Contextual Data

GeoMarker attaches contextual data to your point data by determining the census geographic unit in which each point lies and attaching characteristics of that unit to the point record. The initial release of GeoMarker attaches data from the 2019 American Community Survey 5-year data at the census tract level. Future releases of GeoMarker will include additional datasets, geographic levels, and contextual variables. We want to know what additional data are important to you! Please send us your suggestions by emailing

Point in Polygon: Determining Points’ Contexts

GeoMarker uses a basic point-in-polygon spatial operation to determine the geographic unit in which each latitude-longitude point is located. GeoMarker uses the 2018 Census Tract, 2018 Tiger/Line + basis shapefile, as provided by NHGIS as the definition of the polygons for the point-in-polygon operation. Contextual variables are derived from the 2018 ACS 5-year (2014-2018) dataset in NHGIS, using the following definitions.

Contextual Variables

Proportion unemployed
Source Table B23025. Employment Status for the Population 16 Years and Over
Universe Population 16 years and over
Numerator [ALY3E005] In labor force: Civilian labor force: Unemployed
Denominator [ALY3E003] In labor force: Civilian labor force
Proportion population in poverty
Source Table C17002. Ratio of Income to Poverty Level in the Past 12 Months
Universe Population for whom poverty status is determined
Numerator [ALWVE002] Under .50
+[ALWVE003] .50 to .99
Denominator [ALWVE001] Total
Median household income
Source Table B19013. Median Household Income in the Past 12 Months (in 2018 Inflation-Adjusted Dollars)
Universe Households
Numerator [ALW1E001] Median household income in the past 12 months (in 2018 inflation-adjusted dollars)
Denominator n/a
Income inequality
Source Table B19083. Gini Index of Income Inequality
Universe Households
Numerator [AMEME001] Gini Index
Denominator n/a
Proportion of family households headed by single woman
Source Table B11001. Household Type (Including Living Alone)
Universe Households
Numerator [ALU9E006] Family households: Other family: Female householder, no husband present
Denominator [ALU9E002] Family households
Proportion of occupied housing units that are owner occupied
Source Table B25003. Tenure
Universe Occupied housing units
Numerator [ALZLE002] Owner occupied
Denominator [ALZLE001] Total
Proportion African American
Source Table B02001. Race
Universe Total population
Numerator [ALUCE003] Black or African American alone
Denominator [ALUCE001] Total
Proportion of adults who completed high school
Source Table B15003. Educational Attainment for the Population 25 Years and Over
Universe Population 25 years and over
Numerator [ALWGE017] Regular high school diploma
+[ALWGE018] GED or alternative credential
+[ALWGE019] Some college, less than 1 year
+[ALWGE020] Some college, 1 or more years, no degree
+[ALWGE021] Associate’s degree
+[ALWGE022] Bachelor’s degree
+[ALWGE023] Master’s degree
+[ALWGE024] Professional school degree
+[ALWGE025] Doctorate degree
Denominator [ALWGE001] Total
Persons per square kilometer
Source Table B01003. Total Population
Universe Total population
Numerator [ALUBE001] Total
Denominator [ALAND]/1000000 Current land area (2019 TIGER/Line census tracts shapefile), converted to sq km
Housing units per square kilometer
Source Table B25001. Housing Units
Universe Housing units
Numerator [ALZJE001] Total
Denominator [ALAND]/1000000 Current land area (2019 TIGER/Line census tracts shapefile), converted to sq km

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Yost Index Quintiles for Census Tracts

As part of a collaboration between GeoMarker and the ADMIRAL Study (University of Minnesota Medical School, Department of Pediatrics), we are providing data files containing Yost Index quintiles for census tracts. ADMIRAL Study contributing institutions may download Yost index data files for the appropriate years and merge them with their geolocated patient records. You may download ZIP files from the table below; and, when you unzip the files, you will find comma-separated value (CSV) files that contain the Yost Index quintiles.


The Yost Index files are available in the table below. Each row in the table represents the year the patient was diagnosed with B-cell acute lymphoblastic leukemia (B-ALL).

  1. Find the Yost Index file corresponding to the year of diagnosis and download it to your computer
  2. Unzip the downloaded file
  3. Merge the Yost Index CSV file with the geocoded patient data file for only those patients diagnosed that same year using the GISJOIN field
  4. Create an output file containing the following fields:
    • STUDYID (unique code used for ADMIRAL Study for samples and data)
    • NAACCRGISCoordinateQualityName
    • YEAR
  5. Repeat the process for other diagnosis years, as needed

Yost Index Data Files

Year of diagnosis
2000 2010
2001 2011
2002 2012
2003 2013
2004 2014
2005 2015
2006 2016
2007 2017
2008 2018
2009 2019

Yost Index Data File Record Layout

Variable Description Data type
GISJOIN GIS Join Match Code String
STATE State name String
STATEA State FIPS code String
COUNTY County name String
COUNTYA County FIPS code String
TRACTA Census tract code String
QUINTILE Yost index quintile for the census tract Integer
YEAR Year of diagnosis Integer

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