The goal of this paper is to present a new model of fuzzy topological relations for simple spatial objects in Geographic Information Sciences (GIS). The concept of computational fuzzy topological space is applied to simple fuzzy objects to efficiently and more accurately solve fuzzy topological relations, extending and improving upon previous research in this area. Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on computational fuzzy topology. And then, we also propose a new model to compute fuzzy topological relations between simple spatial objects, an analysis of the new model exposes:(1) the topological relations of two simple crisp objects; (2) the topological relations between one simple crisp object and one simple fuzzy object; (3) the topological relations between two simple fuzzy objects. In the end, we have discussed some examples to demonstrate the validity of the new model, through an experiment and comparisons of existing models, we showed that the proposed method can make finer distinctions, as it is more expressive than the existing fuzzy models.

In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models.

Web app for global DEM data with spatial resolution of about 30m. This data can be used for mapping and spatial modelling in GIS or other computer programs.

A digital elevation model (DEM) at a resolution of 1 m that shows the topography of the land. This DEM is what is called a bare earth model; in that the elevation model is representative of the land without taking into account the trees and buildings. The original LiDAR data that the DEM was derived from was captured by PHB Lasermap for HRM in April 2007 and was processed by Applied Geomatics Research Group at NSCC in 2012. Metadata

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The Office of the Geographer’s Global Large Scale International Boundary Detailed Polygons file combines two datasets, the Office of the Geographer’s Large Scale International Boundary Lines and NGA shoreline data. The LSIB is believed to be the most accurate worldwide (non- W. Europe) international boundary vector line file available. The lines reflect U.S. government (USG) policy and thus not necessarily de facto control. The 1:250,000 scale World Vector Shoreline (WVS) coastline data was used in places and is generally shifted by several hundred meters to over a km. There are no restrictions on use of this public domain data. The Tesla Government PiX team performed topology checks and other GIS processing while merging data sets, created more accurate island shoreline in numerous cases, and worked closely with the US Dept. of State Office of the Geographer on quality control checks.

Methodology: Tesla Government’s Protected Internet Exchange (PiX) GIS team converted the LSIB linework and the island data provided by the State Department to polygons. The LSIB Admin 0 world polygons (Admin 0 polygons) were created by conflating the following datasets: Eurasia_Oceania_LSIB7a_gen_polygons, Africa_Americas_LSIB7a_gen_polygons, Africa_Americas_LSIB7a, Eurasia_LSIB7a, additional updates from LSIB8, WVS shoreline data, and other shoreline data from United States Government (USG) sources. The two simplified polygon shapefiles were merged, dissolved, and converted to lines to create a single global coastline dataset. The two detailed line shapefiles (Eurasia_LSIB7a and Africa_Americas_LSIB7a) were merged with each other and the coastlines to create an international boundary shapefile with coastlines. The dataset was reviewed for the following topological errors: must not self overlap, must not overlap, and must not have dangles. Once all topological errors were fixed, the lines were converted to polygons. Attribution was assigned by exploding the simplified polygons into multipart features, converting to centroids, and spatially joining with the newly created dataset. The polygons were then dissolved by country name. Another round of QC was performed on the dataset through the data reviewer tool to ensure that the conversion worked correctly. Additional errors identified during this process consisted of islands shifted from their true locations and not representing their true shape; these were adjusted using high resolution imagery whereupon a second round of QC was applied with SRTM digital elevation model data downloaded from USGS. The same procedure was performed for every individual island contained in the islands from other USG sources.
After the island dataset went through another round of QC, it was then merged with the Admin 0 polygon shapefile to form a comprehensive world dataset. The entire dataset was then evaluated, including for proper attribution for all of the islands, by the Office of the Geographer.

Data available online through the Arkansas Spatial Data Infrastructure (ASDI) at http://gis.arkansas.gov. The subject file represents the Arkansas portion of the Intermodal Terminal Facilities data set contains geographic data for trailer-on-flatcar (TOFC) and container-on-flatcar (COFC) highway-rail and/or rail-water transfer facilities in Arkansas. The locations of TOFC/COFC facilities were determined using available facility address information and MapExpert, a commercial nationwide digital map database and software package, and recording the longitude/latitude of the approximate center of the facility. Facility locations are not bound to any current or previous highway, railway, or waterway network models. After completing topological and attribute verification using ARC/INFO, the data was extracted and reformatted into the BTS ACSII format. The scale and resolution of this data set and source data are unknown.

A seamless hydro-flattened digital surface model (DSM) with 1 metre resolution. The DSM captures the land surface and natural and built features on the earth's surface, such as trees and buildings.The LiDAR data was captured for the Halifax Regional Municipality's coastal flood mapping project through topographic andtopo-bathymetric lidar data collection in 2017-2018 and was processed by the Applied Geomatics Research Group at NSCC.Due to limitations of the sensor used in the topography lidar data acquisition mission, the dataset excludes a significant number of buildings and other asphalt surfaces such as driveways. Users should use caution when using the data for those purposes. Metadata

Residual biomass is a renewable resource with attractive characteristics to produce energy and biofuels. Diverse studies have stated that residual biomass used for biofuels and energy production can contribute partially to solve the energy demand problem, decreasing fossil fuels carbon emissions. Most works have focused on developing new technologies, processes, and processing systems based on biomass. Other works have addressed the supply chain-planning problem to determine optimal locations considering diverse objectives. A third group of works have proposed schemes based on Geographic Information Systems (GIS) to determine suitable locations in different types of systems. Nevertheless, works capable to combine the advantage of GIS, mathematical programming, and process design have not been properly conducted. Therefore, this paper presents a sequential approach for the optimal planning of a residual biomass processing system. The methodology considers selecting potential locations through a multicriteria methodology based on GIS. Also, this paper proposes a mathematical programming approach for the optimal planning of a residual biomass processing system, which considers as input the locations predefined by GIS methodology, as well as six potential products, six processing routes, and eight raw materials. The mathematical programming approach consists of mass balances to obtain the interconnections between the different supply chain nodes, as well as constraints to model the considered technologies involving capital investment and production costs. The GIS approach was applied to a case study in Mexico, which produced 764 harvesting sites and 334 processing plants for all considered residual biomass types. The optimization approach conducted used 33 processing plants, 467 harvesting sites, and 2 products from 3 biomass types in order to determine the final supply chain topology. Results show that the proposed methodology is a useful tool to determine the optimal supply chain topology during the decision process.

A seamless hydro-flattened digital surface model (DSM) with 1 metre resolution. The DSM captures the land surface and natural and built features on the earth's surface, such as trees and buildings.The LiDAR data was captured for the Halifax Regional Municipality's coastal flood mapping project through topographic andtopo-bathymetric lidar data collection in 2017-2018 and was processed by the Applied Geomatics Research Group at NSCC.Due to limitations of the sensor used in the topography lidar data acquisition mission, the dataset excludes a significant number of buildings and other asphalt surfaces such as driveways. Users should use caution when using the data for those purposes. Metadata

ABSTRACT Watershed delineation, drainage network generation and determination of river hydraulic characteristics are important issues in hydrological sciences. In general, this information can be obtained from Digital Elevation Models (DEM) processing within GIS commercial softwares, such as ArcGIS and IDRISI. On the other hand, the use of open source GIS tools has increased significantly, and their advantages include free distribution, continuous development by user communities and full customization for specific requirements. Herein, we present the IPH-Hydro Tools, an open source tool coupled to MapWindow GIS software designed for watershed topology acquisition, including preprocessing steps in hydrological models such as MGB-IPH. In addition, several tests were carried out assessing the performance and applicability of the developed tool, given by a comparison with available GIS packages (ArcGIS, IDRISI, WhiteBox) for similar purposes. The IPH-Hydro Tools provided satisfactory results on tested applications, allowing for better drainage network and less processing time for catchment delineation. Regarding its limitations, the developed tool was incompatible with huge terrain data and showed some difficulties to represent drainage networks in extensive flat areas, which can occur in reservoirs and large rivers.

The Office of the Geographer’s Global Large Scale International Boundary Detailed Polygons file combines two datasets, the Office of the Geographer’s Large Scale International Boundary Lines and NGA shoreline data. The LSIB is believed to be the most accurate worldwide (non- W. Europe) international boundary vector line file available. The lines reflect U.S. government (USG) policy and thus not necessarily de facto control. The 1:250,000 scale World Vector Shoreline (WVS) coastline data was used in places and is generally shifted by several hundred meters to over a km. There are no restrictions on use of this public domain data. The Tesla Government PiX team performed topology checks and other GIS processing while merging data sets, created more accurate island shoreline in numerous cases, and worked closely with the US Dept. of State Office of the Geographer on quality control checks.

Methodology: Tesla Government’s Protected Internet Exchange (PiX) GIS team converted the LSIB linework and the island data provided by the State Department to polygons. The LSIB Admin 0 world polygons (Admin 0 polygons) were created by conflating the following datasets: Eurasia_Oceania_LSIB7a_gen_polygons, Africa_Americas_LSIB7a_gen_polygons, Africa_Americas_LSIB7a, Eurasia_LSIB7a, additional updates from LSIB8, WVS shoreline data, and other shoreline data from United States Government (USG) sources. The two simplified polygon shapefiles were merged, dissolved, and converted to lines to create a single global coastline dataset. The two detailed line shapefiles (Eurasia_LSIB7a and Africa_Americas_LSIB7a) were merged with each other and the coastlines to create an international boundary shapefile with coastlines. The dataset was reviewed for the following topological errors: must not self overlap, must not overlap, and must not have dangles. Once all topological errors were fixed, the lines were converted to polygons. Attribution was assigned by exploding the simplified polygons into multipart features, converting to centroids, and spatially joining with the newly created dataset. The polygons were then dissolved by country name. Another round of QC was performed on the dataset through the data reviewer tool to ensure that the conversion worked correctly. Additional errors identified during this process consisted of islands shifted from their true locations and not representing their true shape; these were adjusted using high resolution imagery whereupon a second round of QC was applied with SRTM digital elevation model data downloaded from USGS. The same procedure was performed for every individual island contained in the islands from other USG sources.
After the island dataset went through another round of QC, it was then merged with the Admin 0 polygon shapefile to form a comprehensive world dataset. The entire dataset was then evaluated, including for proper attribution for all of the islands, by the Office of the Geographer.

A seamless hydro-flattened digital elevation model (DEM) with 2 metre resolution. The DEM is what is called a bare earth model; in that the elevation model is representative of the land without taking into account the trees and buildings.The LiDAR data was captured for the Halifax Regional Municipality's coastal flood mapping project through topographic andtopo-bathymetric lidar data collection in 2017-2018 and was processed by the Applied Geomatics Research Group at NSCC. Metadata

These watershed boundaries were delineated by the Martha's Vineyard Commission (MVC) and the SMAST during the Mass Estuaries studies. Local knowledge and field data along with a computer watershed model were utilized to generate these boundaries. Topological checks were performed to make sure that neighboring boundaries do not overlap. Also, were appropriate, shared boundaries are identical (i.e. where the edge of a sub-watershed overlaps with its major watershed boundary).The Major Watershed boundaries are stored as a separate data layer and can be found within the Dukes County GIS AGOL data collection.

Background: Between 2011 and 2018, the NASA Dawn spacecraft visited asteroid (4) Vesta and dwarf planet (1) Ceres to investigate the surfaces of both protoplanets through optical and hyperspectral imaging and their composition through gamma-ray and neutron spectroscopy from orbit.
For both Vesta and Ceres, a geologic mapping investigation was realized based on optical and hyperspectral data as well as a photogrammetrically derived digital terrain model. For the global mapping investigation, mappers employed Geographic Information System (GIS) software to map 15 quadrangles. The results were published as individual map sheets alongside research papers discussing the geologic evolution. The style of collaborative mapping to produce a consistent global view represented by individual quadrangle maps is comparably new despite abundantly available mapping experiences. Ongoing data acquisition during mapping created considerable challenges for the coordination and homogenization of mapping results.

To handle this issue simultaniously to the active mission phase as best as possible a GIS-based environment was needed in order to conduct one homogenous dataset (w.r.t. geometrical and visual character) that represents one geologically-consistent map at the end. Therefore, the mapping team was supported by an predefined mapping template which was generated in the proprietary ArcGIS environment. The template contains different layers (called feature classes) for the different object/geomoetry types and contains predefined attribute values as well as cartographic symbols. The cartographic symbols follow international standards as far as possible. The colours for the geological units refering to established colour values used in geologic maps, e.g., standardized planetary maps generated by USGS, but considering individual needs and requests within the mapping team, too.

The data product pubished here based on the mentioned GIS-based template and represents the merged global GIS-dataset of the 15 individually conducted geological maps of Ceres within the Dawn Mission. The detailed descriptions of all those scientific interpretions are published in the papers listed within the reference section. Based on team-internal decisions the dataset is provided within the properitary format of ESRIs ArcGIS environment. However, in order to use the data product also outside this software environment, single shapefiles with additional information about the symbology are also included. All available data are available within the compressed folder and the readme-file gives some informative remarks for the useage of the data

Additional remark: The data set provided here does not represent a holistic (in term of topological and scientifical) unification of the 15 individual mapping data as primarily geometric and content-related inconsistencies at quadrangle boundaries prohibited a unified compilation. On the one side, this is due to the fact that the the aim of the mapping project was not to produce a uniform global map, but rather to gain a first impression of the geology of Ceres and publish associated scientific papers. On the other side, that the geological mapping project ran parallel to the regular mission phase, and a finalizing review process for creating a global geological dataset wasn´t scheduled in the mission planning. This deficiency cannot be remedied simply by merging topological missmatches or changing the visualisation. Rather it will require ongoing and detailed scientific discussion of the interpretation results, which could be solved within an updating version of the global map.

Portal built for the release and sharing of data resources of "Big Earth Data Science Engineering Program (CASEarth)" launched by the Chinese Academy of Sciences. A global raster data of land cover and land use. This data can be used for mapping and spatial modeling in Geographic Information Systems (GIS) or other computer programs. This website is not accessible from the USA.

DESCRIPTION

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).

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 ! 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 eda2.3 "dbeel_rivers.rn.backup"
#france
pg_restore -U postgres -d eda2.3 "france.rn.backup"
# spain
pg_restore -U postgres -d eda2.3 "spain.rn.backup"   
# portugal
pg_restore -U postgres -d eda2.3 "portugal.rn.backup" 
# with the schema you will probably want to be able to use the functions
psql -U postgres -d eda2.3 -f "function_dbeel_rivers.sql"

3. RNA (Attributes)

This corresponds to tables

• dbeel_rivers.rna
• france.rna
• spain.rna
• portugal.rna

Columns (See Atlas)

idsegment

altitudem

distanceseam

distancesourcem

cumnbdam

medianflowm3ps

surfaceunitbvm2

surfacebvm2

strahler

shreeve

codesea

name

pfafriver

pfafsegment

basin

riverwidthm

temperature

temperaturejan

temperaturejul

wettedsurfacem2

wettedsurfaceotherm2

lengthriverm

emu

cumheightdam

riverwidthmsource

slope

dis_m3_pyr_riveratlas

dis_m3_pmn_riveratlas

dis_m3_pmx_riveratlas

drought

drought_type_calc

Code :

pg_restore -U postgres -d eda2.3 "dbeel_rivers.rna.backup"
pg_restore -U postgres -d eda2.3 "france.rna.backup"
pg_restore -U postgres -d eda2.3 "spain.rna.backup"   
pg_restore -U postgres -d eda2.3 "portugal.rna.backup"  

4. RNE (eel predictions)

These layers contain eel data (see Atlas for detail)

• dbeel_rivers.rne
• france.rne
• spain.rne
• portugal.rne

Columns (see

These watershed boundaries were delineated by the Martha's Vineyard Commission (MVC) and the SMAST during the Mass Estuaries studies. Local knowledge and field data along with a computer watershed model were utilized to generate these boundaries. Topological checks were performed to make sure that neighboring boundaries do not overlap. Also, were appropriate, shared boundaries are identical (i.e. where the edge of a sub-watershed overlaps with its major watershed boundary).The Major Watershed boundaries are stored as a separate data layer and can be found within the Dukes County GIS AGOL data collection.

NZ Regional River Names (based on REC2) Layer can be used to display rivers in New Zealand and their names.The River Environment Classification (REC) is a database of catchment spatial attributes, summarised for every segment in New Zealand's network of rivers. The attributes were compiled for the purposes of river classification. Examples where the REC can be used include, catchment rainfall calculations, catchment river flows, flood forecasting, land use and catchment associations.CreationThe REC was originally created using hydrological networking tools and a digital elevation model.River names have been attached where available.REC2 (River Environment Classification, v2.5) - June 2019 [Hosted Feature Layer]This service depicts rivers as linesThe River Environment Classification (REC) is a database of catchment spatial attributes, summarised for every segment in New Zealand's network of rivers. The attributes were compiled for the purposes of river classification, while the river network description has been used to underpin models. Typically, models (e.g. CLUES and TopNet) would use the dendritic (branched) linkages of REC river segments to perform their calculations. Since its release and use over the last decade, some errors in the location and connectivity of these linkages have been identified. The current revision corrects those errors, and updates a number of spatial attributes with the latest data. REC2 provides a re-cut framework of rivers for modelling and classification. It is built on a newer version of the 30m digital elevation model, in which the original 20m contours were supplemented with, for example, more spot elevation data and a better coastline contour. Boundary errors were minimised by processing contiguous areas (such as the whole of the North Island) together, which wasn't possible when it was originally created.Major updates include the revision of catchment land use information, by overlaying with the land cover database (LCDB3, current as at 2008), and the update of river and rainfall statistics with data from 1960-2006.
The river network and associated attributes have been assembled within an ArcGIS geodatabase. Topological connectivity has been established to allow upstream and downstream tracing within the network. REC2 can be downloaded or streamed and used directly in ArcMap. (A file geodatabase version for ArcGIS of REC2 can be downloaded as a zip file and used directly for analyses in ArcMap from here)This is based on REC2 (Version 5) , June 2019 - a publicly available dataset from NIWA Taihoro Nukurangi.NIWA acknowledges funding from the MBIE SSIF towards the preparation of REC v2.5Coordinate Reference System: NZTM (New Zealand Transverse Mercator, EPSG: 2193)Geometric Representation of Rivers: LinesExtent (Bounding Box):

Top(Latitude) -33.9534Bottom(Latitude) -47.4867

Left (Longitude) 166.2634

Right (Longitude) 178.9733

_Item Page Created: 2022-01-19 03:16 Item Page Last Modified: 2025-04-05 16:14Owner: NIWA_OpenDataNZ Rivers and NamesNo data edit dates availableFields: CATAREA,CUM_AREA,nzsegment,LENGTHDOWN,Headwater,StreamOrde,upElev,downElev,headw_dist,segslpmean,LID,reachtype,FROM_NODE,TO_NODE,Shape_Leng,RivName

The important attributes are nzsegment (primary key), which can be used to join the watershed polygons to the river network, and the old_nzreach (which can be used to retrieve values from REC1 river classification, and other previously calculated properties). The shape_area gives the area of the watershed in meters squared. REC2 (River Environment Classification, v2.3)The River Environment Classification (REC) is a database of catchment spatial attributes, summarised for every segment in New Zealand's network of rivers. The attributes were compiled for the purposes of river classification, while the river network description has been used to underpin models.Typically, models (e.g. CLUES and TopNet) would use the dendritic (branched) linkages of REC river segments to perform their calculations. Since its release and use over the last decade, some errors in the location and connectivity of these linkages have been identified. The current revision corrects those errors, and updates a number of spatial attributes with the latest data.REC2 provides a recut framework of rivers for modelling and classification. It is built on a newer version of the 30m digital elevation model, in which the original 20m contours were supplemented with, for example, more spot elevation data and a better coastline contour. Boundary errors were minimised by processing contiguous areas (such as the whole of the North Island) together, which wasn't possible a decade ago. Major updates include the revision of catchment land use information, by overlaying with the latest land cover database (LCDB3, current as at 2008), and the update of river and rainfall statistics with data from 1960-2006.The river network and associated attributes have been assembled within an ArcGIS geodatabase. Topological connectivity has been established to allow upstream and downstream tracing within the network. REC2 can be downloaded as a zip file and used directly in ArcMap. Alternatively, the layers can be extracted as shape files.NIWA acknowledges funding from the Terrestrial and Freshwater Biodiversity Information System (TFBIS) towards the preparation of REC v2.