Equity is often confused with equality - they are both concepts that stem from a desire to liberate people from conditions of oppression, but the distinction between the two is incredibly important:
Equality indicates that individuals or communities receive the exact same resources or opportunities.
Equity, however, takes this a step further and acknowledges that despite equal resources, the foundation that one stands on may not provide them with the same capacity to make use of these resources.
For example, taking the approach of equality to school funding would mean that each school receives money based on the number of students they have - i.e., a basic amount per head. Conversely, when we take equity into account, we would see that educational disparities may exist between schools, making the basic amount per head unnecessary for some and not enough for others. A school located in a low-income area may require more resources for school breakfasts or after school programming. In this way, we can see that equality is about equal opportunity, while equity is about equal outcomes.
Source: Tony Ruth (@LunchBreath for Design in Tech, 2019)
With these definitions and frameworks in mind, it isn’t a large stretch to say that implementing policies with the lens of equality is simple - give every community the exact same resources and the exact same funding. If we want to follow the equitable path however, we are confronted with a slightly more complex problem: How do we decide where the resources should be allocated based on need, in order to achieve equitable outcomes and progress towards a just society?
This is where Geographic Information Systems (GIS) comes in! Equity - and thus inequity - like most people-based phenomena, has a spatial component. One’s background, identity, occupation, place of residence and affiliations all have equity implications. Data, while imperfect, can still help us understand many indicative determinants of equity at a regional as well as a local scale. By operationalizing place-based decision making (a key goal of geospatial analysis), it is possible to assess the relationships between determinants and begin the process of framing opportunities and constraints and potentially prioritize communities for engagement, action and additional insight.
What does this look like in practice? In advance of any substantive efforts, the usage of GIS and geospatial analysis should be limited to ethical and community-aligned equity projects. That is to say, equity analysis can be used for good and ill. One analyst’s worthy community is another analyst's high-profit redevelopment project. As such, transparent objectives with regards to the intended use of equity analysis is critical to ensure positive outcomes. If the ethics are sound, then analysis is reasonably straightforward. Additionally, data sources should also be questioned, as many data are collected under inequitable circumstances and effectively act to perpetuate inequity. Many equity-related variables (while limited in terms of their disaggregation) are available from numerous data sources, such as Statistics Canada, Environics, and various survey-based research projects such as My Health, My Community or the Early Childhood Development Index. While these data-sets have some bias, they are considered large and neutral enough to provide some value. Mapping these variables can establish a baseline of inequitable conditions, but can also be analysed in contrast to each other.
For example, let’s consider the percentage of seniors within a community, something that is measured by Statistics Canada. Seniors can be economically vulnerable, with their cost of living often exceeding their income. However, seniors are a heterogeneous group with diverse needs, abilities and opportunities. While percentages of Seniors may not be an equity indicator on it’s own, the percentage of Seniors contrasted with percentage of population living below the Low income cut off (LICO) could reveal areas with increased need for senior-based social services such as below market housing or low-cost/barrier recreational facilities.
Source: Licker Geospatial for Metro Vancouver, 2020
This type of mapping is but one small and basic example of the application of GIS to view both our communities and equity-denied populations through a spatial lens. To that end, the analysis shown above produced actionable results: indicating specific neighbourhoods where public outreach and consultation can be targeted in an effort to ascertain the needs of the community.
By understanding where there are populations in need of additional services, increased funding, infrastructure improvements, and additional measures can be deployed to improve equity, and improve the lives of those that need it the most.
Importantly, this work can be expanded to add value to the analysis. Further statistical analysis, like a Principal Component Analysis, can create indexes that take into account all available data and transform it into a usable single indicator. That being said, explaining PCA is worthy of a whole blog post to itself - Stay tuned for more!