Author: jennifer.nhieu

Time-series Animation of Power Centre Growth in the Greater Toronto Area for the Last 25 Years

By: Jennifer Nhieu
Geovisualization Class Project @RyersonGeo, SA8905, Fall 2018

Introduction:

In 1996, there were 29 power centres with 239 retail tenants accounting for just under five million square feet of retail space (Webber and Hernandez, 2018). 22 years later, in 2018, there are 125 power centres with 2,847 retail tenants accounting for 30 million more square feet of retail space (Webber and Hernandez, 2018). In addition, power centres expand in an incremental manner, either through the purchase and integration of adjoining parcels or the conversion of existing parking space into new stores (Webber and Hernandez, 2018). This development process often leads to retail centres becoming “major clusters of commercial activity that significantly exceed the original approved square footage total” (Webber and Hernandez, 2018, pg. 3).

Data and Technology:

To visualize this widespread growth of power centres from 1996 to 2017, a time-series animation map was created on Kepler.gl. (beta version) using power centre growth data provided by the Centre for the Study of Commercial Activity (CSCA) at Ryerson University, who undertakes an annual field survey-based inventory of retail activity in the Greater Toronto Area. Kepler.gl was created by Uber’s visualization team and released to the public on the summer of 2018. It is an “open source, high-performance web-based application for visual exploration of large-scale geolocation data sets. Kepler.gl can render millions of points representing thousands of trips and perform spatial aggregations on the fly” and is partnered with Mapbox, a “location data platform for mobile and web applications that provides open-source location features”. (Uber Technologies Inc., 2018 and Mapbox, 2018).

Methodology:

The data provided by the CSCA includes information on the shopping centre’s name, a unique identification code for each power center, the longitude and latitude coordinates for each power centre, its square footage information from the year it was built to 2017 and etc. The data had to be restructured on Microsoft Excel to include a pseudo date and time column, which would include the years 1992 to 2017, as well as 1-hour time intervals to allow Kelper.gl to create an animation based on date and time. The table below is an example of the data structure required to create a time animation on Kepler.gl.

Table 1: Data structure example*

Table 1: Data structure example*
*The data in this table has been modified due to confidentiality reasons.

Below is the time series animation set up for visualizing power centre growth on Kepler.gl. This process can also be replicated to produce another time-series animations:

  1. Visit https://kepler.gl/#/
  2. Click Get Started.
  3. Drag and drop .csv file onto application.
  4. Hold and drag to navigate to the Toronto GTA.
  5. On the contents bar, click + Add Layer on the Layers
  6. Under Basic Layer Type, click Select A Type, then select Point.
  7. Under Columns, Lat* = COORDY and Lng* = COORDX.
  8. Under Color, click Color Based On, then select SQ FT.
  9. Under Color, click the color scheme bar, select a preferred light to dark colour scheme.
  10. Under Color, Color Scale, select quantize.
  11. Under Color, Opacity, set to 4.
  12. Under Radius, Radius Based on, Select a field, select
  13. Under Radius, Radius Range, set the range from 1 to 60.
  14. On the contents bar, click + Add Layer on the Filters
  15. Click Select a field, then select
  16. In the slider, drag the rightmost square notch to highlight only 2 bars with the left square notch.
  17. Press the play button the start the animation.

Notes:

  • The speed of the animation can be adjusted.
  • The legend can be shown by clicking the bottom circular button located to the top right corner of the screen.
  • Hover your mouse over a point to see the metadata of the selected power centre.
Figure 1: Power Centre Growth in the Toronto GTA (1992 – 2017)

Limitations:

During the implementation process, it became apparent that Kepler.gl is more focused on graphics and visuals than it does on cartographic standards. The program does not allow the user to manually adjust class ranges on the legend, nor does it accurately display continuous data. The proportional symbols used to represent power centre growth displays flashing or blinking symbols rather than a gradual growth in the symbols. There was an attempt to correct this problem by duplicating the values in the date and time column, and then adding additional pseudo date and time values between each year. However, when tested, the animation exhibited the same flashing and blinking behaviour, therefore it became apparent that is problem exists in the programming of Kepler.gl and not in the data itself. Furthermore, by duplicating these values, the file would exceed the maximum file size on chrome (250mb), and limit performance on Safari, the two web browsers it runs on.

Conclusion:

Regardless of the limitations, as the current Kepler.gl is still in its early beta version, it still has a lot of potential to incorporate user feedback from industry professionals and run additional testing before the final release.

References:

Webber, S. and Hernandez, T. (2018). Retail Development and Planning Policy.   Centre for the Study of Commercial Activity, Ryerson University. Toronto, CA.Uber Technologies Inc. (2018). Kepler.gl. Retrieved from http://kepler.gl/#/
Mapbox. (2018). About Mapbox. Retrieved from https://www.mapbox.com/about/