| Province of residence | Unweighted | Weighted |
|---|---|---|
| Percentage | ||
| Newfoundland and Labrador | 7.8 | 7.5 |
| Prince Edward Island | 5.9 | 6.0 |
| Nova Scotia | 14.0 | 13.9 |
| New Brunswick | 12.4 | 12.5 |
| Quebec | 19.4 | 18.0 |
| Ontario | 21.8 | 20.8 |
| Manitoba | 13.6 | 13.0 |
| Saskatchewan | 12.7 | 12.1 |
| Alberta | 16.0 | 15.6 |
| British Columbia | 18.4 | 17.9 |
| Canada | 15.4 | 17.9 |
Catalogue number: Catalogue number: 89200005
Issue number: 2020009
Release date: February 25, 2020
QGIS Demo 9
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Visualizing Vector Data (Part 1) – Symbology Styles")
So now that we know how to edit the attributes and geometries of vector data, a complementary skill is using their fields for visualization. As we briefly introduced, these parameters can be set in the Layer Properties box. Today we'll focus on the Symbology tab learning:
The available styles and their application to different field types.
Then in a follow up video we'll show how to use rule-based visualizations, save and load created symbology files, and apply labelling schemes.
Before beginning let's discuss some considerations for visualization.
First - Are there established conventions for symbolizing the specific features in a layer? If there are multiple conventions - which is best aligned with the geographic location being visualized.
Second - Have you selected logical colour schemes for specific features?
Third - What is the appropriate style based on the field type.
And some additional factors that influence interpretability, such as:
The simplest symbology edits can be done by right-clicking the layer, expanding Style and dragging on the colour wheel to change the colour. Using the white circle we can alter the specific hue and brightness. This is already an improvement for this layer, being immediately decipherable as water features.
Now lets explore the Single Symbol style, the default in QGIS, where a single colour is applied to all features - using the boundary layers. Specifically, we'll alter the styles to visualize both boundary levels simultaneously.
So opening the Layer Properties Box, in the Symbology tab, we can change the colour or transparency of the entire layer, as done with the hydrology layer, using these tabs. However, we want to change only the transparency of the Fill Color retaining the boundary outlines, so we'll click on Simple Fill and then Fill Colour. We can use the sliders on the right or the interactive selections in the various tabs to set the applied colour. For our purpose we will set the opacity to 0, making our layer fully transparent. We can use Apply to verify we're satisfied with the visualization and OK to finalize.
Now we'll repeat with the Census Division layer, going to Simple Fill and changing the outline width to 0.86 and then clicking on the colour – we'll alter it to Red to distinguish them from the subdivision boundaries. As seen in the Canvas, we can now visualize both boundaries simultaneously.
Alternatively we could have applied a categorized symbology, which enables us to symbolize discrete features, classes or categories within a specified field - selected in the Column drop-down. Using the Provincial Unique Identifier we can click Classify and Apply. So now each subdivision is coloured according to its province. We could switch this out for another categorical variable, like Census Division Names, hitting Classify – Yes and OK. So this offers an alternative means to visualize both boundary levels.
But due to the random colour ramp there are two issues:
So we'll use some colour presets within the Symbology tab and the Topological Coloring tool in the Processing Toolbox to address these issues. The topological colouring tool is explicitly meant for symbolizing boundaries, and will ensure adjacent features are not assigned the same colour.
Within the tool we'll specify the Input layer, the number of colours to use and the Mode which determines how colours are assigned – and are explained in the tool description on the right. We’ll use Assigned Area, helpful given variations in the size of features in divisions and even more pertinent for the subdivision layer.
The tool outputs a new layer with the exact same properties as the input, except with one new field called “color id” which we can use for visualization. Selecting it from the drop-down, we will now expand Colour Ramp drop-down beside and select Create New. Here are a variety of presets available for symbolizing a layer. Today we'll use the Catalog: ColorBrewer.
So first we'll match the number of colours to that specified in the tool, and then we can select a colour scheme including palettes and ramps, to use - in this case Pastel1. Then click OK. and in the Symbology tab, click Classify and OK. Now adjacent features are distinctly coloured and the overall visualization is much easier to interpret.
Now lets explore graduated symbologies using the grain elevators layer. Graduated styles can be used to visualize concentrations, magnitudes or frequencies of a variable with a specific colour ramp – like vehicle collisions, earthquakkes and population sizes. The style is restricted to numeric field types. So we'll use the Capacity_Tonne field.
The Mode determines the method used to establish value ranges or “break values” used in visualization. So we can use Pretty Breaks which defaults to easily interpreted value ranges. The Precision parameter determines the number of decimal places in the Legend Values and we can alter the Classes value in the bottom right corner to change the number of value ranges used in visualization. Clicking Apply and looking in the Canvas , nearly all elevators are coloured white, with select elevators on the coasts being red. This is because there are many more primary and process elevators with smaller capacities than there are export elevators, with much larger storage.
In the Histogram tab, we can click Load Values to assess the distribution of data and value ranges, which informs the most appropriate Mode to apply. So here, given that most features are in the first value range, we'd want to apply a different mode, in this case a Quantile (Equal Count), so that an equal number of features are in each value range.
We still want to edit the Value ranges to be more intuitive, double-clicking and entering the new values which should also update the Legend values. So we'll alter to 5000 and 50000. We'll also alter the Legend values for the Upper and Lower bounds switching to greater than 50000 and less than 5000. Clicking apply and looking in the Canvas there is a much better distribuiton of colour across the features, reaffirmed back in the Histogram tab – where features are more evenly distributed across the value ranges.
For point and line geometries we can also alter the size of symbols between ranges to enhance visualization. So let's increase the size of the point symbol by 0.5 for each value range. Click Apply and OK. So as seen in the Map Canvas this offers a visualization of the differing storage capacities between individual elevators.
There are three additional symbology styles for point geometries. We can apply a Point Cluster symbology, and within the Render Settings - see our Graduated symbology style is still applied. The cluster style will provide a dynamic count of features based on the scale of the Canvas and specified Distance – determining the radius for clustering. As we can see zooming out – the counts of clustered features becomes larger and zooming in – we can begin to see individual features.
The Point Displacement symbology is effectively the same as Cluster, but depicts the individual features displayed in a particular geometry around the Cluster . Additionally features can be labelled using a specified field, here using the Capacity Tonne field. As shown, this style is not the best for detailed datasets or coarser scales, but is suitable when the features are sparser or the Canvas is at a finer scale enabling the properties and attributes of individual features to be distinguished within the Cluster.
The final style is the Heat Map, which will create a dynamic, raster style interpolation according to the spatial distribution of points. So lets switch the colour ramp to Red-Yellow-Greens and reexpand the drop-down to Invert the Colour Ramp. Additionally we can weight the visualization by a numeric field. We'll use the Capacity Tonne field as the Weight parameter – clicking Apply and OK. Zooming in and out we have a dynamic visualization of storage capacity that changes with scale. Now perhaps we want to alter the colour ramp, because in this case the green areas effectively mean zero storage capacity. Rather than expanding the drop-down click the Colour Ramp itself. Now we can edit the colour gradient relative to the value ranges or change the Transparency for only one colour by clicking on the stop and altering the Opacity slider.
Back in the Canvas we can now see the underlying boundaries along with our heat map. Finally we'll Save the Project File As, giving it a name – calling it VectorVisualization, so that we can use it in our follow up video, where we'll cover rule-based symbologies, and labelling schemes.
(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.)
| Duration of Visit | Main Trip Purpose | Country or Region of Expenditures | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | Canada | United States | Overseas | ||||||
| $ '000 | C.V. | $ '000 | C.V. | $ '000 | C.V. | $ '000 | C.V. | ||
| Total Duration | Total Main Trip Purpose | 27,521,742 | A | 17,683,076 | A | 4,839,752 | B | 4,998,914 | B |
| Holiday, leisure or recreation | 16,327,462 | A | 9,824,816 | A | 3,365,144 | B | 3,137,502 | B | |
| Visit friends or relatives | 6,477,084 | A | 4,585,518 | A | 613,497 | B | 1,278,069 | C | |
| Personal conference, convention or trade show | 357,441 | B | 247,563 | C | 95,198 | D | 14,680 | E | |
| Shopping, non-routine | 763,620 | B | 616,190 | B | 147,430 | C | .. | ||
| Other personal reasons | 1,221,365 | B | 888,535 | B | 183,224 | E | 149,606 | E | |
| Business conference, convention or trade show | 926,902 | B | 546,367 | B | 247,906 | C | 132,629 | D | |
| Other business | 1,447,868 | B | 974,087 | B | 187,353 | C | 286,428 | E | |
| Same-Day | Total Main Trip Purpose | 5,013,925 | A | 4,614,295 | A | 347,927 | B | 51,703 | E |
| Holiday, leisure or recreation | 2,335,908 | B | 2,124,745 | B | 159,461 | C | 51,703 | E | |
| Visit friends or relatives | 1,255,164 | B | 1,198,787 | B | 56,377 | D | .. | ||
| Personal conference, convention or trade show | 66,346 | C | 63,112 | C | 3,234 | E | .. | ||
| Shopping, non-routine | 624,822 | B | 521,532 | B | 103,291 | C | .. | ||
| Other personal reasons | 322,718 | B | 315,858 | B | 6,860 | E | .. | ||
| Business conference, convention or trade show | 71,933 | D | 61,464 | D | 10,469 | E | .. | ||
| Other business | 337,033 | C | 328,797 | C | 8,235 | E | .. | ||
| Overnight | Total Main Trip Purpose | 22,507,816 | A | 13,068,781 | A | 4,491,825 | B | 4,947,211 | B |
| Holiday, leisure or recreation | 13,991,554 | A | 7,700,072 | A | 3,205,683 | B | 3,085,799 | B | |
| Visit friends or relatives | 5,221,920 | B | 3,386,731 | A | 557,120 | B | 1,278,069 | C | |
| Personal conference, convention or trade show | 291,095 | C | 184,451 | C | 91,964 | D | 14,680 | E | |
| Shopping, non-routine | 138,797 | C | 94,658 | C | 44,139 | D | .. | ||
| Other personal reasons | 898,647 | B | 572,677 | B | 176,364 | E | 149,606 | E | |
| Business conference, convention or trade show | 854,969 | B | 484,903 | B | 237,437 | C | 132,629 | D | |
| Other business | 1,110,835 | B | 645,289 | B | 179,118 | C | 286,428 | E | |
Estimates contained in this table have been assigned a letter to indicate their coefficient of variation (c.v.) (expressed as a percentage). The letter grades represent the following coefficients of variation:
|
|||||||||
| Duration of Trip | Main Trip Purpose | Country or Region of Trip Destination | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | Canada | United States | Overseas | ||||||
| Person-Trips (x 1,000) | C.V. | Person-Trips (x 1,000) | C.V. | Person-Trips (x 1,000) | C.V. | Person-Trips (x 1,000) | C.V. | ||
| Total Duration | Total Main Trip Purpose | 101,520 | A | 91,523 | A | 7,514 | A | 2,482 | A |
| Holiday, leisure or recreation | 46,551 | A | 41,114 | A | 4,055 | A | 1,382 | B | |
| Visit friends or relatives | 36,220 | A | 33,848 | A | 1,558 | B | 814 | B | |
| Personal conference, convention or trade show | 1,683 | C | 1,505 | C | 158 | C | 21 | E | |
| Shopping, non-routine | 5,060 | B | 4,136 | B | 924 | B | .. | ||
| Other personal reasons | 5,827 | B | 5,440 | B | 314 | D | 73 | D | |
| Business conference, convention or trade show | 1,713 | B | 1,370 | B | 279 | D | 65 | D | |
| Other business | 4,465 | B | 4,111 | B | 227 | C | 127 | D | |
| Same-Day | Total Main Trip Purpose | 59,108 | A | 55,946 | A | 3,161 | B | .. | |
| Holiday, leisure or recreation | 24,199 | A | 22,851 | A | 1,348 | B | .. | ||
| Visit friends or relatives | 21,064 | A | 20,490 | A | 574 | C | .. | ||
| Personal conference, convention or trade show | 1,040 | C | 1,016 | C | 24 | E | .. | ||
| Shopping, non-routine | 4,688 | B | 3,845 | B | 844 | C | .. | ||
| Other personal reasons | 4,254 | B | 4,030 | B | 224 | E | .. | ||
| Business conference, convention or trade show | 719 | C | 640 | C | 79 | E | .. | ||
| Other business | 3,143 | B | 3,075 | B | 68 | E | .. | ||
| Overnight | Total Main Trip Purpose | 42,412 | A | 35,577 | A | 4,353 | A | 2,482 | A |
| Holiday, leisure or recreation | 22,352 | A | 18,263 | A | 2,707 | A | 1,382 | B | |
| Visit friends or relatives | 15,156 | A | 13,358 | A | 983 | B | 814 | B | |
| Personal conference, convention or trade show | 643 | C | 489 | C | 134 | C | 21 | E | |
| Shopping, non-routine | 371 | C | 292 | C | 80 | D | .. | ||
| Other personal reasons | 1,573 | B | 1,410 | B | 90 | D | 73 | D | |
| Business conference, convention or trade show | 994 | B | 730 | B | 200 | C | 65 | D | |
| Other business | 1,322 | B | 1,036 | B | 159 | C | 127 | D | |
Estimates contained in this table have been assigned a letter to indicate their coefficient of variation (c.v.) (expressed as a percentage). The letter grades represent the following coefficients of variation:
|
|||||||||
| Release date | 2018 | 2019 | |||
|---|---|---|---|---|---|
| Q4 | Q1 | Q2 | Q3 | Q4 | |
| Quarterly (percentage) | |||||
| February 25, 2020 | 83.5 | 85.2 | 81.9 | 75.4 | 62.4 |
| November 26, 2019 | 83.5 | 84.6 | 80.1 | 64.9 | .. |
| August 23, 2019 | 83.5 | 81.9 | 65.2 | .. | .. |
| May 24, 2019 | 83.5 | 67.5 | .. | .. | .. |
| February 26, 2019 | 60.0 | .. | .. | .. | .. |
| .. not available for a specific reference period Source: Quarterly Survey of Financial Statements (2501) |
|||||
Statistics Canada is modernizing its methods of data access to improve its service to users of Statistics Canada data. The goal of modernization is to fully realize the potential of the data holdings created for the public good and increase collaboration and partnerships between data users and providers while ensuring that all data assets are protected against unauthorized use and disclosure.
This privacy impact assessment identifies and explores privacy, confidentiality and security issues associated with the use of video surveillance monitoring (camera monitoring) in secure facilities designed for the purposes of data access and makes recommendations for issue resolution or mitigation.
Statistics Canada is planning on expanding controlled access to anonymized microdata for statistical research projects, by establishing Secure Access Points on the premises of federal, provincial, territorial, and municipal governments, universities, and other organizations. Each Secure Access Point is a secure Statistics Canada facility that meets Statistics Canada's departmental security standards for data access, including controlled access monitoring. Any data stored on these premises remain under the care and control of Statistics Canada and subject to the confidentiality provisions of the Statistics Act.
In each Secure Access Point, video surveillance cameras will be used to monitor activity and access. Only Statistics Canada employees and deemed employees (individuals providing statistical services to Statistics Canada under contract or written arrangement) will have access to the microdata within Secure Access Points.
Statistics Canada's use of the camera monitoring includes making recordings of activities in the secure designated room to offer enhanced protection of employees and assets.
The PIA also identifies the risk areas and categorizes the level of potential risk (level 1 representing the lowest level of potential risk and level 4, the highest) associated with the collection and use of personal information of employees.
This assessment concludes that, with the existing Statistics Canada safeguards, any remaining risks are either negligible or are such that Statistics Canada is prepared to accept and manage the risk.
| Geography | CVs for operating revenue |
|---|---|
| percent | |
| Canada | 0.74 |
| Newfoundland and Labrador | 0.10 |
| Prince Edward Island | 0.03 |
| Nova Scotia | 0.38 |
| New Brunswick | 0.36 |
| Quebec | 1.05 |
| Ontario | 1.45 |
| Manitoba | 0.63 |
| Saskatchewan | 0.81 |
| Alberta | 0.42 |
| British Columbia | 0.38 |
| Yukon | 0.00 |
| Northwest Territories | 0.00 |
| Nunavut | 0.00 |
| Geography | Month | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201812 | 201901 | 201902 | 201903 | 201904 | 201905 | 201906 | 201907 | 201908 | 201909 | 201910 | 201911 | 201912 | |
| percentage | |||||||||||||
| Canada | 0.63 | 0.69 | 0.63 | 0.57 | 0.54 | 0.56 | 0.60 | 0.60 | 0.57 | 0.59 | 0.56 | 0.58 | 0.61 |
| Newfoundland and Labrador | 1.35 | 2.14 | 1.84 | 2.36 | 2.04 | 2.16 | 1.79 | 2.45 | 2.48 | 3.13 | 3.19 | 2.76 | 3.03 |
| Prince Edward Island | 3.46 | 3.11 | 2.65 | 3.37 | 3.12 | 0.57 | 1.99 | 6.84 | 4.93 | 4.01 | 4.53 | 4.04 | 3.28 |
| Nova Scotia | 2.49 | 2.42 | 3.49 | 3.37 | 2.42 | 2.90 | 2.65 | 4.67 | 4.63 | 2.77 | 2.94 | 3.46 | 3.50 |
| New Brunswick | 1.48 | 1.66 | 1.18 | 1.78 | 1.96 | 1.69 | 2.09 | 2.27 | 1.30 | 1.56 | 1.87 | 1.51 | 1.46 |
| Quebec | 1.17 | 1.21 | 1.14 | 1.01 | 1.26 | 1.07 | 1.48 | 1.35 | 1.41 | 1.32 | 1.26 | 1.37 | 1.18 |
| Ontario | 1.15 | 1.29 | 1.11 | 1.00 | 0.93 | 0.98 | 1.00 | 1.03 | 0.93 | 1.04 | 0.96 | 0.99 | 1.03 |
| Manitoba | 2.09 | 2.03 | 1.76 | 1.58 | 1.68 | 1.52 | 1.62 | 2.43 | 2.74 | 2.18 | 2.42 | 2.06 | 2.11 |
| Saskatchewan | 1.29 | 1.74 | 2.34 | 1.74 | 1.59 | 1.72 | 1.62 | 1.39 | 1.92 | 1.58 | 1.59 | 1.69 | 1.58 |
| Alberta | 1.72 | 2.01 | 1.80 | 1.81 | 1.25 | 1.42 | 1.39 | 1.31 | 1.23 | 1.18 | 1.23 | 1.22 | 1.27 |
| British Columbia | 1.64 | 1.66 | 1.68 | 1.49 | 1.52 | 1.60 | 1.65 | 1.65 | 1.55 | 1.61 | 1.62 | 1.63 | 1.96 |
| Yukon Territory | 4.18 | 3.78 | 3.69 | 3.65 | 3.09 | 4.72 | 4.89 | 4.04 | 4.89 | 5.91 | 7.04 | 6.07 | 6.72 |
| Northwest Territories | 0.89 | 0.85 | 0.73 | 1.03 | 0.80 | 0.96 | 1.03 | 0.99 | 0.91 | 1.00 | 1.46 | 1.58 | 0.90 |
| Nunavut | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Geography | CVs for operating revenue |
|---|---|
| percent | |
| Canada | 0.17 |
| Newfoundland and Labrador | 0.46 |
| Prince Edward Island | 0.37 |
| Nova Scotia | 0.34 |
| New Brunswick | 0.32 |
| Quebec | 0.31 |
| Ontario | 0.36 |
| Manitoba | 0.53 |
| Saskatchewan | 0.27 |
| Alberta | 0.50 |
| British Columbia | 0.25 |
| Yukon | 0.34 |
| Northwest Territories | 0.25 |
| Nunavut | 0.22 |
Catalogue number: Catalogue number: 89200005
Issue number: 2020008
Release date: February 19, 2020
QGIS Demo 8
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Adding Fields and Editing Feature Attributes with the Field Calculator")
So in today's demo we'll use the Field Calculator to edit fields and feature attributes, including those storing geometric, numeric and text information. The Field Calculator is used to add and update fields or edit attributes of selected features. Like the Select by Expression tool, introduced in the previous demo, it uses expression syntax. Today we'll use the two tools in conjunction to select and update large selections of features.
So let's start by adding some spatial measures to our subset and created datasets.
So we can select them in the Layers Panel, and click the Field Calculator icon on the Attribute Toolbar. Since we are adding a new field, much like when we added the Fields to our AOI layer, we need to specify a field name, the field type as well as provide the parameters for the number of characters it stores. The same drop-downs from the Select by Expression tool are also found in the Field Calculator which we can use to help create our expressions. In the Geometry drop-down we can see the general expression syntax, which tends to be dollar sign followed by the measure of interest. So if we wanted to add coordinate information to a point dataset we could use $x or $y or for our polygons $perimeter and $area.
The Preview shows the output, which is calculated in Map units making the projected coordinate system with units in meters applied to these layers helpful in adding these measures.
To alter the units just apply an appropriate conversion factor, in this case divided by 10000 for area in hectares. We'll also copy the Expression and now repeating with our AOI layer- we can also opt to Update Existing Fields – selecting the field to update from the drop-down – meaning we can finally populate the area in hectares field we added when we first created the AOI layer – pasting the expression and clicking OK.
It is important to note that these measures are not automatically updated if the geometry of a layer is edited. So if we split a polygon or clipped it we would have to update these fields using the Field Calculator and once again paste in the appropriate expression. We can then Save the Edits and turn the toggle editor off.
Now let's take a look at updating large feature selections using the road_segment_1 layer. First we'll change the projection using the Reproject Layer tool. Much like the Save Vector Layer As box it can be used to transform a layer to a new projection, and can be applied as a batch process to multiple layers. So we'll change the coordinate reference system to UTM Zone 14N and use a temporary file for the output.
Then using our Interactive Selection tool, with our AOI layer highlighted, we'll select the merged Census Division feature, which we'll use to clip the reprojected road segments. So within the tool Reprojected is selected as the Input and the Overlay layer is the AOI. The Overlay layer is restricted to polygon vectors. And we'll also check Selected Features Only, so that only the road segments overlapping with the selected AOI feature are retained. This can be used to standardize the extent of analysis for multiple layers, and in this case, reduce processing times with editing the attributes of our road features. I saved the file in our Intro Demo folder, and called it CPRoads for Clipped Projected Roads.
So now we'll use the Select by Expression and Field Calculator sequentially to isolate and update the attributes for large selections of features in the Road Segments layer. So the road classes were interpreted using the CanVEC catalogue and Road Class field shown in the downloading data from the Federal Geospatial Platform demo.
We'll enter three expressions together and the remaining expressions are found in the video description. The first we'll enter is road classes greater than 309 AND less than 312 – for selecting within a value range which corresponds with our Highway features. Clicking select - 10000 features are returned.
Since this is the first selection we have to create a New Field, which we'll call Class, specifying it as a Text field type with a length of 50. As seen, when there are selected features by default the Only Update Selected Features box is checked. To update all features we could simply uncheck the box.
Remembering our syntax rules from the Selecting by Attributes demo we need to apply a single quotes around text-based entries.
Back in the Select by Expression tool let's populate another class, changing values to greater than 311 and less than 315. So 25000 features were selected. Since we have already created our field we will use Update Existing field and scroll down to the bottom of the list and selecting our Class field. Our current selection corresponds to local classes.
Now let's create one final selection - changing to road_class to = 309, which corresponds with Collector roads. Once again in the Field Calculator, we'll select the field to update and enter the corresponding attribute. The remaining expressions are provided in the video description – which can be used to populate the remainder of the field.
So now we'll use fields in the CP_Roads layer to update the Speed Limit field which is currently empty. The first one is simple entering "is_trans_c" = 11 – meaning is TransCanada Highway is TRUE. In the Calculator click Update and find the Speed_Rest Field. So the limit for the TransCanada in Manitoba is 110 kilometres an hour and we recall numbers can be entered as is.
The next few expressions are slightly more complex, since we are using a variety of fields to approximate speed restrictions. So we can use the Official S field, which contains the full name and type of the road, and add LIKE wildcard way to isolate Highways, Freeways and Expressways. But we want to avoid selecting other roads containing WAY - like Stoneway or Wallford Way - so we will also specify of_street_6 which is the Road Type and use the IN operator so to avoid repeating the field for each attribute. We'll scroll through the unique entries and add Highway, comma Freeway. Had we left the Road Segments unclipped we would also need to add Expressway. And finally to avoid overwriting the Speed Limits we've already populated with previous expressions we'll use AND "speed_rest" IS NULL in all subsequent expressions. In the Field Calculator we'll add the corresponding speed limit, defaulting to 100 kilometres an hour.
Now we can remove the components except Speed Rest IS NULL from our previous expression, and use the Official 2 field which provides a general indication of the road settings. Once again we'll use the IN operator and add Unorganized, Reserve and Rural Municipality. Then we'll use the road_class field, specifying greater than 307 and less than 310, to isolate non-urban roads that are collector or arterial. Once the selection is created, we'll enter an average speed limit of 80 kilometres per hour in the Field Calculator.
As with our Class field, the remaining expressions and associated speed limits to update in the Field Calculator are found in the video description, along with an explanation of the expressions.
The final thing I'd like to show is adding two final fields. The first is the length of the road segments in kilometers. I recommend populating both these fields fully prior to applying these calculations to avoid repeating these procedures later, as we are only currently updating the selected features. So applying the skills from earlier in the video the syntax is $length, and divided by 1000 for units in kilometres.
Now we'll add a field called TimeMin, which we will use the Length and Speed Limit fields to calculate. Speed equals distance over time, so time equals distance over speed, dividing our Length KM field by the speed_rest field. And left as-is the output is currently in hours, so we will just multiply by 60 to calculate the time to traverse each road segment in Minutes. Now ensure to Save Edits and Toggle the Editor off.
Congratulations! You should now feel confident using the Field Calculator to add and update fields and edit attributes for large feature selections in vector datasets. You should also feel comfortable applying expression syntax to perform these procedures – such as applying the appropriate operators and syntax for different attributes, and using multiple fields to isolate features by criteria of interest. We'll continue expanding these syntax skills throughout the tutorials. Additionally you should feel comfortable adding geometric, numeric and text attributes, and derive new information using the Field Calculator. Apply these skills to datasets of interest to you.
In the next demo we will discuss procedures for visualizing vector data specifically focusing on the Symbology and Labels tabs in the Layer Properties box to visualize different fields. We'll use the CPRoads layer from this tutorial to demonstrate rule-based visualizations as well.
(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.)
