About this dataset

Context

The dataset tabulates the Brisbane median household income by race. The dataset can be utilized to understand the racial distribution of Brisbane income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Brisbane, CA median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Brisbane, CA (2021, in 2022 inflation-adjusted dollars)

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Brisbane median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the Brisbane household income by gender. The dataset can be utilized to understand the gender-based income distribution of Brisbane income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Brisbane, CA annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Brisbane, CA annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Brisbane income distribution by gender. You can refer the same here

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.

The Australian Marine Microbial Biodiversity Initiative (AMMBI) provides methodologically standardized, continental scale, temporal phylogenetic amplicon sequencing data describing Bacteria, Archaea and microbial Eukarya assemblages. Sequence data is linked to extensive physical, biological and chemical oceanographic contextual information. Samples are collected monthly to seasonally from multiple depths at seven National Reference Stations (NRS) sites: Darwin Harbour (Northern Territory), Yongala (Queensland), North Stradbroke Island (Queensland), Port Hacking (New South Wales), Maria Island (Tasmania), Kangaroo Island (South Australia), Rottnest Island (Western Australia). The Integrated Marine Observing System (IMOS) NRS network is described at http://imos.org.au/facilities/nationalmooringnetwork/nrs/ North Stradbroke Island NRS is located 6.6 nm north east of North Stradbroke Island at a depth of 60 m over sandy substrate. It is 30 km southeast of the major city of Brisbane, Queensland (population 2.099 million), at the opening to large, shallow, Moreton Bay. The site is impacted by the southerly flowing EAC and its eddies, which may cause periodic nutrient enrichment through upwelling. This latitude is the biogeographic boundary for many tropical and subtropical species. The water column is well mixed between May-August and stratified for the remainder of the year and salinity may at times be affected by floodwaters from the nearby Brisbane River outflow.

Site details from Brown, M. V. et al. Continental scale monitoring of marine microbiota by the Australian Marine Microbial Biodiversity Initiative. Sci. Data 5:180130 doi: 10.1038/sdata.2018.130 (2018). Site location: North Stradbroke Island National Reference Station (NRS), Queensland, Australia Note on data download/processing: Data downloaded from Australian Microbiome Initiative via Bioplatforms Australia Data Portal on 17 June 2022. The search filter applied to download data from Bioplatforms Australia Data portal are stored in the Darwin Core property (identificationRemarks). Taxonomy is assigned according to the taxonomic database (SILVA 138) and method (Sklearn) which is stored in the Darwin Core Extension DNA derived data property (otu_db). Prefix were removed from the taxonomic names as shown in the example (e.g. d_Bacteria to Bacteria). Scientific name is assigned to the valid name available from the highest taxonomic rank. This collection is published as Darwin Core Occurrence, so the event level measurements need to be replicated for every occurrence. Instead of data replication, the event level eMoF data are made available separately at https://www.marine.csiro.au/data/services/obisau/emof_export.cfm?ipt_resource=bioplatforms_mm_nrs_nsi Please see https://www.australianmicrobiome.com/protocols/acknowledgements/ for citation examples and links to the data policy.

About this dataset

Context

The dataset tabulates the median household income in Brisbane. It can be utilized to understand the trend in median household income and to analyze the income distribution in Brisbane by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Brisbane, CA Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Brisbane, CA: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Brisbane, CA
• Brisbane, CA households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Brisbane median household income. You can refer the same here

This dataset is the June 2022 release of Geoscape Planning for a single SA2 area (Brisbane City) with SA2 code (31105). 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 Brisbane City SA2 by AURIN.

Change in prevalent cases and mortality over the life course of the Brisbane adult population (95% uncertainty interval).

PM2.5 values baseline and sensitivity scenarios.

Health care costs and health outcomes for base case by sex over the life course of the Brisbane adult population (95% uncertainty interval).

The Australian commercial real estate market, valued at $34.07 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 8.46% from 2025 to 2033. This expansion is fueled by several key drivers. Strong population growth in major cities like Sydney, Melbourne, and Brisbane is increasing demand for office, retail, and industrial spaces. Furthermore, the burgeoning e-commerce sector is driving significant growth in the logistics and warehousing segments. Government infrastructure investments and a generally positive economic outlook also contribute to this positive market trajectory. While rising interest rates and potential economic slowdown pose some constraints, the long-term fundamentals of the Australian economy and the ongoing need for modern commercial spaces are expected to mitigate these risks. The market is segmented by property type (office, retail, industrial & logistics, hospitality, and others) and by city (Sydney, Melbourne, Brisbane, Adelaide, Canberra, Perth), reflecting diverse investment opportunities and regional variations in growth rates. Sydney and Melbourne are expected to remain dominant, given their established business ecosystems and high population densities. However, other cities such as Brisbane are witnessing significant growth driven by infrastructure development and population influx. The key players in this dynamic market, including Lendlease Corporation, Scentre Group Limited, and Mirvac, are well-positioned to capitalize on these growth opportunities. The segmentation of the market reveals significant potential within specific sectors. The industrial and logistics sector, driven by the e-commerce boom and supply chain optimization efforts, is anticipated to experience particularly strong growth. Similarly, the office sector, while facing some challenges from remote work trends, remains resilient due to the ongoing need for collaborative workspaces and central business district locations. The retail sector will continue to adapt to evolving consumer preferences, with a focus on experience-driven retail and omnichannel strategies. Careful consideration of factors like interest rate fluctuations, construction costs, and regulatory changes will be crucial for investors navigating the complexities of this dynamic market. The forecast period of 2025-2033 offers a promising outlook for sustained growth within this sector. Recent developments include: • October 2023: Costco is planning a major expansion in Australia, with several new warehouses under construction and several prime locations being considered for future locations. Costco currently operates 15 warehouses in Australia, with plans to expand to 20 within the next five years, based on current stores and potential locations., • July 2023: A 45-storey BTR tower will be developed by Lendlease and Japanese developer Daiwa House, completing the final phase of Lendlease's Melbourne Quarter project and its second Build-to-Rent (BTR) project in Australia. The USD 650 million deal, similar to Lend lease's first 443-unit BTR project under construction in the 5.5 hectares of mixed-use space at Brisbane Showground, is a stand-alone investment and is separate from the company's ongoing efforts to build a wider BTR partnership, which will include several assets.. Key drivers for this market are: Rapid Urbanization, Government Initiatives Actively promoting the Construction Activities. Potential restraints include: Rapid Urbanization, Government Initiatives Actively promoting the Construction Activities. Notable trends are: Retail real estate is expected to drive the market.

The South East Queensland Peri-urban SuperSite is a member of the Australian SuperSite Network (SuperSites, http://www.supersites.net.au/), a facility within the Australian Terrestrial Ecosystem Network (TERN, http://www.tern.org.au/). The SEQ Peri-urban Supersite’s (SEQP) core infrastructure is located at the sub-tropical 50 ha Samford Ecological Research Facility (SERF) of the Queensland University of Technology in Brisbane (https://www.qut.edu.au/research/why-qut/infrastructure/samford-ecological-research-facility). SERF is located at the western extent of the Pine longitudinal transect north of Brisbane where the urban footprint is rapidly expanding. The transect extends from the upper reaches of the Pine River catchment through the Samford Valley to Central Moreton Bay. The traditional custodians of the Samford Valley are the Yugara nation. Clan relations may well have extended into and from the neighbouring Jinibara and Kabi Kabi. The Supersite focuses on the impact of urban development and low frequency, high flow (ephemeral) events on terrestrial biogeochemistry, biodiversity and downstream water quality. Rapid population growth in SEQ is expected to continue particularly in peri-urban areas. The development, transformation of land use and exploitation of resources associated with this population growth will intensify the pressure on catchment, aquatic and coastal environments, potentially leading to significant habitat fragmentation, water quality issues, biodiversity loss and loss of economic and amenity values. The vulnerability of SEQ’s high biodiversity ecosystems will be compounded by climate change in the region. Key research questions: • Can ecosystem services be maintained in an urbanising environment? • How do carbon and energy balances change under different land uses in transition from a natural dry sclerophyll forest to a peri-urban area? • What impact will Brisbane’s peri-urban development have on water quality and soil borne greenhouse gases (carbon dioxide, nitrous oxide and methane) and surrounding vegetation? • What are the long-term effects of urbanisation on remnant vegetation? • What impact does pasture composition and management have on greenhouse gas emissions? • How will changes in the climate, land-use (e.g. from rural to residential) affect soil nutrient balances and water leaving the catchment? • How can bio-acoustic monitoring be used for measuring ecosystem biodiversity and health?

1. There is mounting concern that people living more urbanised, modern lifestyles have fewer and lower quality interactions with nature, and therefore have limited access to the associated health and wellbeing benefits. Yet, variation in the different types of nature interactions and the factors that influence these interactions across populations are poorly understood.
2. We compared four types of nature interactions by administering surveys across two cities that differ markedly in urbanisation pattern and population density—Singapore and Brisbane—: (i) indirect (viewing nature through a window at work or at home); (ii) incidental (spending time in nature as part of work); (iii) intentional interactions in gardens; and (iv) intentional interactions in public urban greenspaces.
3. Our results show that Singapore respondents spent about half as much time (25.8 hours per week) interacting with nature as Brisbane respondents (52.3 hours per week), and indirect interactions were the most prevalent across both cities.
4. Nature orientation, age, income and gender significantly predicted the duration of nature interactions in both cities, while self-reported health, education and ethnicity additionally predicted duration of nature interactions only for Brisbane. Also, the relationship(s) between each factor and duration could differ in direction and effect size between types of nature interactions.
5. As such, we conclude that there is much local variation in the dynamics of interactions between people and nature, and that focused studies are needed to develop effective interventions addressing declines in nature interactions in different locations.

Road trauma rates per 100 million kilometres travelled by transport mode.

Proportional multi-state life table Markov model input parameters.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED September 2016

DESCRIPTION
Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES
U.S. Census Bureau 1960-1990 Decennial Census http://factfinder2.census.gov

California Department of Finance 1961-2016 Population and Housing Estimates http://www.dof.ca.gov/research/demographic/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, tract) are as of January 1, 2010, released beginning November 30, 2010 by the U.S. Census Bureau. A priority development area (PDA) is a locally-designated infill area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are as current as July 2016. Population estimates for PDAs were derived from Census population counts at the block group level for 2000-2014 and at the tract level for 1970-1990.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average). Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average.

Estimates of density for tracts and PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside InlandCoastalDelta: American Canyon, Benicia, Clayton, Concord, Cotati, Danville, Dublin, Lafayette, Martinez, Moraga, Napa, Novato, Orinda, Petaluma, Pleasant Hill, Pleasanton, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Walnut Creek, Antioch, Brentwood, Calistoga, Cloverdale, Dixon, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Livermore, Morgan Hill, Oakley, Pittsburg, Rio Vista, Sonoma, St. Helena, Suisun City, Vacaville, Windsor, Yountville Unincorporated: all unincorporated towns

Mode share travel targets.

Additional mean minutes per week of transport physical activity undertaken in the travel targets scenario compared to the baseline scenario (statu-quo), by age and sex.

Mean trips per week (weekdays only) for baseline and travel targets scenario, by age and sex.

Violent and property crime rates per 100,000 population for San Mateo County and the State of California. The total crimes used to calculate the rates for San Mateo County include data from: Sheriff's Department Unincorporated, Atherton, Belmont, Brisbane, Broadmoor, Burlingame, Colma, Daly City, East Palo Alto, Foster City, Half Moon Bay, Hillsborough, Menlo Park, Millbrae, Pacifica, Redwood City, San Bruno, San Carlos, San Mateo, South San Francisco, Bay Area DPR, BART, Union Pacific Railroad, and CA Highway Patrol.

The final Australian National Liveability Study 2018 datasets comprise a suite of policy relevant spatial indicators of local neighbourhood liveability and amenity access estimated for residential address points across Australia's 21 largest cities, and summarised at range of larger area scales (Mesh Block, Statistical Areas 1-4, Suburb, LGA, and overall city summaries). The indicators and measures included encompass topics including community and health services, employment, food, housing, public open space, transportation, walkability and overall liveability. The datasets were produced through analysis of built environment and social data from multiple sources including OpenStreetMap the Australian Bureau of Statistics, and public transport agency GTFS feed data. These are provided in CSV format under an Open Data Commons Open Database licence. The 2018 Australian National Liveability data will be of interest to planners, population health and urban researchers with an interest in the spatial distribution of built environment exposures and outcomes for data linkage, modelling and mapping purposes. Area level summaries for the data were used to create the indicators for the Australian Urban Observatory at its launch in 2020. A detailed description of the datasets and the study has been published in Nature Scientific Data, and notes and code illustrating usage of the data are located on GitHub. The spatial data were developed by the Healthy Liveable Cities Lab, Centre for Urban Research with funding support provided from the Australian Prevention Partnership Centre #9100003, NESP Clean Air and Urban Landscapes Hub, NHMRC Centre of Research Excellence in Healthy, Liveable Communities #1061404 and an NHMRC Senior Principal Research Fellowship GNT1107672; with interactive spatial indicator maps accessible via the Australian Urban Observatory. Any publications utilising the data are not necessarily the view of or endorsed by RMIT University or the Centre of Urban Research. RMIT excludes all liability for any reliance on the data.