The overall goal of this presentation is to demonstrate a complex, real-world spatial analysis workflow from start to finish. The specific analysis focuses on assessing how the Supreme Court decision overturning Roe v. Wade affects access to women’s healthcare across the United States. This case study highlights how decisions made at national and global levels can produce varied impacts at local scales. In this analysis, the primary focus is the loss of access to women’s healthcare, measured in terms of increased travel times to available services. Additionally, it examines the potential surge in demand at the remaining clinics providing these services and explores the spatial distribution of the cumulative burden resulting from these changes.
Feature to Point vs. Mean Center Tools
To represent area features, such as census tracts, representative points were generated using the Feature to Point and Mean Center tools. Each tool has distinct advantages and limitations:
- Feature to Point: Straightforward, fast, and easy to interpret. However, the centroids it produces may not accurately reflect where the population actually resides, especially in large, sparsely populated rural tracts.
- Mean Center: Calculates a population-weighted center based on more granular census block data from the Living Atlas, producing a more representative location for the bulk of the population. The trade-off is that processing large datasets with the Mean Center tool can require significant computation time.
Multivariate Clustering
Multivariate clustering was applied to identify spatial patterns among census tracts with similar scores on an index representing the cumulative burden of multiple factors. This tool employs the k-means clustering method to reveal clusters where residents are disproportionately affected, highlighting areas with heightened vulnerability.
Conclusion
The analysis underscores the profound impact of the Supreme Court decision on access to women’s healthcare. Key findings indicate that the primary consequences are increased travel times for women and heightened demand at the remaining clinics. These impacts are spatially uneven, disproportionately affecting communities that are already disadvantaged or marginalized. Notably, the analysis highlights that residents of Texas are likely to face some of the heaviest burdens. Overall, the workflow demonstrates the power of spatial analysis to quantify and visualize the local-level consequences of high-level policy decisions, providing actionable insights for policymakers and stakeholders.