Our address datasets contain all geospatial address data of United States. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in United States

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Polygons in this layer represent low food access areas: areas of the District of Columbia which are estimated to be more than a 10-minute walk from the nearest full-service grocery store. These have been merged with Census poverty data to estimate how much of the population within these areas is food insecure (below 185% of the federal poverty line in addition to living in a low food access area).Office of Planning GIS followed several steps to create this layer, including: transit analysis, to eliminate areas of the District within a 10-minute walk of a grocery store; non-residential analysis, to eliminate areas of the District which do not contain residents and cannot classify as low food access areas (such as parks and the National Mall); and Census tract division, to estimate population and poverty rates within the newly created polygon boundaries.Fields contained in this layer include:Intermediary calculation fields for the aforementioned analysis, and:PartPop2: The total population estimated to live within the low food access area polygon (derived from Census tract population, assuming even distribution across the polygon after removing non-residential areas, followed by the removal of population living within a grocery store radius.)PrtOver185: The portion of PartPop2 which is estimated to have household income above 185% of the federal poverty line (the food secure population)PrtUnd185: The portion of PartPop2 which is estimated to have household income below 185% of the federal poverty line (the food insecure population)PercentUnd185: A calculated field showing PrtUnd185 as a percent of PartPop2. This is the percent of the population in the polygon which is food insecure (both living in a low food access area and below 185% of the federal poverty line).Note that the polygon representing Joint Base Anacostia-Bolling was removed from this analysis. While technically classifying as a low food access area based on the OP Grocery Stores layer (since the JBAB Commissary, which only serves military members, is not included in that layer), it is recognized that those who do live on the base have access to the commissary for grocery needs.Last updated November 2017.

Our address datasets contain all geospatial address data of The Netherlands. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in The Netherlands

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Abstract

The Sahel Women Empowerment and Demographic Dividend (P150080) project is a regional project aiming to accelerate the demographic transition by addressing both supply- and demand-side constraints to family planning and reproductive and sexual health. To achieve its objective, the project targets adolescent girls and young women mainly between the ages of 8 and 24, who are vulnerable to early marriage, teenage pregnancy, and early school drop-out. The project targeted 9 countries of the Sahel and Western Africa (Benin, Burkina Faso, Cameroon, Chad, Côte d’Ivoire, Guinea, Mali, Mauritania, and Niger) and is expanding in other African countries. The SWEDD is structured into three main components: component 1 seeks to generate demand for reproductive, maternal, neonatal, child health and nutrition products and services; component 2 seeks to improve supply of these products and qualified personnel; and component 3 seeks to strengthen national capacity and policy dialogue.

The World Bank Africa Gender Innovation Lab and its partners are conducting rigorous impact evaluations of key interventions under component 1 to assess their effects on child marriage, fertility, and adolescent girls and young women’s empowerment. The interventions were a set of activities targeting adolescent girls and their communities, designed in collaboration with the government of Côte d’Ivoire. These were (i) safe spaces to empower girls through the provision of life skills and SRH education; (ii) support to income-generating activities (IGA) with the provision of grants and entrepreneurship training; (iii) husbands’ and future husbands’ clubs, providing boys of the community with life skills and SRH education; and finally (iv) community sensitization by religious and village leaders. The latter two have the objective to change restrictive social norms and create an enabling environment for girls’ empowerment.

These data represent the first round of data collection (baseline) for the impact evaluation.

Geographic coverage

Mali, Regions of Kayes, Ségou and Sikasso

Analysis unit

Households, individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The baseline sample comprises 8776 households and 7463 girls living in the regions of Kayes, Sikasso and Ségou in Mali. To define the sample, we partnered with INSTAT Mali. At first, INSTAT conducted a census of the population living in the areas around the 49 schools selected by the education focal point that will all benefit from the SWEDD program. Therefore, census activities were concentrated in 287 villages located within a radius of 10/15km around these schools. Eventually, 10 villages had to be dropped due to security reasons. Keeping with the eligibility criteria of surveying villages where there were at least 10 households with a girl aged between 12 and 24 years old, 270 villages were eventually sampled. Households were surveyed before randomization into groups assigned to receive the SWEDD program.

The objective of the baseline survey was to build a comprehensive dataset, which would serve as a reference point for the entire sample, before treatment and control assignment and program implementation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Health and family planning 5. Knowledge of HIV/AIDS 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credit 10. Personal relationships and social networks 11. Committee members and community participation

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to an eligible pre-selected girl within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The questionnaires were written in French, translated into Bambara, and programmed on tablets in French using the CAPI program.

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

Our model is a full-annual-cycle population model {hostetler2015full} that tracks groups of bat surviving through four seasons: breeding season/summer, fall migration, non-breeding/winter, and spring migration. Our state variables are groups of bats that use a specific maternity colony/breeding site and hibernaculum/non-breeding site. Bats are also accounted for by life stages (juveniles/first-year breeders versus adults) and seasonal habitats (breeding versus non-breeding) during each year, This leads to four states variable (here depicted in vector notation): the population of juveniles during the non-breeding season, the population of adults during the non-breeding season, the population of juveniles during the breeding season, and the population of adults during the breeding season, Each vector's elements depict a specific migratory pathway, e.g., is comprised of elements, {non-breeding sites}, {breeding sites}The variables may be summed by either breeding site or non-breeding site to calculate the total population using a specific geographic location. Within our code, we account for this using an index column for breeding sites and an index column for non-breeding sides within the data table. Our choice of state variables caused the time step (i.e. (t)) to be 1 year. However, we recorded the population of each group during the breeding and non-breeding season as an artifact of our state-variable choice. We choose these state variables partially for their biological information and partially to simplify programming. We ran our simulation for 30 years because the USFWS currently issues Indiana Bat take permits for 30 years. Our model covers the range of the Indiana Bat, which is approximately the eastern half of the contiguous United States (Figure \ref{fig:BatInput}). The boundaries of our range was based upon the United States boundary, the NatureServe Range map, and observations of the species. The maximum migration distance was 500-km, which was based upon field observations reported in the literature \citep{gardner2002seasonal, winhold2006aspects}. The landscape was covered with approximately 33,000, 6475-ha grid cells and the grid size was based upon management considerations. The U.S.~Fish and Wildlife Service considers a 2.5 mile radius around a known maternity colony to be its summer habitat range and all of the hibernaculum within a 2.5 miles radius to be a single management unit. Hence the choice of 5-by-5 square grids (25 miles(^2) or 6475 ha). Each group of bats within the model has a summer and winter grid cell as well as a pathway connecting the cells. It is possible for a group to be in the cell for both seasons, but improbable for females (which we modeled). The straight line between summer and winter cells were buffered with different distances (1-km, 2-km, 10-km, 20-km, 100-km, and 200-km) as part of the turbine sensitivity and uncertainty analysis. We dropped the largest two buffer sizes during the model development processes because they were biologically unrealistic and including them caused all populations to go extinct all of the time. Note a 1-km buffer would be a 2-km wide path. An example of two pathways are included in Figure \ref{fig:BatPath}. The buffers accounts for bats not migrating in a straight line. If we had precise locations for all summer maternity colonies, other approaches such as Circuitscape \citep{hanks2013circuit} could have been used to model migration routes and this would have reduced migration uncertainty.

BackgroundAssessing exposure to infections in early childhood is of interest in many epidemiological investigations. Because exposure to infections is difficult to measure directly, epidemiological studies have used surrogate measures available from routine data such as birth order and population density. However, the association between population density and exposure to infections is unclear. We assessed whether neighbourhood child population density is associated with respiratory infections in infants.MethodsWith the Basel-Bern lung infant development study (BILD), a prospective Swiss cohort study of healthy neonates, respiratory symptoms and infections were assessed by weekly telephone interviews with the mother throughout the first year of life. Using population census data, we calculated neighbourhood child density as the number of children < 16 years of age living within a 250 m radius around the residence of each child. We used negative binomial regression models to assess associations between neighbourhood child density and the number of weeks with respiratory infections and adjusted for potential confounders including the number of older siblings, day-care attendance and duration of breastfeeding. We investigated possible interactions between neighbourhood child population density and older siblings assuming that older siblings mix with other children in the neighbourhood.ResultsThe analyses included 487 infants. We found no evidence of an association between quintiles of neighbourhood child density and number of respiratory symptoms (p = 0.59, incidence rate ratios comparing highest to lowest quintile: 1.15, 95%-confidence interval: 0.90–1.47). There was no evidence of interaction with older siblings (p = 0.44). Results were similar in crude and in fully adjusted models.ConclusionsOur study suggests that in Switzerland neighbourhood child density is a poor proxy for exposure to infections in infancy.

This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Settlements boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 2.3, the population usually resident in Ireland by usual residence 1 year before Census Day. Attributes include population breakdown by usual residence (e.g. same address, outside Ireland). Census 2016 theme 2 represents Migration, Ethnicity and Religion. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO. In order to distinguish between the urban and rural population for census analysis, the boundaries of distinct settlements need to be defined. This requires the creation of suburbs and extensions to existing cities and legal towns as well as delineating boundaries for settlements which are not legally defined (called Census towns). From 1971 to 2006, Census towns were defined as a cluster of fifty or more occupied dwellings where, within a radius of 800 metres there was a nucleus of thirty occupied dwellings (on both sides of a road, or twenty on one side of a road), along with a clearly defined urban centre e.g. a shop, a school, a place of worship or a community centre. Census town boundaries where extended over time where there was an occupied dwelling within 200 metres of the existing boundary. To avoid the agglomeration of adjacent towns caused by the inclusion of low density one off dwellings on the approach routes to towns, the 2011 criteria were tightened, in line with UN criteria. In Census 2011 a new Census town was defined as being a cluster with a minimum of 50 occupied dwellings, with a maximum distance between any dwelling and the building closest to it of 100 metres, and where there was evidence of an urban centre (shop, school etc). The proximity criteria for extending existing 2006 Census town boundaries was also amended to include all occupied dwellings within 100 metres of an existing building. Other information based on Tailte Éireann mapping and orthogonal photography was taken into account when extending boundaries. Boundary extensions were generally made to include the land parcel on which a dwelling was built or using other physical features such as roads, paths etc. Extensions to the environs and suburbs of legal towns and cities were also constructed using the 100 metre proximity rule applied to Census towns. For census reports, urban settlements are towns with a population of 1,500 or more, while settlements with a population of less than 1,500 are classified as rural.

This dataset includes all data pertaining to a long-term demographic study of Cladonia perforata (perforate reindeer lichen), a federally endangered lichen endemic to Florida, including fine-scale cover, occupancy, and population area data, conducted by the Archbold Biological Station Plant Ecology Program. This includes 13 years of data (2011-2024) from nine subpopulation (including seven at Archbold Biological Station, and two at the Lake Wales Ridge Wildlife and Environmental Area, Royce Unit), all located in rosemary scrub habitat within the Lake Wales Ridge metapopulation. This study sought to characterize the fire ecology and long-term population trends for the species, and thus also includes data on prescribed burn severity and time since fire. Data were collected using a stratified random plot design, with occupancy plots (presence/absence within 1.5 meter radius) throughout the subpopulation and a subset of these designated as cover plots only, with this cover data collected as point intercept hits within a 48x48cm area. Cover data also includes microhabitat data – canopy cover in densiometer reading and dominant ground cover. Cover and occupancy data were taken every 3 years for each subpopulation (subpopulations were on different yearly schedules). Subpopulation area was mapped using a submeter GPS unit every 6 years. Subpopulations were resampled for all metrics as soon as possible following a fire, and the sampling schedule was then reset.

Our address datasets contain all geospatial address data of Iceland. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Iceland

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Our address datasets contain all geospatial address data of Norway. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Norway

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in France, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: · Python Package: https://github.com/datasciencecampus/transport-network-performance · Docker Image: https://github.com/datasciencecampus/transport-performance-docker Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: · Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): · Does not include effects due to delays (such as congestion and diversions). · Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning. Transport Network Routing: · “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details. How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

Small planets (Rp<=4R_{Earth}) are divided into rocky super-Earths and gaseous sub-Neptunes separated by a radius gap, but the mechanisms that produce these distinct planet populations remain unclear. Binary stars are the only main-sequence systems with an observable record of the protoplanetary disk lifetime and mass reservoir, and the demographics of planets in binaries may provide insights into planet formation and evolution. To investigate the radius distribution of planets in binary star systems, we observed 207 binary systems hosting 283 confirmed and candidate transiting planets detected by the Kepler mission, then recharacterized the planets while accounting for the observational biases introduced by the secondary star. We found that the population of planets in close binaries ({rho}300au) or single stars. In contrast to planets around single stars, planets in close binaries appear to have a unimodal radius distribution with a peak near the expected super-Earth peak of Rp ~1.3R{Earth}_ and a suppressed population of sub-Neptunes. We conclude that we are observing the direct impact of a reduced disk lifetime, smaller mass reservoir, and possible altered distribution of solids reducing the sub-Neptune formation efficiency. Our results demonstrate the power of binary stars as a laboratory for exploring planet formation and as a controlled experiment of the impact of varied initial conditions on mature planet populations.

CC: Carrying capacity.The carrying capacity of pods was calculated by fitting the maximum number of pods, including their radius distance, into the convex hull area encompassing the entire population. The carrying capacity of whales is the number of pods multiplied by the mean pod size.

This feature layer was was created using Census 2016 data produced by the Central Statistics Office (CSO) and Settlements boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 2.1, the population usually resident in Ireland by place of birth and nationality. Attributes include population breakdown by place of birth and nationality (e.g. UK Birthplace, Poland Nationality). Census 2016 theme 2 represents Migration, Ethnicity and Religion. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO.In order to distinguish between the urban and rural population for census analysis, the boundaries of distinct settlements need to be defined. This requires the creation of suburbs and extensions to existing cities and legal towns as well as delineating boundaries for settlements which are not legally defined (called Census towns). From 1971 to 2006, Census towns were defined as a cluster of fifty or more occupied dwellings where, within a radius of 800 metres there was a nucleus of thirty occupied dwellings (on both sides of a road, or twenty on one side of a road), along with a clearly defined urban centre e.g. a shop, a school, a place of worship or a community centre. Census town boundaries where extended over time where there was an occupied dwelling within 200 metres of the existing boundary. To avoid the agglomeration of adjacent towns caused by the inclusion of low density one off dwellings on the approach routes to towns, the 2011 criteria were tightened, in line with UN criteria. In Census 2011 a new Census town was defined as being a cluster with a minimum of 50 occupied dwellings, with a maximum distance between any dwelling and the building closest to it of 100 metres, and where there was evidence of an urban centre (shop, school etc). The proximity criteria for extending existing 2006 Census town boundaries was also amended to include all occupied dwellings within 100 metres of an existing building. Other information based on Tailte Éireann mapping and orthogonal photography was taken into account when extending boundaries. Boundary extensions were generally made to include the land parcel on which a dwelling was built or using other physical features such as roads, paths etc. Extensions to the environs and suburbs of legal towns and cities were also constructed using the 100 metre proximity rule applied to Census towns. For census reports, urban settlements are towns with a population of 1,500 or more, while settlements with a population of less than 1,500 are classified as rural.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in Great Britain, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: · Python Package: https://github.com/datasciencecampus/transport-network-performance · Docker Image: https://github.com/datasciencecampus/transport-performance-docker Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: · Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): · Does not include effects due to delays (such as congestion and diversions). · Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning. Transport Network Routing: · “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details. How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

This method returns Crystal Roof’s proprietary crime rate map overlays. These overlays are taken directly from our main Crime Rates map.

The overlays are circular PNG images, available in 1,000, 1,500, or 2,000-meter radii.

You can request overlays showing either total crime rates or crime rates for a specific crime type (controlled by the variant parameter).

About Crystal Roof’s Crime Rates Map

• Crime rates are calculated for small geographic areas known as Lower Layer Super Output Areas (LSOAs).
• Rates are calculated per 1,000 residents, using population data from the 2021 Census.
• Crime levels are grouped into 10 categories using our proprietary algorithm, which balances both the distribution of crime values and the number of areas with similar rates. These categories are not standard deciles.
• All figures represent annual data (covering the most recent 12 months).
• The dataset is updated monthly, with a three-month lag between the current date and the most recent available data.

Integration examples

• Google Maps integration example
• Leaflet integration example

Local Perspective is a configurable app template that provides information based on a user defined location. A buffered distance around the user defined location is used to return features from features layers in the map. Use CasesDisplays the amenities, demographic, lifestyle, and weather information within a buffer of an address or point. This is a good choice for showing data that describes resources such as restaurants, parking lots, theaters, and museums available near an address.Provides directions to features within a radius of a user-selected point. This is a good choice when you want to allow users to find directions to a point of interest in a local area or for routing to destinations in more than one feature layer.Compares layers within a buffered distance of an address or point. The collection of layers can be scrolled through to gain an understanding of the variation between the layers within the current buffer. This is a good choice for showing data comparing availability of resources like schools, police stations, fire stations, and hospitals, or for comparing different types of crimes committed near an address.Configurable OptionsLocal Perspective can be used to show local amenities and can be configured using the following options:Choose a title, logo image, and color scheme.Enable US demographics, US lifestyles, and live weather.Enable routing directions as well as opt to store organization subscription credentials for public use of the app (users cannot see credentials).Set the default and maximum distance values for the buffered area, as well as distance units.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis application requires a feature layer to take full advantage of its capabilities. For more information, see the Layers help topic for more details.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Settlements boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 4.1, families, family members and children in families. Attributes include family size by number of families, number of persons and number of children (e.g. 2 persons (No. of families), 3 persons (No. of persons), 5 persons (No. of children)). Census 2016 theme 4 represents Families. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO. In order to distinguish between the urban and rural population for census analysis, the boundaries of distinct settlements need to be defined. This requires the creation of suburbs and extensions to existing cities and legal towns as well as delineating boundaries for settlements which are not legally defined (called Census towns). From 1971 to 2006, Census towns were defined as a cluster of fifty or more occupied dwellings where, within a radius of 800 metres there was a nucleus of thirty occupied dwellings (on both sides of a road, or twenty on one side of a road), along with a clearly defined urban centre e.g. a shop, a school, a place of worship or a community centre. Census town boundaries where extended over time where there was an occupied dwelling within 200 metres of the existing boundary. To avoid the agglomeration of adjacent towns caused by the inclusion of low density one off dwellings on the approach routes to towns, the 2011 criteria were tightened, in line with UN criteria. In Census 2011 a new Census town was defined as being a cluster with a minimum of 50 occupied dwellings, with a maximum distance between any dwelling and the building closest to it of 100 metres, and where there was evidence of an urban centre (shop, school etc). The proximity criteria for extending existing 2006 Census town boundaries was also amended to include all occupied dwellings within 100 metres of an existing building. Other information based on Tailte Éireann mapping and orthogonal photography was taken into account when extending boundaries. Boundary extensions were generally made to include the land parcel on which a dwelling was built or using other physical features such as roads, paths etc. Extensions to the environs and suburbs of legal towns and cities were also constructed using the 100 metre proximity rule applied to Census towns. For census reports, urban settlements are towns with a population of 1,500 or more, while settlements with a population of less than 1,500 are classified as rural.

This map shows the population density and total population in the United States in 2010. This is shown by state, county, tract, and block group. The color shows the population per square mile (population density), while the size of each feature shows the total population living there. This is a valuable way to represent population by understanding the quantity and density of the people living there. Areas with high population density are more tightly packed, while low population density means the population is more spread out.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.