Northern Manitoba and Alaska Interior
Energy access is often defined by the 4 A's of Energy Security: Affordability, Accessibility, Adaptability, and Acceptability. However, while energy access is a pressing issue, especially for roughly 1.3 billion people who have no off-grid access, there is no central method to visualize the various forms of energy access in these communities. While data exists it is often scattered or segmented which leads to less efficient analaysis and solutions to address off-grid access. Our project's concrete visualization problem was to visualize energy access in remote regions of Alaska, USA and Manitoba, CA with our novel solution being layering the various energy sources alongside community data to illustrate and inform relations between the two.
This project, conducted for EE 559: Energy and Sustainability and CSE 512: Data Visualization, explores equity-centered challenges in off-grid electrification, particularly focusing on Indigenous communities in Northern Manitoba (by Caitie) and Alaska Interior (by Lin). The aim is to examine energy access issues rooted in geography, policy, and technology, with a particular emphasis on diesel dependence and multi-renewable evergy access.
Please view our Northern Manitoba and Alaska Interior Energy Access Visualization - Video Introduction🎥
For more information on our research, please view our Alaska Interior Presentation⭐ and our Northern Manitoba Presentation⭐
Energy access is a pressing issue globally with over 900 million people expected without energy access by 2030, with the largest communities projected to be without energy access by 2030 in the Sub-Saharan Africa and South Asia (Alstone et al., 2015). Our visualization attempts to understand energy access from the perspective of visual interaction; we want to inform the user with clear metrics what energy access exists in communities. Rather than using an abstract chart, such as a pie chart, bar graph, or flow chart, we wanted to showcase the access in a method that users can tangibly visualize with a map. While raw numeric charts are a necessary complement to understanding the data, such as our bar graph for diesel and fuel energy, we developed the novel map visualization of layered energy access. We are aware of the novelty of our idea, especially, due to our EE 559: Energy and Sustainability course which emphasized knowledge gaps in energy and the lack of a central graph to compare, contrast, and visualize energy access in remote communities.
The research question our team addressed was remote energy access in sub-arctic and arctic communities. Our problem was unique in that we coupled it with our research projects in the EE 559: Energy and Sustainability course which gave us a unique insight and outlook into energy transition problems and issues. We complimented the work for that course through developing this visualization to pitch our policy recommendations and therefore were keen on making a defensible and clear visualization.
Originally, we wanted to showcase energy access in the two regions, but as we continued, we wanted to go deeper and showcase the problem for the relevant communities. Instead of leaving the viewer wondering how this energy access impacted communities we included community data to educate the viewer. We further wanted to show comparisons of fuel prices and fuel usage, as diesel dependece is a massive issue (Bhattarai, 2016). We included the data we found to address this issue in a comparison bar chart of communities. Our main goal was to show how energy security and sovereignty often intersects with energy access, population size, and heightened costs.
Our team's first goal was to find datasets for the various energy sources which exist for communities. We sought out government reported data, first, and only looked at data which was in the last 10 years to ensure accruate and high-quality data. The Alaska Energy Data Gateway and the Alaska Open Data Geoportal are two key sources for energy-related data in Alaska. Our focus is on communities participating in the Power Cost Equalization (PCE) program, which provides financial support to rural areas where electricity costs are significantly higher than in urban regions. Administered by the Alaska Energy Authority (AEA) and the Regulatory Commission of Alaska (RCA), the program supports 82,000 residents in 193 largely diesel-dependent communities. We further investigated and found oil and geothermal data to include from the AEA. We found population data and sizes of communities from the Alaska Labor Statistics.
For Manitoba we used datasets from Canada Energy Regulator (CER), Province of Manitoba Oil Data, Manitoba Hydro, and Geology Survey of Canada. We immediately noticed that data for Canada was more segmented and hard to find; upon further researching we found that this was the case due to the government monopolies in place for energy. Energy was provided freely to the residents of Canada but, in turn, the transparency and need for stakeholder engagement was lacking because it was all handled by the government internally. We further had a hard time finding community data, fuel price data, and fuel usage data for the same reasons. However we did find the community size and population data from the Canada Population Census of 2021.
We chose to layer all the energy types to help the user see and compare the metrics that were of most interest to them. We began by layering the electric, hydro, natural gas, and transmission lines as this data was most readily available and easy to plot. We then found oil fields and geothermal layering somewhat tricky to plot as these were polygons instead of lines and points on the GeoJSON. However, we found a way to bound the data to our regions and highlight them in a semi-transparent layer for easy visualization. Afterwards, we went to layer the community data and initially started with simple markers.
As we looked and tinkered with our visualization we found that we actually wanted to make the viewer have an easier time visualizing community size and make population-scaled markers instead. We adjusted the scaling in a way that was easy to see at a zoomed-out scale but also different enough that large communities were clearly bigger in scale. Our layered techniques were designed in a way that were easy to navigate with a tooltip and see relevant community information metrics as well as instructions provided for the user to further investigate and compare the fuel data through clicking.
Our comparison bar chart is only generated upon the clicking of relevant communities for the sake of clear and clean visualization. We designed this bar chart to compare as many metrics as the user is interested in and adjusted the bars so they changed size and colors as the user went through the communities. We went further and matching the outer border of the clicked community to match its bar chart color, so the user could easily see which data matched which population bubble. We further designed this graph to have the functionality of clicking the community-name in the key to undo it, as well as the option of clicking the community itself, to give multi-functionality at engaging with community data.
Another methodology to make this project come to fruition was careful coordination, group-level milestones, and rigorous bug-fixes. We set internal deadlines for each other every day and collaborated in person biweekly while also communicating before and after we made revisions to keep the visualization on track with our group-level goals. We further worked on bug fixes together to ensure that we could find unique solutions instead of being stuck alone with bugs. In total this project was an astounding 90 hours between the two of us with all bug-fixes, design, and adjustments. While the workload was heavy we were passionate about making the graph for not just our coursework but also developing this into further research!
Our profiency for this graphic is evident in the three core features of:
All of these features compliment each other with clear visualization techniques, colors, and layers, and colors. Our checkbox functionality gives the user full control as well as the zooming-in feature so they can navigate and identify key areas. Alternatively, the user can check to see all community data and still see relevant trends and areas of interest, such as the interior east side of Alaska or the Northern Manitoba region.
The purpose of our visualization is to identify and address energy access in remote regions and we are certain that this graphic proves to be a valuable feature. We hope that this graphic is useful for individuals from all backgrounds from classmates to policy makers. Through making a data visualization with all relevant features, we hope to cut down the time spent looking for data and instead increase the time and efficiency used to address the challenges in the region! This is another core reason we spent so long making the graphic utilize different features, colors, and elements for a more educational experience.