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.
The r n_persist_hh Persistent Hot Spot tracts are most heavily concentrated in the South Phoenix and Maryvale corridors, especially in parts of the West Valley.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.
New hot spots are popping up in places like Mesa and Glendale that weren’t struggling ten years ago. This is often caused by the displacement, because as older neighborhoods closer to downtown become “trendy” and expensive (gentrification), the original residents can no longer afford to live there.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.
Even though more than half of the neighborhoods in the county show improved scores, in places like Central Phoenix and Tempe, the hardship scores are dropping. This might just be because wealthier people are moving into new apartments while the people who used to live there can no longer afford the rent.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.
We recommend that Maricopa County build a new community resource center in the Maryvale and West Glendale area because the data shows this neighborhood stays in a hot spot no matter what. Since this area has very high scores for being far from buses and having lower graduation rates, a center that offers job training and childcare right on a bus line would help residents overcome these specific barriers. This plan puts help exactly where the map shows the most stuck or long-term financial struggles.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.
We need to focus on neighborhoods in the Mesa and Gilbert area that are just now starting to show high levels of financial struggle. This is happening because as some parts of the city get more expensive, people are forced into these emerging areas where rent is rising fast but there aren’t enough resources to help. We recommend that the state starts a rent protection program there immediately to help families stay in their homes before these neighborhoods become permanently stuck in poverty.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.
The fact that r pct_worsened_23% of tracts have worsened over the last decade, combined with a strong Moran’s I of r round(moran_global$estimate[1], 3), proves that economic hardship is not random but deeply concentrated in specific geographic clusters. To prevent these hardship corridors from expanding further into the suburban fringe, regional leaders should implement a real-time data monitoring system that tracks shifts in renter burden and transportation access before they solidify into permanent poverty traps. By using the mobility Sankey to identify which neighborhoods are descending in stability, housing and transit authorities can align their budgets to provide targeted relief exactly where the data shows the most rapid decline.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.