Circles proportional to the 2011 population located in the centre of the IRIS of Ile-de-France and associating variables from the 2011 population census. Confined to the limits of their original IRISs, these abstract cartographic objects visually reflect information more rooted in the reality of their demography and can be used as a medium for thematic analysis of other information derived from the data awarded to this population and expressed in rates.

This map shows youth population in Dallas, Texas by census block group areas. The size shows a count of youth population, and the transparency represents a ratio of youth population in relation to the total population. Bigger circles show areas with a large amount of youth. Circles with less transparency show areas where the youth population makes up a larger amount of the total population.Boundaries: Living Atlas Census Block AreasData: 2016 USA Esri Demographics

Mali Circle Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2023 boundaries are provided by Michael Bauer Research GmbH. These were published in October 2023. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country Region

This map shows demographic and income data in Detroit. Assuming an assignment where the poverty fighting charity I work for would like to alleviate suffering among impoverished children in Detroit. Detroit is a Michigan city that always ranks among America's poorest urban centers. Orange circles have below average median household income, the darker shades indicate households with a very low income-close to poverty level. The size of the circles: larger circles indicate a greater number of children in the area.What stands out is the obvioud pattern of low-income households in the city center combined with areas of high child population. This pattern helps answer where in Detroit our charity will focus its resources to help children living in poverty-in places shown on the map where there is a cluster of several large dark Orange circles like Dearborn and Pontiac (for example). The charity may and will offer free after school care and/Or but not limited to breakfast programs.

This map shows the total population in the United States in 2010. This is shown by state, county, tract, and block group. The size of the circles represent the amount of people living within an area. The map shows this pattern for states, counties, tracts, and block groups. There is increasing geographic detail as you zoom in, and only one geography is configured to show at any time. The data source is the US Census Bureau, and the vintage is 2010. The original service and data metadata can be found here.

Contained within the 1st Edition (1906) of the Atlas of Canada is a plate that shows two maps. The maps show the density of population per square mile for every township the Maritime Provinces, Quebec and Ontario, circa 1901. Cities and towns of 5000 inhabitants or more are shown as black dots. The size of the circle is proportionate to the population. The map uses eight classes, seven of which are shades of brown, more densely populated portions are shown in the darker tints. Numbers make it clear which class is being shown in any one township.

Contained within the 1st Edition (1906) of the Atlas of Canada is a plate that shows two maps. The maps show the density of population per square mile for every township in Manitoba, Saskatchewan, British Columbia, Alberta, circa 1901. The statistics from the 1901 census are used, yet the population of Saskatchewan and Alberta is shown as confined within the vicinity of the railways, this is because the railways have been brought up to date of publication, 1906. Cities and towns of 5000 inhabitants or more are shown as black dots. The size of the circle is proportionate to the population. The map uses eight classes, seven of which are shades of brown, more densely populated portions are shown in the darker tints. Numbers make it clear which class is being shown in any one township. Major railway systems are shown. The map also displays the rectangular survey system which records the land that is available to the public. This grid like system is divided into sections, townships, range, and meridian from mid-Manitoba to Alberta.

Urban density and frequent transit including population by block group, jobs from the Census Longitudinal Employer-Household Dynamics (LEHD) survey, light rail with half-mile buffer and frequent transit stops with quarter-mile buffer.This map displays the interplay between frequent transit, population and employment density in the region. Navigate the map to see how population density and employment census tract density overlaps with walking sheds of frequent transit and light rail. Brown block groups identify areas with higher population density, and blue circle sizes represent employment density in a given census tract. Click on a block group or census tract for additional details.

This map shows the percent change in family households in the United States from 2000 to 2010. The map is available for states, counties, tracts, and block groups.The color of the features highlight an increase or decrease over time. Areas in red experienced a decrease in family households, while areas in blue experienced an increase. Areas in white had a minimal change over time.The size of the symbol represents the total amount of family households in an area. Larger circles have more family households, and smaller circles have less.The map shows this pattern for states, counties, tracts, and block groups. There is increasing geographic detail as you zoom in, and only one geography is configured to show at any time. The data source is the US Census Bureau, and the vintage is 2010. The original service and data metadata can be found here.

This map shows demographic and income data in Round Rock. What stands out is a pattern of low-income households in the central & downtown area combined with areas of high child population. This pattern helps answer where in Round Rock our charity should focus its resources to help children living in poverty.

Contains all data (in shapefile form) and Arcmap documents (.mxd) for the map "Native Population, Economies and Movement, ca 1820" from the unit Native Canada, ca 1820 in the Historical Atlas of Canada Online Learning Project. From the Historical Atlas Online Learning Project Website: "On this map, population is shown by circles, colour coded by linguistic groups, and scaled according to estimated population size. This gives the general picture of population distribution at this time. These were greatly affected by disease and migration resulting from European contact. Information on patterns of population movement is also depicted - both traditional seasonal movements, and large scale migration due to warfare or other disruptive influences. Native economic zones are broadly characterized by how traditional economies had been affected by European contact. These range from those severely disrupted by European settlement and resource depletion, to traditional economies relatively untouched by contact. Information on European commercial activity is also shown, to put the former into context: both trading posts, and routes of maritime commerce are depicted." For documentation explaining the data, see file: HACOLP_Nat_Canada_ca_1820_Distribution_Info_20161207.pdf To view the full maps, download all the files preserving file hierarchy, and unzip the shapefiles to the folder HACOLP_Nat_Canada_1820/GISData/HACOLP/Data/UNIT_14/Shapefiles/

Abstract

Following an outbreak of violence on 25 August 2017 in Rakhine State, Myanmar, a new massive influx of Rohingya refugees to Cox’s Bazar, Bangladesh started in late August 2017. Most of the Rohingya refugees settled in Ukhia and Teknaf Upazilas of Cox’s Bazar, a district bordering Myanmar identified as the main entry area for border crossings.

This dataset presents the result of the NPM Round 12 Site Assessment exercise, which collected information related to the Rohingya refugee population distribution and needs during the months of August, September and October 2018.

• The data collection for NPM baseline survey was conducted between 9 August and 4 September 2018: it provides an update about the population distribution and movements.
• The data collection for NPM Site Assessment survey was conducted between 23 September and 10 October 2018: in addition to an update about the population figures, this includes a multi-sectoral needs assessment.

The full maps and GIS packages by camp produced based on NPM Baseline and Site Assessment 12 are available at the links below:

• Please click here to access the data by camp as of September/October 2018.
• Please click here to access the data by camp as of August 2018.

Rohingya refugee population distribution by para in Teknaf upazila. - Please click here.

Geographic coverage

Bangladesh

Kind of data

Observation data/ratings [obs]

Abstract

The Gauteng City-Region Observatory (GCRO) (based at the University of Johannesburg (UJ)) in partnership with the Gauteng Provincial Government, contracted Development Research Africa (DRA) to conduct an integrated Quality of Life/Customer Satisfaction Survey in the Gauteng City-Region (GCR). The objective of the GCRO is to assist the Gauteng Government to build Gauteng as an integrated and globally competitive region, where the economic activities of different parts of the province complement each other in consolidating Gauteng as an economic hub of Africa, and an internationally recognised global city-region. The this end, the main aim of the survey, conducted from July to October 2009, was to inform the GCRO and the Provincial Government, as well as other role-players about the perceived state of the municipalities within the GCR footprint about the quality of life of their inhabitants.

Geographic coverage

The Quality of Life Survey covers the whole of Gauteng and also areas with GCR 'footprints' in the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Analysis unit

Households and individuals

Universe

The Gauteng City-Region Observatory Quality of Life Survey 2009 covered all household residents of Gauteng and selected areas of the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Kind of data

Qualitative and quantitative data

Sampling procedure

For the purpose of this study, multi-stage cluster sampling was used as no sampling frame containing all members in the universe or population exists. The sample was drawn in stages, with wards being selected at the first stage, dwelling units within the wards being selected in the second stage and respondents selected at the third stage.

Phase 1 The wards formed the primary sampling units (PSUs). A random starting point(s) was used as a method to select the dwelling units to be surveyed. A total number of 602 wards in 4 provinces (Gauteng 448 wards), (Mpumalanga 72 wards), (North-West 70 wards) and (Free State 12 wards) were completed. A total of 6639 interviews were completed in these wards.

Phase 2 During the second phase, the field teams were required to complete a certain number of interviews, depending on the population size of that particular ward. The teams had to complete for an example in ward X 3 interviews and in ward Y they had to complete 33 interviews. This meant that the field teams had different target number of interviews that they needed to complete in all the pre-selected wards. Ward maps were obtained before fieldwork commenced, and random starting points were identified, marked and numbered on the map. This allowed for the random selection of one (if more than one existed) starting point. The field managers concerned will firstly identify where the starting point(s) is/are on the ground. Oncethat has been established he/she will from the starting point count 20 households from the starting point moving to his/her left. The 20th household that he/she has selected was the household were the interviews was supposed to take place Thereafter, the next 20th household was selected and approached until the target number of interviews was obtained.

The following process of household selection was adhered to: From the starting point 20 houses were counted in a ward. However, if there were:

• 1-5 target number of interviews to be completed in a ward; 01 starting point was used; • 6-10 target number of interviews to be completed in a ward; 02 starting points were used; • 11-15 target number of interviews to be completed in the ward; 03 starting points were used; • 16-20 target number of interviews to be completed in the ward; 04 starting points were used; • 21-25 target number of interviews to be completed in the ward; 05 starting points were used; and • 25 and above target number of interviews to be completed in a ward; 06 starting points were used

In the case of a household refusal or if a selected respondent was mentally disabled, the household was immediately substituted with the household on the left. If still there was no interview completed then another substitution, going to the right of the originally selected household, was done. In case of non-contact whereby there was no-one home after two visits at two different times (afternoon and evenings) on the same day, the same substitution method was followed. Therefore, at least two-revisits at different times were done in cases where selected dwelling units, households or individuals were not at home i.e. non-contact. However, in some cases households visited after 19:00 on the day were substituted as agreed to in order to ensure that all the target number of households would be completed in the allocated time per ward.

Phase 3 For the purpose of this study, one randomly selected household respondent was selected per household. All household members qualified if they met the following criteria: • Resident(s) of the household irrespective of nationality but excluding nonresidents and visitors; and • 18 years of age or older • In the event of a child headed household (all household members are under 18 years old), the oldest child was assumed to be the head of household, and should be interviewed If more than one eligible person was found per dwelling unit, the ideal and most practical and accurate method of random selection of an individual was the use of a KISH grid. One individual per household was selected using the KISH grid after a comprehensive listing exercise was completed of all eligible individuals at the dwelling unit. Once the respondent had been selected the fieldworker will follow up only that person per household. If selected, substitutions could not be made where there were refusals or non-contact over a period of a day after two or more re-visits on the same day.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Gauteng City-Region Observatory and Data Research Africa (DRA) developed the quantitative evaluation tool for the survey. DRA reformatted the pre-pilot questionnaire and provided input into the layout and flow as well as question structure to ensure accurate data capturing. DRA field managers piloted the questionnaire with 30 interviews with individuals from households with different demographic characteristics . The Gauteng City-Region Observatory Quality of Life Survey 2009 questionnaire collected data on demographic details of the enumerated population (population group, gender, age, language) and on housing (dwelling type, tenure, satisfaction with dwelling, perceived quality of housing and housing allocation) as well as household services (water, sanitation, refuse, energy sources). Questions included those on migration, health (including disability), education and employment (including employment sector). Questions on community services and amenities were included, and questions on transport, leisure activities and safety and crime. Financial data was collected (including on debts, income, and social grants) and data on household assets. Finally, data on public participation and governance was also collected, and data on the perceived personal wellbeing and quality of life of respondents.

Layer Information: -Weather Events: Convection in Las Cruces and the Significant Flood Event of 2006 in El Paso are displayed. Clicking on the icon can give information of the phenomena or event. - Major Cities: Major cities and populations are mapped. The bigger the circle the bigger the population of that city. - Observation/ Data collection sites: This layer contains the location of where atmospheric soundings launched from the surface and where in-stu surface observations are gathered. The later includes Weather, Ocean, Lake, River, Water Quality, and Air Quality. - Köppen-Geiger Climate Divisions: General temperatures, precipitation, and latitude define these climate classes. The World Meteorological Organization (WMO) defined a classic climate record to be 30 years, so this current map is based of off the average weather an area has experienced from 1981 to 2010. New normals will be calculated in 2021. To read more click here. -National Weather Service Forecast Offices (WFO): Locations of the continental United States weather forecast offices, including office contact information. App Information: How to use it: Zooming in and out will turn on and off different layers. A zoomed out map will show the global Koppen climate classification. Zooming in will turn off the climate layer, while enabling the National Weather Service (NWS) Offices, Weather Events and other layers. Clicking on a Weather event or NWS office in the map will bring up a window with more information. - The legends and layers are shown by toggling the menus on via the icons at the bottom of the map.

This map highlights two overlapping patterns in the United States:High average wildfire hazard potentialHigh counts of minority population (largest circles on the map)The pattern is shown by states, counties, ZIP Codes, tracts, block groups, and optionally 50km hex bins. It helps us answer questions such as "where are minority populations also at a high risk for wildfires?"The Living Atlas layer in this map contains wildfire hazard potential (WHP) data for the conterminous United States aggregated from states to block groups and 50 km hex bins then enriched with demographic data. The data is from the USDA Forest Service Fire Modeling Institute providing an index of WHP at a 270 meter resolution. Wildfire hazard potential provides information on the relative potential for wildfire that would be difficult for fire crews to contain. "Areas with higher wildfire potential values represent fuels with a higher likelihood of experiencing high-intensity fire with torching, crowning, and other forms of extreme fire behavior." - Fire Modeling Institute. A score of 5 is very high risk and a score between 0-1 is likely non-burnable area such as water or asphalt. "On its own, WHP is not an explicit map of wildfire threat or risk, but when paired with spatial data depicting highly valued resources and assets such as communities, structures, or powerlines, it can approximate relative wildfire risk to those resources and assets. WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic planning and fuels management."Each layer has been enriched with 2020 Esri demographic attributes to better approximate wildfire hazard risk. A hosted imagery layer of this data is available in ArcGIS Living Atlas for additional analysis.Data notes:Zonal Statistics as Table were run against a local copy of the WHP data using US standard geographies as the feature zone input for the analysis. Geographies included are: State, County, Congressional District, ZIP Code, Tract, and Block Group. Statistical tables were joined to geographies. To learn more about zonal statistics, view the documentation here. 50 km hex bins were created using Generate Tessellation and then joined to zonal statistics as described above (step 1).Data was enriched with 2020 Esri Demographics. Attributes include population, households & housing units, growth rate, and calculated variables such as population change over time. To create the population-weighted attributes on the state, congressional district, and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average WHP were multiplied.The hex bins were converted into centroids and summarized within the state, congressional district, and county boundaries.The summation of these values were then divided by the total population of each respective geography.

This map contains demographic variables for block groups in the Boston area. The map shows a comparison of two variables: per capita income growth from 2015-2020 and unemployment. Size and color are used to show the two variables.The size of the circle represents the unemployed population over the age of 16, so the largest circles show the areas with the most unemployment. The colors showcase a range of personal income growth from 2015 to 2020. Green areas have the least projected growth, and yellow areas have the highest projected income growth.The data comes from Esri's ArcGIS Online data enrichment using the Living Atlas Block Group Analysis layer. The vintage of the data is 2015.

OverviewThis feature layer shows population change compared to pre-crisis baseline in Pakistan on a daily basis for all level 2 administrative units of Pakistan. The layer has time enabled to show the change from 2022-08-13 to the latest date when population change data harvested by Data for Good at Meta is available.Population maps provided by Data for Good at Meta are generated based on users of Facebook. For more information about the disaster population maps provided by Data for Good at Meta, please refer to this link.Default data visualizationA divergent color ramp was employed to create a choropleth map for % population change compared to the pre-crisis baseline. The size of pre-crisis baseline is visualized using circles in different sizes. Each circle represents one Level 3 administrative unit in Pakistan.This feature layer contains the following metrics for mapping and analysis:Baseline population - an estimated number of Facebook users during the pre-crisis period. It is calculated as an average of 90 days before the crisis (in this case, 2022-08-14 was used as the onset of crisis).Crisis population - an estimated number of Facebook users during the crisis. Original data are provided every 8 hours.Difference in population - the difference between crisis population and the baseline populationPercent change in population - the percentage of population change from baseline to a given date during the crisisZ-score - a unitless normalized measurement to quantify the population change from baselineDate - Date of data acquisition. Original data are provided three times a day (8-hour interval). We calculated a daily average using all three timestamps available for each day. Users can filter by Date to create a subset showing the population change on a selected dateMethod of data preparationRemove data points without a valid baseline population or percent change in populationCalculate daily average using the three timestamps available for each dayAggregate the original point data to Level 3 administrative units of PakistanAppend all daily average level 3 administrative units data to a single file to enable time option of the layer