The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

blockgroupcartographics Opportunity This publication re-shares the TIGER/Line dataset from the US Census Bureau to supplement the other datasets available in Open Environment's Block Group Dataverse. This share is valuable for two reasons. First, the original publication uses geodatabase file format, which requires GIS software to open and interpret. This publication uses CSV formats for access with simpler tools. Second, many models based on the U.S. Census data benefit from a measure of population density. That is, population counts divided by the land area of the geography. In this case, the ALAND variable is only available from TIGER/Line. The 2023blockgroupdemographics dataset, then, is dependent upon this publication for land and water area within block group. Dataset details -- The dataset offers 242,336 rows, one for each block group. |Variable|Description| |---|---| |GEO_ID|The fully qualified block group geographic identifier| |STATEFP|US State FIPS code, 2 digits| |COUNTYFP|US County FIPS code, 3 digits| |TRACTCE|Census tract identifier, 6 digits| |BLKGRPCE|Census block group identifier, 6 digits| |GEOID|The block group GEO ID starting with the state code| |GEOIDFQ|The block group GEO ID starting with the country codes| |NAMELSAD|Legal/statistical area description| |MTFCC|5 digit MAF/TIGER Feature Class Code, see https://www.census.gov/library/reference/code-lists/mt-feature-class-codes.html| |FUNCSTAT|Functional status code, see https://www.census.gov/library/reference/code-lists/functional-status-codes.html| |ALAND|Land area of the block group geography| |AWATER|Water area of the block group geography| |INTPTLAT|Latitude of the block group's centroid| |INTPTLON|Longitude of the block group's centroid| |geometry|Provides the point and polygon shape as a text string.| Additional Caveats It is import to note, analytically, the following: The Census revises Block Group shapes with each decennial census survey. As a result, merging datasets from different decades will result in data loss. Shapes that have the same GEO_ID may have different land area. New geographies and geographies no longer available can be expected then. All questions or feedback is most welcome by email at support@openenvironments.com Citations U.S. Census Bureau, “TIGER/Line Shapefiles", https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html, 2023 U.S. Census Bureau, “TIGER/Line Shapefiles FTP Archives",https://www2.census.gov/geo/tiger/TIGER2023/BG/ Python Package Index - PyPI. Python Software Foundation. "A simple wrapper for the United States Census Bureau’s API.". Retrieved from https://pypi.org/project/census/ Kelsey Jordahl, Joris Van den Bossche, Martin Fleischmann, Jacob Wasserman, James McBride, Jeffrey Gerard, … François Leblanc. (2020, July 15). geopandas/geopandas: v0.8.1 (Version v0.8.1). Zenodo. http://doi.org/10.5281/zenodo.3946761

Colorado County BRFSS Binge Drinking Prevalence represents the Percent of Adults who Binge Drink calculated from the 2018-2022 Colorado Behavioral Risk Factor Surveillance System (County Estimates) data set. These data represent the estimated prevalence of Binge Drinking among adults (Age 18+) for each county in Colorado. Binge Drinking is defined for males as having five or more drinks on one occasion and for females as having four or more drinks on one occasion within the past 30 days. Binge Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days. Data is suppressed if there was not enough data to calculate a reliable estimate. The estimate for each county was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2018-2022). This file was developed for use in activities and exercises within the Colorado Department of Public Health and Environment (CDPHE), including the Alcohol Outlet Density StoryMap. COUNTY (County Name)FULL (Full County Name)LABEL (Proper County Name)County FIPS (County FIPS Code as String)NUM FIPS (County FIPS Code as Number)CENT LAT (County Centroid Latitude)CENT LONG (County Centroid Longitude)US FIPS (Full FIPS Code)Binge Percent (County estimate for prevalence of Binge Drinking among adults Age 18+)Lower Confidence Limit (Lower 95% Confidence Interval for Binge Percent Value)Upper Confidence Limit (Upper 95% Confidence Interval for Binge Percent Value)Years (2018-2022)

Data Dictionary:We are also including a tabular version that’s slightly more comprehensive (would include anything that didn’t join to the parcel basefile due to lot alterations or resubdivisions since 2024). This Excel file can be downloaded [HERE], and does not contain latitude and longitude information. Attribute Label Definition Source

TAX_ID Unique 26 character property tax identification number Onondaga County Planning

SHAPE_Leng Shape length NA - Calculated field

SHAPE_Area Shape area NA - Calculated field

PRINTKEY Abbreviated tax identification number (section-block-lot) Onondaga County Planning

ADDRESSNUM Property’s physical street address Onondaga County Planning

ADDRESSNAM Property’s physical street name Onondaga County Planning

TAX_ID City Tax ID number (26 digit number used for parcel mapping) City of Syracuse - Assessment

SBL Property Tax Map Number (Section, Block, Lot) City of Syracuse - Assessment

PNUMBR Property Number (10 digit number) City of Syracuse - Assessment

StNum Parcel street number City of Syracuse - Assessment

StName Parcel street name City of Syracuse - Assessment

FullAddress Street number and street name City of Syracuse - Assessment

Zip Parcel zip code City of Syracuse - Assessment

desc_1 Lot description including dimensions City of Syracuse - Assessment

desc_2 Lot description including dimensions City of Syracuse - Assessment

desc_3 Lot description including dimensions City of Syracuse - Assessment

SHAPE_IND

City of Syracuse - Assessment

LUC_parcel New York State property type classification code assigned by assessor during each roll categorizing the property by use. For more details: https://www.tax.ny.gov/research/property/assess/manuals/prclas.htm City of Syracuse - Assessment

LU_parcel New York State property type classification name City of Syracuse - Assessment

LUCat_Old Legacy land use category that corresponds to the overarching NYS category, i.e. all 400s = commercial, all 300s = vacant land, etc. NA

land_av Land assessed value City of Syracuse - Assessment

total_av Full assessed value City of Syracuse - Assessment

Owner Property owner name (First, Initial, Last, Suffix) City of Syracuse - Assessment

Add1_OwnPOBox Property owner mailing address (PO Box) City of Syracuse - Assessment

Add2_OwnStAdd Property owner mailing address (street number, street name, street direction) City of Syracuse - Assessment

Add3_OwnUnitInfo Property owner mailing address unit info (unit name, unit number) City of Syracuse - Assessment

Add4_OwnCityStateZip Property owner mailing address (city, state or country, zip code) City of Syracuse - Assessment

FRONT Front footage for square or rectangular shaped lots and the effective front feet on irregularly shaped lots in feet City of Syracuse - Assessment

DEPTH Actual depth of rectangular shaped lots in feet (irregular lots are usually measured in acres or square feet) City of Syracuse - Assessment

ACRES Number of acres (where values were 0, acreage calculated as FRONT*DEPTH)/43560) City of Syracuse - Assessment

yr_built Year built. Where year built was "0" or null, effective year built is given. (Effective age is determined by comparing the physical condition of one building with that of other like-use, newer buildings. Effective age may or may not represent the actual year built; if there have been constant upgrades or excellent maintenance this may be more recent than the original year built.) City of Syracuse - Assessment

n_ResUnits Number of residential units NA - Calculated field

IPSVacant Is it a vacant structure? ("Commercial" or "Residential" = Yes; null = No) City of Syracuse - Division of Code Enforcement

IPS_Condition Property Condition Score assigned to vacant properties by housing inspectors during routine vacant inspections (1 = Worst; 5 = Best) City of Syracuse - Division of Code Enforcement

NREligible National Register of Historic Places Eligible ("NR Eligible (SHPO)," or "NR Listed") City of Syracuse - Neighborhood and Business Development

LPSS Locally Protected Site Status ("Eligible/Architecturally Significant" or "Local Protected Site or Local District") City of Syracuse - Neighborhood and Business Development

WTR_ACTIVE Water activity code ("I" = Inactive; "A" = Active) City of Syracuse - Water

RNI Is property located in Resurgent Neighborhood Initiative (RNI) Area? (1 = Yes; 0 = No) City of Syracuse - Neighborhood and Business Development

DPW_Quad Geographic quadrant property is located in. Quadrants are divided Northwest, Northeast, Southwest, and Southeast based on property location in relation to I-81 and I-690. DPW uses the quad designation for some types of staff assignments. City of Syracuse - Department of Public Works

DPW_Sani DPW sanitation trash and recycling pick-up day (trash service weekly, recycling biweekly) City of Syracuse - Department of Public Works

DPW_Recycle DPW recycling biweekly pick-up group (either Week A or Week B), collection occurs every other week City of Syracuse - Department of Public Works

TNT_NAME TNT Sector property is located in City of Syracuse - Neighborhood and Business Development

NHOOD City Neighborhood Syracuse-Onondaga County Planning Agency (SOCPA)

NRSA Is property located in Neighborhood Revitilization Strategy Area (NRSA)? (1 = Yes; 0 = No) City of Syracuse - Neighborhood and Business Development

DOCE_Insp1 Geographic boundary use to assign Division of Code Enforcement cases for housing inspectors City of Syracuse - Division of Code Enforcement

DOCE_Insp2 Geographic boundary use to assign Division of Code Enforcement cases for building inspectors City of Syracuse - Division of Code Enforcement

DOCE_Permit Geographic boundary use to assign Division of Code Enforcement cases for permit inspectors City of Syracuse - Division of Code Enforcement

DOCE_Comm Geographic boundary use to assign Division of Code Enforcement cases for commercial and electrical inspectors City of Syracuse - Division of Code Enforcement

FIRE_DIST Fire engine districts City of Syracuse - Fire Department

ZONE_DIST_PREV Former zoning district code Syracuse-Onondaga County Planning Agency (SOCPA)

REZONE ReZone designation (adopted June 2023, last updated 2024-12-17) City of Syracuse - Neighborhood and Business Development

CC_DIST Current Common Council District property is located in Onondaga County Board of Elections

CTID_2020 Census Tract ID (2020) U.S. Census Bureau

CTLAB_2020 Census Tract Label (2020) U.S. Census Bureau

CT_2020 Census Tract (2020) U.S. Census Bureau

SpecNhood Is property located in a special Neighborhood historic preservation district? (1 = Yes; 0 or null = No) Syracuse-Onondaga County Planning Agency (SOCPA)

InPD Is property located in preservation district? (1 = Yes; 0 or null = No) Syracuse-Onondaga County Planning Agency (SOCPA)

PDNAME Preservation District name Syracuse-Onondaga County Planning Agency (SOCPA)

ELECT_DIST Election district number Onondaga County Board of Elections

CITY_WARD City ward number Onondaga County Board of Elections

COUNTY_LEG Onondaga County Legislative District number (as of Dec 2024) Onondaga County Board of Elections

NYS_ASSEMB New York State Assembly District number (as of Dec 2024) Onondaga County Board of Elections

NYS_SENATE New York State Senate District number (as of Dec 2024) Onondaga County Board of Elections

US_CONGR United States Congressional District number Onondaga County Board of Elections

LAT Parcel latitude (centroid y-coordinate) in decimal degrees NA - Calculated field

LONG Parcel longitude (centroid x-coordinate) in decimal degrees NA - Calculated field

Annual crop data from 1972 to 1998 are now available on EOS-WEBSTER. These data are county-based acreage, production, and yield estimates published by the National Agricultural Statistics Service. We also provide county level livestock, geography, agricultural management, and soil properties derived from datasets from the early 1990s.

 The National Agricultural Statistics Service (NASS), the statistical
 arm of the U.S. Department of Agriculture, publishes U.S., state, and
 county level agricultural statistics for many commodities and data
 series. In response to our users requests, EOS-WEBSTER now provides 27
 years of crop statistics, which can be subset temporally and/or
 spatially. All data are at the county scale, and are only for the
 conterminous US (48 states + DC). There are 3111 counties in the
 database. The list includes 43 cities that are classified as
 counties: Baltimore City, MD; St. Louis City, MO; and 41 cities in
 Virginia.

 In addition, a collection of livestock, geography, agricultural
 practices, and soil properties variables for 1992 is available through
 EOS-WEBSTER. These datasets were assembled during the mid-1990's to
 provide driving variables for an assessment of greenhouse gas
 production from US agriculture using the DNDC agro-ecosystem model
 [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776;
 Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data
 (except nitrogen fertilizer use) were all derived from publicly
 available, national databases. Each dataset has a separate DIF.

 The US County data has been divided into seven datasets.

 US County Data Datasets:

 1) Agricultural Management
 2) Crop Data (NASS Crop data)
 3) Crop Summary (NASS Crop data)
 4) Geography and Population
 5) Land Use
 6) Livestock Populations
 7) Soil Properties

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

Soil Geochemistry Spatial Database - General DescriptionLaboratory data were produced by the USDA-NRCS Kellogg Soil Survey Laboratory, located in the National Soil Survey Center, Lincoln, NE. The National Cooperative Soil Survey (NCSS) Soil Characterization Database contains the analytical results from the Kellogg Soil Survey Laboratory (KSSL) at the National Soil Survey Center (NSSC) in Lincoln, Nebraska, as well as the results from numerous cooperating State university laboratories in the United States. Properties measured in the laboratory serve as the basis for interpretations related to soil use and management. Standardized methodologies and procedures used in the laboratory are contained in the Kellogg Soil Survey Laboratory Methods Manual, Soil Survey Investigations Report (SSIR) No. 42 (by the Soil Survey Staff). The KSSL data are provided in reports (for example, Primary and Supplementary Characterization Data Sheets.) The database includes pedons that represent the central concept of a soil series, pedons that represent the central concept of a map unit but not of a series, and pedons sampled to bracket a range of properties within a series or landscape. Not all analyses are conducted for every soil. Suites of analytical procedures are run based upon anticipated or known conditions regarding the nature of the soil being analyzed. Results are reported in tiers. For example, soils of arid environments are routinely analyzed for salts and carbonates as part of the standard analysis suite.The geographic display consists of two major sets of geochemistry data:Current Geochemistry Project — The Soil Geochemistry Spatial Dataset is a collection of soil geochemistry data produced by the U.S. Department of Agriculture, Natural Resources Conservation Service, Kellogg Soil Survey Laboratory in Lincoln, Nebraska. The website is ongoing and updated periodically to reflect additional available data. Soil pedons were sampled and analyzed by horizons. Pedons represent either the central concept of a soil series, the central concept of a map unit, or unspecified sites on a project specific basis. Sites are indentified as either contaminated or non-contaminated (cited as “unknown”) based on knowledge of land use history. When labeled as “unknown”, it suggests the elemental concentrations represent native values. These data are represented in four layers: Site Info, Major Elements, Trace Elements, and Selected Characterization Data. Data in Major Element and Trace Element tiers are defined by specific digestion. Digestion method for elements in the “Major Elements” tier consists of an acid combination of HF+HNO3+HCL. Elements in the “Trace Elements” tier are recovered from soil with a HNO3+HCL digestion. Elemental data are incomplete on certain samples. Additional elements have been added to the analytical suite over time and certain data are absent on earlier projects. Also, certain projects were specific for only major or trace elements.Currently, the dataset contains data for over 4,800 sites. Each point or location on the map represents one or more pedons. Data for pedons generally include multiple horizons listed sequentially in a tabular format. Each location has four types of data associated: site, trace element, major element, and characterization data. For certain sites, only trace or major elemental data may be available. The Soil Geochemistry Spatial Dataset contains data collectively produced by the National Cooperative Soil Survey Program. Sites were generally selected and sampled by soil survey personnel in respective states. Laboratory data were produced by the U.S. Department of Agriculture, Natural Resources Conservation Service, Kellogg Soil Survey Laboratory, located in the National Soil Survey Center, Lincoln, Nebraska.Summary and analysis of these data are documented in:Burt, R., M.A. Wilson, M.D. Mays, and C.W. Lee. 2003. Major and Trace Elements of Selected Pedons in the U.S. J. of Environ. Qual. 32:2109-2121.Wilson, M.A., R. Burt, S.J. Indorante, A.B. Jenkins, J.V. Chiaretti, M.G. Ulmer, J.M. Scheyer. 2008. Geochemistry in the modern soil survey program. Environmental Monitoring and Assessment. 139:151–171.For detailed information on Kellogg Soil Survey Laboratory (KSSL) methods (e.g., procedures, interferences), refer to “Soil Survey Laboratory Investigations Report No. 42”.For information on the applications of laboratory data, refer to “Soil Survey Laboratory Investigations Report No. 45”.For the complete characterization data dataset, pedon and site information, additional qualifications and limitations on the regarding characterization data, refer to the National Cooperative Soil Survey Soil Characterization Database at https://ncsslabdatamart.sc.egov.usda.gov.Locations of each site are identified as either “Geographic”, a known location identified by latitude and longitude or “Centroid”, the location is unknown within county and the point on the map is located at the latitude and longitude of the county centroid. Note that locations in the past were recorded only as latitude and longitude in the Soil Survey Laboratory Characterization Database. Therefore, georeference locations for sites can be considered only approximate unless the map datum (NAD27 or WGS84) can be indentified for each point. It can be assumed that data prior to 1990 was recorded from maps with a NAD27 datum and with a WGS84 datum after 1995. 2. Holmgren Dataset — A second group of data was produced by the Soil Survey Laboratory during the 1970’s and 1980’s for a project documenting the content of selected trace elements in agricultural soils of the U.S. This dataset contains over 3,400 sites in conterminous U.S. These data are available as a separate spatial layer on a county centroid basis.These data are discussed in:Holmgren, G.G.S., M.W. Meyer, R.L. Chaney, and R.B. Daniels. 1993. Cadmium, lead, zinc, copper, and nickel in agricultural soils in the United States of America. J. Environ. Qual. 22:335-348.Use ConstraintsYou are most WELCOME to use the database, but you should be aware that the assessment of the accuracy and applicability is strictly a USER RESPONSIBILITY and the NRCS and NCSS take no responsibility for problems that arise from use of these data.The U.S. Department of Agriculture, Natural Resources Conservation Service and the National Cooperative Soil Survey, should be acknowledged as the data source in products derived from these data.This dataset is not designed for use as a primary regulatory tool in permitting or citing decisions, but may be used as a reference source. This is public information and may be interpreted by organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application. Federal, State, or local regulatory bodies are not to reassign to the Natural Resources Conservation Service or the National Cooperative Soil Survey any authority for the decisions that they make. The Natural Resources Conservation Service will not perform any evaluations of these data for purposes related solely to State or local regulatory programs.Digital data files are periodically updated. Files are dated, and users are responsible for obtaining the latest version of the data.

The Bureau of Health Professions Area Resource File is a county-based data file summarizing secondary data from a wide variety of sources into a single file to facilitate health analysis. The file contains over 6,000 data elements for all counties in the United States with the exception of Alaska, for which there is a state total, and certain independent cities that have been combined into their appropriate counties. The data elements include: (1) County descriptor codes (name, FIPS, HSA, PSRO, SMSA, SEA, BEA, city size, P/MSA, Census Contiguous County, shortage area designation, etc.), (2) Health professions data (number of professionals registered as M.D., D.O., DDS, R.N., L.P.N., veterinarian, pharmacist, optometrist, podiatrist, and dental hygienist), (3) Health facility data (hospital size, type, utilization, staffing and services, and nursing home data), (4) Population data (size, composition, employment, housing, morbidity, natality, mortality by cause, by sex and race, and by age, and crime data), (5) Health Professions Training data (training programs, enrollments, and graduates by type), (6) Expenditure data (hospital expenditures, Medicare enrollments and reimbursements, and Medicare prevailing charge data), (7) Economic data (total, per capita, and median income, income distribution, and AFDC recipients), and (8) Environment data (land area, large animal population, elevation, latitude and longitude of population centroid, water hardness index, and climate data).