According to a survey conducted by Ipsos on predictions for global issues in 2020, ** percent of Australian respondents believed it unlikely that one of their online accounts would be hacked in 2020. This was slightly less than the global average.

Australia Probability of Dying at Age 20-24 Years: per 1000 data was reported at 2.000 Ratio in 2019. This stayed constant from the previous number of 2.000 Ratio for 2018. Australia Probability of Dying at Age 20-24 Years: per 1000 data is updated yearly, averaging 2.850 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 4.500 Ratio in 1990 and a record low of 2.000 Ratio in 2019. Australia Probability of Dying at Age 20-24 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Health Statistics. Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Australia Probability of Dying at Age 15-19 Years: per 1000 data was reported at 1.500 Ratio in 2019. This stayed constant from the previous number of 1.500 Ratio for 2018. Australia Probability of Dying at Age 15-19 Years: per 1000 data is updated yearly, averaging 2.000 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 3.300 Ratio in 1990 and a record low of 1.500 Ratio in 2019. Australia Probability of Dying at Age 15-19 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Health Statistics. Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Australia Probability of Dying at Age 5-9 Years: per 1000 data was reported at 0.400 Ratio in 2019. This stayed constant from the previous number of 0.400 Ratio for 2018. Australia Probability of Dying at Age 5-9 Years: per 1000 data is updated yearly, averaging 0.500 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 0.900 Ratio in 1991 and a record low of 0.400 Ratio in 2019. Australia Probability of Dying at Age 5-9 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Health Statistics. Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

This dataset, released January 2020, contains for the years 2017-2018 the Estimated number of people aged 18 years and over with high or very high psychological distress based on the Kessler 10 Scale; Estimated number of people aged 18 years and over who had high blood pressure, overweight and obesity; Smoking (modelled estimates); Alcohol: lifetime risky drinking (modelled estimates); Fruit consumption (modelled estimates); Exercise (modelled estimates). The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS; estimates at the LGA and PHN level were derived from the PHA estimates. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Key information about Australia Geopolitical Risk Index

• Australia Geopolitical Risk Index was reported at 0.165 % in Feb 2025.
• This records an increase from the previous number of 0.082 % for Jan 2025.
• Australia Geopolitical Risk Index data is updated monthly, averaging 0.069 % from Jan 1985 to Feb 2025, with 482 observations.
• The data reached an all-time high of 0.515 % in Mar 2022 and a record low of 0.005 % in Feb 1989.
• Australia Geopolitical Risk Index data remains active status in CEIC and is reported by Dario Caldara and Matteo Iacoviello.
• The data is categorized under World Trend Plus’s Geopolitical Risk Index – Table: Geopolitical Risk Index: By Country.

Australia Probability of Dying at Age 10-14 Years: per 1000 data was reported at 0.400 Ratio in 2019. This stayed constant from the previous number of 0.400 Ratio for 2018. Australia Probability of Dying at Age 10-14 Years: per 1000 data is updated yearly, averaging 0.600 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 1.000 Ratio in 1991 and a record low of 0.400 Ratio in 2019. Australia Probability of Dying at Age 10-14 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Health Statistics. Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

According to a survey conducted by Ipsos on predictions for global issues in 2019, ** percent of Australians believed it likely that Donald Trump would be impeached in 2019. The results of the survey showed that Australians were amongst the top six countries who believed that he would be impeached this year.

Bureau of Meteorology Australian Digital Forecast Database Grid. Forecasts are issued by the Bureau of Meteorology on a routine basis for each state and merged into one forecast grid for Australia …Show full descriptionBureau of Meteorology Australian Digital Forecast Database Grid. Forecasts are issued by the Bureau of Meteorology on a routine basis for each state and merged into one forecast grid for Australia for each forecast element. This forecast element is the Daily Rainfall Amount - 25% Chance of More Than (mm). There is a 25% chance during the twenty four hour period to 15 UTC of getting the stated amount of rainfall, or more.

Abstract

The Namoi Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/).

An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin.

The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Namoi Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2018) NAM Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58.

Dataset Ancestors

• Derived From River Styles Spatial Layer for New South Wales

• Derived From Geofabric Surface Network - V2.1

• Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition

• Derived From HUN SW footprint shapefiles v01

• Derived From HUN Groundwater footprint polygons v01

• Derived From Namoi Environmental Impact Statements - Mine footprints

• Derived From Namoi CMA Groundwater Dependent Ecosystems

• Derived From Landscape classification of the Namoi preliminary assessment extent

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From Bioregional Assessment areas v03

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Asset list for Namoi - CURRENT

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From Namoi bore locations, depth to water for June 2012

• Derived From Victoria - Seamless Geology 2014

• Derived From Murray-Darling Basin Aquatic Ecosystem Classification

• Derived From HUN SW GW Mine Footprints for IMIA 20170303 v03

• Derived From Climate model 0.05x0.05 cells and cell centroids

• Derived From Namoi hydraulic conductivity measurements

• Derived From Namoi groundwater uncertainty analysis

• Derived From Historical Mining footprints DTIRIS HUN 20150707

• Derived From Namoi NGIS Bore analysis for 2012

• Derived From Australian 0.05º gridded chloride deposition v2

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From Bioregional Assessment areas v06

• Derived From NAM Analysis Boundaries 20160908 v01

• Derived From Namoi groundwater drawdown grids

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From BOM, Australian Average Rainfall Data from 1961 to 1990

• Derived From Namoi Existing Mine Development Surface Water Footprints

• Derived From Surface water Preliminary Assessment Extent (PAE) for the Namoi (NAM) subregion - v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From [National Surface Water sites

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

This dataset, created in June 2013, provides an indication of the likelihood of a flood occurring from one or more sources: creek, river, and storm tide inside the Brisbane City Council local government area. This layer contributes to the overall Flood Awareness Mapping for Brisbane City Council.

Brisbane City Council has developed the Flood Awareness Maps and adopted the terms ‘high’, ‘medium’, ‘low’ and ‘very low’ likelihood areas to help residents and businesses better understand the likelihood of a flood affecting their property. The Flood Awareness Maps are an awareness tool and the maps do not provide information about the depth or speed of flood water. Information on potential flood levels for a property can be found in the FloodWise Property Report online.

The Flood Awareness Maps are an awareness tool to provide an indication of the likelihood of a flood occurring from one or more sources: creek, river, overland flow and storm tide. The maps do not provide information about the depth or speed of flood water. Use the FloodWise Property Report for information about flood levels specific to your property.

Many properties within the high and medium flood likelihood were affected by flooding in the 1974 and 2011 Brisbane River floods.

Residents in the low and very low flood likelihood areas should still be aware of their risk of flooding and understand how they, as well as others in the area, may be affected.

High likelihood area

Flooding is almost certain to occur in a high likelihood area. Residents and businesses are strongly advised to learn about the flood likelihood for their property so they can be prepared to help minimise the impact on their home, business and family.

Medium likelihood area

Flooding is likely to occur in a medium likelihood area. Residents and businesses are advised to learn about the flood likelihood for their property so they can be prepared to help minimise the impact on their home, business and family.

Low likelihood area

Low flood likelihood areas may experience flooding in a rare flood event. Residents and businesses should consider how flooding may affect their local area, suburb or community. Flooding is unlikely in a low flood likelihood area but it may still occur.

Very low likelihood area

Very low likelihood areas are unlikely to flood except in a very rare or extreme flood event. Residents and businesses should consider how flooding may affect their local suburb, area or community. Flooding is very unlikely in a very low flood likelihood area, but may still occur.

Brisbane City Council is working hard to reduce the impact of flooding but we all have a responsibility to understand our flood risk and be better prepared to minimise the impact of flooding on our homes, property and businesses.

For further information please refer to Council's website.

The Disadvantage-Need-Risk (DNR) scores and rank are a method of identifying target populations and locations across South Australia and have been developed so that DCSI services can be planned to provide maximum support for those most in need. The DNR scores and ranks are a composite of: Disadvantage: the Index of Relative Socio-economic Disadvantage, part of the Socio-Economic Index For Areas (SEIFA) developed by the ABS. Need (or Sentinel Indicators): For the Vulnerable Families with Children target group the Sentinel Indicators identified were: jobless families with children under 15, divorce or separation, women smoking during pregnancy, Centrelink payments, substantiated child protection notifications. For Youth at Risk target group the Sentinel Indicators were: rates of teen pregnancy, student absenteeism, young people receiving unemployment benefits, youth Justice Orders, single parent families. Risk: the proportion of the target population within the general population.

Australia Risk of Impoverishing Expenditure for Surgical Care: % of People at Risk data was reported at 0.100 % in 2021. This stayed constant from the previous number of 0.100 % for 2020. Australia Risk of Impoverishing Expenditure for Surgical Care: % of People at Risk data is updated yearly, averaging 0.100 % from Dec 2003 (Median) to 2021, with 17 observations. The data reached an all-time high of 0.200 % in 2008 and a record low of 0.000 % in 2019. Australia Risk of Impoverishing Expenditure for Surgical Care: % of People at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Health Statistics. The proportion of population at risk of impoverishing expenditure when surgical care is required. Impoverishing expenditure is defined as direct out of pocket payments for surgical and anaesthesia care which drive people below a poverty threshold (using a threshold of $2.15 PPP/day).;The Program in Global Surgery and Social Change (PGSSC) at Harvard Medical School (https://www.pgssc.org/);Weighted average;

BackgroundThe risk of preterm birth (PTB) and low birthweight (LBW) may change over time the longer that immigrants reside in their adopted countries. We aimed to study the influence of acculturation on the risk of these outcomes in Australia.MethodsA retrospective cohort study using linked health data for all non-Indigenous births from 2005–2013 in Western Australia was undertaken. Acculturation was assessed through age on arrival, length of residence, interpreter use and having an Australian-born partner. Adjusted odds ratios (aOR) for term-LBW and PTB (all, spontaneous, medically-indicated) were calculated using multivariable logistic regression in migrants from six ethnicities (white, Asian, Indian, African, Māori, and ‘other’) for different levels of acculturation, compared to the Australian-born population as the reference.ResultsThe least acculturated migrant women, those from non-white non-Māori ethnic backgrounds who immigrated at age ≥18 years, had an overseas-born partner, lived in Australia for < 5 years and used a paid interpreter, had 58% (aOR 1.58, 95% CI 1.15–2.18) higher the risk of term-LBW and 40% (aOR 0.60, 95% CI 0.45–0.80) lower risk of spontaneous PTB compared to the Australian-born women. The most acculturated migrant women, those from non-white non-Māori ethnic backgrounds who immigrated at age 10 years and did not use an interpreter, had similar risk of term-LBW but 43% (aOR 1.43, 95% CI 1.14–1.78) higher risk of spontaneous PTB than the Australian-born women.ConclusionAcculturation is an important factor to consider when providing antenatal care to prevent PTB and LBW in migrants. Acculturation may reduce the risk of term-LBW but, conversely, may increase the risk of spontaneous PTB in migrant women residing in Western Australia. However, the effect may vary by ethnicity and warrants further investigation to fully understand the processes involved.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Rasters of the probability of drawdown in the weathered zone exceeding 20cm for baseline, crdp and acrd conditions in the surface weathered and fractured rock layer . The 10,000 posterior parameter ensembles are evaluated for each receptor location using the tailor-made emulator for that particular maximum drawdown (dmax) or time to maximum drawdown (tmax) prediction. A limited set of 200 parameter combinations, randomly selected from the posterior parameter ensembles is evaluated with the original model to compute dmax and tmax at the output nodes.

This is described in product 2.6.2 Groundwater numerical modelling (Peeters et al. 2016).

Peeters L, Dawes W, Rachakonda P, Pagendam D, Singh R, Pickett T, Frery E, Marvanek S, and McVicar T (2016) Groundwater numerical modelling for the Gloucester subregion. Product 2.6.2 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia.

Dataset History

This dataset contains gridded surfaces of the probability of drawdown exceeding 20cm for baseline, crdp and acrd conditions. Grids were interpolated using results from model cells. The interpolation is based on the exceedance probabilities calculated for the computational nodes based on the 200 randomly sampled parameter combinations of the posterior parameter distributions.

Dataset Citation

Bioregional Assessment Programme (XXXX) AEM dmax exceedance probability v01. Bioregional Assessment Derived Dataset. Viewed 11 July 2018, http://data.bioregionalassessments.gov.au/dataset/81bd21cb-ca48-443a-b47a-3fc578c80cee.

Dataset Ancestors

• Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW)

• Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014

• Derived From Groundwater Economic Assets GLO 20150326

• Derived From Gloucester digitised coal mine boundaries

• Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

• Derived From GLO Geological Model Extracted Horizons Final Grid XYZ V01

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Asset database for the Gloucester subregion on 12 September 2014

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From National Groundwater Information System (NGIS) v1.1

• Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved

• Derived From Asset database for the Gloucester subregion on 8 April 2015

• Derived From R-scripts for uncertainty analysis v01

• Derived From New South Wales 2 kilometers Residential Exclusions Zone

• Derived From Geofabric Surface Cartography - V2.1

• Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320

• Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From EIS Gloucester Coal 2010

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Geological Maps Combined for NSW

• Derived From Asset database for the Gloucester subregion on 28 May 2015

• Derived From Gloucester Deep Wells Completion Reports - Geology

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014

• Derived From EIS for Rocky Hill Coal Project 2013

• Derived From GLO AEM dmax v01

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From GLO Deep Well Locations and Depths of Formations V01

• Derived From Gloucester - Additional assets from local councils

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From GLO RMS Model Depth Structure Eroded v01

• Derived From Asset database for the Gloucester subregion on 29 August 2014

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted

• Derived From Groundwater Modelling Report for Stratford Coal Mine

• Derived From AGL Gloucester Gas Project AECOM report location map features

• Derived From Report for Director Generals Requirement Rocky Hill Project 2012

• Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013

• Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases

• Derived From Freshwater Fish Biodiversity Hotspots

• Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014

• Derived From GLO AEM Model v02

• Derived From Australia - Species of National Environmental Significance Database

• Derived From GLO DEM 1sec SRTM MGA56

• Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal

• Derived From NSW Office of Water Groundwater Entitlements Spatial Locations

• Derived From GLO Receptors 20150828

• Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public)

• Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

• Derived From [Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current

Forecast: Maternal Death Rate (Lifetime Risk) in Australia 2024 - 2028 Discover more data with ReportLinker!

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

Salinity risk mapping derived from land quality attribution associated with soil-landscape mapping at the subsystem/phase level. See Resource Management Technical Report 298, Section 2.8, Department of Agriculture, 2005. This version updates two previous versions (metadata dates 08/09/2003 and 29/04/2004). Show full description

This dataset, created in June 2013, provides an indication of areas subject to overland flow flooding inside the Brisbane City Council local government area. The overall overland flow layer consists of the 1% Annual Exceedance Probability (AEP) (100 year Average Recurrence Interval (ARI)) flood extent sourced from the Citywide Creek and Overland Flow Path mapping study (GHD, 2017).High impact area:In high impact areas, overland flow is almost certain to occur during a single lifetime (70 years). An event of this size or larger has a 5% chance of occurring in any year. The overland flow is generally unsafe for people, vehicles and buildings.Medium impact area:For the majority of medium impact areas, overland flow is very likely to occur during a single lifetime (70 years). An event of this size or larger has a 2% chance of occurring in any year. The overland flow is generally unsafe for people, vehicles and buildings, however these hazards are experienced less frequently than in high impact areas.Low impact area:For the majority of low impact areas, overland flow is likely to occur during a single lifetime (70 years). An event of this size or larger has a 1% chance of occurring in any year. The overland flow is generally safe for people, vehicles and buildings; however, certain areas can experience greater hazards.link in the Data and resources section on this page.

Due to a system issue, this data is not displayed here. To access the data, please use the ArcGIS Hub Datasets link in the Data and resources section on this page.

Australia Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data was reported at 28,182.000 NA in 2023. This records an increase from the previous number of 18,170.000 NA for 2022. Australia Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data is updated yearly, averaging 10,377.000 NA from Dec 1985 (Median) to 2023, with 39 observations. The data reached an all-time high of 28,182.000 NA in 2023 and a record low of 5,969.000 NA in 1985. Australia Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Health Statistics. Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.;WHO, UNICEF, UNFPA, World Bank Group, and UNDESA/Population Division. Trends in maternal mortality estimates 2000 to 2023. Geneva, World Health Organization, 2025;Weighted average;