IMPORTANT NOTICE This item has moved to a new organization and entered Mature Support on February 3rd, 2025. This item is scheduled to be Retired and removed from ArcGIS Online on June 27th, 2025. We encourage you to switch to using the item on the new organization as soon as possible to avoid any disruptions within your workflows. If you have any questions, please feel free to leave a comment below or email our Living Atlas Curator (livingatlascurator@esri.ca) The new version of this item can be found here A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000, based on data from the current Census of Population Program, of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program.If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts.The CMA boundaries were obtained from the Statistics Canada website.

This data includes the characteristics of Ontario Works and Ontario Disability Support Program cases, by census metropolitan area, and by the province including: * family type * family size * primary applicant's age and sex * consecutive months on social assistance A census metropolitan area (CMA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core. *[CMA]: census metropolitan area

The metropolitan influence zone classification, developed by researchers at Statistics Canada, classifies communities (census subdivisions) that lie outside census metropolitan areas (CMAs) and census agglomerations (CAs) according to the degree of influence that CMA/CAs have on them. The classification was used in this mapping project on quality of life to compare similar communities (or census subdivisions), in order to recognize inherent differences in the social and economic characteristics of different communities and differences in their geographic locations, which may have important influences on quality of life.

Housing Assessment Resource Tools (HART) This dataset contains 2 tables and 5 files which draw upon data from the 2021 Census of Canada. The tables are a custom order and contain data pertaining to older adults and housing need. The 2 tables have 6 dimensions in common and 1 dimension that is unique to each table. Table 1's unique dimension is the "Ethnicity / Indigeneity status" dimension which contains data fields related to visible minority and Indigenous identity within the population in private households. Table 2's unique dimension is "Structural type of dwelling and Period of Construction" which contains data fields relating to the structural type and period of construction of the dwelling. Each of the two tables is then split into multiple files based on geography. Table 1 has two files: Table 1.1 includes Canada, Provinces and Territories (14 geographies), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); and Table 1.2 includes Canada and the CMAs of Canada (44). Table 2 has three files: Table 2.1 includes Canada, Provinces and Territories (14), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); Table 2.2 includes Canada and the CMAs of Canada excluding Ontario and Quebec (20 geographies); and Table 2.3 includes Canada and the CMAs of Canada that are in Ontario and Quebec (25 geographies). The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and data fields: Geography: - Country of Canada as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia) as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), as a whole as well as all census divisions (CDs) within the 3 territories - All 43 census metropolitan areas (CMAs) in Canada Data Quality and Suppression: - The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. - Area suppression is used to replace all income characteristic data with an 'x' for geographic areas with populations and/or number of households below a specific threshold. If a tabulation contains quantitative income data (e.g., total income, wages), qualitative data based on income concepts (e.g., low income before tax status) or derived data based on quantitative income variables (e.g., indexes) for individuals, families or households, then the following rule applies: income characteristic data are replaced with an 'x' for areas where the population is less than 250 or where the number of private households is less than 40. Source: Statistics Canada - When showing count data, Statistics Canada employs random rounding in order to reduce the possibility of identifying individuals within the tabulations. Random rounding transforms all raw counts to random rounded counts. Reducing the possibility of identifying individuals within the tabulations becomes pertinent for very small (sub)populations. All counts are rounded to a base of 5, meaning they will end in either 0 or 5. The random rounding algorithm controls the results and rounds the unit value of the count according to a predetermined frequency. Counts ending in 0 or 5 are not changed. Universe: Full Universe: Population aged 55 years and over in owner and tenant households with household total income greater than zero in non-reserve non-farm private dwellings. Definition of Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing Need, regardless of their housing circumstances. Data Fields: Table 1: Age / Gender (12) 1. Total – Population 55 years and over 2. Men+ 3. Women+ 4. 55 to 64 years 5. Men+ 6. Women+ 7. 65+ years 8. Men+ 9. Women+ 10. 85+ 11. Men+ 12. Women+ Housing indicators (13) 1. Total – Private Households by core housing need status 2. Households below one standard only...

This table presents the 2021 and 2016 population and dwelling counts, land area, population density and population ranking for census metropolitan areas or census agglomerations. It also shows the percentage change in the population and dwelling counts between 2016 and 2021.

This table presents the 2021 population counts for census metropolitan areas and census agglomerations, and their population centres and rural areas.

According to Statistics Canada, Census Metropolitan Areas consisting of one or more neighbouring municipalities situated around a core. A census metropolitan area must have a total population of at least 100,000 of which 50,000 or more live in the core.Statistics Canada Census Metropolitan Area boundary for 2021, lcma000b21a_e (cartographic boundary file)Metadata

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset contains a modified version of the source Hunter CMA GDE dataset. Modifications include the addition of fields containing polygon areas. Also includes further derived datasets used to facilitate mapping. Including a version dissolved on the Keith Form field of the source dataset, a version clipped to the Hunter subregion boundary and one combined with the input Greater Hunter Vegetation Mapping dataset.

Dataset History

The polygons from the source GDE dataset were dissolved on the Keith Form field (HunterGDEs_KeithForm.shp). This was then clipped to the Hunter subregion boundary with an AREA field added and areas computed on the Albers projection (HunterGDEs_KeithForm_clipped.shp) . Also Keith Form Classes from the Greater Hunter Vegetation Mapping input dataset (see lineage) were UNIONed to produce a combined GDE and Non-GDE vegetation covereage (HunterGDEs_KeithForm_plusGHM_KeithForm.shp). Some summary area stats were also generated and included as a table (HUN_GDE_KF_Areas.csv)

Dataset Citation

Bioregional Assessment Programme (2015) Hunter GDEs v02. Bioregional Assessment Derived Dataset. Viewed 07 June 2018, http://data.bioregionalassessments.gov.au/dataset/6e64d63f-5af5-475e-b479-11ef16db12e0.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v05

• Derived From Greater Hunter Native Vegetation Mapping with Classification for Mapping

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v06

• Derived From GEODATA TOPO 250K Series 3

• Derived From Victoria - Seamless Geology 2014

• Derived From Hunter CMA GDEs (DRAFT DPI pre-release)

• Derived From Geological Provinces - Full Extent

• Derived From Greater Hunter Native Vegetation Mapping

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

The census is Canada's largest and most comprehensive data source conducted by Statistics Canada every five years. The Census of Population collects demographics and linguistic information on every man, woman and child living in Canada. The data shown here is provided by Statistics Canada from the 2006 Census as a custom profile data order for the City of Vancouver, using the City'​s 22 local planning areas. The data may be reproduced provided they are credited to Statistics Canada, Census 2006, custom order for City of Vancouver Local Areas. Data accessThis dataset has not yet been converted to a format compatible with our new platform. The following links provide access to the files from our legacy site: Census local area profiles 2006 (CSV) Census local area profiles 2006 (XLS)Dataset schema (Attributes)Please see the Census local area profiles 2006 attributes page. NoteThe 22 Local Areas is defined by the Census blocks and is equal to the City'​s 22 local planning areas and includes the Musqueam 2 reserve. Vancouver CSD (Census Subdivision) is defined by the City of Vancouver municipal boundary which excludes the Musqueam 2 reserve but includes Stanley Park. Vancouver CMA (Census Metropolitan Area) is defined by the Metro Vancouver boundary which includes the following Census Subdivisions: Vancouver, Surrey, Burnaby, Richmond, Coquitlam, District of Langley, Delta, District of North Vancouver, Maple Ridge, New Westminster, Port Coquitlam, City of North Vancouver, West Vancouver, Port Moody, City of Langley, White Rock, Pitt Meadows, Greater Vancouver A, Bowen Island, Capilano 5, Anmore, Musqueam 2, Burrard Inlet 3, Lions Bay, Tsawwassen, Belcarra, Mission 1, Matsqui 4, Katzie 1, Semiahmoo, Seymour Creek 2, McMillian Island 6, Coquitlam 1, Musqueam 4, Coquitlam 2, Katzie 2, Whonnock 1, Barnston Island 3, and Langley 5. In 2006 there were changes made to the definition of households. A number of Single Room Occupancy and Seniors facilities were considered to be dwellings in 2001, and collective dwellings in 2006. As a result the residents of those buildings would not be considered to be households in 2006. There is a high likelihood that residents of such facilities have low incomes, and there will have been an impact on the count of households considered to have a low income.A number of changes were made to the census family concept for 2001 which account for some of the increase in the total number of families, single parent families and children living at home.Occupied Dwellings are those with a household living in them. The change to the definition of households (already noted) also affects the number of occupied dwellings.In 2006 there was a change made to the definition of duplex. While it is still defined as a dwelling in a building with two dwellings, one above the other, in 2001 these were only detached properties. In 2006 the definition changed so they could be joined to other similar properties. In 2006 Statistics Canada also seem to have identified more duplexes than before.In 2006 Statistics Canada conducted the Census with a mail-in or online response. To facilitate this, they identified more secondary addresses in houses. This probably also contributes to the increase from 2001 in the number of duplexes, and the reduction in the number of single-family dwellings.Data products that are identified as 20% sample data refer to information that was collected using the long census questionnaire. For the most part, these data were collected from 20% of the households; however they also include some areas, such as First Nations communities and remote areas, where long census form data were collected from 100% of the households. Data currencyThe data for Census 2006 was collected in May 2006. Data accuracyStatistics Canada is committed to protect the privacy of all Canadians and the confidentiality of the data they provide to us. As part of this commitment, some population counts of geographic areas are adjusted in order to ensure confidentiality.Counts of the total population are rounded to a base of 5 for any dissemination block having a population less than 15. Population counts for all standard geographic areas above the dissemination block level are derived by summing the adjusted dissemination block counts. The adjustment of dissemination block counts is controlled to ensure that the population counts for dissemination areas will always be within 5 of the actual values. The adjustment has no impact on the population counts of census divisions and large census subdivisions. Websites for further information Statistics Canada 2006 Census Dictionary Local area boundary dataset

UNI-CEN Standardized Census Data Tables contain Census data that have been reformatted into a common table format with standardized variable names and codes. The data are provided in two tabular formats for different use cases. "Long" tables are suitable for use in statistical environments, while "wide" tables are commonly used in GIS environments. The long tables are provided in Stata Binary (dta) format, which is readable by all statistics software. The wide tables are provided in comma-separated values (csv) and dBase 3 (dbf) formats with codebooks. The wide tables are easily joined to the UNI-CEN Digital Boundary Files. For the csv files, a .csvt file is provided to ensure that column data formats are correctly formatted when importing into QGIS. A schema.ini file does the same when importing into ArcGIS environments. As the DBF file format supports a maximum of 250 columns, tables with a larger number of variables are divided into multiple DBF files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.

This table contains data on the number and the assessment value of residential properties, by property type, living area and residency type for the census metropolitan areas of Toronto and Vancouver and their census subdivisions.

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 …Show full descriptionAbstract 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. The dataset is a geodatabase containing various mapping layers relating to the Hunter groundwater model uncertainty analysis. This dataset contains the grids of assessment units for the 50th percentiles of drawdown for baseline, coal resource development pathway and additional coal resources development. Also included are point shapefiles attributed with the exceedence probabilities and the acceptance threshold for the groundwater level objective function for each model node as well as the locations of groundwater observations used in the uncertainty analysis. HUN_GW_Observation (feature class) - Groundwater observation locations were sourced from the dataset HUN_Bores_v01 and a distance matrix for each bore location calculated between all observation sites in the attribute table. That is, for each bore location the distance to all other bores has been calculated and added to the attribute table. HUN_GW_dmax_tmax_excprob (feature class) - The fraction of evaluated parameter combinations of the design of experiment that meets the groundwater level acceptance criterion (threshold). Dataset History The grids of assessment units of drawdowns for baseline, CRDP and ACRD scenarios were derived from the results of drawdown from HUN_GW_Uncertainty_Analysis at the model nodes for baseline, crdp and acrd for the 50th percentile. HUN_GW_Observation (feature class) - Groundwater observation locations were sourced from the dataset HUN_Bores_v01 and a distance matrix for each bore location calculated between all observation sites in the attribute table. That is, for each bore location the distance to all other bores has been calculated and added to the attribute table. HUN_GW_dmax_tmax_excprob (feature class) - The fraction of evaluated parameter combinations of the design of experiment that meets the groundwater level acceptance criterion (threshold). Dataset Citation Bioregional Assessment Programme (2016) HUN GW dmax tmax mapping layers v01. Bioregional Assessment Derived Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/176e3e2b-a9ab-40fc-a7de-6d78a3e3571e. Dataset Ancestors Derived From Camerons Gorge Grassy White Box Endangered Ecological Community (EEC) 2008 Derived From HUN GW Model code v01 Derived From NSW Office of Water - National Groundwater Information System 20140701 Derived From Travelling Stock Route Conservation Values Derived From HUN GW Model v01 Derived From NSW Wetlands Derived From Climate Change Corridors Coastal North East NSW Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From Climate Change Corridors for Nandewar and New England Tablelands Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Fauna Corridors for North East NSW Derived From R-scripts for uncertainty analysis v01 Derived From Asset database for the Hunter subregion on 27 August 2015 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004 Derived From Bioregional Assessment areas v04 Derived From Hunter CMA GDEs (DRAFT DPI pre-release) Derived From HUN GW Uncertainty Analysis v01 Derived From Asset database for the Hunter subregion on 16 June 2015 Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129 Derived From Gippsland Project boundary Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition Derived From Asset database for the Hunter subregion on 24 February 2016 Derived From Natural Resource Management (NRM) Regions 2010 Derived From Asset database for the Hunter subregion on 12 February 2015 Derived From NSW Office of Water Surface Water Offtakes - Hunter v1 24102013 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013 Derived From HUN groundwater flow rate time series v01 Derived From Asset list for Hunter - CURRENT Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From HUN GW Model simulate ua999 pawsey v01 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Ramsar Wetlands of Australia Derived From Native Vegetation Management (NVM) - Manage Benefits Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From HUN bores v01 Derived From NSW Office of Water Surface Water Licences Processed for Hunter v1 20140516 Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514 Derived From Atlas of Living Australia NSW ALA Portal 20140613 Derived From Bioregional Assessment areas v03 Derived From Gosford Council Endangered Ecological Communities (Umina woodlands) EEC3906 Derived From Bioregional Assessment areas v05 Derived From Lower Hunter Spotted Gum Forest EEC 2010 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Climate Change Corridors (Dry Habitat) for North East NSW Derived From Groundwater Entitlement Hunter NSW Office of Water 20150324 Derived From Asset database for the Hunter subregion on 20 July 2015 Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From NSW Office of Water Groundwater Licence Extract, North and South Sydney - Oct 2013 Derived From Commonwealth Heritage List Spatial Database (CHL) Derived From Australia World Heritage Areas Derived From Northern Rivers CMA GDEs (DRAFT DPI pre-release) Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From NSW Office of Water GW licence extract linked to spatial locations for NorthandSouthSydney v3 13032014 Derived From Threatened migratory shorebird habitat mapping DECCW May 2006 Derived From Groundwater Economic Elements Hunter NSW 20150520 PersRem v02 Derived From NSW Office of Water - GW licence extract linked to spatial locations for North and South Sydney v2 20140228 Derived From HUN AssetList Database v1p2 20150128 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From Climate Change Corridors (Moist Habitat) for North East NSW Derived From Operating Mines OZMIN Geoscience Australia 20150201 Derived From NSW Office of Water - National Groundwater Information System 20141101v02 Derived From Bioregional Assessment areas v06 Derived From Asset database for the Hunter subregion on 22 September 2015 Derived From Groundwater Economic Assets Hunter NSW 20150331 PersRem Derived From Australia - Species of National Environmental Significance Database Derived From Monitoring Power Generation and Water Supply Bores Hunter NOW 20150514 Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From HUN GW Model Mines raw data v01 Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From Victoria - Seamless Geology 2014 Derived From HUN Alluvium (1:1m Geology) Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From Darling River Hardyhead Predicted Distribution in Hunter River Catchment NSW 2015

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the Corangamite Catchment Management Authority (CMA) area. The method used in this research is based upon the characteristics of a potential GDE containing area as one that: 1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community. 2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased. 3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns. 4. Has growth patterns similar to verified GDEs. The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this.

The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon.

In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to groundwater.

Separate datasets and associated metadata records have been created for GDE species tolerance.

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the North East Catchment Management Authority (CMA) area. The method used in this research is based upon the characteristics of a potential GDE containing area as one that: 1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community. 2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased. 3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns. 4. Has growth patterns similar to verified GDEs. The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this.

The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon.

In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to groundwater.

Separate datasets and associated metadata records have been created for GDE species tolerance.

Annual population estimates as of July 1st, by census metropolitan area and census agglomeration, single year of age, five-year age group and gender, based on the Standard Geographical Classification (SGC) 2021.

This table contains data for gross domestic product (GDP), in current dollars, for all census metropolitan area and non-census metropolitan areas.

Differences in the number and proportion of persons with and without disabilities, aged 15 years and over, by census metropolitan areas.

People who have been granted permanent resident status in Canada. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

Family characteristics of seniors by housing indicators for Canada, provinces and territories, census metropolitan areas and census agglomerations. Includes age of seniors, tenure including presence of mortgage payments and subsidized housing, condominium status, value (owner-estimated) of dwelling and number of bedrooms.