High Economic Hardship Clusters (HH) — Hot Spot Tracts Statewide:
267
Low Economic Hardship Clusters (LL) — Cold Spot Tracts Statewide:
269
Tracts Improved (2013→2023)
53.7%
Tracts Worsened (2013→2023)
31.1%
Persistently High Hardship (2013–2023)
17.8%
Emerging Hot Spots (2013–2023)
2.7%
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 0.684 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.
[TODO: Identify the specific geographic corridor in your county where these tracts are concentrated.]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: 31.1% 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 0.684.
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.
Targeting the South Phoenix to Maryvale corridor, which contains 158 Persistent Hot Spot tracts (17.8%), should be the top priority. These areas consistently show high hardship and are reinforced by strong spatial clustering (Moran’s I = 0.684), meaning the conditions are deeply embedded across neighboring tracts. Maricopa County, in coordination with the City of Phoenix and Valley Metro, should implement a place-based strategy that combines affordable housing preservation, workforce development, and expanded transit access. Without coordinated investment across these sectors, isolated interventions are unlikely to break the cycle of entrenched hardship.Target: 24 Emerging Hot Spot tracts (2.7% 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.
The 24 Emerging Hot Spot tracts 2.7% located in west Phoenix and nearby transition areas should be treated as early warning zones. These areas likely reflect outward displacement from central Phoenix, where rising housing costs are pushing vulnerable populations into new locations. Maricopa County and local planning agencies should implement early intervention strategies, such as rental assistance programs, zoning protections, and monitoring of housing cost trends to prevent further concentration of hardship. Acting early in these areas is critical to avoid repeating the long term patterns seen in persistent hot spot corridors.Evidence base: 53.7% of tracts improved (2013→2023) but 31.1% worsened; Moran’s I = 0.684 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 53.7% of tracts improved, the fact that 31.1% worsened and clustering remains high (Moran’s I = 0.684) shows that hardship is still spatially concentrated and uneven. The County should establish an ongoing data monitoring and cross sector coordination strategy that integrates housing, transportation, and economic development data. Using tools like the trajectory map and mobility patterns, agencies can identify where hardship is shifting and respond in real time. This approach ensures that improvements are sustained and that emerging problem areas are addressed before they become persistent clusters.The baseline EHI consists of 3 measures: Poverty + Unemployment + Income (inv.)
Current index: 7-component EHI: Poverty + Unemployment + Income (inv.) + Renter Burden + Low Ed. Attainment + Food Insecurity (SNAP) + Transp. Disadvantage
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 + Low Ed. Attainment + Food Insecurity (SNAP) + Transp. Disadvantage 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.