The thesis the data comes from analyses patterns of growth, decline, clustering and dispersal of live music in Sydney and Melbourne between the 1980s and 2000s. It demonstrates the use of historical Geographic Information Systems, combined with interviews, as a methodological approach for understanding the impacts of restructuring in cultural industries. It offers a practical example of applied social research with GIS.

The project developed a novel methodology combining GIS with interviews with music scene participants. A substantial part of the research project comprised the development of a historical geodatabase, leveraging the spatial and temporal data embedded in historical live music performance listings (‘gig listings’) sourced from archived publications in Sydney and Melbourne. This geodatabase ultimately incorporates over 20,000 live music listings and over 2500 geocoded venues.

The historical geodatabase was built incrementally to adapt to the format of the historical data. The structure maintains a one-to-one relationship to primary sources from different publications, allowing for quality checks, but can produce normalised outputs that allow live music venues, performances, and bands to be analysed separately. Outputs from the geodatabase have facilitated the quantitative analysis and geovisualisation of live music data over the study time frame in Sydney and Melbourne.

This dataset consists of various raster and gpkg feature classes which formed most of the data used to guide construction of tectonic elements and GPlates model discussed in chapters 3 and 4 of the thesis. Most of these datasets are from freely-available government sources. Those which were mentioned in the thesis but not included in this data package are predominantly sourced from supplementary data packages in other research studies. A README file is contained with more information.

Remoteness Areas divide Australia into five classes of remoteness on the basis of a measure of relative access to services. The five remoteness classes are: Major Cities, Inner Regional, Outer Regional, Remote and Very Remote. Remoteness Areas are derived from the Accessibility/Remoteness Index of Australia Plus (ARIA+) produced by the University of Adelaide.Data and geography referencesSource data publication: Australian Statistical Geography Standard (ASGS) Edition 2 - Defining Remoteness AreasFurther information: Australian Statistical Geography Standard (ASGS) Edition 2 - Remoteness StructuresSource: Australian Bureau of Statistics (ABS)Made possible by the Digital Atlas of AustraliaThe Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.Contact the Australian Bureau of StatisticsEmail geography@abs.gov.au if you have any questions or feedback about this web service.Subscribe to get updates on ABS web services and geospatial products.Privacy at the Australian Bureau of StatisticsRead how the ABS manages personal information - ABS privacy policy.

This dataset consists of various raster and gpkg feature classes which formed most of the data used to guide construction of tectonic elements and GPlates model discussed in chapters 3 and 4 of the thesis. Most of these datasets are from freely-available government sources. Those which were mentioned in the thesis but not included in this data package are predominantly sourced from supplementary data packages in other research studies. A README file is contained with more information.

The Open Accessibility and Remoteness Index for Australia. The "Open Accessibility and Remoteness Index for Australia" is an open-data, national GIS accessibility index for Australia. Many researchers are familiar with the Australian Bureau of Statistics Remoteness Structure, which categorises places in Australia into five classes according to their remoteness. Often it would be more useful to have remoteness measured as a continuous variable, rather than a categorical one however. The ABS Remoteness Structure is based on ARIA+, a continuous remoteness index produced by the National Centre for Social Applications of GIS at the University of Adelaide. Unfortunately, ARIA+ is a commercial product which costs hundreds of dollars. The "Open accessibility and remoteness index for Australia" reimplements the ARIA+ methodology and makes the results freely available in the public domain. The major divergence from the ARIA+ methodology is that where ARIA+ uses network distance along highways to measure proximity, this project uses Euclidian distance. The Open Accessibility and Remoteness Index for Australia is available for 2001, 2006 and 2011 census geographies. Files available for download include 1-km raster data , population-weighted mean values for selected ABS census geographic units and a brief technical report. Please contact Francis Markham with any inquiries.

The GILMORE project is a pilot study designed to test holistic systems approaches to mapping mineral systems and dryland salinity in areas of complex regolith cover. The project is coordinated by the Australian Geological Survey Organisation, and involves over 50 scientists from 14 research organisations. Research partners include: Cooperative Research Centres for Advanced Mineral Exploration Technologies (CRC AMET), Landscape Evolution and Mineral Exploration (CRC LEME), the CRC for Sensor Signal and Information Processing, and the Australian Geodynamics Cooperative Research Centre (AGCRC) Land and Water Sciences Division of Bureau of Rural Sciences (BRS) NSW Department of Land & Water Conservation and the NSW Department of Mineral Resources. Various universities including the Australian National University, University of Canberra, Macquarie University, Monash University, University of Melbourne, and Curtin University of Technology, and Australian National Seismic Imaging Resource (ANSIR). The project area lies on the eastern margin of the Murray-Darling Basin in central-west NSW. The project area was chosen for its overlapping mineral exploration (Au-Cu) and salinity management issues, and the availability of high-resolution geophysical datasets and drillhole materials and datasets made available by the minerals exploration industry. The project has research agreements with the minerals exploration industry, and is collaborating with rural land-management groups, and the Grains Research and Development Corporation. The study area (100 x 150 km), straddles the Gilmore Fault Zone, a major NNW-trending crustal structure that separates the Wagga-Omeo and the Junee-Narromine Volcanic Belts in the Lachlan Fold Belt. The project area includes tributaries of the Lachlan and the Murrumbidgee Rivers, considered to be two of the systems most at risk from rising salinities. This project area was chosen to compare and contrast salt stores and delivery systems in floodplain (in the Lachlan catchment) and incised undulating hill landscapes (Murrumbidgee catchment). The study area is characteristic of other undulating hill landscapes on the basin margins, areas within the main and tributary river valleys, and the footslopes and floodplains of the Murray-Darling Basin itself. Studies of the bedrock geology in the study area reveal a complex architecture. The Gilmore Fault Zone consist of a series of subparallel, west-dipping thrust faults, that juxtapose, from west to east, Cambro-Ordovician meta-sediments and granites of the Wagga Metamorphics, and further to the east, a series of fault-bounded packages comprising volcanics and intrusions, and siliciclastic meta-sediments. Two airborne electromagnetic (AEM) surveys were flown in smaller areas within the two catchments. Large-scale hydrothermal alteration and structural overprinting, particularly in the volcanics, has added to the complexity within the bedrock architecture. The data were originally published on 6 CDs. For ease of download the data have been zipped into the original structure. The contents are as follows: CD1 - An overview of the GILMORE Project with geophysical images, regolith map, drillhole locations, geophysical survey information and maghemite geochemistry. CD2 - Airborne Electromagnetic (AEM) images from the TEMPEST survey with vertical cross-sections linked to the flight lines CD3 - Integrated images of the Airborne Electromagnetic (AEM) data draped over the First Vertical Derivative of the Total Magnetic Intensity CD4 - Integrated images of the Airborne Electromagnetic (AEM) data draped over the First Vertical Derivative of the Total Magnetic Intensity CD5 - High resolution geophysical images from three detailed surveys and data from the Airborne Electromagnetic (AEM) QUESTEM survey CD6 - Geology, geochemistry, downhole data, 3 dimensional models, seismic data, and images linked to downhole point data.

This data-set is a supplementary material related to the generation of synthetic images of a corridor in the University of Melbourne, Australia from a building information model (BIM). This data-set was generated to check the ability of deep learning algorithms to learn task of indoor localisation from synthetic images, when being tested on real images. =============================================================================The following is the name convention used for the data-sets. The brackets show the number of images in the data-set.REAL DATAReal
---------------------> Real images (949 images)

Gradmag-Real -------> Gradmag of real data (949 images)SYNTHETIC DATASyn-Car
----------------> Cartoonish images (2500 images)

Syn-pho-real ----------> Synthetic photo-realistic images (2500 images)

Syn-pho-real-tex -----> Synthetic photo-realistic textured (2500 images)

Syn-Edge --------------> Edge render images (2500 images)

Gradmag-Syn-Car ---> Gradmag of Cartoonish images (2500 images)=============================================================================Each folder contains the images and their respective groundtruth poses in the following format [ImageName X Y Z w p q r].To generate the synthetic data-set, we define a trajectory in the 3D indoor model. The points in the trajectory serve as the ground truth poses of the synthetic images. The height of the trajectory was kept in the range of 1.5–1.8 m from the floor, which is the usual height of holding a camera in hand. Artificial point light sources were placed to illuminate the corridor (except for Edge render images). The length of the trajectory was approximately 30 m. A virtual camera was moved along the trajectory to render four different sets of synthetic images in Blender*. The intrinsic parameters of the virtual camera were kept identical to the real camera (VGA resolution, focal length of 3.5 mm, no distortion modeled). We have rendered images along the trajectory at 0.05 m interval and ± 10° tilt.The main difference between the cartoonish (Syn-car) and photo-realistic images (Syn-pho-real) is the model of rendering. Photo-realistic rendering is a physics-based model that traces the path of light rays in the scene, which is similar to the real world, whereas the cartoonish rendering roughly traces the path of light rays. The photorealistic textured images (Syn-pho-real-tex) were rendered by adding repeating synthetic textures to the 3D indoor model, such as the textures of brick, carpet and wooden ceiling. The realism of the photo-realistic rendering comes at the cost of rendering times. However, the rendering times of the photo-realistic data-sets were considerably reduced with the help of a GPU. Note that the naming convention used for the data-sets (e.g. Cartoonish) is according to Blender terminology.An additional data-set (Gradmag-Syn-car) was derived from the cartoonish images by taking the edge gradient magnitude of the images and suppressing weak edges below a threshold. The edge rendered images (Syn-edge) were generated by rendering only the edges of the 3D indoor model, without taking into account the lighting conditions. This data-set is similar to the Gradmag-Syn-car data-set, however, does not contain the effect of illumination of the scene, such as reflections and shadows.*Blender is an open-source 3D computer graphics software and finds its applications in video games, animated films, simulation and visual art. For more information please visit: http://www.blender.orgPlease cite the papers if you use the data-set:1) Acharya, D., Khoshelham, K., and Winter, S., 2019. BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images. ISPRS Journal of Photogrammetry and Remote Sensing. 150: 245-258.2) Acharya, D., Singha Roy, S., Khoshelham, K. and Winter, S. 2019. Modelling uncertainty of single image indoor localisation using a 3D model and deep learning. In ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, IV-2/W5, pages 247-254.

The University of Ballarat through its Centre for eCommerce and Communications (CeCC) was engaged by the Golden Plains Shire and the City of Ballarat to review the extent to which key objectives of past Geographic Information System (GIS) strategies have been achieved and also to gather information which can assist with future planning during.

This dataset contains interviews to gauge current usage of online GIS systems and to obtain feedback to ascertain priorities which were conducted online and face-to-face in January 2011.

Observation data (towed video, BRUVs) collected in Victorian state waters at South Australia to Cape Otway.This footage was collected by researchers from Deakin University, Victorian Department of Primary Industries - Marine and Freshwater Resources Institute (MAFRI) and Parks Victoria.The original footage has been converted from various formats including VHS and MiniDV to digital format, with funds supplied by Deakin University Library. Underwater footage gathered from other geographical locations around Victoria from the Victorian Marine Habitat Mapping Program can be accessed via the links featured at the bottom of this record.High quality versions of the videos may be requested via Deakin University Library.

The FedUni Spatial groundwater bore database is part of an interoperable web-GIS maintained by Federation University Australia. It records data on groundwater research and monitoring bores that are either owned, maintained or monitored by the University for groundwater research projects. The FedUni Spatial website was initially developed with funding support from the Corangamite Catchment Management Authority and contained four environmental datasets: groundwater bores, salinity, erosion and landslides, covering the Corangamite region.

The FedUni Spatial groundwater bore database contains information on bore location, bore ownership, bore construction details, aquifer parameters, groundwater level monitoring, groundwater chemistry and isotopes, bore lithology and stratigraphy. It includes links to images, documents, datafiles and weblinks that are relevant to individual bore records. The FedUni spatial groundwater bore database is also used to clean and enhance data, most of which is then revised in the Victorian Groundwater Management System owned and managed by the Department of Sustainability and Environment.

The FedUni Spatial groundwater bore data is also included in the Visualising Victoria's Groundwater web-portal

Federation University Australia ’s Centre for eCommerce and Communications were engaged by
DIIRD to assist in researching the business case for a GIS application as part of the GippsLink Project, 2010.

The project stakeholders across six LGAs -Bass Coast Shire; Baw Baw Shire; East Gippsland Shire; Latrobe City; South Gippsland Shire, Wellington Shire participated in an online survey to gauge current usage of online GIS systems and to obtain feedback to ascertain priorities. The results of that survey form this dataset which is stored using Lime survey software. 23 responses were obtained.

The aerial photographs, taken on the 6th of February 1975 at a scale 1: 50 000, were obtained from the Survey of Kenya and were used to generate my original data.

This collection comprises a report developed from the analysis of data output from the POSTool and written by Paula Hooper, Bryan Boruff and Fiona Bull of the Centre for the Built Environment and Health, University of Western Australia. The report examined the spatial distribution of Public Open Space (POS) across the Perth Metropolitan Region (PMR) focusing specifically on parks, park type, park amenity, and park catchment by Local Government Area (LGA). Summary statistics were derived for each LGA in the PMR using the POS Tool and park and park amenity provision compared across the region.

The report outlines the spatial disparities in the provision of parks and park amenity in Perth highlighting where certain LGA’s have underprovided for the citizens they represent. Furthermore, through the examination of park catchments and the population serviced by each park, the report identifies the percent of each LGA’s population which does not have easy access to parks in their neighborhood. The results of this report identify the varying range of park and park amenity provision across the PMR whilst providing an example of the robust analysis which can be conducted using results generated by the POS Tool.

The Corangamite Catchment Management Authority Knowledge Base is part of an interoperable web-GIS maintained by Federation University Australia. The site provides an extensive collection of publications and technical reports on all aspects of the catchment. The collection focuses on information written specifically for the Corangamite Region. The database has been indexed by subject and locality for information retrieval and analysis. Federation University Australia's Centre for eResearch and Digital Innovation is hosting the site on behalf of the CCMA.

The Federation University Australia Corangamite Catchment Management Authority Knowledge Base was established to ensure the protection and sustainable development of land, vegetation and water resources within a boundary stretching from Geelong to Ballarat and along the coast to Peterborough.

About 380,000 people live in the catchment’s 13,340 square kilometres of south-western Victoria and 175 kilometres of coastal fringe. The region is defined by four river basins – the Moorabool, Barwon, Lake Corangamite and Otway Coast. It includes all or part of the cities of Ballarat and Greater Geelong, the Borough of Queenscliff and the shires of Moorabool, Surf Coast, Corangamite, Golden Plains, Colac Otway and Moyne.

Related initiatives include Soil Health, an online repository of soil health information and knowledge: including reports, research papers, maps and descriptions related to current and past soil series mapping, land capability and suitability assessments, agricultural trials, and soil research and investigations; and, NRM Planning, a pilot project testing how online mapping can be used to match local and regional priorities for catchment management in the Corangamite Catchment Management Authority region.