Accessing Geolocation Data

As part of the curation process, some participant identifiable information has been removed from the Registered and Controlled Tier datasets to protect participant privacy and reduce the risk of re-identification. This includes some participant geolocation data. Geolocation data may come from participant provided information (PPI), or from the electronic health records (EHR) data. In most cases, participants’ self-reported addresses will be used as the primary source of geolocation data. Below is information about available geolocation fields specific to the Registered and Controlled Tier datasets, respectively.  For additional information on how All of Us protects participant privacy by means of data removal, suppression, or generalizations, click here.

Geolocation Data Available within the Registered Tier

State-level data are the most granular geolocation available within the Registered Tier dataset and it is subject to Registered Tier state generalization rules. Researchers will not be able to identify the specific All of Us site where a participant enrolled. 

Participants’ state-level geolocation data are generalized, with the value_as_concept_id = 2000000011, in the Registered Tier dataset if any of the following conditions are met:

• A participant has EHR data available in the dataset and self-reported state of residence differs from the state of the health provider organization sharing the EHR data.  
• The participant has reported a state of residence from which there are 200 or fewer total participants enrolled.

The state location data are stored in the data tables in all rows with the observation_source_concept_id = 1585249. The state code is stored in value_as_concept_id in the observation table. 

Geolocation Data Available within the Controlled Tier

Three digit zip code level data are the most granular geolocation data available within the Controlled Tier dataset and are subject to Controlled Tier state generalization rules. If a participant resides in a zip code that is reported to have fewer than 20,000 residents per Census, the participant is generalized to a random neighboring zip3 to protect their privacy. The zip codes with fewer than 20k residences are sourced from Methods for De-identification of PHI | HHS.gov. 

Participants who have self-reported as American Indian or Alaskan Native (AI/AN) will have three digit zip code generalized to 000* in CDR v7 through present CDR versions. 

In the event we receive invalid zip codes from our upstream data contributors, the Data and Research Center (DRC) will recodes these invalid zip codes to 000*. 

Note: Researchers will not be able to identify the specific All of Us site where a participant enrolled using geolocation data.

Data Extraction Query Examples

Registered Tier

To obtain state level information in the Registered Tier: 

1. Join the value_as_concept_id in the observation table with concept_id from the concept table.
2. Use concept_name from the concept table to extract the state.

Coding examples for how to retrieve state location are below. 

Example of Code in Python:

# Install dependencies
import pandas_gbq

# Load the CDRv9 dataset (wb-affable-acorn-7941.R2025Q4R6)
dataset = %env WORKSPACE_CDR

# Query the count of participants by states
query = f"""
SELECT 
    concept_name, 
    COUNT(DISTINCT person_id) as n_participant
FROM `{dataset}.observation`
JOIN `{dataset}.concept` on value_as_concept_id=concept_id
WHERE observation_source_concept_id=1585249
GROUP BY 1
"""

# Save states in a dataframe
df = pandas_gbq.read_gbq(query, dialect="standard")
df.head()

Example of Code in R: 

# Import dependencies 
library(bigrquery)
library(DBI)
library(tidyverse)


# Load the CDRv9 dataset (wb-affable-acorn-7941.R2025Q4R6)
dataset <- Sys.getenv('WORKSPACE_CDR')

# Query the count of participants by states
geo_sql <- str_glue("SELECT DISTINCT
   concept_name,
   COUNT(DISTINCT person_id) as n_participant
FROM `{dataset}.observation`
JOIN `{dataset}.concept` ON value_as_concept_id = concept_id
WHERE observation_source_concept_id=1585249
GROUP BY 1
")

# Save states in a dataframe
tb <- bq_project_query(Sys.getenv('GOOGLE_PROJECT'), geo_sql)
geo_df <-  bq_table_download(tb)

head(geo_df)

Controlled Tier

Geolocation data by zip code in the Controlled Tier can be accessed via the Data Explorer or by directly querying in the Jupyter notebook. 

1. Access via the Data Explorer

Create a cohort. When you add new criteria, search “Postal code” in the criteria search bar and select “Add.”

2. Direct query

You can also access geolocation by zip code by directly querying the data, as shown in the following example queries. 

Example of code in Python: 

# Install dependencies
import pandas_gbq 


# Load the CDRv9 dataset (wb-affable-acorn-7941.R2025Q4R6)
dataset = %env WORKSPACE_CDR


# Query the count of participants by states and zip code
query =  f'''
SELECT COUNT(DISTINCT person_id) AS n_participant, 
    state_of_residence_source_value AS State,
    value_as_string AS Zip_code                
FROM `{dataset}.person_ext`
JOIN `{dataset}.observation` USING(person_id)
WHERE observation_source_concept_id = 1585250 
GROUP BY 2,3'''

# Save states in a dataframe
df = pandas_gbq.read_gbq(query)
df.head()

Example of code in R:

# Install dependencies
library(bigrquery)
library(DBI)
library(tidyverse)

# Load the CDRv9 dataset (wb-affable-acorn-7941.R2025Q4R6)
dataset <- Sys.getenv('WORKSPACE_CDR')


# Query the count of participants by states and zip code
query <- str_glue("
SELECT COUNT(DISTINCT person_id) AS n_participant, 
    state_of_residence_source_value AS State,
    value_as_string AS Zip_code                
FROM `{dataset}.person_ext`
JOIN `{dataset}.observation` USING(person_id)
WHERE observation_source_concept_id = 1585250 
GROUP BY 2,3")


# Save states in a dataframe
tb <- bq_project_query(Sys.getenv('GOOGLE_PROJECT'), query)
df <-  bq_table_download(tb)
head(df)

Registered Tier State and Site Generalizations 

OBSERVATION TABLE 

For rows in the OBSERVATION table where observation_source_concept_id = 1585249 (observation_source_value = StreetAddress_PIIState) and either of the two conditions below are met, we generalize the value_source_concept_id (and the derived value_as_concept_id) of the row to 2000000011 (State information suppressed for privacy).

For additional information on how All of Us protects participant privacy by means of data removal, suppression, or generalization, click here.