Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This database is an initial Asset database for the Central West subregion on 29 April 2015. This dataset contains the spatial and non-spatial (attribute) components of the Central West subregion Asset List as one .mdb files, which is readable as an MS Access database and a personal geodatabase. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Central West are included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Central West subregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "CEN_asset_database_doc_20150429.doc ", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "CEN_asset_database_doc_20150429.doc" located in the zip file. Some of the source data used in the compilation of this dataset is restricted.

Dataset History

This is initial asset database.

The Bioregional Assessments methodology (Barrett et al., 2013) defines a water-dependent asset as a spatially distinct, geo-referenced entity contained within a bioregion with characteristics having a defined cultural indigenous, economic or environmental value, and that can be linked directly or indirectly to a dependency on water quantity and/or quality.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. Elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2) - assets considered to be water dependent.

Elements may be represented by a single, discrete spatial unit (polygon, line or point), or a number of spatial units occurring at more than one location (multipart polygons/lines or multipoints). Spatial features representing elements are not clipped to the preliminary assessment extent - features that extend beyond the boundary of the assessment extent have been included in full. To assist with an assessment of the relative importance of elements, area statements have been included as an attribute of the spatial data. Detailed attribute tables contain descriptions of the geographic features at the element level. Tables are organised by data source and can be joined to the spatial data on the "ElementID" field

Elements are grouped into Assets, which are the objects used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy.

The "Element_to_asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Following delivery of the first pass asset list, project teams make a determination as to whether an asset (comprised of one or more elements) is water dependent, as assessed against the materiality tests detailed in the BA Methodology. These decisions are provided to ERIN by the project team leader and incorporated into the Assetlist table in the Asset database. The Asset database is then re-registered into the BA repository.

The Asset database dataset (which is registered to the BA repository) contains separate spatial and non-spatial databases.

Non-spatial (tabular data) is provided in an ESRI personal geodatabase (.mdb - doubling as a MS Access database) to store, query, and manage non-spatial data. This database can be accessed using either MS Access or ESRI GIS products. Non-spatial data has been provided in the Access database to simplify the querying process for BA project teams. Source datasets are highly variable and have different attributes, so separate tables are maintained in the Access database to enable the querying of thematic source layers.

Spatial data is provided as an ESRI file geodatabase (.gdb), and can only be used in an ESRI GIS environment. Spatial data is represented as a series of spatial feature classes (point, line and polygon layers). Non-spatial attribution can be joined from the Access database using the AID and ElementID fields, which are common to both the spatial and non-spatial datasets. Spatial layers containing all the point, line and polygon - derived elements and assets have been created to simplify management of the Elementlist and Assetlist tables, which list all the elements and assets, regardless of the spatial data geometry type. i.e. the total number of features in the combined spatial layers (points, lines, polygons) for assets (and elements) is equal to the total number of non-spatial records of all the individual data sources.

Dataset Citation

Department of the Environment (2013) Asset database for the Central West subregion on 29 April 2015. Bioregional Assessment Derived Dataset. Viewed 08 February 2017, http://data.bioregionalassessments.gov.au/dataset/5c3f9a56-7a48-4c26-a617-a186c2de5bf7.

Dataset Ancestors

• Derived From Macquarie Marshes Vegetation 1991-2008 VIS_ID 3920

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

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

• Derived From Travelling Stock Route Conservation Values

• Derived From NSW Wetlands

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

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

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

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Ramsar Wetlands of Australia

• Derived From Native Vegetation Management (NVM) - Manage Benefits

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

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

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

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From [New South Wales NSW Regional CMA Water Asset

Data types for landform classification.

Explanatory power of each factor.

Conservation criteria for each of the landform categories.

ssurgoOnDemandThe purpose of these tools are to give users the ability to get Soil Survey Geographic Database (SSURGO) properties and interpretations in an efficient manner. They are very similiar to the United States Department of Agriculture - Natural Resource Conservation Service's distributed Soil Data Viewer (SDV), although there are distinct differences. The most important difference is the data collected with the SSURGO On-Demand (SOD) tools are collected in real-time via web requests to Soil Data Access (https://sdmdataaccess.nrcs.usda.gov/). SOD tools do not require users to have the data found in a traditional SSURGO download from the NRCS's official repository, Web Soil Survey (https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm). The main intent of both SOD and SDV are to hide the complex relationships of the SSURGO tables and allow the users to focus on asking the question they need to get the information they want. This is accomplished in the user interface of the tools and the subsequent SQL is built and executed for the user. Currently, the tools packaged here are designed to run within the ESRI ArcGIS Desktop Application - ArcMap, version 10.1 or greater. However, much of the Python code is recyclable and could run within a Python intepreter or other GIS applications such as Quantum GIS with some modification.NOTE: The queries in these tools only consider the major components of soil map units.Within the SOD tools are 2 primary toolsets, descibed as follows:<1. AreasymbolThe Areasymbol tools collect SSURGO properties and interpretations based on a user supplied list of Soil Survey areasymbols (e.g. NC123). After the areasymbols have been collected, an aggregation method (see below) is selected . Tee aggregation method has no affect on interpretations other than how the SSURGO data aggregated. For soil properties, the aggregation method drives what properties can be run. For example, you can't run the weighted average aggregation method on Taxonomic Order. Similarly, for the same soil property, you wouldn't specify a depth range. The point here is the aggregation method affects what parameters need to be supplied for the SQL generation. It is important to note the user can specify any number of areasymbols and any number of interpretations. This is another distinct advantage of these tools. You could collect all of the SSURGO interpretations for every soil survey area (areasymbol) by executing the tool 1 time. This also demonstrates the flexibility SOD has in defining the geographic extent over which information is collected. The only constraint is the extent of soil survey areas selected to run (and these can be discontinuous).As SOD Areasymbol tools execute, 2 lists are collected from the tool dialog, a list of interpretations/properties and a list of areasymbols. As each interpretation/property is run, every areasymbol is run against the interpretation/property requested. For instance, suppose you wanted to collect the weighted average of sand, silt and clay for 5 soil survey areas. The sand property would run for all 5 soil survey areas and built into a table. Next the silt would run for all 5 soil survey areas and built into a table, and so on. In this example a total of 15 web request would have been sent and 3 tables are built. Two VERY IMPORTANT things here...A. All the areasymbol tools do is generate tables. They are not collecting spatial data.B. They are collecting stored information. They are not making calculations(with the exception of the weighted average aggregation method).<2. ExpressThe Express toolset is nearly identical to the Areasymbol toolset, with 2 exceptions.A. The area to collect SSURGO information over is defined by the user. The user digitizes coordinates into a 'feature set' after the tool is open. The points in the feature set are closed (first point is also the last) into a polygon. The polygon is sent to Soil Data Access and the features set points (polygon) are used to clip SSURGO spatial data. The geomotries of the clip operation are returned, along with the mapunit keys (unique identifier). It is best to keep the points in the feature set simple and beware of self intersections as they are fatal.B. Instead of running on a list of areasymbols, the SQL queries on a list of mapunit keys.The properties and interpretations options are identical to what was discussed for the Areasymbol toolset.The Express tools present the user the option of creating layer files (.lyr) where the the resultant interpretation/property are joined to the geometry and saved to disk as a virtual join. Additionally, for soil properties, an option exists to append all of the selected soil properties to a single table. In this case, if the user ran sand, silt, and clay properties, instead of 3 output tables, there is only 1 table with a sand column, a silt column, and a clay column.<Supplemental Information<sAggregation MethodAggregation is the process by which a set of component attribute values is reduced to a single value to represent the map unit as a whole.A map unit is typically composed of one or more "components". A component is either some type of soil or some nonsoil entity, e.g., rock outcrop. The components in the map unit name represent the major soils within a map unit delineation. Minor components make up the balance of the map unit. Great differences in soil properties can occur between map unit components and within short distances. Minor components may be very different from the major components. Such differences could significantly affect use and management of the map unit. Minor components may or may not be documented in the database. The results of aggregation do not reflect the presence or absence of limitations of the components which are not listed in the database. An on-site investigation is required to identify the location of individual map unit components. For queries of soil properties, only major components are considered for Dominant Component (numeric) and Weighted Average aggregation methods (see below). Additionally, the aggregation method selected drives the available properties to be queried. For queries of soil interpretations, all components are condisered.For each of a map unit's components, a corresponding percent composition is recorded. A percent composition of 60 indicates that the corresponding component typically makes up approximately 60% of the map unit. Percent composition is a critical factor in some, but not all, aggregation methods.For the attribute being aggregated, the first step of the aggregation process is to derive one attribute value for each of a map unit's components. From this set of component attributes, the next step of the aggregation process derives a single value that represents the map unit as a whole. Once a single value for each map unit is derived, a thematic map for soil map units can be generated. Aggregation must be done because, on any soil map, map units are delineated but components are not.The aggregation method "Dominant Component" returns the attribute value associated with the component with the highest percent composition in the map unit. If more than one component shares the highest percent composition, the value of the first named component is returned.The aggregation method "Dominant Condition" first groups like attribute values for the components in a map unit. For each group, percent composition is set to the sum of the percent composition of all components participating in that group. These groups now represent "conditions" rather than components. The attribute value associated with the group with the highest cumulative percent composition is returned. If more than one group shares the highest cumulative percent composition, the value of the group having the first named component of the mapunit is returned.The aggregation method "Weighted Average" computes a weighted average value for all components in the map unit. Percent composition is the weighting factor. The result returned by this aggregation method represents a weighted average value of the corresponding attribute throughout the map unit.The aggregation method "Minimum or Maximum" returns either the lowest or highest attribute value among all components of the map unit, depending on the corresponding "tie-break" rule. In this case, the "tie-break" rule indicates whether the lowest or highest value among all components should be returned. For this aggregation method, percent composition ties cannot occur. The result may correspond to a map unit component of very minor extent. This aggregation method is appropriate for either numeric attributes or attributes with a ranked or logically ordered domain.

Hierarchical landform classifications by scale.

Major ecosystems and biota of the Korean Peninsula (sources: National Ecosystem Survey and DPRK Flora and Fauna of Coreana).

Number of indicator species in each habitat.

Factor analysis results.

The relationship between the geographic environment and human health has been a long-standing focus of scientific inquiry. Sn as an essential trace element for the human body, play vital roles in individual health and may influence longevity. However, the extent to which the statistical characteristics of population longevity are associated with elemental geochemical background values at a regional scale remains an important question. Based on the geochemical survey data of Yunnan Province and Chinese census data, the article utilizes Arcgis spatial analysis and mathematical statistics to explore the relationship between ω(Sn) and regional longevity level. The results of the study show that: (1) There is a close correlation between ω(Sn) and regional longevity levels. Within Yunnan Province, regions with high ω(Sn) have higher levels of longevity index and Ultra-octogenarian Index. (2) Spearman’s correlation coefficient shows that ω(Sn) is significantly positively (P 

The relationship between the geographic environment and human health has been a long-standing focus of scientific inquiry. Sn as an essential trace element for the human body, play vital roles in individual health and may influence longevity. However, the extent to which the statistical characteristics of population longevity are associated with elemental geochemical background values at a regional scale remains an important question. Based on the geochemical survey data of Yunnan Province and Chinese census data, the article utilizes Arcgis spatial analysis and mathematical statistics to explore the relationship between ω(Sn) and regional longevity level. The results of the study show that: (1) There is a close correlation between ω(Sn) and regional longevity levels. Within Yunnan Province, regions with high ω(Sn) have higher levels of longevity index and Ultra-octogenarian Index. (2) Spearman’s correlation coefficient shows that ω(Sn) is significantly positively (P 

The characteristic scale of clustering (hc) for the 234-spatial point patterns.

ANOVA test for the characteristic scale of clustering among the spatial point patterns based on temporal aggregations.