| Data source | ||
|---|---|---|
| Response or edited | Imputed | |
| % | ||
| Sales of goods manufactured | 88.1 | 11.9 |
| Raw materials and components | 78.6 | 21.4 |
| Goods / work in process | 83.2 | 16.8 |
| Finished goods manufactured | 76.5 | 23.5 |
| Unfilled Orders | 89.9 | 10.1 |
| Capacity utilization rates | 76.6 | 23.4 |
December 11, 2020 (Previous notice)
The Variant of NAPCS Canada version 2.0 – Manufacturing and Logging has been updated this December 11 2020, to help the Annual Survey of Manufacturing and Logging Industries (ASML) program with improving the measurement of the use and production of plastic in the manufacturing industries. The updated variant was renamed Variant of NAPCS Canada version 2.0 – Manufacturing and Logging Rev.1 (for Revision 1). There are four (4) variant codes that have been expanded to twelve (12) codes, along with title changes to five (5) other codes, as shown below:
| Old ASML variant Code | Old ASML variant English Title | Updated ASML variant Code | Updated ASML variant English Title | GSIM Type of Change |
|---|---|---|---|---|
| 28111110 | Polyester resins | 28111111 | Polyethylene terephthalate (PET) resins | RC4.1 - Breakdown |
| 28111110 | Polyester resins | 28111112 | Other thermoplastic polyester resins | RC4.1 - Breakdown |
| 28111210 | Polyethylene, low-density | 28111210 | Low-density polyethylene resins | VC2 - Name change |
| 28111220 | Polyethylene, linear low-density | 28111220 | Linear low-density polyethylene resins | VC2 - Name change |
| 28111230 | Polyethylene, high-density | 28111230 | High-density polyethylene resins | VC2 - Name change |
| 28111410 | Acrylonitrile-butadiene-styrene | 28111410 | Acrylonitrile-butadiene-styrene resins | VC2 - Name change |
| 28111420 | Polyvinyl chloride | 28111420 | Polyvinyl chloride resins | VC2 - Name change |
| 28111430 | All other thermoplastic resins | 28111431 | Polypropylene resins | RC4.2 - Split off |
| 28111430 | All other thermoplastic resins | 28111432 | Thermoplastic polyurethane resins | RC4.2 - Split off |
| 28111430 | All other thermoplastic resins | 28111433 | Polyamide (nylon) resins | RC4.2 - Split off |
| 28111430 | All other thermoplastic resins | 28111434 | All other thermoplastic resins, n.e.c. | RC4.2 - Split off |
| 28111510 | Phenol-formaldehyde resins | 28111511 | Phenolic resins | RC4.1 - Breakdown |
| 28111510 | Phenol-formaldehyde resins | 28111512 | Urea formaldehyde resins | RC4.1 - Breakdown |
| 28111510 | Phenol-formaldehyde resins | 28111513 | All other formaldehyde based resins | RC4.1 - Breakdown |
| 28111610 | Other thermosetting resins | 28111611 | Unsaturated polyester (thermosetting) resins | RC4.2 - Split off |
| 28111610 | Other thermosetting resins | 28111612 | Thermosetting polyurethane resins | RC4.2 - Split off |
| 28111610 | Other thermosetting resins | 28111613 | Other thermosetting resins, n.e.c. | RC4.2 - Split off |
The social and economic impacts of COVID-19 have fuelled an extraordinary demand for timely, high-quality data on the health of Canada's people, society and economy. In response to this demand, Statistics Canada has enhanced many of its programs, including its Labour Force Survey (LFS), with the creation of the Labour Force Survey (LFS) Expert Panel.
Comprised of national and international experts from government, academia and non-governmental agencies, the LFS Expert Panel will provide independent advice and guidance on one of Statistics Canada's most important statistical programs.
To ensure that these enhancements result in a deeper and broader understanding of evolving market conditions, the Panel will:
Listed below, Panel members were selected to reflect a wide-range of expertise and experience, including in the use of LFS data and international experience in the management of similar large-scale statistical programs. With the help of these experts, the LFS will provide even better data and insights to Canadians on evolving labour market conditions in our country.
Chair: Lynn Barr-Telford, Assistant Chief Statistician, Social, Health and Labour Statistics
Secretary: Josée Bégin, Director General, Labour Market, Education and Socioeconomic Well-being Statistics Branch
Agency subject matter: Centre for Labour Market Information, Modern Statistical Methods and Data Science Branch, and Collection and Regional Services Branch.
Assistant Director, Research and Methodology, United States Census Bureau
John Eltinge is the U.S. Census Bureau Liaison to Statistics Canada's LFS Expert Panel. Mr. Eltinge is the Assistant Director for Research and Methodology at the United States Census Bureau. Before 2016, he served as the Associate Commissioner for Survey Methods Research at the Bureau of Labor Statistics (BLS). Prior to that, he served as a senior mathematical statistician at BLS, and an associate professor with tenure in the Department of Statistics at Texas A&M University. He gave the Roger Herriot Memorial Lecture on Innovation in the Federal Statistical System; and was previously the President of the Washington Statistical Society, the overall chair of the 2003 Joint Statistical Meetings, an associate editor for The American Statistician, and an associate editor for the Applications and Case Studies Section of Journal of the American Statistical Association. In addition, at the 2018 Joint Statistical Meetings, he presented the annual plenary Deming Memorial Lecture, "Improving the Quality and Value of Statistical Information: 14 Questions on Management". A webcast of this lecture is available through: Plenary Session Webcasts
His research interests include the following: data quality; design optimization; integration of multiple data sources; imputation; time series; and small domain estimation.
Mr. Eltinge holds a Ph.D. from the Department of Statistics at Iowa State University; is a fellow of the American Statistical Association; an editor of the Harvard Data Science Review; an associate editor for Journal of Official Statistics and for Survey Methodology Journal; and a member of the Federal Committee on Statistical Methodology.
Professor, Sociology Department, Western University
Howard Ramos is a professor at Western University as well as the Chair of the Department of Sociology. He investigates issues of social justice and social change and has published four books and over 50 articles and chapters on social movements, human rights, Indigenous issues, environmental advocacy, urban change, economic and tourism development, technology, ethnicity, race, immigration, and equity, diversity and inclusion in higher education. Dr. Ramos has worked with a wide range of advocacy and community organizations and is committed to knowledge translation and evidence-based policy.
Director, Prices, Labour and Housing Division, Canadian Economic Analysis Department, Bank of Canada
Karyne B. Charbonneau is the Director of the Canadian Economic Analysis (CEA) Department's Prices, Labour and Housing division. She is primarily responsible for the evolution of the labour market and inflation in the near term.
Ms. Charbonneau joined the Bank of Canada in 2013 as a Senior Economist in the International Economic Analysis Department. Prior to occupying her current role, she was a Policy Advisor in CEA and provided guidance on the impact of trade policy changes on the Canadian economy.
Her research focuses on applied econometrics, international trade and labour economics. She received her PhD in economics from Princeton University.
Director of Economics and Statistics, Nova Scotia Department of Finance and Treasury Board
Thomas Storring is the Director of Economics and Statistics for the Nova Scotia Department of Finance and Treasury Board. His work focuses on macroeconomic conditions in the Province: how macroeconomics affects the government's fiscal choices and how government decisions affect the economy. He is the focal point for Statistics Canada within the Province of Nova Scotia, and advises Statistics Canada on the Province's needs and priorities for the national statistical system.
Mr. Storring has worked as an economist for over 20 years in provincial finance departments in Ontario and Nova Scotia, as well as for J. D. Irving, Limited. Over the last 10 years, Thomas has taught at both Saint Mary's University and Dalhousie University, lecturing on money and banking, public finance, statistics, principles of economics, as well as global economics for Saint Mary's MBA program. He completed his undergraduate degree in economics at Acadia University and received his Master's in economics at the University of Oxford.
Professor, Economics Department, University of Waterloo
Mikal Skuterud is a full-time Professor in the Department of Economics at the University of Waterloo and is affiliated with the Canadian Labour Economics Forum (CLEF) and the Institute of Labor Economics (IZA). He received his Master's degree in Economics from the University of British Columbia and his PhD in Economics from McMaster University.
His research interests include: the labour market integration of immigrants, labour market policies that influence hours of work, and the economics of trade unions. His work has appeared in the American Economic Review, the Journal of Labor Economics, and the Canadian Journal of Economics and has received national media coverage in the New York Times and the Globe and Mail.
Program Manager, Labour Surveys Branch, Australian Bureau of Statistics
Bjorn Jarvis is the head of the Labour Surveys Branch at the Australian Bureau of Statistics, which comprises the Labour Force Survey and related household surveys, and employer (establishment/business) surveys. In this role, he has overseen innovative transformation of Labour Force Survey methods, analysis and communication. This role included managing the impacts of COVID-19 on Labour Force statistics. Over his 16 years in official statistics, Bjorn has held a broad range of survey and administrative statistics roles in the labour and population statistics programs. He is a highly regarded survey statistician and communicator, with deep connections to the labour statistics user community in Australia.
Senior Economist, Canadian Union of Public Employees, Broadbent Institute
Angella MacEwen is a Senior Economist at the Canadian Union of Public Employees, a policy fellow with the Broadbent Institute and a member of the National Stakeholder Advisory Panel (NSAP) at the Labour Market Information Council (LMIC). Her primary focus is understanding precarity and inequality in the Canadian labour market and evaluating policy solutions proposed for these issues. She also studies the impacts of Canadian economic and social policy on workers, especially climate policy and international trade and investment treaties. Ms. MacEwen writes a quarterly publication, Economy at Work, which aims to communicate current economic issues to a broad audience. She holds an MA in Economics from Dalhousie University.
This variant of the North American Product Classification System (NAPCS) Canada 2017 V2.0 was approved as a departmental standard on October 16, 2017. It replaces the NAPCS 2017 Version 1.0 Manufacturing and Logging variant.
The Annual Survey of Manufacturing and Logging Industries (ASML) is a survey of the manufacturing and logging industries in Canada. It is intended to cover all establishments primarily engaged in manufacturing and logging activities as well as some sales offices and warehouses which support these establishments.
The details collected include principal industrial statistics (such as revenue, salaries and wages, cost of materials and supplies used, cost of energy and water utility, inventories, etc.), as well as information about the commodities produced and consumed. Data collected by the ASML industries help measure the production of Canada's industrial and primary resource sectors, as well as provide an indication of the well-being of each industry covered by the survey and its contribution to the Canadian and Provincial economy.
Within Statistics Canada, the data are used by the Canadian System of National Accounts, the Monthly Survey of Manufacturing and Prices programs. The data are also used by the business community, trade associations, federal and provincial departments, as well as international organizations and associations to profile the manufacturing and logging industries, undertake market studies, forecast demand and develop trade and tariff policies. The manufacturing variant was created to capture additional details on products that NAPCS Canada 2017 Version 1.0 would otherwise not have collected. By adding an extra (eighth) digit to the classification, additional detail can be collected. In NAPCS Canada 2017 Version 2.0, some of those eight digit variant codes were brought up to the standard seven digit level. Those products include tobacco (see NAPCS Canada code 212112 - Cigars, chewing and smoking tobacco), chemicals (see codes 26321 - Petrochemicals, 27113 - Basic organic chemicals, n.e.c., 27211 - Ammonia and chemical fertilizers and 28111 - Plastic resins), cement (see code 46511 - Cement) and asphalt (see code 26211 - Asphalt (except natural) and asphalt products). The variant 8 digits codes left in Version 2.0 are for wood products, such as codes under NAPCS Canada 1451221 - Fuel products of waste wood, 157112 - Waste and scrap of wood, 24122 - Reconstituted wood products, 24124 - Other sawmill products, and treated wood products, and 462134 - Other wood millwork products.
The Variant of NAPCS Canada version 2.0 – Manufacturing and Logging has been updated this December 11 2020, to help the Annual Survey of Manufacturing and Logging Industries (ASML) program with improving the measurement of the use and production of plastic in the manufacturing industries. The updated variant was renamed Variant of NAPCS Canada version 2.0 – Manufacturing and Logging Rev.1 (for Revision 1). There are four (4) variant codes that have been expanded to twelve (12) codes, along with title changes to five (5) other codes, as shown below:
| Old ASML variant Code | Old ASML variant English Title | Updated ASML variant Code | Updated ASML variant English Title | GSIM Type of Change |
|---|---|---|---|---|
| 28111110 | Polyester resins | 28111111 | Polyethylene terephthalate (PET) resins | RC4.1 - Breakdown |
| 28111110 | Polyester resins | 28111112 | Other thermoplastic polyester resins | RC4.1 - Breakdown |
| 28111210 | Polyethylene, low-density | 28111210 | Low-density polyethylene resins | VC2 - Name change |
| 28111220 | Polyethylene, linear low-density | 28111220 | Linear low-density polyethylene resins | VC2 - Name change |
| 28111230 | Polyethylene, high-density | 28111230 | High-density polyethylene resins | VC2 - Name change |
| 28111410 | Acrylonitrile-butadiene-styrene | 28111410 | Acrylonitrile-butadiene-styrene resins | VC2 - Name change |
| 28111420 | Polyvinyl chloride | 28111420 | Polyvinyl chloride resins | VC2 - Name change |
| 28111430 | All other thermoplastic resins | 28111431 | Polypropylene resins | RC4.2 - Split off |
| 28111430 | All other thermoplastic resins | 28111432 | Thermoplastic polyurethane resins | RC4.2 - Split off |
| 28111430 | All other thermoplastic resins | 28111433 | Polyamide (nylon) resins | RC4.2 - Split off |
| 28111430 | All other thermoplastic resins | 28111434 | All other thermoplastic resins, n.e.c. | RC4.2 - Split off |
| 28111510 | Phenol-formaldehyde resins | 28111511 | Phenolic resins | RC4.1 - Breakdown |
| 28111510 | Phenol-formaldehyde resins | 28111512 | Urea formaldehyde resins | RC4.1 - Breakdown |
| 28111510 | Phenol-formaldehyde resins | 28111513 | All other formaldehyde based resins | RC4.1 - Breakdown |
| 28111610 | Other thermosetting resins | 28111611 | Unsaturated polyester (thermosetting) resins | RC4.2 - Split off |
| 28111610 | Other thermosetting resins | 28111612 | Thermosetting polyurethane resins | RC4.2 - Split off |
| 28111610 | Other thermosetting resins | 28111613 | Other thermosetting resins, n.e.c. | RC4.2 - Split off |
The structure of the NAPCS Canada 2017 variant for Manufacturing and Logging is hierarchical. It is composed of five levels.
Catalogue number: Catalogue number: 89200005
Issue number: 2020019
Release date: December 1, 2020
QGIS Demo 19
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Introduction to Raster Data (Part 1): Processing and Visualizing Single-Band Rasters")
So in today's tutorial we'll introduce using raster data in QGIS - specifically focussing on single-band rasters. These rasters depict changes in a single continuous variable, such as precipitation, slope or elevation. One of the most common single-band rasters are Digital Elevation Models or DEMs for short, which show changes in height above sea level. We'll cover some generic raster functions, such as Merge and Reproject; discuss the parameters for their visualization, and then in a follow up demo cover some DEM specific tools and the Raster Calculator. This will provide you with foundational skills for processing, combining and visualizing single-band rasters. Raster datasets epitomize the finer resolution and powerful analyses that are possible with publically available datasets, with resolutions of 15 to 30 metres being common.
As established, for a selection of files in the Browser Panel we can right-click and Add Selected Layers to load them simultaneously in to the Layers Panel. The boundaries between the DEMs are pronounced, resulting in their splotchy appearance. And this is because visualization is tailored to their specific value ranges, which vary significantly due to the mountainous terrain.
To address this we can merge the DEMs into one file, which will create a uniform value range for visualization. In the Processing Toolbox search and open the Merge tool under GDAL - Raster miscellaneous. Click Select All in the Multiple Selection box for the Input Layers. Since we want the full value range to be used in visualization leave the Grab pseudocolour from first layer parameter unchecked. The separate band parameter applies to composite rasters, such as satellite imagery. Since we are using single-band rasters we'll leave it unchecked. NoData values often relate to the cells at the edge of rasters, and may appear as a black perimeter. Here we'll leave the NoData values as default, as well as the compression parameters. So run with a temporary output file, since the merged file is around 1 gigabyte. And the process takes around 6 minutes to complete. If one tool fails there are often alternatives. For example, here we could use r.patch, a GRASS tool, to merge the rasters.
Once complete, the merged file appears like this. The visualization is a marked improvement, with the full value range being used in its rendering.
Now we'll use the Warp (reproject) tool to transform the projection and coordinate reference system. In general, it is best practice to avoid projecting rasters due to potential adverse effects on cell alignments or values but in this case we want to use a projected system for spatial analysis. There are a few additional parameters to specify within the tool. This includes the Source CRS, selecting NAD 83 (CSRS), a geographic coordinate system, from the drop-down. Then we can specify the Target coordinate reference system to transform to, opening the system selector and entering 26911 for NAD83 UTM Zone 11 N, which corresponds with the current location. Change the Resampling method to Bilinear, as Nearest Neighbour is better suited to thematic rasters. Once again we'll leave the NoData values unset, as we'll define them in our output raster. Leave the georeferenced units as-is to use the source layer's resolution. And we'll leave all other parameters with defaults and once again save to a temporary output file. And the tool takes roughly 12 minutes to complete.
As we can see, there were some effects on cell alignment and values, with the warped DEM containing slightly different value ranges. To address this open the Layer Properties box and within the Histogram tab, click Compute Values to assess the distribution of raster data values. Zooming in, based on the distribution, our minimum value is similar to the merged DEM, with 0 being a NoData Value. Clicking on the Hand icon we can interactively select the minimum value in the Histogram, or enter it manually – matching it to the minimum value in the merged DEM: 552. In the Transparency tab, we'll enter 0 as the NoData value to remove the black perimeter around the warped DEM. The min and max values could also be defined in the Symbology tab. So as we can see, the available Layer Properties Box tabs for rasters partly overlap with those of vector datasets. Clicking OK, removing the NoData value and adjusting the minimum has improved the visualization of the warped DEM.
To export a raster to a new dataset we can apply the same procedures applied to vectors. Right-click the raster, Export and click Save as to open the Save Raster Layer As box. We can select the file Format from the drop-down – with GeoTIFF being the most common, and we can optionally create a virtual raster or VRT file. This links the source datasets and applied processes, reducing processing times, file sizes as well as providing other processing advantages. For this format, a subfolder needs to be specified, which will also be the filename. Otherwise, provide an output filename and directory here entering PmBCDEM for projected merged British Columbia digital elevation model.
We can also specify the output cell-size, the source resolution being 15 m by 15 m or 225 square meters per pixel. Finer resolutions result in larger file sizes. So to reduce the total file-size, there is a trade-off, requiring a coarser resolution. Alternatively we can specify the number of rows and columns for the output raster, which will adjust the cell-size accordingly. Here we'll use the source resolution. There are also compression options and a parameter to build pyramids, which we'll cover momentarily with a separate tool. To remove any known NoData values or unrealistic values in the exported raster we could expand and check the NoData values box, click the Plus icon and enter the value range such as -9999 to -1. After clicking OK, the permanent file will be created and added to the Layers Panel.
You may have noticed the longer rendering times for the rasters. This is because the source resolution is being used regardless of the canvas scale. To improve it we can run the output through the Build Pyramids tool, which will create multiple coarser resolution versions of the input, which are then applied for rendering based on the canvas scale. We can then specify the Resampling method and whether the pyramids should be created internally within the DEM file or as an external .ovr file for GeoTiffs. As you can see, clicking Run, this significantly improves rendering times within the Canvas, as we zoom in and out and change the canvas location.
Now let's discuss raster visualization. Once again we'll repeat defining the NoData Value within our permanent raster – entering 0. The data distribution in the Histogram tab is the same as our temporary reprojected file. And finally in the Symbology tab we'll match the minimum value to the merged raster. Clicking apply, the visualization of the DEM now matches that of the temporary merged and reprojected DEMs.
The Symbology tab, as with vector datasets, is used for visualization. The Render type drop-down is equivalent to the Style drop-down, where we can apply different visualization schemes. For the single band rasters - Singleband gray - is the default, but we can also apply Singleband pseudocolour and, specifically for the DEMs, Hillshade. Paletted / Unique is used for thematic rasters and multi-band is used for composite imagery to assign bands to the visible spectrum for analysis and visualization.
There are various Contrast enhancement options in the drop-down. We'll leave it with the default - Stretch to Min/Max. Expanding the Min/Max Value Settings we can specify how the value ranges are applied in rendering. So switch to cumulative count cut. This enhanced the contrast and brightness between cells, using values between the 2nd and 98th percentile. So change the values to 0.5% and 99.5%. And this reduces the contrast, since we are using a larger range of the data – resulting in less values falling outside of the minimum and maximum values. Conversely using a smaller distribution of the total values, entering 5.0% and 95.0%, once again intensifies the contrast and brightness. We could also define the range in standard deviations. So changing it to 5 standard deviations, the contrast and brightness is noticeably reduced. Conversely, using 0.5 has the opposite effect, with more of the DEM values occurring outside of that range. Switch back to Cumulative Count Cut with default values and click Apply.
The Statistics Extent determines the raster values used based on the canvas. By default they apply to the Whole Raster, meaning zooming in or out produces no change in rendering. Alternatively we could switch to the Current extent to optimize visualization for a specific location and scale, or select Updated extent for a dynamic visualization. Now as we change the scale and location of the canvas, the values and visualization of the DEM is adjusted accordingly.
Finally, the Color Rendering drop-down of the symbology tab can be used to fine-tune the visualization. Now let's switch to a Pseudocolour style in the Render type drop-down. Apply the Red-Yellow-Green ramp from the expanded All Ramps side-bar. Reopen the colour ramp, and click Invert Colour Ramp. The particular Interpolation method can be specified as linear, discrete or exact, which varies according to the intended use. We'll leave the other settings as default, clicking Apply and OK. So this is another common visualization for DEMs, with red showing mountain peaks and greens visualizing valley bottoms. Ensure you have a permanent file of the DEM saved which we'll use in the Part II of this demo and then save the project file with a distinctive name.
(The words: "For comments or questions about this video, GIS tools or other Statistics Canada products or services, please contact us: statcan.sisagrequestssrsrequetesag.statcan@canada.ca" appear on screen.)
(Canada wordmark appears.)
Under the authority of the Statistics Act, Statistics Canada is hereby requesting the following information, which will be used solely for statistical and research purposes and will be protected in accordance with the provisions of the Statistics Act and any other applicable law. This is a mandatory request for data.
Statistics Canada is requesting information about tickets sold by VIA Rail including date of travel, train number, origin and destination, fare class, type of ticket, total cost, and an indicator of whether the ticket was refunded.
This request does not contain any personal information.
Monthly data from January 2018 onwards.
This information is being requested from Transport Canada.
Statistics Canada requires this information to benchmark the number of train trips and spending on train travel within Canada. This will improve the estimates of the National Travel Survey (NTS) and the Visitor Travel Survey (VTS).
The information may also be used to replace questions on train fare spending, thereby reducing the burden on survey respondents.
The estimates from the NTS and the VTS are essential for measuring tourism’s impact and are critical for policy makers, researchers, and industry stakeholders to help support Canada's tourism sector and to better serve visitors during their stay in Canada.
Statistics Canada may also use the information for other statistical and research purposes.
Transport Canada is the department who receives and possesses this data under federal regulations and are responsible for the development and maintenance of the VIA Rail Ticketing Database.
September 2024
August 8, 2024
Statistics Canada is requesting contact information (email addresses) from travellers using Canada Border Services Agency's electronic customs declaration mobile application, as well as the following information from their declarations made in the application: first name(s), surname(s) and initial(s) of traveller(s), language(s) spoken by traveller(s), mode of transportation, port of entry, flight number, date of arrival, purpose of trip, date(s) of birth, country(ies) of issue for documents, document type(s) and document number(s).
Only data for non-resident visitors to Canada will be retained.
This request contains personal information such as travellers' names, dates of birth, email addresses and travel document information for visitors to Canada. Personal identifiers are required to reliably identify and contact potential survey respondents for the Visitor Travel Survey. The personal identifiers are removed once survey responses are received and all published data files or tables are either anonymized or aggregated.
The information is being requested from Canada Border Services Agency (CBSA).
This information is required to obtain travellers' contact and customs declaration information so that the redesigned Visitor Travel Survey can build a reliable list of international visitors to Canada from which to select a sample. Without this information, it is virtually impossible to reliably contact international visitors to complete the survey, except those travelling by commercial air.
The estimates from the Visitor Travel Survey are essential for measuring tourism's impact and are critical for policy makers, researchers, and industry stakeholders to help support Canada's tourism sector and to better serve visitors during their stay in Canada. Individual Canadians benefit from the survey because a thriving tourism industry, driven by smart decisions from reliable data, leads to jobs and investment in local communities and infrastructure.
Statistics Canada may also use the information for other statistical and research purposes.
Canada Border Services Agency is responsible for the development and maintenance of the electronic customs declaration mobile applications, so they are the owners of this data once collected from travellers.
January 2024 and onward (daily).
October 3, 2023
Anonymized and aggregate information on Canadians travelling in Canada and Americans travelling to Canada is being requested. Information will include number of visitors, number of trips, number of nights, nights per visitor, and nights per trip at the geography level of FSA (forward sortation area: the three first characters of the postal code).
This request does not include personal information.
Monthly data beginning with January 2018 (ongoing).
This information is being requested from Environics Analytics Group
Statistics Canada requires this information to produce and publish statistics on domestic travel within Canada by both Canadians and Americans. This will help fill important data gaps and provide travel information that would normally be collected from travel surveys, ultimately eliminating response burden. Data can be used by policy makers, researchers, and industry stakeholders to help support Canada's tourism sector which employs Canadians directly and indirectly in the transportation, restaurant and food services, accommodation and cultural as well as recreational industries. High quality detailed tourism data benefits the Canadian economy and, in turn, all Canadians.
Statistics Canada may also use the information for other statistical and research purposes.
Environics Analytics Group (EAG) provides a range of large-sample-size surveys, administrative data, mobile movement data and detailed current estimates of the population. The data span both Canada and the US and are maintained and updated regularly. EAG takes research data, and administrative data and uses statistical and geographic techniques to express the data for small geographies for all of Canada providing datasets that are needed to produce timely and quality detailed tourism data.
December 2020 and onward (monthly).
December 8, 2020
Catalogue number: Catalogue number: 89200005
Issue number: 2020020
Release date: December 1, 2020
QGIS Demo 20
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Introduction to Raster Data (Part 2): Digital Elevation Models (DEM) Tools and the Raster Calculator")
So welcome back everyone. For Part 2 of Intro to Raster Data we'll explore some DEM specific tools, particularly the Slope, Hillshade and Aspect tools. For each of these, there are multiple alternatives available in the Processing Toolbox, each with slightly different parameters and applications. Then we'll move on to the raster calculator, first using it to select cells by criteria of interest, and then to combine raster variables for further analysis. Broadly, DEMs and related datasets can be used to extract 3D information and conduct a wide-variety of analyses and visualizations. This is because elevation and its related characteristics significantly influence both biophysical and socio-economic processes. Just a few examples include daylight hours, growing season lengths, soil erosion potential, visibility and optimal routes for transportation or movement across a landscape.
So with the pseudocolour DEM loaded and duplicated in the Layers Panel, the first tool we'll explore is the Slope tool. As alluded to, there are many options. We'll use the GDAL Slope tool. The ratio of vertical to horizontal units is 1 to 1, since we transformed the merged DEM to a projected coordinate system, with units in metres in the previous demo. Otherwise we'd have to calculate and enter the appropriate ratio of 1 metre to 1 degree. Optionally, we can calculate slope as a percentage instead of degrees, which would be suitable for flatter locations. However, with rise over run slope at 45° is 100%, and as it approaches 90 degrees approaches infinity. And therefore we'll leave it unchecked to avoid astronomically large and unintuitive values in the output, given the mountainous terrain. We will check Compute Edges to avoid potential adverse edge effects and then run with a temporary file. So I've used the Build Pyramids tool on most rasters in this demo to improve rendering times, which you can do as well.
So as we can see Slope varied between 0 to 80 degrees. And unsurprisingly there are many more changes in slope in the mountainous southwest area of the DEM compared to the flatter region in the northeast. Slope is a key parameter in many analyses, such as determining the optimal route across a landscape or examining risk potential for mass movements like land-slides or avalanches.
The next tool that we'll examine is the Hillshade tool, once again using the GDAL version. If we were in a flatter location, such as the Prairies, we could apply a vertical exaggeration factor such as 5 or 10. And this effectively acts as a multiplier to enhance the visualization of elevation changes. For our area the default of 1, meaning no exaggeration, is appropriate. Leaving the next three parameters as default, we can check Compute Edges and run the tool once with Combined Shading and then again with Multidirectional Shading checked.
But as noted in the previous demo, we can also apply Hillshade as a visualization style to our DEM in the Rendering drop-down of the Symbology tab. And this is the traditional single-light source hillshade. So let's click Apply – and then I'll adjust the canvas scale to better see the visualization.
So the azimuth defines the horizontal direction of sunlight, from north, south, east or west, in degrees. So the default of 315, means that lighting is coming from the northwest, with shading occurring on the southeast facing slopes. We can alter the values in the box and click Apply. So lighting from the East adjusts shaded areas to the western facing slopes. Alternatively, we can also spin the Azimuth wheel. With lighting from the south, logically, the shade occurs on the north facing slopes. I've included links to the guides for determining realistic azimuth and angle values for a given location and at a time of interest in the video description. So the Altitude parameter adjusts the vertical angle of the sun in the sky. Altering the value to 25, shadow lengths shrink, because as it approaches 0° lighting is occurring from directly overhead. Then changing to a value of 75, the length of shadows increases substantially. As the angle approaches 90° lighting is occurring from the horizon, like with sunrise or sunsets. Now we'll switch back to the default settings by clicking on the tabs, and click OK.
Now let's take a look at the multi-directional and combined hillshades. So as we can see the multidirectional hillshade is relatively similar to the traditional hillshade, with just a few additional lighting sources incorporated in the output. Conversely, the combined hillshade is notably different from the others, since lighting is occurring from all directions. In the Transparency tab set the global opacity to 50%. Now let's toggle on the duplicated DEM and drape the hillshade over it. This is another standard visualization of DEMs. It provides a pseudo-3D visualization, with the texture of the mountainous terrain draped over the pseudocolour visualization of elevational changes. And we could use any of the output hillshades for this purpose. To create an actual 3D visualization of the overlaid rasters, expand the View drop-down and select New 3D Map View. Click the Configure icon and specify the DEM raster in the drop-down. Clicking OK, back in the 3D Map View window we can hold the Ctrl key and left-click to change the tilt of the 3D view. Then we can use the scroll-bar on the mouse to zoom in or out.
Hillshades also have multiple analytical applications, since they are another key factor influencing properties such as melt periods, growing conditions and vegetation distribution. And this influence also extends to our next tool, the Aspect tool.
The Aspect tool creates a raster showing the direction of hillslopes expressed in degrees. Once again we'll use the GDAL tool. Check return 0 instead of -9999 for flat areas and Compute Edges, selecting the DEM as the input, and then run with a temporary output file. The output then appears as such. For a more intuitive visualization switch the Render type to Singleband Pseudocolour with a Spectral colour ramp and click Apply. This should distinctively colour the four cardinal directions, and we can substitute values in the Label column for their text equivalents: entering E for east for 90 degrees, S for 180 degrees, W for 270 and N for 360, as well as flat areas for 0. If we change the mode to Equal Interval and then the classes to 9 we can also add inter-cardinal directions and relabel as needed. So 45 would be northeast, 135 would be southeast, and so on.
So now we'll move on to the Raster Calculator. It is akin to the Select by Expression, Field Calculator and Intersection tools for vector data combined. It is a versatile tool that can be used to query, reclassify and combine raster files. We'll explore some of these applications with a simple scenario. So say we want to isolate the locations for a new farm with some known environmental constraints corresponding to our existing rasters. We can use the Raster Calculator to isolate those areas. So let's begin by selecting cells by criteria of interest. We'll use the Slope raster, specifying cells with a slope less than or equal to 10 degrees.
"Slope@1" <= 10
And here, the @1 symbol refers to the band number, relevant for composite rasters. So for single-band and thematic rasters there is only one band, meaning all of them will be followed by @1. The only other required parameter is a reference layer, here using the Slope raster. Then we can run the tool with a temporary output.
When it completes, the output appears like this. So open the Layer Properties box and change the render type to Paletted - clicking Classify. As we can see– the values are 0 or 1, indicating False and True for the specified criteria. So re-enabling the duplicate DEM, unsurprisingly the zero values mostly occur in the mountainous area, while ones correspond to select valley bottoms and flatter areas in the northeast.
So reopen the calculator. To retain the input values that match our criteria of interest we must multiply our original query by the input raster. This is because cells that match the criteria will be multiplied 1 by the original value, which equals the original value. Conversely those that do not meet the criteria will be 0 multiplied by the original values, which will always be 0 – retaining only the cells that match that particular criteria. Once again provide the reference layer and then run.
("Slope@1" <= 10)* "Slope@1"
Now we could repeat with a slightly more complex example, using the Aspect raster to isolate east, south and west facing slopes. Start with a double-open bracket, then enter aspect greater than or equal to 90 AND less than or equal to 270 degrees – which will exclude our north facing slopes. However, if we also wanted to include flat areas we could add OR equal to 0 as a separate component using our Expression syntax. Then close the brackets and multiply by the Aspect raster to retain their specific values. Copy this expression for reuse in the next example. If any issues were encountered using the Raster Calculator, the r.reclass tool could be used to retain and reformat values of interest within a raster – and we'll cover this tool in an upcoming demo.
(The words: "(("Aspect@1" >= 90 AND "Aspect@1" <= 270) OR ("Aspect@1" = 0))* "Aspect@1"" appear on screen.)
Now let's use the Raster Calculator to combine variables of interest. Here we'll combine the previous queries to create a weighted raster in assessing the suitability of locations for a new farm according to the specific conditions of interest. So start with adding open brackets and slope less than or equal to 10 degrees.
Close the bracket and then multiply the expression by 0.75 to assign the weighting value and close the bracket.
(The words: "(("Slope@1" <= 10 )*0.75)" appear on screen.)
Provided the weighting values add up to 1, we can combine as many raster layers as are relevant to the analysis. Here we're assigning a greater weight to slope conditions. Now add plus, open bracket and paste in the Aspect query from the previous example. Multiply the query by 0.25 and close the bracket.
(The words: " + ((("Aspect@1" >= 90 AND "Aspect@1" <= 270) OR ("Aspect@1" = 0))*0.25)" appear on screen.)
Now we can run the tool and create our weighted raster of the slope and aspect variables.
The values of the output are between 0 and 1, but unlike the binary true-false raster we initially created, this raster contains a gradient or range of values. Switch the Render type to pseudocolour and apply a Greens colour ramp. So values closer to 0 are less suitable according to the specific conditions, while areas closer to 1 are more suitable. As we established, the most suitable locations correspond with the flatter northeast region, while the mountainous areas are largely unsuitable. We could incorporate other rasters for a more realistic assessment, such as masking out any lakes, rivers or existing for farmland, or incorporating soils layers, in isolating suitable sites for a new farm.
We can then also use the Contour Tool (GDAL) in the Processing Toolbox to create isolines from the raster datasets. Isolines delineate areas of equal value, with contours specifically referring to elevation. Using the DEM as an input, we can switch the Attribute Name to Z - a common abbreviation. And change the Interval to 250 or 500 to reduce processing time and the file size. We could Check 3D vector to enable 3D visualizations of our output. Finally we'll enter a NoData value of -9999 – and run the tool. Although applied to the DEM here, it could be applied to any single-band raster, including slope, aspect or as another example using agricultural soil samples we could create a raster and contours showing areas of equal fertilizer, pesticide or nutrient application. We'll use this tool again in an upcoming demo.
We can overlay the output contours over the DEM and adjust visualization, such as adding labels as required.
And to conclude I'd just like to highlight some additional analysis that can be done with the available tools to demonstrate the finer resolution analyses that can be performed using raster datasets. So the first is a cost direction and cumulative cost raster, generated using the DEM, Slope and Annual Crop Inventory rasters for the region, with defined Start and End Points. The Slope and Annual Crop Inventories were reclassified and combined to create a Friction raster, dictating the costs associated with travelling between cells. So Urban, Agricultural and Forest classes and areas of lower slope values assigned smaller costs than other covering other land-cover classes and higher slope areas. Switching to the Cumulative Cost raster, once again we can see that the costs of traversing the mountainous area were much greater than the foothills and plains to the northeast.
The second example is a Viewshed from one of the mountain tops, showing the areas that were visible from a selected point. Viewsheds are used in landscape planning and architectural applications to ensure viewlines are retained and safety requirements are adhered to. So the tutorial and additional examples demonstrate how we can derive diverse analytical products from just a few simple layers – ranging from routing optimization to planning applications to environmental analysis.
(The words: "For comments or questions about this video, GIS tools or other Statistics Canada products or services, please contact us: statcan.sisagrequestssrsrequetesag.statcan@canada.ca" appear on screen.)
(Canada wordmark appears.)
| NAPCS-CANADA | Month | |||
|---|---|---|---|---|
| 202006 | 202007 | 202008 | 202009 | |
| Total commodities, retail trade commissions and miscellaneous services | 0.64 | 0.69 | 0.69 | 0.59 |
| Retail Services (except commissions) [561] | 0.64 | 0.69 | 0.68 | 0.58 |
| Food at retail [56111] | 0.64 | 0.61 | 0.81 | 0.61 |
| Soft drinks and alcoholic beverages, at retail [56112] | 0.55 | 0.52 | 0.52 | 0.52 |
| Cannabis products, at retail [56113] | 0.00 | 0.00 | 0.00 | 0.00 |
| Clothing at retail [56121] | 1.15 | 1.04 | 1.07 | 0.87 |
| Footwear at retail [56122] | 2.26 | 2.05 | 2.17 | 1.70 |
| Jewellery and watches, luggage and briefcases, at retail [56123] | 9.57 | 10.12 | 9.08 | 10.81 |
| Home furniture, furnishings, housewares, appliances and electronics, at retail [56131] | 0.71 | 0.71 | 0.73 | 0.68 |
| Sporting and leisure products (except publications, audio and video recordings, and game software), at retail [56141] | 2.04 | 2.66 | 3.00 | 3.45 |
| Publications at retail [56142] | 8.08 | 7.42 | 8.50 | 8.62 |
| Audio and video recordings, and game software, at retail [56143] | 3.19 | 6.29 | 7.86 | 5.56 |
| Motor vehicles at retail [56151] | 2.20 | 2.64 | 2.58 | 1.96 |
| Recreational vehicles at retail [56152] | 5.71 | 3.50 | 3.79 | 4.00 |
| Motor vehicle parts, accessories and supplies, at retail [56153] | 1.58 | 1.91 | 1.67 | 1.52 |
| Automotive and household fuels, at retail [56161] | 3.40 | 2.65 | 2.13 | 2.10 |
| Home health products at retail [56171] | 2.56 | 2.77 | 2.26 | 2.62 |
| Infant care, personal and beauty products, at retail [56172] | 3.47 | 3.72 | 2.70 | 2.31 |
| Hardware, tools, renovation and lawn and garden products, at retail [56181] | 2.11 | 1.59 | 1.22 | 1.36 |
| Miscellaneous products at retail [56191] | 3.12 | 2.57 | 2.37 | 2.46 |
| Total retail trade commissions and miscellaneous servicesFootnotes 1 | 1.66 | 1.62 | 1.65 | 1.65 |
Footnotes
|
||||
| Geography | CVs for operating revenue | |
|---|---|---|
| percent | ||
| Amusement parks and arcades | Other amusement and recreation industries | |
| Canada | 1.80 | 0.41 |
| Newfoundland and Labrador | 0.00 | 2.23 |
| Prince Edward Island | 0.00 | 1.30 |
| Nova Scotia | 0.00 | 2.28 |
| New Brunswick | 0.00 | 1.58 |
| Quebec | 2.85 | 0.75 |
| Ontario | 3.10 | 0.50 |
| Manitoba | 0.00 | 0.90 |
| Saskatchewan | 0.00 | 1.66 |
| Alberta | 2.82 | 2.17 |
| British Columbia | 4.45 | 0.65 |
| Yukon | .. | 0.00 |
| Northwest Territories | 0.00 | 0.00 |
| Nunavut | .. | 0.00 |
This is an audio recording of The Daily article A profile of Canadians with a mobility disability and visible minorities with a disability, American Sign Language.
