High Economic Hardship Clusters (HH) — Hot Spot Tracts Statewide:
201
Low Economic Hardship Clusters (LL) — Cold Spot Tracts Statewide:
260
Tracts Improved (2013→2023)
63.9%
Tracts Worsened (2013→2023)
29.4%
Persistently High Hardship (2013–2023)
17%
Emerging Hot Spots (2013–2023)
2.9%
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 0.624 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.
The persistent high-hardship tracts appear to cluster mainly in the southern and western portions of Phoenix, especially around South Phoenix and areas near Glendale and Maryvale. These neighborhoods consistently show higher hardship scores across multiple years, suggesting that economic hardship is deeply rooted rather than temporary. The clustering pattern also shows that hardship spills across tract boundaries, meaning nearby neighborhoods are affected by similar structural conditions.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.
Emerging hot spot tracts appear to be forming along outer suburban edges and lower-cost areas surrounding central Phoenix. Rising housing costs and redevelopment in historically lower-income neighborhoods may be pushing vulnerable residents outward into areas with fewer services and transportation options. This supports the displacement paradox because some “improving” tracts may actually reflect population turnover instead of improved living conditions for long-term residents.The majority of tracts improved over the decade-long window. However, aggregate improvement masks significant variation: 29.4% 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.
Maricopa County experienced overall improvement across many tracts between 2013 and 2023, likely influenced by regional economic growth and population expansion. However, improvement was uneven, with some neighborhoods continuing to experience concentrated hardship despite broader county growth. This suggests that countywide economic gains are not reaching all communities equally.Target: 150 Persistent Hot Spot tracts (17% of all Maricopa tracts): spatially concentrated, entrenched hardship confirmed by a Global Moran’s I of 0.624.
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.
Place-based investment should prioritize the South Phoenix and Maryvale corridors, where persistent hardship clusters remained across both time periods. These tracts consistently recorded high Economic Hardship Index values and remained surrounded by similarly distressed neighborhoods, reinforcing spatial concentration. Maricopa County and the City of Phoenix should coordinate investments in public transit access, workforce development, and affordable housing preservation to address multiple dimensions of hardship simultaneously.Target: 25 Emerging Hot Spot tracts (2.9% 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 hardship clusters should be monitored closely in outer suburban areas where lower-income households may be relocating because of rising housing costs in central Phoenix. The county should develop an early-warning monitoring system using annual ACS indicators, eviction data, and rent burden trends to identify neighborhoods at risk before hardship becomes deeply entrenched. This would allow policymakers to intervene earlier through rental assistance, transportation planning, and community stabilization programs.Evidence base: 63.9% of tracts improved (2013→2023) but 29.4% worsened; Moran’s I = 0.624 confirms strong spatial clustering persists.
TODO: Using the trajectory map and mobility Sankey, make a forward-looking data-monitoring or cross-sector coordination argument.
The trajectory map and Sankey diagram show that hardship conditions are not static and can shift substantially over time across neighborhoods. Because many tracts moved between hardship quintiles, county agencies should maintain continuous spatial monitoring instead of relying on one-time assessments. Stronger coordination between housing, transportation, workforce, and public health agencies would help address the interconnected causes of hardship that tend to cluster geographically.The baseline EHI consists of 3 measures: Poverty + Unemployment + Income (inv.)
Current index: 3-component EHI: Poverty + Unemployment + Income (inv.)
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 [your chosen component] 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.