# Analyzing Census Data with Data Commons

## Learning Objectives

By the end of this vignette, you will be able to:

- Understand what Google Data Commons is and why it offers unique value for data integration
- Navigate the knowledge graph structure using relationships between geographic entities
- Find and use DCIDs (Data Commons IDs) and statistical variables
- Retrieve demographic data across different geographic levels using the `datacommons` R package
- Integrate data from multiple sources through the unified Data Commons API
- Handle multiple data sources (facets) and select appropriate ones for your analysis
- Build progressively complex demographic analyses that combine disparate datasets

## Why Start with Census Data?

Census data provides an ideal introduction to Data Commons for several reasons:

1. Familiar territory: Many analysts have worked with census data, making it easier to focus on learning the Data Commons approach rather than the data itself.

2. Rich relationships: Census data showcases the power of the knowledge graph through natural hierarchies (country → state → county → city), demonstrating how Data Commons connects entities.

3. Integration opportunities: Census demographics become even more valuable when combined with health, environmental, and economic data—showing the true power of Data Commons.

4. Real-world relevance: The examples we’ll explore address actual policy questions that require integrated data to answer properly.

## Why Data Commons?

The R ecosystem has excellent packages for specific data sources. For example, the tidycensus package provides fantastic functionality for working with U.S. Census data, with deep dataset-specific features and conveniences.

So why use Data Commons? The real value is in data integration.

Data Commons is part of Google’s philanthropic initiatives, designed to democratize access to public data by combining datasets from organizations like the UN, World Bank, and U.S. Census into a unified knowledge graph.

Imagine you’re a policy analyst studying the social determinants of health. You need to analyze relationships between:

- Census demographics (population, age, income)
- CDC health statistics (disease prevalence, obesity rates)
- EPA environmental data (air quality indices)
- Bureau of Labor Statistics (unemployment rates)

With traditional approaches, you’d need to:

1. Learn multiple different APIs
2. Deal with different geographic coding systems
3. Reconcile different time periods and update cycles
4. Match entities across datasets (is “Los Angeles County” the same in all datasets?)

Data Commons solves this by providing a unified knowledge graph that links all these datasets together. One API, one set of geographic identifiers, one consistent way to access everything. The `datacommons` R package is your gateway to this integrated data ecosystem, enabling reproducible analysis pipelines that seamlessly combine diverse data sources.

## Understanding the Knowledge Graph

Data Commons organizes information as a graph, similar to how web pages link to each other. Here’s the key terminology:

### DCIDs (Data Commons IDs)

Every entity in Data Commons has a unique identifier called a DCID. Think of it like a social security number for data:

- `country/USA` = United States
- `geoId/06` = California (using FIPS code 06—Federal Information Processing Standards codes are essentially ZIP codes for governments, providing standard numeric identifiers for states, counties, and other geographic areas)
- `Count_Person` = the statistical variable for population count

### Relationships

Entities are connected by relationships, following the Schema.org standard—a collaborative effort to create structured data vocabularies that help machines understand web content. For Data Commons, this means consistent, machine-readable relationships between places and data:

- California –(`containedInPlace`)–> United States
- California –(`typeOf`)–> State
- Los Angeles County –(`containedInPlace`)–> California

This structure lets us traverse the graph to find related information. Want all counties in California? Follow the `containedInPlace` relationships backward.

### Statistical Variables

These are the things we can measure:

- `Count_Person` = total population
- `Median_Age_Person` = median age
- `UnemploymentRate_Person` = unemployment rate
- `Mean_Temperature` = average temperature

The power comes from being able to query any variable for any place using the same consistent approach through the `datacommons` R package.

## Prerequisites

    # Core packages
    library(datacommons)
    library(dplyr)
    library(tidyr)
    library(stringr)
    library(ggplot2)
    library(scales)
    library(knitr)

    # Set a consistent theme for plots
    theme_set(theme_minimal())

## Setting Up API Access

You’ll need a free API key from https://docs.datacommons.org/api/#obtain-an-api-key

    # Set your API key
    dc_set_api_key("YOUR_API_KEY_HERE")

    # Or manually set DATACOMMONS_API_KEY in your .Renviron file

Don’t forget to restart your R session after setting the key to automatically load it.

## Finding What You Need

The `datacommons` R package requires three key pieces:

1. Statistical Variables: What you want to measure
   - Find these at: https://datacommons.org/tools/statvar
2. Place DCIDs: Where you want to measure it
   - Find these at: https://datacommons.org/place
3. Dates: When you want the measurement
   - Use `"all"` to retrieve the entire time series
   - Use `"latest"` for the most recent available data
   - Use specific years like `"2020"` or date ranges
   - See the API documentation for advanced date options

Let’s see how to find these in practice.

## Example 1: National Population Trends

Real-world motivation: Understanding population growth patterns is fundamental for policy planning, from infrastructure investments to social security projections. Let’s examine U.S. population trends to see how growth has changed over time.

    # Get U.S. population data
    # We found these identifiers using the Data Commons website:
    # - Variable: "Count_Person" (from Statistical Variable Explorer)
    # - Place: "country/USA" (from Place Explorer)
    us_population <- dc_get_observations(
      variable_dcids = "Count_Person",
      entity_dcids = "country/USA",
      date = "all",
      return_type = "data.frame"
    )

    # Examine the structure
    glimpse(us_population)
    #> Rows: 490
    #> Columns: 8
    #> $ entity_dcid   <chr> "country/USA", "country/USA", "country/USA", "country/US…
    #> $ entity_name   <chr> "United States of America", "United States of America", …
    #> $ variable_dcid <chr> "Count_Person", "Count_Person", "Count_Person", "Count_P…
    #> $ variable_name <chr> "Total population", "Total population", "Total populatio…
    #> $ date          <chr> "1900", "1901", "1902", "1903", "1904", "1905", "1906", …
    #> $ value         <int> 76094000, 77584000, 79163000, 80632000, 82166000, 838220…
    #> $ facet_id      <chr> "2176550201", "2176550201", "2176550201", "2176550201", …
    #> $ facet_name    <chr> "USCensusPEP_Annual_Population", "USCensusPEP_Annual_Pop…

    # Notice we have multiple sources (facets) for the same data
    unique(us_population$facet_name)
    #>  [1] "USCensusPEP_Annual_Population"
    #>  [2] "CensusACS5YearSurvey_AggCountry"
    #>  [3] "CensusACS5YearSurvey"
    #>  [4] "USDecennialCensus_RedistrictingRelease"
    #>  [5] "WorldDevelopmentIndicators"
    #>  [6] "USCensusPEP_AgeSexRaceHispanicOrigin"
    #>  [7] "OECDRegionalDemography_Population"
    #>  [8] "CensusACS5YearSurvey_SubjectTables_S0101"
    #>  [9] "CensusACS5YearSurvey_SubjectTables_S2601A"
    #> [10] "CensusACS5YearSurvey_SubjectTables_S2602"
    #> [11] "CensusACS5YearSurvey_SubjectTables_S2603"
    #> [12] "CDC_Mortality_UnderlyingCause"
    #> [13] "CensusPEP"
    #> [14] "CensusACS1YearSurvey"
    #> [15] "WikidataPopulation"

### Handling Multiple Data Sources

Data Commons aggregates data from multiple sources. This is both a strength (comprehensive data) and a challenge (which source to use?). Let’s handle this:

    # Strategy 1: Use the official Census PEP (Population Estimates Program)
    us_pop_clean <- us_population |>
      filter(facet_name == "USCensusPEP_Annual_Population") |>
      mutate(
        year = as.integer(date),
        population_millions = value / 1e6
      ) |>
      filter(year >= 2000) |>
      select(year, population_millions) |>
      arrange(year)

    # Visualize the clean data
    ggplot(us_pop_clean, aes(x = year, y = population_millions)) +
      geom_line(color = "steelblue", linewidth = 1.5) +
      geom_point(color = "steelblue", size = 3) +
      scale_y_continuous(
        labels = label_number(suffix = "M"),
        limits = c(280, 350)
      ) +
      scale_x_continuous(breaks = seq(2000, 2025, 5)) +
      labs(
        title = "U.S. Population Growth",
        subtitle = "Using Census Population Estimates Program data",
        x = "Year",
        y = "Population (millions)",
        caption = "Source: U.S. Census Bureau via Data Commons"
      )

    # Calculate average annual growth
    growth_rate <- us_pop_clean |>
      mutate(
        annual_growth =
          (population_millions / lag(population_millions) - 1) * 100
      ) |>
      summarize(
        avg_growth = mean(annual_growth, na.rm = TRUE),
        pre_2020_growth = mean(annual_growth[year < 2020], na.rm = TRUE),
        post_2020_growth = mean(annual_growth[year >= 2020], na.rm = TRUE)
      )

    message(
      "Average annual growth rate: ", round(growth_rate$avg_growth, 2), "%\n",
      "Pre-2020 growth rate: ", round(growth_rate$pre_2020_growth, 2), "%\n",
      "Post-2020 growth rate: ", round(growth_rate$post_2020_growth, 2), "%"
    )

    # Note for statistics enthusiasts: We're using arithmetic mean for simplicity.
    # For compound growth rates, geometric mean would be more precise, which you can
    # calculate as geometric_mean_growth = (last_value/first_value)^(1/n_years) - 1.
    # But given the small annual changes (~0.7%), the difference is negligible.

Key insight: Notice the marked change in growth patterns around 2020. While pre-2020 showed steady growth, the post-2020 period reveals a significant slowdown. This dramatic shift raises important policy questions: How much is due to COVID-19 mortality versus changes in immigration patterns or birth rates? The `datacommons` package makes it easy to pull in additional variables like death rates, immigration statistics, and birth rates to investigate these questions further.

### Alternative: Aggregate Across Sources

    # Strategy 2: Take the median across all sources for each year
    us_pop_aggregated <- us_population |>
      filter(!is.na(value)) |>
      mutate(year = as.integer(date)) |>
      filter(year >= 2000) |>
      group_by(year) |>
      summarize(
        population_millions = median(value) / 1e6,
        n_sources = n_distinct(facet_name),
        .groups = "drop"
      )

    # Show which years have the most sources
    us_pop_aggregated |>
      slice_max(n_sources, n = 5) |>
      kable(caption = "Years with most data sources")

Years with most data sources
year | population_millions | n_sources
--- | --- | ---
2017 | 325.0538 | 12
2019 | 328.2395 | 12
2018 | 326.6875 | 11
2020 | 329.8250 | 11
2021 | 331.9714 | 10
2022 | 332.1845 | 10
2023 | 333.6512 | 10

## Example 2: Using Relationships to Find States

Real-world motivation: State-level demographic analysis is crucial for understanding regional variations in aging, which impacts healthcare planning, workforce development, and social services allocation.

    # Method 1: Use the parent/child relationship in observations
    state_data <- dc_get_observations(
      variable_dcids = c("Count_Person", "Median_Age_Person"),
      date = "latest",
      parent_entity = "country/USA",
      entity_type = "State",
      return_type = "data.frame"
    )

    # The code automatically constructs the following query:
    # Find all entities of type State contained in country/USA

    glimpse(state_data)
    #> Rows: 724
    #> Columns: 8
    #> $ entity_dcid   <chr> "geoId/04", "geoId/11", "geoId/17", "geoId/01", "geoId/1…
    #> $ entity_name   <chr> "Arizona", "District of Columbia", "Illinois", "Alabama"…
    #> $ variable_dcid <chr> "Count_Person", "Count_Person", "Count_Person", "Count_P…
    #> $ variable_name <chr> "Total population", "Total population", "Total populatio…
    #> $ date          <chr> "2023", "2023", "2020", "2024", "2024", "2023", "2023", …
    #> $ value         <dbl> 7268175, 672079, 12812508, 5157699, 11180878, 21928881, …
    #> $ facet_id      <chr> "1964317807", "1145703171", "1541763368", "2176550201", …
    #> $ facet_name    <chr> "CensusACS5YearSurvey_SubjectTables_S0101", "CensusACS5Y…

    # Process the data - reshape from long to wide
    state_summary <- state_data |>
      filter(str_detect(facet_name, "Census")) |> # Use Census data
      select(
        entity = entity_dcid,
        state_name = entity_name,
        variable = variable_dcid,
        value
      ) |>
      group_by(entity, state_name, variable) |>
      # Take first if duplicates
      summarize(value = first(value), .groups = "drop") |>
      pivot_wider(names_from = variable, values_from = value) |>
      filter(
        !is.na(Count_Person),
        !is.na(Median_Age_Person),
        Count_Person > 500000 # Focus on states, not small territories
      )

    # Visualize the relationship
    ggplot(state_summary, aes(x = Median_Age_Person, y = Count_Person)) +
      geom_point(aes(size = Count_Person), alpha = 0.6, color = "darkblue") +
      geom_text(
        data = state_summary |> filter(Count_Person > 10e6),
        aes(label = state_name),
        vjust = -1, hjust = 0.5, size = 3
      ) +
      scale_y_log10(labels = label_comma()) +
      scale_size(range = c(3, 10), guide = "none") +
      labs(
        title = "State Demographics: Population vs. Median Age",
        x = "Median Age (years)",
        y = "Population (log scale)",
        caption = "Source: U.S. Census Bureau via Data Commons"
      )

    # Find extremes
    state_summary |>
      filter(
        Median_Age_Person == min(Median_Age_Person) |
          Median_Age_Person == max(Median_Age_Person)
      ) |>
      select(state_name, Median_Age_Person, Count_Person) |>
      mutate(Count_Person = label_comma()(Count_Person)) |>
      kable(caption = "States with extreme median ages")

States with extreme median ages
state_name | Median_Age_Person | Count_Person
--- | --- | ---
Maine | 44.8 | 1,377,400
Utah | 31.7 | 3,331,187

Key insight: The 13-year gap between Maine (44.8) and Utah (31.7) in median age represents dramatically different demographic challenges. Maine faces an aging workforce and increasing healthcare demands, while Utah’s younger population suggests different priorities around education and family services. These demographic differences drive fundamentally different policy needs across states.

## Example 3: Cross-Dataset Integration

Real-world motivation: Public health researchers often need to understand how socioeconomic factors relate to health outcomes. Let’s explore potential connections between age demographics, obesity rates, and economic conditions at the county level—exactly the type of integrated analysis that would be extremely difficult without Data Commons.

    # Get multiple variables for California counties
    # Notice how we can mix variables from different sources in one query!
    ca_integrated <- dc_get_observations(
      variable_dcids = c(
        "Count_Person", # Census
        "Median_Age_Person", # Census
        "Percent_Person_Obesity", # CDC
        "UnemploymentRate_Person" # BLS
      ),
      date = "latest",
      parent_entity = "geoId/06", # California
      entity_type = "County",
      return_type = "data.frame"
    )

    # Check which sources we're pulling from
    ca_integrated |>
      group_by(variable_name, facet_name) |>
      summarize(n = n(), .groups = "drop") |>
      slice_head(n = 10) |>
      kable(caption = "Data sources by variable")

Data sources by variable
variable_name | facet_name | n
--- | --- | ---
Median age of population | CensusACS5YearSurvey | 58
Median age of population | CensusACS5YearSurvey_SubjectTables_S0101 | 58
Percentage of Adult Population That Is Obese | CDC500 | 232
Total population | CDC_Mortality_UnderlyingCause | 58
Total population | CDC_Social_Vulnerability_Index | 58
Total population | CensusACS1YearSurvey | 41
Total population | CensusACS5YearSurvey | 58
Total population | CensusACS5YearSurvey_SubjectTables_S0101 | 58
Total population | CensusPEP | 56
Total population | USCensusPEP_AgeSexRaceHispanicOrigin | 58

    # Process the integrated data
    ca_analysis <- ca_integrated |>
      # Pick one source per variable for consistency
      filter(
        (variable_dcid == "Count_Person" &
           str_detect(facet_name, "CensusACS5Year")) |
          (variable_dcid == "Median_Age_Person" &
             str_detect(facet_name, "CensusACS5Year")) |
          (variable_dcid == "Percent_Person_Obesity" &
             str_detect(facet_name, "CDC")) |
          (variable_dcid == "UnemploymentRate_Person" &
             str_detect(facet_name, "BLS"))
      ) |>
      select(
        entity = entity_dcid,
        county_name = entity_name,
        variable = variable_dcid,
        value
      ) |>
      group_by(entity, county_name, variable) |>
      summarize(value = first(value), .groups = "drop") |>
      pivot_wider(names_from = variable, values_from = value) |>
      drop_na() |>
      mutate(
        county_name = str_remove(county_name, " County$"),
        population_k = Count_Person / 1000
      )

    # Explore relationships between variables
    ggplot(ca_analysis, aes(x = Median_Age_Person, y = Percent_Person_Obesity)) +
      geom_point(aes(size = Count_Person, color = UnemploymentRate_Person),
        alpha = 0.7
      ) +
      geom_smooth(method = "lm", se = FALSE, color = "red", linetype = "dashed") +
      scale_size(range = c(2, 10), guide = "none") +
      scale_color_viridis_c(name = "Unemployment\nRate (%)") +
      labs(
        title = "California Counties: Age, Obesity, and Unemployment",
        subtitle = "Integrating Census, CDC, and BLS data through Data Commons",
        x = "Median Age (years)",
        y = "Obesity Rate (%)",
        caption = "Sources: Census ACS, CDC PLACES, BLS via Data Commons"
      ) +
      theme(legend.position = "right")

    # Show correlations
    ca_analysis |>
      select(Median_Age_Person, Percent_Person_Obesity, UnemploymentRate_Person) |>
      cor() |>
      round(2) |>
      kable(caption = "Correlations between demographic and health variables")

Correlations between demographic and health variables
| Median_Age_Person | Percent_Person_Obesity | UnemploymentRate_Person
--- | --- | --- | ---
Median_Age_Person | 1.00 | -0.36 | -0.43
Percent_Person_Obesity | -0.36 | 1.00 | 0.62
UnemploymentRate_Person | -0.43 | 0.62 | 1.00

Key insight: The negative correlation (-0.30) between median age and obesity rates is counterintuitive—we might expect older populations to have higher obesity rates. However, the strong positive correlation (0.61) between unemployment and obesity suggests economic factors may be more important than age. This type of finding, made possible by the `datacommons` package’s easy data integration, could inform targeted public health interventions that address economic barriers to healthy living.

## Example 4: Time Series Across Cities

Real-world motivation: Urban planners and demographers track city growth patterns to inform infrastructure investments, housing policy, and resource allocation. Let’s examine how major U.S. cities have grown differently over the past two decades.

    # Major city DCIDs (found using datacommons.org/place)
    major_cities <- c(
      "geoId/3651000", # New York City
      "geoId/0644000", # Los Angeles
      "geoId/1714000", # Chicago
      "geoId/4835000", # Houston
      "geoId/0455000" # Phoenix
    )

    # Get historical population data
    city_populations <- dc_get_observations(
      variable_dcids = "Count_Person",
      entity_dcids = major_cities,
      date = "all",
      return_type = "data.frame"
    )

    # Process - use Census PEP data for consistency
    city_pop_clean <- city_populations |>
      filter(
        str_detect(facet_name, "USCensusPEP"),
        !is.na(value)
      ) |>
      mutate(
        year = as.integer(date),
        population_millions = value / 1e6,
        city = str_extract(entity_name, "^[^,]+")
      ) |>
      filter(year >= 2000) |>
      group_by(city, year) |>
      summarize(
        population_millions = mean(population_millions),
        .groups = "drop"
      )

    # Visualize growth trends
    ggplot(
      city_pop_clean,
      aes(x = year, y = population_millions, color = city)
    ) +
      geom_line(linewidth = 1.2) +
      geom_point(size = 2) +
      scale_color_brewer(palette = "Set1") +
      scale_y_continuous(labels = label_number(suffix = "M")) +
      labs(
        title = "Population Growth in Major U.S. Cities",
        x = "Year",
        y = "Population (millions)",
        color = "City",
        caption = "Source: Census Population Estimates Program via Data Commons"
      ) +
      theme(legend.position = "bottom")

    # Calculate growth rates
    city_pop_clean |>
      group_by(city) |>
      filter(year == min(year) | year == max(year)) |>
      arrange(city, year) |>
      summarize(
        years = paste(min(year), "-", max(year)),
        start_pop = first(population_millions),
        end_pop = last(population_millions),
        total_growth_pct = round((end_pop / start_pop - 1) * 100, 1),
        .groups = "drop"
      ) |>
      arrange(desc(total_growth_pct)) |>
      kable(
        caption = "City population growth rates",
        col.names = c("City", "Period", "Start (M)", "End (M)", "Growth %")
      )

City population growth rates
City | Period | Start (M) | End (M) | Growth %
--- | --- | --- | --- | ---
Phoenix | 2000 - 2024 | 1.327196 | 1.673164 | 26.1
Houston | 2000 - 2024 | 1.977408 | 2.390125 | 20.9
New York City | 2000 - 2024 | 8.015209 | 8.478072 | 5.8
Los Angeles | 2000 - 2024 | 3.702574 | 3.878704 | 4.8
Chicago | 2000 - 2024 | 2.895723 | 2.721308 | -6.0

Key insight: The stark contrast between Sun Belt cities (Phoenix +26%, Houston +21%) and Rust Belt Chicago (-6%) reflects major economic and demographic shifts in America. These patterns have profound implications for everything from housing affordability to political representation. The `datacommons` package makes it trivial to extend this analysis—we could easily add variables like temperature, job growth, or housing costs to understand what drives these migration patterns.

## Working with Data Sources (Facets)

Each data source in Data Commons is identified by a `facet_id` and `facet_name`. Understanding these helps you choose the right data:

    # Example: What sources are available for U.S. population?
    us_population |>
      filter(date == "2020") |>
      select(facet_name, value) |>
      distinct() |>
      mutate(value = label_comma()(value)) |>
      arrange(desc(value)) |>
      kable(caption = "Different sources for 2020 U.S. population")

Different sources for 2020 U.S. population
facet_name | value
--- | ---
WorldDevelopmentIndicators | 331,577,720
USCensusPEP_AgeSexRaceHispanicOrigin | 331,577,720
USCensusPEP_Annual_Population | 331,526,933
OECDRegionalDemography_Population | 331,526,933
USDecennialCensus_RedistrictingRelease | 331,449,281
CensusACS5YearSurvey_AggCountry | 329,824,950
CDC_Mortality_UnderlyingCause | 329,484,123
CensusACS5YearSurvey_SubjectTables_S0101 | 326,569,308
CensusACS5YearSurvey_SubjectTables_S2601A | 326,569,308
CensusACS5YearSurvey_SubjectTables_S2602 | 326,569,308
CensusACS5YearSurvey_SubjectTables_S2603 | 326,569,308

Common sources and when to use them:

- `USCensusPEP_Annual_Population`: Annual estimates (good for time series)
- `CensusACS5YearSurvey`: American Community Survey (good for detailed demographics)
- `USDecennialCensus`: Official census every 10 years (most authoritative)
- `CDC_*`: Health data from CDC
- `BLS_*`: Labor statistics from Bureau of Labor Statistics
- `EPA_*`: Environmental data from EPA

## Tips for Effective Use

1. Start with the websites: Use Data Commons web tools to explore what’s available before writing code.

2. Use relationships: The `parent_entity` and `entity_type` parameters are powerful for traversing the graph.

3. Be specific about sources: Filter by `facet_name` when you need consistency across places or times.

4. Expect messiness: Real-world data has gaps, multiple sources, and inconsistencies. Plan for it.

5. Leverage integration: The real power is in combining datasets that would normally require multiple APIs.

## Going Further

The `datacommons` R package currently focuses on data retrieval, which is the foundation for analysis. While more advanced features may come in future versions, you can already:

- Combine demographic, health, environmental, and economic data
- Build models that incorporate multiple data sources
- Create visualizations that would require extensive data wrangling otherwise
- Track changes across time for any available variable

The package integrates seamlessly with the tidyverse, making it easy to incorporate Data Commons into your existing R workflows and create reproducible analysis pipelines.

Explore more at:

- Statistical Variable Explorer: https://datacommons.org/tools/statvar
- Place Explorer: https://datacommons.org/place
- Knowledge Graph Browser: https://datacommons.org/browser/

## Summary

In this vignette, we’ve explored how the `datacommons` R package provides unique value through data integration. While specialized packages like `tidycensus` excel at deep functionality for specific datasets, the `datacommons` package shines when you need to:

- Combine data from multiple sources
- Use consistent geographic identifiers across datasets
- Access a wide variety of variables through one API
- Navigate relationships in the knowledge graph

The power lies not in any single dataset, but in the connections between them. By using the `datacommons` package, you can focus on analysis rather than data wrangling, enabling insights that would be difficult or impossible to achieve through traditional approaches.

## Resources

- Get API Key: https://docs.datacommons.org/api/#obtain-an-api-key
- Statistical Variable Explorer: https://datacommons.org/tools/statvar
- Place Explorer: https://datacommons.org/place
- Knowledge Graph Browser: https://datacommons.org/browser/
- API Documentation: https://docs.datacommons.org/api/rest/v2/