High Economic Hardship Clusters (HH) — Hot Spot Tracts Statewide:
262
Low Economic Hardship Clusters (LL) — Cold Spot Tracts Statewide:
270
Tracts Improved (2013→2023)
56%
Tracts Worsened (2013→2023)
31.6%
Persistently High Hardship (2013–2023)
18.6%
Emerging Hot Spots (2013–2023)
1.7%
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 0.678 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: 31.6% 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: 165 Persistent Hot Spot tracts (18.6% of all Maricopa tracts): spatially concentrated, entrenched hardship confirmed by a Global Moran’s I of 0.678.
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.
[Many tracts in the urban core near South Phoenix and West near Glendale report hardship index above 1. For example, tract 929 displays an index of 1.544. Heading towards South Phoenix, tract 1169 has a hardship Index of 1.086. Within South Phoenix, is where the hardship index trends the highest with tract 1133.01 reporting an index of 2.282 and neighboring tract 1139 an index of 2.809. It is not a surprise that the two highest indices are next to each other considering the county’s Moran I of 0.678, supporting strong positive spatial correlation. To improve hardship in the county, policy should focus on the South and West Phoenix hardship corridor. Persistent Hardship spread more throughout the area when adding the Rental Burden index. The county should implement a policy focused on rent control & eviction protection. Addressing housing first will introduce housing stability into the area that can help other initiatives. The City of Phoenix housing department should be the responsibility entity with insight into housing costs, landlord incentives, and the ability to provide tenant support services]Target: 15 Emerging Hot Spot tracts (1.7% 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 number of Emerging Hot Spots dropped to 15 in the 5-index calculations compared to 25 with only 3 indices. Comparing the two maps you can see two types of shifts. The first is the tracts have now shifted to Persistent Hot Spot with the new indices. This is the case with tracts 1162.03, 4211.02, and 923.07. The second type of shift is a tract no longer considered significant. We see this occur at tracts 3195, 1171, and 1045.01. These shifts may be attributed to additional indices demonstrating true spatial trends. However, due to the manner of shifting occurring we must also consider the displacement paradox. This occurs when rising costs displace vulnerable citizens to neighboring tracts. This would align with the tracts that are no longer considered in significant hardship neighboring those who are Emerging and Persistent Hot Spots. Most Emerging Hot Spots are bordering current Persistent Hot Spots. A cluster of Emerging Hot Spots have popped up Northeast of Mesa, in West Phoenix, North of Glendale, as well as a few spots just north of the city center Persistent Hot Spots. With a high Moran’s I of 0.678, it is expected that hardship may effect neighboring tracts. Another clue that these are truly Emerging Hot Spots, is that they are occurring near existing clusters with the additional indices. The spread and shift of Emerging Hot Spots should be monitored on a regular basis. Tracking evictions, rent increases/ burdens, and migration patterns can help determine the direction of hardship index the tracts are trending. ]Evidence base: 56% of tracts improved (2013→2023) but 31.6% worsened; Moran’s I = 0.678 confirms strong spatial clustering persists.
TODO: Using the trajectory map and mobility Sankey, make a forward-looking data-monitoring or cross-sector coordination argument.
[Improving tracts should also be considered against worsening tracts to determine true improvement. In this case, 56% of tracts have improved with 31.6% of tracts worsening. The Moran’s I of 0.678 supports strong clustering, meaning the improving tracts are clustered together and worsening tracts clustering with like. The cluster trajectory map also supports with the Persistent Hot Spots clustered together in the urban core, and the Persistent Cold Spot clustered north and east of the city, clear polarization. The Sankey points to how the population is moving between economic hardship quintiles. 38 tracts moved out of Q1 least hardship, with some of those tracts dipping as far down to Q4 in their movement. 39 tracts moved up from Q5 most hardship, with only 1 tract making it as far up as Q3. Analyzing the movement of the two extremes, Q1 and Q5, we can see that tracts in Q1 fall further into hardship (down 3 quintiles) than Q5 tracts can work up (up 2 quintiles). A key takeaway from the mobility Sankey is the importance of addressing Emerging Hot Spots right away. It is much harder for a tract in deep hardship to correct. Monitoring that movement is occurring is important but also considering which quintile the tract is moving to is key. Q1 tracts falling to Q4 is an indication of sharp decline and structural issues. As mentioned in previous recommendation, housing stability and the surround inputs should be sharply monitored. Income is also a big piece of the puzzle. Low job availability & high unemployment can compound other affordability issues and make recovery for an area near impossible. Both job vacancies and unemployment numbers should be regularly monitoring along with housing stability to help determine the hardship health of the county. ]The baseline EHI consists of 3 measures: Poverty + Unemployment + Income (inv.)
Current index: 5-component EHI: Poverty + Unemployment + Income (inv.) + Renter Burden + Low Ed. Attainment
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 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.