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

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

The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.

This is a worship data from OSM of Pakistan. It is in SQL format for postgresql with postgis extension enabled.

The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.

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 Flood Inundation Mapping (FIM) Visualization Deck is a web-based application designed to display and compare flood extent and depth information across various temporal and scenario conditions. It provides a front-end interface for accessing geospatial flood data and interacting with mapped outputs generated from hydraulic modeling.

Core Functions: • Flood Extent Mapping: Visualizes flood extents from modeled scenarios (e.g., 2-year, 10-year, 100-year events) and real-time conditions based on streamflow observations or forecasts. • Flood Depth Visualization: Displays depth rasters over affected areas, derived from hydraulic simulations (e.g., HEC-RAS). • Scenario Comparison: Allows side-by-side viewing of multiple FIM outputs to support calibration or decision analysis. • Layer Management Toolbox: Users can toggle basemaps, adjust layer transparency, load datasets, and control map extents.

Data Inputs: • Precomputed flood inundation extents (raster/tile layers) • Depth grids • Stream gauge metadata • Associated hydraulic model outputs

Technical Stack: • Front-end: Built with JavaScript, primarily using Leaflet.js for interactive map rendering. • Back-end Services: Uses GeoServer to serve raster tiles and vector layers (via WMS/WFS). Uses OGC-compliant services and REST endpoints for data queries. • Data Formats: Raster layers (e.g., GeoTIFF, PNG tiles), vector layers (GeoJSON, shapefiles), elevation models, and model-derived grid outputs. • Database: Integrates with a PostgreSQL/PostGIS backend or similar spatial database for hydrologic and geospatial data management. • Deployment: Hosted via University of Iowa infrastructure, with modular UI elements tied to specific watersheds or study areas.

CyberGIS-Jupyter for Water Quarterly Release Announcement (2020 Q2)

Dear HydroShare Users,

We are pleased to announce a new quarterly release of CyberGIS-Jupyter for Water (CJW) platform at https://go.illinois.edu/cybergis-jupyter-water. This release includes new capabilities to support the geoanalytics suite of GRASS for model pre/post-processing, PostGIS database, and Landlab Earth surface modelling toolkit along with several enhancements to job submission middleware, system security as well as service infrastructure. Please refer to the following list for details and examples.

Please let us know if you have any questions or run into any problems (help@cybergis.org). Any feedback would be greatly appreciated.

Best regards, CyberGIS-Hydro team

GRASS GIS for model pre/post-processing: Learn how to consolidate the features of the GRASS geoanalytics suite to support pre/post-processing for SUMMA and RHESSYs models in CJW. Example notebooks: https://www.hydroshare.org/resource/4cbcfdd6e7f943e2969dd52e780bc52d/

Manage geospatial data with PostGIS: PostGIS is an extension to the PostgreSQL object-relational database system which allows geospatial data to be efficiently stored while providing various advanced functions for in-situ data analysis and processing. Example notebooks: https://www.hydroshare.org/resource/bb779d4cce564dd6afcf463c8910786f/

Security and service infrastructure enhancements Trusted group: Starting from this release, all users are required to join the “CyberGIS-Jupyter for Water” trusted group at https://www.hydroshare.org/group/157 in order to access the CJW platform, which is a preventive measure to protect the shared computing resources from being abused by malicious users. A complete user profile page is highly recommended to expedite the approval process. User metric submission to XSEDE: CJW, as a science gateway, is now sending unique user usage metrics to XSEDE to comply with its requirements.

Landlab for enabling collaborative numerical modeling in Earth sciences using knowledge infrastructure Example notebooks: https://www.hydroshare.org/resource/370c288b61b84794b847ef85c4dd4ffb/ https://www.hydroshare.org/resource/6add6bee06bb4050bfe23e1081627614/

Job submission enhancements Refactored the structure of the cyberGIS job submission system Data-driven implementation for avoiding excessive data transmission between HydroShare and CJW Add the specification of input parameters into a JSON file to improve the flexibility and generality of model management Enable HPC-SUMMA object that can directly call SUMMA Example notebooks: https://www.hydroshare.org/resource/4a4a22a69f92497ead81cc48700ba8f8/

The ckanext-tsbsatellites extension appears to be designed to enhance CKAN's capabilities, primarily focusing on geospatial data management, potentially within the context of a project or organization named "TSB Satellites". The extension leverages other CKAN extensions like ckanext-spatial and ckanext-harvest to provide a more comprehensive solution. Set up involves Solr for spatial search, GeoNetwork integration, and proper configuration parameters. Key Features: Leverages ckanext-spatial: Relies on the ckanext-spatial extension to provide geospatial search and display functionalities within CKAN, enabling users to search for datasets based on location. Employs ckanext-harvest: Uses ckanext-harvest to automatically import or synchronize metadata from external sources, potentially including GeoNetwork, through a Redis backend. GeoNetwork Integration: Supports integration with GeoNetwork, a metadata catalog, ostensibly for importing and managing geospatial metadata, with instructions for installing and configuring GeoNetwork with PostgreSQL and PostGIS. Instructions are for GeoNetwork v2.10.3 running on Ubuntu 12.04 64bit.

The SIRIUS inventory integrates data from (i) the Sentinel-1/2 derived Arctic coastal human impact dataset (SACHI) (Bartsch et al., 2021), (ii) OpenStreetMap dataset for the infrastructure and land use information (OpenStreetMap Contributors and Geofabrik GmbH, 2018), (iii) the pan-Arctic catchments summary database (ARCADE) for the watersheds (Speetjens et al., 2022), (iv) the modeled Northern Hemisphere permafrost map by Obu et al. (2018), and (v) the contaminated sites database and reports by the State of Alaska Department of Environmental Conservation (2023) (DEC) to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska.

The dataset is deployed as a GeoPackage and can be imported to spatial databases (e.g. PostgreSQL/PostGIS), a Geographic Information System (e.g. QGIS), and used within geospatial processing libraries (e.g. Python's GeoPandas). All layers can be queried either in dependence or combination with one another.

Each GeoPackage contains the following layers:

• ARCADE_WatershedsDB
• DEC_ContaminatedSitesAK
• OSM_Point_InfrastructureHIElements
• SACHI_OSM_InfrastructureHIElements
• SACHI_OSM_InfrastructureHIElements_RRNetwork
• UiO_MAGT
• UiO_PermafrostProbability
• UiO_PermafrostZones

A corresponding manuscript, including application examples and a thorough description of the individual components, was submitted to be published in an open-access journal.

Download Data

• Python Scripts
  • 01_InfrastructureDataETL: reprojects the input Shapefiles and raster datasets to a common coordinate system (EPSG:5936) and then clips datasets to the boundary of Alaska. It also includes a step for filtering the permafrost probability raster dataset based on a minimum probability threshold of 50% and rounds the values in the mean annual ground temperature raster dataset.
  • 02_OSM-aggregation: processes the OpenStreetMap (OSM) geospatial data. It imports and merges OSM polygon and point data, cleans and extracts unique values of "fclass" and "osm_type", and aggregates these values for manual categorization, based on the OSM key-value-scheme. The script assigns Land Use/Cover Area frame Statistical Survey (LUCAS) categories to the data, filters out natural objects and places, and resolves unknown categories by identifying intersections between datasets.
  • 03_SACHI-aggregation: assigns LUCAS categories to the SACHI dataset based on the 'Use' column.
  • 04_SACHI-OSM_decisiontree: performs a series of geospatial operations to determine the overlap between polygonal OSM features and SACHI features and assigns LUCAS categories to the overlapping features based on certain criteria and dissolves them. The overlapping and non-overlapping features are then combined into a single dataset: the harmonized critical infrastructure and human-impacted areas dataset.
  • 05_TextMiningNLTK-CSSites: performs text mining and data preprocessing on the reports of the DEC contaminated sites database. It extracts dates, calculates cleanup times for inactive sites, identifies contaminants based on abbreviations and text entries, and extracts information related to contaminants and the medium they are found in.
• GeoPackages
  • PermaRisk_RRNetworkLine_v01_r00.gpkg contains the rail and road network as line geometries.
  • PermaRisk_RRNetworkPolygonal_v01_r00.gpkg contains the rail and road network as polygon geometries.

This is the authors' version of the work. The definitive version was published in the proceedings of "Quinta conferenza italiana sul software geografico e sui dati geografici liberi" (GFOSS DAY 2012), November 2012. http://www.gfoss.it/drupal/gfossday2012

Fire news management in the context of the European Forest Fire Information System (EFFIS) Paolo Corti¹, Jesús San-Miguel-Ayanz¹, Andrea Camia¹, Daniel McInerney¹, Roberto Boca¹, Margherita Di Leo¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via Fermi, 2749, I-21027 Ispra, Italy.

Introduction. The European Forest Fire Information System (EFFIS) has been established by the Joint Research Centre (JRC) and the Directorate General for Environment (DG ENV) of the European Commission (EC) in close collaboration with the Member States and neighbor countries. EFFIS is intended as complementary system to national and regional systems in the countries, which provides harmonized information required for international collaboration on forest fire prevention and fighting and in cases of trans-boundary fire events. It provides updated information before (fire danger forecast), during (burnt area perimeters, hot spots) and after (damage assessments, erosion, emission, dispersion) forest fires, harmonized at the EU level, enhancing international cooperation (e.g., by aerial fire fighting) and supporting decision making. The system is built using Free and Open Source Software for Geospatial (FOSS4G), and the current (2012) implementation is based on frameworks such as Django, OpenLayers, jQuery, MapServer, MapProxy, GDAL, PostgreSQL and PostGIS. All the datasets available in the EFFIS map viewer are also published through interoperable Web services (OGC WMS/WFS/WCS) and supplied with an open data license. The process of geoparsing, collecting and managing fire news, together with the management of the MODIS hot spots, is a vital task for the operation of the EFFIS, in order to identify fire burnt area perimeters and is performed daily during fire season by the EFFIS operators. To this aim, the FireNews EFFIS module, a web based application integrated within the EFFIS portal, is a toolset which provides automatic geoparsing and simplifies the management of the fire news collected by the operator.

Description

This map represents a classification of forest types based on the influence of the edge effect (distance to the forest edge) and elevation effect (temperature and area) on tree community richness.

The edge effect influences tree diversity through an environmental aridity filter. In New Caledonia, the maximum temperature recorded at the forest edge is 41°C in February, while it never exceeds 24°C beyond 100 meters from the edge. This temperature difference induces a selection for species that tolerate the most arid conditions, leading to a reduction in the biological richness of tree communities (Ibanez et al., 2017; Birnbaum et al., 2022; Blanchard et al., 2023).

Altitude also affects tree diversity due to temperature variation and available area (Ibanez et al., 2014; Birnbaum et al., 2015; Pouteau et al., 2015; Ibanez et al., 2016; Ibanez et al., 2018). In New Caledonia, observed tree community richness ranges from 35 to 121 species per hectare within the NC-PIPPN network, peaking at mid-altitude ranges (refer to figure 'amap_elevation_richness.png'). Potential richness was assessed using the S-SDM model, with the 80th percentile used as a threshold to distinguish low and high potential richness across three elevation classes: [0 - 400m[, [400 - 900m[, and [900 - 1628m[.

The classification of forest types combines distance from the forest edge and potential richness by elevation into three major categories, as illustrated in the figure 'amap_forest_types_nc.png':

Edge Forest: Parts of the forest located less than 100 meters from the forest edge.

Mature Forest: Parts of the forest located beyond 100 meters from the edge with a lower potential richness of tree communities.

Core Forest: Parts of the forest located more than 300 meters from the edge with a higher potential richness of tree communities.

Content

The map is computed from the Forest Map of New Caledonia (v2024) and the Potential Tree Species Richness in the Forests of New Caledonia (v2024). This dataset was produced, analyzed, and verified using a combination of open-source software, including QGIS, PostgreSQL, PostGIS, Python, R, and the GDAL library, all running on Linux.

amap_forest_types_nc.png is a picture illustrating the forest type classification

amap_forest_types_nc.zip is a compressed file contains the six essential files for an ESRI-format GIS system, using the WGS84 international coordinate system, and can be uploaded to a spatial database such as PostgreSQL/PostGIS. Each row of the attribute table represents a forest type (a multi-polygon) with associated fields :

Field Type Description

type TEXT One of the three forest types ("Edge Forest", "Mature Forest", "Core forest")

area_ha NUMERIC (2 DECIMALS) Area of the multi-polygon in hectares

description TEXT Description of the three forest types

geom GEOMETRY (MULTIPOLYGON, 4326)) Geometry with datum EPSG: 4326 (WGS 84 – World Geodetic System 1984)

Limitations

We caution users that the distinction between the three classes is based on an ecological interpretation and does not reflect directly perceptible breaks in the forest. The ecological transition from the edge to the core of the forest follows multiple gradient modulated by environmental conditions.

Moreover, this classification is based on local observations and measurements, which are complex to generalize and extrapolate across a territory as environmentally diverse as New Caledonia. Nevertheless, it allows us to address the impact of fragmentation at the scale of New Caledonia.

The SIRIUS inventory integrates data from

1. OpenStreetMap for infrastructure and land use information (OpenStreetMap Contributors and Geofabrik GmbH, 2018),
2. the modeled Northern Hemisphere permafrost map by Obu et al. (2018),
3. the contaminated sites database and reports by the State of Alaska Department of Environmental Conservation (2023) (DEC),
4. the inventory of contaminated sites in Canada by the Treasury Board of Canada Secretariat (FCSI) (2024),
5. a compilation of contaminated sites in Greenland and Norway from the NGO Robin des Bois (2009a, 2009b) (RdB),
6. projections of the timing of first talik formation, a model output from the CryoGridLite permafrost model (Langer et al., 2024)

for the Arctic regions of Alaska (AK), Canada (CA), Greenland (GL), and Norway (NO).

This dataset is provided as a GeoPackage, enabling seamless integration with spatial databases (e.g., PostgreSQL/PostGIS), Geographic Information Systems (e.g., QGIS), and geospatial processing libraries (e.g., Python's GeoPandas). All layers can be queried either independently or in combination with one another.

The GeoPackage contains 17 separate layers.

The contaminated sites layers contain detailed information on contaminants haromized into chemical classifications based on the Agency for Toxic Substances and Disease Registry (ATSDR). Additionally, each layer includes projections of talik formation as complementary attributes. These projections are provided for SSP1-2.6 and SSP5-8.5 scenarios, specifying the 5th, 50th (median), and 95th percentiles of the year when talik formation is expected to occur for the first time.

The layers of the infrastructure and human-impacted areas are further divided into points of interest (POI), polygonal infrastructure, and linear road and rail networks (RRNetwork) for each country (AK, CA, GL, and NO). The permafrost extent is provided as a single pan-Arctic layer.

Contaminated Sites:

• AK_ContaminatedSites_DEC
• CA_ContaminatedSites_FCSI
• GL_ContaminatedSites_RdB
• NO_ContaminatedSites_RdB

Infrastructure and Human-Impacted Areas:

• NO_POI_InfrastructureHIElements_OSM
• NO_InfrastructureHIElements_OSM
• NO_InfrastructureHIElements_RRNetwork_OSM
• GL_POI_InfrastructureHIElements_OSM
• GL_InfrastructureHIElements_OSM
• GL_InfrastructureHIElements_RRNetwork_OSM
• AK_POI_InfrastructureHIElements_OSM
• AK_InfrastructureHIElements_OSM
• AK_InfrastructureHIElements_RRNetwork_OSM
• CA_POI_InfrastructureHIElements_OSM
• CA_InfrastructureHIElements_OSM
• CA_InfrastructureHIElements_RRNetwork_OSM

Permafrost Extent:

• PermafrostZones_UiO
  
  

Download Data

• pan-arctic_SIRIUS_v1.0.gpkg

Summary: This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable. The contained files were developed by the NY Cities Program of The Nature Conservancy and the NYC Environmental Justice Alliance through the Just Nature NYC Partnership. Additional context and interpretation of this work is available in a blog post.

References: Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. Urban Climate 31:100583. https://doi.org/10.1016/j.uclim.2020.100583

Files in this Repository: Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, EPSG 2263): Points with unique identifiers (fid) and data on proportion tree canopy cover (prop_canopy), proportion grass/shrub cover (prop_grassshrub), and proportion total vegetation cover (prop_veg) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage): nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data) nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif Computer Code: Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database. nyc_point_buffer_vegetation_overlay.sql

Data Sources and Methods: We used two openly available datasets from the City of New York for this analysis: Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page Six-inch resolution land cover data for New York City as of 2017, available at https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns All data were used in the New York State Plane Coordinate System, Long Island Zone (EPSG 2263). Land cover data were used in a polygonized form for these analyses. The general steps for developing the data available in this repository were as follows: Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions Delete any points that do not overlap the areas in the Borough Boundaries dataset. Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020). Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles. Relate the results from the overlay analysis back to the associated points. Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data." All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in nyc_point_buffer_vegetation_overlay.sql. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the gdal_rasterize function.

Version 1.0 - December 1, 2022

This dataset contains the shapefile of the Caledonian forest produced by digitization at a scale of 1:3,000 from a mosaic of satellite images (Sentinel 2, Quickbird, Pléiades) and aerial photographs provided by the department of infrastructure, topography and land transport (DITTT) of the government of New Caledonia and available on the georep map server (georep.nc). Satellite images and aerial photographs were taken between 2009 and 2021 with a maximum spatial resolution of 0.5 m. We interpreted vegetation as forest when the trees (which should be taller than 5 m) formed a continuous canopy cover, obscuring the ground surface for a minimum of 0.5 ha (FAO 2020). The minimum distance between two polygons is 10 m. Below the polygons were merged

The polygons were verified through databases of plant occurrences collected in the forest, in particular the Herbarium of New Caledonia database (NOU) and the Network of plant inventories and permanent plots of New Caledonia (NC-PIPPN). So this dataset is periodically updated with corrections made by field observations and the addition of new expert interpretations, especially on small islands that have not been digitized before. We plan to produce the whole map step by step

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Currently, only the North and South provinces are available, an area of 16,395 km² out of a total of 18,345 km². Digitization of the Loyaulties Islands will be available soon.

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The file contains the 6 files required for the GIS and can be uploaded to a spatial database such as PostgreSQL/PostGis
The attribute table displays information on features of forest polygons. Each row in the table represents a forest fragment (a polygon) with associated fields :

• id (INTEGER) = unique identifier
• created_by (TEXT)= The creator of the data
• created_for (TEXT)= The recipient and funder
• area_ha (NUMERIC) = area of the polygon in hectare
• ps (BOOLEAN) = True or False the polygon overlaps the Southern province
• pn (BOOLEAN) = True or False the polygon overlaps the Northern province
• pil (BOOLEAN) = True or False the polygon overlaps the Islands province (Loyaulties islands)
• geom (geometry(polygon,4326)) = Polygon type geometry and datum EPSG: 4326 (= WGS 84– World Geodetic System 1984)