A Glimpse of Short Term Rentals in Calgary Using Tableau

by Bryan Willis
Geovis Project Assignment @RyersonGeo, SA8905, Fall 2020

Project linkhttps://thebryanwillis.github.io/CalgaryShortTermRentals.html

Background

Over the years, many homeowners have decided to turn their place of residence into short term rentals, allowing their place of residence to be rented out for short periods of time. Short term rentals have also seen an increase in popularity due to their better pricing when compared with hotels and the unique neighbourhood characteristics it provides. Although Calgary has not seen the increase of short term rentals as dramatics as that of Toronto and Vancouver, Calgary has continued to see growth in the short term rental supply. The City of Calgary defines a short term rental as a place of residence that provides temporary accommodation and lodging for up to 30 days and all short term rentals in Calgary must legally obtain a business license to run.

This interactive dashboard will aim to highlight some key components related to short term rentals in Calgary such as the locations, the license status, the composition of the housing type and licenses per month

Data

The data used in this dashboard is based off of the Short Term Rentals data set which was acquired through the City of Calgary’s Open Data Portal.

Methods

  1. Data Cleaning – After downloading the data from the open data portal, the data needed to be cleaned for it to properly display the attributes we want. All rows containing NULL values were removed from the data set via MS Excel.
  2. Map Production – After importing the cleaned data into Tableau, we should quickly be able to create our map that shows where the locations of the short term rentals are. To do this, drag both the auto generated into the middle of the sheet which should automatically generate a map with the location points. To differentiate LICENSED and CANCELLED points, drag the License Status column into the ‘Color’ box.
  1. Monthly Line Graph – To produce the line graph that shows the number of licenses produced by month, drag into the COLUMN section at the top and right click on it and select MONTH. For the ROWS section, again use but right click on it after dragging and select MEASURE and COUNT. Lastly, drag License Status into the ‘Color’ box.
Finalized monthly line graph
  1. City Quadrant Table – To create this table, we first need to create a new column value for the city quadrant. Right click the white space under ‘Tables’ and click on ‘Create Calculated Field’ which will bring up a new window. In the new window input RIGHT([Address],2) into the blank space. This code will create a new field with the last two letters in the Address field which is the quadrant. Once this field is created, drag it into the ROW section and drag it again into the ROW but this time right clicking it and clicking on Measure and then Count. Finish off by dragging License Status to the ‘Color’ box.
Finalized City Quadrant Table
  1. Dwelling Type Pie Chart – For the pie chart, first right click on the ROW section and click ‘New Calculation’. In the box, type in avg(0) to create a new ‘Mark’. There should now be an AGG(avg(0)) section under “Marks’, make sure the dropdown is selected at ‘Pie’. Then drag the Type of Residence column into the ‘Angle’ and ‘Color’ boxes. To further compute the percentage for each dwelling type, right click on the angle tab with the Type of Residence column in it then go the ‘Quick Table calculation’ and finally ‘Percent of Total’ .
Finalized pie chart
  1. Dashboard Creation – Once the above steps are complete, a dashboard can be made with the pieces by combining all 4 sheets in the Dashboard tab.
Finalized dashboard with the 4 created components

Limitations

The main limitations in this project comes from the data. Older licensing data is removed from the data set when the data set is updated daily by city staff. This presents the problem of not being able to compare full year to date data. As seen in the data set used in the dashboard, majority of the January data has already been removed from the data set with the except of January 26, 2020. Additionally, there were also quite a few entries in the data set that had null addresses which made it impossible to pinpoint where those addresses were. Lastly, as this data set is for 2020, the COVID-19 pandemic might have disrupted the amount of short term rentals being licensed due to both the city shifting priorities as well as more people staying home resulting in less vacant homes available for short term rentals.

Geovisualization of the York Region 2018 Business Directory


(Established Businesses across Region of York from 1806 through 2018)

Project Weblink (ArcGIS Online): Click here or direct weblink at https://ryerson.maps.arcgis.com/apps/opsdashboard/index.html#/82473f5563f8443ca52048c040f84ac1

Geovisualization Project @RyersonGeo
SA8905- Cartography and Geovisualization, Fall 2020
Author: Sridhar Lam

Introduction:

York Region, Ontario as identified in Figure 1, with over one million people from a variety of cultural backgrounds is across 1,776 square kilometres stretching from Steeles Avenue in the south to Lake Simcoe and the Holland Marsh in the north. By 2031, projections indicate 1.5 million residents, 780,000 jobs, and 510,000 households. Over time, York Region attracted a broad spectrum of business activity and over 30,000 businesses.

Fig.1: Region of York showing context within Ontario, Greater Toronto Area (GTA) and its nine Municipalities.
(Image-Sources: https://www.fin.gov.on.ca/en/economy/demographics/projections/ , https://peelarchivesblog.com/about-peel/ and https://www.forestsontario.ca/en/program/emerald-ash-borer-advisory-services-program)

Objective:

To create a geovisualization dashboard for the public to navigate, locate and compare established Businesses across the nine Municipalities within the Region of York.

The dashboard is intended to help Economic Development market research divisions sort and visualize businesses’ nature, year of establishment (1806 through 2018), and identify clusters (hot-spots) at various scales.

Data-Sources & References:

  1. Open-Data York Region
  2. York Region Official Plan 2010

Methodology:

First, the Business Directory updated as of 2018, and the municipal boundaries layer files, which are made available at the Open-Data Source of York Region, are downloaded. As shown in Figure 2, the raw data is analyzed to identify the Municipal data based on the address / municipal location distribution. It is identified that the City of Markham and the City of Vaughan have a major share.

Fig.2: The number of businesses and the percentage of share within the nine Municipalities of the York Region.

The raw-data is further analyzed, as shown in Figure 3, to identify the major business categories, and the chart below presents the top categories within the dataset.

Fig.3: Major Business Categories identified within the dataset.

Further, the raw data is analyzed, as shown in figure 4, to identify the businesses by the year of establishment, and identifies that most of the businesses within the dataset were established after the 1990s.

Fig 4: Business Establishment Years identified within the dataset.

The Business addressed data is checked for consistency, and Geocodio service is used to geocode the address list for all the business location addresses. The resulting dataset is imported into ArcGIS Map, as shown in figure 5, along with the municipal boundaries layers and checked for inconsistent data before being uploaded onto ArcGIS Online as hosted layers.

Fig.5: Business Locations identified after geocoding of the addresses across the York Region.

Once hosted on ArcGIS Online, a new dashboard titled: ‘Geovisualization of the York Region 2018 Business Directory’ is created. To the dashboard, the components are tested for visual hierarchy, and careful selection is made to use the following components to display the data:

  1. Dashboard Title
  2. Navigation (as shown in figure 6, is placed on the left of the interface, which provides information and user-control to navigate)
  3. Pull-Down/ Slider Lists for the user to select and sort from the data
  4. Maps – One map to display the point data and the other to display cluster groups
  5. Serial Chart (List from the data)- To compare the selected data by the municipality
  6. Map Legend, and
  7. Embedded Content – A few images and videos to orient the context of the dashboard

The user is given a choice to select the data by:

Fig.6: User interface for the dashboard offering selection in dropdown and slider bar.

Thus a user of the dashboard can select or make choices using one or a combination of the following to display the results in on the right panes (Map, data-chart and cluster density map):

  1. Municipality: By each or all Municipalities within York Region
  2. Business Type: By each type or multiple selections
  3. Business Establishment Year Time-Range using the slider (the Year 1806 through 2018)

For the end-user of this dashboard, results are also provided based on business locations identified after geocoding the addresses across the York Region, comparative and quantifiable by each of the nine municipalities shown in Figure 7.

Fig.7: Data-Chart displayed once the dashboard user makes a selection.

By plotting the point locations on a map, and simultaneously showing the clusters within the selected range (Region/ by Municipality / by Business Type / Year of Establishment selections), Figure 8.

Fig.8: Point data map and cluster map indicate the exact geolocation as well as the cluster for the selection made by the user across the York Region at different scales.

Results:

Overall, the dashboard provides an effective geovisualization with a spatial context and location detail of the York Region’s 2018 businesses. The business type index with an option to select one/ multiple at a time and the timeline slider bar offers an end-user of the dashboard to drill down to the information they seek to obtain from this dashboard. The dashboard design offers a dark theme interface maintaining a visual hierarchy of the different map elements such as the map title, legend, colour scheme, colour combinations ensuring contrast and balance, font face selection and size, background and map contrast, choice of hues, saturation, emphasis etc. The maps also offer the end-user to change the background map base layers to see the data in the context of their choice. As shown in figure 9 of location data and quantifiable data at different scales, the dashboard interface offers visuals to display the 30,000+ businesses across the York Region.

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Fig.9: Geovisualization Dashboard to display the York Region 2018 Business Directory across the Nine Municipalities of the York Region.

The weblink to access the ArcGIS Online Dashboard where it is hosted is: https://ryerson.maps.arcgis.com/apps/opsdashboard/index.html#/82473f5563f8443ca52048c040f84ac1

(Please note an ArcGIS Online account is required)

Limitation:

The 2018 business data across York Region contains over 38,000 data points, and the index/ legend of the business types may look cluttered while a selection is made as well. The fixed left navigation panel width is definitely a technical limitation because the pull-down display cannot be made wider. However, the legend screen could be maximized to read all the business categories clearly. There may be errors, incomplete or missing data in the compilation of business addresses. This dashboard can be updated quickly but requires a little effort, whenever there is an update of the York Region business directory’s new release in the coming years.

A Century of Airplane Crashes

Laine Gambeta
Geovisualization Project, @RyersonGeo, Fall 2019

Tableau is an exceptionally useful tool in visualizing data effectively.  It allows many variations of charts in which the software suggests the best type based on data content.  The following project uses a data-set obtained from the National Transportation and Safety Board identifying locations and details of plane crashes between 1908-2009. The following screenshot is a final product and a run through of how it was made.

Map Feature:

To create the map identifying accident location, a longitude and latitude is required.  Once inputted into the Columns and Rows, Tableau automatically recognizes the location data and creates a map. 

The Pages function is populated with the date of occurrence and filtered by month in order to create a time animation based on a monthly scale. When the Pages function is populated with a date the software automatically recognizes a time series animation and creates a time slide.

The size of the map icon indicates the total number of fatalities at a specific location and time.  To create this effect, the fatalities measure is inputted into the Size function.  This same measure is inserted into the label function to show the total number of occurrences with each icon appearance.

When you scroll over the icons on the map the details of each occurrence appear.  To create this tool, the measures you want to appear are inserted into the Details function.  In this function, Date, Sum Aboard, Sum Fatalities, Sum Survivors, and Summary of accident appears when you scroll over the icon on the map.

Vertical Bar Chart Feature:

To create the vertical bar chart you must insert the date on the Y axis (columns), and the X axis (rows) with people aboard and fatalities.

Next, we must create a calculation to pull the number of survivors by subtracting the two measures.  To do so, right click on a column title cell and click create calculated field.  Within this calculation you select the two columns

you want to subtract and it will populate the fields. We will use this to identify the number of survivors.

The next step is creating a dual- axis to show both values on the same chart.  Right click one of the measures in the rows field and click dual-axis.  This will combine the measures onto the same chart and overlap each other.

Following this we need to filter the data to move along the animation by month.  It tallies the monthly numbers and adds it to the chart. In order to combine the monthly tallies to show on an annual bar chart, the following filters are used.  First filter by year which tallies the monthly counts into a single column on the bar chart.  The Page’s filter identifies the time period increments used in the time slider animation, this value must be consistent across all charts in order to sync.  In this case, we are looking at statistics on a monthly basis.

To split the colours between green and red to identify survivors and fatalities, the Measure Names (which is created automatically by Tableau) is inserted into the colour function.  This will identify each variable as a different colour.

When you bring your mouse over top the bar chart it selects and identifies the statistics related to the specific year.  To create this feature, the measures must be added to the tooltip function and formatted as you please.

Horizontal Bar Chart Feature:

The second bar chart is similar to the previous one.  The sum of fatalities is put in Columns and the Date is put in Rows to switch the axis to have the date on the Y axis.  The Pages function uses the same time frame as other charts calculating monthly and adding the total to the bar chart as the time progresses.

Total Count Features:

To create the chart you must insert the date on the Y axis (columns), and the X axis (rows) with people aboard and fatalities.

Adding in running counts is a very simple calculation feature and is built into Tableau.  You build the table by putting the measure into the text function, this enable‚Äôs the value to show as text and not a chart.  You will notice below that the Pages function must be populated with a date measure on a monthly basis to be consistent with the other charts.   

In order to create the running total values, a calculation must be added to the measure.  Clicking the SUM measure opens the options and allows us to select Edit Table Calculation.  This opens a menu where you can select Running Total, Sum on a monthly basis.  We apply this to 3 separate counters to total occurrences, fatalities, and survivors.

Pie Chart Feature:

Creating a pie chart requires the following measures to be used.  Under the marks drop down you must select pie chart.  This automatically creates a function for angular measure values.  The fatality and survivor measures are used and filtered monthly.  The Measure Values which is automatically created by Tableau identifies the values of these measures and is inputted into the Angle function to calculate the pie chart.  Again, the Measures Names are inputted into the colour function to separate the values by fatalities and survivors. The Pages function is populated with date of occurrence by month to sync with the other charts.

Lastly, a dashboard is created which allows the placement of the features across a single page.  They can be arranged to be aesthetically pleasing and informative.  Formatting can be done in the dashboard page to manipulate the colors and fonts.

Limitations:

Tableau does not allow you to select your map projection. Tableau Online has a public server to publish dashboards to, however it does not support timeline animation. Therefore, the following link to my project is limited to selecting the date manually to observe the statistics.

https://prod-useast-a.online.tableau.com/t/lainegambeta/views/ACenturyofAirplaneCrashes/Dashboard2?:origin=card_share_link&:embed=n