The Survey Control Information Management System (SCIMS) is a database that contains all of the coordinates, heights and related information for NSW survey marks that form the official State Survey Control Network.

The network is represented physically by over 250,000 survey marks positioned at varying densities across NSW. Each survey mark is assigned a horizontal and vertical spatial position and a class and order, according to accuracy, monument and other factors. Detailed metadata information is also recorded. SCIMS data is supplied to the surveying and spatial industries through the SCIMS online internet product.

Twice a year we carry out walking count surveys to give us a picture of walking trends across the local area. The counts take place at around 100 locations from 6am to midnight in fair weather conditions, on a weekday and a day on the weekend in March and October.The survey locations were selected based on the Liveable Green Network, such as locations of interest or where change is occurring or expected. Visit the interactive mapMore information on walking count sites

No notes provided

Business Needs Survey 2022 – Impact of the Covid-19 pandemic on the needs of businesses in the City.The City conducted the 2020 Business Needs Survey following the first lockdown initiated in response to Covid-19. The survey aimed to provide insight into the needs of small business operators to determine the best approach in supporting them to remain economically viable.The City has conducted 2021 and 2022 Covid-19 Business Needs Surveys. The responses document how organisations, industry sectors and members were impacted by the pandemic immediately before the 2021 four-month lockdown.See previous surveys

Board of Surveying and Spatial Information (BOSSI) Annual Report for the year ending 30 June 2015

The City conducted the 2020 Business Needs Survey following the first lockdown initiated in response to Covid-19. The survey aimed to provide insight into the needs of small business operators to determine the best approach in supporting them to remain economically viable. The City conducted the 2021 Covid-19 Business Needs Survey 12 months after the first survey in 2020. The responses document how organisations, industry sectors and members were impacted by the pandemic immediately before the 2021 four-month lockdown.

Positioning is NSW’s authoritative, reliable, high-accuracy spatial referencing system. The Positioning theme includes the coordinates and their uncertainty of all location-based data promulgated from, or related to, the Geocentric Datum of Australia (GDA94) and the Australian Height Datum (AHD71).

This is a database that contains all of the coordinates, heights and related information for NSW survey marks that form the official State Survey Control Network (SCIMS).

The scourced Geotiff file is cropped to the map window only, with no legend, disclaimers, map grip, scale bar or north arrow displayed. The NSW Topographic Map series is derived from the Digital Topographic Database (DTDB).

Information viewed in this web service includes: • Roads

• Points of Interest

• Localities

• Contours

• Drainage

• Cultural data

• Parks and forests

• Property boundaries.

The Annual Report for the Board of Surveying & Spatial Information for the 2020 - 2021 financial year

The City conducted the 2020 Business Needs Survey following the first lockdown initiated in response to Covid-19. The survey aimed to provide insight into the needs of small business operators to determine the best approach in supporting them to remain economically viable.

Spot Height is a point feature class representing individual points on the earth’s surface, the elevation of which has been related to a datum by ground or photogrammetric survey. It is a part of …Show full descriptionSpot Height is a point feature class representing individual points on the earth’s surface, the elevation of which has been related to a datum by ground or photogrammetric survey. It is a part of the NSW Digital Topographic Database (DTDB).

Abstract Australia's Land Borders is a product within the Foundation Spatial Data Framework (FSDF) suite of datasets. It is endorsed by the ANZLIC – the Spatial Information Council and the Intergovernmental Committee on Surveying and Mapping (ICSM) as the nationally consistent representation of the land borders as published by the Australian states and territories. It is topologically correct in relation to published jurisdictional land borders and the Geocoded National Address File (G-NAF). The purpose of this product is to provide:

a building block which enables development of other national datasets; integration with other geospatial frameworks in support of data analysis; and visualisation of these borders as cartographic depiction on a map.

Although this service depicts land borders, it is not nor does it purport to be a legal definition of these borders. Therefore it cannot and must not be used for those use-cases pertaining to legal context. Termination Points are the point at which the state border polylines meet the coastline. For the purpose of this product, the coastline is defined as the Mean High Water Mark (MHWM). In the absence of a new MHWM for NSW, the Jervis Bay termination points are defined by the NSW cadastre. This feature layer is a sub-layer of the Land Borders service. Currency Date modified: 10 November 2021 Modification frequency: None Data extent Spatial extent North: -14.88° South: -38.06° East: 153.55° West: 129.00° Source information Catalog entry: Australia's Land Borders The Land Borders dataset is created using a range of source data including:

Australian Capital Territory data was sourced from the ACT Government GeoHub – ‘ACT Boundary’. No changes have been made to the polylines or vertices of the source data. In the absence of any custodian published border for Jervis Bay – New South Wales, a border has been constructed from the boundary of the NSW cadastre supplied by NSW Spatial Services. Geoscience Australia’s GEODATA TOPO 250K data was considered as an alternative, however, that border terminated short of the coastline as it stops at the shoreline of the major water bodies. Therefore, a decision was made to use the NSW and OT supplied cadastre to create a new representation of the Jervis Bay border that continued to the coastline (MHWM), in place of the TOPO 250K data. In the absence of publicly available data from New South Wales, the land borders for New South Wales have been constructed using the data of adjoining states Queensland, South Australia, Victoria and the Australian Capital Territory. This approach is agreeable to New South Wales Government for this interim product. In the absence of publicly available data from the Northern Territory the land borders for the Northern Territory have been constructed using the data of adjoining states Western Australia, Queensland and South Australia. This approach is agreeable to Northern Territory Government for this interim product. Queensland state border and coastline data have been download from the Queensland Spatial, Catalogue – QSpatial. Publicly available data for the state borders of South Australia was downloaded from data.gov.au and is ‘SA State Boundary - PSMA Administrative Boundaries’. Downloaded as a file geodatabase in GDA2020. Victorian state border data has been downloaded from the Victorian state Government Spatial Datamart, it is titled ‘FR_FRAMEWORK_AREA_LINE’. The Victorian state border data was used for the NSW/VIC section of border due to the absence of any publicly available data from New South Wales for this section of the border. Western Australian state border data was downloaded from the WA Government as publicly available. The Western Australia state border data has been used for the WA/NT section of the border due to the absence of publicly available data from Northern Territory for this section of the border. Selecting the SA data for the WA/SA border would introduce mismatches with the WA cadastre. It would also not improve the SA relationship with the SA cadastre. Using the WA data for the WA/SA section of the border aligns each state with its own cadastre without causing overlaps.

Sources specific to the Termination Points are as follows:

Jurisdictions Coastline data source

NT/QLD Publicly available Queensland Coastline and State Border data

QLD/NSW Publicly available Queensland Coastline and State Border data

NSW/VIC VIC Framework (1:25K) line

VIC/SA Coastline Capture Program (of SA by Tasmania)

SA/WA Coastline Capture Program (of SA by Tasmania)

WA/NT Coastline Capture Program (of NT by Tasmania)

JBT (OT) NSW Cadastre

Lineage statement At the southwest end of the NT/SA/WA border the South Australian data for the border was edited by moving the end vertex ~1.7m to correctly create the intersection of the 3 states (SA/WA/NT). At the southeast end of the NT/QLD/SA border the South Australian data for the border was edited by moving the end vertex ~0.4m to correctly create the intersection of the 3 states (NT/SA/QLD). Queensland data was used for the NT/QLD border and the QLD/NSW border due to the absence of publicly available data from the Northern Territory for these section of the border. Data published by Queensland also included a border sections running westwards along the southern Northern Territory border and southwards along the western New South Wales border. These two sections were excluded from the product as they are not within the state of Queensland. Queensland data was also used in the entirety for the SA/QLD segment of the land borders. Although the maximum overlap between SA and QLD state border data was less than ~5m (and varied along the border), the Queensland data closely matched its own cadastre and that of South Australia. The South Australian data overlapped the Queensland data, it also did not match the South Australian cadastre. Therefore, a decision to use the Queensland data for the QLD/SA section of the border ensured the best possible topological consistency with the published cadastre of each state. The South Australian/Victorian state border, north-south, were generally very similar with some minor deviations from each other from less than 1m to ~60m (there is one instance of deviation of 170m). The section of border that follows the Murray River is matched, for the most part by both states. Over three quarters of the border running along the river is matched with both states. There is a mismatch between the states in the last quarter of the border along the river, the northern section, however, both states still have the border running inside, or along, the river polygon (Surface hydrology), the Victorian data was chosen for this section purely for consistency as the Victorian data was used for the preceding arcs. Overall, the Victorian data was selected for use as the South Australia/Victoria land border. After taking the existing cadastre and GNAF points into account and it did not introduce extra errors into the relationship between the land borders and the cadastre of either state. In parts, it improved the relationship between the South Australian cadastre and the SA/VIC state border. This interim product will be updated when all states and territories have published agreed, authoritative representations of their land borders. This product will also be updated to include land mass polygons at time when the Coastline Capture Program is complete. This dataset is GDA 2020 compliant - transformed into GDA2020 from it's original source datum. Reference System Code 2020.00. Data dictionary All Layers

Attribute name Description

CREATE_DATE Date on which the positional data point was created in the data set

Field All features in this data set are labelled "TERMINATION_POINT"

SOURCE Project from which the data point information is derived

STATEMENT Legal disclaimer for the positional data

STATES Termination points divide at least two states and/or territories

Contact Geoscience Australia, clientservices@ga.gov.au

This dataset contains employment, internal floor area and businesses by employment zone collected in the 2022 floor space and employment survey. The 2022 survey was the fourth full survey within the current City of Sydney local area boundaries. Previous surveys were undertaken in 2007, 2012 and 2017. View the interactive map More information about the floor space and employment survey

This GIS data package contains airborne electromagnetic (AEM) datasets and interpreted data products for the Speewa survey area, as part of the River Murray Corridor (RMC) Salinity Mapping and Interpretation Project. The RMC project was undertaken between 2006 and 2010 to provide information on a range of salinity and land management issues along a 450 kilometre reach of the Murray River from the South Australian border to Gunbower, northwest of Echuca in Victoria. The Speewa survey area extends from Speewa south to Swan Hill.

This metadata briefly describes the contents of the data package. The user guide included in the package contains more detailed information about the individual datasets and available technical reports.

The main components in the package are: AEM data and images derived from a holistic inversion of the RMC RESOLVE AEM survey; a composite digital elevation model (DEM); a range of interpreted data products designed to map key elements of the hydrogeological system and salinity hazards using the AEM dataset; and a series of ESRI ArcGIS map documents.

The AEM data component consists of grids and images of modelled conductivity data derived from a holistic inversion of the RMC RESOLVE AEM survey. They include: layer conductivity grids below ground surface; depth slice grids representing the average conductivity of various regular depth intervals below ground surface; floodplain slice grids representing the average conductivity of various depth intervals relative to the elevation above or below a surface that approximates the River Murray floodplain; watertable slice grids representing the average conductivity of various intervals relative to the elevation above or below the regional watertable; and AEM cross sections of conductivity versus depth along each of the flight lines. The holistic inversion AEM data are derived from the 'River Murray Corridor RESOLVE AEM Survey, VIC & NSW, 2007 Final Data (P1141)', available as Geoscience Australia product number 67212 (GeoCat #67212).

The DEM data component consists of a 10 metre horizontal resolution composite DEM for the River Murray Corridor AEM survey area derived from airborne light detection and ranging (LiDAR) surveys, AEM surveys, and the shuttle radar topography mission (SRTM) survey.

The interpreted data component is organised into product themes to address salinity and land management questions and to map key elements of the hydrogeological system and salinity hazards. An ArcGIS map document is included for each product theme. The products include: Blanchetown Clay; conductive soils; flush zones; groundwater conductivity; stratigraphic extents and reliability; near surface conductive zones; near surface resistive zones; Parilla Sands; Quaternary alluvium; recharge; salt store; surface salt; vegetation health; and Woorinen Formation.

The RMC project was funded through the National Action Plan for Salinity and Water Quality with additional funding from the Lower Murray Catchment Management Authority (CMA), Mallee CMA, Goulburn-Murray Water and the Murray-Darling Basin Authority. The project was administered by the Australian Government Department of Agriculture, Fisheries and Forestry through the Bureau of Rural Sciences, now known as the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). Geoscience Australia (GA) were contracted to provide geophysical services to manage the AEM system selection and data acquisition, and to process and calibrate the AEM data. The AEM survey was flown by Fugro Airborne Geophysical Services in 2007 using the helicopter-borne RESOLVE frequency domain system. The Cooperative Research Centre for Landscape Environments and Mineral Exploration was sub-contracted through GA to manage the interpretation and reporting component of the RMC project.

Data contains: ACAD survey Boardman Peasley Survey Council GIS DEM Hunter River survey *rainfall data Station Information Data contains: ACAD survey Boardman Peasley Survey Council GIS DEM Hunter River survey *rainfall data Station Information

This GIS data package contains airborne electromagnetic (AEM) datasets and interpreted data products for the Lindsay-Wallpolla and Lake Victoria-Darling Anabranch survey area, as part of the River Murray Corridor (RMC) Salinity Mapping and Interpretation Project. The RMC project was undertaken between 2006 and 2010 to provide information on a range of salinity and land management issues along a 450 kilometre reach of the Murray River from the South Australian border to Gunbower, northwest of Echuca in Victoria. The Lindsay-Wallpolla survey area extends from the South Australian border to approximately 10 kilometres west of Mildura, incorporating Lake Victoria and the lower reaches of the Darling and Darling Anabranch river systems. This metadata briefly describes the contents of the data package. The user guide included in the package contains more detailed information about the individual datasets and available technical reports. The main components in the package are: AEM data and images derived from a holistic inversion of the RMC RESOLVE AEM survey; a composite digital elevation model (DEM); a range of interpreted data products designed to map key elements of the hydrogeological system and salinity hazard; and a series of ESRI ArcGIS map documents. The AEM data component consists of grids and images of modelled conductivity data derived from a holistic inversion of the RMC RESOLVE AEM survey. They include: layer conductivity grids below ground surface; depth slice grids representing the average conductivity of various regular depth intervals below ground surface; floodplain slice grids representing the average conductivity of various depth intervals relative to the elevation above or below a surface that approximates the River Murray floodplain; watertable slice grids representing the average conductivity of various intervals relative to the elevation above or below the regional watertable; and AEM cross sections of conductivity versus depth along each of the flight lines. The holistic inversion AEM data are derived from the 'River Murray Corridor RESOLVE AEM Survey, VIC & NSW, 2007 Final Data (P1141)', available as GA product (GeoCat #67212). The DEM data component consists of a 10 metre resolution composite DEM for the River Murray Corridor AEM Survey area, derived from airborne light detection and ranging (LiDAR) surveys, AEM surveys and the shuttle radar topography mission (SRTM) survey. The interpreted data component is organised into product themes to address salinity and land management questions and to map key elements of the hydrogeological system and salinity hazards. An ArcGIS map document is included for each product theme. The products include: Blanchetown Clay; conductive soils; flush zones; groundwater conductivity; strategic extents and reliability; near surface conductive zones; near surface resistive zones; Parilla Sands; Quaternary alluvium; recharge; salt store; surface salt; vegetation health; and Woorinen Formation. The RMC project was funded through the National Action Plan for Salinity and Water Quality, with additional funding from the Lower Murray Catchment Management Authority (CMA), Mallee CMA, Goulburn-Murray Water and the Murray-Darling Basin Authority. The project was administered by the Australian Government Department of Agriculture, Fisheries and Forestry through the Bureau of Rural Sciences, now known as the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). Geoscience Australia (GA) were contracted to provide geophysical services to manage the AEM system selection and data acquisition, and to process and calibrate the AEM data. The AEM survey was flown by Fugro Airborne Geophysical Services in 2007 using the helicopter-borne RESOLVE frequency domain system. The Cooperative Research Centre for Landscape Environments and Mineral Exploration was sub-contracted through GA to manage the interpretation and reporting component of the RMC project.

This repository provides a 25-hectare (500m x 500m) resolution spatial grid for New South Wales.

This grid layer was used to align systematic drone surveys and spatially structure binomial N-mixture models for estimating the abundance of koalas at the landscape-scale. It supports presence/absence and abundance frameworks and is suitable for use in large-scale ecological monitoring programs.

The grid was used in the following study:

Ryan, S.A., Southwell, D.M., Beranek, C.T., Clulow, J., Jordan, N.R., Witt, R.R., 2025.
Estimating the landscape-scale abundance of an arboreal folivore using thermal imaging drones and binomial N-mixture modelling
Biological Conservation. Manuscript ID: 111207. https://doi.org/10.1016/j.biocon.2025.111207

📘 Abstract

Estimating the abundance of wildlife populations at a landscape-scale is vital for conservation, but is often hampered by survey costs, data processing and imperfect detection. In this study, we developed a framework that combines a protocol for validating nocturnal thermal drone detections in real-time with N-mixture modelling to estimate the landscape-scale abundance of arboreal folivores. As a case study, we estimated the abundance of koalas (Phascolarctos cinereus) across seven reserves (673 km²) in New South Wales, Australia. We conducted thermal drone surveys of 208, 25-ha sites stratified across vegetation type and fire history, on average, three times over consecutive nights (range 1–12 repeats), between 18:00–04:00 h (May to September). All koala detections were validated by field personnel or in real-time with drones equipped with a thermal camera and searchlight. Koalas were detected on 245 occasions. We fitted N-mixture models to validated repeat count data to quantify the effect of site and observation variables on abundance and detectability. Using our top set of competing models, we estimated that 4357 koalas (95 % CI = 2319–8307) occupy the seven reserves, with a mean detection probability of 0.22 (95 % CI = 0.15–0.31) over all survey occasions. We found detection probability decreased with increases in relative humidity and temperature. Koala abundance was negatively associated with fire severity, elevation, tree height and soil clay content, and positively associated with available water content, forest cover and soil organic carbon. Our framework, which combines real-time field validated drone data while accounting for imperfect detection, improves the accuracy of abundance estimates for arboreal folivores across large-scales.

• Grid_Albers_00500m_NSW_Polys.shp and associated files
  A shapefile representing 25-ha (500 m × 500 m) grid cells across New South Wales.

• CRS: GDA94 / Australian Albers (EPSG:3577)
• Geometry Type: Polygon
• Cell Size: 500 m × 500 m (25 hectares)
• Total Features: 3,222,693
• Attribute Fields: Id (unique cell identifier)
• Bounding Box (minx, miny, maxx, maxy):
  (826250.0, –4212250.0, 2082750.0, –3181250.0)

• Thermal drone survey planning
• Spatial alignment of repeatable wildlife monitoring
• Koala and arboreal mammal detection
• Binomial or Poisson N-mixture model design
• Landscape-scale ecological stratification

This grid layer was provided by Allen Mcilwee (NSW Government) and is published with permission as open-access supplementary material to support the following paper:

Ryan, S.A., Southwell, D.M., Beranek, C.T., Clulow, J., Jordan, N.R., Witt, R.R. (2025)
Estimating the landscape-scale abundance of an arboreal folivore using thermal imaging drones and binomial N-mixture modelling
Biological Conservation. Manuscript ID: 111207. https://doi.org/10.1016/j.biocon.2025.111207

The dataset is made available to support open ecological research and systematic drone survey planning in New South Wales.

Users applying this grid for survey or monitoring purposes in NSW are encouraged to submit resulting species detection records to NSW BioNet to contribute to state-wide biodiversity data and conservation efforts.

The Soil and Land Information System (SALIS) of New South Wales (NSW) provides a substantial database of information on soils, landscapes, and other geographic features, and is used by the NSW Government, other organizations and individuals to improve planning and decision-making for natural resource management. SALIS contains: (1) physical and chemical soil profile data from more than 70,000 points across NSW; AND (2) several soil map data sets, including the NSW Soil Landscapes (based on 1:100,000 or 1:250,000 map tiles), NSW Soil and Land Resources (seamless coverages based on major catchment areas), and Land Systems of Western NSW. Data users can access soil and land information from SALIS free-of-charge using the eSPADE spatial viewer system, which provides access to both soil profile and soil map information from SALIS and other sources. Digital spatial soil data are also accessible from the NSW Office of Environment and Heritage (OEH) data download site. The SALIS database is constantly updated as new information on the State's soil resources becomes available.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Classification and descriptions of native vegetation types of southeast NSW (including the South Coast and parts of the eastern tablelands), and map of extant distribution of these veg types at 1:100 000 interpretation scale. Based on the South Coast - Illawarra Vegetation Integration (SCIVI) Project, which aimed to integrate many previous vegetation classification and mapping works to produce a single regional classification and map plus information on regional conservation status of vegetation types, to inform the South Coast and Illawarra Regional Strategies. Vegetation classification based on a compilation of ~ 8,500 full-floristic field survey sites from previous studies. Classified vegetation types refered to previous studies. Distribution of veg types was mapped by spatial interpolation (modelling) from classified sites, using a hybrid decision-tree/expert system. Final model was cut to \'extant\' boundaries using a compiled coverage of aerial photograph interpretation (API) of woody and wetland vegetation boundaries. A total of 189 vegetation types were identified, and types related to Endangered Ecological Communities are highlighted. Tozer et al 2006. Native vegetation of southeast NSW: a revised classification and map for the coast and eastern tablelands. ANZNS0359100156 VIS ID 2230

Dataset History

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Data Quality

Lineage: Refer to project report for details. Vegetation classification and mapping based on ~ 8,500 field survey sites compiled from numerous previous surveys by many workers between the 1980s and 2005. Extant boundaries of native vegetation delineated by compilation of new and existing spatial data derived from aerial photo interpretation, augmented in parts by on-screen interpretation from digital orthorectified imagery of 1998 or later.

Scope: dataset

Completeness: Spatial completeness: final map of extant native vegetation boundaries relies on compilation of API of extant native vegetation. API standards vary across the study area. Smaller patches of woody vegetation and areas of non-woody non-wetland vegetation (eg. primary and secondary/derived grasslands) are not mapped as extant native vegetation. Classification completeness: Classification based on ~8,500 full-floristic field samples compiled from numerous previous surveys. This is the most comprehensive classification of the native vegetation of this region to date, however every classification can be improved by further sampling. Report gives the number of field samples classified as each veg type (=map unit) - this gives a general indication of how comprehensive the description of each unit is, and the likely reliability of modelling for that vegetation type. Verification completeness: No verification has been undertaken across the full study area, as all available site data was used to maximise power of model. Verification / statements of accuracy will be possible in future.

Logical consistency: Distribution of veg types was mapped by spatial interpolation (modelling) from ~ 8,500 classified field survey sites, using a hybrid decision-tree/expert system to explore relationships between veg types and environmental variables including substrate, topography and climate. Final map is based on an explicit set of rules defining the environmental space occupied by each vegetation type. See report for discussion of the modelling process and its limitations.

Positional accuracy: Spatial accuracy of modelled boundaries between vegetation types not tested, as no independent classified site data were available on completion of project. Accuracy of extant vegetation boundaries varies across the study area due to compilation of large number of previous coverages. Generally estimated to be 20-50m.

Attribute accuracy: Refer to project report for details. Accuracy of modelled vegetation types not tested as no independent classified site data were available following modelling. Accuracy of extant native vegetation boundaries varies across the study area according to standards of compiled API coverages: northern part (Sydney south to Araluen/Batemans Bay) delineated remnants andge;1ha, southern end andge;~2ha, small central area (Narooma/Cobargo) has minimum polygon size of 10ha.

Dataset Citation

NSW Department of Environment, Climate Change and Water (2010) Southeast NSW Native Vegetation Classification and Mapping - SCIVI VIS_ID 2230 20030101. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/0f1aeb33-1b49-4839-88fa-8b635cf9d3ab.

This dataset contains the results of the bicycle count survey in the City of Sydney.The City has conducted twice yearly intersection cycle counts at various sites in peak hours (6-9 am and 4-7 pm) on one day in March and October every year, since March 2010 (excluding March 2018). This data shows the total number of cyclists at the site during peak hours as well as individual survey hours.