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Intro to ArcGIS Pro

VISUALIZING DATA

Let’s take a look at data again to understand what we’re working with.

1 Open the attribute table of the popMotherTongTractVan layer if it’s not already open.

Reflection Question

(Click the text for the answer)

What do the numbers representing each language in each Census tract represent?
Some numbers are over 100, so we know these are not percentages. These numbers represent actual language speakers of each language in each Census tract.


2 Scroll to the right and then down through the attribute table and notice what languages are represented by larger numbers of language speakers and which languages don’t have very many speakers represented.

Just a visual glance at the data shows there are relatively large numbers of speakers of Cantonese, Mandarin, and Panjabi, with Ukrainian, Urdu, and Polish representing relatively small numbers of speakers, among others.

3 Take a look at the Aboriginal Languages column. There are not large numbers of mother tongue speakers of Indigenous languages in Vancouver according to this data.

This is also a good example of how data can both lend greater visibility to certain people and phenomena and erase or render less visible certain people and phenomena. Canada’s first languages are made invisible in this dataset and lumped into one group instead of their own languages. This may be a function of the small numbers of speaker numbers and also how data is collected, but it is good to approach data with a critical eye.

Select by Attributes

Let’s take a closer look at the distribution of Aboriginal Languages by using a tool called Select by Attributes which enables us to query the data using different expressions.

1 On the Map Tab, go to the Selection section and click on the Select by Attributes button, which will open a new dialog window.

2 Set the following parameters:

  • Input Rows: popMotherTongTractVan
  • Selection type: New Selection
  • Click: New Expression

For the Expression, Where:

  • Field: diameter
  • is equal to (this is the default)
  • click the dropdown in the third box of the clause to see the range of values

This is a good example of how you can use the Select by Attributes tool to better view the data before you even make a selection.

In this dropdown, the values are sorted by default in order of smallest to largest.

Reflection Question

(Click the text for the answer)

What is the largest value in the dataset?
133.


Let’s return to the Select by Attributes query we were building, where we’ll select the records representing tracts with speakers numbering over 100. For the Expression, Where:

  • Field: Aboriginal Languages
  • is greater than or equal to
  • ‘113’

1 Click the OK button at the bottom of the window to calculate the expression and apply the selection.

In the attribute table, three records are selected, but it may not be apparent at first.

2 At the bottom of the attribute table, note that it says “3 of 117 selected”. Click the cyan button that is not selected to the left of these words to see only the selected records.

3 Close the attribute table to better see the selected records on the map.

Change the Symbology

1 Click on the Appearance tab at the top of the map and click the dropdown arrow under Symbology and select Single Symbol.

2 In the Symbology window, click on the symbol itself, the yellow box, or whatever colour yours appears to be.

3 Click on Properties if it’s not already selected and click the dropdown arrow next to Colour and select No colour.

4 Click on Apply.

Now we just see the outlines of the Census tracts. Let’s change the basemap to get a better idea of where these are located.

5 As you did before, from the Map tab, select the Basemap dropdown menu and select the Streets basemap.

6 Zoom into the selected tracts to see where in Vancouver larger numbers of mother tongue speakers of Indigenous languages are spoken.

We see that these tracts are in an area known as the Downtown Eastside. If you are familiar with Vancouver, you may associate this area with large disparities between rich and poor, with fancy stores and restaurants and housing, along with a large homeless population.

A simple data query and selection and visual analysis is enough to ask further questions about why a certain spatial pattern exists in a certain area. We are going to further explore symbology as a way to understand a dataset, but think about how you might go about understanding why the Census tracts with the highest numbers of speakers are clustered where they are. Is there a historical connection to this area? Would income data be informative? Are there relevant community institutions?