The Population Centre Boundary Files portray the population centre boundaries for which census data are disseminated. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

This table presents the 2021 population counts for census metropolitan areas and census agglomerations, and their population centres and rural areas.

Canada's largest metropolitan area is Toronto, in Ontario. In 2022. Over 6.6 million people were living in the Toronto metropolitan area. Montréal, in Quebec, followed with about 4.4 million inhabitants, while Vancouver, in Britsh Columbia, counted 2.8 million people as of 2022.

This table contains 5 series, with data for years 1871 - 1971 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (1 items: Canada ...) Population (5 items: Total population; Population in incorporated centres of 30,000 to 99,999 persons; Population in incorporated centres of 5,000 to 29,999 persons; Population in incorporated centres of 100,000 persons and over ...).

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

Distribution of the household population by Children's body mass index (BMI) - Center for Disease Control (CDC) classification system, by sex and age group

This statistic shows the population distribution of British Columbia, Canada in 2016, by urban/rural type. In 2016, 61.8 percent of British Columbia's population lived in large urban population centers.

Canada CA: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 1.441 % in 2015. This records a decrease from the previous number of 1.483 % for 2000. Canada CA: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 1.483 % from Dec 1990 (Median) to 2015, with 3 observations. The data reached an all-time high of 1.518 % in 1990 and a record low of 1.441 % in 2015. Canada CA: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Population below 5m is the percentage of the total population living in areas where the elevation is 5 meters or less.;Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. 2021. Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/d1x1-d702.;Weighted average;

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)

#Description of the distribution areas of local populations of woodland caribou, forest ecotype, in Quebec The data represent the ranges of the 13 local populations and two areas of knowledge acquisition of woodland caribou, a forest ecotype in Quebec (hereinafter forest caribou). The file contains the polygons and the name assigned to each population or knowledge acquisition sector, the period covered by the telemetry data used during the delimitation exercise, and the date these boundaries were last updated. The information contained in the file of occurrences of species in a precarious situation of the Quebec Natural Heritage Data Center (CDPNQ) is also present in the file (CDPNQ occurrence number, French, English and scientific name of the species, the type of occurrence, the rank of precariousness [rank S] and the status under the Act Respecting Threatened or Vulnerable Species, see MELCCFP 2023 for a description of these fields.). ## #Contexte The Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks (hereinafter MELCCFP) is responsible for the monitoring and management of forest caribou in Quebec (Government of Quebec, 2021 a). In 2017-2018, the MELCCFP improved its monitoring activities in order to obtain an accurate and up-to-date portrait of the situation of the various populations on its territory (see Literature review on the factors involved in the decline of caribou populations in Quebec, Government of Quebec, 2021 b). The local population has been identified as the appropriate monitoring and management scale for this species since each population may face different threats depending on the habitat and socio-economic context in which it is found (Environment Canada, 2008). It is within this framework that the work to identify and delimit the distribution areas of forest caribou populations carried out in 2021-2022 is part of. The local population is defined as a group of caribou occupying a defined territory that is spatially distinct from the territories occupied by other caribou groups. The dynamics of the local population are determined primarily by local factors influencing birth and mortality rates, rather than by the contributions or losses resulting from immigration or emigration between groups. Therefore, the ranges of local forest caribou populations in this file are defined as the geographic area where a group of individuals exposed to similar factors influencing their demographics live and which meets the needs of their life cycle over a given period of time (e.g. calving, rutting, wintering). Note that for the two knowledge acquisition sectors, Baie-James and Matamec, the number of caribou monitored and the temporal scale of monitoring do not allow to date clearly conclude that they are distinct local populations or to associate these caribou with an adjacent population. ## #Méthodologie in short The forest caribou populations in Charlevoix and Val-d'Or were excluded from the following manipulations, as their geographic isolation is sufficient to demonstrate that they are local populations distinct from other caribou groups. A minimal convex polygon was made using 100% of telemetry data from 2004-2012 and 2017-2021 for the population of Charlevoix and from 1986 to 2020 for that of Val-d'Or. For other forest caribou populations, telemetry data from collars placed on caribou by the MELCCFP and various partners (Université Laval, Université du Québec à Rimouski, Hydro-Québec, Government of Ontario and Government of Ontario and Government of Newfoundland and Labrador) were used to identify and delimit the distribution areas of local populations. The data ranges from 2001 to 2021, but the period covered by the data varies by sector of study (see field: Layer tempo for information). Forest caribou populations in Quebec were identified by assigning caribou monitored by telemetry to a population using the fuzzy classification method (c-mean fuzzy clustering; Schaefer et al., 2001; Schaefer et al., 2001; Schaefer and Wilson, 2002). This method aims to unite individuals into groups in order to maximize the distance between members of distinct groups and to minimize the distance between members of the same group. Centroids from individual home ranges were used in this analysis. The ranges were delineated by creating minimal convex polygons including 100% of caribou locations (100% MCP) assigned to a population. For the Baie-James sector, the area was delimited by removing the overlaps between the area inventoried in 2020 (Szor and Gingras, 2020) and the distribution areas of the surrounding local populations. For the Matamec sector, the area represents the non-overlap between the distribution area of the local population of the Lower North Shore and a minimal convex polygon encompassing all telemetry data for caribou in the Matamec sector and the local Lower North Shore population. The acquisition of knowledge on the distribution of caribou and the presence of distinct populations continues in the Baie-James sector (Nord-du-Québec) and the Matamec sector (Côte-Nord). The last update was made in 2021-2022. ## #Mise on guard and limitations of data use: * The ranges of forest caribou populations in Quebec in this file represent the state of knowledge on land use by caribou between 2001 and 2021. * The ranges of the Detour and Nottaway populations overlap both the province of Quebec and that of Ontario, while the ranges of the populations Caniapiscau, Lower North Shore, Lac Joseph and Joir River also straddle the province of Newfoundland and Labrador. Only the portion of the ranges of forest caribou populations in the province of Quebec is presented in the file. * Information on the ranges of local populations does not make it possible to establish with certainty that caribou is absent in territories outside the ranges. * The ranges represent areas where it is likely to find caribou belonging to the same population. They do not make it possible to identify the sectors used more or less strongly by caribou in this population or the sectors used by caribou to move within the range or from one population to another (connectivity sectors). * The ranges are subject to change, depending on the new telemetry data that will be acquired, the refinement of our local knowledge and the modifications of land use patterns by caribou. ## #Littérature Environment Canada. 2008. Scientific review for the identification of critical habitat for the boreal population of woodland caribou (Rangifer tarandus caribou) in Canada. August 2008. Ottawa: Environment Canada. 80 pp. + 192 p. appendices Government of Quebec. 2021 a. Monitoring system for forest caribou populations in Quebec and mountain caribou in Gaspésie 2020-2031: summary document, Ministry of Forests, Wildlife and Parks, Directorate of Expertise on Terrestrial Wildlife, Herpetofauna and Avifauna, 16 pp. Government of Quebec. 2021 b. Literature review on the factors involved in the decline of forest caribou populations in Quebec and mountain caribou in Gaspésie, Ministry of Forests, Wildlife and Parks, Directorate of Expertise on Terrestrial Wildlife, Herpetofauna and Avifauna, 244 pp. + 15p. appendices Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks (MELCCFP). 2023. The Quebec Natural Heritage Data Center — Information document, Government of Quebec, Quebec, 32 pp. Schaefer, J.A., Veitch, A.M., Harrington, F.H., Harrington, F.H., Brown, W.K., Theberge, J.B., & Luttich, S.N. 2001. Fuzzy structure and spatial dynamics of a declining woodland caribou population. Oecologia, 126 (4), 507—514. https://doi.org/10.1007/s004420000555 Schaefer, J.A., & Wilson, C. C. 2002. The fuzzy structure of populations. Canadian Journal of Zoology, 80 (12), 2235—2241. https://doi.org/10.1139/z02-184 Szor, G, and G. Gingras. 2020. Aerial inventory of forest caribou (Rangifer tarandus caribou) in the James Bay, Rupert and La Grande sectors, Nord-du-Québec, in winter 2020, Ministry of Forests, Wildlife and Parks, Direction de la gestion de la fauna du Nord-du-Québec, 31 p.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

In 2024, Russia had the largest population among European countries at 144.8 million people. The next largest countries in terms of their population size were Turkey at 87.5 million, Germany at 84.5 million, the United Kingdom at 69.1 million, and France at 66.5 million. Europe is also home to some of the world’s smallest countries, such as the microstates of Liechtenstein and San Marino, with populations of 39,870 and 33,581 respectively. Europe’s largest economies Germany was Europe’s largest economy in 2023, with a Gross Domestic Product of around 4.2 trillion Euros, while the UK and France are the second and third largest economies, at 3.2 trillion and 2.8 trillion euros respectively. Prior to the mid-2000s, Europe’s fourth-largest economy, Italy, had an economy that was of a similar sized to France and the UK, before diverging growth patterns saw the UK and France become far larger economies than Italy. Moscow and Istanbul the megacities of Europe Two cities on the eastern borders of Europe were Europe’s largest in 2023. The Turkish city of Istanbul, with a population of 15.8 million, and the Russian capital, Moscow, with a population of 12.7 million. Istanbul is arguably the world’s most famous transcontinental city with territory in both Europe and Asia and has been an important center for commerce and culture for over two thousand years. Paris was the third largest European city with a population of 11 million, with London being the fourth largest at 9.6 million.

Canada CA: Rural Land Area data was reported at 9,197,138.473 sq km in 2015. This records a decrease from the previous number of 9,198,346.026 sq km for 2000. Canada CA: Rural Land Area data is updated yearly, averaging 9,198,346.026 sq km from Dec 1990 (Median) to 2015, with 3 observations. The data reached an all-time high of 9,199,344.420 sq km in 1990 and a record low of 9,197,138.473 sq km in 2015. Canada CA: Rural Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Rural land area in square kilometers, derived from urban extent grids which distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.;Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.;Sum;

Maps of the analysis of change between * mapping of heat islands/freshness 2020-2022 * and * mapping of heat/freshness islands using 2013-2014 data * on all major urban centers by two methods, i.e. - The map of the Difference between the differences of temperatures in °C (* 2020-2022 * minus * 2013-2014*)), which is calculated at the pixel level and produced at the scale of the Quebec ecumene (2016 census, 2016 census, 167,764 km2). The temperature difference is the difference in temperature in the city compared to a nearby wooded area. A positive value of the difference in temperature differences represents an increase in the temperature gap in 2020-2022 compared to 2013-2014, a negative value represents a decrease in the temperature difference in 2020-2022 compared to 2013-2014. - The map of _SUHII index variation between 2020-2022 and 2013-2014 (%) _, which represents the percentage of change in the Surface Urban Heat Island Intensity (SUHII) index between the two years. This map covers the extent of * 2021 census population centers * () * (CTRPOP) with at least 1,000 inhabitants and a density of at least 400 inhabitants per km2 to which a 2 km buffer zone is added and the values are calculated at the scale of the * dissemination island * of Statistics Canada. The SUHII index highlights areas with higher heat island intensity, by calculating a weighted average from the temperature difference classes, giving more weight to the hottest classes. Index change values below 100% represent a decrease in the intensity of UHIs in 2020-2022 compared to 2013-2014. Values greater than 100% represent an increase in UHI intensity between 2013-2014 and 2020-2022. Values around 100% correspond to an absence of change. The temperature difference classes were produced by the k-means algorithm, which takes into account the distribution of temperature difference values in a population center in a given year. The limits of temperature difference classes may therefore differ between the two years, which will influence the variation value of the SUHII index. For more details on the creation of the various maps as well as their advantages, limitations and potential uses, consult the * Technote * (simplified version) and/or the * methodological report * (full version). The production of this data was coordinated by the National Institute of Public Health of Quebec (INSPQ) and carried out by the forest remote sensing laboratory of the Center for Forestry Education and Research (CERFO), funded under the * 2013-2020 Climate Change Action Plan * of the Quebec government entitled Le Québec en action vert 2020.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Canada CA: Urban Land Area data was reported at 13,983.114 sq km in 2015. This records an increase from the previous number of 12,775.562 sq km for 2000. Canada CA: Urban Land Area data is updated yearly, averaging 12,775.562 sq km from Dec 1990 (Median) to 2015, with 3 observations. The data reached an all-time high of 13,983.114 sq km in 2015 and a record low of 11,777.168 sq km in 1990. Canada CA: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.;Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.;Sum;

Mise with caution and limitations of data use: * The report by Lesmerises and St-Laurent (2018) must be cited when using this file (see Literature section). * The distribution area of the mountain caribou population of Gaspesia in this file represents the state of knowledge on the use of land by montane caribou between 1988 and 2016. * Information on the distribution of the Gaspesie mountain caribou population in this file represents the state of knowledge on land use by montane caribou between 1988 and 2016. * Information on the distribution of the Gaspesie mountain caribou population in this file represents the state of knowledge on the use of land by montane caribou between 1988 and 2016. * Information on the distribution of the Gaspesie mountain caribou population in this file represents the state of knowledge on land use by mountain caribou between 1988 and 2016. * Information on the distribution of local population does not allow us to establish with certainty that caribou are absent in territories outside of this range. * The range is subject to change, depending on the new telemetry data that will be acquired, the refinement of our local knowledge and the changes in the patterns of land use by caribou. ## #Description of the range of the local population of woodland caribou, mountain ecotype, population of Gaspesie The data represent the range of woodland caribou, mountain ecotype, population of Gaspesie (hereinafter mountain caribou of Gaspesie). The file contains the polygon and the name assigned to the population, the period covered by the telemetry data used during the delimitation exercise, and the date of the last update. The information contained in the file of occurrences of species in a precarious situation of the Quebec Natural Heritage Data Center (CDPNQ) is also present in the file (CDPNQ occurrence number, French, English and scientific name of the species, the type of occurrence, the rank of precariousness (rank S) and the status under the Act Respecting Threatened or Vulnerable Species, see MELCCFP 2023 for a description of these fields.). ## #Contexte of the publication The Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks (hereinafter MELCCFP) is responsible for the monitoring and management of mountain caribou in Gaspésie (Government of Quebec, 2021 a, b). The local population has been identified as the appropriate monitoring and management scale for this species since each population may face different threats depending on the habitat and socio-economic context in which it is found (Environment Canada, 2008). The local population is defined as a group of caribou occupying a defined territory that is spatially distinct from the territories occupied by other caribou groups. The dynamics of the local population are determined primarily by local factors influencing birth and mortality rates, rather than by the contributions or losses resulting from immigration or emigration between groups. As a result, the distribution area of the local population of mountain caribou in Gaspésie is defined as the geographic area where a group of individuals exposed to similar factors influencing their demographics live and which meets the needs of their life cycle during a given period of time (e.g. calving, rutting, wintering). In 2018, Lesmerises and St-Laurent produced the report Influence of the rate of habitat disturbance, regional coyote abundance, and predator control on the demographic parameters of the Gaspesia-Atlantic caribou population report presented to the Canadian Wildlife Service (Environment Canada). One of the objectives of the report was to delineate the range of this population. The MELCCFP uses the distribution area delimited by Lesmerises and St-Laurent (2018) as part of the monitoring and management of mountain caribou in Gaspésie. For any questions related to the Lesmerises and St-Laurent report (2018), please contact Mr. St-Laurent (Martin-hugues_St-laurent@uqar.ca). ## #Méthodologie in a nutshell The methodology below is a summary of that described in Lesmerises and St-Laurent (2018). Telemetry data from three follow-ups carried out during different periods of time were used (1988-1991, 1998-2008 and 2013-2016). The caribou were equipped with VHF collars during the first two follow-ups. Aerial flights were carried out at various times in order to locate the caribou. During the follow-up from 2013 to 2016, the caribou were equipped with GPS/Argos collars programmed to acquire locations every 2 or 3 hours depending on the collar model used. The range of the mountain caribou population in Gaspesie was defined by estimating a minimum convex polygon comprising 99% of telemetry locations plus a 10 km buffer zone. Finally, the parts superimposed on the St. Lawrence River were removed. ## #Littérature Environment Canada. 2008. Scientific review for the identification of critical habitat for the boreal population of woodland caribou (Rangifer tarandus caribou) in Canada. August 2008. Ottawa: Environment Canada. 80 pp. + 192 pp. appendices. Government of Quebec. 2021 a. Monitoring system for forest caribou populations in Quebec and mountain caribou in Gaspésie 2020-2031: summary document, Ministry of Forests, Wildlife and Parks, Directorate of Expertise on Terrestrial Wildlife, Herpetofauna and Avifauna, 16 pp. Government of Quebec. 2021 b. Literature review on the factors involved in the decline of forest caribou populations in Quebec and mountain caribou in Gaspésie, Ministère des Forêts, de la Faune et des Parcs, Direction of expertise on terrestrial fauna, herpetofauna and avifauna, 244 pp. + 15p. appendices Lesmerises, F. and M.-H. St-Laurent. 2018. Influence of the rate of habitat disturbance, regional coyote abundance, and predator control on the demographic parameters of the caribou population in Gaspesia-Atlantique. Scientific report submitted to Environment Canada — Canadian Wildlife Service, Rimouski (Quebec). 22 pp. + 8 appendices. Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks (MELCCFP). 2023. The Quebec Natural Heritage Data Center — Information document, Government of Quebec, Quebec, 32 pp.This third party metadata element was translated using an automated translation tool (Amazon Translate).

Population trend estimates based on data collected through the North American Breeding Bird Survey (BBS), for approximately 300 Canadian bird species. The trend results are Canadian trends and thus use data from Canadian BBS routes only. Results are updated annually; these estimates use BBS data from 1970 through 2012. Trends are presented for species, provinces, territories, Bird Conservation Regions (BCR), as well as the sub-regions that make up the analytical strata (intersections of BCRs and provinces/territories) for which there are sufficient BBS data for statistical analysis. These estimates were produced using a hierarchical Bayesian model, which differs from the maximum likelihood model that was used to generate BBS trend estimates prior to 2011. The estimates and details on statistical methods used are available on the BBS results website. The BBS is jointly coordinated by Environment Canada, Canadian Wildlife Service and the U.S. Geological Survey (USGS), Patuxent Wildlife Research Center. Any use of these BBS results for Canada should acknowledge the hundreds of skilled volunteers in Canada who have participated in the BBS over the years and those who have served as provincial or territorial coordinators for the BBS.

The data presented on this page concern the 2020-2022 mapping of temperature differences, the classification maps of these temperature differences (i.e. urban heat and freshness islands) and the map of the urban heat island intensity index. These different maps are detailed below: - The mapping of Temperature differences in °C represents the temperature difference in the city compared to a nearby forest. It was produced at the scale of the ecumene of Quebec (2021 census, 185,453 km2). This mapping, provided on a grid with a spatial resolution of 15 m, was carried out with a predictive machine learning model built on Landsat-8 satellite data provided by the *United States Geological Survey (USGS) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. _Islands of urban heat and freshness (ICFU) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. _Islands of urban heat and freshness (ICFU) _ was conducted for * population centers from the 2021 census * (CTRPOP) with at least 1,000 inhabitants and a density of at least 400 inhabitants per km2 to which is added a 2 km buffer zone. It thus covers all major urban centers, i.e. 14,072 km2. The method for categorizing ICFUs is the ranking of predicted temperature differences for each population center into 9 levels. Classes 8 and 9 are considered Urban Heat Islands and classes 1, 2, and 3 as Urban Freshness Islands. The interval values for each class and population center are shown in the production metadata file. Since surface temperatures were analyzed at the Quebec ecumene scale, but the classification intervals were calculated for each population center individually, the differences in temperature grouped into the different classes vary from region to region. Thus, there are differences observed in the predicted temperature differences between North and South Quebec and according to urban realities. For example, a temperature difference of 2°C may be present in class 1 (cooler) in a population center located in southern Quebec, but may be present in class 9 (very hot) in a population center in northern Quebec. It is therefore important to interpret the identification of heat islands in relation to the relative temperature difference data produced at the Quebec ecumene scale. In addition to this map, the map of * Temperature variations for the urbanization perimeters of the smallest municipalities 2020-2022 * covers all the urbanization perimeters that are not (or only partially) covered by the ICFU map. Thus, the two maps put side by side allow a complete coverage of all population centers and urbanization perimeters in Quebec. - The _Urban Heat Island Intensity Index (SUHII) _ map _ represents the Surface Urban Heat Island Intensity (SUHII) index _ represents the Surface Urban Heat Island Intensity (SUHII) index. This index is calculated for each * dissemination island * (ID) of Statistics Canada included in the * 2021 census population centers * (CTRPOP) * () * (CTRPOP). It highlights areas with higher heat island intensity, by calculating a weighted average from temperature difference classes, giving more weight to the hottest classes. This weight is proportional to the class number (e.g. a class 9 surface is 9 times more important in the index than the same area with a class 1). These maps as well as those of * 2013-2014 * are used for the * Analysis of change between the mapping of heat/freshness islands 2013-2014 and 2020-2022 *. For more details on the creation of the various maps as well as their advantages, limitations and potential uses, consult the * Technote * (simplified version) and/or the * methodological report * (version complete). The production of this data was coordinated by the National Institute of Public Health of Quebec (INSPQ) and carried out by the forest remote sensing laboratory of the Center for Forestry Education and Research (CERFO), funded under the * 2013-2020 Climate Change Action Plan * of the Quebec government entitled Le Québec en action vert 2020.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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Teleradiology Market Size 2024-2028

The teleradiology market size is forecast to increase by USD 3.83 billion at a CAGR of 17.55% between 2023 and 2028.

The increasing prevalence of diseases, coupled with the growing geriatric population, is the key driver of the teleradiology market. Aster Medical Imaging LLC is a key company in this space, offering teleradiology services such as urgent after-hours consultations with radiologists, urgent daytime support for emergency cases, routine overflow, and backlog management. The company's services help healthcare providers efficiently manage radiology needs, improving patient care and addressing the rising demand for timely diagnostic solutions.
Technological advancements and upgrades in teleradiology modalities enable radiologists to provide accurate diagnoses from a distance, improving accessibility and efficiency in healthcare delivery and mhealth solutions. Furthermore, government initiatives encouraging the adoption of healthcare IT have created a favorable regulatory environment for teleradiology services. These factors collectively contribute to the expansion of the teleradiology market, offering opportunities for growth and innovation in the healthcare industry.

What will be the Size of the Teleradiology Market During the Forecast Period?

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The market is experiencing significant growth due to the integration of Artificial intelligence (AI) in healthcare, particularly In the interpretation of medical imaging for various conditions. Preliminary and final reports are shared between healthcare professionals and Neuroradiologists through XERO Exchange Network and other teleradiology platforms. The geriatric population, with target diseases such as cardiovascular conditions, cancer, and osteoarthritis (OA), significantly benefits from timely interventions based on accurate and efficient diagnostic data.
However, higher-resolution imaging and 3D imaging are essential for diagnosis and treatment planning In the elderly population. AI enhances image interpretation, ensuring diagnostic accuracy and improving the overall quality of healthcare. The market is driven by the increasing demand for cutting-edge imaging solutions to manage musculoskeletal ailments like OA. The Osteoarthritis Action Alliance focuses on the importance of early diagnosis and intervention for improved patient outcomes.

How is this Teleradiology Industry segmented and which is the largest segment?

The teleradiology industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Modality

  CT
  X-ray
  Ultrasound
  MRI
  Nuclear imaging


Component

  Hardware
  Software
  Telecom and networking


End Use

  Hospital
  Radiology Clinics
  Ambulatory Imaging Center


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China


  Rest of World (ROW)

By Modality Insights

The CT segment is estimated to witness significant growth during the forecast period. In the dynamic and evolving teleradiology industry, healthcare professionals leverage advanced imaging systems to deliver accurate and timely diagnostic reports remotely. Artificial Intelligence (AI) is increasingly being integrated into medical imaging to enhance preliminary reports, ensuring that final reports are more precise and efficient. Hospitals and neuroradiologists rely on various modalities of CT scanners for diagnosing target diseases such as cardiovascular conditions, cancer, and osteoarthritis (OA) In the elderly population. Agfa Healthcare's XERO Exchange Network plays a crucial role in facilitating the secure exchange of diagnostic data between healthcare providers, enabling timely interventions and improving the quality of healthcare. Multi-detector CT (MDCT) scanners, with their ability to acquire images more quickly and with greater resolution, are a significant modality In the teleradiology industry. These scanners are essential for interpreting complex medical images, particularly In the context of cardiovascular conditions and cancer. The integration of AI in image interpretation further enhances the diagnostic capabilities of these systems, ensuring accuracy and efficiency In the diagnostic processes.

Moreover, the teleradiology industry is witnessing a shift towards streamlining workflows and cost-cutting measures during economic recessions. Broadband networks and data security are critical considerations in ensuring connectivity and data privacy. Security protocols are essential to mitigate potential connectivity issues and maintain the confidentiality of diagnostic data. The integration of AI in teleradiology platforms and services is expected to revolutionize the industry, providing 3D imaging c

Map illustrating the differences in relative surface temperatures for all small urban areas in Quebec. The relative temperature difference is the temperature difference in the city compared to a nearby wooded area. With a 9-level scale for classifying relative differences in temperature, this map indicates areas that are relatively cooler or warmer within urbanization perimeters. This map is complementary to the * map of urban heat/fresh islands (ICFU) *. In fact, it covers all areas of urbanization that are not (or only partially) covered by the ICFU card. Thus, the two maps placed side by side allow a complete coverage of all population centers and urbanization perimeters in Quebec. The interval values for each class of temperature difference within the urbanization perimeters also come from the ICFU map: the classification thresholds for the temperature differences of an urbanization perimeter are reproduced from those of the ICFU map for the population center closest to the urbanization perimeter. The production of this data was carried out by the National Institute of Public Health of Quebec (INSPQ) and was funded under the * Plan for a Green Economy * of the Government of Quebec.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**