The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Australia: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Changelog Version 1.0.0 (2025-07-05)

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Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

Population density metrics for 2011 Statistical Area Level 2 (SA2) within 2011 Greater Capital City Statistical Areas (GCCSA), including SA2 Population-weighted density (PWD) for 2011 and 2014, PWD change 2011-2014, and ERP population counts by density classes. Selected Density Classes were based on the Australian Population Density Grid published by the ABS, December 2014 (cat. no. 1270.0.55.007). Corresponding population metrics for 2011 GCCSAs. PWD using standardised 1km grid cells provides a more comparable measure of the density in larger regions. It does this by weighting the density using the proportion of population living at that density. In this way the density measure reflects the density at which people actually live. This removes the effect of large unpopulated areas that may be within the regions being compared. In this way comparisons between regions are more valid.

The map service can be viewed at http://soe.terria.io/#share=s-AgXEN0N0Q95icRW7M9JIC9IYBdE

Downloadable spatial data also available below.

Map prepared by the ABS and presented as Figure BLT3 in Built environment theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

It should be noted that this data is now somwhat dated! Human population density is a surrogate indicator of the extent of human pressures on the surrounding landscapes. Areas with high population …Show full descriptionIt should be noted that this data is now somwhat dated! Human population density is a surrogate indicator of the extent of human pressures on the surrounding landscapes. Areas with high population density are associated with higher levels of stream pollution and water diversion through sewers and drains. City and urban environments are substantially changed from their pre-European condition but a changed condition is not of itself necessarily poor by societal standards. It is the impacts such as polluted run-off to waterways, air pollution, sewage disposal, household water use and predation of wildlife by pets that confer impacts on catchment condition. Human population centres have an impact well beyond the built environment. The impact of major population centres is well expressed in the AWRC map, but is best displayed in the 500 map. The main areas of impact are the major coastal and capital cities and suburbs, including popular beachside tourist destinations. Elsewhere, the impact of population density appears to be confined to the Murray and other major river valleys. The Australian Bureau of Statistics compiles population statistics by sampling statistical local areas (SLAas) through the national census. These data can be converted to a per catchment basis. Interpretation of the indicator is largely unequivocal, although there are land-uses/activities (e.g. mining) where population density is not a good indicator of the degree of habitat decline. This indicator has not been validated relative to habitat decline. This indicator is easy to understand. Data are available as: continental maps at 5km (0.05 deg) cell resolution for the ILZ; spatial averages over CRES defined catchments (CRES, 2000) in the ILZ; spatial averages over the AWRC river basins in the ILZ. See further metadata for more detail.

It should be noted that this data is now somwhat dated!

Human population density is a surrogate indicator of the extent of human pressures on the surrounding landscapes.

Areas with high population density are associated with higher levels of stream pollution and water diversion through sewers and drains. City and urban environments are substantially changed from their pre-European condition but a changed condition is not of itself necessarily poor by societal standards. It is the impacts such as polluted run-off to waterways, air pollution, sewage disposal, household water use and predation of wildlife by pets that confer impacts on catchment condition. Human population centres have an impact well beyond the built environment.

The impact of major population centres is well expressed in the AWRC map, but is best displayed in the 500 map. The main areas of impact are the major coastal and capital cities and suburbs, including popular beachside tourist destinations. Elsewhere, the impact of population density appears to be confined to the Murray and other major river valleys.

The Australian Bureau of Statistics compiles population statistics by sampling statistical local areas (SLAas) through the national census. These data can be converted to a per catchment basis.

Interpretation of the indicator is largely unequivocal, although there are land-uses/activities (e.g. mining) where population density is not a good indicator of the degree of habitat decline. This indicator has not been validated relative to habitat decline. This indicator is easy to understand.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

This map shows where senior populations are found throughout the world. Areas with more than 10% seniors are highlighted with a dark red shading while a dot representation reveals the number of seniors and their distribution in bright red.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

This data represents the indicative known and predicted distributions of threatened ecological communities, population and species. These data are a snapshot of data held and maintained in the Bionet – Threatened Species Profiles. The data were extracted mid-November 2013.

The base geometry is derived from a GIS intersection of a NSW Catchment management Authority Layer and IBRA Subregions layer (Interim Bio-regionalisation of Australia). For each NSW (TSC Act) and Cwth (EPBC Act) listed entity the "known" or "predicted" occurrence of each entity is attributed against the base polygon layer based. "Prediction" of occurrence should be treated as having a low confidence.

Attribution of "Known" occurrence is based on the existence of at lease one valid observation record for that polygon (locality). Validation of TS records is completed by nominated Threatened Species experts within NSW OEH (Office of Environment and Heritage). The Assignment is based on expert knowledge and is generally not assisted by distribution modelling approaches.

These data are rendered live from BioNet database to the Office of Environment and Heritage Threatened Species Web site (http://www.environment.nsw.gov.au/threatenedSpeciesApp/). See the following link for an example of a profile with indicative distribution map: http://www.environment.nsw.gov.au/threatenedspeciesapp/profile.aspx?id=10616

These web pages provide a view of the most current indicative distribution data. Users are recommended to check the currency of this product be for use. The data are indicative only and should be used with care - please refer to the readme and Q&A file for further information.

Chart and table of population level and growth rate for the Melbourne, Australia metro area from 1950 to 2025. United Nations population projections are also included through the year 2035.

This product represents the predicted spatial patterns of selected temperate shark and ray species abundance. Species selection was based on ecological risk assessments, threatened species listings and data availability. The maps are based on existing CSIRO National Fish Collection maps, supplemented with fishery catch data, independent survey data and the expert knowledge of 20 shark and ray experts from the region. Structure equates to total species distribution, core distribution – an estimate of where 90% of the population will occur and where possible, nursery areas. The product can be used to identify movement corridors, breeding and feeding areas that overlap between species. This allows managers to identify areas of overlap that are of key conservation value to the species of interest.

Introduction Low-density geochemical surveys provide a cost-effective means to assess the composition of near-surface materials over large areas. Many countries in the world have already compiled geochemical atlases based on such data. These have been used for a number of applications, including: - establish baselines from which future changes can be measured - design geologically sensible targets for remediation of contaminated sites - support decision-making regarding appropriate land-use - explore for natural resources - study links between geology and plant/animal health (geohealth)

A first pilot project was initiated to help establish sampling and analytical protocols relevant to Australian landscapes and climates. The Riverina region was chosen for this study because of its crucial economic, environmental and societal importance within the Murray-Darling basin. The region is a prime agricultural area, is bordered to the south by the Victorian goldfields, and is home to 11% of the Australian population. Results of this study are presented here.

Methods Using a hydrological analysis, 142 sites near the outlets of large catchments were selected within the 123,000 km2 survey area (1 site per 866 km2 on average). At each site, two 10-cm thick overbank sediment samples were taken, one at the surface ('top overbank sediment', TOS) and the other between 60 and 90 cm depth (`bottom overbank sediment', BOS). These were described, dried, sieved (<180 m) and analysed chemically for 62 elements. Exploratory data analysis was undertaken and geochemical maps (various styles are shown here) were prepared.

Results and discussion The geology of the area is dominated by Cainozoic sediments found in low-relief plains over the vast majority of the Riverina. The eastern and southern fringes of the area form higher relief landforms developed on outcropping or subcropping Palaeozoic sedimentary, mafic and felsic volcanic and felsic intrusive rocks.

The geochemical results of the survey are independently corroborated by the good match between the distributions of K, U and Th concentrations in TOS and airborne gamma-ray maps.

The distribution of Ca in BOS indicates generally higher concentrations in the northern part of the study area, which is also reflected in higher soil pH values there. Such data have implications for soil fertility and management in agricultural areas.

In terms of applications to mineral exploration, dispersion trains of typical pathfinder elements for gold mineralisation, like As and Sb are clearly documented by the smoothly decreasing concentrations from south (near the Victorian goldfields) to north (over sediments from the Murray basin).

Chromium is an element that can be associated with ill-health in animals and humans when present over certain levels. There is a smooth increase in Cr concentration from north to south, and the two sites with the highest values can be correlated with a ridge of Cambrian mafic volcanics. High total Cr concentrations in the Riverina are unlikely, however, to lead to serious health problems as only a very small proportion of Cr will be bioavailable.

Conversely, some elements can be present at concentrations that are too low for optimum plant growth, such as potentially Mo. The distribution map for this element shows a general decrease from south to north. Given its lower bioavailability in acid soils, Mo is likely to be deficient in the south of the region, despite higher total concentrations here. Farmers report the necessity to use Mo-enriched fertilisers in this area.

Conclusions Low-density geochemical surveys can be conducted in Australia using common regolith sampling media. They provide a cost-effective, internally consistent dataset that can be used by to support a variety of critical economic, environmental and societal decisions.

This statistic shows the share of ethnic groups in Australia in the total population. 33 percent of the total population of Australia are english.

Australia’s population

Australia’s ethnic diversity can be attributed to their history and location. The country’s colonization from Europeans is a significant reason for the majority of its population being Caucasian. Additionally, being that Australia is one of the most developed countries closest to Eastern Asia; its Asian population comes as no surprise.

Australia is one of the world’s most developed countries, often earning recognition as one of the world’s economical leaders. With a more recent economic boom, Australia has become an attractive country for students and workers alike, who seek an opportunity to improve their lifestyle. Over the past decade, Australia’s population has slowly increased and is expected to continue to do so over the next several years. A beautiful landscape, many work opportunities and a high quality of life helped play a role in the country’s development. In 2011, Australia was considered to have one of the highest life expectancies in the world, with the average Australian living to approximately 82 years of age.

From an employment standpoint, Australia has maintained a rather low employment rate compared to many other developed countries. After experiencing a significant jump in unemployment in 2009, primarily due to the world economic crisis, Australia has been able to remain stable and slightly increase employment year-over-year.

This map shows where youth populations are found throughout the world. Areas with more than 33% youth are highlighted with a dark red shading while a dot representation reveals the number of seniors and their distribution in bright red.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

The Meeberrie earthquake is the largest known onshore Australian earthquake. Its magnitude was ML 7.2 and it was felt over a wide area of Western Australia as shown on the isoseismal map below, from Port Hedland in the north to Albany and Norseman in the south.

Damage from the earthquake was small because of the low population density in the epicentral region, but the shaking at Meeberrie homestead was very severe; all the walls of the homestead were cracked, several rainwater tanks burst, and widespread cracking of the ground occurred. Minor non-structural damage was reported in Perth more than 500km away from the epicentre.

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has …Show full descriptionAbstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details. Originally digitised very poorly, this dataset has been fixed as much as posssible. Important feeding habitats and roosting sites for seven species of migratory shorebirds (waders) were plotted on GIS software using air photos as templates. Data for this process was obtained from a variety of sources to provide numbers of birds recorded at each of the sites. The habitat mapping was done from first hand experience in the field by the author and/or from local ornithologists with particular skills in shorebird identification and local knowledge of habitats used by shorebirds. The accuracy of the data plotted will not always match the topography, such as shoals, sand spits or shorelines because these are constantly changing (the air photos used are at least 10 years old).However this is the most precise shorebird mapping available. Important feeding habitats and roosting sites for seven species of migratory shorebirds (waders) were plotted on GIS software using air photos as templates. Data for this process was obtained from a variety of sources to provide numbers of birds recorded at each of the sites. The habitat mapping was done from first hand experience in the field by the author and/or from local ornithologists with particular skills in shorebird identification and local knowledge of habitats used by shorebirds. The accuracy of the data plotted will not always match the topography, such as shoals, sand spits or shorelines because these are constantly changing (the air photos used are at least 10 years old). However this is the most precise shorebird mapping available. The coastline template provided by DEC was not of sufficient accuracy for the purpose of this project and has not been used in any of the maps other than to illustrate the whole of coast maps. Data were sorted and any obvious errors removed before being used. Only data since 1990 has been used for the purpose of this report due to changes in habitats and population estimates over time. Habitat and species management can only be carried out using the most up to date data and habitat mapping. However as there are no regular population estimates for all coastal estuaries and some data over a 15 year period was used to provide a pattern of habitat usage by threatened migratory shorebirds for the whole coast. There has been no attempt to assess wetlands habitats away from the coast or estuarine habitats due to lack of data for inland sites. However the species concerned are largely coastal during their non-breeding seasons, when the birds are in Australia. This project was commissioned by the Department of Environment and Conservation to identify and map all known shorebird feeding habitat and roost sites along coastal NSW, specifically that of the Sanderling Calidris alba, Great Knot Calidris tenuirostris, Greater Sand Plover Charadrius leschenaultii, Lesser Sand Plover Charadrius mongolus, Broad-billed Sandpiper Limicola falcinellus, Black-tailed Godwit Limosa limosa and Terek Sandpiper Xenus cinereus. The results of the project was a series of shape files and data files using Arcview 9.1 and Microsoft Excel-,compatible with those of the DEC Spatial Analysis & Information Section, to map GIS layers for: the known feeding habitat of Sanderling, Great Knot, Greater Sand Plover, Lesser Sand Plover, Broad-billed Sandpiper, Black-tailed Godwit and Terek Sandpiper along the NSW coast; the boundaries of key foraging sites of the above species along the NSW coast; and the boundaries of roosting sites of the above species along the NSW coast. This dataset has been provided to the BA Programme for use within the programme only. Third parties should contact the NSW Office of Environment & Heritage. Purpose The degree of accuracy of mapping is governed to a large extent on the maps and air photos that are used as a template. Although topographic maps are drawn from air photos the information on the map is what is interpreted by the person responsible for interpreting and plotting the information onto a map. The majority of shorebird habitat is subtidal and as a consequence of this is rarely mapped, unless it happens to be part of a major sand bar. Coastlines are largely delineated by the high tide mark. This in itself is variable depending on whether the tide is high at the time the photograph was taken and whether the tides are spring tides or neap tides at the time. Furthermore the accuracy of the coastline drawn depends on the dedication of the cartographer and at which scale the map is drawn. The coastline provided be DEC for this project is of little use fro drawing shape files to the degree of accuracy that would be required by local government and for drawing b Dataset History Methods: All existing data on shorebird distribution (foraging and roosting records) held by bird groups, specialists and DEC Wildlife Atlas were reviewed and updated where information was available and checked for inaccuracies. , Data was also updated where required as a result of changes to estuarine habitats through the use of aerial photography. Where necessary face to face meetings were arranged with relevant people to obtain input from other shorebird specialists, failing this communication was by phone or email. Digitised air photos provided by DEC were used to map all known shorebird foraging habitat and roost sites along the NSW coast, noting habitat utilised by threatened migratory shorebirds, specifically Sanderling, Great Knot, Greater Sand Plover, Lesser Sand Plover, Broad-billed Sandpiper, Black-tailed Godwit and Terek Sandpiper. Dataset Citation NSW Department of Environment, Climate Change and Water (2010) Threatened Migratory Shorebird Mapping NSW DECCW 2006. Bioregional Assessment Source Dataset. Viewed 31 May 2018, http://data.bioregionalassessments.gov.au/dataset/92a85a3e-11e9-43cf-96d6-f8cd4fb65d03.

Travel Zones (TZs) are the spatial unit of geography defined by Transport Performance and Analytics (TPA), a business unit within Transport for NSW (TfNSW). The TZ spatial layer is applied to data sources used by TfNSW for transport modelling and analysis, including the Household Travel Survey and the Census 2016 Journey to Work data.\r \r \r The Australian Bureau of Statistics (ABS) Statistical Area boundaries form the foundation of the TZ. Generally, a TZ is larger than a Statistical Area Level 1 or Mesh Block, both ABS geography definitions. The ABS Statistical Areas are based on population counts whereas TZ boundaries are defined using population, employment, housing and transport infrastructure.\r \r \r TZs are designed to have standardised trip generation levels across all zones. This causes zones to be different sizes across the metropolitan area. As with many other spatial boundaries, TZs tend to be small in areas with high land-use densities and larger in areas of lower density.\r \r \r This dataset now includes a CSV file mapping the Transit Stop Number (TSN) to the Travel Zone (TZ16). It captures the stop name, suburb and coordinates.\r \r \r Travel Zone Explorer is an interactive map where you can search for Travel Zones (TZ) and find out the current and future population in occupied private dwellings by age and sex.\r \r \r

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

The Koala Likelihood Map (KLM) predicts the likelihood of finding a koala relative to other arboreal mammals across a 10-km grid covering NSW. It is built using existing arboreal mammal records from the past 20 years (currently 1999 to 2019) and represents the likelihood of koalas as the proportion of all records within a grid cell that are koalas. The records of other arboreal mammals provide a measure of survey effort independent of koalas and allow identification of areas where other arboreal mammals have been recorded, but not koalas. The map also includes a measure of the confidence in the koala likelihood estimate. This enables deficiencies in the data to be highlighted, and recommendations to be made for areas requiring further survey. The KLM is a useful tool that can be used to inform a range of koala conservation and management issues, however it is not static and should be updated regularly as new data become available.\r \r The KLM was first developed in 2014 for use in private native forestry regulation, on behalf of the NSW Environment Protection Authority. An updated and refined version of the map (NSW Koala Baseline Likelihood Map 2016) was produced in 2016 and has been used to inform provisions for koala protection under the Coastal Integrated Forestry Operations Approvals and is planned to inform the future review of the Private Native Forestry Code of Practice.\r \r This latest version of the KLM (v2.0 August 2019) includes new data from BioNet and Spot Assessment Technique (SAT) survey databases, as well as SAT data from a targeted state-wide field survey program.\r \r The KLM v2.0 (August 2019) is delivered under the NSW Koala Strategy's Koala Habitat Information Base. This comprises several layers of spatial information, including: Koala Habitat Suitability Model (KHSM) – the probability of finding koala habitat at any location; Koala Tree Suitability Index (KTSI) – the probability of finding a tree species that koalas are known to use for food or shelter; Koala Likelihood Map (KLM) including a confidence layer – predicts the likelihood of finding a koala at a location; Areas of Regional Koala Significance (ARKS) – identifies key koala populations and management areas with potential for long-term viability as well as priority threats to key koala populations; Native vegetation of NSW – this is a high-resolution map of native tree cover and water bodies; and all koala sightings recorded in NSW Bionet.\r \r All Koala Habitat Information Base (KHIB) datasets are available for download below under 'Dataset Relationship'.