High Economic Hardship Clusters (HH) — Hot Spot Tracts Statewide:
244
Low Economic Hardship Clusters (LL) — Cold Spot Tracts Statewide:
254
Tracts Improved (2013→2023)
57.5%
Tracts Worsened (2013→2023)
31.7%
Persistently High Hardship (2013–2023)
17.8%
Emerging Hot Spots (2013–2023)
2.8%
These census tracts had statistically significant high-hardship clustering in both 2013 and 2019. The pattern is not random: a Global Moran’s I of 0.65 confirms that hardship is spatially concentrated, not scattered. Tracts in this category share infrastructure deficits, limited employment access, and concentrated poverty that reinforce one another across neighborhood boundaries.
Implication: Individual-level interventions alone are unlikely to move the needle. Place-based, multi-sector investment is required.
These tracts were not significant hardship clusters in 2013 but became statistically significant by 2019, representing the spatial expansion of hardship beyond historically distressed cores. This is an early warning signal that hardship is spreading, not contained.
Displacement paradox: Some “improving” tracts nearby may be gentrifying, pushing lower-income households outward into these emerging clusters. Declining hardship scores do not necessarily mean existing residents are better off.
[TODO: Identify where emerging hot spots are forming in your county and what may be driving displacement.]The majority of tracts improved over the decade-long window. However, aggregate improvement masks significant variation: 31.7% of tracts worsened over the same period. The data cannot distinguish genuine economic uplift from population turnover: a tract with a declining hardship index may simply have replaced lower-income residents with higher-income newcomers.
Data limitation: Before drawing conclusions from improving scores, ground-truth verification through community engagement and displacement tracking is essential.
[TODO: Customize with your county’s specific context.]Target: 158 Persistent Hot Spot tracts (17.8% of all Maricopa tracts): spatially concentrated, entrenched hardship confirmed by a Global Moran’s I of 0.65.
TODO: Write your first recommendation. Name the specific geographic corridor, cite the hardship index values, and propose a concrete place-based intervention with a named responsible entity.
Focusing on the persistent hot spots in South Phoenix, where the economic hardship index in 2019 ranges from 0.098 to 2.647, a multifaceted place-based intervention strategy is needed to support this chronically disadvantaged area. The City of Phoenix should invest in its Housing Department, workforce development programs, and transportation systems to improve employment access and income stability. Workforce and transportation investments would help residents access and maintain stable jobs, while housing policies, such as expanding affordable housing and rent stabilization, would address ongoing cost burdens.Target: 24 Emerging Hot Spot tracts (2.8% of all Maricopa tracts), new high-hardship clusters not present in 2013, signaling spatial expansion.
TODO: Reference the displacement paradox, identify where these clusters are forming, and propose an early-intervention or monitoring strategy.
Emerging hot spots are forming on the outskirts of east Mesa, signaling the need for an early monitoring and intervention strategy. While the City of Mesa has begun addressing abandoned and neglected properties, these areas may also reflect displacement pressures as housing conditions shift. A monitoring system that tracks property turnover, rent changes, and resident displacement should be implemented, alongside consideration of anti-displacement policies to ensure existing residents benefit from neighborhood changes.Evidence base: 57.5% of tracts improved (2013→2023) but 31.7% worsened; Moran’s I = 0.65 confirms strong spatial clustering persists.
TODO: Using the trajectory map and mobility Sankey, make a forward-looking data-monitoring or cross-sector coordination argument.
Although 63.9% of tracts show improvement, the coexistence of 29.4% worsening tracts and strong spatial clustering (Moran’s I = 0.65) suggests uneven progress across the county. Using both Sankey’s mobility patterns and community engagement can help determine whether improvements reflect real gains for existing residents or displacement. This approach ensures that policy interventions are informed by both quantitative data and lived experiences.The baseline EHI consists of 3 measures: Poverty + Unemployment + Income (inv.)
Current index: 5-component EHI: Poverty + Unemployment + Income (inv.) + Renter Burden + Food Insecurity (SNAP)
After adding your extra component(s), answer the following (minimum 2 sentences each):Q1: What changed spatially?
Compare Hot Spot tract counts and cluster map patterns between your expanded index and the 3-component baseline. Did adding Renter Burden + Food Insecurity (SNAP) shift which tracts or corridors are flagged?
Q2: What stayed the same?
Which Persistent Hot Spot areas appear robustly across index specifications? What does consistency across different index compositions tell us about the reliability of hardship diagnoses in those tracts?
Q3: Policy implications of index choice
If a policymaker targeted place-based investments using the baseline index versus your expanded index, would resource allocation differ? Name specific tracts or geographic corridors and argue which composition better captures the full burden of economic hardship for policy purposes.