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 0.624 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 persistent hot spots are located in Central Phoenix, South Phoenix, and in north 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.

The emerging hot spots are on the borders of the persistent hot spots, mostly around the North Tempe area.

The majority of tracts improved over the decade-long window. However, aggregate improvement masks significant variation: 30.8% 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.

Target: 160 Persistent Hot Spot tracts (18.1% of all Maricopa tracts): spatially concentrated, entrenched hardship confirmed by a Global Moran’s I of 0.624.

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 area west of Grand Canyon University is a persistent hot spot tract, including tract 931.04. The original composition’s hardship index was 2.069 in 2013 and 0.994 in 2023. With renter burden in the composition, the hardship index was 1.999 in 2013 and 1.179 in 2023.

Grand Canyon University, a private University, is the prime candidate to be responsible for this area’s improvement. The median age is ~22.6 (about 3/5ths the age of the median Maricopa County resident) indicating that the college already has many students living in this area.

( U.S. Census Bureau (2024). American Community Survey 5-year estimates. Retrieved from Census Reporter Profile page for Census Tract 931.04, Maricopa, AZ http://censusreporter.org/profiles/14000US04013093104-census-tract-93104-maricopa-az/).

Given that GCU is a private university, there are fewer avenues (or less obvious ones than the state has for publicly funded universities. BUt if they were to intervene, one thing they could do is provide educational opportunities to the local population and permanent residents. This would potentially improve the economic fortunes of the original residents.

Target: 25 Emerging Hot Spot tracts (2.8% 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 displacement paradox is that LISA clusters that show statistical improvements might not be improvements at all. While it could be genuine improvement in the area, another possibility is gentrification: original, poorer residents being outpriced/displaced by wealthy new residents. The lives of the people in that tract didn’t improve, they just moved elsewhere. These emerging hot spot tracts are forming in census tract 3190.01 and the surrounding areas outside of ASU’s Tempe campus.

One monitoring strategy could be to track the number of rentals overall in the area. As ASU grows, so does the need for student housing. One solution could be to increase the supply of housing, including on-campus housing to reduce the impact student growth has on gentrification of outside neighborhoods.

Evidence base: 59.6% of tracts improved (2013→2023) but 30.8% worsened; Moran’s I = 0.624 confirms strong spatial clustering persists.

TODO: Using the trajectory map and mobility Sankey, make a forward-looking data-monitoring or cross-sector coordination argument.

A cross sector strategy could be to have the state add additional funds to Arizona State University to build more on-campus student housing. Adding renter burden to the composite index caused the area around the Tempe campus to go from NS to HH and emerging HH. Removing more students from the housing marketplace should reduce the amount of students displacing poorer people who would like to stay in their homes; on campus, they are at least partially refunded or subsidized by ASU. “The price of living on campus at a four-year college is often much higher or much lower than the average rent paid by 18-to-24-year-olds in the same area. In the most populous states, such as New York, Texas, and California, the price of on-campus housing is lower than average rents” (Room and board - Urban Instittue. (n.d.). https://collegeaffordability.urban.org/prices-and-expenses/room-and-board/)

The baseline EHI consists of 3 measures: Poverty + Unemployment + Income (inv.) + Renter Burden

Current index: 4-component EHI: Poverty + Unemployment + Income (inv.) + Renter Burden

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 shift which tracts or corridors are flagged?

The number of hot spots statewide increased from 201 to 239. The number of low economic hardship clusters lowered from 260 to 237. For example, census tract 3188 went from a hardship index of 0.565 to 1.02 (a hot spot). That tract, along with other surrounding tracts in the area west of ASU’s Tempe campus, became hot spots when they were previously not significant (NS) areas. West Mesa also went from NS to HH.

Renter burden changing the area around ASU makes sense, as many of the people living around ASU probably have high renter burden as students who may be working part-time or going to school full-time (without an income).

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 surrounding Grand Avenue (Southeast Glendale and Phoenix) remained a hot spot in both index specifications. This includes census tract 929 which had a hardship index of 1.377 in the original composition and 1.346 when renter burden was added. Another area that remained a hot spot from the original composite index to the Renter Burden one was South Phoenix. Consistency across multiple index compositions tells us that they are all measuring the same underlying construct (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.

Yes, resource allocation would differ. The original baseline index contains indicators regarding income and wealth. Those indicators measured how much residents make, but not what their expenses are. Adding renter burden better captures the burden of economic hardship, especially as home prices increase year over year. Tract 3188, west of ASU Tempe, is not a significant hot spot or emerging hot spot in the original baseline index . It is, however, an emerging hot spot when renter burden is added.

For resource allocation, it is better to add renter burden because it identifies fewer emerging hot spots than the original (2.8% to 2.9%). Expanding the index actually narrowed the scope of emerging hot spots. This is crucial for efficient resource allocation, as reversing these trends can be done at a lower cost than reversing persistent hot spots with crystallized, systemic issues.