Tool Reference

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Census Data

Download ACS Census Data Download Decennial CensusNEW Download Population EstimatesNEW Download Migration FlowsNEW Download LEHD JobsNEW Download LEHD Commute FlowsNEW Compare Time Periods Join ACS Data to Features

Data Enrichment

Normalize Data MOE Reliability Filter Aggregate DataNEW

Analysis

Demographic Profile Report Hot Spot Analysis

Configuration

Generate Variable Lookup Set Census API Key

Download ACS Census Data

Census Data

Download American Community Survey data with geometry using the tidycensus R library. Creates a feature class with estimate and margin of error fields.

Parameters

ParameterTypeDescription
Year Required String ACS data year (2015–2024 for acs5, 2017–2024 for acs1).
Survey Required String acs5 (5-Year, all geographies) or acs1 (1-Year, 65K+ population areas only)
Category Optional String Filter variable browser by topic category
Table Optional String Select a specific ACS table
Variables Optional String (Multi) Select individual variables from the browser
Manual Variables Optional String Enter variable codes directly, comma-separated (e.g., B19013_001, B25077_001)
Download Entire Table Optional Boolean Download all variables from the selected table instead of individual selections
Geography Level Required String tract, block group, county, state, place, zcta, congressional district, etc.
State Optional String (Multi) Two-letter state abbreviation(s). Required for sub-state geographies.
County Optional String County name or FIPS code(s), comma-separated. Leave blank for entire state.
ZCTA Optional String ZIP Code Tabulation Areas, comma-separated (for ZCTA geography only)
MOE Confidence Level Optional Integer 90% (default), 95%, or 99%
Output Feature Class Required Feature Class Output location. Must be a geodatabase feature class.
Keep Geographic Variables Optional Boolean Retain NAME, STATEFP, ALAND, and other geographic identifier fields

Output Fields

For each variable, two fields are created:

  • [Variable_Name] — The estimate value
  • [Variable_Name]_MOE — The margin of error (90% confidence by default)
Tip: Use the Variable Lookup to find variable codes, or enter them manually if you already know them.

Download Decennial Census Data

Census Data NEW

Download Decennial Census data (2000, 2010, 2020) including block-level geography. Provides core population, race, ethnicity, and housing counts from the redistricting (PL) dataset.

Parameters

ParameterTypeDescription
Census Year Required String 2020, 2010, or 2000
Data Topic Optional String (Multi) Select one or more topics: Total Population, Population by Race, Hispanic or Latino Origin, Population 18 Years and Over, Voting Age by Race, Housing Units, Housing Occupancy, Group Quarters, etc. Available topics vary by Census year.
Manual Variables Optional String Enter Decennial Census variable codes directly, comma-separated (e.g., P1_001N for 2020)
Geography Level Required String state, county, tract, block group, block, place, zcta, congressional district, etc. Block-level geography is unique to Decennial data.
State Optional String (Multi) Required for sub-state geographies
County Optional String Recommended for block-level downloads to limit data size
Output Feature Class Required Feature Class Output location

Available Topics by Year

2020: Total Population, Population by Race, Hispanic/Latino Origin, Population 18+, Voting Age by Race, Voting Age Hispanic/Latino, Total Housing Units, Housing Occupancy Status, Group Quarters Population

2010: All of the above plus Housing Tenure (owner vs. renter)

2000: Total Population, Population by Race, Hispanic/Latino Origin, Population 18+, Total Housing Units, Housing Occupancy Status, Housing Tenure

Important: Block-level data for an entire state can be very large (millions of records). Always specify counties when downloading block-level data.
Tip: Unlike ACS data, Decennial Census counts have no margin of error—they represent a full enumeration. This makes Decennial data ideal for exact population counts and redistricting analysis.

Download Population Estimates

Census Data NEW

Download Census Population Estimates Program (PEP) data for 2020 and later. Includes total population, components of change (births, deaths, migration), and demographic breakdowns by age, race, sex, and Hispanic origin.

Parameters

ParameterTypeDescription
Vintage Year Required String 2020–2024
Geography Required String State or County
Data Type Required String (Multi) Total Population, Components of Change, or demographic breakdowns: AGEGROUP, RACE, SEX, HISP
Label Demographic Values Optional Boolean Display friendly labels (e.g., “Male”/“Female”) instead of numeric codes
State Optional String (Multi) Required for county geography
County Optional String County name or FIPS code. Blank = all counties in state.
Keep Geographic Variables Optional Boolean Retain GEOID, NAME, and other identifier fields
Output Feature Class Required Feature Class Output location
Tip: Population Estimates are released annually and provide the most current intercensal population data. They are produced using a different methodology than the ACS—estimates are based on the Decennial Census baseline updated with administrative records for births, deaths, and migration.

Download Migration Flows

Census Data NEW

Download ACS Migration Flows data showing where people from an origin county moved to (outflows). Creates destination county polygons with flow counts, enabling migration pattern mapping and analysis.

Parameters

ParameterTypeDescription
Year Required String End year of 5-year ACS period (2010–2022). E.g., 2022 = 2018–2022 data.
Origin State Required String State containing the origin county
Origin County Required String County to analyze outbound migration from
Destination States Required String (Multi) States where migrants moved to (select one or more)
Minimum Flow Count Optional Integer Filter out small flows below this threshold. Default: 0 (show all).
Output Feature Class Required Feature Class Destination county polygons with migration data
Tip: Migration Flows data is based on ACS 5-year estimates. Use the minimum flow filter to focus on significant migration patterns and reduce noise from small counts.

Download LEHD Jobs

Census Data NEW

Download LEHD LODES (Longitudinal Employer-Household Dynamics) data showing job counts by geography. Choose between Residence Area Characteristics (RAC, where workers live) or Workplace Area Characteristics (WAC, where workers work).

Parameters

ParameterTypeDescription
State Required String State to download data for
Year Required String 2020–2022 (LODES8, 2020 Census blocks)
Count Jobs By Required String Residence (where workers live) or Workplace (where workers work)
Job Type Required String All Jobs, Primary Jobs, All Private Jobs, Private Primary Jobs, All Federal Jobs, or Federal Primary Jobs
Worker Segment Required String Filter by: All Workers, Age group (29 or younger, 30–54, 55+), Earnings level, or Industry sector (Goods Producing, Trade/Transportation/Utilities, All Other Services)
Geography Level Required String Block Group, Tract, or County
Output Feature Class Required Feature Class Output location with job count data and geometry
Use case: WAC data shows job density—where employment is concentrated. RAC data shows where the workforce lives. Together they reveal commute patterns and jobs-housing balance.

Download LEHD Commute Flows

Census Data NEW

Download LEHD LODES Origin-Destination (OD) data showing commuting patterns between home and work locations. Produces a table of origin-destination pairs with job counts at your chosen geography level.

Parameters

ParameterTypeDescription
State Required String Residence state for commuters
Year Required String 2020–2022 (LODES8, 2020 Census blocks)
Job Type Required String All Jobs, Primary Jobs, Private, or Federal
Worker Segment Required String All Workers, or filter by Age, Earnings, or Industry sector
Include Required String In-state commutes only (main) or Include out-of-state commutes (aux)
Geography Level Required String Block Group, Tract, or County
Output Table Required Table Geodatabase table with origin-destination pairs and job counts

Output Fields

  • home_GEOID — GEOID of the residence (origin) geography
  • work_GEOID — GEOID of the workplace (destination) geography
  • S000 (or segment code) — Number of jobs for this origin-destination pair
Note: This tool outputs a table, not a feature class, since OD data represents pairs of locations rather than single geographies. To visualize, join the table to geographic features using the GEOID fields, or use it to create desire-line features.

Compare Time Periods

Census Data

Compare a single ACS variable across two time periods with statistical significance testing. Handles cross-decade boundary changes using population-weighted interpolation.

Parameters

ParameterTypeDescription
Year 1 (Earlier) Required String The earlier year (baseline) for comparison
Year 2 (Later) Required String The later year for comparison
Survey Required String acs5 or acs1
Variable Required String Single variable code to compare (e.g., B19013_001)
Variable Type Required String Count (summed during interpolation) or Rate/Median (averaged)
Geography Level Required String tract, block group, county, etc.
State Optional String (Multi) State(s) to analyze
County Optional String County filter, comma-separated
MOE Confidence Level Optional Integer 90% (default), 95%, or 99%
Output Feature Class Required Feature Class Output location

Output Fields

  • Est_Y1, MOE_Y1 — Year 1 estimate and MOE
  • Est_Y2, MOE_Y2 — Year 2 estimate and MOE
  • Change — Absolute change (Y2 - Y1)
  • PctChange — Percent change
  • SigChange — Significance: 1 (significant increase), -1 (significant decrease), 0 (not significant)
Important: For ACS 5-Year estimates, use years at least 5 years apart to avoid overlapping data (e.g., 2018 and 2024, not 2020 and 2023).

Join ACS Data to Features

Census Data

Assign Census demographics to your own polygon features (parcels, planning districts, custom boundaries) using spatial interpolation. States and counties are auto-detected from your input features.

Parameters

ParameterTypeDescription
Input Feature Class Required Feature Class Your polygon features to join Census data to
Year / Survey Required String ACS year and survey type
Variables Required String Variable code(s) to join (browser or manual entry)
Variable Type Required String Count (values are summed) or Rate/Median (values are averaged)
Census Geography Required String tract, block group, county, or zcta — the Census level to join from
Join Method Required String Area Weighted or Population Weighted (more accurate, slower)
Output Feature Class Required Feature Class Output location
Tip: For small polygons like parcels, both join methods will assign the value from the Census unit containing the parcel centroid. Area/population weighting matters more for larger target polygons that span multiple Census geographies.

Normalize Data

Data Enrichment

Calculate percentages, per capita rates, or per-thousand rates from raw ACS counts. Properly propagates margins of error using Census Bureau formulas.

Parameters

ParameterTypeDescription
Input Feature Class Required Feature Layer Feature class with ACS data
Numerator Field Required Field The count field to normalize (e.g., population with bachelor's degree)
Denominator Field Required Field The base/universe field (e.g., total population 25+)
Normalization Type Required String Percentage (×100), Per 1,000, Per 10,000, Per 100,000, or Ratio
Output Field Name Required String Name for the new calculated field (MOE field gets _MOE suffix)
Numerator is Subset Required Boolean Check if numerator is a subset of denominator (uses proportion MOE formula vs. ratio formula)
Calculate MOE Optional Boolean Calculate margin of error for the normalized value. MOE fields are auto-detected.
Output Feature Class Optional Feature Class Leave blank to add fields to input, or specify a new feature class

Output Fields

  • [Output Field Name] — The normalized value
  • [Output Field Name]_MOE — Margin of error (if Calculate MOE is checked)

MOE Reliability Filter

Data Enrichment

Calculate Coefficient of Variation (CV) and classify estimates by statistical reliability. Optionally filter or flag unreliable records. MOE fields are auto-detected from the estimate field name.

Parameters

ParameterTypeDescription
Input Feature Class Required Feature Layer Feature class with ACS estimate and MOE fields
Estimate Field Required Field The estimate field to evaluate
MOE Field Optional Field Margin of error field (auto-detected if not specified)
Confidence Level Required String 90% (ACS default), 95%, or 99%
High Reliability Threshold Required Double CV below this = High reliability (default: 12%)
Low Reliability Threshold Required Double CV above this = Low reliability (default: 40%)
Action Required String Flag only (add reliability fields), Set estimate to NULL if Low, or Remove Low reliability records
Output Feature Class Optional Feature Class Leave blank to modify input, or specify a new feature class

Output Fields

  • CV — Coefficient of Variation (percentage)
  • Reliability — High, Medium, or Low

Aggregate Data

Data Enrichment NEW

Aggregate numeric fields from source polygons to larger target zones using area or population weighting. Automatically detects and propagates MOE fields. Ideal for rolling up Census tract data to custom planning zones, council districts, or other boundaries.

Parameters

ParameterTypeDescription
Source Feature Class Required Feature Layer Polygons with data to aggregate (e.g., Census tracts with ACS data)
Target Feature Class Required Feature Layer Polygons to aggregate to (e.g., council districts, planning zones)
Fields to Aggregate Required Field (Multi) Numeric fields from the source to aggregate. Associated MOE fields are automatically detected and propagated.
Variable Type Required String Count (values are summed) or Rate/Median (weighted average)
Aggregation Method Required String Area Weighted or Population Weighted (more accurate)
Output Feature Class Required Feature Class Output target zones with aggregated values
Example workflow: Download ACS tract data → Aggregate to council districts → Normalize to get percentages → Run reliability filter. This gives you statistically sound demographic profiles for any custom geography.

Demographic Profile Report

Analysis

Generate a demographic profile comparing a study area to county, state, and national averages. The tool auto-detects counties from your study area features. Output to Excel, CSV, or HTML.

Parameters

ParameterTypeDescription
Study Area Features Required Feature Layer Polygon layer defining study area. Uses selected features if any, otherwise all features.
Indicators Required String (Multi) Select indicators to include (Total Population, Median Income, etc.)
Census Geography Required String tract or block group — level to aggregate from
Year / Survey Required String ACS year and survey type
Compare to County / State / Nation Optional Boolean Select which comparison geographies to include in the report
County for Comparison Optional String Auto-detected from study area. Select which overlapping county to compare against.
Study Area Name Optional String Label used in the report (default: “Study Area”)
Output Report Required File Output file (.xlsx, .csv, or .html)

Available Indicators

Total Population, Median Age, Median Household Income, Per Capita Income, % Below Poverty Level, % Bachelor's Degree or Higher, % Unemployed, % Owner-Occupied Housing, % Renter-Occupied Housing, Median Home Value, Median Gross Rent, % No Health Insurance, % Foreign Born, % Age 65 and Over, % Under Age 18

Hot Spot Analysis

Analysis

Identify statistically significant spatial clusters (hot spots and cold spots) using Getis-Ord Gi* with optional MOE-aware significance adjustment. Automatically applies symbology when added to a map.

Parameters

ParameterTypeDescription
Input Feature Class Required Feature Layer Polygon or point features with values to analyze
Analysis Field Required Field Numeric field to analyze for clustering
MOE Field Optional Field Margin of error field (auto-detected from analysis field name)
Spatial Relationship Required String Contiguity (shared boundary), Distance band, or K nearest neighbors
Distance Band Optional Double Distance threshold in meters (for distance band method)
Number of Neighbors Optional Integer K value for KNN method (default: 8)
Significance Level Required String 0.10, 0.05, or 0.01
Apply MOE Adjustment Optional Boolean Adjust significance based on estimate reliability (requires MOE field)
Output Feature Class Required Feature Class Output location

Output Fields

  • Gi_Zscore — Getis-Ord Gi* z-score (positive = hot, negative = cold)
  • Gi_Pvalue — Statistical p-value
  • Gi_Bin — Classification (Hot Spot 99%/95%/90%, Cold Spot 99%/95%/90%, Not Significant)
  • Gi_Bin_Adjusted — MOE-adjusted classification (if MOE adjustment enabled)
  • CV — Coefficient of Variation (if MOE field provided)
  • Reliability — High/Medium/Low (if MOE field provided)
MOE Adjustment: When enabled, features with Low reliability (CV > 40%) are marked as “Unreliable Estimate” regardless of Gi* result. Features with Medium reliability have 90% confidence results downgraded to “Not Significant.”

Generate Variable Lookup File

Configuration

Generate the JSON lookup file used by the variable browser in the ACS download and other tools. Run this once for each year/survey combination you need.

Parameters

ParameterTypeDescription
ACS Year Required Integer Year to generate lookup for (e.g., 2024)
Survey Required String acs5 or acs1

Output is saved automatically to lookups/acs_variable_lookup_[year]_[survey].json in the toolbox directory.

Note: This tool takes 1–2 minutes to run as it queries the Census API for all available variables.

Set Census API Key

Configuration

Store your Census API key permanently in R's environment file. You only need to run this once.

Parameters

ParameterTypeDescription
Census API Key Required String (Hidden) Your Census API key (40 characters)

Getting an API Key

Request a free API key at https://api.census.gov/data/key_signup.html. The key is emailed to you within minutes.

Storage: The key is saved in your R .Renviron file and automatically loaded each time R starts. You don't need to enter it again.