GIS and Social Equity: High-Value Strategies for Mapping Intersectionality in Urban Resilience Planning | Green Smith Nepal

Overcome GIS limitations by integrating intersectionality into spatial analysis. Implement High-Value strategies: mixed-method data integration, community-led data governance, and organizational accountability to reveal root causes of inequity. Essential for planners seeking measurable, equitable outcomes and proven Urban Resilience ROI.

The complexity of intersectionality—the overlapping and interdependent nature of multiple forms of marginalization and discrimination (such as race, class, gender, and disability)—presents a profound challenge to quantitative spatial analysis tools like Geographic Information Systems (GIS). While GIS is an invaluable tool for urban planning, providing a "tangible way to highlight inequities", its reliance on quantifiable, standardized data can inherently "gloss over the complexities" of lived experience or "reinforce stigma, discrimination, stereotypes and racism".

The core dilemma is that social cohesion is "inherently based on the existence of social equity". Thus, if spatial planning fails to accurately map intersectional disadvantage, it risks misallocating resources, overlooking the "root causes of inequities", and ultimately failing to build genuine Urban Resilience.

To accurately represent intersectionality and uphold the ethical obligations of equity-focused planning, cities and practitioners must refine GIS through a multi-layered approach: methodological rigor in data overlay, supplementation with qualitative narratives, and institutionalizing ethical data governance that centers community ownership. This framework ensures that high-value data assets lead to equitable outcomes and maximize return on investment (ROI) in social infrastructure.

I. Overcoming the Limitations of GIS and Centralized Data

The fundamental challenge stems from the inherent nature of GIS and the systems that shape the data inputs. These limitations must be acknowledged and mitigated.

A. Acknowledging Data Harm and Systemic Bias

The systems that shape "big data" collection are often "shaped by a dominant colonial culture that puts significant value on quantity over quality". When relying solely on these centralized, quantitative inputs, the resulting maps can inadvertently cause harm:

1. Reinforcing Stigma: Data, if used inappropriately, can "reinforce stigma, discrimination, stereotypes and racism, and can cause harm to individuals and communities". GIS analysis can simplify complex social realities into clean, but misleading, data points that fail to capture the complexities in the ways that multiple forms of marginalization and discrimination overlap.
2. Excluding Indigenous Perspectives: Due to colonial practices, many GIS maps show missing data for First Nations reserves due to data suppression. Furthermore, analyzing where Indigenous people live today "neglects to acknowledge the colonial influences on the distribution of Indigenous people" and ignores traditional territories and rights.
3. Centralizing Power: A key ethical concern is that "the collection, reflection, and learning are now happening in a centralized place, away from the community members". This centralization inherently limits community access to information and removes agency, overriding the core principle that "local expertise is better suited to guiding community development".

B. The Dilemma of Quantification vs. Root Causes

GIS forces attention onto "the numbers and data", which risks drawing attention away from the non-quantifiable root causes of inequities. Practitioners must avoid the trap that the "hunt for more data is [often] a barrier to acting on what we already know". Therefore, refinement must prioritize utility and action over mere descriptive complexity.

II. Methodological Refinement: Mapping Intersectionality via MCE

GIS is most effectively refined to represent intersectionality by moving beyond single-variable mapping to combining multiple distinct vulnerability metrics using Multi-Criteria Evaluation (MCE). This process intentionally overlays and weights different forms of marginalization to identify areas with overlapping, compounding need.

A. Overlapping Measures in Priority Indexes

The core mechanism for mapping intersectionality in planning is designing priority indices that combine measures for different demographic and infrastructural factors. For a metric to successfully capture intersectionality, it must integrate variables that account for race, income, housing status, and health outcomes simultaneously.

1. Utilizing Composite Indices: The most direct way to capture multiple forms of disadvantage is by leveraging existing, validated tools. Cities use the Canadian Index of Multiple Deprivation (CIMD) or similar tools, which incorporate measures across socio-economic status, housing, and other social well-being metrics. This standardized measure can then be weighted alongside other variables.
2. Targeted MCE for Intervention Priority: Planning models use MCE with simple additive weighting to calculate a single numerical index (0 to 1) that prioritizes intervention based on need. In the context of equity, this ensures that the areas with the highest cumulative disadvantage (intersectionality) receive the highest priority rating.

• Example: Urban Tree Planting Priority (Heat/Housing/Income): GIS analysis for urban forest management successfully prioritized areas by overlaying three distinct forms of vulnerability with existing environmental deficit: low tree canopy coverage (environmental deficit); high extreme heat risk (acute shock); and social vulnerability measures such as high percentage of the population living in multi-unit dwellings (a housing marginalization factor) and low income. This synthesis identifies urban cores like downtown Vancouver and the Surrey City Centre, which suffer from a convergence of low tree cover, high heat exposure, and vulnerable populations, demonstrating a spatial intersection of environmental, housing, and socioeconomic factors.

B. Statistical Adjustments to Prevent Bias (High-Value Metrics)

To ensure the resulting maps are reliable and defensible to stakeholders, MCE must incorporate advanced statistical techniques to prevent affluent areas from artificially inflating equity scores.

1. Avoiding Outliers: In measures like park access per person, the presence of "very high outlier results" (e.g., areas next to massive provincial parks) can skew traditional scaling methods. To avoid ignoring variations in park access at the low end—where equity intervention is most needed—GIS must use log normalization instead of min-max normalization. This statistical refinement ensures that data accurately reflects true need, making the output more reliable for monetization purposes.
2. Addressing Data Age and Granularity: Practitioners must acknowledge that maps represent a "snapshot in time" and rely on potentially outdated data (e.g., 2016 Census data). GIS refinement requires a commitment to iterative work, noting that future studies should integrate more recent datasets (like the 2021 Census) to maintain accuracy.

III. Supplementation Strategy: Integrating Qualitative Truths

To prevent "glossing over the complexities" of people’s lives and ensure the data reflects the "unique lived experiences, hopes, and fears", GIS must be systematically supplemented with qualitative information. This is the High-Relevant strategy for connecting data to authentic community engagement.

A. Ground Truthing and Narrative Integration

The use of maps informed by quantitative data alone has "limitations". Therefore, the planning process must mandate integration of qualitative insights.

1. Interviews and Lived Experience: Planning processes must formally include peer review interviews with key stakeholders, municipal staff, and representatives of non-profit organizations. The qualitative data gathered—comments, ideas, and stories from those with lived experiences—is essential to "ground truth" the results of the spatial analysis. For instance, interviewees in one study suggested editing map text, adjusting chosen weights, and seeking different datasets.
2. Holistic Understanding: Qualitative narratives help planners understand the non-quantifiable factors influencing equity, such as available time, opportunity, and the history of the space. This counteracts the simplification inherent in a single numerical index.

B. Community-Led Data Interpretation (Bottom-Up Planning)

Jacobs’ perspective, that "local expertise is better suited to guiding community development", necessitates that the learning process remain within the community.

1. Decentralized Analysis: The data collection must be managed to ensure that the "collection, reflection, and learning are not happening in a centralized place, away from the community members". Instead, the GIS maps should be used as a "starting point to look into potential opportunities for future work".
2. Shifting Focus to Local Scale: While regional-level GIS analysis is useful for identifying areas for future exploration, practitioners found it difficult to relate to some regional maps. Refinement involves scaling the maps to the "local/municipal/neighbourhood level, at the scale in which people live their lives" to better integrate local expertise and address specific needs.

IV. Ethical Data Governance for Accountability and Inclusion

The successful refinement of GIS depends on an organizational commitment to ethical data management that builds accountability. This ensures that the use of spatial data aligns with the goals of social equity rather than reinforcing colonial or exclusionary systems.

A. Institutionalizing a Relationship-First Approach (High Relevant)

To mitigate the risk of data reinforcing stigma or discrimination, cities must mandate respectful relationships before and after data collection.

1. Mandating Community Partnership: Future work must include the "collaboration of Indigenous Nations and peoples as decision-makers and partners in project identification, design, and implementation". This high-value strategy moves beyond mere consultation to "follow the lead of the community" and "support their work with this kind of data".
2. Addressing Data Exclusion: The framework must proactively address missing data for Indigenous peoples and commit to "doing things differently in the future" by acknowledging historical harm and focusing on developing trust and respect.

B. Leveraging Technology for Ubiquity and Spatial Justice (High CPC)

The digitalization dimension of the 15-Minute City model, which leverages ICT infrastructure like IoT and 6G, must be managed to advance equitable outcomes.

1. Ubiquity as the Ethical Mandate: The core principle of ubiquity—the "equitable distribution of services and infrastructure across the entire urban geography"—is the spatial justice mandate for technology use. GIS and smart systems must support urban planners in "contextualising and implementing tailored 15-minute city models" that account for variations in geography, culture, and local needs.
2. Accountability through Data Sharing: The organization-wide equity framework must ensure that the insights gained from GIS analysis help "ensure that the strong voices of marginalized people and communities in the region are heard". This includes transparent decision-making that prevents data from being used as a top-down justification for projects that disregard community needs.

Conclusion

GIS, in its raw form, possesses limitations that threaten to gloss over the complex, non-quantifiable reality of intersectionality and structural inequity. However, by embracing a systematic refinement process—characterized by Multi-Criteria Evaluation that overlays distinct vulnerability measures, mandatory integration of qualitative narratives for "ground truthing," and ethical data governance built on community ownership and accountability—cities can transform spatial analysis into a powerful instrument for social justice. This strategic integration is not only an ethical imperative but a High-ROI mechanism, ensuring that resources are strategically directed to areas of greatest overlapping marginalization, thereby strengthening the social fabric and guaranteeing the long-term resilience of the urban ecosystem.