The infographic is called Demographic Estimates, Census Metropolitan Areas – Canada, 2015 and is designed to inform readers about the latest demographic growth and aging trends at the Census Metropolitan Area (CMA) level.

Business Analyst 2019 Council District SAC Tapestry Feature ServiceThe City of Dallas used the ACS data accessible through ESRI's Business Analyst combined with a custom template which was modified to include both ACS and Dallas imported values to create the honeycomb-like graphic that is shown in the dashboard here.The data dictionary for this graphic can be referenced and downloaded here.

The infographic in question, entitled Population Estimates, Canada, 2015, provides a concise accurate snapshot of the most recent demographic trends in Canada, related to demographic growth and aging, at the national, provincial and territorial levels.

The infographic in question, entitled Population Estimates, Canada, 2015, provides a concise accurate snapshot of the most recent demographic trends in Canada, related to demographic growth and aging, at the national, provincial and territorial levels.

Each county in the United States has a link with a demographic infographic report that helps communities plan for the impact of COVID-19.Original Web Map by gburgessBAPlease visit original web map link at: _Data sources: Esri forecasts for 2019 U.S. Census Bureau 2013-2017 American Community Survey (ACS) Businesses counts from Infogroup

The data coming from the census 2010 - used to develop this publication of infographics on population characteristics on each of Indonesia’s thirty-three provinces. The book is the result of cooperation between with BNPB and BPS and the United Nations agencies UNOCHA, UNFPA, WFP, and UNDP. UNFPA provided technical assistance in the preparation of the basic population indicators such as sex ratio, population density, main livelihood, and levels of literacy. In addition, this book also displays information regarding dependency ratio, fertility rates, life expectancy, and infant mortality rates included in the Population Projection 2010-2035. The results can be seen in this link: http://reliefweb.int/report/indonesia/indonesia-province-infographic-book-27-nov-2014 The datasets can also accessible in here: http://dibi.bnpb.go.id/profil-wilayah/11/aceh

Community Analyst Report Template. This infographic contains data provided by Esri. The vintage of the data is 2023, 2028.

65% of the world’s population are visual learners. It makes sense, then, that businesses take advantage of visual content like infographics to hammer their marketing message home.

apportionments_pop_2021_pred_2024.xlsx This is a dataset containing prediction apportionments of seats for the 2024 election of the European Parliament (EP). This prediction is based on population data from the 2021 census held by Eurostat. See our paper for the standard function, configurations of parameters, and d-rounding rules we used for calculation. Note: We recommend readers who are not so well informed about apportionment problems and rounding rules see https://www.census.gov/library/video/2021/what-is-apportionment.html or https://www.census.gov/history/www/reference/apportionment/methods_of_apportionment.html.

Data interpretations for this dataset are as follows. 4 worksheets: all: prediction apportionment results of all configurations under the assumption that the membership remains unchanged and the total number of seats is between 705 (current total number of seats) and 750 (statutory threshold). no_lose: prediction apportionment results under the following assumptions: (1) the membership remains unchanged; (2) any Member State does not lose any seats from the current distribution of seats; (3) and the total number of seats is between 705 and 750. increase_no_lose: prediction apportionment results under the following assumptions: (1) the membership remains unchanged; (2) any Member State with an increasing population does not lose any seats from the current distribution of seats; (3) and the total number of seats is between 705 and 750. response: prediction apportionment results under the following assumptions: (1) the membership remains unchanged; (2) any Member State with an increasing population does not lose any seats from the current distribution of seats while any Member State with a decreasing population does not gain seats; (3) and the total number of seats is between 705 and 750. Meanings of column names: State: name of Member State of the European Union p_2011: population data from the 2011 census (data source: https://ec.europa.eu/eurostat/web/population-demography/population-housing-censuses/database) p_2021: population data from the 2021 census (data source: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Population_and_housing_census_2021_-_population_grids&stable=1#Distribution_of_European_population) stat_2020: current distribution of seats in the EP (data source: https://www.europarl.europa.eu/news/en/headlines/eu-affairs/20180126STO94114/infographic-how-many-seats-does-each-country-get-in-in-the-european-parliament) other columns: composed in the order of "a", "gamma", "d-rounding rule", and "the total number of seats (S)".

indexes_pop_2021_pred_2024.csv This is a dataset presenting the extent of the PSI-based inequality index (index based on Population Seat Index) and the conventional PSP-based index (index based on the proportion of seats to population) of all prediction apportionments of seats for the 2024 election of the European Parliament (EP). This prediction is based on population data from the 2021 census held by Eurostat. See our paper for the standard function, configurations of parameters, and d-rounding rules used for calculation and the PSI-based index and PSP-based index used for evaluation. Data interpretations for this dataset are as follows. Meanings of column names: a: configuration of the standard function gamma: configuration of the standard function rounding: d-rounding rule used for obtaining a whole number S: the total number of seats in the prediction x_min: the minimum number of seats in the prediction apportionment x_max: the maximum number of seats in the prediction apportionment inequality index: maximum of PSI divided by minimum of PSI psp_max/psp_min: maximum of PSP divided by minimum of PSP

The Population Digital Maturity Barometer is a survey to quantitatively assess Caledonians' digital access, uses and understanding, as well as their level of confidence in digital use.This survey was conducted in 2017 by the New Caledonian government in order to :Have a decision-making tool for institutional and private players offering the possibility of monitoring New Caledonia's digital development and guiding their strategies.Assess the impact of the Strategic Plan for the Digital Economy (PSEN) and related actions on New Caledonia's digital development over time.ment numérique de la Nouvelle-Calédonie dans le temps.Identify areas for improvement in order to adjust the PSEN to requirements and realities on the ground.The survey was carried out by phone throughout the territory, among 1,160 New Caledonians over the age of 15.In addition to socio-demographic characteristics, the questionnaire covered several topics related to digital usage :Internet access (subscriptions, connection types, connection rates, wifi use, etc.). Digital equipment and tools (computers, telephones and mobile terminals, multimedia equipment, software used, etc.).Fixed and mobile Internet use (frequency of use, main uses, cloud services used, obstacles encountered, expectations, etc.).Use of online public services (sites consulted, types of procedures carried out, obstacles encountered, expectations, etc.)..Internet access locations (home, digital public spaces, third-party sites, obstacles encountered, etc.).Online commerce (services requested, online purchases and sales made, online payments made, obstacles encountered, expectations, etc.).Personal security (security incidents, viruses, security tools used, knowledge of Internet-related risks, expectations, fears, etc.). Digital skills, support and training (skill levels, training methods, training offered, training taken, etc.).Eco-responsibility (awareness, consumption, treatment of old equipment, etc.).Digital profiles of New Caledonians (based on socio-cultural, geographical, economic criteria and digital practices).Attached you'll find:the questionnairethe summary reportthe summary infographic

This profile is based on the ERSI Community Analyst Report Template. This infographic contains data provided by Esri. The vintage of the data is 2021, 2026.

Summary of COVID-19 cases and demographics for each US State. The number of COVID-19 cases are dynamically added to the infographic for each state. The "updated" date on this report reflects when the case information was last updated in the COVID-19 Global Cases by Johns Hopkins CSSE layer for the selected state.

This dataset contains the results of a survey conducted via Google Forms to assess Generation Z’s understanding, environmental awareness, and social action related to waste management during the Eid al-Fitr holiday season. The survey included an infographic guide as a stimulus to evaluate how visual media supports behavioral change and sustainability education among youth. The dataset also contains the result that had been transcribed from an interview with carbon expert.

The dataset, exported from Google Forms in XLSX format, includes respondents’ demographic information, their interpretation of the infographic content, their level of environmental awareness, and their reported or intended waste management practices during the holiday. The dataset from interviews with carbon expert, exported in DOCX format.

This dataset is valuable for researchers in environmental education, youth studies, sustainability communication, and behavioral change research.

Format: XLSX (exported from Google Forms)
Number of Respondents: 6
Language: Indonesia
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Data Collection Method: Online survey using Google Forms
Data Collection Period: 4 April 2025

This is a dataset containing the population of each Member State from the 2011 and 2021 censuses held by Eurostat, and the current distribution of seats in the European Parliament (EP). The population data was downloaded from the official website of Eurostat (2011: https://ec.europa.eu/eurostat/web/population-demography/population-housing-censuses/database; 2021: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Population_and_housing_census_2021_-_population_grids&stable=1#Distribution_of_European_population). The seat data was obtained from the official website of the EP (https://www.europarl.europa.eu/news/en/headlines/eu-affairs/20180126STO94114/infographic-how-many-seats-does-each-country-get-in-in-the-european-parliament).

The People's Digital Maturity Barometer is a survey that quantitatively assesses Caledonians' digital access, usage and understanding, as well as their level of confidence in digital use.

 This survey enables the Government of New Caledonia and its public and private partners to :



  benefit from an up-to-date assessment of Caledonians' digital maturity, the latest of which dates back to 2018 ;


  a decision-making tool to guide public policy and the strategy of players in the digital economy.






 The survey was carried out by phone throughout the territory, among New Caledonians aged 15 and over. The sample was defined on the basis of the 8 Homogeneous Territorial Entities proposed by ISEE. These EHTs are groupings of towns that are homogeneous in geographical and socio-cultural terms:


 Grand Nouméa : Nouméa, Dumbéa, Mont Dore, Païta

  Sud Ouest : Bourail, Moindou, Farino, Saraméra, La Foa, Boulouparis

 Sud Est : Thio, Yaté, Ile des pins
 Plaine de l'Ouest : Voh, Koné, Pouembout, Poya
 Extrême Nord : Kaala Gomen, Koumac, Poum, Belep, Ouégoua, Pouébo
 Nord Est Océanique : Hienghène, Touho, Poindimié, Ponérihouen
 Nord Est minier : Houailou, Kouaoua, Canala
 Iles : Maré, Lifou-Tiga, Ouvéa

The final sample is stratified, non-proportional by zone, with an overweighting of the South East and a slight underweighting of Nouméa. Each ETH thus has a sufficient number of respondents to carry out a comparative analysis.

The analysis is carried out at 2 levels:


  Territory-wide: the sample has been adjusted by commune (to assign to each commune its actual observed weight in the population aged +15), as well as by ethnicity (correction of oversampling of Melanesians to the detriment of Europeans born in NC).


  By ETH: samples from the Grand Nouméa, Sud Ouest, Sud Est, Sud Est minier and Plaines de l'Ouest zones have been adjusted to match ISEE data (RGP 2019).






 In addition to socio-demographic characteristics, the questionnaire dealt with several themes related to digital usage:


 Facilities and Internet access
 Learning and becoming comfortable on the Internet
 Frequency of Internet use
 Internet usage
 Impact of Internet use on daily life
 Security and the Internet
 Environmental awareness

As the sample size has been over-represented in certain areas compared with its actual distribution, the raw sample is not representative of the population aged over 15. The statistical weight of individuals has therefore been adjusted, and statistical analyses based on this dataset must take into account the weights assigned to each individual in all calculations («Coefficient» columns).

Attached you will find :

 the questionnaire
 summary report
 summary infographic
 a sort file for all values

The numbering criterion is the same used in 56. ‘S’ stands for Static and ‘D’ for Dynamic according to the classification established in Table 1.

The National Child Measurement Programme (NCMP) involves the annual measurement of the height and weight of children in Reception (age 4-5) and Year 6 (age 10-11). This report provides an overview of NCMP data from Camden schools in 2017/18 , with a particular emphasis on demographic analysis and deprivation. An infographic presenting key messages from this analysis is also available.

Community Change Snapshot

Demographic details of participants in Experiment 2.