This 90 minute session will cover data discovery and extraction via the CHASS Census Analyzer and basic GIS visualization. We will highlight the added value features of using CHASS compared to Statistics Canada Census Profiles. We will provide an overview of the steps involved in visualizing Census data in ArcGIS, including data elements and major processes. This session will also feature a critical discussion on visualizing Census data in GIS software, focusing on the technical expertise required to produce usable visualizations as well as the responsibility (and credit) for producing visualizations.

The US Census Bureau provides a large collection of data files, some of which are encoded separately or do not have an obvious means to integrate. Suppose that the files are located and need to be integrated to make some data-driven decisions using Census population estimates. The resultant files may be very useful to explore, but the user wants to get into visual representation and start considering things spatially and temporally. In this resource, the Jupyter notebook walks through a set of operations created to integrate Census population estimates with the known ESRI shapefile for the equivalent county-scales.

The Census Program Data Viewer (CPDV) is an advanced web-based data visualization tool that helps make statistical information more interpretable by presenting key indicators in a statistical dashboard. It also enables users to easily compare indicator values and identify relationships between indicators.

"Log Into North Carolina" or LINC is an interactive data retrieval service containing historical information for over 900 data items and a variety of geographic areas within the state. Topics include population, labor force, education, transportation, revenue, agriculture, vital statistics, energy and utilities, and other topics for a variety of geographic areas. Most data are available for all 100 counties. Some items are available for municipalities. Others, especially census data, may be retrieved for tracts and townships.LINC is evolving with new data and enhanced features to include data visualization, bulk data downloads, and mapping. The new version of LINC allows the public to view data but also allows registered users to save visualizations and receive notifications of dataset updates.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

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GapMinder collects data from a handful of sources, including the Institute for Health Metrics and Evaluation, the US Census Bureau’s International Database, the United Nations Statistics Division, and the World Bank.

Variable Name & Description of Indicator:

• country: Unique Identifier
• incomeperperson: Gross Domestic Product per capita in constant 2000 US$. The inflation but not the differences in the cost of living between countries has been taken into account.
• Internetuserate: Internet users (per 100 people) Internet users are people with access to the worldwide network.
• urbanrate: Urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects)

More information is available at www.gapminder.org

This downloadable map product includes the Provincial Electoral Divisions (PED) from the most recent provincial election. The data in this information product illustrates the boundaries of Alberta's 87 Provincial Electoral Divisions. Electoral Boundaries are defined by the Alberta Election Act, Chapter E-1, 2018. Provincial Electoral Divisions (PEDs) are territorial units represented by an elected Member to serve in the Alberta Provincial Legislative Assembly. The Provincial Electoral Divisions used in this information product were enacted in December 2017 and came into effect for the 2019 provincial general election. The PED profile contains data created by Statistics Canada in the 2021 Census of Population. The map is a Bivariate map with two factors being shown through the fill colours of each divisions the percentage of CERB recipients versus the percentage of working age people with some post-secondary education. The outline colours are showing the dominant employment sectors of each division. The dominant employment sector is determined by the employment sector with the most employees in each division. The main map is at a scale of 1:3,750,000 and the inset maps are at a scale of 1:500,000.

Population Numbers By New York City Neighborhood Tabulation Areas

The data was collected from Census Bureaus' Decennial data dissemination (SF1).
Neighborhood Tabulation Areas (NTAs), are aggregations of census tracts that are subsets of New York City's 55 Public Use Microdata Areas (PUMAs). Primarily due to these constraints, NTA boundaries and their associated names may not definitively represent neighborhoods.
This report shows change in population from 2000 to 2010 for each NTA.
Compiled by the Population Division – New York City Department of City Planning.

Unadjusted decennial census data from 1950-2000 and projected figures from 2010-2040: summary table of New York City population numbers and percentage share by Borough, including school-age (5 to 17), 65 and Over, and total population.

This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1) && (HRSWK>0)). The prediction task is to determine whether a person makes over $50K a year.

This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell.

Datasets

DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings.

DE_POP_TDBP: This dataset is considered a best product, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers.

DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created.

Please refer to the related publication for details.

Temporal extent

The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894)

The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295)

The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219)

The underlying census data is from 2018.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems.

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de). A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

Census data were provided by the German Federal Statistical Offices.

Funding This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This dataset contains polygons that represent the boundaries of statistical neighborhoods as defined by the DC Department of Health (DC Health). DC Health delineates statistical neighborhoods to facilitate small-area analyses and visualization of health, economic, social, and other indicators to display and uncover disparate outcomes among populations across the city. The neighborhoods are also used to determine eligibility for some health services programs and support research by various entities within and outside of government. DC Health Planning Neighborhood boundaries follow census tract 2010 lines defined by the US Census Bureau. Each neighborhood is a group of between one and seven different, contiguous census tracts. This allows for easier comparison to Census data and calculation of rates per population (including estimates from the American Community Survey and Annual Population Estimates). These do not reflect precise neighborhood locations and do not necessarily include all commonly-used neighborhood designations. There is no formal set of standards that describes which neighborhoods are included in this dataset. Note that the District of Columbia does not have official neighborhood boundaries. Origin of boundaries: each neighborhood is a group of between one and seven different, contiguous census tracts. They were originally determined in 2015 as part of an analytical research project with technical assistance from the Centers for Disease Control and Prevention (CDC) and the Council for State and Territorial Epidemiologists (CSTE) to define small area estimates of life expectancy. Census tracts were grouped roughly following the Office of Planning Neighborhood Cluster boundaries, where possible, and were made just large enough to achieve standard errors of less than 2 for each neighborhood's calculation of life expectancy. The resulting neighborhoods were used in the DC Health Equity Report (2018) with updated names. HPNs were modified slightly in 2019, incorporating one census tract that was consistently suppressed due to low numbers into a neighboring HPN (Lincoln Park incorporated into Capitol Hill). Demographic information were analyzed to identify the bordering group with the most similarities to the single census tract. A second change split a neighborhood (GWU/National Mall) into two to facilitate separate analysis.

Selected housing and population indicators derived from the "2010 Census Summary File 1" published by the U.S. Census Bureau, summarized on a census block basis. All indicators have numerators and denominators except in cases of total population counts and averages. Numerators are denoted with "_N" and Denominators with "_D". These data are intended for use in demographic analysis and visualization. Users are strongly advised to thoroughly read this metadata record and 2010 Summary File 1 documentation available from the U.S. Census Bureau at http://www.census.gov/prod/cen2010/doc/sf1.pdf. A copy of this technical documentation is included with the data download file available from RIGIS. The primary data source,"2010 Census Summary File 1" published by the U.S. Census Bureau, may be accessed online via American Fact Finder (http://factfinder2.census.gov) or directly via http://www2.census.gov/census_2010/04-Summary_File_1/Rhode_Island. The original TIGER/Line shapefile that serves as the spatial reference for these data may be downloaded from https://www.census.gov/geo/maps-data/data/tiger-line.html

Part 1: 2011 Census Dissemination - Summary of products and services line (web module ‘walk-through’) - Part 2: 2011 National Household Survey (NHS) Dissemination - Release strategy and changes to product line compared to census - Data quality … Global non-response (GNR) and coefficients of variation (CVs) - Summary of products and services line (web module ‘walk-through’) - Part 3: What is left to be released for census and NHS? - Part 4: Ongoing and the future …

2011 Census Infographics

The Census Bureau does not recognize or release data for Boston neighborhoods. However, Census block groups can be aggregated to approximate Boston neighborhood boundaries to allow for reporting and visualization of Census data at the neighborhood level. Census block groups are created by the U.S. Census Bureau as statistical geographic subdivisions of a census tract defined for the tabulation and presentation of data from the decennial census and the American Community Survey. The 2020 Census block group boundary files for Boston can be found here. These block group-approximated neighborhood boundaries are used for work with Census data. Work that does not rely on Census data generally uses the Boston neighborhood boundaries found here.

The Census Bureau does not recognize or release data for Boston neighborhoods. However, Census tracts can be aggregated to approximate Boston neighborhood boundaries to allow for reporting and visualization of Census data at the neighborhood level. Census tracts are created by the U.S. Census Bureau as statistical geographic subdivisions of a county defined for the tabulation and presentation of data from the decennial census and the American Community Survey. The 2020 Census tract boundary files for Boston can be found here. These tract-approximated neighborhood boundaries are used for work with Census data. Work that does not rely on Census data generally uses the Boston neighborhood boundaries found here.

The City of Atlanta data visualization suite from ARC & Neighborhood Nexus includes 400 variables all mapped to City of Atlanta neighborhoods, neighborhood planning units (NPUs), and City Council districts’ boundaries. The data includes several City-specific variables such as code enforcement, 911 calls and the results of the recently-conducted windshield survey of housing conditions, as well as hundreds of Census variables like income, poverty, health insurance coverage and disability. When we say “neighborhoods”, we actually mean “Neighborhood Statistical Areas,” which in some cases combine some of Atlanta’s smaller neighborhoods into one.The tools we built include an interactive map, which allows for a deep-dive analysis of all 400 variables, and a dashboard, which is an easy-to-use tool that provides quick comparisons of every neighborhood, neighborhood planning unit, and City Council district to the city as a whole.Visit Neighborhood Nexus and City of Atlanta’s website.

Block Groups:Block groups are statistical subdivisions of census tracts and are the smallest geographic units for which the Census Bureau tabulates sample data. They are designed to cover contiguous areas and are uniquely numbered within each census tract. Block groups do not cross state, county, or census tract boundaries but may cross other geographic entity boundaries.This feature class is used for various purposes, including visualization and analysis of demographic data, urban planning, and resource allocation. It is available for public use and can be accessed through platforms like ArcGIS.Population Range: Each block group generally contains between 600 to 3,000 people.Data Fields:BG20 (BLKGRPCE20): 7-digit census tract and block group number.CT20 (TRACTCE20): 6-digit census tract number.Label (NAMELSAD20): Block group number label.ACS Data:The 2022 American Community Survey (ACS) Block Group Data tables offer detailed estimates on various social, economic, housing, and demographic characteristics at the block group level, which are small statistical divisions of census tracts.The table provides the most comprehensive estimates on all topics for City of Salinas, including block groups. They include detailed information on population, housing, economic, and social characteristics.Selected ACS Fields:Median Age (b01002e1)Population (b01003e1)Households (b11001e1)Households with 200% Federal Poverty Level (c17002e8)Median Household Income (b19301e1)Per Capita Income (b19301e1)Housing Units (b25001e1)Average Household Size (b25010e1)Bachelor's Degree or Higher (b99152e2)High School Degree or Higher (b15003e17)Limited English Households (c16002e1)

This presentation, offered by the Montreal Data Service Center, will provide a brief overview of the new features related to the concepts and variables of the 2021 Census as well as the various products available such as data tables, profiles, visualization tools, analyses, guides, etc. A demonstration on the census program webpage will also be included to teach participants how to effectively find and use census data. Presented by: Thérèse Nguyen (Statistics Canada) Samuel Dupéré (Statistics Canada)