This example demonstrates how to use PostGIS capabilities in CyberGIS-Jupyter notebook environment. Modified from notebook by Weiye Chen (weiyec2@illinois.edu)

PostGIS is an extension to the PostgreSQL object-relational database system which allows GIS (Geographic Information Systems) objects to be stored in the database. PostGIS includes support for GiST-based R-Tree spatial indices, and functions for analysis and processing of GIS objects.

Resources for PostGIS:

Manual https://postgis.net/docs/ In this demo, we use PostGIS 3.0. Note that significant changes in APIs have been made to PostGIS compared to version 2.x. This demo assumes that you have basic knowledge of SQL.

Abstract

The Namoi Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/).

An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin.

The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Namoi Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2018) NAM Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58.

Dataset Ancestors

• Derived From River Styles Spatial Layer for New South Wales

• Derived From Geofabric Surface Network - V2.1

• Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition

• Derived From HUN SW footprint shapefiles v01

• Derived From HUN Groundwater footprint polygons v01

• Derived From Namoi Environmental Impact Statements - Mine footprints

• Derived From Namoi CMA Groundwater Dependent Ecosystems

• Derived From Landscape classification of the Namoi preliminary assessment extent

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1

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

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From Bioregional Assessment areas v03

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Asset list for Namoi - CURRENT

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From Namoi bore locations, depth to water for June 2012

• Derived From Victoria - Seamless Geology 2014

• Derived From Murray-Darling Basin Aquatic Ecosystem Classification

• Derived From HUN SW GW Mine Footprints for IMIA 20170303 v03

• Derived From Climate model 0.05x0.05 cells and cell centroids

• Derived From Namoi hydraulic conductivity measurements

• Derived From Namoi groundwater uncertainty analysis

• Derived From Historical Mining footprints DTIRIS HUN 20150707

• Derived From Namoi NGIS Bore analysis for 2012

• Derived From Australian 0.05º gridded chloride deposition v2

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From Bioregional Assessment areas v06

• Derived From NAM Analysis Boundaries 20160908 v01

• Derived From Namoi groundwater drawdown grids

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From BOM, Australian Average Rainfall Data from 1961 to 1990

• Derived From Namoi Existing Mine Development Surface Water Footprints

• Derived From Surface water Preliminary Assessment Extent (PAE) for the Namoi (NAM) subregion - v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From [National Surface Water sites

Abstract

The Galilee Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/).

An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin.

The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Galilee Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2018) GAL Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/3dbb5380-2956-4f40-a535-cbdcda129045.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From Galilee Drawdown Rasters

• Derived From Galilee Groundwater Model, Hydrogeological Formation Extents v01

• Derived From GAL SW Quantiles Interpolation for IMIA Database

• Derived From SA Petroleum Production License Applications

• Derived From Galilee tributary catchments

• Derived From Springs of the Galilee subregion - Points Geometry

• Derived From GAL Aquifer Formation Extents v01

• Derived From Geofabric Surface Cartography - V2.1

• Derived From SA Mineral and/or Opal Exploration Licenses

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From GAL Assessment Units 1000m 20160522 v01

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

• Derived From Phanerozoic OZ SEEBASE v2 GIS

• Derived From Asset database for the Galilee subregion on 2 December 2014

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From Bioregional Assessment areas v03

• Derived From SA Petroleum Exploration Licences/Permits

• Derived From South Australia Mineral Leases Production, 6 March 2013

• Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01

• Derived From Kevin's Corner Project Environmental Impact Statement

• Derived From Galilee Hydrological Response Variable (HRV) model

• Derived From Asset list for Galilee - 20140605

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From QLD Current Exploration Permits for Minerals (EPM) in Queensland 6/3/2013

• Derived From Victoria - Seamless Geology 2014

• Derived From Galilee groundwater numerical modelling AEM models

• Derived From GAL Surface Water Reaches for Risk and Impact Analysis 20180803

• Derived From Matters of State environmental significance (version 4.1), Queensland

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From GAL Aquifer Formation Extents v02

• Derived From Queensland wetland data version 3 - wetland areas.

• Derived From Galilee surface water modelling nodes

• Derived From GAL Eco HRV SW Quantiles Interpolation for IMIA Database

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From China Stone Coal Project initial advice statement

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From South Australia Mineral Production Claims, 6 March 2013

• Derived From Onsite and offsite mine infrastructure for the Carmichael Coal Mine and Rail Project, Adani Mining Pty Ltd 2012

*

Abstract

The Maranoa-Balonne-Condamine Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP).

The version provided here for public download has been slightly modified to remove restricted material such as the co-ordinates of protected or threatened species. This version was used to populate BA Explorer.

The Analysis Database brings together many of the data sets used in Components 1 and 2 of the assessments and includes hydrology and hydrogeology modelling results, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Component 1 and 2 products under the Assessments tab in http://www.bioregionalassessments.gov.au/.

An Analysis Database of common design and schema was implemented for each subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Databases in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of different specifications and origins.

The Analysis Database includes all the data used for the assessment of the subregion with the exception of those datasets that were not provided to the program with an open access licence. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Maranoa-Balonne-Condamine Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of the Maranoa-Balonne-Condamine Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2017) MBC Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 25 October 2017, http://data.bioregionalassessments.gov.au/dataset/69075f3e-67ba-405b-8640-96e6cb2a189a.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition

• Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 16 June 2015

• Derived From South East Queensland GDE (draft)

• Derived From Geofabric Surface Cartography - V2.1

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From QLD Dept of Natural Resources and Mines, Surface Water Entitlements 131204

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

• Derived From Catchment Scale Land Use of Australia - 2014

• Derived From Surface water preliminary assessment extent for the Maranoa-Balonne-Condamine subregion - v02

• Derived From MBC Groundwater model domain boundary

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From Bioregional Assessment areas v03

• Derived From MBC Groundwater model ACRD 5th to 95th percentile drawdown

• Derived From Permanent and Semi-Permanent Waterbodies of the Lake Eyre Basin (Queensland and South Australia) (DRAFT)

• Derived From Receptors for the Maranoa-Balonne-Condamine subregion

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From MBC Assessment Units 20160714 v01

• Derived From Victoria - Seamless Geology 2014

• Derived From Matters of State environmental significance (version 4.1), Queensland

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From Bioregional Assessment areas v06

• Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 9 June 2015

• Derived From Queensland wetland data version 3 - wetland areas.

• Derived From Groundwater Preliminary Assessment Extent (PAE) for the Maranoa Balonne Condamine (MBC) subregion - v02

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 05 February 2016

• Derived From MBC Groundwater model layer boundaries

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Baseline drawdown Layer 1 - Condamine Alluvium

• Derived From MBC Assessment unit codified by regional watertable

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014

• Derived From MBC Assessment Units 20160714 v02

• Derived From MBC Groundwater model water balance areas

• Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 25 February 2015

• Derived From Australia - Species of National Environmental Significance Database

• Derived From MBC Groundwater model uncertainty analysis

• Derived From Spring vents assessed for the Surat Underground Water Impact Report 2012

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

• **Derived

The Italian geospatial analytics market, valued at €260 million in 2025, is poised for robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 8.17% from 2025 to 2033. This expansion is driven by increasing adoption of precision agriculture techniques, the burgeoning need for efficient infrastructure management within utilities and communication sectors, and rising demand for advanced analytics in defense and intelligence applications. Furthermore, the Italian government's focus on smart city initiatives and the expanding digitalization across various sectors, including healthcare and real estate, significantly contribute to market growth. The market segmentation reveals a strong demand across diverse verticals, with agriculture, utilities, and defense exhibiting substantial growth potential. The prevalent use of surface analysis techniques reflects a focus on immediate application needs, while the growing adoption of network and geo-visualization analytics indicates a shift toward more sophisticated and insightful data interpretation. Leading players such as ESRI, Hexagon AB, and Trimble Geospatial are actively contributing to market development by providing cutting-edge software and services. Competition is likely to intensify as smaller, specialized companies emerge, offering niche solutions and catering to the evolving demands of different market segments. The forecast period (2025-2033) anticipates substantial market expansion, largely attributed to continued technological advancements, particularly in AI and machine learning, enhancing the analytical capabilities of geospatial data. However, challenges exist, potentially including data security concerns, the need for skilled professionals to interpret complex analytical results, and the high initial investment required for advanced geospatial technology implementation. Nevertheless, the long-term outlook for the Italian geospatial analytics market remains positive, driven by sustained government investment in digital infrastructure and the increasing awareness of the value proposition offered by sophisticated geospatial analysis across various sectors. The market's trajectory suggests a significant opportunity for both established and emerging players in the years to come. Recent developments include: March 2023: The Italian space agency and NASA have collaborated to build and launch the Multi-Angle Imager for Aerosols mission, an effort to investigate the health impacts of tiny airborne particles polluting the cities through analyzing data by collecting data from the satellite-based observatories, which would fuel the demand for geospatial analytics market in the country., January 2023: EDB, an open-source database service provider in Italy, announced its partnership with Esri to certify EDB Postgres Advanced Server with Esri ArcGIS Pro and Esri ArcGIS Enterprise, which work together to form Esri's Geospatial analytic solutions, operating in many countries, including Italy. After this partnership, users can connect their EDB Postgres Advanced Server to explore, visualize and analyze their geospatial data and share their work with an Esri ArcGIS Enterprise portal. In addition, EDB customers, especially those in the public sector, can use their database with Esri ArcGIS software to transform their data into something that improves workflows and processes and shapes policies and engagement within their communities.. Key drivers for this market are: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Potential restraints include: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Notable trends are: The Increase in the Number of Smart Cities in The Country Fuels the Market Growth.

Abstract

The Hunter Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/).

An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin.

The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Hunter Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2018) HUN Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 28 August 2018, http://data.bioregionalassessments.gov.au/dataset/298e1f89-515c-4389-9e5d-444a5053cc19.

Dataset Ancestors

• Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

• Derived From HUN ZoPHC and component layers 20171115

• Derived From NSW Office of Water - National Groundwater Information System 20140701

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

• Derived From NSW Wetlands

• Derived From Geofabric Surface Network - V2.1

• Derived From HUN AWRA-R simulation nodes v01

• Derived From Hunter AWRA Hydrological Response Variables (HRV)

• Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition

• Derived From HUN SW footprint shapefiles v01

• Derived From HUN Groundwater footprint polygons v01

• Derived From Asset database for the Hunter subregion on 24 February 2016

• Derived From BA All Regions BILO cells in subregions shapefile

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From HUN AWRA-R River Reaches Simulation v01

• Derived From HUN AWRA-L simulation nodes v02

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

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From Hunter subregion boundary

• Derived From Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)

• Derived From Atlas of Living Australia NSW ALA Portal 20140613

• Derived From Bioregional Assessment areas v03

• Derived From HUN AWRA-R calibration catchments v01

• Derived From HUN AWRA-R Observed storage volumes Glenbawn Dam and Glennies Creek Dam

• Derived From Selected streamflow gauges within and near the Hunter subregion

• Derived From Groundwater Entitlement Hunter NSW Office of Water 20150324

• Derived From Asset database for the Hunter subregion on 20 July 2015

• Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01

• Derived From HUN Alluvium (1:1m Geology)

• Derived From HUN River Perenniality v01

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Hunter bioregion (IBRA Version 7)

• Derived From Climate Change Corridors (Moist Habitat) for North East NSW

• Derived From HUN Riverine Landscape Classes subject to hydrological change

• Derived From Asset database for the Hunter subregion on 22 September 2015

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From HUN bores v01

• Derived From NSW Office of Water Groundwater Licence Extract, North and South Sydney - Oct 2013

• Derived From HUN SW GW Mine Footprints for IMIA 20170303 v03

• Derived From Climate model 0.05x0.05 cells and cell centroids

• Derived From HUN AWRA-LR Model v01

• Derived From HUN Landscape Classification v02

• Derived From [Historical

DESCRIPTION ---------------- VERSIONS version1.0.1 fixes problem with functions version1.0.2 added table dbeel_rivers.rn_rivermouth with GEREM basin, distance to Gibraltar and link to CCM. version1.0.3 fixes problem with functions version1.0.4 adds views rn_rna and rn_rne to the database ---------------- The SUDOANG project aims at providing common tools to managers to support eel conservation in the SUDOE area (Spain, France and Portugal). VISUANG is the SUDOANG Interactive Web Application that host all these tools . The application consists of an eel distribution atlas (GT1), assessments of mortalities caused by turbines and an atlas showing obstacles to migration (GT2), estimates of recruitment and exploitation rate (GT3) and escapement (chosen as a target by the EC for the Eel Management Plans) (GT4). In addition, it includes an interactive map showing sampling results from the pilot basin network produced by GT6. The eel abundance for the eel atlas and escapement has been obtained using the Eel Density Analysis model (EDA, GT4's product). EDA extrapolates the abundance of eel in sampled river segments to other segments taking into account how the abundance, sex and size of the eels change depending on different parameters. Thus, EDA requires two main data sources: those related to the river characteristics and those related to eel abundance and characteristics. However, in both cases, data availability was uneven in the SUDOE area. In addition, this information was dispersed among several managers and in different formats due to different sampling sources: Water Framework Directive (WFD), Community Framework for the Collection, Management and Use of Data in the Fisheries Sector (EUMAP), Eel Management Plans, research groups, scientific papers and technical reports. Therefore, the first step towards having eel abundance estimations including the whole SUDOE area, was to have a joint river and eel database. In this report we will describe the database corresponding to the river’s characteristics in the SUDOE area and the eel abundances and their characteristics. In the case of rivers, two types of information has been collected: River topology (RN table): a compilation of data on rivers and their topological and hydrographic characteristics in the three countries. River attributes (RNA table): contains physical attributes that have fed the SUDOANG models. The estimation of eel abundance and characteristic (size, biomass, sex-ratio and silver) distribution at different scales (river segment, basin, Eel Management Unit (EMU), and country) in the SUDOE area obtained with the implementation of the EDA2.3 model has been compiled in the RNE table (eel predictions). CURRENT ACTIVE PROJECT The project is currently active here : gitlab forgemia TECHNICAL DESCRIPTION TO BUILD THE POSTGRES DATABASE 1. Build the database in postgres. All tables are in ESPG:3035 (European LAEA). The format is postgreSQL database. You can download other formats (shapefiles, csv), here SUDOANG gt1 database. Initial command # open a shell with command CMD # Move to the place where you have downloaded the file using the following command cd c:/path/to/my/folder # note psql must be accessible, in windows you can add the path to the postgres #bin folder, otherwise you need to add the full path to the postgres bin folder see link to instructions below createdb -U postgres eda2.3 psql -U postgres eda2.3 # this will open a command with # where you can launch the commands in the next box Within the psql command create extension "postgis"; create extension "dblink"; create extension "ltree"; create extension "tablefunc"; create schema dbeel_rivers; create schema france; create schema spain; create schema portugal; -- type \q to quit the psql shell Now the database is ready to receive the differents dumps. The dump file are large. You might not need the part including unit basins or waterbodies. All the tables except waterbodies and unit basins are described in the Atlas. You might need to understand what is inheritance in a database. https://www.postgresql.org/docs/12/tutorial-inheritance.html 2. RN (riversegments) These layers contain the topology (see Atlas for detail) dbeel_rivers.rn france.rn spain.rn portugal.rn Columns (see Atlas) gid idsegment source target lengthm nextdownidsegment path isfrontier issource seaidsegment issea geom isendoreic isinternational country dbeel_rivers.rn_rivermouth seaidsegment geom (polygon) gerem_zone_3 gerem_zone_4 (used in EDA) gerem_zone_5 ccm_wso_id country emu_name_short geom_outlet (point) name_basin dist_from_gibraltar_km name_coast basin_name # dbeel_rivers.rn ! mandatory => table at the international level from which # the other table inherit # even if you don't want to use other countries # (In many cases you should ... there are transboundary catchments) download this first. # the rn network must be restored firt ! #table rne and rna refer to it by foreign keys. pg_restore -U postgres -d ed...

The TerriaJS View extension for CKAN provides a framework for integrating TerriaJS, a geospatial data platform, with CKAN datasets. This extension allows users to create and configure views that display CKAN resources within a TerriaJS interface, providing a rich, interactive mapping experience. It leverages JSON schema for configuration, Jinja2 templating for dynamic content, and supports automatic generation of views for common geospatial data formats. Key Features: TerriaJS Integration: Enables display of CKAN resources directly within a TerriaJS viewer embedded in CKAN, enhancing data visualization. JSON Schema Configuration: Utilizes JSON schema for structured and validated configuration of TerriaJS views, allowing for customization of view properties. Dynamic View Generation: Automatically generates TerriaJS views for various data formats like WMS, MVT, and CSV, streamlining the view creation process. Jinja2 Templating: Supports Jinja2 templating within TerriaJS view configurations, allowing for dynamic population of view properties from dataset and resource metadata. Lazy-Loaded Groups: Allows grouping of existing TerriaJS views into virtual, lazy-loaded collections for organization and efficient loading of views. Reference Integrity: Prevents deletion of referenced views to maintain the integrity of dynamically linked view groups. Frontend Validation: Provides UI form generation with frontend JSON schema-based validation using json-editor, ACE editor, and AJV validation. Backend Validation: Utilizes jsonschema for backend API validation, ensuring data consistency and correctness. Configurable Automated View Creation: Defines configurable sets of views to be automatically created based on desired configurations. Dynamic Models: Employs special resource types ('terriajs-group', 'terriajs-view') to resolve existing views at request time, facilitating dynamic linking of views through view IDs. On-the-fly Configurations: During creation of a resource, the plugin provides on-the-fly configurations to provide a default view based on the format of the resource (linked or uploaded). Technical Integration: The extension adds plugins and middleware to CKAN, enabling management of TerriaJS views and configurations through CKAN's API and user interface. The extension leverages postgres + json approach NOT STORING OR CREATING ANY ADDITIONAL TABLE. It requires a properly configured CKAN instance with the plugin enabled in the CKAN configuration file. The type-mapping.json file maps resource types to corresponding JSON schemas for view creation and validation. Benefits & Impact: Implementing the TerriaJS View extension enhances CKAN's geospatial data visualization capabilities, allowing users to explore and interact with data through a TerriaJS interface. By supporting dynamic view generation, Jinja2 templating, and JSON schema configuration, the extension simplifies the creation and maintenance of TerriaJS views, making it easier to publish and share geospatial data through CKAN.

vmap.postgres.danger

vmap.postgres.cours_deau

vmap.postgres.glissement_ccbta

vmap.postgres.natura2000

didon.postgres.exp_corse_zas_2016_o3_nh180

didon.postgres.exp_corse_zas_2017_pm10_moyan

didon.postgres.exp_corse_zas_2017_o3_nh180