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.

Regions: Glendale, Mesa and Tempe

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.

Regions: Emerging hot spots around Tempe, Glendale and Phoenix

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.

Emerging Cold Spots: Sun Tan Valley Dissolving Hot Spots: Phoenix region

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.

The recommended place-based strategy should focus on collaborating with local school districts for education initiatives, coordinating with city transportation agencies to improve transit access, working with housing authorities to expand affordable housing, and partnering with municipal economic development offices to target economic stimulus. These actions will directly address hardship in Glendale and Phoenix, particularly in the surrounding areas that are showing emerging Hot Spots. The City of Phoenix and the Glendale municipal governments should lead this intervention.

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.

Emerging Hot Spot clusters are forming outside of persistent HH tract areas such as Tempe, Mesa, and Glendale. This could mean that residents within HH areas are being pushed out into the surrounding areas. Spatial correlation has been shown to exist and is spreading within these areas. Thus, a place-based intervention targeting the outer boundaries of these emerging hot spot zones is recommended. Interventions are more effective and less costly along these boundary lines and may help limit the spread of HH areas in the future.

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.

Interventions are needed in tracts showing improvement, as progress may unintentionally drive displacement. Although current data does not reveal displacement rates, proactive approaches, such as monitoring school enrolment rates, incoming business proposals, and community engagement, can help identify early signs of unintended displacement. Observing areas surrounding affected areas with increased economic hardship is also important, as individuals often relocate there if they are displaced by gentrification or rising living costs.

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

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?

The Arizona 3-Component baseline shows that there are 201 High Economic Hardship Clusters (HH) and 260 Low Economic Hardship Clusters (LL) statewide. In the expanded 7-Component Arizona map, we see a significant shift, with the number of HH Clusters growing to 267 and the number of LLs to 269. With the introduction of the 4 additional hardship variables, we see that final hardship scores decrease, while the overall total count increases in both HH and LL.

This pattern is reflected in Maricopa County as well. Where the most significant tracts in the 3-variable map remain the same in the expanded version, but their overall hardship scores have generally decreased.

Apache County remains the highest-Economic Hardship County in both versions, but the hardship score decreases significantly from 1.22 to .906. Notably, the 2nd-place county changes as well. In the 3-Component map, La Paz County 1.11 is in second place; in the expanded 7-variable version, it changes to Santa Cruz County in 2ns place with a score of 0.615.

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?

The most persistent Maricopa County Hot Spots are: the Gila Bend Region, the Gila Indian Reservation, Glendale, and Phoenix. These areas remained HH areas regardless of the selected index composition. The continuation and expansion of observed hardship within these areas suggest that they face deeply rooted hardship challenges.

When the 4 additional factors were included, the hardship regions around these HH spots actually expanded. This indicates that the initial variables did not fully capture the extent of hardship experienced within these tracts. This expansion provides a clearer understanding of the various factors that contribute to hardship and what is driving this persistent economic hardship.

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.

If a policymaker targeted place-based investments within the baseline index HH areas, resource and program allocation could potentially be less effective. While the baseline tracts would be targeted, the expanded version shows that hardship is more prevalent and actually extends beyond those initial baseline locations, particularly within the Glendale and Phoenix regions. Research shows that interventions are more effective at the outer edges of economic hardship areas. In this case, hardship tracts could be omitted due to the baseline’s limited scope, potentially leaving out tracts that would benefit from intervention and could help slow the spread of economic hardship to surrounding tracts.