High Economic Hardship Clusters (HH) — Hot Spot Tracts Statewide:
42
Low Economic Hardship Clusters (LL) — Cold Spot Tracts Statewide:
64
Tracts Improved (2013→2023)
57.1%
Tracts Worsened (2013→2023)
38.1%
Persistently High Hardship (2013–2023)
6.3%
Emerging Hot Spots (2013–2023)
4.8%
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 0.449 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: 38.1% 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.]Target: 13 Persistent Hot Spot tracts (6.3% of all Bernalillo tracts): spatially concentrated, entrenched hardship confirmed by a Global Moran’s I of 0.449.
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 persistent hot spot tracts in Bernalillo County are concentrated in the International District along Central Avenue, a corridor long recognized as one of Albuquerque’s most economically distressed areas, where 7.1% of tracts have maintained statistically significant high-hardship clustering across both 2013 and 2019 (Moran’s I = 0.475). The District is home to a large immigrant population with limited access to jobs and services, and has also struggled with gang activity and drug use that pull young residents toward the criminal justice system rather than toward stable employment. The City of Albuquerque should pursue a two-part response: expand youth diversion programs in this corridor to keep school-age kids connected to education and out of the justice system, and increase street-level outreach to immigrant residents to connect them to job training and workforce programs that address the economic isolation this index reflects.Target: 6 Emerging Hot Spot tracts (4.8% of all Bernalillo 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 3.2% of Bernalillo tracts that emerged as new high-hardship clusters between 2013 and 2019 are forming in and around the Nob Hill area along Central Avenue, a neighborhood that has undergone significant transformation as rising rents, new businesses, and an influx of higher-income residents have pushed lower-income households out. Tracts that appear to be improving on the hardship index may simply be replacing lower-income residents rather than lifting them up, and those displaced residents are showing up as new hardship clusters in adjacent neighborhoods. Albuquerque has very limited rent protections and rents have been rising sharply, making early intervention on the housing side critical. The City of Albuquerque should pursue two steps: establish stronger tenant protections in gentrifying corridors to slow displacement, and review zoning rules in these neighborhoods to identify where additional non-luxury housing could be permitted, since new housing supply in Albuquerque has remained limited and what does get built tends to be out of reach for the residents most at risk.Evidence base: 57.1% of tracts improved (2013→2023) but 38.1% worsened; Moran’s I = 0.449 confirms strong spatial clustering persists.
TODO: Using the trajectory map and mobility Sankey, make a forward-looking data-monitoring or cross-sector coordination argument.
While 50.8% of Bernalillo tracts showed improvement between 2013 and 2023, the mobility Sankey shows that most of that movement was modest – tracts shifting slightly within adjacent quintiles rather than making meaningful jumps toward lower hardship. The Q5 tracts that were worst off in 2013 largely remained in Q5 by 2023, and with 40.5% of tracts worsening over the same period, the overall picture is closer to stagnation than progress. The county should not treat the majority-improved headline as a signal that existing conditions are working. Instead, Bernalillo County and the City of Albuquerque should establish a coordinated monitoring approach across housing, workforce, and health agencies that tracks Q5 tracts specifically over time, identifies what is driving continued hardship in those areas, and sets measurable targets for moving tracts out of the worst quintile rather than just counting overall improvement. Cross-agency data sharing is essential here because no single agency sees the full picture. Housing costs, unemployment, and food insecurity interact in ways that only show up when the data is looked at together.The baseline EHI consists of 3 measures: Poverty + Unemployment + Income (inv.)
Current index: 3-component EHI: Poverty + Unemployment + Income (inv.)
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 [your chosen component] 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.