Historical dataset of population level and growth rate for the Sydney, Australia metro area from 1950 to 2025.

Humans have been living on the continent of Australia (name derived from "Terra Australis"; Latin for "the southern land") for approximately 65,000 years, however population growth was relatively slow until the nineteenth century. Europeans had made some contact with Australia as early as 1606, however there was no significant attempt at settlement until the late eighteenth century. By 1800, the population of Australia was approximately 350,000 people, and the majority of these were Indigenous Australians. As colonization progressed the number of ethnic Europeans increased while the Australian Aboriginal population was decimated through conflict, smallpox and other diseases, with some communities being exterminated completely, such as Aboriginal Tasmanians. Mass migration from Britain and China After the loss of its American colonies in the 1780s, the British Empire looked to other parts of the globe to expand its sphere of influence. In Australia, the first colonies were established in Sydney, Tasmania and Western Australia. Many of these were penal colonies which became home to approximately 164,000 British and Irish convicts who were transported to Australia between 1788 and 1868. As the decades progressed, expansion into the interior intensified, and the entire country was claimed by Britain in 1826. Inland colonization led to further conflict between European settlers and indigenous Australians, which cost the lives of thousands of natives. Inward expansion also saw the discovery of many natural resources, and most notably led to the gold rushes of the 1850s, which attracted substantial numbers of Chinese migrants to Australia. This mass migration from non-European countries eventually led to some restrictive policies being introduced, culminating with the White Australia Policy of 1901, which cemented ethnic-European dominance in Australian politics and society. These policies were not retracted until the second half of the 1900s. Independent Australia Australia changed its status to a British dominion in 1901, and eventually became independent in 1931. Despite this, Australia has remained a part of the British Commonwealth, and Australian forces (ANZAC) fought with the British and their Allies in both World Wars, and were instrumental in campaigns such as Gallipoli in WWI, and the South West Pacific Theater in WWII. The aftermath of both wars had a significant impact on the Australian population, with approximately 90 thousand deaths in both world wars combined, as well as 15 thousand deaths as a result of the Spanish flu pandemic following WWI, although Australia experienced a significant baby boom following the Second World War. In the past fifty years, Australia has promoted immigration from all over the world, and now has one of the strongest economies and highest living standards in the world, with a population that has grown to over 25 million people in 2020.

Australia Population: Resident: Estimated: Annual: New South Wales: Greater Sydney data was reported at 5,132,355.000 Person in 2017. This records an increase from the previous number of 5,024,923.000 Person for 2016. Australia Population: Resident: Estimated: Annual: New South Wales: Greater Sydney data is updated yearly, averaging 4,643,072.500 Person from Jun 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 5,132,355.000 Person in 2017 and a record low of 4,256,161.000 Person in 2006. Australia Population: Resident: Estimated: Annual: New South Wales: Greater Sydney data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.G002: Estimated Resident Population.

In June 2022, it was estimated that around 7.3 percent of Australians were aged between 25 and 29, and the same applied to people aged between 30 and 34. All in all, about 55 percent of Australia’s population was aged 35 years or older as of June 2022. At the same time, the age distribution of the country also shows that the share of children under 14 years old was still higher than that of people over 65 years old. A breakdown of Australia’s population growth Australia is the sixth-largest country in the world, yet with a population of around 26 million inhabitants, it is only sparsely populated. Since the 1970s, the population growth of Australia has remained fairly constant. While there was a slight rise in the Australian death rate in 2022, the birth rate of the country decreased after a slight rise in the previous year. The fact that the birth rate is almost double the size of its death rate gives the country one of the highest natural population growth rates of any high-income country.
National distribution of the population Australia’s population is expected to surpass 28 million people by 2028. The majority of its inhabitants live in the major cities. The most populated states are New South Wales, Victoria, and Queensland. Together, they account for over 75 percent of the population in Australia.

Transport for NSW provides projections of population and dwellings at the small area (Travel Zone or TZ) level for NSW. The latest version is Travel Zone Projections 2024 (TZP24), released in January 2025.\r \r TZP24 replaces the previously published TZP22.\r \r The projections are developed to support a strategic view of NSW and are aligned with the NSW Government Common Planning Assumptions .\r \r The TZP24 Population & Dwellings Projections dataset covers the following variables:\r \r * Estimated Resident Population\r \r * Structural Private Dwellings (Regional NSW only)\r \r * Population in Occupied Private Dwellings, by 5-year Age categories & by Sex\r \r * Population in Non-Private Dwellings\r \r The projections in this release, TZP24, are presented annually from 2021 to 2031 and 5-yearly from 2031 to 2066, and are in TZ21 geography.\r \r Please note, TZP24 is based on best available data as at early 2024, and the projections incorporate results of the National Census conducted by the ABS in August 2021.\r \r Key Data Inputs used in TZP24:\r \r * 2024 NSW Population Projections – NSW Department of Planning, Housing & Infrastructure\r \r * 2021 Census data - Australian Bureau of Statistics (including dwellings by occupancy, total dwellings by Mesh Block, household sizes, private dwellings by occupancy, population age and gender, persons by place of usual residence)\r \r For a summary of the TZP24 projection method please refer to the TZP24 Factsheet .\r \r For more detail on the projection process please refer to the TZP24 Technical Guide . \r \r Additional land use information for workforce and employment as well as Travel Zone 2021 boundaries for NSW (TZ21) and concordance files are also available for download on the Open Data Hub.\r \r Visualisations of the population projections are available on the Transport for NSW Website under Data and research/Reference Information .\r \r Cautions\r \r The TZP24 dataset represents one view of the future aligned with the NSW Government Common Planning Assumptions and population and employment projections.\r \r The projections are not based on specific assumptions about future new transport infrastructure but do take into account known land-use developments underway or planned, and strategic plans.\r \r *\tTZP24 is a strategic state-wide dataset and caution should be exercised when considering results at detailed breakdowns.\r \r *\tThe TZP24 outputs represent a point in time set of projections (as at early 2024).\r \r *\tThe projections are not government targets.\r \r *\tTravel Zone (TZ) level outputs are projections only and should be used as a guide. As with all small area data, aggregating of travel zone projections to higher geographies leads to more robust results.\r \r *\tAs a general rule, TZ-level projections are illustrative of a possible future only.\r \r *\tMore specific advice about data reliability for the specific variables projected is provided in the “Read Me” page of the Excel format summary spreadsheets on the TfNSW Open Data Hub.\r \r *\tCaution is advised when comparing TZP24 with the previous set of projections (TZP22) due to addition of new data sources for the most recent years, and adjustments to methodology.\r \r Further cautions and notes can be found in the TZP24 Technical Guide\r \r Important note: \r \r The Department of Planning, Housing & Infrastructure (DPHI) published the 2024 NSW Population Projections in November 2024. As per DPHI’s published projections, the following variables are excluded from the published TZP24 Population and Dwellings Projections:\r \r *\tStructural Private Dwellings for Travel Zones in 43 councils across Greater Sydney, Illawarra-Shoalhaven, Central Coast, Lower Hunter and Greater Newcastle\r \r *\tOccupied Private Dwellings for Travel Zones in NSW.\r \r Furthermore, in TZP24, the Structural Private Dwellings variable aligns with the 2024 Implied Dwelling projections while the Occupied Private Dwellings variable aligns with the 2024 Households projections at SA2 level prepared by DPHI.\r \r The above variables are available upon request by contacting model.selection@transport.nsw.gov.au - Attention Place Forecasting.

The BTS is the primary source of forecasts of population and dwellings at the small area (travel zone) level for the Sydney Greater Metropolitan Area. This area includes the Sydney Greater Capital City Statistical Area, and the Illawarra and Hunter regions. There are 2,949 travel zones in the Sydney GMA. The latest September 2014 Release Population Forecasts provide forecasts at travel zone level for the following variables: Population (Estimated Resident Population) by 5-year Age categories by Sex Occupied Private Dwellings (Households) Population in Occupied Private Dwellings Population in Non-Private Dwellings The forecasts in this release are five-yearly, from 2011 to 2041.

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 population and land use in Sydney and the County of Cumberland. It was prepared by the Department of Main Roads.

The scale is 1 mile = 1 inch. The map is in two parts.


(SR Map Nos.52693-94). 2 sheets.

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This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

Comprehensive demographic dataset for Hampden-Sydney, VA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

A Heat Vulnerability Index was built with Open Data for Metropolitan Sydney, for the years 2011 and 2016. Vulnerability is defined as the propensity of a population to be adversely affected by extreme heat and depends on 3 components: the exposure, sensitivity and adaptive capacity of the population. These 3 sub-indexes were calculated with various indicators that you can find as attributes to this layer. The scale of the study is the Statistical Areas 2 (SA2) of the Australian Bureau of Statistics. Bodilis, Carole ; Yenneti, Komali; Hawken, Scott (2018): Heat Vulnerability Index for Sydney. Faculty of Built Environment, UNSW Sydney.

The 2022 Heat Vulnerability Index (HVI) for Greater Sydney aims to combine information on urban heat, built form and population demographics to provide a fine-grained understanding of the spatial distribution of heat vulnerable populations. The Index combines indicators of heat exposure, sensitivity to heat, and adaptive capacity to produce the composite vulnerability index. The 2022 HVI dataset is built upon the methodology established in the creation of the 2016 Sydney HVI dataset (Sun et al 2018), integrating land cover, urban heat, and demographic data, aggregated to Statistical Area Level 1 (SA1) of the Australian Statistical Geography Standard (ASGS) produced by the Australian Bureau of Statistics (ABS). Broad comparisons can be made between the 2022 and 2016 HVI datasets, however there are multiple factors that may limit direct comparability over time. This includes variations in underlying datasets, the relative nature of the HVI, and the change in size of the study area between 2016 and 2022. When undertaking comparison it is recommended to examine the changes in the underlying datasets and the absolute values of the heat exposure, sensitivity and adaptive capacity indicators. This approach helps to explain the variations in HVI and informs effective heat mitigation strategies. The 2022 HVI is most useful at the SA1 scale. It is not recommended to aggregate the HVI dataset to larger scales (i.e. average HVI for a suburb or LGA). Aggregating spatially specific and individual data to geographic areas smooths out local variation, losing locational specificity and population variation. In cases where individual human exposure is of concern, this may either increase or decrease the representation of the actual exposure of a given individual, causing the neighbourhood effect averaging problem (NEAP) (Kwan 2018). Please refer to the methodology report for more information.

This report provides a comprehensive analysis of Pasifika communities in Australia based on the 2021 Australian Bureau of Statistics (ABS) Census. The Australian Pasifika Educators Network (APEN) define 'Pasifika' as peoples and communities, who are genealogically, spiritually, and culturally connected to the lands, the skies and seas of the Pasifika region (including Aotearoa, New Zealand), and who have chosen to settle in and call Australia home. This analysis seeks to track key trends that have emerged since the 2015 Pacific Communities report published by Professor Jioji Ravulo based on the 2011 Census. In accordance with the intent of the original report, this current version seeks to provide an understanding of the current demographic, and socioeconomic experiences of Pasifika communities with a particular focus on education. This analysis covers population, education levels, employment patterns, as well as family and household characteristics to not only shed light on the unique circumstances faced by Pasifika communities, but also track key trends over the last decade. In addition, an examination of the Western Sydney region has been included, highlighting the significance of place-based insights on Pasifika communities towards informing policy responses and initiatives. Dataset: OVERVIEW This report compiles data from the 2021 Census of Population and Housing conducted by the Australian Bureau of Statistics (ABS). This document provides the second iteration on the human geography of Pasifika peoples within an Australian context,10 aimed at gathering greater insight into Pasifika communities, as well as comparing and tracing key trends through various datapoints. In addition, this report provides a spotlight on Pasifika communities in Western Sydney, where nearly 20% of the nation’s Pasifika population currently resides.

Migrants from the United Kingdom have long been Australia’s primary immigrant group and in 2023 there were roughly 960 thousand English-born people living in Australia. India and China held second and third place respectively with regard to Australia’s foreign-born population. The relative dominance of Asian countries in the list of top ten foreign-born residents of Australia represents a significant shift in Australia’s immigration patterns over the past few decades. Where European-born migrants had previously overshadowed other migrant groups, Australian migration figures are now showing greater migration numbers from neighboring countries in Asia and the Pacific. A history of migration Australia is often referred to as an ‘immigrant nation’, alongside the United States, Canada, and New Zealand. Before the Second World War, migrants to Australia were almost exclusively from the UK, however after 1945, Australia’s immigration policy was broadened to attract economic migrants and temporary skilled migrants. These policy changes saw and increase in immigrants particularly from Greece and Italy. Today, Australia maintains its status as an ‘’Immigrant nation’’, with almost 30 percent of the population born overseas and around 50 percent of the population having both that were born overseas. Australian visas The Australian immigration program has two main categories of visa, permanent and temporary. The permanent visa category offers three primary pathways: skilled, family and humanitarian. The skilled visa category is by far the most common, with more than a million permanent migrants living in Australia on this visa category at the last Australian census in 2021. Of the temporary visa categories, the higher education visa is the most popular, exceeding 180 thousand arrivals in 2023.

This dataset contains time series for monthly precipitation over six sites (Blackheath, Braidwood, Darkes Forest, Goulburn, Lithgow and Moss Vale) in the Sydney Catchment Area (SCA) and monthly mean maximum and mean minimum temperature for three sites (Goulburn, Lithgow, and Moss Vale) in the SCA. This data was used in the study Attribution and Prediction of Precipitation and Temperature Trends within the Sydney Catchment Using Machine Learning. The data was originally from the Australian Bureau of Meteorology Climate Data Online (http://www.bom.gov.au/climate/data/index.shtml), but has been updated to have missing values (8% of data) filled using a moving average centred on the year for which the data is missing.

Below is the abstract for the paper:

Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia's largest city. Increasing population, a warming climate, land surface changes, and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney's water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis is applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, SAM, DMI and measures of global warming such as the Tasman Sea Sea Surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For example, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm2 on the testing data while annual precipitation achieved an RMSE of 613.704 mm2. However, the seasonal models provided valuable insight into whether the season would be wet or dry compared to the climatology.

The workforce dataset contains monthly workforce sizes from July 2005 to June 2018 in the eight Australian capital cities with estimated stratification by indoor and outdoor workers. It is included in both csv and rda format. It includes variables for:

Year Month GCCSA (Greater Capital City Statistical Area, which is used to define capital cities) Date (using the first day of the month) fulltime: Fulltime workers parttime: Parttime workers n. Overall workers outorin. Estimated indoor or outdoor status

This data are derived from the Australian Bureau of Statistics (ABS) Labour Force, Australia, Detailed, LM1 dataset: LM1 - Labour force status by age, greater capital city and rest of state (ASGS), marital status and sex, February 1978 onwards (pivot table). Occupational data from the 2006, 2011 and 2016 Census of Population and Housing (ABS Census TableBuilder Basic data) were used to stratify this dataset into indoor and outdoor classifications as per the "Indooroutdoor classification.xlsx" file. For the Census data, GCCSA for the place of work was used, not the place of usual residence.

Occupations were defined by the Australian and New Zealand Standard Classification of Occupations (ANZSCO). Each 6-digit ANZSCO occupation (the lowest level classification) was manually cross-matched with their corresponding occupation(s) from the Canadian National Occupation System (NOC). ANZSCO and NOC share a similar structure, because they are both derived from the International Standard Classification of Occupations. NOC occupations listed with an “L3 location” (include main duties with outdoor work for at least part of the working day) were classified as outdoors, including occupations with multiple locations. Occupations without a listing of "L3 location" were classified as indoors (no outdoor work). 6-digit ANZSCO occupations were then aggregated to 4-digit unit groups to match the ABS Census TableBuilder Basic data. These data were further aggregated into indoor and outdoor workers. The 4-digit ANZSCO unit groups’ indoor and outdoor classifications are listed in "Indooroutdoor classification.xlsx."

ANZSCO occupations associated with both indoor and outdoor listings were classified based on the more common listing, with indoors being selected in the event of a tie. The cross-matching of ANZSCO and NOC occupation was checked against two previous cross-matches used in published Australian studies utilising older ANZSCO and NOC versions. One of these cross-matches, the original cross-match, was validated with a strong correlation between ANZSCO and NOC for outdoor work (Smith, Peter M. Comparing Imputed Occupational Exposure Classifications With Self-reported Occupational Hazards Among Australian Workers. 2013).

To stratify the ABS Labour Force detailed data by indoors or outdoors, workers from the ABS Census 2006, 2011 and 2016 data were first classified as indoors or outdoors. To extend the indoor and outdoor classification proportions from 2005 to 2018, the population counts were (1) stratified by workplace GCCSA (standardised to the 2016 metrics), (2) logit-transformed and then interpolated using cubic splines and extrapolated linearly for each month, and (3) back-transformed to the normal population scale. For the 2006 Census, workplace location was reported by Statistical Local Area and then converted to GCCSA. This interpolation method was also used to estimate the 1-monthly worker count for Darwin relative to the rest of Northern Territory (ABS worker 1-monthly counts are reported only for Northern Territory collectively).

ABS data are owned by the Commonwealth Government under a CC BY 4.0 license. The attached datasets are derived and aggregated from ABS data.

Transport Performance and Analytics (TPA) provides projections of workforce at the small area (Travel Zone or TZ) level for the Sydney Greater Metropolitan Area (GMA).

The GMA includes the Sydney Greater Capital City Statistical Area (GCCSA), the Southern Highlands and Shoalhaven SA4, Illawarra SA4, Newcastle and Lake Macquarie SA4, and Lower Hunter, Port Stephens, and Maitland SA3s, as defined by the Australian Bureau of Statistics (ABS). TPA workforce projections are five-yearly, from 2011 to 2056 and relate to usual residents of the GMA aged 15 years and over who are employed. They are estimates of employed people based on where they reside. TPA also produces employment projections based on the workplace or job location. They refer to persons aged 15 years and over, working in the GMA regardless of their place of usual residence. The majority of the persons employed in the GMA also reside in the GMA.

Factors considered in the estimation of workforce projections include: population by age and gender; participation rates; unemployment rates; historical labour force data; past trends of employment in each industry and the forecasts of industry growth or decline in each region.

This dataset is the June 2022 release of Geoscape Planning for a single SA2 area (Sydney - Haymarket - The Rocks) with SA2 code (11337). Buildings is a spatial dataset which represents Australia's built environment derived from remotely sensed imagery and aggregated data sources. The Buildings dataset has relationships with the G-NAF, Cadastre, Property and Administrative Boundaries products produced by Geoscape Australia. Users should note that these related Geoscape products are not part of Buildings. For more information regarding Geoscape Buildings, please refer to the Data Product Description and the June 2022 Release Notes. Please note: As per the licence for this data, the coverage area accessed by you can not be greater than a single Level 2 Statistical Area (SA2) as defined by the Australian Bureau of Statistics. If you require additional data beyond a single SA2 for your research, please request a quote from AURIN. Buildings is a digital dataset representing buildings across Australia. Data quality and potential capture timelines will vary across Australia based on two categories, each category has been developed based on a number of factors including the probability of the occurrence of natural events (e.g. flooding), population distribution and industrial/commercial activities. Areas with a population greater than 200, or with significant industrial/commercial activity in a visual assessment have been defined as 'Urban' and all other regions have been defined as 'Rural'. This dataset has been restricted to the Sydney - Haymarket - The Rocks SA2 by AURIN.

Household Travel Survey (HTS) is the most comprehensive source of personal travel data for the Sydney Greater Metropolitan Area (GMA). This data explores average weekday travel patterns for residents in Sydney GMA.\r \r The Household Travel Survey (HTS) collects information on personal travel behaviour. The study area for the survey is the Sydney Greater Metropolitan Area (GMA) which includes Sydney Greater Capital City Statistical Area (GCCSA), parts of Illawarra and Hunter regions. All residents of occupied private dwellings within the Sydney GMA are considered within scope of the survey and are randomly selected to participate.\r The HTS has been running continuously since 1997/981 and collects data for all days through the year – including during school and public holidays.\r \r Typically, approximately 2,000-3,000 households participate in the survey annually. Data is collected on all trips made over a 24-hour period by all members of the participating households.\r \r Annual estimates from the HTS are usually produced on a rolling basis using multiple years of pooled data for each reporting year2. All estimates are weighted to the Australian Bureau of Statistics’ Estimated Resident Population, corresponding to the year of collection3. Unless otherwise stated, all reported estimates are for an average weekday.\r \r \r \r Due to disruptions in data collection resulting from the lockdowns during the COVID-19 pandemic, post-COVID releases of HTS data are based on a lower sample size than previous HTS releases. To ensure integrity of the results and mitigate risk of sampling errors some post-COVID results have been reported differently to previous years. Please see below for more information on changes to HTS post-COVID (2020/21 onwards).\r \r 1. Data collection for the HTS was suspended during lock-down periods announced by the NSW Government due to COVID-19.\r \r 2. Exceptions apply to the estimates for 2020/21 which are based on a single year of sample as it was decided not to pool the sample with data collected pre-COVID-19. \r \r 3. HTS population estimates are also slightly lower than those reported in the ABS census as the survey excludes overseas visitors and those in non-private dwellings.\r \r Changes to HTS post-COVID (2020/21 onwards)\r \r HTS was suspended from late March 2020 to early October 2020 due to the impact and restrictions of COVID-19, and again from July 2021 to October 2021 following the Delta wave of COVID-19. Consequently, both the 2020/21 and 2021/22 releases are based on a reduced data collection period and smaller samples.\r \r Due to the impact of changed travel behaviours resulting from COVID-19 breaking previous trends, HTS releases since 2020/21 have been separated from pre-COVID-19 samples when pooled. As a result, HTS 2020/21 was based on a single wave of data collection which limited the breadth of geography available for release. Subsequent releases are based on pooled post-COVID samples to expand the geographies included with reliable estimates.\r \r Disruption to the data collection during, and post-COVID has led to some adjustments being made to the HTS estimates released post-COVID:\r \r SA3 level data has not been released for 2020/21 and 2021/22 due to low sample collection.\r LGA level data for 2021/22 has been released for selected LGAs when robust Relative Standard Error (RSE) for total trips are achieved\r Mode categories for all geographies are aggregated differently to the pre-COVID categories\r Purpose categories for some geographies are aggregated differently across 2020/21 and 2021/22.\r A new data release – for six cities as defined by the Greater Sydney Commission - is included since 2021/22.\r Please refer to the Data Document for 2022/23 (PDF, 262.54 KB) for further details.\r \r \r RELEASE NOTE\r \r The latest release of HTS data is 15 May 2025. This release includes Region, LGA, SA3 and Six Cities data for 2023/24. Please see 2023/24 Data Document for details.\r \r A revised dataset for LGAs and Six Cities for HTS 2022/23 data has also been included in this release on 15 May 2025. If you have downloaded HTS 2022/23 data by LGA and/or Six Cities from this link prior to 15/05/2025, we advise you replace it with the revised tables. If you have been supplied bespoke data tables for 2022/23 LGAs and/or Six Cities, please request updated tables.\r \r Revisions to HTS data may be made on previously published data as new sample data is appended to improve reliability of results. Please check this page for release dates to ensure you are using the most current version or create a subscription (https://opendata.transport.nsw.gov.au/subscriptions) to be notified of revisions and future releases.\r

Wildlife species impacted by habitat loss and fragmentation often require conservation efforts to maintain populations. Long-nosed bandicoots (Perameles nasuta) still persist within the highly urbanised matrix of northern Sydney (Australia). These data are from a conservation genetics project investigating population structure and genetic diversity of the North Head Long-nosed bandicoot (Perameles nasuta)Â population and individuals from surrounding suburbs throughout northern Sydney.

The population at North Head, Sydney, is currently listed as an Endangered population due to its small size, apparent isolation and other threats. To support future management, we used 1,446 single nucleotide polymorphism markers (SNPs) from 167 bandicoots to: i) assess the assumption of isolation and determine if genetic structuring is present between North Head and individuals from 11 other localities in northern Sydney, and ii) investigate genetic diversity over time in the North Head population from ...

This publication, which was issued annually, gives details, statistics and comparative tables relating to population, births, deaths, diseases, and marriages (including divorces), for Sydney and suburbs.

(6/5601 part). 1 box (part).

Note:

This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.