This is a point shape file representing 2 kilometre incremental distances along each of the 8 NWT highways.  These 2km points do not represent the actual location of 2km highway posts found along the sides of the highways.  The feature class points are placed every 2 kilometres along a highway and represent the distance from a fixed commencement point, the beginning of that highway.

Established Protected and Conservation Areas in the NWT

Important Wildlife Areas In The NWT

Mineral Claims

Escape Room GIS Week Event Game shape file for Final Destination tracks.

Coal Exploration Licences

The Canadian Protected and Conserved Areas Database (CPCAD) is the authoritative source of data on protected and conserved areas in Canada. The database consists of the most up-to-date spatial and attribute data on marine and terrestrial protected areas in all governance categories recognized by the International Union for Conservation of Nature (IUCN), as well as Other effective area-based conservation measures (OECMs, or conserved areas) across the country. Indigenous Protected and Conserved Areas (IPCAs) are also included if they are recognized as protected or conserved areas. CPCAD adheres to national reporting standards and is freely available to the public. CPCAD is compiled and managed by Environment and Climate Change Canada (ECCC), in collaboration with federal, provincial, territorial, and other reporting authorities that provide the data. The database contains combined data from all these Canadian reporting authorities, who have determined that their areas meet the Canadian criteria as protected or conserved areas. CPCAD is used by a wide range of organizations, including governments, environmental non-governmental organizations (ENGOs), academia, land managers, industry, and the general public. CPCAD supports many of the Government of Canada’s priorities including Canada’s national reporting on protected areas, Canada’s international reporting on protected areas as a result of Canada’s commitments under the United Nations Convention on Biological Diversity, and Canada’s protected areas program by providing baseline information. More detailed information on CPCAD is available by downloading the User Manual. The data is current as of the date of the most recent revision. For prior years, please reach out to scf-geocarto-cws-geomapping@ec.gc.ca.

A database of verified tornado tracks across Canada has been created covering the 30-year period from 1980 to 2009. The tornado data have undergone a number of quality control checks and represent the most current knowledge of past tornado events over the period. However, updates may be made to the database as new or more accurate information becomes available. The data have been converted to a geo-referenced mapping file that can be viewed and manipulated using GIS software.

Description: These commercial whale watching data are comprised of two datasets. First, the ‘whale_watching_trips_jun_sep_british_columbia’ data layer summarizes commercial whale watching trips that took place in 2019, 2020 and 2021 during the summer months (June to September). The second data layer, ‘wildlife_viewing_events_jun_sep_british_columbia’ contains estimated wildlife viewing events carried out by commercial whale watching vessels for the same years (2019, 2020 and 2021) and months (June to September). Commercial whale watching trips and wildlife viewing events are summarized using the same grid, and they can be related using the unique cell identifier field ‘cell_id’. The bulk of this work was carried out at University of Victoria and was funded by the Marine Environmental Observation, Prediction and Response (MEOPAR) Network under the ‘Whale watching AIS Vessel movement Evaluation’ or WAVE project (2018 – 2022). The aim of the WAVE project was to increase the understanding of whale watching activities in Canada’s Pacific region using vessel traffic data derived from AIS (Automatic Identification System). The work was finalized by DFO Science in the Pacific Region. These spatial data products of commercial whale watching operations can be used to inform Marine Spatial Planning, conservation planning activities, and threat assessments involving vessel activities in British Columbia. Methods: A list of commercial whale watching vessels based in British Columbia and Washington State and their corresponding MMSIs (Maritime Mobile Service Identity) was compiled from the whale watching companies and Marine Traffic (www.marinetraffic.com). This list was used to query cleaned CCG AIS data to extract AIS positions corresponding to commercial whale watching vessels. A commercial whale watching trip was defined as a set of consecutive AIS points belonging to the same vessel departing and ending in one of the previously identified whale watching home ports. A classification model (unsupervised Hidden Markov Model) using vessel speed as the main variable was developed to classify AIS vessel positions into wildlife-viewing and non wildlife viewing events. Commercial whale watching trips in the south and north-east of Vancouver Island were limited to a duration of minimum 1 hour and maximum 3.5 hours. For trips in the west coast of Vancouver island the maximum duration was set to 6 hours. Wildlife-viewing events duration was set to minimum of 10 minutes to a maximum of 1 hour duration. For more information on methodology, consult metadata pdf available with the Open Data record. References: Nesdoly, A. 2021. Modelling marine vessels engaged in wildlife-viewing behaviour using Automatic Identification Systems (AIS). Available from: https://dspace.library.uvic.ca/handle/1828/13300. Data Sources: Oceans Network Canada (ONC) provided encoded AIS data for years 2019, 2020 and 2021, within a bounding box including Vancouver Island and Puget Sound used to generate these products. This AIS data was in turn provided by the Canadian Coast Guard (CCG) via a licensing agreement between the CCG and ONC for the non-commercial use of CCG AIS Data. More information here: https://www.oceannetworks.ca/science/community-based-monitoring/marine-domain-awareness-program/ Molly Fraser provided marine mammal sightings data collected on board a whale watching vessels to develop wildlife-viewing events classification models. More information about this dataset here: https://www.sciencedirect.com/science/article/pii/S0308597X20306709?via%3Dihub Uncertainties: The main source of uncertainty is with the conversion of AIS point locations into track segments, specifically when the distance between positions is large (e.g., greater than 1000 meters).

Canadian Homogenized Surface Air Temperature – Version 3.1 (CanHomT V3.1) This dataset is an update of the Third Generation Homogenized Temperature dataset (Vincent et al. (2020), extended through 2023. It was developed for climate trend analysis and includes long-term daily maximum, minimum, and mean temperature series for 780 stations (776 locations) in Canada. The key enhancement in V3.1 is the inclusion of both Original and Adjusted data, together with their associated data flags and the source station climate ID, providing improved traceability, reproducibility, and transparency. Key processing steps include: • Merging observations from nearby sites to create long records • Quality control of data • Adjusting daily minimum temperatures since July 1961 for the observing-time (climatological day definition) change at 96 principal stations (Vincent et al., 2009) • Detecting non-climatic shifts in annual and seasonal average temperature data series using homogeneity tests with reference and station metadata. • Applying Quantile-Matching (QM) adjustments with reference data (including parallel observations) to correct non-climatic changes (Vincent et al., 2018; Wang et al., 2010). Parallel observations —simultaneous measurements from old and new instruments or locations—were used whenever available because they provide the most reliable basis for data homogenization. Adjustments made without suitable reference data carry greater uncertainty, and this increased uncertainty should be quantified and clearly communicated (WMO, 2020). The QM method adjusts the entire distribution of data in one segment to match another (Wang et al., 2026; Vincent et al., 2018; Wang & Feng, 2013; Wang et al. 2010), ensuring that distribution changes—including variance shifts—at identified changepoints are homogenized. The gridded version of monthly CanHomT V3.1, called CanGridT mlyV3.1 [https://open.canada.ca/data/en/dataset/781e02cc-6c1b-462e-b61b-f96c607b23bd], was found to represent Canada’s warming trend reasonably well since 1900, despite changes in data availability over time (Wang et al., 2026, see their Figure S3). References Vincent, L.A., M.M. Hartwell and X.L. Wang, 2020: A Third Generation of Homogenized Temperature for Trend Analysis and Monitoring Changes in Canada’s Climate. Atmosphere-Ocean, 58(3), 173–191. https://doi.org/10.1080/07055900.2020.1765728. Vincent, L.A., E.J. Milewska, R. Hopkinson and L. Malone, 2009: Bias in minimum temperature introduced by a redefinition of the climatological day at the Canadian synoptic stations. J. Appl. Meteor. Climatol, 48, 2160-2168. DOI: 10.1175/2009JAMC2191.1. Vincent, L.A., E.J. Milewska, X. L. Wang, and M. M. Hartwell, 2018. Uncertainty in homogenized daily temperatures and derived indices of extremes illustrated using parallel observations in Canada, Intl. J. Climatol., 38:2, 692-707. DOI: 10.1002/JOC.5203. Wang, X. L. and Y. Feng, published online July 2013: RHtestsV4 User Manual. Climate Research Division, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada. 28 pp. [Available online at https://github.com/ECCC-CDAS] DOI: 10.13140/RG.2.2.17309.17125. Wang, X. L., H. Chen, Y. Wu, Y. Feng, and Q. Pu, 2010: New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol., 49, 2416-2436. DOI: 10.1175/2010JAMC2376.1. WMO. (2020). Guidelines on Homogenization (2020 edition). World Meteorological Organization. WMO-No. 1245. https://library.wmo.int/records/item/57130-guidelines-on-homogenization?offset=1. Wang, X. L., Feng, Y., Zwiers, F. W., & Cheng, V. Y. S. (2026). Precipitation trends in version 2 of the Canadian homogenized monthly precipitation dataset. Atmosphere-Ocean, 1–16, https://doi.org/10.1080/07055900.2026.2617861.