October App of the Month: The Population of Ottawa is Aging

One of the greatest challenges Canada will face in the next decade is our aging population. Factors such as infrastructure, social services and health care will need to be reevaluated to support this demographic change. Learn how United Way Ottawa used ArcGIS Online analytic tools to identify neighbourhoods where resources to better serve the city’s aging population are needed most today and into the future, and how Esri Story Maps were used to visually compare analytic predictions.

On occasion, I volunteer at a retirement home, where I try to envision the lives of the seniors who are living there. This age group of people in our society is not something we think hard about unless we interact with these individuals or have grandparents. Questions like “what are their health needs as they age?” and “what type of care do they need?” are questions that we don’t always consider however important as this group of population increases.

With Canada’s demographic landscape changing, there are many factors to consider when supporting our senior population. Do we have enough infrastructure to accommodate them? How will the social dynamic change as our senior population requires more services? What is the economic impact of a large portion of our workforce moving into retirement? What are the services needed to ensure the senior population is not isolated and marginalized?

In June 2017, United Way Ottawa, a non-profit organization, released A Profile of Vulnerable Seniors in the Ottawa Region—a report that analyzes the state of vulnerable seniors in Ottawa and makes recommendations to address this emerging issue. To connect different audiences with the report, the organization wanted to create a dynamic tool that allowed people to interact with the report and understand how an aging population will affect their neighbourhood. They began by analyzing where this population exists. For October’s App of the Month, we explore the story map, The Population of Ottawa is Aging, which they used to identify the neighbourhoods in Ottawa that require the most attention.

United Way Ottawa’s story map provides a striking visual to their web page, engaging readers to find out more.

The first thing you’ll notice in this story map is the swipe tool between two maps, which allows you to see how Ottawa’s senior population is projected to significantly increase by 2025. United Way Ottawa used the Story Map Swipe template to leverage the power of storytelling, with narrative content and a legend on the left panel. However, the real story focuses on the maps being compared. The two maps move in sync, providing a powerful tool for comparing data as seen below.

Using the swipe tool, you can easily compare and analyze data on the two maps.

Since the story map renders the web maps side by side, you can see the differences in the attributes of both maps as you move the swipe tool. For example, observe how the popup text and colour change as the swipe tool crosses the popup box. This allows you to compare quickly the contents of the maps.

Note the change in content and colour of the popup as the swipe tool crosses it.

Now diving in further, I want to discuss the analysis shown on the map. Using boundary and Statistics Canada data from the Ottawa Neighbourhood Study to map the current percentage of the city’s aging population, United Way Ottawa then leveraged Esri Demographics and Lifestyle Data to produce the percentage of the population aged 65 and over in 2025. This can be done through ArcGIS Online, where you can use the Enrich Layer tool to attach certain data (categories of attributes shown in the image below) to a feature or within a proximity of a feature. To view the different types of data available for a region, you can use the data browser, choose your region, and browse data by category. There are many useful datasets which you can select to enhance your data layer. While using the enrichment tool does consume credits, the analytics that comes with it is very powerful and offers numerous opportunities for revealing insights about your data.

There are numerous demographic data categories available to enrich your data layer.

Now that we’ve identified the neighbourhoods in Ottawa with a large aging population, what’s next?

Looking at the four types of analytics that Chris North pointed out in his article, the first three (descriptive, diagnostic and predictive) have already been completed in this case. To answer what’s next, let’s proceed with the fourth type – prescriptive analytics. For example, within those neighbourhoods identified with large aging populations, United Way Ottawa uses projected growth data to better mobilize local partners and resources to improve the wellbeing of seniors, reduce isolation and help them maintain a connection to their community. This ensures that their city’s most vulnerable people have access to the right place-based programs and services.

United Way Ottawa’s use of GIS analytic tools is outstanding because it’s creating a better understanding of Ottawa’s aging population. Applying predictive analysis in their research not only illustrates the understanding of demographic change, but it can also provide insight on possible solutions to help improve the services for seniors now and in the future.