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.] The strongest corridor of persistent economic hardship in Maricopa County appears to be mostly concentrated in the west central area around Phoenix, Glendale area. There is also a pocket in Mesa area that has persistent hot spots are concentrated. Collectively these are the clusters of census tracts remained statistically significant hot spots in both 2013 and 2019. These areas are associated with some of the highest hardship index values in the county and are part of the 158 (125 were the same from 2013 and 33 were added from Q4 hardship level to Q5) persistent hot spot tracts identified in the dashboard.

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 24 emerging hot spot tracts (2.7%) are mostly forming along the outer edges of the urban core, particularly in Tempa and Mesa. These areas were not clustered in 2013 but became significant by 2019, showing that hardship is spreading outward. This pattern may be occuring because of rising housing costs in central areas pushing lower-income households into more affordable peripheral areas. For example, Maricopa County has a 34.4% renter burden rate, and 4.7% unemployment rate suggesting housing affordability pressures that may be driving this shift. Even though the percentage is small, it signals the early stages of spatial expansion of hardship.

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.] While 53.7% of tracts improved between 2013 and 2023, a significant 31.1% of tracts worsened, showing uneven progress across the county. The Sankey diagram also shows that many tracts remain in the same hardship quintiles over time, especially those in Q4 and Q5. This suggests that improvement is not evenly distributed. Additionally, Maricopa’s EH index only improved slightly (from -0.211 to -0.116 after adding variables), indicating that underlying hardship still exists despite overall gains. This highlights the importance of verifying improvements with community-level data.

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.

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.

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 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?

In context of Maricopa county, after adding renter burden, low eductaion Attainment, food insecurity and transportation disadvantage to the index, the share of tracts that improved dropped from 63.9% to 53.7%, while those that worsened increased from 29.4% to 31.1% indicating the expanded index captures additional hardship. Spatially, new hot spots appear in areas like Sun City and Surprise in the northwest, as well as around Tempe and just north of Phoenix’s downtown core. In broader context of Arizona state, hot spots increased from 201 to 267 and northwest, west and mid-eastern areas doing better while metropolitian areas like Phoenix, Tucson and Tempe picking up more hot spots. These shifts suggest that hardship is no longer confined to traditional inner-city areas but is spreading into suburban and transitional neighborhoods, likely driven by rising housing costs and cost-of-living pressures. Maricopa’s EH index also increased from -0.211 to -0.116, reinforcing that overall measured hardship rises when these additional factors are included.

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 area around the south-central Phoenix corridor, Glendale area and eastern Maricopa county (Tempe and Mesa) remained a persistent hot spot across both versions, with about 158 tracts (17.8%) consistently identified. Out of 158 tracts, 125 tracts remained unchanged from 2013 to 2019 whereas 33 tracts upgraded from Q4 level hardship to Q5 level. The strong clustering (Moran’s I = 0.684) also did not change, reinforcing that hardship is spatially concentrated. For broader Arizona, northeastern areas predominantly indian reservation remained in the hot spot. This consistency across different index specifications increases confidence that these areas are truly disadvantaged and not just sensitive to how the index is constructed.

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.

Using the baseline index (poverty, unemployment, and income) would likely focus resources mainly on historically disadvantaged areas like central and south Phoenix. However, the expanded index shifts some attention toward newer areas of concern, particularly in the western and southwestern parts of the county, where housing cost burden and food insecurity are more prominent. For example, even though Maricopa County ranks relatively low in hardship statewide (around 13th out of 15 counties after expansion), the added variables reveal more localized stress that the baseline index misses. Also, Overall, the expanded index provides a more complete picture of hardship and is better suited for policy because it captures both traditional economic disadvantage and newer cost-of-living pressures, leading to more targeted interventions.