cover a larger area than just the intersection so it is critical to view these spatially.

From left to right: the yeast and human data division; Type, real data or random model; Networks, intersections of network models; # Edges, number of protein pairs in the intersections; # Nodes, number of different proteins (nodes) in the intersections; #Ed./#Nod., or network density is ratio of the number of edges divided by the number of nodes; %PGe, percentage of the PG model's edges backed by the KG model. R. 1, p-values random model and R. 2, adjacency random model (see the section: Network randomisation) the randomisation process was realized over the matrix of possible binary associations of all the proteins (nodes) in the PG and KG models. Calculation of intersections for the random models went through 1,000 iterations. For further statistics see Table S2 in Text S1.

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: 06/21/2025.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

aFraction of edges in the intersection respect to the total edges in the union.

A criticality analysis dataset of the phenomenon occlusion on a T-intersection. Simulation was done using CARLA.

Analysis of ‘Street Intersections’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/29a36886-3c07-4883-a034-36ecba64754f on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Street Intersections, includes all street names that intersect at an intersection. Street Nodes can have one or street names associated with it.

--- Original source retains full ownership of the source dataset ---

GO terms follow the GO Consortium hierarchy. Higher level (generic) terms are numbered and in bold text, lower level (specific) terms are in regular text.*The cut-off value was set to 1E-4.

Abstract We characterized both discursive expressions of three sixth grade students on the phrase intersect each other and the effect they have on the construction of collective and shared meanings of intersection. Recording data, extracting information without losing the context where the interaction is framed, and selecting expressions to analyze were all done by using a classroom-based qualitative strategy. The analysis, following techniques typical of ethnomethodology, was done in terms of: (i) intelligibility and authenticity, (ii) features of the discourse proposed by Sfard and (iii) our conceptualization of collective voice and shared voice. We found a geometry classroom in which student voices are collectively constructed and generate shared meaning about intersection, which is reflected in a punctual discursive transition. The contribution of this paper resides in the use of the discursive features regarded in the analysis to focus the gaze to the discourse on mathematical content.

Data analysis worksheets and average crash rates by intersection type and roadway functional classification.

This data set contains intersection points used to compute rate of change statistics for New York State coastal wetlands. Analysis was performed in ArcMap 10.5.1 using historical vector shoreline data from the National Oceanic and Atmospheric Administration (NOAA). Rate of change statistics were calculated using the Digital Shoreline Analysis System (DSAS), created by U.S. Geological Survey, version 5.0. End-point rates, calculated by dividing the distance of shoreline movement by the time elapsed between the oldest and the most recent shoreline, were generated for wetlands where fewer than three historic shorelines were available. Linear regression rates, determined by fitting a least-squares regression line to all shoreline points for a particular transect, were used in areas where three or more shorelines were present. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing the intersection measurement points presented here, which were then used to calculate the rates.

Here are a few use cases for this project:

1. Traffic Flow Analysis: The "Intersection" model could be used to monitor and analyze traffic flow at busy intersections. The model can detect various vehicle types and provide data that aids in understanding traffic patterns, congestions, peak hours, and more.

2. City Transportation Planning: The ability to distinguish between various classes of vehicles makes "Intersection" valuable for city planning departments. They can use the data from this model to make more informed decisions relating to road design, public transportation infrastructure, and vehicular traffic regulation.

3. Autonomous Vehicle Development: "Intersection" can be a useful tool in developing smarter self-driving cars. The ability to correctly identify various vehicle types can inform decision-making algorithms used in autonomous vehicles, leading to safer and more efficient rides.

4. Vehicle Based Advertising: For advertising companies, the "Intersection" model can be used to assess the types of vehicles passing through a specific location. This data can guide strategies such as billboard placement or targeted advertisements, focusing on demographic profiles associated with certain vehicle types.

5. Crash Analytics and Insurance Risk Evaluation: Insurance companies could use the "Intersection" model to analyze accident-prone areas by identifying the types of vehicles typically involved. This information could assist in adjusting insurance premiums or identifying high-risk areas.

Analysis of ‘Street Closures due to construction activities by Intersection’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c3166c2c-d13d-4056-8cab-2b16c2e4d881 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

DOT Street Closure data identifies locations in the New York City Street Closure map where a street is subject to a full closure, restricting through traffic, for the purpose of conducting construction related activity on a City street. Details about DOT construction permits can be found at Street Works Manual, http://streetworksmanual.nyc/. Full Closure Permits are issued for a period of time during which the street may be closed to through traffic for only a portion of the time, and open at other times.

Additional information regarding which part of the street and during which period of time it is fully closed is included only on the permit itself.

--- Original source retains full ownership of the source dataset ---

The Vulnerable Road User Safety Assessment is an assessment of the safety performance of the state of Iowa with respect to vulnerable road users and the plan of the state of Iowa to improve the safety of vulnerable road users. It is required by law and is described under 23 U.S.C. 148(1).The assessment is driven by demographic and performance-related data developed in consultation with local governments that represent high-risk areas. This assessment is used to identify areas of high risk to vulnerable road users, and then utilize that assessment to drive strategy and investment decisions around further safety improvements to mitigate identified safety risks.VRU Safety Assessment GoalsIdentify areas of higher risk for bicyclist and pedestrian crashesProvide insight on areas of necessary infrastructure improvements on Iowa roadsFurther the objective of achieving zero fatalities on the nation’s Roads

Market Size and Growth: The global Intersection Violation Systems market is projected to experience substantial growth in the coming years, driven by increasing traffic congestion and the need for enhanced road safety. The market size is estimated to reach USD XX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The rise in traffic accidents at intersections, along with the increasing adoption of advanced traffic management systems, is fueling the demand for efficient intersection violation detection solutions. Drivers, Trends, and Restraints: Key drivers of the market include government regulations mandating the implementation of traffic safety technologies, advancements in sensor technologies and artificial intelligence, and the growing focus on smart cities. Major trends shaping the market are the increasing proliferation of connected vehicles and the adoption of cloud-based services for traffic management. However, factors such as high installation and maintenance costs, data privacy concerns, and regulatory complexities may act as restraints to market growth. The market is segmented by application (city road, highway, others), type (3MP, 5MP, 9MP, others), and region. Prominent players in the market include Hikvision, AxxonSoft, Vehant Technologies, and Dell Technologies.

BackgroundDespite the Canadian healthcare system’s commitment to equity, evidence for disparate access to primary care (PC) providers exists across individual social identities/positions. Intersectionality allows us to reflect the realities of how social power shapes healthcare experiences at an individual’s interdependent and intersecting social identities/positions. The objectives of this study were to determine: (1) the extent to which intersections can be used classify those who had/did not have a PC provider; (2) the degree to which each social identity/position contributes to the ability to classify individuals as having a PC provider; and (3) predicted probabilities of having a PC provider for each intersection.Methods and findingsUsing national cross-sectional data from 241,445 individuals in Canada aged ≥18, we constructed 320 intersections along the dimensions of gender, age, immigration status, race, and income to examine the outcome of whether one had a PC provider. Multilevel analysis of individual heterogeneity and discriminatory accuracy, a multi-level model using individual-level data, was employed to address intersectional objectives. An intra-class correlation coefficient (ICC) of 23% (95%CI: 21–26%) suggests that these intersections could, to a very good extent, explain individual variation in the outcome, with age playing the largest role. Not all between-intersection variance in this outcome could be explained by additive effects of dimensions (remaining ICC: 6%; 95%CI: 2–16%). The highest intersectional predicted probability existed for established immigrant, older South Asian women with high income. The lowest intersectional predicted probability existed for recently immigrated, young, Black men with low income.ConclusionsDespite a “universal” healthcare system, our analysis demonstrated a substantial amount of inequity in primary care across intersections of gender, age, immigration status, race, and income.

Parameters of the ASSET method employed for the analysis of stochastic and network data. Each column shows the parameters employed for the corresponding step of the method.

A. SUMMARY This dataset contains the list of intersecting Analysis Neighborhoods and ZIP Codes for the City and County of San Francisco. It can be used to identify which ZIP codes overlap with Analysis Neighborhoods and vice verse. B. HOW THE DATASET IS CREATED The dataset was created with a spatial join between the Analysis Neighborhoods and ZIP codes. C. UPDATE PROCESS This is a static dataset D. HOW TO USE THIS DATASET This dataset is a many-to-many relationship between analysis neighborhoods and ZIP codes. A single neighborhood can contain or intersect with multiple ZIP codes and similarly, a single ZIP code can be in multiple neighborhoods. This dataset does not contain geographic boundary data (i.e. shapefiles/ GEOMs). The datasets below containing geographic boundary data should be used for analysis of data with geographic coordinates. E. RELATED DATASETS Analysis Neighborhoods San Francisco ZIP Codes Supervisor District (2022) to ZIP Code Crosswalk Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods

All calculations were performed using NeAT [28]. The query is PSGN and used reference corresponds to each biomolecular interactomes. In bold, those significant p-values.aThe limit of precision for the hypergeometric test.