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.

The 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.

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

Infographic displaying current education, income, and employment in San Bernardino County.Report was generated February 2024 using Business Analyst for ArcGIS

Using the coronavirus infographic template in Business/Community Analyst Web (ArcGIS Blog).Business Analyst (BA) Web infographics are a powerful way to understand demographics and other information in context. This blog article explains how your organization can use the Coronavirus infographic template that was added to the infographics gallery on March 1, 2020._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This infographic template provides an overview of a community’s demographics using a color palette of reds and yellows on a dark background. It contains demographic data provided by Esri and the U.S. Census Bureau, from the Esri Updated Demographics, American Community Survey, and Census 2010 datasets. Variables included in the template present information on population, occupation, housing, income, age, education, and commute times. This infographic may be useful for learning about basic demographic and work-related information in an area.

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.

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.

Users with the most appearances in the AC rows (in bold) are depicted in Fig. 8.

Migration Summary (2011-2020) Infographic to be embedded in 2022 BBTN Migration Story Map. Data for maps and tables was retrieved from: Internal Revenue Service, Statistics of Income Division Migration Data, 2011 - 2020.

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.

This data contains the length of walls used at various locations in Baghdad during the US combat operations from 2003-2008. The walls were used for two main purposes- (i) protection against blast and (ii) enclosing neighborhoods to curtail inter tribal conflict. This data therefore separates the walls used for these proposes. The total length of blast and neighborhood wall was extracted using Fiji ImageJ software (Schindelin et al. 2012) from an infographic of concrete walls in Baghdad developed by Izady (2020) for the Gulf 2000 Project at Columbia University, a repository of infographics and maps of demographic and socio-political indicators of the Gulf Region

Militaries are among the most resource intensive institutions in the world, requiring vast volumes of material and energy for both domestic and foreign operations. As a result, militaries are some of the most polluting institutions as well, but very little is known about military contributions to climate change and other forms of environmental degradation, nor about their total material consumption. Furthermore, the accessibility of reliable data about military resource use and environmental damage is highly variable, and depends on military transparency, the context of military operations, and broader emissions reporting requirements between countries. Our preliminary research has shown that one novel, workable approach to examining a military's material footprint is to focus on the logistics that move raw materials move across global military and civilian supply chains. For example, by concentrating on procurement, purchase, and distribution of hydrocarbon-based fuels, we revealed that the U.S. military is a larger polluter than as many as 140 countries. However, a systematic study of the sourcing of raw materials and their circulation supply chains, including the resultant environmental damage, is entirely lacking.

This research will build on our previous work on the climate impacts of US military operations to look at other kinds of materials that have significant environmental impacts. We will source and collate secondary datasets that allow us to quantifying and visualize US military acquisition and use of three seemingly banal materials - sand, water, and concrete- that have serious environmental, social, and economic impacts when purchase and deployed in the large volumes that the US military did during the occupation of Iraq from 2003-2011. We will source this data from publicly available reports produced by the US Congressional Budget Office and individual procurement orders made by the Defense Logistics Agency, supplemented with data from Freedom of Information Act requests as needed. As the most extensive military operation of the 21st century, Iraq from 2003-2011 provides an ideal case study because procurement and supply chains are documented on digital spreadsheets and accessible for analysis, and because analysis of that data can help researchers, governments, and the public understand the consequences and impacts of foreign intervention in new and dynamic ways.

We will undertake a number of activities to make this data useful and available to a range of users, including policymakers and the US military itself. First, we will create a GIS database that collates currently disparate datasets and geographically situates the procurement, distribution and use of sand, water, and concrete. This spatial approach will allow us, and other researchers, to consider all manner of adjacent questions around the social, economic, and environmental impacts of material practices of US military during wartime. Additionally, following our previous research on US military fuel consumption, we will conduct life cycle analyses on all the materials we study, calculating not only the climate change impact of these materials in practice, but also other environmental consequences, such as local air pollution impacts. We will collate all this data and our analysis and visualizations thereof onto a public-facing data lab website, enabling anyone with a web browser to conduct high-powered quantitative analysis of the data for themselves. Further, we will produce policy-relevant literature on the environmental implications of war beyond the usual kinds of analysis in time for the next round of global climate change negotiations at COP26 in Glasgow in November 2021. We seek significant outreach to non-academic partners, such as the US and UK military, climate and environmental policymakers and civil society groups in our current network and beyond.

Enrollment and test signatures used to compute the genuine scores in the template update experiments.

Infographics: City of Sherman

Division of the feature set introduced in 56 according to the type of information they contain.

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

In November 2014, 3,674 Londoners took part in the first London Survey run by Talk London, to tell us what they thought of the city and their neighbourhood. The London Survey enables us to: • Assess Londoners’ priorities across the breadth of Mayoral responsibilities • Understand Londoners’ perceptions of their quality of life • Identify those areas that require improvement, or where we need to improve outcomes for particular groups of people. TECHNICAL DETAILS • Results are based on interviews with 3,674 London residents aged 18+. • Interviews were carried out online via the Talk London community between 3 Oct and 5 Nov. • Interviews were not randomly sampled, but self-selecting via a number of known databases. This achieved a non-representative sample of Londoners. • The data has been weighted by age, gender and ethnicity to reflect that of the London population. • A minimum number of responses were achieved for each key demographic group to maintain a robust sample. • Where results do not sum to 100% this may be due to multiple responses, computer rounding or the exclusion of don’t knows/not stated. • The qualitative analysis of the open-ended questions 36, 37 and 38 was undertaken by SPA Future Thinking. Top level themes and sub themes are reported as a percentage of the overall base number of respondents (3,421 to all three questions). The top three sub themes are presented where available. • This is the first London Survey conducted by Talk London for City Hall. INFOGRAPHICS