This dataset contains estimates of the prevalence of homelessness on Census night 2011, derived from the Census of Population and Housing using the Australian Bureau of Statistics (ABS) definition …Show full descriptionThis dataset contains estimates of the prevalence of homelessness on Census night 2011, derived from the Census of Population and Housing using the Australian Bureau of Statistics (ABS) definition of homelessness. Prevalence is an estimate of how many people experienced homelessness at a particular point-in-time. The ABS uses six homeless operational groups to present the estimates of homelessness. Estimates are also presented for selected groups of people who may be marginally housed and whose living arrangements are close to the statistical boundary of homelessness and who may be at risk of homelessness. Data is by SA3 2011 boundaries. Periodicity: 5 yearly. For more information visit the 'http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/2049.0Explanatory%20Notes12016?OpenDocument' 'target='_blank' >Australian Bureau of Statistics. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2018): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

This dataset presents the total number of distinct specialist homeless services clients. The client counts are based on the location where the client resided in the week before their first support period in the financial year. Each client contributes only once, even if they had multiple support periods during the financial year. The data spans the financial years of 2014-15 to 2018-19 and is aggregated to 2016 Australian Statistical Geography Standard (ASGS) Statistical Area Level 3 (SA3).

The Specialist Homelessness Services Collection (SHSC) data accompanies the Specialist Homelessness Services Annual Report 2018-19.

For further information about this dataset, visit the Australian Institute of Health and Welfare - SHSC Data Cubes User Guide.

Notes:

• Caution should be used when comparing data for 2017-18 onwards with data for 2014-15 to 2016-17 in sub-state data cubes. Data for 2011-12 to 2016-17 at the state, territory and national levels are weighted to account for agency non-response and invalid statistical linkage keys (SLK), and have been rounded to the nearest integer. Due to improvements in agency response and rates of SLK validity, data from 2017–18 are no longer weighted. The removal of weighting does not constitute a break in time series, and these data are directly comparable with weighted counts for earlier years. As the weighting method is not suitable for sub-state units, the data in the sub-state cubes are not weighted.

• Clients are considered to be homeless if they are living in any of the following circumstances: No shelter or improvised dwelling, Short-term temporary accommodation, House, townhouse or flat (couch surfing or with no tenure).

• Clients are considered to be at risk of homelessness if they are living in any of the following circumstances: Public or community housing (renter or rent free), Private or other housing (renter, rent-free or owner), Institutional settings.

  AURIN has spatially enabled the original data.

This dataset presents the total number of distinct specialist homeless services clients. The client counts are based on the location where the client resided in the week before their first support period in the financial year. Each client contributes only once, even if they had multiple support periods during the financial year. The data spans the financial years of 2014-15 to 2018-19 and is aggregated to 2016 Australian Statistical Geography Standard (ASGS) Greater Capital City Statistical Areas (GCCSA).

The Specialist Homelessness Services Collection (SHSC) data accompanies the Specialist Homelessness Services Annual Report 2018-19.

For further information about this dataset, visit the Australian Institute of Health and Welfare - SHSC Data Cubes User Guide.

Notes:

• Caution should be used when comparing data for 2017-18 onwards with data for 2014-15 to 2016-17 in sub-state data cubes. Data for 2011-12 to 2016-17 at the state, territory and national levels are weighted to account for agency non-response and invalid statistical linkage keys (SLK), and have been rounded to the nearest integer. Due to improvements in agency response and rates of SLK validity, data from 2017–18 are no longer weighted. The removal of weighting does not constitute a break in time series, and these data are directly comparable with weighted counts for earlier years. As the weighting method is not suitable for sub-state units, the data in the sub-state cubes are not weighted.

• Clients are considered to be homeless if they are living in any of the following circumstances: No shelter or improvised dwelling, Short-term temporary accommodation, House, townhouse or flat (couch surfing or with no tenure).

• Clients are considered to be at risk of homelessness if they are living in any of the following circumstances: Public or community housing (renter or rent free), Private or other housing (renter, rent-free or owner), Institutional settings.

  AURIN has spatially enabled the original data.

This dataset presents the number of distinct specialist homeless services clients by client type, sex and age group. The client counts are based on the location where the client resided in the week before their first support period in the financial year. Each client contributes only once, even if they had multiple support periods during the financial year. The data spans the financial years of 2014-15 to 2018-19 and is aggregated to 2018 Australian Statistical Geography Standard (ASGS) Local Government Areas (LGA).

The Specialist Homelessness Services Collection (SHSC) data accompanies the Specialist Homelessness Services Annual Report 2018-19.

For further information about this dataset, visit the Australian Institute of Health and Welfare - SHSC Data Cubes User Guide.

Notes:

• Caution should be used when comparing data for 2017-18 onwards with data for 2014-15 to 2016-17 in sub-state data cubes. Data for 2011-12 to 2016-17 at the state, territory and national levels are weighted to account for agency non-response and invalid statistical linkage keys (SLK), and have been rounded to the nearest integer. Due to improvements in agency response and rates of SLK validity, data from 2017–18 are no longer weighted. The removal of weighting does not constitute a break in time series, and these data are directly comparable with weighted counts for earlier years. As the weighting method is not suitable for sub-state units, the data in the sub-state cubes are not weighted.

• Clients are considered to be homeless if they are living in any of the following circumstances: No shelter or improvised dwelling, Short-term temporary accommodation, House, townhouse or flat (couch surfing or with no tenure).

• Clients are considered to be at risk of homelessness if they are living in any of the following circumstances: Public or community housing (renter or rent free), Private or other housing (renter, rent-free or owner), Institutional settings.

  AURIN has spatially enabled the original data and has set suppressed values (".") as Null.

Abstract copyright UK Data Service and data collection copyright owner.

A comparative study of the causes of new episodes of homelessness among people aged 50 or more years was undertaken in Boston, Massachusetts (USA), Melbourne, Australia, and four English cities. The aims were to make a substantial contribution to the predominantly American debate on the causes of homelessness, and to make practice recommendations for the improvement of prevention.

The study had several objectives. It aimed to collect information about the antecedents, triggers and risk factors for becoming homeless in later life and about the national and local policy and service contexts. Furthermore, the researchers aimed to analyse and interpret the findings with reference to an integrated model of the causes of homelessness that represented structural and policy factors, including housing, health and social service organisation and delivery factors, and personal circumstances, events, problems and dysfunctions. The aim was to do this collaboratively, by drawing on the project partners' experience and knowledge. Finally, it was hoped to develop recommendations for housing, primary health care and social welfare organisations for the prevention of homelessness. This was to be done by identifying the common sequences and interactions of events that precede homelessness and their markers (or 'early warning' indicators) and by holding workshops in England with practitioners and their representative organisations on new ways of working.

By the study of contrasting welfare and philanthropic regimes in a relatively homogeneous category of homeless incidence (i.e. recent cases among late middle-aged and older people), it was hoped that valuable insights into the relative contributions of the policy, service and personal factors would be obtained. The study focused on older people who had recently become homeless, purposely to gather detailed and reliable information about the prior and contextual circumstances. To have included people who had been homeless for several years would have reduced the quality of the data because of 'recall' problems.

Users should note that data from the Australian sample for the study are not included in this dataset.
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The data file includes information about the English respondents and those from Boston. It was compiled in two stages. The first stage involved each project partner entering the pre-coded responses into the file. All partners then identified themes and created codes for the open-ended responses, and the resulting variables were added. Data quality-control procedures included blind checks of the data coding and keying.

The first 200 variables pertain to information collected from the respondents. They comprise descriptive variables of the circumstances prior to homelessness, including housing tenure during the three years prior to the survey, previous homelessness, employment history, income, health and addiction problems, and contacts with family, friends and formal services. The respondents were asked to rate whether specific factors were implicated in becoming homeless, and where appropriate, a following open-ended question sought elaboration.

The remaining variables comprise information collected from the respondents' 'key workers' about their understanding of the events and states that led to their clients becoming homeless.

This dataset presents the number of distinct specialist homeless services clients by client characteristics. The client counts are based on the location where the client resided in the week before their first presentation of the 2018-19 financial year. The data is aggregated to 2016 Australian Statistical Geography Standard (ASGS) Statistical Area Level 4 (SA4).

The Specialist Homelessness Services Collection (SHSC) data accompanies the Specialist Homelessness Services Annual Report 2018-19.

For further information about this dataset, visit the Australian Institute of Health and Welfare - Technical Information.

Notes:

• 'Homeless' status is derived for a client based on the client's housing circumstances at the beginning of their first support period within the reference year. All other clients not meeting these criteria are considered to be at risk of homelessness (excluding clients who did not provide sufficient information to make this assessment).

• Housing circumstances are determined based on the client's type of residence, tenure and conditions of occupancy.

• Rates are crude rates based on the Australian estimated resident population at 30 June of the reference year, as detailed in the online technical information.

• Includes clients from 'Other territories' and those that have not provided location information.

• Data presented have not been adjusted for partial or non-response (unweighted).

• Clients are assigned to a region based on where they lived in the week before presenting to a SHS agency. Clients are assigned to only one region, based on the location details provided in the first support period in the reference year. Regions are defined by the 2016 Australian Statistical Geography Standard (ASGS).

  AURIN has spatially enabled the original data and has excluded data from 'Unknown' SA4s.