Background
Renewable energy is the energy that is derived from natural processes that are replenished at a rate that is equal to or faster than the rate at which they are consumed. There are various forms of renewable energy, getting directly or indirectly from the sun, or from the heat that is generated deep within the earth. They include energy generated from wind, solar, hydropower and ocean resources, geothermal, solid biomass, biogas and liquid biofuels. Over time, there has been a wide range of energy-producing technologies and equipment that took advantage of these natural resources. Consequently, the utilizable energy can be produced in many forms. These forms include industrial heat, electricity, thermal energy for space and water conditioning, and transportation fuels.
Canada has an abundance of renewable resources that can be used to produce energy due to its large landmass and diversified geography. Canada is a world leader in the production and use of energy from renewable resources. For this project, we would be focusing on renewable energy installations in the Greater Toronto Area (GTA), Canada. There are 58 renewable energy installations in GTA. Renewable energy resources currently provide 0.6% of GTA’s total renewable energy supply. Deep Lake Water Cooling, Geothermal, Solar Air Heating, Solar Hot Water, Solar Photovoltaic (PV) and Wind Turbine are the forms of renewable energy used in GTA. Solar photovoltaic is the most important used form of renewable energy produced in the GTA. Solar hot water also contributes to the GTA’s renewable energy mix. Recently, more wind and solar photovoltaic is being used within the GTA.
Project Description
My geo-visualization project includes one interactive map and two graphs:
My map illustrates renewable energy locations in the GTA. This map is a proportional symbol map where the size of the circles depends on the size of the installation. Depending on the year and type chosen, the size varies. Users can view the results for different years between 1986 and 2014. Users can select years to see how many installed within that year and select the type of installation to see how many of that specific installation is within the GTA. The bar graph compares the type of installation and its size. The bars are stacked by the years each renewable energy was installed. The pie chart looks at the % of total counts of system owners. 79.31% of the system owners are generated.
Technology
Tableau is a data visualization software used to see and understand data. For my data visualization project, I used Tableau Public to create my dashboard. I chose to use Tableau because it has built-in visualizing and interacting tools and provides limitless data exploration. It allows you to import many different types of files such as shapefiles, text files and excel files to the maps.
Data & Methods
The data used for this project was downloaded from the Toronto Open Data Portal. I used the Renewable Energy Installations shapefile (Click here) for my map. This data consists of point data of the renewable energy locations in the GTA. It displays data from 1863 to 2014. The attribute for this data includes Building Name, Location, Type, Year Installed, Size (ekW), etc. This data was imported into Tableau’s data source as a ‘Spatial file”.
For my map, I added the “Geometry” field into the “Marks” card in Sheet. This added the generated Longitude field to the “Columns” tab and the generated Latitude field to the “Rows” tab. The background map was set to a dark theme and in the upper-right “Show Me” tab, the map icon can be selected to generate the base map.
From the Tables column, I added multiple features to the sheet. Systems Owner, Geometry, Building Name, Type, Year Installed, and Size field were added to the “Marks” card. The Type field was set to Colour and Sum Size field was set to Size. Then under the “Marks” card, I set it to Circle to allow the Size field to symbolized using a proportional symbol. A year filter was added to the map. Users can use the slider to look at the installations by year.
Next, I opened a second sheet. This was used for the bar graph. For the bar graph, under the “Marks” card, I set it to Bar. I used Type for the “Rows” tab and Size for the “Columns” tab. Under the “Marks” card, the Year Installed field was set to Colour, this produced a stacked bar graph.
Next, I opened the third sheet. This was used for the pie chart. For the pie chart, under the “Marks” card, I set it to Pie. I added the System Owners into the “Marks” card. For the System Owners, I right clicked on that field, and the “Measure” was set to “Count”. Next, I right clicked on that field again, and set the “Quick Table Calculation” to “Percent of Total”. This computed the percentage for each System Owners count.
Finally, a new “dashboard sheet” was added and the 3 sheets were dragged into it. The legend for the map had “floating” items. This was done by right-clicking on the legend item in the dashboard and from the layout column on the left side, floating was clicked. The bar graph and pie chart were also floating items. They were placed at the bottom of the interactive map with their respective legends.
Limitations and Future Works
One of my main limitation for this project was getting data. Initially, I planned to create a Canada-wide level dataset map. There are over 600 renewable energy locations across Canada. However, for the Renewable Energy installations data, I was not able to find Canada wide level dataset. This restriction made me change my focus to the GTA. As a result, my map only focuses on 58 installations. The downloaded file was a shapefile data. Moreover, the downloaded data was incomplete. It included City divisions but not all Agencies or Corporations. When I imported the data into Tableau, it had a lot of null columns; it was missing ward names, etc. On the Open Data Portal, the same data was in a .xlsx format. However, this format had more fields within it (such as ward names, etc.). When I tried using that in Tableau, it was missing the geometry field, as a result, it did not display any data on the map. Additionally, this data was in months, so I was not able to connect both this table and the shapefile table on Tableau.
Another limitation is with the proportional symbols for the size of the installations. The EditSize feature for the proportional symbols was very limited with its edit options. It does not allow you to select the number of divisions you want for your data. If Tableau enables this feature, it will help users customize what they want to their symbols.
To expand on this project, it would be more beneficial to add additional information/context on Renewable Energy Installations. For the future of this project, if I had information on the units and energy used for each installation, I would have been able to look at how efficient each installation is. This would state the measuring cost and the benefits of energy transitions. Moreover, using location data such as urban vs. rural would add more information to the base map. It would allow users to understand and see where these installations are located visually. As a result, with additional information and a complete dataset, this geo-visualization project can be expanded and improved.