This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

This dataset provides geographical information on projects issued by or on behalf of Western Economic Diversification Canada.

Annual counts of enterprises by gender (men+, women+) of owner for Canada, provinces and the territories.

Annual counts of enterprises by age of owner (younger than 30 years, 30 to 39 years, 40 to 49 years, etc.) for Canada, provinces and the territories.

Ontario Land Cover (OLC) is a primary data layer. It provides a comprehensive, standardized, landscape level inventory of Ontario’s natural, rural and anthropogenic (human made) features.Product Packages:Esri-compatible PackageOpen source compatible PackageService:Now also available through a web service which circumvents the need to download data by exposing it for visualization over the internet. When using the ESRI Image Server URL in ESRI software full geoprocessing and analysis can also be done using just the service URL.Services can be accessed directly in ArcPro by using Add Data -> Add Data From Path and copying the desired service URL below into the text box. They can also be accessed by setting up an ArcGIS server connection in ESRI software using the ArcGIS Image Server REST endpoint URL.Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS Rest Server, WCS, WMS/WMTS) and add the appropriate URL in the resultant popup window.. All services are in Web Mercator projection.For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca.Service URL’sArcGIS Image Server Resthttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Thematic/Ontario_Land_Cover_Baseline_V1/ImageServerWeb Mapping Service (WMS)https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/Thematic/Ontario_Land_Cover_Baseline_V1/ImageServer/WMSServer/Web Coverage Service (WCS)https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/Thematic/Ontario_Land_Cover_Baseline_V1/ImageServer/WCSServer/Additional DocumentationBaseline Class Descriptions - Ontario Land Cover Version 1 (TEXT)Changes Descriptions - Ontario Land Cover Version 1 (TEXT)StatusCompleted: Production of the data has been completedMaintenance and Update FrequencyAs needed: Data is updated as deemed necessaryContactJoel Mostoway, Natural Resources and Forestry, Science and Research Branch, joel.mostoway@ontario.ca

Canadian Internet use survey, Internet use by location of access, for Canada, provinces and selected census metropolitan areas (CMA), from 2005 to 2009. (Terminated)

The international boundary data featured in this shapefile consists of the boundary between the United States and Canada and the United States and Mexico. Each country's section is administered independently. The United States and Canada border data was provided by the International Boundary Commission, United States and Canada (IBC). The International Boundary and Water Commission (IBWC) provided the United States and Mexico section of the border data. Geospatial data files provided individually by the IBC and IBWC were used to re-align the Census Bureau's MAF/TIGER System data for the agency's representation of the international boundaries of United States with Canada and Mexico. The Census Bureau's MAF/TIGER System and the IBWC source file data for the portion of the United States and Mexico border featured a gap between Cameron County, Texas and the three-mile limit in the Gulf of Mexico. The National Oceanic and Atmospheric Administration Coast Survey Office's representation of the United States and Mexico boundary used to fill this gap.Download: https://www2.census.gov/geo/tiger/TIGER2023/INTERNATIONALBOUNDARY/tl_2023_us_internationalboundary.zipMetadata: https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19115/tl_2023_us_internationalboundary.shp.iso.xml

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

Counties in California intended for the NEVI Map.Data downloaded in May 2021 from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2021.html#list-tab-VGDZBC72KXZ7CWIQNY.

The power plant locations and characteristics are part of the California Energy Commission’s (CEC) California Energy Infrastructure geospatial data sets. The data is derived from the CEC’s QFER-1304 Power Plant Owner Reporting Database and is updated annually. Among other information, a number of identifying attributes are given for each power plant as well as the generator units at each plant, their energy type, the total nameplate capacity, and their owners and operators.

This California Power Plants data set has identical information to the many tables making up the QFER data set, however this single feature layer is derived by condensing several QFER tables into one. Some fields of the original tables have been omitted, and point geometries, determined by each plants’ address fields, have been appended for geospatial display. Four new fields have been compiled from QFER’s Annual Generation Table. These are listed and defined as:Nameplate Capacity (MW): The total nameplate capacity from every unit that makes up the power plant, regardless of status Units: List of the unit names at each power plant Primary Energy Source: A list of the primary energy sources used by every generator at the plantLast Reported Year: The last year that the power plant was recorded in the Annual Generation Table.Primary Energy Source Descriptions: Source Type Description

AB Biomass Agriculture Crop Byproducts/Straw/Energy Crops

BAT Battery Battery Storage - not to be counted as a primary fuel/energy source

BFG Natural Gas Blast Furnace Gas

BIT Coal Bituminous Coal

BLQ Biomass Black Liquor

COL Coal Anthracite Coal

DFO Oil Distillate Fuel Oil (includes all Diesel and No. 1, No. 2, and No. 4 Fuel Oils)

GAS Oil Gasoline

GEO Geothermal Geothermal

JF Oil Jet Fuel

KER Oil Kerosene

LFG Biomass Landfill Gas

LIG Coal Lignite Coal

LWAT Large Hydro Large Hydro

MSW Biomass Municipal Solid Waste

N/A Unspecified Other, non-specified

NA Unspecified Not Available

NG Natural Gas Natural Gas (Methane - Pipeline Weighted National Average w/ HHV 1,050 Btu/scf)

NUC Nuclear Nuclear (Uranium, Plutonium, Thorium)

OBG Biomass Other Biomass Gases (Digester Gas, Methane, and other biomass gases)

OBL Biomass Other Biomass Liquid (Ethanol, Fish Oil, Liquid Acetonitrile Waste, Medical Waste, Tall Oil, Waste Alcohol, and other Biomass not specified)

OBS Biomass Other Biomass Solid (Animal Manure and Waste, Solid Byproducts, and other solid biomass not specified)

OG Natural Gas Other Gas (Butane, Coal Processes, Coke-Oven, Refinery, and other processes)

OGW Biomass Other gases, waste products

OIL Oil Non-specified oil products, may include distillate fuel oil

OTH Other Other (Batteries, Chemicals, Coke Breeze, Hydrogen, Pitch, Sulfur, Tar Coal, and miscellaneous technologies)

PC Petroleum Coke Petroleum Coke (Solid)

PG Natural Gas Propane

PUR Other Purchased Steam

RFO Oil Residual Fuel Oil (includes No. 5 and No. 6 Fuel Oils and Bunker C Fuel Oil)

SC Coal Coal-based Synfuel and include briquettes, pellets, or extrusions, which are formed by binding materials and processes that recycle material

SLW Biomass Sludge Waste (Waste Oil blended with Residual Fuel Oil)

SUB Coal Sub-bituminous Coal

SUN Solar Solar (Photovoltaic, Thermal)

SWAT Small Hydro Small Hydro, Eligible Hydroelectric for RPS

TDF Biomass Tires

UNK Unspecified Other, non specified

UNSP Unspecified Unspecified

WAT Hydro (Large and Small) Water (Conventional, Pumped Storage)

WC Coal Waste/Other Coal (Anthracite Culm, Bituminous Gob, Fine Coal, Lignite Waste, Waste Coal)

WDL Biomass Wood Waste Liquids (Red Liquor, Sludge Wood, Spent Sulfite Liquor, and other wood related liquids not

WDS Biomass Wood/Wood Waste Solids (Paper Pellets, Railroad Ties, Utility Poles, Wood Chips, and other wood solids)

WH Waste Heat Waste Heat

WND Wind Wind

WO Oil Oil-Other and Waste Oil (Butane (Liquid), Crude Oil, Liquid Byproducts, Oil Waste, Propane (Liquid), Re-refined The purpose of this feature layer is to:Support the CEC/Energy Assessments Division/Supply Analysis Office in electric generation report;Support the CEC/REAT by providing information on renewable power plant location and capacity;Support the CEC/STEP/Engineering Office/Geo Science in water management report;Support CEC/STEP/Siting Office, Compliance Office, Environmental Office, Engineering Office, and /Strategic Transmission Planning and Corridor Designation Office by providing information on power plant location, capacity, fuel type, operational status, CEC docket id, etc. Support the CEC/STEP/Strategic Transmission Planning and Corridor Designation Office in corridor study and transmission line siting; Support the CEC staff's various analysis by providing general geographic reference information;Enhance communication between government agencies on emergency management, resource management, economic development, and environmental study;Provide illustration of critical infrastructure spatial data to the public or other agencies

This study includes two components of the research infrastructure developed by CCRI for the 1941 Census of Canada: selected digitized published tables of aggregate data and a reconstruction of the census geography.

This study includes two components of the research infrastructure developed by CCRI for the 1921 Census of Canada: selected digitized published tables of aggregate data and a reconstruction of the census geography.

A geographic representation of Public Work’s Operational Zones. Operational Zones are a subdivision of Management Area geography (which are a subdivision of District geography). Zones are designed such that each road is assigned to a nearby facility ('base') for service. Facilities usually central within their zone to maximize efficiency.

The 3 combined land cover databases:

Far North Land Cover v1.4 Southern Ontario Land Resource Information System (SOLRIS) v1.2 the Provincial Land Cover 2000 Edition

While each of these source products has differing pixel resolutions, projections and classifications, the resulting OLCC database is a standardized product with a:

pixel resolution of 15 metres coordinate system of Ontario Lambert Conformal Conic class structure of 29 land cover classes.

The standardized classification has been accomplished at the expense of the more detailed class structures in the source land cover products. Where possible, the original land cover products should be used for analysis.

This is an update to OLCC v1.0. In this version the Far North Land Cover component has been updated and extends further south into the Area of Undertaking (AOU) and into Manitoba than the previous version.Now also available through a web service which exposes the data for visualization and geoprocessing.The service is best accessed through the ArcGIS REST API, either directly or by setting up an ArcGIS server connection using the REST endpoint URL. The service draws using the Web Mercator projection.For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Land Information Ontario (LIO) at lio@ontario.ca.Service Endpointshttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Thematic/Ontario_Land_Cover_Compilation_v2/ImageServerhttps://intra.ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Thematic/Ontario_Land_Cover_Compilation_v2/ImageServer (Government of Ontario Internal Users)

Additional Documentation

Data Specification Document - Ontario Land Cover Compilation Version 2 (PDF)

Data Specification Document - Far North Land Cover Version 1.4 (PDF)

Data Specification Document - SOLRIS Version 1.2 (PDF)

Data Specification Document - Provincial Land Cover (2000 Edition) (PDF)

Status

Completed: Production of the data has been completed

Maintenance and Update Frequency

As needed: Data is updated as deemed necessary

Contact

Joel Mostoway, Forest Resources Inventory Program, Science and Research Branch, joel.mostoway@ontario.ca

Feature layer generated from running the Join Features solution

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California formed from the multiplicative product of the spring, summer, and winter HSI surfaces. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons. Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and season using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. For each season, subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell. The three seasonal HSI rasters were then multiplied to create a composite HSI. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

Practice using GeoSearch to find geographic names and codes for different levels of census geography for a given area on a map, as well as look at how boundaries fit inside one another.

Dwelling condition by Indigenous identity, Registered or Treaty Indian status, residence by Indigenous geography, age and gender for the population in private households.

Number and percent of Indigenous populations attending school (high school, trades/college or university), including data on reserves and Inuit Nunangat.

Canadian Internet use survey, Internet use, by location of access and household type for Canada, urban area or rural area from 2005 to 2009. (Terminated)