This map answers one question: if a child lives in this tract, how many youth-serving organizations can they reach?
🗺️ The Youth Org / 1K Children rate measures the number of accessible non-profit youth programs (after-school programs, mentoring, tutoring, sports leagues, youth development organizations) per 1,000 children aged 0-17 living in each tract, counted within a 5-mile radius for urban areas and 10-mile radius for rural areas.
Three views are available:
👉🏽 Map tab:
🟢 Green (200+) — strong absolute access
🟡 Yellow (75–200) — moderate
🔴 Red (under 75) — care desert
👉🏽 Regional View tab:
🟢 Green — above average for urban/rural peers
🟡 Yellow — near peer average
🔴 Red — below peers, underserved relative to context
Hover over any tract for name, district, and reach counts. Click clusters to see individual organizations. Switch between Map, Regional View, and Data Table.
Radius: 5mi urban / 10mi rural • Urban: Salt Lake, Utah, Davis, Weber, Tooele, Cache, Washington, Iron counties • Source: NCCS BMF v1.2, 2019-2024 • Window: 2019-2024 BMF filings
Youth-serving organizations are among the strongest evidence-based tools for preventing juvenile justice involvement.
👉🏽 Sharkey, Torrats-Espinosa & Takyar (2017) used instrumental variable methods across 264 cities to establish that community nonprofits causally reduce violent crime — every 10 additional organizations per 100,000 residents produced a 9% year-to-year decline in murder, with cumulative long-term effects reaching 12% for homicide, 10% for violent crime, and 7% for property crime. Youth development programs contributed substantially to the overall effect.
👉🏽 Summer youth employment programs produce some of the largest effect sizes in the youth violence prevention literature. RCTs in Boston and Chicago found that providing youth with structured summer work experience reduced violent crime arraignments by 35% and violent crime arrests by 43%, respectively, during the 16–18 months after participation (Modestino, 2019; Heller, 2014). The reductions were driven not simply by keeping youth busy but by measurable improvements in social skills, conflict resolution, and community connection, with effects concentrating among the most at-risk youth. Estimated net savings range from $1,700 to $1,900 per participant from crimes averted alone.
👉🏽 Hawks et al. (2021) identified high-quality childcare as one of the most effective anticrime interventions in a large-scale scoping review, with sizable effects on arrest rates persisting into adulthood.
👉🏽 Afterschool and park-based programs target the hours when youth are most vulnerable — research consistently identifies 2–6 p.m. on school days as peak time for juvenile crime and victimization (Gottfredson et al., 2004; Farrington et al., 2023). A spatial evaluation of a park-based afterschool program in Miami-Dade County found that neighborhoods with the program showed significantly greater decreases in youth arrest rates compared to matched comparison areas, with the program operating daily during precisely those peak-risk hours and combining academic support, sports, mental health promotion, and life skills development (D’Agostino et al., 2019, AJPH; 2020, JAMA Network Open). Recent systematic reviews confirm that program quality matters more than program existence alone. Programs emphasizing social skills and character development show the strongest effects on delinquent behavior, particularly for middle-school youth (Garcia et al., 2023).
Red tracts are where children have the fewest structured (non-profit) alternatives to unsupervised time — where the 2–6 p.m. vulnerability window is widest, gang recruitment faces the least competition from legitimate youth programs, and the protective effects documented across these studies are least likely to reach the children who need them most.
Density of community organizations that Sharkey et al. (2017) found reduce neighborhood violence — youth development, community improvement, crime prevention, human services, housing, and civic groups.
🟢 Green — strong community institutional infrastructure
🟡 Yellow — moderate institutional presence
🔴 Red — institutional desert, few community orgs within reach
Every 10 additional community nonprofits per 100,000 residents → 9% year-to-year decline in murder, with cumulative long-term effects of 12% reduction in homicide, 10% in violent crime, and 7% in property crime (Sharkey et al. 2017), through:
Radius: 7mi urban / 15mi rural • Rate: per 100,000 total pop • NTEE: S, O, I2, I4, P2/P3/P8, W, L2/L4 • Window: 2019-2024 BMF filings
Community organizations are the institutional infrastructure through which neighborhoods build and maintain the capacity to prevent crime. The evidence is causal, not merely correlational.
👉🏽 Sharkey et al. (2017; 2018) used instrumental variable methods across 264 cities to establish that community nonprofits causally reduce violent crime — every 10 additional organizations per 100,000 residents produced a 9% year-to-year decline in murder, with cumulative long-term effects reaching a 12% reduction in homicide, 10% in violent crime, and 7% in property crime. Community development, violence prevention, workforce, and substance abuse treatment groups drove these effects, contributing to the historic crime decline since the 1990s through cumulative community investment: providing after-school programs, treating addiction, offering reentry services, and reclaiming public spaces.
👉🏽 Sampson (1997, 2012) established that collective efficacy — mutual trust among neighbors combined with willingness to intervene for the common good — explains why some disadvantaged neighborhoods have low crime while others do not. Neighborhoods scoring high on collective efficacy showed crime rates approximately 40% lower than those scoring low, and collective efficacy explained over half the variance in homicide rates across Chicago neighborhoods. Community organizations are the institutional vehicles through which collective efficacy is built and sustained.
👉🏽 Harding, Western & Sandelson (2022) argued that investments in community-based support systems can achieve what supervision alone is limited to to deliver. People on probation and parole are nearly three times more likely to be unemployed, twice as likely to be in poverty, and twice as likely to be in psychological distress compared to the general population. These are the conditions that community organizations are positioned to address through direct service provision, referral networks, and social connection.
👉🏽 Branas et al. (2018) demonstrated in a cluster RCT in Philadelphia that even simple community investment in neglected spaces of cleaning vacant lots, planting grass, and installing low fences reduced gun violence by 29% and overall crime by 13% in the poorest neighborhoods. Residents near treated lots reported 37% reduced perceptions of crime. The finding reinforces that community-level investment in shared spaces, the kind of work community organizations coordinate, produces measurable safety returns.
Red tracts on this map are (non-profit) institutional deserts — places where the organizational infrastructure that Sharkey, Sampson, and Branas have shown to prevent crime is thinnest. These are neighborhoods where few organizations are coordinating services, supervising public spaces, connecting residents to resources, or building the social cohesion that keeps communities safe. Investing in community organizations in these tracts addresses the mechanism, not just the symptoms.
This map answers one question: if a person in this tract needs substance abuse treatment, mental health services, or behavioral health support, how many (non-profit) providers can they reach?
🟢 Green — residents can access multiple treatment providers within the service radius. These tracts have the strongest “treatment safety net.”
🟡 Yellow — some treatment access but below the regional average. Residents may face wait times, limited specialty options, or longer travel.
🔴 Red/Orange — treatment deserts where residents have few or no behavioral health providers within reach. These are the places where untreated conditions are most likely to be addressed through arrest rather than care.
Hover over any tract to see its name, county, and treatment reach counts. Click numbered clusters to zoom in and see individual treatment facilities. Toggle layers with the checkboxes. Switch to Data Table to search tracts by name, county, or treatment rate.
Radius: 15mi urban / 30mi rural • Rate: per 100,000 total pop • Source: NCCS BMF v1.2, 2019-2024 • Classification: NTEE codes F (Mental Health), I (Crime/Legal-Related treatment) • Window: 2019-2024 BMF filings
Access to behavioral health and substance use treatment is one of the strongest evidence-based pathways for reducing criminal justice contact.
👉🏽 Treatment nonprofits and crime reduction: Sharkey et al. (2017) found substance abuse treatment nonprofits have the largest per-organization crime reduction impact of any nonprofit type — each additional organization reduces murders by 23%, exceeding workforce development and all other community organization categories. The specific type of organization mapped on this tab has the highest marginal return on public safety of any community investment measured.
👉🏽 Healthcare access as criminal justice policy: A convergent body of causal research demonstrates that expanding health insurance coverage reduces both crime and recidivism. Medicaid expansion was associated with 20–30% reductions in violent arrests and 25–40% reductions in drug arrests across 3,035 U.S. counties (Simes & Jahn, 2022), an approximately 5% reduction in violent crime at the county level (Vogler, 2020), and overall crime rate reductions through earlier HIFA waiver expansions (Wen et al., 2017). The mechanism is consistent across studies: when treatment is unavailable, law enforcement become the default responders to behavioral health crises, and untreated conditions are addressed through arrest rather than care.
👉🏽 Recidivism and return on investment: Medicaid coverage causally reduced recidivism by 11.5–13.5%, driven by increased referrals to addiction treatment (Aslim et al., 2022). Every $1 spent providing Medicaid to people exiting prison generates $3.45 to $10.62 in social benefits, with the majority of returns from reduced future criminal involvement (Aslim, Mungan & Yu, 2024). People on community supervision are more than twice as likely to lack health insurance and be in psychological distress compared to the general population, yet supervision systems monitor compliance rather than connecting people to treatment (Harding, Western & Sandelson, 2022).
Red tracts are where the treatment care desert pathway is most active — where residents face the longest distances to care and the highest likelihood that their next contact will be law enforcement rather than a provider. These are the tracts where every additional treatment facility would produce the largest returns and where insurance coverage alone cannot translate into actual treatment without nearby providers to deliver it.
Accessibility of public safety organizations — crime prevention, victim services, reentry programs, restorative justice, and gang intervention.
🟢 Blue/Green — strong safety org access
🟡 Yellow — moderate access
🔴 Red — safety infrastructure desert
Public safety nonprofits are the institutional infrastructure that mediates between community risk factors and actual harm. Their presence creates pathways for violence intervention, victim support, and successful reentry. Tracts shown in red lack these organizational resources.
Two methods are used together:
NTEE I-codes — all Crime and Legal Related organizations, including crime prevention (I20), youth violence prevention (I21), offender rehabilitation (I40), legal services, and victim advocacy.
Name keyword matching — organizations in any NTEE category whose name contains: VIOLENCE, GANG, SAFETY, JUSTICE, REENTRY, PRISON, VICTIM, PROBATION, PAROLE, RESTORATIVE, or CRIME PREVENT.
For example, Polynesian Community Services Organization (NTEE I21) is classified as both Youth Enrichment and Public Safety — it is a youth violence prevention org that serves the Polynesian community in Salt Lake City. Many organizations like this sit at the intersection of youth development and public safety, which is why this is an overlapping category: an org can be counted in youth enrichment, Sharkey community, and public safety simultaneously.
These organizations were active at some point between 2019 and 2024 based on IRS BMF filing records. Some may no longer be operating — smaller community organizations can dissolve without immediate updates to the BMF. The map reflects the institutional landscape during this window, not necessarily the current state of every individual organization.
Radius: 7mi urban / 15mi rural • Rate: per 100,000 total pop • ID: NTEE I* + name keywords (overlapping with other categories) • Window: 2019-2024 BMF filings
This dashboard maps IRS-registered nonprofit organizations across Utah using the NCCS Unified BMF v1.2 (Urban Institute). The BMF contains every 501(c) organization with geocoded addresses and NTEE activity classifications.
Each nonprofit is assigned a service radius (euclidean distance) based on organization type. Urban counties (Salt Lake, Utah, Davis, Weber, Tooele, Cache, Washington, Iron) use different radii than rural counties to reflect different travel patterns.
| Category | Urban | Rural | Rationale |
|---|---|---|---|
| Youth Enrichment | 5 miles | 10 miles | Urban: UTA bus + parent driving. Rural: families drive further for youth programs |
| Community Orgs | 7 miles | 15 miles | Urban: neighborhoods span multiple tracts. Rural: community orgs serve wider areas |
| Treatment | 15 miles | 30 miles | Urban: valley is one treatment market. Rural: specialized services draw from large catchment |
| Public Safety | 7 miles | 15 miles | Urban: victim services serve county-wide. Rural: safety orgs cover multi-county regions |
Reach is computed by buffering each tract centroid (UTM Zone 12N projection for accurate distance in meters) and counting nonprofit points within the radius.
Rate stabilization: child population floor of 100 prevents extreme per-capita rates in tracts with few resident children. For community, treatment, and public safety reach rates (per 100K total population), a total population floor of 500 is applied to prevent inflated rates in low-population tracts.
The service reach model uses Euclidean distance buffering with regionally-adjusted radii, following the spatial access framework described by the Urban Institute's Spatial Equity Data Tool. This approach was selected over travel-time isochrones (which require road network and transit data that may not reflect actual travel patterns in rural Utah) and network distance (which ignores speed differentials). The Euclidean method's primary limitation — treating geographic distance as equivalent to travel time — is partially addressed through the urban/rural radius adjustment, which assigns wider service radii to rural counties where residents routinely travel longer distances. Future iterations could incorporate travel-time isochrones using the r5r routing engine with UTA GTFS transit data for urban counties.
The Regional View (available on the Youth Reach tab) compares each tract against its urban or rural peers rather than the statewide average. This addresses a fundamental problem: rural and urban nonprofit landscapes are so different that a single statewide scale masks meaningful variation within each context.
Raw reach counts (number of accessible orgs) are z-scored within region: urban tracts are standardized against the urban mean and standard deviation, rural tracts against the rural mean. A z-score of 0 means average for that region; negative means below peers; positive means above.
A rural tract with 39 accessible youth orgs (the rural average) gets z ≈ 0 on the regional scale but z ≈ -1.0 on the statewide scale (because the urban average is 149). Without regional comparison, every rural tract looks like a care desert — which is technically true in absolute terms but analytically unhelpful for identifying which rural communities are most underserved relative to what is feasible.
| Z-score | Meaning | Color |
| > 1.5 | Far above regional peers | Dark green |
| 0.5 to 1.5 | Above average | Light green |
| -0.5 to 0.5 | Near regional average | Yellow |
| -1.5 to -0.5 | Below peers | Orange |
| < -1.5 | Far below peers | Red |
Nonprofit density was estimated using three independent methods to test robustness of care desert identification:
Method 1: Child Population Floor — HQ-based count divided by max(child_pop, 100). Simple, transparent. Problem: treats a nonprofit's mailing address as its entire service area.
Method 2: Empirical Bayes — shrinks unstable small-population rates toward a reference mean. Three variants tested: statewide prior, regional prior (urban vs rural), and spatial prior (local neighbors via spdep::EBlocal). Regional EB uses separate priors — urban prior of 2.1 per 1K vs rural prior of 2.4 per 1K — preventing rural tracts from being pulled toward urban levels.
Method 3: Reach Counts (z-scored) — raw count of accessible orgs within the service radius, z-scored within region. Avoids the denominator problem entirely by asking “how many orgs can a child reach?” rather than computing a per-capita rate.
Methods 1/2 and Method 3 are nearly uncorrelated (r = 0.10 statewide). HQ-based rates and reach-based counts measure fundamentally different things: institutional presence vs service accessibility.
Care desert agreement: 20 tracts are flagged as care deserts by all four methods — these are highest-confidence findings. Methods 1 and 3 agree on classification 68% of the time. The 32% disagreement is a finding: it shows presence and accessibility are distinct dimensions.
Urban vs rural: Urban tracts average 149 accessible youth orgs; rural tracts average 39. The regional z-score prevents this baseline difference from dominating the composite scoring.
The Map tab uses Method 1 (absolute reach rates with colorBin) for policy audiences who need intuitive values. The Regional View uses Method 3 (within-region z-scores) for analytical comparison.
Three systems applied simultaneously. COI categories are mutually exclusive. Sharkey and Public Safety categories overlap.
| System | Denominator | Categories | Source |
|---|---|---|---|
| COI Categories | Per 1,000 children 0-17 | Youth Enrichment, General Enrichment, Health, Safety, General | Child Opportunity Index 3.0 |
| Sharkey Categories | Per 100,000 total pop | Community, Treatment, Workforce | Sharkey et al. (2017, ASR) |
| Public Safety | Per 100,000 total pop | Crime prevention, victim services, reentry, restorative justice | NTEE I-codes + name keywords |
| Limitation | Description |
|---|---|
| Coverage | BMF captures IRS-registered 501(c) organizations only. Government agencies, for-profit providers, and informal community organizations are not included. |
| Geocoding | Addresses represent headquarters or mailing addresses, not service delivery locations. The service reach model partially addresses this. |
| Classification | NTEE codes are self-reported and may not reflect current program focus. Name keyword search supplements but cannot identify all youth-serving orgs. |
| Temporal | 5-year window (2019-2024) balances currency against BMF filing lag for smaller organizations. |
| Urban/Rural | County-level urban/rural designation. Some urban-fringe tracts in rural counties may be misclassified. |
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