The data set provides a summary of regional Western Australia population by Regional Development Commission boundaries, and by regional centres. Additional information is provided on Aboriginal and …Show full descriptionThe data set provides a summary of regional Western Australia population by Regional Development Commission boundaries, and by regional centres. Additional information is provided on Aboriginal and Torres Strait Islander population by Regional Development Commission boundaries.

The data set provides a summary of regional Western Australia data by Regional Development Commission boundaries including population, Gross Regional Product, jobs growth and unemployment. Show full description

Estimated resident population (ERPs) is the official measure of the Australian population, published quarterly by the ABS. This dataset contains quarterly ERP by age, at state/territory and Australia level.

Western Australia Tomorrow, Population Report No. 10, Medium-term Forecasts for Western Australia 2014-2026 and Sub-regions 2016-2026 Show full description

The data set provides a summary of regional Western Australia population by Regional Development Commission boundaries, and by regional centres. Additional information is provided on Aboriginal and Torres Strait Islander population by Regional Development Commission boundaries.

Original provider: Dr Belinda Cannell, Murdoch University

Dataset credits: Data provider Murdoch University - Biological Sciences and Biotechnology Originating data center Satellite Tracking and Analysis Tool (STAT) Project partner Murdoch University
University of NSW Project sponsor or sponsor description This project has been funded under the Australian Research Council Linkage Project Scheme. Funds have also been contributed by Department of Environment and Conservation,
Fremantle Ports, Department of Defence, Tiwest and the Winifred Violet Scott Trust fund.

Abstract: Little Penguins from Penguin and Garden islands in Perth, Western Australia, are tracked to determine the areas in which they travel and feed throughout the breeding season. Once the areas they regularly use are determined, the threats the penguins are exposed to, and their likelihood of occurrence, can be elucidated. This forms part of a broader project to determine the population viability analysis of the Little Penguins in the Perth metropolitan region.

This dataset is derived from data stored in Landgate’s medium scale Topographic Geodatabase (TGDB). It provides a very broad view of the states population centres. License Information Use of Fundamental Land Information published to data.wa.gov.au is subject to the conditions of a Personal Use Agreement. © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions.

Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses.

The data set provides a summary of regional Western Australia data by Regional Development Commission boundaries including population, Gross Regional Product, jobs growth and unemployment.

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.

Estimated Resident Population (ERP) is the official measure of the Australian population. ERP for sub-state regions (including SA2s and LGAs) is published annually, with a reference date of 30 June.

This dataset is the June 2022 release of Geoscape Planning for a single SA2 area (Perth City) with SA2 code (51041). 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 Perth City SA2 by AURIN.

Common Noddies (Anous stolidus) were first recorded on Lancelin Island off south-western Australia in January 1992. A study of the population dynamics of this colony began in the 1994-95 breeding season and was continued for over 10 years, until 2011. The demography of the colony was modelled using information on adult survivorship, age of first breeding and natal recruitment from the analysis of banding-recapture data and annual colony census data.

The extent and scope of the seasonal distribution movements and habitat usage of dugongs fitted with remote location recording and transmitting devices within the Shark Bay World Heritage Property (SBWHP) on the mid-West Coast of Western Australia were measured from 2000-2002. In addition to defining movement patterns and habitat preferences of individual dugongs an aerial survey was undertaken during the summer of 2002 to define population distribution and abundance estimates.

All data/script descriptions, and details for executing the analyses, are outlined in the README file, "README_Thia_2022_JEB.pdf".

Conservation translocations have become an increasingly popular method to restore or secure vulnerable populations. However, translocations greatly vary in success. The use of population viability analysis (PVA) may increase the likelihood of meeting translocation goals. However, the quality of PVAs to inform translocations is dependent on the availability of ecological data and clear translocation objectives to guide them. Here, we used PVAs to inform the planned conservation translocation of the Western Grasswren (Amytornis textilis textilis) from mainland Shark Bay onto Dirk Hartog Island, Western Australia. A range of translocation scenarios was modelled and scored against success criteria as determined by translocation objectives. Simulations of 20-year outcomes found that a minimum founder population of 112 individuals meets all success criteria. PVA supported sourcing individuals from two subpopulations to maximise genetic diversity. No impact to source populations was detected for the proposed harvest quantities despite conservative estimates of initial source population sizes. Here we demonstrate that creating clear, measurable objectives alongside a PVA lessens ambiguity about which translocation scenarios could be viable. In doing so, we have identified the minimum translocation sizes needed to maintain genetic diversity and population growth, thus increasing the likelihood of translocation success without impacting the source population. Methods Allele dataset was generated through next generation SNP sequencing of blood samples from Western Grasswren (Amytornis textilis textilis). After filtering SNPs in the methods described in Gibson Vega et al. 2023, a subset of 1000 neutral loci were extracted for use in the program VORTEX to model population genetic mixing. Gibson Vega, A., Hall, M. L., Ridley, A., Cowen, S. J., Slender, A. L., Burbidge, A. H., Louter, M., and Kennington, W. J. (2023). Population genetic structure associated with a landscape barrier in the Western Grasswren (Amytornis textilis textilis). Ibis.

Text file (GenAlEx format) containing allele scoring for 28 microsatellite loci for 78 individual dolphins sampled in the metropolitan waters of Perth, Western Australia.Format of the dataset with detailed information:- 1st column: sample ID- 2nd column: (Pop) community allocated to the sample (based on socio-spatial structure, GR- Gage Roads; SCR - Swan Canning Riverpark; OA - Owen Anchorage; and CS - Cockburn Sound, Chabanne et al. 2017)- 3rd and 4th columns: sample location - longitude and latitude in decimal degree- 5th column: allocated haplotype (Genbank Accession numbers: Hap1- MW221830; Hap2 - MW221831; Hap3 - MW221832; Hap4 - MW221833; and Hap5 - MW221834)- 6th to end: alleles scoring, two columns per locus, missing allele is scored as '0'.

The data were collected to test hypotheses that microplastic concentrations in stormwater drains would be able to be predicted from: (1) the proportions of different land uses in stormwater catchments; (2) catchment population and land area; (3) rainfall preceding sample collection. The data show that microplastic fibres were the most common morphology across all drains, followed by fragments. Most microplastics detected were in the 100-530 µm size range, with lower proportions ≤ 25 µm or > 530 µm. The most common colour was black, followed by red, blue, and green with other colours < 5% of total particle counts. There was no statistically significant variation in microplastic concentrations between or within stormwater catchments. Linear mixed-effects models showed significant positive effects of catchment area, catchment population, and the proportion of industrial land, natural land and public open space on microplastic concentrations. The proportion of residential land had a significant negative effect on microplastic concentrations. The proportion of agricultural land in each catchment, and preceding rainfall, had no effect on microplastic concentrations. The majority of data are presented as a single comma-separated value file with 144 rows representing 3 replicates of 4 size fractions from 12 sampling sites. Samples have unique names and are categorised by Size (4 categories), Drain (6 categories) and Site (12 categories, 2 per Drain). Quantitative data relating to microplastics measurement include: sample volume; raw counts of total microplastics and microplastics separated into fragment, fibre, film, and microbead categories; concentrations of total microplastics and microplastics separated into fragment, fibre, film, and microbead categories; blank corrections (fibres only); corrected raw counts and concentrations of fibres; corrected raw counts and concentrations of total microplastics. Catchment demographic and land use data are: catchment area and population; proportions of land use in residential, industrial, services, agricultural, natural, and public open space categories. Rainfall for the 7 days prior to sample collection is also recorded. A separate comma-separated value file summarises the microplastic colour data, and an image shows aerial photograph maps of each site.

This dataset contains 80 SNP loci from 15 locations with 25-47 samples per locations. Each column contains data from two alleles. All genotyped samples are included. Prior to population genetic analysis clone mates were identified and removed.

Starch-gel electrophoresis was used to investigate population structure of the commercially and recreationally exploited abalone, Haliotis roei. Samples were obtained from abalone taken from 10 sites in south-western Australia between March 1997 and April 1998.