Data Dictionary | Durham Population Lab

This is a data dictionary for the Durham County Social Determinants of Health Data. Please contact Mark Yacoub at mark.yacoub@duke.edu if you would like access to the data.

Variable Name	Full Name	Description	Data Type	Data Level	Years	Percent Missing	Source	Table	Notes
Add_Number	Address Number	The whole number identifier of a location along a thoroughfare or within a defined community	Cross-sectional	Address	2024	0	National Address Database	nad
St_PreDir	Street Name Pre-Directional	Word preceding the Street Name element that indicates the direction taken by the street from an arbitrary starting point or line, or the sector where it is located	Cross-sectional	Address	2024	0	National Address Database	nad
St_Name	Street Name	The element of the complete street name that identifies the particular street (as opposed to any street types, directionals, and modifiers)	Cross-sectional	Address	2024	0	National Address Database	nad
St_PosTyp	Street Name Post Type	Word or phrase that follows the Street Name element and identifies a type of thoroughfare in a complete street name	Cross-sectional	Address	2024	0	National Address Database	nad
St_PosDir	Street Name Post Directional	A word following the Street Name element that indicates the direction taken by the street from an arbitrary starting point or line, or the sector where it is located	Cross-sectional	Address	2024	0	National Address Database	nad
Floor	Floor	A floor, story, or level within a building.	Cross-sectional	Address	2024	0	National Address Database	nad
Unit	Unit	A group or suite of rooms within a building that are under common ownership or tenancy, typically having a common primary entrance	Cross-sectional	Address	2024	0	National Address Database	nad
Inc_Muni	Incorporated Municipality	Name of the incorporated municipality or other general-purpose local governmental unit where the address is located	Cross-sectional	Address	2024	0	National Address Database	nad
Post_City	Postal City Name	A city name for the ZIP code of an address, as given in the USPS City State file	Cross-sectional	Address	2024	0	National Address Database	nad
Zip_Code	Zip Code	For standard street mail delivery (with a corresponding geographic delivery area), the system of 5-digit codes that identifies the individual USPS Post Office associated with an address	Cross-sectional	Address	2024	0	National Address Database	nad
Longitude	Address Longitude	Address Longitude, derived based on point placement	Cross-sectional	Address	2024	0	National Address Database	nad
Latitude	Address Latitude	Address Latitude, derived based on point placement	Cross-sectional	Address	2024	0	National Address Database	nad
NatGrid	National Grid Coordinates	National Grid Coordinate, derived based on point placement. Useful for GIS work	Cross-sectional	Address	2024	0	National Address Database	nad
DateUpdate	Date Updated		Cross-sectional	Address	2024	0	National Address Database	nad
NAD_Source	Source		Cross-sectional	Address	2024	0	National Address Database	nad
nad_durm_combine	Entire Address Combined		Cross-sectional	Address	2024	0	National Address Database	nad
building_type	Building Type		Cross-sectional	Address	2024	0	National Address Database	nad
errors	Property/NAD Merge Errors		Cross-sectional	Address	2024	0	National Address Database	nad
year	Tax Year		Cross-sectional	Address	2024	25	Durham Real Property Database	property
reid	Record ID		Cross-sectional	Address	2024	25	Durham Real Property Database	property
city_pcnt	City Percent		Cross-sectional	Address	2024	37	Durham Real Property Database	property
fire_district	Fire District		Cross-sectional	Address	2024	0	Durham Real Property Database	property
fire_pcnt	Fire Percent		Cross-sectional	Address	2024	88	Durham Real Property Database	property
land_class	Land Class		Cross-sectional	Address	2024	0	Durham Real Property Database	property
calculated_acres	Calculated Acres		Cross-sectional	Address	2024	25	Durham Real Property Database	property
zoning	Zoning Code		Cross-sectional	Address	2024	0	Durham Real Property Database	property
total_land_value_assessed	Total Land Value Assessed		Cross-sectional	Address	2024	25	Durham Real Property Database	property
total_bldg_value_assessed	Total Building Value Assessed		Cross-sectional	Address	2024	25	Durham Real Property Database	property
land_use_value	Land Use Value		Cross-sectional	Address	2024	25	Durham Real Property Database	property
use_value_deferred	Use Value Deferred		Cross-sectional	Address	2024	99.6	Durham Real Property Database	property
historic_value_deferred	Historic Value Deferred		Cross-sectional	Address	2024	25	Durham Real Property Database	property
total_deferred_value	Total Deferred Value		Cross-sectional	Address	2024	25	Durham Real Property Database	property
total_prop_value	Total Property Value		Cross-sectional	Address	2024	25	Durham Real Property Database	property
deed_date	Deed Date		Cross-sectional	Address	2024	0	Durham Real Property Database	property
revenue_stamps	Revenue Stamps		Cross-sectional	Address	2024	25	Durham Real Property Database	property
pkg_sale_date	Package Sale Date		Cross-sectional	Address	2024	0	Durham Real Property Database	property
pkg_sale_price	Package Sale Price		Cross-sectional	Address	2024	63	Durham Real Property Database	property
land_sale_date	Land Sale Date		Cross-sectional	Address	2024	0	Durham Real Property Database	property
land_sale_price	Land Sale Price		Cross-sectional	Address	2024	94	Durham Real Property Database	property
heated_area	Heated Area		Cross-sectional	Address	2024	40	Durham Real Property Database	property
number_of_buildings	Number of Buildings		Cross-sectional	Address	2024	25	Durham Real Property Database	property
total_bedrooms	Total Bedrooms		Cross-sectional	Address	2024	25	Durham Real Property Database	property
total_bathrooms	Total Bathrooms		Cross-sectional	Address	2024	25	Durham Real Property Database	property
total_half_bathrooms	Total Half Bathrooms		Cross-sectional	Address	2024	25	Durham Real Property Database	property
neighborhood	Neighborhood		Cross-sectional	Address	2024	0	Durham Real Property Database	property
total_obldg_value	Total Outbuilding Value		Cross-sectional	Address	2024	27	Durham Real Property Database	property
gross_leasable_area	Gross Leasable Area		Cross-sectional	Address	2024	25	Durham Real Property Database	property
EHR_Address	Electronic Health Record Address	The address formated similar to that of Duke's Electronic Health Record	Cross-sectional	Address	2024	0	Self-derived	property
FIPS.GEOID	FIPS GEOID		Cross-sectional	Address	2024	0	Census Geocoder	property
blockgroup	Block Group		Cross-sectional	Address	2024	0	Census Geocoder	property
census_tract	Census Tract	An area roughly equivalent to a neighborhood established by the Bureau of Census for analyzing populations. Derived from address longitude and latitude	Cross-sectional	Address	2024	0	Census Geocoder	property
n_ret		Number of Returns	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_single		Filing status is single	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_joint		Number of Joint Returns	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_head		Number of Head of Household Returns	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_rac		Number of Refund Anticipation Check Returns	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_elderly		Number of Elderly Returns	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
agi		Adjusted Gross Income	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_total_inc		Number of Returns with Total Income	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_total_inc		Total Income Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_wage		Number of Returns with Salaries and Wages	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_wage		Salaries and Wages	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_farm		Number of Farm Returns	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_unemp _comp		Number of Returns with Unemployment Compensation	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_unemp _comp		Unemployment Compensation Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_txbl_ss		Number of Returns with Taxable Social Security Benefits	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_txbl_ss		Taxable Social Security Benefits Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_item		Number of Returns With Itemized Deductions	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_item		Total Itemized Deductions Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_alt_min _tax		Number of Returns with Alternative Minimum Tax	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_alt_min _tax		Alternative Minimum Tax Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_dep_care _credit		Number of Returns with Child and Dependent Care Credit	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_dep_care _credit		Child and Dependent Care Credit Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_child_tax _credit		Number of Returns with Child Tax Credit	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_child_tax _credit		Child Tax Credit Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_total_tax _pay		Number of Returns with Total Tax Payments	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_total_tax _pay		Total Tax Payments Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_eic		Number of Returns with Earned Income Credit	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_eic		Earned Income Credit Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_excess _eic		Number of Returns with Excess Earned Income Credit	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_excess _eic		Excess Earned Income Credit (Refundable) Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_tax_due		Number of Returns with Tax Due at Time of Filing	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_tax_due		Tax Due at Time of Filing Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
n_overpay		Number of Returns with Overpayments Refunded	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
a_overpay		Overpayments Refunded Amount	Time series	Zip code	2015-2021	<1	IRS Individual Income Tax Statistics	irs	Note the naming conventions by this example column name: n_item_16_b3. The first section is the variable name, n_item, followed by a number denoting the tax year, and then an income bucket number, ranging from b1 to b6 or aggregate.
ACCESS2	Current Lack of Health Insurance among adults aged 18-64	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults aged 18-64 who report having no current health insurance coverage. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
ARTHRITIS	Arthritis among adults aged > or = 18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having arthritis (reporting 'yes' to the question: "Have you ever been told by a doctor or other health professional that you have some form of arthritis, rheumatoid arthritis, gout, lupus, or fibromyalgia?") The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
BINGE	Binge drinking among adults aged > or = 18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report having > or =5 drinks (men) or > or =4 drinks (women) on > or =1 occasion during the previous 30 days. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
BPHIGH	High blood pressure among adults aged > or = 18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report ever having been told by a doctor, nurse, or other health professional that they have high blood pressure. Women who were told they had high blood pressure only during pregnancy and those who were told they had borderline hypertension are not included. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
BPMED	Taking medicine for high blood pressure control among adults aged > or =18 years with high blood pressure	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults with high blood pressure who reported currently taking medicine for high blood pressure. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
CANCER	Cancer (excluding skin cancer) among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having arthritis (reporting 'yes' to the question: "Have you ever been told by a doctor, nurse, or other health professional that you had melanoma or any other types of cancer?" and "no" to the question, "Have you ever been told by a doctor, nurse, or other health professional that you had skin cancer that is not melanoma?"). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
CASTHMA	Current asthma prevalence among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having current asthma (reporting 'yes' to both of the questions, "Have you ever been told by a doctor, nurse, or other health professional that you have asthma?" and the question, "Do you still have asthma?"). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
CERVICAL	Cervical cancer screening among women aged 21-65 years	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among female respondents aged 21-65 years who did not report having had a hysterectomy and who report having had a Papanicolaou (Pap) test within the previous 3 years OR female respondents aged 30-65 years who reported having had a human papilloma virus (HPV) test alone or in combination with a PAP test (also known as a co-test) within the previous 5 years. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2020	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
CHD	Coronary heart disease among adults aged > or = 18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report ever having been told by a doctor, nurse, or other health professional that they had angina or coronary heart disease. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
CHECKUP	Visits to doctor for routine checkup within the past year among adults aged > or =18 years	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report having been to a doctor for a routine checkup (e.g., a general physical exam, not an exam for a specific injury, illness, or condition) in the previous year. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
CHOLSCREEN	Cholesterol screening among adults aged > or =18 years	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among respondents aged > or =18 years who report having their cholesterol checked within the previous 5 years. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
COGNITION	Cognitive disability among adults	A multi-level regression, post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a cognitive disability (reporting 'yes' to the question: "Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?"). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
COLON_ SCREEN	Colorectal cancer screening among adults aged 45-75 years	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults aged 45-75 years who report having one of the following: a fecal occult blood test (FOBT) within the previous year, a fecal immunochemical test (FIT)-DNA test within the previous 3 years, a sigmoidoscopy within the previous 5 years, a sigmoidoscopy within the previous 10 years with a FIT in the past year, a colonoscopy within the previous 10 years, or a CT colonography (virtual colonoscopy) within the previous 5 years. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2020	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
COPD	Chronic obstructive pulmonary disease among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report having ever been told by a doctor, nurse, or other health professional they had chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
COREM	Older adult men aged > or =65 years who are up to date on a core set of clinical preventive services	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults aged 65 years and older as follows: Number of men aged > or =65 years reporting having received all of the following: an influenza vaccination in the past year; a pneumococcal vaccination (PPV) ever; and either a fecal occult blood test (FOBT or FIT) within the previous year, a FIT-DNA test within the previous 3 years, a sigmoidoscopy within the previous 5 years, a sigmoidoscopy within the previous 10 years with a FOBT in the previous year, a colonoscopy within the previous 10 years, or a CT colonography (virtual colonoscopy) within the previous 5 years. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2020	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
COREW	Older adult women aged > or =65 years who are up to date on a core set of clinical preventive services	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults aged 65 years and older as follows: Number of women aged > or =65 years reporting having received all of the following: an influenza vaccination in the past year; a pneumococcal vaccination (PPV) ever; either a fecal occult blood test (FOBT or FIT) within the previous year, a FIT-DNA test within the previous 3 years, a sigmoidoscopy within the previous 5 years, a sigmoidoscopy within the previous 10 years with a FOBT in the previous year, a colonoscopy within the previous 10 years, or a CT colonography (virtual colonoscopy) within the previous 5 years; and a mammogram in the past 2 years. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2020	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
CSMOKING	Current cigarette smoking among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report having smoked > or = 100 cigarettes in their lifetime and currently smoke every day or some days. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
DENTAL	Visits to dentist or dental clinic in the past year among adults aged > or =18 years	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report having been to the dentist or dental clinic in the past year. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2020	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
DEPRESSION	Depression among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who responded yes to having ever been told by a doctor, nurse, or other health professional they had a depressive disorder, including depression, major depression, dysthymia, or minor depression. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
DIABETES	Diagnosed diabetes among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report being told by a doctor or other health professional that they have diabetes (other than diabetes during pregnancy for female respondents). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
DISABILITY	Any disability among adults aged > or =18 years	A multi-level regression, post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who said yes to at least one of six disability questions: 1) "Are you deaf or do you have serious difficulty hearing?" 2) "Are you blind or do you have serious difficulty seeing, even when wearing glasses?" 3) "Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions? 4) "Do you have serious difficulty walking or climbing stairs?" 5) "Do you have difficulty dressing or bathing?" 6) "Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone, such as visiting a doctor's office or shopping?". The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
GHLTH	Fair or poor self-rated health status among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report their general health status as "fair" or "poor". The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
HEARING	Hearing disability among adults aged > or =18 years	A multi-level regression, post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a hearing disability (reporting 'yes' to the question: "Are you deaf or do you have serious difficulty hearing?"). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
HIGHCHOL	High cholesterol among adults aged > or =18 years who have been screened in the past 5 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report having ever been screened for high cholesterol and told by a doctor, nurse, or other health professional that they had high cholesterol. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
INDEPLIVE	Independent living disability among adults aged > or =18 years	A multi-level regression, post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having an independent living disability (reporting 'yes' to the question: "Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone, such as visiting a doctor's office or shopping?"). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
KIDNEY	Chronic kidney disease among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults aged 18 years and older who report ever having been told by a doctor, nurse, or other health professional that they have kidney disease. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
LPA	No leisure-time physical activity among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having no leisure-time physical activity (reporting 'No' to the question: "During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?"). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
MAMMOUSE	Mammography use among women aged 50-74 years	A multilevel regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among women aged 50-74 years who report having had a mammogram within the previous 2 years. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2020	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
MHLTH	Mental health not good for > or =14 days among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults aged > or = 18 years who report that their mental health (including stress, depression, and problems with emotions) was not good for 14 or more days during the past 30 days. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
MOBILITY	Mobility disability among adults aged > or =18 years	A multi-level regression, post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a mobility disability (reporting 'yes' to the question: "Do you have serious difficulty walking or climbing stairs?"). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
OBESITY	Obesity among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among respondents aged > or =18 years who have a body mass index (BMI) > or =30.0 kg/m calculated from self-reported weight and height. Exclude the following: Height: data from respondents measuring or =8 ft; Weight: data from respondents weighing or =650 lbs; BMI: data from respondents with BMI or =100 kg/m2. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
PHLTH	Physical health not good for > or =14 days among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who reported 14 or more days, during the past 30 days, that their physical health (including physical illness and injury) was not good. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
SELFCARE	Self-care disability among adults aged > or =18 years	A multi-level regression, post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a self-care disability (reporting 'yes' to the question: "Do you have difficulty dressing or bathing?"). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
SLEEP	Sleeping less than 7 hours among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report usually getting insufficient sleep duration (<7 hours, on average, during a 24-hour period). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2020	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
STROKE	Stroke among adults aged > or =18 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who report ever having been told by a doctor, nurse, or other health professional that they have had a stroke. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
TEETHLOST	All teeth lost among adults aged > or =65 years	A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults aged 65 years and older who report having lost all of their natural teeth due to tooth decay or gum disease. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2020	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
VISION	Vision disability among adults aged > or =18 years	A multi-level regression, post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a vision disability (reporting 'yes' to the question: "Are you blind or do you have serious difficulty seeing, even when wearing glasses?"). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence.	Cross-sectional	Census tract	2021	22	PLACES	places	Note the naming conventions by this example column name: MHLTH_16_ucl. The first section is the variable name, MHLTH, followed by either a 15 or 16 denoting the year, and then a second optional post-fix noting the value as the upper confidence limit (_ucl) or lower confidence limit (_lcl) of that year's measure.
AVEAGE	Average Age of Death	The average age of death here is reported for 2009 (reflecting records from 2005-2009) and 2014 (reflecting records from 2010-2014).	Cross-sectional	Block Group	2009, 2014	49	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
RAVGYR	Average Year of Residential Construction	The average age of all residential units - including single-family, multi-family, townhouse and all other residential categories.	Time series	Block Group	2012-2016	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
KWH	Avg. Monthly Household Electricity Use	This data is derived from household energy use for Duke Energy customers throughout Durham County for the years 2013-2014. Household use is averaged for each bockgroup for each month of the calendar year and then averaged across the year as well. The numbers reported here are calendar year averages.	Cross-sectional	Block Group	2013, 2014	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
LUDIV	Land Use Diversity	The calculation of this measurement uses the Simpson Index of Diversity, which measures the variety and evenness of different groups. A higher number in this measure reflects both a higher number of land use types in each area and a more balanced number of properties among them. Parcels are categorized as: agricultural, residential, commercial, recreation, industrial, community services, public services (utilities), wild and forested lands. A measure of 1 would mean each of the possible types of land use is present and that there is an equal number of each. A measure of 0 would mean only one type is present.	Cross-sectional	Block Group	2001, 2005, 2010, 2015, 2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
REDIV	Race/Ethnic Diversity	The calculation of this measurement uses the Simpson Index of Diversity, which measures the variety and evenness of different groups. The component numbers for this measure are blockgroup counts of White (not Hispanic), Black or African American (not Hispanic), Asian (not Hispanic), Hispanic or Latino/a, Two or More Races (not Hispanic), and Other (not Hispanic). A higher number in this measure reflects both a higher number of race/ethnicity categories present and more people of each identity present. A diversity measurement of 1 would mean all race and ethnicity identities are present and equally represented, while a measure of 0 would indicate only one race or ethnicity is present.	Cross-sectional	Block Group	2010, 2015, 2020, 2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
RESIDENTIAL-SALE-PRICE	Median Sale Price	We calculate median home price as the median sale price for single-family residential properties over a three year period, centered on the metric year, for time periods with more than five sales total. For example, for the year 2019, the metric value will be the median sale price for any residential sales which occurred between Jan 1, 2018 and Dec 31, 2020. If there were five or fewer sales during that time period within the selected census area, the metric value will show no data. We count as a sale any qualified real estate transaction with a non-zero sale price. This metric excludes, for example, foreclosure sales, sales between related parties, and multi-parcel sales. It only includes single-family residential properties.	Time series	Block Group	1998-2020	48	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
RES-SALE-PRICE-SQFT	Median Sale Price per Square Foot	We calculate median home price as the median sale price per heated square foot for single-family residential properties over a three year period, centered on the metric year, for time periods with more than five sales total. For example, for the year 2019, the metric value will be the median sale price for any residential sales which occurred between Jan 1, 2018 and Dec 31, 2020. If there were five or fewer sales during that time period within the selected census area, the metric value will show no data. We count as a sale any qualified real estate transaction with a non-zero sale price. This metric excludes, for example, foreclosure sales, sales between related parties, and multi-parcel sales. It only includes single-family residential properties.	Time series	Block Group	1998-2020	48	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
VCODE	Automotive Code Violations	The number of automotive code violations divided by the square miles of each blockgroup that lie within the City boundary. This measurement includes abandoned and hazardous, as well as junked vehicle violations. It excludes calls found to be not in violation. This measurement reflects a City-only service and parts of Durham County are not represented by this data.	Time series	Block Group	2012-2022	48	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
MEANRPMT	Average Residential Building Permit Value	The average value of residential building permits for each boundary. These permits include new construction as well as renovations and exclude demolitions. As with the value of permits when normalized by square miles, the average permit values shown here are skewed strongly toward areas of Durham that see substantial private investment in apartment building construction.	Time series	Block Group	2012-2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CCC	Child Care Centers per Square Mile	This measure includes both child care centers and family child care homes which are licensed by the state of North Carolina's Division of Child Care and Early Development. It does not include unlicensed locations. Data is collected each year in April and reflects a snapshot of the calendar year's licensed centers.	Time series	Block Group	2013-2022	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CC45	Child Care Centers with 4 or 5 Star Ratings	This measure includes both child care centers and family child care homes which are licensed by the state of North Carolina's Division of Child Care and Early Development. It does not include unlicensed locations. Data is collected each year in April and reflects a snapshot of the calendar year's licensed centers.	Time series	Block Group	2013-2022	64	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CKD_ TOTAL	Chronic Kidney Disease Rate (Adult Population)	These chronic kidney disease rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Rates selected for inclusion in this site are those matching CDC guidelines as reported by Duke Health.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CKD_ ASIAN	Chronic Kidney Disease Rate (Asian Population)	These chronic kidney disease rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Rates selected for inclusion in this site are those matching CDC guidelines as reported by Duke Health.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	93	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CKD_ BLACK	Chronic Kidney Disease Rate (Black or African American Population)	These chronic kidney disease rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Rates selected for inclusion in this site are those matching CDC guidelines as reported by Duke Health.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	57	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CKD_ FEMALE	Chronic Kidney Disease Rate (Female)	These chronic kidney disease rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Rates selected for inclusion in this site are those matching CDC guidelines as reported by Duke Health.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CKD_ HISPANIC	Chronic Kidney Disease Rate (Hispanic or Latino/a Population)	These chronic kidney disease rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Rates selected for inclusion in this site are those matching CDC guidelines as reported by Duke Health.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	92	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CKD_ MALE	Chronic Kidney Disease Rate (Male)	These chronic kidney disease rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Rates selected for inclusion in this site are those matching CDC guidelines as reported by Duke Health.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	49	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CKD_ WHITE	Chronic Kidney Disease Rate (White Population)	These chronic kidney disease rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Rates selected for inclusion in this site are those matching CDC guidelines as reported by Duke Health.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	57	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CPMTS	Commercial Building Permit Values Per Sq Mile	The value of commercial, business, and industrial building permits for each boundary divided by the area (in square miles) of each. These permits include new construction as well as renovations and exclude demolitions.	Time series	Block Group	2012-2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
COB	Commercial Certificates of Occupancy per Sq Mile	COs included here are the following types: business, mercantile, factory industrial, and mixed use commercial. The count of these per neighborhood or blockgroup is divided by the area in square miles within the City/County Inspections districts.	Time series	Block Group	2012-2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
AFTERNOON-COOL-ISLANDS	Afternoon Cool Oases	This metric reports the percentage of area in each census blockgroup or tract which was in the bottom quintile for temperature according to the CAPA heat watch study model. In this case, that equated to temperatures at or below 83 degrees Fahrenheit. The CAPA study modeled temperature between 3-4pm on a typical summer day in degrees Fahrenheit using a random forest machine learning model, trained on data points collected in July 2021 by Durham volunteers and the Museum of Life and Science as part of the CAPA Heat Watch Program.	Cross-sectional	Block Group	2021	66	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
EVENING-COOL-ISLANDS	Evening Cool Oases	This metric reports the percentage of area in each census blockgroup or tract which was in the bottom quintile for temperature according to the CAPA heat watch study model. In this case, that equated to temperatures at or below 78.8 degrees Fahrenheit. The CAPA study modeled temperature between 7-8pm on a typical summer day in degrees Fahrenheit using a random forest machine learning model, trained on data points collected in July 2021 by Durham volunteers and the Museum of Life and Science as part of the CAPA Heat Watch Program.	Cross-sectional	Block Group	2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PRUNSD	Poor or Unsound State of Repair	State of repair for all properties is reported in 5 categories in the Tax Administration's property records. The five categories are good, normal, fair, poor, and unsound. The latter two are defined as follows: poor, showing marked deterioration; unsound, may be unfit for habitation or condemned. For the measure reported here, these two categories are combined.	Time series	Block Group	2013-2015	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
DIABETES_ TOTAL	Diabetes Rate (Adult Population)	These type 2 diabetes rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
DIABETES_ ASIAN	Diabetes Rate (Asian Population)	These type 2 diabetes rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	81	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
DIABETES_ BLACK	Diabetes Rate (Black or African American Population)	These type 2 diabetes rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	67	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
DIABETES_ FEMALE	Diabetes Rate (Female)	These type 2 diabetes rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
DIABETES_ HISPANIC	Diabetes Rate (Hispanic or Latino/a Population)	These type 2 diabetes rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	76	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
DIABETES_ MALE	Diabetes Rate (Male)	These type 2 diabetes rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	48	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
DIABETES_ WHITE	Diabetes Rate (White Population)	These type 2 diabetes rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	57	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
D_SQM	Drug Crimes per Square Mile	Drug-related crimes include all incidents involving drug and paraphernalia manufacturing, distributing, and possession charges. For more detailed Durham crime reporting visit RAIDS Online.	Time series	Block Group	2012-2016	53	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
SUMEJECT	Summary Ejectments Per Square Mile	DataWorks acquires civil process records from the Durham County Sheriff's Department for use at the neighborhood level. These are records of the Sheriff's Department notifications to tenants and do not include any personally-identifiable information. The number of these summary ejectment filings per Census blockgroup is divided by the area of the blockgroup in square miles. NOTE: Summary ejectment counts published here are revised as of May 17, 2019. These counts represent a modest increase across the county per year with the largest change (an additional 203 summary ejectments) in 2013. Among the blockgroups of the county, these new counts vary from previous summaries published here, with 81% of blockgroups seeing less than a 10-count change in any given year. These Compass summaries are now managed within our databases to ensure future stability in counts and reproducibility. To learn more about this change and how we manage evictions data contact tech@dataworks-nc.org.	Time series	Block Group	2000-2022	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PTGNRL	General Election Participation	The Board of Elections codes voters 'inactive' when their most recent address may be inaccurate. Due to the potentially inaccurate addresses of inactive voters, the spatial analysis for 2012 relied only on the active voters. For the 2012 general election, there were 175,721 active voters as compared with 212,654 total registered voters in Durham County. For 2020, this metric uses data from a commercial voter database published by L2 Political. For 2020, this data was generated using data from the Redistricting Data Hub.	Cross-sectional	Block Group	2012, 2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
HEARTATTACK_ TOTAL	Heart Attack (Adult Population)	Heart attack or MI diagnoses are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Heart attack or MI incidents are those coded as ICD 410.01, 410.11, 410.21, 410.31, 410.41, 410.51, 410.61, 410.71, 410.81, 410.91, I21, I22 and I25.2.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	62	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
HEARTATTACK_ BLACK	Heart Attack (Black or African American Population)	Heart attack or MI diagnoses are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Heart attack or MI incidents are those coded as ICD 410.01, 410.11, 410.21, 410.31, 410.41, 410.51, 410.61, 410.71, 410.81, 410.91, I21, I22 and I25.2.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	85	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
HEARTATTACK_ FEMALE	Heart Attack (Female)	Heart attack or MI diagnoses are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Heart attack or MI incidents are those coded as ICD 410.01, 410.11, 410.21, 410.31, 410.41, 410.51, 410.61, 410.71, 410.81, 410.91, I21, I22 and I25.2.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	87	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
HEARTATTACK_ MALE	Heart Attack (Male)	Heart attack or MI diagnoses are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Heart attack or MI incidents are those coded as ICD 410.01, 410.11, 410.21, 410.31, 410.41, 410.51, 410.61, 410.71, 410.81, 410.91, I21, I22 and I25.2.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	75	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
HEARTATTACK_ WHITE	Heart Attack (White Population)	Heart attack or MI diagnoses are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017). Heart attack or MI incidents are those coded as ICD 410.01, 410.11, 410.21, 410.31, 410.41, 410.51, 410.61, 410.71, 410.81, 410.91, I21, I22 and I25.2.	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	83	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
AFTERNOON-HEAT-ISLANDS	Heat Islands (Summer Afternoon)	This metric reports the percentage of area in each census blockgroup or tract which was in the top quintile for temperature according to the CAPA heat watch study model. In this case, that equated to temperatures at or above 84.5 degrees Fahrenheit. The CAPA study modeled temperature between 3-4pm on a typical summer day in degrees Fahrenheit using a random forest machine learning model, trained on data points collected in July 2021 by Durham volunteers and the Museum of Life and Science as part of the CAPA Heat Watch Program.	Cross-sectional	Block Group	2021	66	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
EVENING-HEAT-ISLANDS	Heat Islands (Summer Evening)	This metric reports the percentage of area in each census blockgroup or tract which was in the top quintile for temperature according to the CAPA heat watch study model. In this case, that equated to temperatures at or above 81.8 degrees Fahrenheit. The CAPA study modeled temperature between 7-8pm on a typical summer day in degrees Fahrenheit using a random forest machine learning model, trained on data points collected in July 2021 by Durham volunteers and the Museum of Life and Science as part of the CAPA Heat Watch Program.	Cross-sectional	Block Group	2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
RESIDENTIAL-DEMOLITION-PERMITS	Home Demolitions	Demolitions are permitted by the Durham City-County Inspections Department, along with permits for new construction, additions and alterations. The information included here reflects only full demolitions of residential structures. For every year of data available, the number of these for each area on the map is divided by the square mileage of the area to create the rate displayed here.	Time series	Block Group	2012-2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PROXBANK	Households Within Walking Distance to Banks	This measurement includes both commercial bank locations and credit unions. These are identified primarily by North American Industry Classification System (NAICS) codes 52211 and 52213 and then conducting a qualitative scan of additional community businesses. Households are identified in this case as parcels with dwelling units and the 1/4 mile distance is Euclidean, or as-the-crow-flies. The rate is calculated by dividing the number of dwelling units within a 1/4 mile of banks or credit unions by the total number of dwelling units in the blockgroup. Which blockgroup a parcel belongs to is determined by where its centroid is placed.	Cross-sectional	Block Group	2014, 2018, 2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PROXBUS	Households Within Walking Distance to Bus Stops	Households include all residential units, not just residential parcels. Bus stop locations used in this calculation are those active during the spring of each calendar year. Households are identified in this case as parcels with dwelling units and the 1/4 mile distance is Euclidean, or as-the-crow-flies. The rate is calculated by dividing the number of dwelling units within a 1/4 mile of bus stops by the total number of dwelling units in the blockgroup. Which blockgroup a parcel belongs to is determined by where its centroid is placed.	Time series	Block Group	2013-2018	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PROXCF	Homes Near Fast Food and Convenience Stores	Fast food and convenience store locations include fast food chains, gas station/convenience stores, dollar stores and pizza places. These are identified first by using North American Industry Classification System (NAICS) codes for convenience stores and fast food in the InfoUSA data set and then conducting a qualitative scan of additional community businesses. Households are identified in this case as parcels with dwelling units and the 1/4 mile distance is Euclidean, or as-the-crow-flies. The rate is calculated by dividing the number of dwelling units within a 1/4 mile of these food retailers by the total number of dwelling units in the blockgroup. Which blockgroup a parcel belongs to is determined by where its centroid is placed.	Cross-sectional	Block Group	2018	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PROXGR	Households Within Walking Distance to Full Service Grocers	Full service grocers include chain grocery stores and independent full-service stores but do not include farmers' markets. Full service grocers are identified primarily by North American Industry Classification System (NAICS) code 44511 and then conducting a qualitative scan of additional community businesses. Households are identified in this case as parcels with dwelling units and the 1/4 mile distance is Euclidean, or as-the-crow-flies. The rate is calculated by dividing the number of dwelling units within a 1/4 mile of grocers by the total number of dwelling units in the blockgroup. Which blockgroup a parcel belongs to is determined by where its centroid is placed.	Cross-sectional	Block Group	2013, 2018	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
CLINIC	Households Within Walking Distance to Health Clinics	Clinic locations are current as of May 2018 and sourced from the Durham County Social Services Department, Lincoln Community Health and Duke Division of Community Health. Households are identified in this case as parcels with dwelling units and the 1/4 mile distance is Euclidean, or as-the-crow-flies. The rate is calculated by dividing the number of dwelling units within a 1/4 mile of health clinics by the total number of dwelling units in the blockgroup.	Cross-sectional	Block Group	2018	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PROXPH	Households Within Walking Distance to Pharmacies	Pharmacies include traditional storefront locations of independent and chain drug stores, as well as big-box retail locations. These are identified primarily by North American Industry Classification System (NAICS) code 44611 and then conducting a qualitative scan of additional community businesses. Households are identified in this case as parcels with dwelling units and the 1/4 mile distance is Euclidean, or as-the-crow-flies. The rate is calculated by dividing the number of dwelling units within a 1/4 mile of pharmacies by the total number of dwelling units in the blockgroup. Which blockgroup a parcel belongs to is determined by where its centroid is placed.	Cross-sectional	Block Group	2014, 2018, 2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
RCODE	Minimum Housing Code Violations per Square Mile	The number of minimum housing code violations divided by the square miles of each blockgroup that lie within the City boundary. This measurement includes all minimum housing code violations, excluding calls found to be not in violation. This does include orders to repair, demolish, unsafe structures, and boarded properties.	Time series	Block Group	2012-2022	48	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PCTIMP	Impervious Area	This measurement is derived from the National Land Cover Database (NLCD) 30-meter national dataset. It represents the percent of total land within each block group that is impervious and not covered by tree canopy.	Cross-sectional	Block Group	2001, 2006, 2011, 2016, 2019	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
MEDAGE	Median Age	The age at the midpoint of the population. Half of the population is older than this age, and half is younger.	Time series	Block Group	2011-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
HMINC	Median Homebuyer Income	Median homebuyer income is reported here in annual nominal dollar values and are therefore comparable to the dollar values reported in median household income and median home price. Records included in this analysis are for originated, owner-occupant mortgages only. Starting with this update (April 2023), prior years of data have been revised to incorporate updates from the Consumer Financial Protection Bureau. The CFPB receives late reporting from lending institutions and revises data retrospectively. Prior Compass updates of this variable continually incorporated each new year's release.	Time series	Census Tract	2007-2022	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
OUT-OF-STATE-VALUE	Out of State Ownership (all properties)	This dataset is sourced from parcel ownership information acquired from the Durham County Tax Assessor's office and Durham's Open Data Portal. These data show property ownership for each year as of roughly the beginning of the year. This compass variable identifies the percent of total assessed value owned by entities whose mailing address is located in a state other than North Carolina, taken as a percent of total assessed value where the owner has a valid mailing address (excluding, for example, property which has been taken as Right-of-Way). We are missing 2006 data in our dataset, so that year is omitted.	Time series	Block Group	2001-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
OUT-OF-STATE-RES-VALUE	Out of State Ownership (residential properties)	This dataset is sourced from parcel ownership information acquired from the Durham County Tax Assessor's office and Durham's Open Data Portal. These data show property ownership for each year as of roughly the beginning of the year. This Compass variable identifies the percent of total residential assessed value owned by entities whose mailing address is located in a state other than North Carolina, taken as a percent of total residential assessed value where the owner has a valid mailing address (excluding, for example, property which has been taken as Right-of-Way). Residential properties are identified as any parcels with a residential land use or with an apartment building. We are missing 2006 data in our dataset, so that year is omitted.	Time series	Block Group	2001-2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
POPU	Population	This data is from the 2010 and 2020 Decennial Censuses. The Neighborhood Compass will report 100% population counts from the Decennial Census until population estimates for blockgroups and incorporate mid-decade updates from the ACS for Census tracts. This will allow for the best-available data at the sub-county level.	Time series	Block Group	2010, 2015-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
POPDENS	Population Density	This measurement provides the population per square mile based on the 2010 and 2020 Censuses using blockgroups.	Time series	Block Group	2010, 2015-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PTPRIM	Primary Election Participation	For 2012, the data included in this measure reflect active voters only. The Board of Elections codes voters 'inactive' when their most recent address may be inaccurate. Due to the potentially inaccurate addresses of inactive voters, this spatial analysis relies only on the active voters. As of January 2014, there were 176,148 active voters out of a total 200,885 registered in Durham County. For 2020, this metric uses data from a commercial voter database published by L2 Political. For 2020, this data was generated using data from the Redistricting Data Hub.	Cross-sectional	Block Group	2012, 2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
P_SQM	Property Crimes per Square Mile	Property crimes are commonly reported with parts 1 and 2 separate. This measurement is different, including all part 1 property crimes and these part 2 crimes: fraud, forgery, embezzlement, counterfeiting, stolen property and vandalism. The number of property crimes occurring in each boundary is divided by the area (in square miles) of the boundary, rather than the population. This is intended to control for how crime often happens in areas that are less populated. For more detailed Durham crime reporting visit RAIDS Online.	Time series	Block Group	2012-2016	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
RPMTS	Residential Building Permit Value Per Sq Mile	The value of residential building permits for each boundary divided by the area (in square miles) of each. These permits include new construction as well as renovations and exclude demolitions. Even when normalized by square miles, as these numbers are, strong outliers skew the data. Some areas of Durham receive tremendous amounts of housing investment as private apartment development continues near downtown.	Time series	Block Group	2012-2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
COR	Residential Certificates of Occupancy per Sq Mile	COs included here are the following types: residential, single family, townhouse, condominium, duplex, manufactured homes, and mixed-use residential. The count of these per neighborhood or blockgroup is divided by the area in square miles within the City/County Inspections districts.	Time series	Block Group	2012-2020	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
SWTORD	Sidewalks to Roadways	The intention of this measurement is to indicate how well neighborhood roads serve pedestrians. Pedestrian or bike paths not adjacent to roadways are not among the sidewalks counted here. Areas reporting 'N/A' are outside City limits and do not contain annexed communities - the City does not maintain or build sidewalks in those areas.	Time series	Block Group	2013-2016	48	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
STROKE_ TOTAL	Stroke (Adult Population)	These stroke rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Block Group	2015, 2017, 2018, 2019	56	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
STROKE_ BLACK	Stroke (Black or African American Population)	These stroke rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	76	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
STROKE_ FEMALE	Stroke (Female)	These stroke rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	66	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
STROKE_ MALE	Stroke (Male)	These stroke rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	71	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
STROKE_ WHITE	Stroke (White Population)	These stroke rates are based on health care visits documented in this combined dataset, including a total of 169,115 adults of a countywide total of 245,572 (2017).	Cross-sectional	Census Tract	2015, 2017, 2018, 2019	80	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
AFTERNOON-TEMPERATURE	Summer Afternoon Temperature	This metric reports the average modeled temperature between 3-4pm on a typical summer day in degrees Fahrenheit across each blockgroup or tract. The temperatures were modeled using a random forest machine learning model, trained on data points collected in July 2021 by Durham volunteers and the Museum of Life and Science as part of the CAPA Heat Watch Program.	Cross-sectional	Block Group	2021	69	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
EVENING-TEMPERATURE	Summer Evening Temperature	This metric reports the average modeled temperature between 7-8pm on a typical summer day in degrees Fahrenheit across each blockgroup or tract. The temperatures were modeled using a random forest machine learning model, trained on data points collected in July 2021 by Durham volunteers and the Museum of Life and Science as part of the CAPA Heat Watch Program.	Cross-sectional	Block Group	2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
REVAL	Percent Change in Residential Property Values	Data here show the median rate of tax value change for each residential parcel in the blockgroup. Half the residential properties in each blockgroup changed at a rate higher than that shown and half at a rate lower than that shown.	Cross-sectional	Block Group	2016, 2019	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PCTTREE	Tree Coverage	This measurement is derived from the National Land Cover Database (NLCD) 30-meter national dataset. It represents the percent of total land cover within each block group that is tree canopy, whether in forests, along streets, or individual trees.	Cross-sectional	Block Group	2001, 2006, 2011, 2016	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
WCODE	Unmaintained Property Violations per Sq Mile	The number of unmaintained property violations divided by the square miles of each blockgroup that lie within the City boundary. This measurement includes all unmaintained property ('weedy lot') violations, not including calls found to be not in violation or those involving commercial properties. This measurement, along with residential building code violations and abandoned, hazardous or junked vehicles reflect a City-only service and parts of Durham County are not represented by this data.	Time series	Block Group	2012-2022	48	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
V_SQM	Violent Crimes per Square Mile	The violent crimes per square mile metric shows the average number of arrests per square mile for violent crimes by the City of Durham Police. Reported violent crimes include simple assaults, certain sex offenses, child abuse and kidnapping. The number of property crimes occurring in each boundary is divided by the area (in square miles) of the boundary, rather than the population. This is intended to control for how crime often happens in areas that are less populated.	Time series	Block Group	2012-2016	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PTASNL	Asian	The percent of the total population reporting their race to be Asian and ethnicity as not Latino or Hispanic.	Time series	Block Group	2010, 2015-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PTBLKNL	Black or African American	The percent of the total population reporting their race to be Black or African American and ethnicity as not Latino or Hispanic.	Time series	Block Group	2010, 2015-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
BIKEWK	Commuting to Work by Bicycle	As with all measurements from the American Community Survey in the Neighborhood Compass, this data represents 5 years' worth of surveying. With each annual update, the 5-year period advances by dropping one year and incorporating the next. For this reason, annual releases of this measurement are not suitable for true time series comparison until no overlap exists among the survey periods.	Time series	Block Group	2012-2021	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
MEDGRENT	Median Gross Rent	The median gross rent is at the midpoint of all rent costs for each neighborhood. Half of the rental units in this Census tract cost more and half cost less. Gross rent includes the contract rent plus estimated monthly costs of utilities and heating fuels if these are paid for by the renter (ACS).	Time series	Block Group	2011-2021	52	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
UNFOWN	Cost-Burdened Mortgage Holders	This includes selected monthly ownership costs such as mortgage or similar debts, taxes, insurance, utilities, and condo or homeowners fees.	Time series	Block Group	2011-2022	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
UNFRENT	Cost-Burdened Renters	Gross rent as a percentage of household income is a computed ratio of monthly gross rent to monthly household income.	Time series	Block Group	2011-2022	48	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PTLAT	Hispanic/Latino	The percent of the total population reporting their ethnicity to be Latino or Hispanic.	Time series	Block Group	2010, 2015-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PTAIAN	Indigenous Population	The Census Bureau categorizes respondents' race into one of 6 racial categories, one of which is "American Indian and Alaska Native," or AIAN. The census labels people's race as AIAN when they meet the definition of "a person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment."	Time series	Block Group	2010, 2015-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PCTC30	Commuting 30 Minutes or More	This data includes all those commuting to the workplace, whether in personal vehicles, bicycles, walking or by public transit. As of 2010, the Durham County mean commuting time was 21.7 minutes.	Time series	Block Group	2011-2021	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
MEDINC	Median Household Income	The household income at the midpoint of all households. Half of the households in this Census tract earn a higher annual income and half earn a lower annual income.	Time series	Census Tract	2011-2022	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
MEDINC-BLACK	Median Household Income (Black Households)	This metric value shows the household income at the midpoint of all households with householders who were identified as Black by the census. In most cases, the householder is the person or one of the people in whose name the home is owned, being bought, or rented. Within each census area, half of the households with Black householders earn an annual income that's greater than the median, and half earn an annual income lower than the median.	Time series	Census Tract	2011-2022	25	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
MEDINC-HISPANIC	Median Household Income (Hispanic or Latina/o Households)	This metric value shows the household income at the midpoint of all households with householders who were identified as Hispanic or Latino/a by the census. In most cases, the householder is the person or one of the people in whose name the home is owned, being bought, or rented. Within each census area, half of the households with Hispanic or Latino/a householders earn an annual income that's greater than the median, and half earn an annual income lower than the median.	Time series	Census Tract	2010-2022	53	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
MEDINC-WHITE	Median Household Income (White Households)	This metric value shows the household income at the midpoint of all households identified as white (and not Latinx) by the US Census. In most cases, the householder is the person or one of the people in whose name the home is owned, being bought, or rented. Within each census area, half of the households with white householders earn an annual income that's greater than the median, and half earn an annual income lower than the median.	Time series	Census Tract	2010-2022	24	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PTPOC	People of Color	The percent of the total population reporting their race on the census as non-white and/or their ethnicity to be Hispanic or Latino.	Time series	Block Group	2010, 2013-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PCI	Per Capita Income	Average obtained by dividing aggregate income by total population of an area. These amounts are inflation-adjusted for 2011.	Time series	Block Group	2011-2022	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
BACH	Percent of Adults with a Bachelors Degree or More	Along with other tract data from US 2020 Census included here, this measure is derived from 1970-2020 US Census Bureau surveys for 2020-normalized Census tract boundaries.	Time series	Census Tract	2000, 2010, 2018-2021	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
EDUCATION-HEALTH-CARE- SOCIAL-SERVICE-WORKERS	Percent of Workers in Educational, Health Care and Social Assistance Services	These estimates refer to the civilian employed population 16 years and over who are working in these sectors: educational services, and health care and social assistance.	Time series	Census Tract	2010-2018	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
ACCOMMODATION-WORKERS	Percent of Workers in the Arts, Entertainment, Recreation, and Accommodation & Food Services	These estimates refer to the civilian employed population 16 years and over who are working in these sectors: arts, entertainment, and recreation, and accommodation and food services.	Time series	Census Tract	2010-2019	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
RETAIL-WORKERS	Percent of Workers in the Retail Trade Industry	These estimates refer to the civilian employed population 16 years and over who are working in these sectors: retail trade.	Time series	Census Tract	2010-2018	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PCTRENT	Renter-Occupied Housing	All occupied units which are not owner occupied, whether they are rented for cash rent or occupied without payment of cash rent, are classified as renter-occupied. Renter-occupied status also applies to units in continuing care arrangements, such as assisted living.	Time series	Block Group	2011-2022	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PT65UP	Retirement-Age Population	The percent of population 65 years of age and older is calculated using the same Decennial Census counts of population as those used for total population, population density, and race and ethnicity. The population 65 years and older is divided by the total population for each blockgroup.	Time series	Block Group	2010, 2015-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
DRALONE	Single-Occupancy Commuters	This data indicates the percentage of surveyed residents who commute by driving a personal vehicle such as a car, truck or van with no additional passengers.	Time series	Census Tract	2011-2021	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PCTSSI	Supplemental Security Income	Supplemental Security Income (SSI) is a nationwide U.S. assistance program administered by the Social Security Administration that guarantees a minimum level of income for aged, blind, or disabled individuals.	Time series	Census Tract	2010-2021	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
BUSWK	Taking Public Transportation to Work	Public transportation" in this national data set refers to buses, trolleys, streetcars, subways and el trains, railroads, or ferryboats.	Time series	Census Tract	2011-2019	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
WLKWK	Walking to Work	Unclear/Not specified on DataWorks's Website.	Time series	Census Tract	2011-2021	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PTWHNL	White or Caucasian	The percent of the total population reporting their race to be White and ethnicity as not Latino or Hispanic.	Time series	Block Group	2010, 2015-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
WKHOME	Working from Home	Unclear/Not specified on DataWorks's Website	Time series	Census Tract	2011-2021	22	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
PTUND18	Youth Population	The percent of population under 18 years of age is calculated using the same Decennial Census counts of population as those used for total population, population density, and race and ethnicity. The population under 18 years of age is divided by the total population for each blockgroup.	Time series	Block Group	2010, 2015-2021	47	DataWorks NC	dataworks	Note the naming conventions by this example column name: MEDINC_11_moe. The first section is the variable name, MEDINC, followed by a number denoting the year, and then a second optional post-fix noting the value as the margin of error (_moe) of that year's measure.
rac_TotNum _Jobs		Total number of jobs	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _<29		Number of jobs for workers age 29 or younger	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _30-54		Number of jobs for workers age 30 to 54	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _55+		Number of jobs for workers age 55 or older	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _<1250		Number of jobs with earnings $1250/month or less	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _1251-3333		Number of jobs with earnings $1251/month to $3333/month	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _3333+		Number of jobs with earnings greater than $3333/month	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _AFFH		Number of jobs in NAICS sector 11 (Agriculture, Forestry, Fishing and Hunting)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _MQOG		Number of jobs in NAICS sector 21 (Mining, Quarrying, and Oil and Gas Extraction)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Util		Number of jobs in NAICS sector 22 (Utilities)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Const		Number of jobs in NAICS sector 23 (Construction)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Manuf		Number of jobs in NAICS sector 31-33 (Manufacturing)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _WSale		Number of jobs in NAICS sector 42 (Wholesale Trade)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _RSale		Number of jobs in NAICS sector 44-45 (Retail Trade)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _TrWa		Number of jobs in NAICS sector 48-49 (Transportation and Warehousing)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Info		Number of jobs in NAICS sector 51 (Information)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _FinIns		Number of jobs in NAICS sector 52 (Finance and Insurance)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _RERL		Number of jobs in NAICS sector 53 (Real Estate and Rental and Leasing)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _PSTS		Number of jobs in NAICS sector 54 (Professional, Scientific, and Technical Services)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Mgmt		Number of jobs in NAICS sector 55 (Management of Companies and Enterprises)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _ASWSR		Number of jobs in NAICS sector 56 (Administrative and Support and Waste Management and Remediation Services)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Edu		Number of jobs in NAICS sector 61 (Educational Services)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _HlthSA		Number of jobs in NAICS sector 62 (Health Care and Social Assistance)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _AER		Number of jobs in NAICS sector 71 (Arts, Entertainment, and Recreation)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _AFS		Number of jobs in NAICS sector 72 (Accommodation and Food Services)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Other		Number of jobs in NAICS sector 81 (Other Services [except Public Administration])	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _PA		Number of jobs in NAICS sector 92 (Public Administration)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _White		Number of jobs for workers with Race: White, Alone	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _BAA		Number of jobs for workers with Race: Black or African American Alone	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _AIAN		Number of jobs for workers with Race: American Indian or Alaska Native Alone	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Asian		Number of jobs for workers with Race: Asian Alone	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _NHPI		Number of jobs for workers with Race: Native Hawaiian or Other Pacific Islander	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Two		Number of jobs for workers with Race: Two or More Race Groups	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _NotHL		Number of jobs for workers with Ethnicity: Not Hispanic or Latino	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _HL		Number of jobs for workers with Ethnicity: Hispanic or Latino	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _<HS		Number of jobs for workers with Educational Attainment: Less than high school	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _HS		Number of jobs for workers with Educational Attainment: High school or equivalent	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _SomeCollege		Number of jobs for workers with Educational Attainment: Some college or Associate	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _BachDegree+		Number of jobs for workers with Educational Attainment: Bachelor's degree or advanced degree	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Male		Number of jobs for workers with Sex: Male	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
rac_NumJobs _Female		Number of jobs for workers with Sex: Female	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_rac	Note the naming conventions by this example column name: rac_NumJobs_<1250_02. The first section is the variable name, rac_NumJobs, followed by a 2-digit number denoting the year.
wac_TotNum _Jobs		Total number of jobs	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _<29		Number of jobs for workers age 29 or younger	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _30-54		Number of jobs for workers age 30 to 54	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _55+		Number of jobs for workers age 55 or older	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _<1250		Number of jobs with earnings $1250/month or less	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _1251-3333		Number of jobs with earnings $1251/month to $3333/month	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _3333+		Number of jobs with earnings greater than $3333/month	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _AFFH		Number of jobs in NAICS sector 11 (Agriculture, Forestry, Fishing and Hunting)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _MQOG		Number of jobs in NAICS sector 21 (Mining, Quarrying, and Oil and Gas Extraction)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Util		Number of jobs in NAICS sector 22 (Utilities)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Const		Number of jobs in NAICS sector 23 (Construction)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Manuf		Number of jobs in NAICS sector 31-33 (Manufacturing)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _WSale		Number of jobs in NAICS sector 42 (Wholesale Trade)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _RSale		Number of jobs in NAICS sector 44-45 (Retail Trade)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _TrWa		Number of jobs in NAICS sector 48-49 (Transportation and Warehousing)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Info		Number of jobs in NAICS sector 51 (Information)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FinIns		Number of jobs in NAICS sector 52 (Finance and Insurance)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _RERL		Number of jobs in NAICS sector 53 (Real Estate and Rental and Leasing)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _PSTS		Number of jobs in NAICS sector 54 (Professional, Scientific, and Technical Services)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Mgmt		Number of jobs in NAICS sector 55 (Management of Companies and Enterprises)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _ASWSR		Number of jobs in NAICS sector 56 (Administrative and Support and Waste Management and Remediation Services)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Edu		Number of jobs in NAICS sector 61 (Educational Services)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _HlthSA		Number of jobs in NAICS sector 62 (Health Care and Social Assistance)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _AER		Number of jobs in NAICS sector 71 (Arts, Entertainment, and Recreation)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _AFS		Number of jobs in NAICS sector 72 (Accommodation and Food Services)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Other		Number of jobs in NAICS sector 81 (Other Services [except Public Administration])	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _PA		Number of jobs in NAICS sector 92 (Public Administration)	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _White		Number of jobs for workers with Race: White, Alone	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _BAA		Number of jobs for workers with Race: Black or African American Alone	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _AIAN		Number of jobs for workers with Race: American Indian or Alaska Native Alone	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Asian		Number of jobs for workers with Race: Asian Alone	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _NHPI		Number of jobs for workers with Race: Native Hawaiian or Other Pacific Islander	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Two		Number of jobs for workers with Race: Two or More Race Groups	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _NotHL		Number of jobs for workers with Ethnicity: Not Hispanic or Latino	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _HL		Number of jobs for workers with Ethnicity: Hispanic or Latino	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _<HS		Number of jobs for workers with Educational Attainment: Less than high school	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _HS		Number of jobs for workers with Educational Attainment: High school or equivalent	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _SomeCollege		Number of jobs for workers with Educational Attainment: Some college or Associate	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _BachDegree+		Number of jobs for workers with Educational Attainment: Bachelor's degree or advanced degree	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Male		Number of jobs for workers with Sex: Male	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _Female		Number of jobs for workers with Sex: Female	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmAge _<1		Number of jobs for workers at firms with Firm Age: 0-1 Years	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmAge _2-3		Number of jobs for workers at firms with Firm Age: 2-3 Years	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmAge _4-5		Number of jobs for workers at firms with Firm Age: 4-5 Years	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmAge _6-10		Number of jobs for workers at firms with Firm Age: 6-10 Years	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmAge _11+		Number of jobs for workers at firms with Firm Age: 11+ Years	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmSize _<19		Number of jobs for workers at firms with Firm Size: 0-19 Employees	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmSize _20-49		Number of jobs for workers at firms with Firm Size: 20-49 Employees	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmSize _50-249		Number of jobs for workers at firms with Firm Size: 50-249 Employees	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmSize _250-499		Number of jobs for workers at firms with Firm Size: 250-499 Employees	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.
wac_NumJobs _FirmSize _500+		Number of jobs for workers at firms with Firm Size: 500+ Employees	Number of Jobs	Census Tract	2002-2019	22	Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, Resident Area Characteristics	lodes_wac	Note the naming conventions by this example column name: wac_NumJobs_<1250_02. The first section is the variable name, wac_NumJobs, followed by a 2-digit number denoting the year.