This data package has the purpose to offer data for demographic indicators, part of 5-years American Community Census, that could be needed in the analysis made along with health-related data or as stand-alone. The American Community Survey based on 5-years estimates is, according to U.S Census Bureau, the most reliable, because the samples used are the largest and the data collected cover all country areas, regardless of the population number.

This layer shows housing units broken down by owner occupied and renter occupied in Tempe Zip Codes.Data is from US Census American Community Survey (ACS) 5-year estimates.To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online).A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2016-2020ACS Table(s): S2502 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table downloaded and joined with Zip Code boundaries in the City of Tempe.Date of Census update: March 17, 2022National Figures: data.census.gov

This American Community Survey (ACS) dataset identifies demographic and housing estimates by zip code tabulation areas within the United States, from 2012 through 2016. The dataset identifies sex and age, race and housing units by Zip Code Tabulation Area.

This layer shows population broken down by race and Hispanic origin. Data is from US Census American Community Survey (ACS) 5-year estimates.To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2016-2020ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table downloaded and joined with Zip Code boundaries in the City of Tempe.Date of Census update: March 17, 2022National Figures: data.census.gov

This data comes from the 2010 Census Profile of General Population and Housing Characteristics. Zip codes are limited to those that fall at least partially within LA city boundaries. The dataset will be updated after the next census in 2020. To view all possible columns and access the data directly, visit http://factfinder.census.gov/faces/affhelp/jsf/pages/metadata.xhtml?lang=en&type=table&id=table.en.DEC_10_SF1_SF1DP1#main_content.

This American Community Survey (ACS) dataset specifies the place of birth for the foreign-born population by zip code tabulation areas within the United States.

This layer shows age and sex demographics. Data is from US Census American Community Survey (ACS) 5-year estimates.To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). Layer includes:Key demographicsTotal populationMale total populationFemale total populationPercent male total population (calculated)Percent female total population (calculated)Age and other indicatorsTotal population by AGE (various ranges)Total population by SELECTED AGE CATEGORIES (various ranges)Total population by SUMMARY INDICATORS (including median age, sex ratio, age dependency ratio, old age dependency ratio, child dependency ratio)Percent total population by AGE (various ranges)Percent total population by SELECTED AGE CATEGORIES (various ranges)Male by ageMale total population by AGE (various ranges)Male total population by SELECTED AGE CATEGORIES (various ranges)Male total population Median age (years)Percent male total population by AGE (various ranges)Percent male total population by SELECTED AGE CATEGORIES (various ranges)Female by ageFemale total population by AGE (various ranges)Female total population by SELECTED AGE CATEGORIES (various ranges)Female total population Median age (years)Percent female total population by AGE (various ranges)Percent female total population by SELECTED AGE CATEGORIES (various ranges)A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Current Vintage: 2016-2020ACS Table(s): S0101 (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: March 17, 2022Data Preparation: Data table downloaded and joined with Zip Code boundaries in the City of Tempe.National Figures: data.census.gov

This annual publication focuses on the Social Security beneficiary population at the ZIP code level. It presents basic program data on the number and type of beneficiaries and the amount of benefits paid in each state, Social Security Administration field office, and ZIP code. It also shows the number of beneficiaries aged 65 or older. Report for 2016.

The Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., 2000-2016, v1.0 data set contains daily and annual concentration predictions for Fine Particulate Matter (PM2.5), Ozone (O3), and Nitrogen Dioxide (NO2) pollutants at ZIP Code-level for the years 2000 to 2016. Ensemble predictions of three machine-learning models were implemented (Random Forest, Gradient Boosting, and Neural Network) to estimate the daily PM2.5, O3, and NO2 at the centroids of 1km x 1km grid cells across the contiguous U.S. for 2000 to 2016. The predictors included air monitoring data, satellite aerosol optical depth, meteorological conditions, chemical transport model simulations, and land-use variables. The ensemble models demonstrated excellent predictive performance with 10-fold cross-validated R-squared values of 0.86 for PM2.5, 0.86 for O3, and 0.79 for NO2. These high-resolution, well-validated predictions allow for estimates of ZIP Code-level pollution concentrations with a high degree of accuracy. For general ZIP Codes with polygon representations, pollution levels were estimated by averaging the predictions of grid cells whose centroids lie inside the polygon of that ZIP Code; for other ZIP Codes such as Post Offices or large volume single customers, they were treated as a single point and predicted their pollution levels by assigning the predictions using the nearest grid cell. The polygon shapes and points with latitudes and longitudes for ZIP Codes were obtained from Esri and the U.S. ZIP Code Database and were updated annually. The data include about 31,000 general ZIP Codes with polygon representations, and about 10,000 ZIP Codes as single points. The aggregated ZIP Code-level, daily predictions are applicable in research such as environmental epidemiology, environmental justice, health equity, and political science, by linking with ZIP Code-level demographic and medical data sets, including national inpatient care records, medical claims data, census data, U.S. Census Bureau American CommUnity Survey (ACS), and Area Deprivation Index (ADI). The data are particularly useful for studies on rural populations who are under-represented due to the lack of air monitoring sites in rural areas. Compared with the 1km grid data, the ZIP Code-level predictions are much smaller in size and are manageable in personal computing environments. This greatly improves the inclusion of scientists in different fields by lowering the key barrier to participation in air pollution research. The Units are ug/m^3 for PM2.5 and ppb for O3 and NO2.

This American Community Survey (ACS) data set identifies selected characteristics of the total and native population by zip code tabulation area within the United States. The dataset identifies population by native and foreign-born, including age, sex, language spoken at home, ability to speak English, marital status, educational attainment, income, poverty and citizenship status by zip code tabulation area.

This dataset denotes ZIP Code centroid locations weighted by population. Population weighted centroids are a common tool for spatial analysis, particularly when more granular data is unavailable or researchers lack sophisticated geocoding tools. The ZIP Code Population Weighted Centroids allows researchers and analysts to estimate the center of population in a given geography rather than the geometric center.

This American Community Survey (ACS) data set identifies age and sex for the population by zip code tabulation areas within the United States. The data includes an estimate of the population by gender and age categories, total estimates as well as these category’s margin of error and margin of error ratio.

This dataset aggregates extensive public data corresponding to 34,928 zip codes from the contiguous United States, spanning from 2000 to 2016. It encompasses 580,244 zip code-year observations, capturing a myriad of variables to portray a comprehensive picture of each region. The variables include, but are not limited to, education rate, median household income, median house value, poverty rate, percentages of Hispanic and Black populations, and meteorological variables, offering nuanced insights into the socio-economic conditions, demographic composition, and environmental contexts of each area. This rich, multifaceted dataset serves as a valuable resource for exploratory research, specifically designed to facilitate the evaluation of potential causal relationships, with a focus on educational attainment, although its extensive range of variables allows for a multitude of applications across various domains.

US Census American Community Survey (ACS) 2016, 5-year estimates of the key demographic characteristics of ZIP Code Tabulation Areas geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2016 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

1. Zip code boundaries change, in ways small and large - these are not a stable analysis unit. Data you received keyed to zip codes may have used an earlier and very different boundary for your zip codes of interest.
2. Historically, the United States Postal Service has not published zip code boundaries, and instead, boundary datasets are compiled by third party vendors from address data. That means that the boundary data are not authoritative, and any data you have keyed to zip codes may use a different, vendor-specific method for generating boundaries from the data here.
3. Zip codes are designed to optimize mail delivery, not social, environmental, or demographic characteristics. Analysis using zip codes is subject to create issues with the Modifiable Areal Unit Problem that will bias any results because your units of analysis aren't designed for the data being studied.

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 Pacific Time.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Rows included for Citywide case counts Rows are included for the Citywide case counts and incidence rate every day. These Citywide rows can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling bases.

Related dataset See the dataset of the most recent cumulative counts for all geographic areas here: https://data.sfgov.org/COVID-19/COVID-19-Cases-and-Deaths-Summarized-by-Geography/tpyr-dvnc

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by geography over time are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

This study contains measures of neighborhood-school gap for 2009-2010 and 2015-2016. Neighborhood-school gap (NS gap) refers to the discrepancy between the demographics of a public school and its surrounding community. For example, if 60 percent of a school's student body is Black, but 30 percent of the neighborhood population is Black, the school has a positive Black neighborhood-school gap. These datasets measure gaps in race and poverty between elementary school student populations and the census tracts and ZIP code tabulation areas (ZCTAs) that those elementary schools serve. Data is at the census tract and ZCTA level. Supplemental data containing component variables used to calculate NS gap at the school and block group level is also available.

American Community Survey Census data includes demographics, education level, commute information, and more subset to Colorado by the Department of Local Affairs (DOLA).

Annual report providing Social Security beneficiary population data by state and ZIP code. Report for 2016.

PurposeWe examined openly shared substance-related tweets to estimate prevalent sentiment around substance use and identify popular substance use activities. Additionally, we investigated associations between substance-related tweets and business characteristics and demographics at the zip code level.MethodsA total of 79,848,992 tweets were collected from 48 states in the continental United States from April 2015-March 2016 through the Twitter API, of which 688,757 were identified as being related to substance use. We implemented a machine learning algorithm (maximum entropy text classifier) to estimate sentiment score for each tweet. Zip code level summaries of substance use tweets were created and merged with the 2013 Zip Code Business Patterns and 2010 US Census Data.ResultsQuality control analyses with a random subset of tweets yielded excellent agreement rates between computer generated and manually generated labels: 97%, 88%, 86%, 75% for underage engagement in substance use, alcohol, drug, and smoking tweets, respectively. Overall, 34.1% of all substance-related tweets were classified as happy. Alcohol was the most frequently tweeted substance, followed by marijuana. Regression results suggested more convenience stores in a zip code were associated with higher percentages of tweets about alcohol. Larger zip code population size and higher percentages of African Americans and Hispanics were associated with fewer tweets about substance use and underage engagement. Zip code economic disadvantage was associated with fewer alcohol tweets but more drug tweets.ConclusionsThe patterns in substance use mentions on Twitter differ by zip code economic and demographic characteristics. Online discussions have great potential to glorify and normalize risky behaviors. Health promotion and underage substance prevention efforts may include interactive social media campaigns to counter the social modeling of risky behaviors.