This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest.

6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals.

7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives.

8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest.

9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group.

10. scChldHome:

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES COMPUTERS AND INTERNET USE - DP02 Universe - Total households Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 The 2008 Broadband Improvement Act mandated the collection of data about computer and internet use. As a result, three questions were added to the 2013 American Community Survey (ACS) to measure these topics. The computer use question asked if anyone in the household owned or used a computer and included four response categories for a desktop or laptop, a smartphone, a tablet or other portable wireless computer, and some other type of computer. Respondents selected a checkbox for “Yes” or “No” for each response category. Respondents could select all categories that applied. Question asked if any member of the household has access to the internet. “Access” refers to whether or not someone in the household uses or can connect to the internet, regardless of whether or not they pay for the service. If a respondent answers “Yes, by paying a cell phone company or Internet service provider”, they are asked to select the type of internet service.

This dataset contains Iowa households by presence of a computer and type of internet subscription for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Table B28003.

Computer Presence includes the following: Total, Yes, and No.

Internet subscription includes the following: Total, Total w/Computer, Broadband, Dial-up and None.

Dataset

This dataset was created by valcho valev

Contents

This layer shows Computers and Internet Use. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show Percentage of Households with a Broadband Internet Subscription. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): DP02, S2801Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2022National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Dataset and details can be found at the US Energy Information Admininstration (EIA)'s RBECs website

Context

EIA administers the Residential Energy Consumption Survey (RECS) to a nationally representative sample of housing units. Traditionally, specially trained interviewers collect energy characteristics on the housing unit, usage patterns, and household demographics. For the 2015 survey cycle, EIA used Web and mail forms, in addition to in-person interviews, to collect detailed information on household energy characteristics. This information is combined with data from energy suppliers to these homes to estimate energy costs and usage for heating, cooling, appliances and other end uses — information critical to meeting future energy demand and improving efficiency and building design.

First conducted in 1978, the fourteenth RECS collected data from more than 5,600 households in housing units statistically selected to represent the 118.2 million housing units that are occupied as a primary residence. Data from the 2015 RECS are tabulated by geography and for particularly characteristics, such as housing unit type and income, that are of particular interest to energy analysis.

The results of each RECS include data tables, a microdata file, and a series of reports. Data tables are generally organized across two headings; "Household Characteristics" and "Consumption & Expenditures." See RECS data tables.

The RECS and many of the EIA supplier surveys are integral ingredients for some of EIA's more comprehensive data products and reports, such as the Annual Energy Outlook (AEO) and Monthly Energy Review (MER). These products allow for broader comparisons across sectors, as well as projections of future consumption trends.

Content

The Residential Energy Consumption Survey (RECS) is a periodic study conducted by the U.S. Energy Information Administration (EIA) that provides detailed information about energy usage in U.S. homes. RECS is a multi-year effort (Figure 1) consisting of a Household Survey phase, data collection from household energy suppliers, and end-use consumption and expenditures estimation.

The Household Survey collects data on energy-related characteristics and usage patterns of a national representative sample of housing units. The Energy Supplier Survey (ESS) collects data on how much electricity, natural gas, propane/LPG, fuel oil, and kerosene were consumed in the sampled housing units during the reference year. It also collects data on actual dollar amounts spent on these energy sources.

EIA uses models (energy engineering-based models in the 2015 survey and non-linear statistical models in past RECS) to produce consumption and expenditures estimates for heating, cooling, refrigeration, and other end uses in all housing units occupied as a primary residence in the United States. Originally conducted by trained interviewers with paper and pencil, the 2015 study used a combination of computer-assisted personal interview (CAPI), web, and mail modes to collect data for the Household and Energy Supplier Surveys.

Banner image credit: https://www.flickr.com/photos/caribb/1518299093

These datasets contain measures of internet access per United States census tract and ZIP code tabulation area (ZCTA) from the 2015-2019 American Community Survey five-year estimate. Key variables include the number and percent of households per tract or ZCTA with any type of internet subscription, with broadband internet, and with a computer or smartphone.

Data source

All data was collected from US Census official site: data.census.gov

Data Processing Nuances

The first row in all data files contains column descriptions. It should be ignored in the load, e.g.: df = pd.read_csv('ACSST5Y2018.S0101-Data.csv', skiprows=[1], low_memory=False)

Next, if you need county CFIPS, it can be exctracted from the GEO_ID column: df['CFIPS'] = df['GEO_ID'].apply(lambda x: int(x.split('US')[-1]))

Content List

1. County Demographics

American Community Survey (ACS) data derived from S0101 AGE AND SEX: - ACSST5Y2018.S0101-Data.csv - ACSST5Y2018.S0101-Column-Metadata.csv - ACSST5Y2019.S0101-Data.csv - ACSST5Y2019.S0101-Column-Metadata.csv - ACSST5Y2020.S0101-Data.csv - ACSST5Y2020.S0101-Column-Metadata.csv - ACSST5Y2021.S0101-Data.csv - ACSST5Y2021.S0101-Column-Metadata.csv

Includes basic info on population and age structure

2. Detailed County Demographics

American Community Survey (ACS) data derived from DP05ACS DEMOGRAPHIC AND HOUSING ESTIMATES: - ACSDP5Y2018.DP05-Data.csv - ACSDP5Y2018.DP05-Column-Metadata.csv - ACSDP5Y2019.DP05-Data.csv - ACSDP5Y2019.DP05-Column-Metadata.csv - ACSDP5Y2020.DP05-Data.csv - ACSDP5Y2020.DP05-Column-Metadata.csv - ACSDP5Y2021.DP05-Data.csv - ACSDP5Y2021.DP05-Column-Metadata.csv

Includes detailed info on demographic structure: age, race, sex, etc

3. Business Statistics

County Business Patterns (CBP) data derived from: - CB1800CBP All Sectors: County Business Patterns, including ZIP Code Business Patterns, by Legal Form of Organization and Employment Size Class for the U.S., States, and Selected Geographies: 2018 - CBP2018.CB1800CBP-Data.csv - CBP2018.CB1800CBP-Column-Metadata.csv - CB1900CBP All Sectors: County Business Patterns, including ZIP Code Business Patterns, by Legal Form of Organization and Employment Size Class for the U.S., States, and Selected Geographies: 2019 - CBP2019.CB1900CBP-Data.csv - CBP2019.CB1900CBP-Column-Metadata.csv - CB2000CBP All Sectors: County Business Patterns, including ZIP Code Business Patterns, by Legal Form of Organization and Employment Size Class for the U.S., States, and Selected Geographies: 2020 - CBP2020.CB2000CBP-Data.csv - CBP2020.CB2000CBP-Column-Metadata.csv

Includes info on number of establishments, payroll, and other metrics by different business size (less than 5 employees, 5 to 9 employees, etc).

4. Presence of computer and internet

American Community Survey (ACS) data derived from B28003 PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD: - ACSDT5Y2018.B28003-Data.csv - ACSDT5Y2018.B28003-Column-Metadata.csv - ACSDT5Y2019.B28003-Data.csv - ACSDT5Y2019.B28003-Column-Metadata.csv - ACSDT5Y2020.B28003-Data.csv - ACSDT5Y2020.B28003-Column-Metadata.csv - ACSDT5Y2021.B28003-Data.csv - ACSDT5Y2021.B28003-Column-Metadata.csv

5. Computer and internet types

American Community Survey (ACS) data derived from S2801 TYPES OF COMPUTERS AND INTERNET SUBSCRIPTIONS: - ACSST5Y2018.S2801-Data.csv - ACSST5Y2018.S2801-Column-Metadata.csv - ACSST5Y2019.S2801-Data.csv - ACSST5Y2019.S2801-Column-Metadata.csv - ACSST5Y2020.S2801-Data.csv - ACSST5Y2020.S2801-Column-Metadata.csv - ACSST5Y2021.S2801-Data.csv - ACSST5Y2021.S2801-Column-Metadata.csv

This layer contains information on technology access by Household. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer represents the underlying data for several data visualizations on the Tempe Equity Map.Data visualized as a percent of total population in households in given census tract.Values shown of -999 represent no data for those cells.Layer includes:Key demographicsTotal Population in Households % Broadband Internet Subscription: American Indian and Alaska Native alone% Broadband Internet Subscription: Asian Alone% Broadband Internet Subscription: Black or African American alone% Broadband Internet Subscription: Native Hawaiian and Other Pacific Islander alone% Broadband Internet Subscription: White Alone% Broadband Internet Subscription: Hispanic or Latino origin% Without an internet Subscription: American Indian and Alaska Native alone% Without an internet Subscription: Asian alone% Without an internet Subscription: Native Hawaiian and Other Pacific Islander alone% Without an internet Subscription: Black or African American Alone% Without an internet Subscription: White Alone% Without an internet Subscription: Hispanic or Latino origin% No computer in household: American Indian and Alaska native alone% No computer in household: Asian alone% No computer in household: Black or African American alone% No computer in household: Native Hawaiian or Pacific Islander% No computer in household: White Alone% No computer in household: Hispanic or Latino origin Current Vintage: 2017-2021ACS Table(s): S2802 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: Dec 8, 2022Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

This layer contains information on technology access by Household. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer represents the underlying data for several data visualizations on the Tempe Equity Map.Data visualized as a percent of total population in households in given census tract.Layer includes:Key demographicsTotal Population in Households % Broadband Internet Subscription: American Indian and Alaska Native alone% Broadband Internet Subscription: Asian Alone% Broadband Internet Subscription: Black or African American alone% Broadband Internet Subscription: Native Hawaiian and Other Pacific Islander alone% Broadband Internet Subscription: White Alone% Broadband Internet Subscription: Hispanic or Latino origin% Without an internet Subscription: American Indian and Alaska Native alone% Without an internet Subscription: Asian alone% Without an internet Subscription: Native Hawaiian and Other Pacific Islander alone% Without an internet Subscription: Black or African American Alone% Without an internet Subscription: White Alone% Without an internet Subscription: Hispanic or Latino origin% No computer in household: American Indian and Alaska native alone% No computer in household: Asian alone% No computer in household: Black or African American alone% No computer in household: Native Hawaiian or Pacific Islander% No computer in household: White Alone% No computer in household: Hispanic or Latino origin Current Vintage: 2019-2023ACS Table(s): S2802 (Not all lines of this ACS table are available in this feature layer.)Census API: Census Bureau's API for American Community Survey Date of Census update: Dec 12, 2024Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

This layer contains information on technology access by Household. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer represents the underlying data for several data visualizations on the Tempe Equity Map.Data visualized as a percent of total population in households in given census tract.Layer includes:Key demographicsTotal Population in Households% Broadband Internet Subscription: American Indian and Alaska Native alone% Broadband Internet Subscription: Asian Alone% Broadband Internet Subscription: Black or African American alone% Broadband Internet Subscription: Native Hawaiian and Other Pacific Islander alone% Broadband Internet Subscription: White Alone% Broadband Internet Subscription: Hispanic or Latino origin% Without an internet Subscription: American Indian and Alaska Native alone% Without an internet Subscription: Asian alone% Without an internet Subscription: Native Hawaiian and Other Pacific Islander alone% Without an internet Subscription: Black or African American Alone% Without an internet Subscription: White Alone% Without an internet Subscription: Hispanic or Latino origin% No computer in household: American Indian and Alaska native alone% No computer in household: Asian alone% No computer in household: Black or African American alone% No computer in household: Native Hawaiian or Pacific Islander% No computer in household: White Alone% No computer in household: Hispanic or Latino originCurrent Vintage: 2018-2022ACS Table(s): S2802 (Not all lines of this ACS table are available in this feature layer.)Census API: Census Bureau's API for American Community SurveyDate of Census update: Dec 15, 2023Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Source:

Creator: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA -- culled from 1990 US Census, 1995 US FBI Uniform Crime Report, 1990 US Law Enforcement Management and Administrative Statistics Survey, available from ICPSR at U of Michigan. -- Donor: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA -- Date: July 2009

Data Set Information:

Many variables are included so that algorithms that select or learn weights for attributes could be tested. However, clearly unrelated attributes were not included; attributes were picked if there was any plausible connection to crime (N=122), plus the attribute to be predicted (Per Capita Violent Crimes). The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units.

The per capita violent crimes variable was calculated using population and the sum of crime variables considered violent crimes in the United States: murder, rape, robbery, and assault. There was apparently some controversy in some states concerning the counting of rapes. These resulted in missing values for rape, which resulted in incorrect values for per capita violent crime. These cities are not included in the dataset. Many of these omitted communities were from the midwestern USA.

Data is described below based on original values. All numeric data was normalized into the decimal range 0.00-1.00 using an Unsupervised, equal-interval binning method. Attributes retain their distribution and skew (hence for example the population attribute has a mean value of 0.06 because most communities are small). E.g. An attribute described as 'mean people per household' is actually the normalized (0-1) version of that value.

The normalization preserves rough ratios of values WITHIN an attribute (e.g. double the value for double the population within the available precision - except for extreme values (all values more than 3 SD above the mean are normalized to 1.00; all values more than 3 SD below the mean are normalized to 0.00)).

However, the normalization does not preserve relationships between values BETWEEN attributes (e.g. it would not be meaningful to compare the value for whitePerCap with the value for blackPerCap for a community)

A limitation was that the LEMAS survey was of the police departments with at least 100 officers, plus a random sample of smaller departments. For our purposes, communities not found in both census and crime datasets were omitted. Many communities are missing LEMAS data.

Attribute Information:

'(125 predictive, 4 non-predictive, 18 potential goal) ', ' communityname: Community name - not predictive - for information only (string) ', ' state: US state (by 2 letter postal abbreviation)(nominal) ', ' countyCode: numeric code for county - not predictive, and many missing values (numeric) ', ' communityCode: numeric code for community - not predictive and many missing values (numeric) ', ' fold: fold number for non-random 10 fold cross validation, potentially useful for debugging, paired tests - not predictive (numeric - integer) ', ' population: population for community: (numeric - expected to be integer) ', ' householdsize: mean people per household (numeric - decimal) ', ' racepctblack: percentage of population that is african american (numeric - decimal) ', ' racePctWhite: percentage of population that is caucasian (numeric - decimal) ', ' racePctAsian: percentage of population that is of asian heritage (numeric - decimal) ', ' racePctHisp: percentage of population that is of hispanic heritage (numeric - decimal) ', ' agePct12t21: percentage of population that is 12-21 in age (numeric - decimal) ', ' agePct12t29: percentage of population that is 12-29 in age (numeric - decimal) ', ' agePct16t24: percentage of population that is 16-24 in age (numeric - decimal) ', ' agePct65up: percentage of population that is 65 and over in age (numeric - decimal) ', ' numbUrban: number of people living in areas classified as urban (numeric - expected to be integer) ', ' pctUrban: percentage of people living in areas classified as urban (numeric - decimal) ', ' medIncome: median household income (numeric - may be integer) ', ' pctWWage: percentage of households with wage or salary income in 1989 (numeric - decimal) ', ' pctWFarmSelf: percentage of households with farm or self employment income in 1989 (numeric - decimal) ', ' pctWInvInc: percentage of households with investment / rent income in 1989 (numeric - decimal) ', ' pctWSocSec: percentage of households with social security income in 1989 (numeric - decimal) ', ' pctWPubAsst: pe...

DO NOT EDIT THIS DATASET.

This dataset, which is automatically updated contains American Community Survey 5-Year Estimates. This dataset is updated by a Socrata process; please contact support@socrata.com if you encounter any questions or issues.

This dataset contains variables from Data Profile 2. Topics include: households by type, relationship, marital status, fertility, grandparents, school enrollment, educational attainment, veteran status, disability status, residence, place of birth, language spoken at home, ancestry, computer access, and internet use, all at the Census Tract level for the entire County.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Geocoded for Baltimore City County. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM(r) classification, census block group, and latitude-longitude. PRIZM(r) classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM(r) classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey. This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because of its higher spatial accuracy than other databases describing US Census geographies, including those provided by the US Census. This database includes data only for environmental behaviors: How likely would you be to take part in the following efforts to improve and maintain the quality of the watershe... Visit https://dataone.org/datasets/knb-lter-bes.335.570 for complete metadata about this dataset.

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

This dataset contains measures of internet access per United States census tract from the 2015-2019 American Community Survey five-year estimate. Key variables include the number and percent of households per tract with any type of internet subscription, with broadband internet, and with a computer or smartphone. A curated version of this data is available through ICPSR at https://doi.org/10.3886/ICPSR38559.v1

This layer shows computer ownership and internet access by education. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count of people age 25+ in households with no computer and the percent of the population age 25+ who are high school graduates (includes equivalency) and have some college or associate's degree in households that have no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28006 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.