Updated May 8, 2026 – Weave: The Social Fabric Project
Introduction
Weave’s Social Trust Index and Map show the strength of certain neighborhood traits that are correlated with social trust. Trusting behaviors, intentions, and spaces are judged separately, but they influence and complement each other. You can think of them as the ecosystem that supports building relationships and trust in a community.
We had two requirements for our data. It had to be very local, collected for census block groups, which are neighborhoods that include from 600-3,000 people. And it had to be as up-to-date as possible. We chose sources that update the data at least annually, so we can have a new iteration of the Trust Map each year.
These two requirements were important to be sure we had data that was timely and specific to neighborhoods. These requirements limited the data available to us, since not many groups collect information at the neighborhood level across the US every year.
Weave assembled a group of advisors deeply involved in social trust research. You can see their names in the Trust Map credits. We and our advisors chose data that research has shown to be correlated to social trust. We hired the firm Datastory to create the first version of the Social Trust Map in 2023. In 2025, LocAItion Matters was engaged to facilitate data collection, documentation, mapping, and analysis, further refining the data selection for the updated version.
The Index and Map data came from Esri, Spatial.ai, SafeGraph, and OpenStreetMaps. These groups acquire data from the U.S. Census, the Bureau of Labor Statistics, MRI-Simmons, social media (e.g., X, Instagram, Facebook), and ground surveys, among others. Datastory and LocAItion Matters used the criteria below to score each neighborhood as a percentile compared to the rest of the country, which we converted to a 0-100 scale. This means that if a particular neighborhood was in the 80th percentile compared to the rest of the country for book club attendance, their score on that criterion would be 80. These scores are then rolled up by subcategory and category. Finally, Datastory and LocAItion Matters created an interactive map to display the results.
What We’re Measuring: The Three Categories
Weave’s Social Trust Map and Index don’t measure social trust itself, as there is not one way that people define trust. Different efforts like Pew Research or The Social Connection in America report measure trust through surveys, where they ask people to answer whether they agree or disagree with statements like “Most people can be trusted” or “I can trust most of my neighbors” to inform public policy and research. We took a different approach.
Our goal was to find data that could be actionable and disaggregated at the neighborhood level to support people who want to improve their communities. The Social Trust Index measures the components that help people build trust where they live — whether that is their proximity to shared spaces where they can gather, how many people show up for their neighbors through local institutions, or whether people feel invested in the place where they live. The three-category framework below was originally developed by Datastory for the 2023 Social Trust Map in collaboration with Weave’s advisory group. LocAItion Matters refined and expanded the variable selection for the 2025 update. Together, the 45 variables span three dimensions of trust as it appears in everyday community life.
A. Trusting Behaviors
Trusting Behaviors reflects how actively engaged people are in community life. Volunteering, joining local organizations, donating, and voting locally are all expressions of trust in collective action. This category is measured across three sub-dimensions.
A.1 Community Groups and Activities
Membership in organizations that bring neighbors together: church boards, fraternal orders, religious and veterans’ clubs, book clubs, environmental groups, and committees for local organizations.
A.2 Nonprofit Support
Whether people invest time and money in community causes — volunteering for charitable organizations and donating to arts, educational, health, and social services nonprofits.
A.3 Civic Participation
Engagement in local democracy and public life: voting in local elections, attending town or school board meetings, participating in religious services, high school events, and political rallies or organized protests.
B. Trusting Intentions
Trusting Intentions captures whether people feel connected to and invested in the community around them — even before they act on it. This includes how people see themselves as neighbors, whether they follow local events and organizations, and the community-oriented values they express.
B.1 Feelings of Trustworthiness
Whether people report being trusted by family and friends for advice on everyday decisions (groceries, healthcare, restaurants) and whether social trust shapes their consumer behavior, such as supporting brands aligned with their values.
B.2 Interest in Local Events and Organizations
Social media behavior analyzed at the neighborhood level: how many people prioritize local information and follow charitable groups, community organizations, politicians, religious groups, and school groups online.
B.3 Community-Oriented Values
Aggregated, anonymized social media content from each neighborhood, analyzed for the presence of three community-oriented orientations:
- Humanitarian: desire to give back and serve others through charity or community action.
- Civic Attentiveness: interest in improving the neighborhood’s appearance, needs, and local affairs.
- Activist: advocacy for social justice and a more equitable community.
C. Trusting Spaces
Trusting Spaces identifies whether people have physical places where they can meet and build relationships with neighbors. Shared spaces — third places outside of home and work — are foundational to the informal interactions that strengthen community bonds. This category counts two types of spaces within a quarter-mile of each neighborhood.
C.1 Third Spaces
Informal gathering places outside of home and work: religious organizations, barbershops, beauty and nail salons, bowling centers, libraries, coffee shops, bars, and fitness and recreation centers.
C.2 Publicly Accessible Leisure and Green Spaces
Outdoor and recreational spaces where communities can gather informally: parks, playgrounds, basketball courts, pools, dog parks, gardens, nature reserves, recreation areas, zoos, and botanical gardens, identified through OpenStreetMap’s leisure classification system.
How the Index Is Built
The map draws on four primary sources, selected for national coverage, annual update frequency, and consistency at the census block group level. There is no standardized definition of a neighborhood in the United States. Because this analysis spans the entire country, it requires a consistent geographic unit that can be applied uniformly across all 50 states while still approximating the scale of a neighborhood. Census block groups meet this requirement. Defined by the U.S. Census Bureau, block groups are the smallest geographic unit for which a wide range of demographic and socioeconomic data is publicly available. Each contains roughly 600 to 3,000 residents. Their boundaries are drawn based on local population density and geography — smaller in dense urban areas, larger in sparse rural ones.This prevents the loss of local variation that occurs when data is aggregated across larger geographies.
Each of the 45 variables is scored as a percentile relative to all other block groups in the United States, expressed on a 0–10 scale. Variable scores are combined within each sub-dimension and re-ranked using the same method, producing sub-dimension scores. Sub-dimension scores are then combined within each category and re-ranked again, producing the three category scores used for Trusting Behaviors, Trusting Intentions, and Trusting Spaces. The three category scores are then combined and re-ranked one final time to produce the overall Social Trust Index score. This layered structure ensures no single variable dominates the result and that each category contributes equally to the map.
Data Sources
Esri Market Potential Data (MRI-Simmons + Tapestry Segmentation)
The 28 variables in categories A and B are drawn from Esri’s Market Potential database. As described in Esri’s Market Potential Database Methodology Statement (2024), Esri’s Data Development team builds these estimates by combining two sources: the MRI-Simmons Survey of the American Consumer Doublebase® and Esri’s Tapestry™ Segmentation system.
The MRI-Simmons Doublebase integrates responses from four continuous consumer surveys — representing more than 70,000 U.S. adults sampled across spring and fall periods — and covers more than 2,400 products, services, activities, values, and attitudes (MRI-Simmons, 2024). Each survey respondent is assigned to one of Esri’s 67 Tapestry demographic-lifestyle segments. This allows Esri to calculate a consumption rate for a given behavior within each segment, then apply those rates to the corresponding segment populations within any target geography to produce a local demand estimate (Esri, 2024).
The result is an Expected Number of Consumers — a projected estimate of how many adults or households in a block group are likely to exhibit a given behavior — alongside a Market Potential Index (MPI) that compares local demand to the national average. An MPI of 100 equals national demand; above 100 indicates higher local demand, below 100 indicates lower (Esri, 2024). These are modeled estimates, not direct observations. A variable like “Participated in Book Club (Last 12 Mo.)” does not reflect a count of people surveyed in that specific neighborhood — it is a statistically derived projection based on who lives there and how similar populations behave nationally. All estimates represent a 12-month reference period and are updated annually.
GeoSocial Segments
Three variables in category B draw on data from Spatial.ai Proximity, which derives neighborhood-level lifestyle and values profiles from aggregated, anonymized social media activity across platforms including X (formerly Twitter), Instagram, and Facebook. GeoSocial does not track individuals — it clusters publicly expressed social content into segment-level signals at the neighborhood scale (Spatial.ai Proximity). The three variables used here — Humanitarian, Civic Attentiveness, and Activism — reflect the relative presence of community-oriented values in each block group’s social media activity.
Points of Interest (POI) Data
Some of the variables in category C are identified using point-of-interest (POI) data from Safegraph’s POI data, compiled from business registrations, ground surveys, and location intelligence services. For each block group, the map counts qualifying third-space locations — such as religious organizations, libraries, coffee shops, and fitness centers — within a quarter-mile radius of the neighborhood boundary.
OpenStreetMap (OSM)
Publicly accessible leisure and green spaces are identified using OpenStreetMap (OSM), a free and open-source collaborative mapping platform maintained by a global volunteer community (OpenStreetMap contributors, 2025). OSM’s leisure and land-use classification tags are used to identify parks, playgrounds, recreation areas, nature reserves, and other publicly accessible outdoor spaces. Like the POI data, OSM-based spaces are counted within a quarter-mile radius of each block group.
Variable Summary
The table below summarizes the 45 variables by category and sub-dimension. For the full technical data inventory, contact LocAItion Matters or Weave directly.
| Category | ID | Variable Name | Data Source |
|---|---|---|---|
| A. Trusting Behaviors | A.1.1 | Member of a Church Board | Esri / MRI-Simmons MPI |
| A.1.2 | Member of a Fraternal Order | Esri / MRI-Simmons MPI | |
| A.1.3 | Member of a Religious Club | Esri / MRI-Simmons MPI | |
| A.1.4 | Member of a Veterans Club | Esri / MRI-Simmons MPI | |
| A.1.5 | Participated in Book Club (Last 12 Mo.) | Esri / MRI-Simmons MPI | |
| A.1.6 | Participated in Environmental Groups (Last 12 Mo.) | Esri / MRI-Simmons MPI | |
| A.1.7 | Served on Committee for Local Organization | Esri / MRI-Simmons MPI | |
| A.2.1 | Volunteered for Charitable Organization | Esri / MRI-Simmons MPI | |
| A.2.2 | Contributed to Arts or Cultural Organization | Esri / MRI-Simmons MPI | |
| A.2.3 | Contributed to Educational Organization | Esri / MRI-Simmons MPI | |
| A.2.4 | Contributed to Health Organization | Esri / MRI-Simmons MPI | |
| A.3.1 | Voted in Local Elections — Always | Esri / MRI-Simmons MPI | |
| A.3.2 | Attended High School Sports Events | Esri / MRI-Simmons MPI | |
| A.3.3 | Attends Religious Services Regularly | Esri / MRI-Simmons MPI | |
| A.3.4 | Attended Political Rally or Protest | Esri / MRI-Simmons MPI | |
| A.3.5 | Attended Public Meeting on Town/School Affairs | Esri / MRI-Simmons MPI | |
| B. Trusting Intentions | B.1.1 | Family & Friends Trust Advice — Grocery Shopping | Esri / MRI-Simmons MPI |
| B.1.2 | Family & Friends Trust Advice — Healthcare | Esri / MRI-Simmons MPI | |
| B.1.3 | Family & Friends Trust Advice — Healthy Lifestyles | Esri / MRI-Simmons MPI | |
| B.1.4 | Family & Friends Trust Advice — Restaurants | Esri / MRI-Simmons MPI | |
| B.1.5 | Will Pay More for Product from Trusted Company | Esri / MRI-Simmons MPI | |
| B.1.6 | Expects Brands to Support Social Causes | Esri / MRI-Simmons MPI | |
| B.2.1 | Social Media: Finds Local Information Important | Esri / MRI-Simmons MPI | |
| B.2.2 | Social Media: Follows Charitable Groups | Esri / MRI-Simmons MPI | |
| B.2.3 | Social Media: Follows Local Groups | Esri / MRI-Simmons MPI | |
| B.2.4 | Social Media: Follows Politicians | Esri / MRI-Simmons MPI | |
| B.2.5 | Social Media: Follows Religious Groups | Esri / MRI-Simmons MPI | |
| B.2.6 | Social Media: Follows School Groups | Esri / MRI-Simmons MPI | |
| B.3.1 | Humanitarian — GeoSocial Segment | Esri / GeoSocial Segments | |
| B.3.2 | Civic Attentiveness — GeoSocial Segment | Esri / GeoSocial Segments | |
| B.3.3 | Activism — GeoSocial Segment | Esri / GeoSocial Segments | |
| C. Trusting Spaces | C.1.1 | Religious Organizations (within ¼ mi.) | Esri / POI (NAICS) |
| C.1.2 | Barber Shops (within ¼ mi.) | Esri / POI (NAICS) | |
| C.1.3 | Beauty Salons (within ¼ mi.) | Esri / POI (NAICS) | |
| C.1.4 | Nail Salons (within ¼ mi.) | Esri / POI (NAICS) | |
| C.1.5 | Bowling Centers (within ¼ mi.) | Esri / POI (NAICS) | |
| C.1.6 | Libraries and Archives (within ¼ mi.) | Esri / POI (NAICS) | |
| C.1.7 | Coffee Shops and Snack Bars (within ¼ mi.) | Esri / POI (NAICS) | |
| C.1.8 | Drinking Places / Bars (within ¼ mi.) | Esri / POI (NAICS) | |
| C.1.9 | Fitness and Recreation Centers (within ¼ mi.) | Esri / POI (NAICS) | |
| C.2.1 | Zoos and Botanical Gardens (within ¼ mi.) | Esri / POI (NAICS) | |
| C.2.2 | Nature Parks (within ¼ mi.) | Esri / POI (NAICS) | |
| C.2.3 | Publicly Accessible Leisure Spaces — OSM (within ¼ mi.) | OpenStreetMap |
Limitations
Scale
Relying on Census-aligned geographies and standardized annual sources makes the index replicable and comparable across every neighborhood in the country year over year. The tradeoff is that the index is bounded by what these sources track — not every meaningful dimension of social trust can be captured through existing national datasets.
Proximity
Third spaces and leisure spaces are counted within a quarter-mile radius of each block group boundary — a practical approximation of walkable access, but an imperfect one. What constitutes a meaningful distance varies considerably by context: urban density, transportation access, age, income, and physical ability all shape how far a “walkable” space actually is.
References
Datastory. (2023). “Weave Social Trust Map: Methodology.” Prepared for Weave: The Social Fabric Project, The Aspen Institute. https://weavers.org/trust/methodology/
Esri. (2024). “Market Potential Database Methodology Statement.” Esri Data Development. https://content.esri.com/esri_content_doc/dbl/us/j9672_market_potential_db_methodology_statement_2024_final.pdf
Esri. (2024). “Use and Interpret Market Potential Data.” ArcGIS StoryMaps. https://storymaps.arcgis.com/stories/0dc218cb1ff74e45b9d6413d8ee585a1
Esri. (n.d.). “GeoSocial Segments.” Esri Demographics. ArcGIS Business Analyst. https://doc.arcgis.com/en/esri-demographics/data/geosocial.htm
MRI-Simmons. (2024). “MRI-Simmons USA: Survey of the American Consumer Doublebase.” https://www.mrisimmons.com/our-data/national-studies/usa/
OpenStreetMap contributors. (2025). “OpenStreetMap.” https://www.openstreetmap.org
U.S. Census Bureau. (n.d.). “Census Block Groups.” https://www.census.gov/programs-surveys/geography/about/glossary.html
If you have further questions, contact Weave at trustmap@aspeninstitute.org.