Population: Resident: Estimated: Annual: South Australia: Greater Adelaide data was reported at 1,334,167.000 Person in 2017. This records an increase from the previous number of 1,324,057.000 Person for 2016. Population: Resident: Estimated: Annual: South Australia: Greater Adelaide data is updated yearly, averaging 1,270,970.500 Person from Jun 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 1,334,167.000 Person in 2017 and a record low of 1,189,243.000 Person in 2006. Population: Resident: Estimated: Annual: South Australia: Greater Adelaide 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.

The City of Port Adelaide Enfield Community Profile provides demographic and economic analysis for the Council area and its suburbs based on results from the 2016, 2011, 2006, 2001, 1996 and 1991 Censuses of Population and Housing. The profile is updated with population estimates when the Australian Bureau of Statistics (ABS) releases new figures. This is an interactive query tool where results can be downloaded in various formats. Three reporting types are available from this resource: 1. Social atlas that delivers the data displayed on a map showing each SA1 area (approx 200 households), 2. Community Profile which delivers data at a District level which contain 2 to 3 suburbs, and 3. Economic Profile which reports statistics of an economic indicators. The general community profile/social atlas themes available for reporting on are: -Age -Education -Ethnicity -Disability -Employment/Income -Household types -Indigenous profile -Migration -Journey to work -Disadvantage -Population Estimates -Building approvals. It also possible to navigate to the Community Profiles of some other Councils as well.

Official population projections for: • South Australia and regions for 2016 to 2041 • Local government areas (LGAs) and Statistical Areas level 2 (SA2s) for 2016 to 2036. Users should familiarise themselves with the assumptions, qualifications and background information provided on the DPTI population projections webpage at http://www.dpti.sa.gov.au/planning/population in order to choose the projection that best suits their needs. Updated every 5 years.

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

As of June 2023 in the state of South Australia, about 6.8 percent of the population was between 25 and 29 years old. In comparison, just 2.6 percent of the population was over the age of 85.

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.

With population growth in the Adelaide city centre a priority for both the Government of South Australia and the City of Adelaide, a joint research study was commissioned by the two organisations into the market for Adelaide city living.

The work, undertaken by Hudson Howells in South Australia, identified key market segments for Adelaide, as well as providing insights into perceptions of Adelaide as a residential proposition, product preferences, competitor environment and relocation decision making.

The research comprised four surveys and seven focus groups. Reports outlining the results of each study and the round of focus groups are available here as pdf files. Survey raw data is also provided here in excel and spss formats for use by the development community and others.

In particular providing the data in spss format allows for further statistical interrogation and additional insights into the Adelaide city living market.

澳大利亚 Population: Resident: Estimated: Annual: South Australia: Greater Adelaide在2017达1,334,167.000 人口，相较于2016的1,324,057.000 人口有所增长。澳大利亚 Population: Resident: Estimated: Annual: South Australia: Greater Adelaide数据按每年更新，2006至2017期间平均值为1,270,970.500 人口，共12份观测结果。该数据的历史最高值出现于2017，达1,334,167.000 人口，而历史最低值则出现于2006，为1,189,243.000 人口。CEIC提供的澳大利亚 Population: Resident: Estimated: Annual: South Australia: Greater Adelaide数据处于定期更新的状态，数据来源于Australian Bureau of Statistics，数据归类于Global Database的澳大利亚 – Table AU.G002: Estimated Resident Population。

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

Increase regional populations, outside of Greater Adelaide, by 20 000 to 320 000 or more by 2020.

BackgroundFew studies have investigated the relationship between physical activity (PA) of low intensity and duration with quality of life (QoL) among individuals at risk or with cardiovascular disease (CVD).ObjectivesTo investigate whether PA of different intensity and duration moderates the relationship between CVD and its risk factors (obesity, hypertension, diabetes, dyslipidaemia) and QoL in adults.MethodsPopulation-based cross-sectional studies using data from the EpiFloripa Cohort Study (Southern Brazil; n = 1,220, 38.8±12.0 years, 48.2% males) and the North West Adelaide Health Study (NWAHS, South Australia; n = 1,661, 43.7±11.1 years, 49.7% males). The physical and psychological domains of QoL were assessed using the WHOQOL-Bref (EpiFloripa) or the SF-36 (NWAHS) questionnaires. The diagnosis of CVD and its risk factors were self-reported. PA was self-reported and quantified by its intensity [“walking” or moderate/vigorous (MVPA)] and duration (none, 1–150, ≥150 min/week). Both studies were analysed separately, and results were adjusted for sociodemographic variables.ResultsParticipants at risk or with CVD from both studies showed a lower QoL than ‘healthy’ individuals with a stronger relationship for the physical domain. PA duration showed a direct-trend relationship with QoL, but the associations were stronger for MVPA in both studies. However, when stratified by health status, the magnitude of the association between “walking” duration and a higher physical QoL was greater among those at risk or with CVD compared to ‘healthy’ individuals. Conversely, among Australians with CVD, MVPA was associated with a better physical QoL only when its duration was ≥150 min/week. All associations were stronger in the NWAHS than in the Brazilian study.Conclusions“Walking” was more prevalent than MVPA and was consistently associated with a better physical QoL among those at risk or with CVD. These findings should be considered in the design of public health interventions designed to increase PA and improve QoL.

This dataset is the June 2022 release of Geoscape Planning for a single SA2 area (Adelaide) with SA2 code (41001). 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 Adelaide SA2 by AURIN.

SD: Standard Deviation, IRSD: Index of Relative DisadvantageNote: Socio-demographic characteristics are current for SAMSS participants and baseline for NWAHS participants. Obesity status is reported one year ago for SAMSS participants and measured at baseline for NWAHS participants.Socio-demographic characteristics of participants who were obese and not obese, South Australian Monitoring and Surveillance System (SAMSS) and North West Adelaide Health Study (NWAHS).

Occurrence records for Thylacine used for predicting habitat suitability

The statistic depicts Australia's gross domestic product (GDP) from 1987 to 2024, with projections up until 2030. In 2024, GDP in Australia amounted to about 1.8 trillion US dollars. See global GDP for a global comparison. Australia’s economy and population Australia’s gross domestic product has been growing steadily, and all in all, Australia and its economic key factors show a well-set country. Australia is among the countries with the largest gross domestic product / GDP worldwide, and thus one of the largest economies. It was one of the few countries not severely stricken by the 2008 financial crisis; its unemployment rate, inflation rate and trade balance, for example, were hardly affected at all. In fact, the trade balance of Australia – a country’s exports minus its imports – has been higher than ever since 2010, with a slight dip in 2012. Australia mainly exports wine and agricultural products to countries like China, Japan or South Korea. One of Australia’s largest industries is tourism, which contributes a significant share to its gross domestic product. Almost half of approximately 23 million Australian residents are employed nowadays, life expectancy is increasing, and the fertility rate (the number of children born per woman) has been quite stable. A look at the distribution of the world population by continent shows that Australia is ranked last in terms of population and population density. Most of Australia's population lives at the coast in metropolitan areas, since parts of the continent are uninhabitable. Unsurprisingly, Australia is known as a country with very high living standards, four of its biggest cities – Melbourne, Adelaide, Sydney and Perth – are among the most livable cities worldwide.

Prevalence of sociodemographic and clinical variables among adults in the EpiFloripa study (Southern Brazil, 2012–2013) and North West Adelaide Health Study (South Australia, 2008–2010).

Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

Generally, urbanization is a major threat to biodiversity; however, urban areas also provide habitats that some species can exploit. Flying-foxes (Pteropus spp.) are becoming increasingly urbanized; which is thought to be a result of increased availability and temporal stability of urban food resources, diminished natural food resources, or both. Previous research has shown that urban-roosting grey-headed flying-foxes (Pteropus poliocephalus) preferentially forage in human-modified landscapes. However, which land-use areas and food plants support its presence in urban areas is unknown. We tracked nine P. poliocephalus roosting in Adelaide, South Australia, between December 2019 and May 2020, using global positioning systems (GPS), to investigate how individuals used the urban landscape mosaic for feeding. The most frequently visited land-use category was “residential” (40% of fixes) followed by “road-side,” “reserves” and “primary production” (13–14% each). However, “reserves” were visited four times more frequently than expected from their areal availability, followed by the “residential” and “road-side” categories that were visited approximately twice more than expected each; in contrast, the “primary production” category was visited approximately five times less than expected. These results suggest that while residential areas provide most foraging resources supporting Adelaide’s flying-fox population, reserves contain foraging resources that are particularly attractive to P. poliocephalus. Primary production land was relatively less utilized, presumably because it contains few food resources. Throughout, flying-foxes visited an eclectic mixture of diet plants (49 unique species), with a majority of feeding fixes (63%) to locally indigenous Australian native species; however, in residential areas 53% of feeding visits were to non-locally indigenous species, vs only 13% in reserves. Flowering and fruiting phenology records of the food plants visited further indicated that non-locally indigenous species increase the temporal availability of foraging resources for P. poliocephalus in urban Adelaide. Our findings demonstrate the importance of residential areas for urban-roosting P. poliocephalus, and suggest that the anthropogenic mixture of food resources available in the urban landscape mosaic supports the species’ year-round presence in urban areas. Our results further highlight the importance of conserving natural habitats within the urban landscape mosaic, and stress the need for accounting for wildlife responses to urban greening initiatives. Methods Pteropus poliocephalus individuals were captured at the Adelaide Botanic Park on 10 and 11 December 2019. Flying-foxes returning to the roost before dawn were captured using two double banked mist nets (18m × 5m and 12m × 5m, 38mm mesh, Ecotone Telemetry, Poland) suspended 15 m high in the canopy of the colony. Nets were run on pulley systems that were continuously monitored by volunteers and two or three trained researchers. Each flying-fox was removed from the net by researchers and placed into a pillowcase hung from a horizontal pole. Females that were lactating, pregnant or carrying a pup were released immediately upon capture. Ten focal individuals (five adult males and five non-reproductively active adult females) were transported to Adelaide Zoo where they were processed. Individuals were anaesthetized using 5% vaporized isoflurane via facemask and maintained at 2% isoflurane until processing was complete. While anaesthetized, morphometrics were taken and all bats were banded with a single stainless-steel band (Australian Bird and Bat Banding Scheme). The ten individuals were fitted with a collar supporting a GPS and accelerometry unit (CREX GPS Logger, Ecotone Telemetry, Poland, hereafter: transmitter). The five females had a mass of 725 g ± 93.1 (568 - 806) and the five males had a body mass of 818 g ± 127.0 (685 - 954). The transmitter and collar weighed 10 g and 3 g, respectively, giving a total weight of 13 g, representing 1.6-2.3% and 1.4-1.9% of the body weight of females and males, respectively. After animals recovered from anesthesia, they were placed in animal holding facilities at the Adelaide Zoo for recovery. The five male flying-foxes required surgery to implant a temperature-sensitive VHF FM radio transmitter (model PD-2THX, 3.9 g; battery life: 5 months; Holohil) as part of another study (Walker, unpublished). Tracking devices represented < 3.0 % of the body mass of the lightest individual. Individuals that did not require surgery (n = 5 females) were released back into the colony within 6 h of capture; the five males were released back into the colony the next morning following an assessment by a wildlife veterinarian (Author WB). Research was conducted under Animal Research Authority no. A12217, issued by Western Sydney University. Data collection Global positioning systems and accelerometer data were collected for 5 months from 13 December 2019 to 23 May 2020 (Austral Summer and Autumn). Accelerometer data were recorded on three orthogonal axes and were used to identify the GPS fixes associated with feeding, as opposed to other behaviors such as flying (see below). Transmitters were programmed to collect accelerometer data in three burst types, dependent on battery voltage (solar recharge): 12 s at 5 Hz every 15 mins, 2 s at 30 Hz every 30 mins, or 3 s at 10 Hz every 30 mins. Transmitters were set to record GPS data every 30 mins during the night when there was sufficient solar recharge. Accelerometer data were linked with the GPS duty cycle. The duty cycles of the transmitters were monitored and changed remotely via Global System for Mobile (GSM) network using the web-panel depending upon the solar recharge of the batteries. Data were collected via a GSM link from GPS trackers to 3G-enabled mobile phone towers that then reported the data to a File Transfer Protocol (FTP) server, accessed through the GPS data processing software package “NGA Analyzer” (Ecotone Telemetry, Poland). The amount of time that the trackers produced usable data varied from 3 to 154 days (Mean = 78.4; Supplementary Table 1). Some of the collared individuals left the Adelaide region (defined as ≥75 km from the center of the Adelaide Botanic Garden roost) during our study (Supplementary Table 1). One female (FFOX05) left the Adelaide region before the transmitter began collecting data and hence her data were excluded from analyses herein. Another female (FFOX02) left the Adelaide region on 18 January 2020, and returned on 2 April 2020. Two males (FFOX07 and FFOX09) left the Adelaide region on 31 December 2019 and on 19 March 2020, respectively, and did not return to the region for the duration of the study. We removed GPS fixes associated with travel outside of the Adelaide region (GPS fixes greater than 75 km from the colony). GPS fixes less than 500 m from the center of the colony were also removed to exclude resting time within the colony. As the boundary of the roost varies with the number of flying-foxes present, this conservative approach means that we are potentially excluding some nearby foraging fixes. For those individuals that left the Adelaide region, GPS fixes recorded on the day of departure or day of return were excluded as any food resources associated with these fixes were likely supporting the individual’s journey to or from another roost rather than their stay in Adelaide. Identifying feeding fixes Global positioning systems locations were designated “feeding fixes” if they were temporally aligned with moderate levels of activity, as determined by variance in the acceleration data. We applied a principal component analysis (PCA) using the “prcomp” function from the “stats” package in R to the three recorded axes of acceleration forces to maximize the amount of variation caused by movement that is expressed in a single vector (i.e., principal component 1). We did this also to account for variation in the spatial orientation of the transmitter between individuals, which could have influenced the distribution of acceleration forces caused by movement across the three axes. We plotted a frequency histogram of the standard deviation (SD) of the PC1 scores over each burst of acceleration data for each individual and used troughs between primary modes in the distribution of SD values as thresholds for designating among broad categories of acceleration intensity (Collins et al., 2015). Firstly, we identified a mode of greatest SD values, most likely associated with wing flapping during flight, and assigned data above a trough threshold defining the lower limit of this mode to high-level activity (Supplementary Figure 1A). The remaining data were subject to another PCA. We identified two modes in the frequency distribution of SD values of the new PC1 vector: a lower mode clearly associated with inactivity (i.e., little, if any, and body movement), and an upper mode that included values of greater acceleration intensity, but excluded the highest values previously assigned to high-level activity. We assigned data above the trough between these two modes as moderate activity that likely pertains to tree-based movements, including feeding (i.e., the behavior of eating a dietary component and any associated movement within a foraging tree; Supplementary Figure 1B). Date-time stamps associated with moderate activity were rounded to the nearest second. We then aligned the closest activity date-time stamps to the GPS date-time stamp. To minimize the possibility of misinterpreting the level of activity at each GPS coordinate, we calculated the time discrepancy between each pair of activity and GPS data, using the “difftime” function from the “lubridate” package in R. We excluded the data to include only data pairs that were within a ±60 s discrepancy buffer. We used the “suncalc”

Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

The Stansbury Basin is a relatively unexplored Cambrian sedimentary basin in South Australia. Good quality seismic data has defined a large untested prospect, the Investigator prospect, which has the potential to contain 200 to 300 mmbbl of oil,... The Stansbury Basin is a relatively unexplored Cambrian sedimentary basin in South Australia. Good quality seismic data has defined a large untested prospect, the Investigator prospect, which has the potential to contain 200 to 300 mmbbl of oil, or 170+ bcf of gas and 4+ mmbbl of condensate. The area is considered to have strong affinities with prolific Late Proterozoic and Cambrian petroleum-producing basins in Central Siberia and the Sultanate of Oman. The Stansbury Basin covers about 4000 square miles and lies partly onshore on Yorke Peninsula and partly offshore beneath Gulf St Vincent, which consists of relatively quiet and sheltered shallow (80-120 feet) waters. It lies in close proximity to the State's capital city, Adelaide (population 1 million), and to refining facilities and an undersupplied market for gas. No exploration wells have yet been drilled in the most prospective section of the basin beneath the waters of the gulf. The main trend of interest is a reef-rimmed platform and shelf margin developed in a back-arc basin, with hydrocarbon source potential from black marine shales occurring in deeper parts of the section to the east. There is abundant evidence to indicate that the reef-like facies are dolomitized with resultant secondary porosity, and that they have remained unbreached since deposition. Other structural plays are present in the form of large, gently dipping anticlinal structures after the style of the giant Siberian fields, plus structures associated with wrench faults. Petroleum exploration carried out to date has been sparse considering the areal extent of the basin. Much of the early effort was based on technologically poor and inadequate seismic data. Exploration has, however, established from shallow onshore well cores and logs that the primary reservoir possesses excellent reservoir characteristics. Drill stem tests conducted in several Yorke Peninsula wells, drilled on doubtful structural closure, yielded traces of gas and highly saline water confirming the integrity of the seal and that no flushing has occurred. The best quality seismic available for mapping was acquired offshore in a speculative survey carried out by GSI in late 1985. The results of this work have been interpreted and form the basis of this proposal. The Investigator Prospect is a drillable structure, delineated in shallow water (80 to 100 feet). The Stansbury Basin tenure comprises Petroleum Exploration Licence 53, awarded on 4 December 1990 to Preview Resources Pty Ltd and Oakman Pty Ltd. PEL 53 covers approximately 10,136 sq. km (2,505,000 acres) and not only contains proven structures but also encompasses all known trends. An exploration programme is in progress which started with a detailed review and the reprocessing of available seismic data. The programme also includes an organic geochemistry evaluation, involving total organic carbon, Rock-Eval pyrolysis, gas chromatography - mass spectrometry and thermal modelling studies. Basement tectonics are also being appraised using potential field data together with reprocessed seismic.