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We have found 64 datasets for the keyword " taxi". You can continue exploring the search results in the list below.
Datasets: 106,156
Contributors: 42
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64 Datasets, Page 1 of 7
Ministry of Transportation (MOT) Railroad Crossing
A Railroad Crossing is where the road surface is common to both the Railway Authority and the Ministry of Transportation Road Network; also includes overhead and under passes. It is a Point feature
Public transport - Stop
All stops in the public transport network managed by the City of Rouyn-Noranda**This third party metadata element was translated using an automated translation tool (Amazon Translate).**
Transportation
Transportation networks (transportation)The means and aids for conveying people and goods. For example, resources describing roads, airports and airstrips, shipping routes, tunnels, nautical charts, vehicle or vessel location, aeronautical charts, and railways.
Ministry of Transportation (MOT) Special Lane
A Special Lane defines the number of special lanes that are not defined as through travel lanes, passing lanes, or truck lanes which are described in the Highway Profile dataset. It is a Linear feature
Canada Basemap Transportation (CBMT) - Raster Tile
The Canada Basemap Transportation (CBMT) is a raster tile service that provides spatial reference context with an emphasis on transportation networks across Canada. It is designed especially for use as a background layer in a web mapping application or geographic information system (GIS). Access: Access is free of charge under the terms of the Open Government Licence - Canada.Data Sources: Data for the CBMT is sourced from the following datasets:- Topographic data of Canada – CanVec Series- Official names from the Canadian Geographical Names Database (CGNDB).Projections:- Data is provided in the EPSG:3978 (NAD83 Canada Atlas Lambert) projected coordinate system.- Data is provided in the EPSG:3857 (WGS84 Pseudo-Mercator) projected coordinate system.Geographic Coverage: - The CBMT in the EPSG:3857 has complete coverage of the world, with full datasets in Canada and only partial data in other parts of the world including boundaries, Country Names, and major cities.- The CBMT in the EPSG:3978 covers the entire geographic area of Canada and some major transportation routes and cities in the northern States of the USA.Additional Versions: - The CBMT is available as a dynamic service (WMS) or a tiled service (ESRI REST and WMTS). - A geometry-only version (CBMT GEOM) and a text-only version (CBMT TXT) are available. - French versions of the basemap are accessible via the Carte de base du Canada - Transport (CBCT).
Ministry of Transportation (MOT) Sign
A Sign is a lettered board, message or other display which includes all regulatory, warning, guide, informational, advisory, construction and maintenance and route markers, but excluding electronically controlled messages/displays. It is a Point feature
Ministry of Transportation (MOT) Highway Profile
A Highway Profile defines the number of through travel lanes, including passing and truck lanes, and whether the road is divided or not. It is a Linear feature
Railroad Station - 50k - Canvec
Transport Features is composed of, among others, the National Road Network (NRN) and the National Railway Network (NRWN). Transport Features entities are: Nautical Facility, Track Segment, Track Junction, Railway Station, Track Crossing, Track Marker Post, Track Structure, Rail Ferry, Road Segment, Road Ferry, Road Junction, Blocked Passage, Toll Point, Aerial Cableway, Footbridge, Trail, Navigational Aid, Marina, and Runway. CanVec is a digital cartographic reference product of Natural Resources Canada (NRCan). It originates from the best available data sources covering Canadian territory, offers quality topographical information in vector format and complies with international geomatics standards. CanVec is a multi-source product coming mainly from the National Topographic Data Base (NTDB), the Mapping the North process conducted by the Canada Center for Mapping and Earth Observation (CCMEO), the Atlas of Canada data, the GeoBase initiative and the data update using satellite imagery coverage (e.g. Landsat 7, Spot, Radarsat, etc.).Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)
Railroad Station - 250k - Canvec
Transport Features is composed of, among others, the National Road Network (NRN) and the National Railway Network (NRWN). Transport Features entities are: Nautical Facility, Track Segment, Track Junction, Railway Station, Track Crossing, Track Marker Post, Track Structure, Rail Ferry, Road Segment, Road Ferry, Road Junction, Blocked Passage, Toll Point, Aerial Cableway, Footbridge, Trail, Navigational Aid, Marina, and Runway. CanVec is a digital cartographic reference product of Natural Resources Canada (NRCan). It originates from the best available data sources covering Canadian territory, offers quality topographical information in vector format and complies with international geomatics standards. CanVec is a multi-source product coming mainly from the National Topographic Data Base (NTDB), the Mapping the North process conducted by the Canada Center for Mapping and Earth Observation (CCMEO), the Atlas of Canada data, the GeoBase initiative and the data update using satellite imagery coverage (e.g. Landsat 7, Spot, Radarsat, etc.).Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)
Recreational Vessel Traffic Model for British Columbia
Description:Data on recreational boating are needed for marine spatial planning initiatives in British Columbia (BC). Vessel traffic data are typically obtained by analyzing automatic identification system (AIS) vessel tracking data, but recreational vessels are often omitted or underrepresented in AIS data because they are not required to carry AIS tracking devices. Transport Canada’s National Aerial Surveillance Program (NASP) conducted aerial surveys to collect information on recreational vessels along several sections of the BC coast between 2018 and 2022. Recreational vessel sightings were modeled against predictor variables (e.g., distance to shore, water depth, distance to, and density of marinas) to predict the number of recreational vessels along coastal waters of BC.The files included here are:--A Geodatabase (‘Recreational_Boating_Data_Model’), which includes: (1) recreational vessel sightings data collected by NASP in BC and used in the recreational vessel traffic model (‘Recreational_Vessels_PointData_BC’); (2) aerial survey effort (or number of aerial surveys) raster dataset (‘surveyeffort’); and (3) a vector grid dataset (2.5 km resolution) containing the predicted number of recreational vessels per cell and predictor variables (‘Recreational_Boating_Model_Results_BC).--Scripts folder which includes R Markdown file with R code to run the modelling analysis (‘Recreational_Boating_Model_R_Script’) and data used to run the code.Methods:Data on recreational vessels were collected by NASP during planned aerial surveys along pre-determined routes along the BC coast from 2018 to 2022. Data on non-AIS recreational vessels were collected using video cameras onboard the aircraft, and data on AIS recreational vessels using an AIS receiver also onboard the aircraft. Recreational boating predictors explored were: water depth, distance to shore, distance to marinas, density of marinas, latitude, and longitude. Recreational vessel traffic models were fitted using Generalized Linear Models (GLM) R packages and libraries used here include: AED (Roman Lustrik, 2021) and MASS (Venables, W. N., Ripley, 2002), pscl package (Zeileis, Kleiber, and Jackman, 2008) for zeroinfl() and hurdle() function. Final model was selected based on the Akaike’s information criterion (AIC) and the Bayes’ information criterion (BIC). An R Markdown file with code use to run this analysis is included in the data package in a folder called Script. Spatial Predictive Model: The selected model, ZINB, consist of two parts: one with a binomial process that predicts the probability of encountering a recreational vessel, and a second part that predicts the number of recreational vessels via a count model. The closer to shore and to marinas, and the higher the density of marinas, the higher the predicted number of recreational vessels. The probability of encountering recreational vessels is driven by water depth and distance to shore. For more information on methodology, consult metadata pdf available with the Open Data record.References:Serra-Sogas, N. et al. 2021. Using aerial surveys to fill gaps in AIS vessel traffic data to inform threat assessments, vessel management and planning. Marine Policy 133: 104765. https://doi.org/10.1016/j.marpol.2021.104765Data Sources:Recreational vessel sightings and survey effort: Data collected by NASP and analyzed by Norma Serra to extract vessel information and survey effort (more information on how this data was analyzed see SerraSogas et al, 2021). Bathymetry data for the whole BC coast and only waters within the Canadian EEZ was provided by DFO – Science (Selina Agbayani). The data layer was presented as a raster file of 100 meters resolution. Coastline dataset used to estimate distance to shore and to clip grid was provided by DFO – Science (Selina Agbayani), created by David Williams and Yuriko Hashimoto (DFO – Oceans). Marinas dataset was provided by DFO – Science (Selina Agbayani), created by Josie Iacarella (DFO – Science). This dataset includes large and medium size marinas and fishing lodges. The data can be downloaded from here: Floating Structures in the Pacific Northwest - Open Government Portal (https://open.canada.ca/data/en/dataset/049770ef-6cb3-44ee-afc8-5d77d6200a12)Uncertainties:Model results are based on recreational vessels sighted by NASP and their related predictor variables and not always might reflect real-world vessel distributions. Any biases caused by the opportunistic nature of the NASP surveys were minimized by using survey effort as an offset variable.
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