The global GIS Mapping Software market size is projected to reach USD 14.7 billion by 2029, exhibiting a CAGR of 12.5% during the forecast period. The rising adoption of GIS software in various industries, including utilities, transportation, retail, and government, is driving market growth. Additionally, the increasing availability of high-resolution satellite imagery and the growing trend of smart cities are contributing to the demand for GIS software. The market is segmented based on type into continuous flow dryer, horizontal band dryer, tower grain dryer, portable dryer, and others. The continuous flow dryer segment holds the largest market share due to its high efficiency and ability to handle large volumes of grain. Based on application, the market is divided into agricultural, industrial, and commercial. The agricultural segment dominates the market owing to the widespread use of GIS software for precision farming and crop monitoring. Geographically, North America held the largest market share in 2021, followed by Europe and Asia-Pacific. The high adoption rate of GIS software in developed countries, such as the United States and Canada, is a major factor driving the market growth in North America.

The Census Tract Boundary Files portray the census tract boundaries for which census data are disseminated. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

As of March 2018, these files have been converted to GeoJSON and Esri shapefiles format using GDAL OGR2OGR from Mapinfo format files. The original .e00 files still remain, so files are available in multiple formats suitable for a variety of systems.

The Designated Place Boundary Files portray the designated place boundaries for which census data are disseminated. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

The boundary files portray the geographic limits used for census dissemination. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

The boundary files portray the geographic limits used for census dissemination. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

The 2009 Census Subdivision Boundary File portrays the boundaries of all census subdivisions which combined cover all of Canada. The file depicts the full extent of census subdivisions, including the coastal water area. It provides a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

The boundary files portray the geographic limits used for census dissemination. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. Each file contains the NPRI map layers in a KMZ format that you can use with virtual globe software such as Google Earth™. Data are available for the last two reporting years. You can filter the data by province or industry type. Select a facility to view a report that summarizes its pollutant releases, disposals and transfers. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html

The Urban Area Boundary Files portray the urban area boundaries for which census data are disseminated. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

The Annual Minimum Snow and Ice (MSI) Extent of the Atlas of Canada National Scale Data, are data sets compiled containing annual data from 2000 to present. The data sets were derived from research published by the Canada Centre for Remote Sensing which classified satellite imagery over Canada and neighbouring regions for the continued presence or absence of snow and ice from April 1 to September 20 each year. The Atlas of Canada MSI products consist of a vector dataset and a raster time-series animation application.

VECTOR DATASET

The vector dataset has been generalized to display at the scale of 1:1,000,000.

TIME-SERIES ANIMATION APPLICATION

The time-series animation application has not been generalized from its original scale (250 m pixels).

The application is disseminated through the Data Cube Platform, implemented by the Canada Centre for Mapping and Earth Observation, Natural Resources Canada using geospatial big data management techniques. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The time-series is also available as a Web Map Service (WMS) and Web Coverage Service (WCS).

CREDIT

Source data provided by Alexander P. Trishchenko, Canada Centre for Remote Sensing, Natural Resources Canada

Metadata record: https://open.canada.ca/data/en/dataset/808b84a1-6356-4103-a8e9-db46d5c20fcf

These Dissemination Area (DA) are provided by the City of Ottawa, obtained from Statistics Canada. These areas make up Ottawa and surrounding area and have been extracedt from the 2016 Statistics Canada Census Boundary product: https://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-2016-eng.cfm Please reference Statistics Canada for more information about the data and use guidelines. DA geographies are typically joined to Census and other attribute data, allowing one to create informative maps.From Statistics Canada:The Dissemination Area Boundary Files portray the dissemination area boundaries for which Census data are disseminated. A dissemination area is a small area composed of one or more neighbouring dissemination blocks and is the smallest standard geographic area for which all census data are disseminated. The files contain the boundaries of all dissemination areas which combine to cover all of Canada.There are two types of boundary files: digital and cartographic. Digital files depict the full extent of the geographical areas, including the coastal water area. Cartographic files depict the geographical areas using only the major land mass of Canada and its coastal islands. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems or other mapping software.

Note - This boundary files have been projected into WGS84 for web mapping use. Original source files, served by Statistics Canada are delivered in Lambert Conformal Conic, a projection better for analysis when considering geographic areas.Further information regarding DA from Statistics Canada: https://www150.statcan.gc.ca/n1/en/catalogue/92-169-XAdapted from Statistics Canada, Census Boundary Files, October 2019. This does not constitute an endorsement by Statistics Canada of this product.

The Dissemination Area Boundary Files portray the dissemination area boundaries for which census data are disseminated. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

Bathymetry of Lake Ontario has been compiled as a component of a NOAA project to rescue Great Lakes lake floor geological and geophysical data and make it more accessible to the public. The project is a cooperative effort between investigators at the NOAA National Geophysical Data Center's Marine Geology & Geophysics Division (NGDC/MGG) and the NOAA Great Lakes Environmental Research Laboratory (GLERL). was compiled utilizing the entire historic sounding data base. The entire historic hydrographic sounding data base from the U.S. and Canada, originally collected for nautical charting purposes, was used to create a complete and accurate representation of Lake Ontario bathymetry. The U.S. data primarily came from the NOS Hydrographic Survey Data. This and other bathymetric sounding data collected by the U.S. National Ocean Service's (NOS) Coast Survey and the U. S. Army Corps of Engineers was employed to construct bathymetric contours at 1 meter intervals from 1-10 meters depth and 2 meter intervals at depths greater than 10 meters. Compilation scales ranged from 1:10,000 to 1:50,000. Bathymetric sounding data collected by the Canadian Hydrographic Service (CHS) were employed to construct bathymetric contours at 1 meter intervals and compilation scales ranging from 1:1,000 to 1:30,000. Digitization of the bathymetric contours, merging of the bathymetric contour data sets, poster construction, and preparation of a CD-ROM, were accomplished at the NGDC. Multibeam bathymetric data collected by the University of New Brunswick's Ocean Mapping Group (UNB-OMG), with support of the Geological Survey of Canada (GSC) and the CHS, were kindly made available in gridded form. In the two areas where multibeam bathymetric data were available, no other bathymetric data were used in the compilations. In some areas all available Canadian and U. S. bathymetric sounding data, collected at different times on different survey expeditions, were used to derive the contours. The U.S. coastline used was primarily the GLERL Medium Resolution Vector Shoreline dataset (Lee, 1998). Where needed for more coverage, the NOS Medium Resolution Vector Shoreline for the Conterminous U.S. (1994) dataset was used. Coastlines from the CHS bathymetric sounding data field sheets were used to complete the Canadian coastline. Images were constructed using the publicly-available software Generic Mapping Tools (GMT).

This web mapping application shows the monitoring networks used to track drought conditions across Manitoba. Each tab displays a different source of data, including: streamflow and water level, groundwater, precipitation, reservoir supply status, and Canadian and United States Drought Monitor contours. Each of the data sources are explained in more detail below. Please note the following information when using the web mapping application:

When you click on a data point on the River and Lake, Groundwater or Reservoir maps, a pop-up box will appear. This pop-up box contains information on the streamflow (in cubic feet per second; ft3/s), water level (in feet), groundwater level (in metres), storage volume (acre-feet), or supply status (in per cent of full supply level; %) for that location. Click on the Percentile Plot link at the bottom of the pop-up box to view a three-year time series of observed conditions (available for river and lake and groundwater conditions only). A toolbar is located in the top right corner of the web mapping application. The Query Tool can be used to search for a specific river, lake or reservoir monitoring station by name or aquifer type by location. The Layer List enables the user to toggle between precipitation conditions layers (1-month, 3-month, and 12-month) and increase or decrease the transparency of the layer. Data is current for the date indicated on the pop-up box, percentile plot, or map product. Near-real time data are preliminary and subject to change upon review.

River and lake conditions are monitored to determine the severity of hydrological dryness in a watershed. River and lake measurements are converted to percentiles by comparing daily measurements from a specified day to historical measurements over the monitoring station’s period of record for that particular day. A percentile is a value on a scale of zero to 100 that indicates the percent of a distribution that is equal to or below it. In general:

Streamflow (or lake level) which is greater than the 90th percentile is classified as “much above normal”. Streamflow (or lake level) which is between the 75th and 90th percentile is classified as “above normal”. Streamflow (or lake level) which is between the 25th and 75th percentiles is classified as “normal”. Streamflow (or lake level) which is between the 10th and 25th percentile is classified as “below normal”. Streamflow (or lake level) which is less than the 10th percentile is classified as “much below normal”.

"Median" indicates the midpoint (or 50th percentile) of the distribution, whereby 50 per cent of the data falls below the given point, and 50 per cent falls above. Other flow categories include:

"Lowest" indicates that the estimated streamflow (or lake level) is the lowest value ever measured for the day of the year. "Highest" indicates that the estimated streamflow (or lake level) is the highest value ever measured for the day of the year.

Monitoring stations classified as “No Data” do not have current estimates of streamflow (or lake level) available. Click on the Percentile Plot link at the bottom of the pop-up box to view a graph (in PDF format) displaying a three-year time series of observed conditions relative to the historical percentiles described above. The period of record used to compute the percentiles is stated, alongside the station ID, and if the river or lake is regulated (i.e. controlled) or natural. Hydrometric data are obtained from Water Survey of Canada, Manitoba Infrastructure, and the United States Geological Survey. Near real-time data are preliminary as they can be impacted by ice, wind, or equipment malfunction. Preliminary data are subject to change upon review. Groundwater conditions are monitored to determine the severity of hydrological dryness in an aquifer. Water levels are converted to percentiles by comparing daily measurements from a specified day to historical measurements over the monitoring station’s period of record for that particular day. A percentile is a value on a scale of zero to 100 that indicates the percent of a distribution that is equal to or below it. In general:

A groundwater level which is greater than the 90th percentile is classified as “much above normal”. A groundwater level which is between the 75th and 90th percentile is classified as “above normal”. A groundwater level which is between the 25th and 75th percentiles is classified as “normal”. A groundwater level which is between the 10th and 25th percentile is classified as “below normal”. A groundwater level which is less than the 10th percentile is classified as “much below normal”. Monitoring stations classified as “No Data” do not have current measurements of groundwater level available.

"Median" indicates the midpoint (or 50th percentile) of the distribution, whereby 50 per cent of the data falls below the given point, and 50 per cent falls above. Click on the Percentile Plot link at the bottom of the pop-up box to view a graph (in PDF format) displaying a three-year time series of observed conditions relative to the historical percentiles described above. The period of record used to compute the percentiles is stated, alongside the station ID. Precipitation conditions maps are developed to determine the severity of meteorological dryness and are also an indirect measurement of agricultural dryness. Precipitation indicators are calculated at over 40 locations by comparing total precipitation over the time period to long-term (1971 – 2015) medians. Three different time periods are used to represent: (1) short-term conditions (the past month), (2) medium-term conditions (the past three months), and (3) long-term conditions (the past twelve months). These indicator values are then interpolated across the province to produce the maps provided here. Long-term and medium-term precipitation indicators provide the most appropriate assessment of dryness as the short term indicator is influenced by significant rainfall events and spatial variability in rainfall, particularly during summer storms. Due to large distances between meteorological stations in northern Manitoba, the interpolated contours in this region are based on limited observations and should be interpreted with caution. Precipitation conditions are classified as follows:

Per cent of median greater than 115 per cent is classified as “above normal”. Per cent of median between 85 per cent and 115 per cent is classified as “normal”. Per cent of median between 60 per cent and 85 per cent is classified as “moderately dry”. Per cent of median between 40 per cent and 60 per cent is classified as a “severely dry”. Per cent of median less than 40 per cent is classified as an “extremely dry”.

Precipitation data is obtained from Environment and Climate Change Canada, Manitoba Agriculture, and Manitoba Sustainable Development’s Fire Program. Reservoir conditions are monitored at 15 locations across southern Manitoba to track water availability, including possible water shortages. Conditions are reported both as a water level and as a “supply status”. The supply status is the current amount of water stored in the reservoir compared to the target storage volume of the reservoir (termed “full supply level”). A supply status greater than 100 per cent represents a reservoir that is exceeding full supply level. Canadian and U.S Drought Monitors: Several governments, agencies, and universities monitor the spatial extent and intensity of drought conditions across Canada and the United States, producing maps and data products available through the Canadian Drought Monitor and United States Drought Monitor websites. The Canadian Drought Monitor is managed through Agriculture and Agri-Food Canada, while the United States Drought Monitor is a joint effort between The National Drought Mitigation Centre (at the University of Nebraska-Lincoln), the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. The drought monitor assessments are based on a suite of drought indicators, impacts data and local reports as interpreted by federal, provincial/state and academic scientists. Both the Canadian and United States drought assessments have been amalgamated to form this map, and use the following drought classification system:

D0 (Abnormally Dry) – represents an event that occurs every 3 - 5 years; D1 (Moderate Drought) – 5 to 10 year event; D2 (Severe Drought) – 10 to 20 year event; D3 (Extreme Drought) – 20 to 50 year event; and D4 (Exceptional Drought) – 50+ year event.

Additionally, the map indicates whether drought impacts are: (1) short-term (S); typically less than six months, such as impacts to agriculture and grasslands, (2) long-term (L); typically more than six months, such as impacts to hydrology and ecology, or (3) a combination of both short-term and long-term impacts (SL). The Canadian Drought Monitor publishes its assessments monthly, and United States Drought Monitor maps are released weekly on Thursday mornings. The amalgamated map provided here will be updated on a monthly basis corresponding to the release of the Canadian Drought Monitor map. Care will be taken to ensure both maps highlight drought conditions for the same point in time; however the assessment dates may differ between Canada and the United States due to when the maps are published. Please click on an area of drought on the map to confirm the assessment date. Canadian Drought Monitor data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. United States Drought Monitor data are available on the United States Drought Monitor website: https://droughtmonitor.unl.edu. For more information, please visit the Manitoba Drought Monitor website.

This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. F1000Research 2021, 10:1312, DOI: 10.12688/f1000research.75891.1). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..

We also provide a guide which provides a general description of the contents of the four sections in this archive (Documentation_for_Sections_of_Zenodo_Archive.docx). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.

Data Files:

Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"

This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.

Section 2. "Section_2.Localized_Hotspot_Lists.zip"

All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.

Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"

Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).

Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"

Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan).

Software:

This Zenodo archive also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries.

This distribution of the Geostatistical Epidemiology Toolbox does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from DMTI (https://www.dmtispatial.com/canmap/) through the Scholar's Geoportal at the University of Western Ontario (http://geo2.scholarsportal.info/). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).

Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"

The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes.

The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images.

This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).

Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"

In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:

“Ontario_City_Closest_Postal_Code_Identification.pl”

Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.

The output of this program (for the same municipal region being evaluated) is required for the following two Perl

The Federal Electoral District Boundary Files portray the federal electoral district boundaries for which census data are disseminated. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

The Dissemination Area Boundary Files portray the dissemination area boundaries for which census data are disseminated. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

Annual and five-year (5YA) average wet deposition maps for the non-sea-salt sulfate ion are available. The file formats include geodatabase files (*.gdb) compatible with geospatial software (e.g. ESRI ArcGIS) and KMZ files compatible with virtual globe software (e.g. Google Earth™). Maps can also be viewed online via Open Maps and the ArcGIS online viewer. Annual deposition from each site was screened for completeness using the following criteria: (1) precipitation amounts were recorded for >90% of the year and >60% of each quarter, and (2) sulfate concentrations were reported for >70% of the precipitation measured over the year and for >60% of each quarter. Five-year average wet deposition values are averaged annual deposition values with a completeness criterion >60% for the five-year period. Units for wet deposition fluxes are in kg of xSO4 per hectare per year (kg ha-1 y-1). Sources of measurement data and spatial interpolation method are described here: https://doi.org/10.18164/e8896575-1fb8-4e53-8acd-8579c3c055c2. Recommended citation: Environment and Climate Change Canada, [year published]. xSO4 Wet Deposition Maps. Air Quality Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada. [URL/DOI], accessed [date]. Recommended acknowledgement: The author(s) acknowledge Environment and Climate Change Canada for the provision of Canada-U.S. wet deposition kriging maps accessed from the Government of Canada Open Government Portal at open.canada.ca, and the data providers referenced therein.

The 2013 Census Subdivision Boundary File portrays the boundaries of all census subdivisions which combined cover all of Canada. The file depicts the full extent of census subdivisions, including the coastal water area. It provides a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.