How the immigrant share of the population shifts across the country. Press play to animate. Source: US Census ACS 5-year.
Spatial access by the enhanced two-step floating catchment area method (E2SFCA). Darker green = better access; orange = a service desert (people who need it, but no organization within reach); gray = no foreign-born population. Source: org locations + US Census ACS 2023.
How inclusive each state’s policies are toward immigrants, across seven domains: health coverage, language access, higher education, identification, integration & licensure, legal protection, and economic inclusion. Darker green = more inclusive; orange/red = more restrictive or limited. Click any state for its full profile.
Standardized gaps between foreign-born and US-born residents (uninsured, crowded housing, rent burden, education, home internet, vehicle access, homeownership) from US Census ACS. Green = smaller gaps; red = larger gaps. This is an outcome measure, separate from the Policy map: it describes how immigrant and US-born residents differ, and is shaped by who has immigrated where (recency and composition), not by state policy alone. Click any state for its full profile.
Project Implicit is a self-selected volunteer sample, not a representative survey; within each area, respondents are reweighted to the Census population by age, sex, race and ethnicity, and citizenship. Higher = less welcoming. Areas with too few respondents are left blank. See the note below for the exact survey item. Source: Project Implicit 2023-2025.
Project Implicit is a self-selected volunteer sample, not a representative survey; within each area, respondents are reweighted to the Census population by age, sex, race and ethnicity, and citizenship. Higher = less welcoming. Areas with too few respondents are left blank. See the note below for the exact survey item. Source: Project Implicit 2023-2025.
12774 WISTERIA DR, GERMANTOWN, MD 20874-9998
Chinese American Parents Association of Montgomery County is a nonprofit organization in Germantown, Maryland that serves immigrants and refugees.
CAPA-MC serves Chinese American immigrant families in Montgomery County, MD through student academic and leadership programs, scholarships, cultural festivals, civic engagement, and education advocacy.
Our mission is to promote immigrant family involvement in school communities, to advocate and serve, and to establish a strong partnership with all education stakeholders to address educational issues and challenges.
Read from the organization's website.
Services delivered in Chinese.
Website in more than one language.
Chinese.
Service area: Montgomery County, Maryland.
Founded 2016. IRS NTEE code B01. Revenue $25,000-99,999. Latest Form 990: 2024. EIN 81-3893959.
All organizations in Germantown | All organizations in Maryland
Sources: IRS exempt-organization records, Form 990 filings, federal and network directories, and the organization's own website. Data as of October 19, 2026. Information here can change; contact the organization to confirm.
Pick a group to see its own profile: how large it is and how it lives, with the all-foreign-born and US-born averages shown for context. National estimates from US Census ACS 1-year microdata (PUMS).
Created and maintained by Nari Yoo, PhD, University of Michigan School of Social Work.
The underlying dataset is being prepared for publication as a data descriptor: “Service and Language Access Profiles of U.S. Immigrant- and Refugee-Serving Nonprofits: A National Geocoded Dataset Augmented with Large Language Models.”
Collaboration and data-access inquiries are welcome: nariyoo@umich.edu.
Immigrants and refugees in the United States depend on a dispersed patchwork of nonprofit organizations for legal aid, resettlement, English classes, health and mental health care, and basic needs. There has been no national, openly available map that identifies these organizations, describes what each one actually does, and records whether they can serve people in languages other than English. Administrative records cannot answer these questions on their own: the IRS classification system undercounts immigrant- and refugee-serving organizations and is silent on the services they deliver and the languages they use (Gleeson & Bloemraad, 2013). The inventories that do exist tend to cover a single domain, such as immigration legal services, or rely on hand coding that does not scale to the whole country.
This project addresses those gaps. It assembles a national, geocoded census of immigrant- and refugee-serving nonprofits from IRS records, Form 990 program text and federal and sector directories, and screens every candidate with a panel of language models reading the evidence that candidate actually has. A language-model layer then reads each organization’s website to tag the specific services it offers and to audit its language access, separating genuinely multilingual sites with professionally translated content from those that only add an automatic-translation widget, and recording the languages each organization states it serves. Producing these organization-level service and language-access measures at national scale is the dataset’s distinctive contribution; they are not derivable from administrative data and have not previously been compiled into a single open resource.
The supply side is then placed next to demand. Each organization is linked to Census American Community Survey measures of the foreign-born population, its growth over time, and limited-English proficiency by language, at the state, county, city, and metropolitan level. Seeing provision and capacity alongside where immigrants live and which languages they need makes it possible, for any place in the country, to identify both who provides help and where the gap between need and available, language-accessible help is widest.
It is built for three audiences. Newcomers and the people who assist them can see what resources exist in a new place, identify the organizations that provide a specific service, and use the directory to make referrals in the language a client needs. Practitioners and funders can see where coverage is thin relative to need. Researchers and policymakers get a reproducible, openly documented dataset that links the immigrant nonprofit sector to local linguistic demand.
The dashboard now extends beyond the directory. The Access map measures spatial access to organizations, and to public (SAMHSA) and private (a private practice directory) mental-health care, with a two-step floating catchment method, by language, for households without a car, and for providers that accept Medicaid. The Policy map carries a state Immigrant Policy Inclusion Index (immigrant-specific inclusive minus restrictive laws across health, language, education, identification, and legal protection), and the Disparity map shows a companion Immigrant-Native Disparity Index of foreign-born versus US-born outcome gaps. A Language access guide explains the right to free interpreting and translation in health and mental-health care, and a grounded assistant answers questions from the underlying data.
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A national directory and dashboard of US nonprofit organizations serving immigrants, refugees, asylees, and other foreign-born newcomers, paired with Census foreign-born, language-need, and immigrant social/economic context at state, county, and city levels.
Every demographic figure is foreign-born specific and available at state, county, city, and metro levels, with US, Census-region, and state averages shown as benchmarks. Time-series (2009–2023 / 2011–2023) cover foreign-born share, language need by language, immigrant and racial/ethnic diversity, and residential segregation.
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General educational information only, not legal, medical, immigration, or mental-health advice; using it creates no attorney-client or provider-patient relationship. Compiled from public sources and may be incomplete or out of date, so verify with the official source before relying on any listing. Outside links are for convenience and do not imply endorsement, and individual providers are not vetted. Provided “as is,” without warranty. © 2026 Nari Yoo. Data CC BY 4.0.