This is the 2024 data update. For the 2018 update, please see the link below.

Starting with the most recently updated polygon shapefile of ASPAs (Terauds and Lee 2016), which contained some minor improvement on the original ASPA spatial layer first made publicly available in 2011, we first cross-checked the location of ASPA polygons with the spatially explicit locations provided in the ASPAs Management Plans. Once polygons were aligned with the Management Plans, we then georeferenced the maps provided in the management plans to check the ASPA boundaries in relation to known landscape features, In some cases, there was a lack of concurrence between co-ordinates, PDF map, coastline, rock layer or Google Earth. In these cases the following protocol was followed: snap to coordinates (unless clearly wrong), otherwise align to rock outcrop layer based on the PDF map, otherwise align to coastline. Full details of the updates made to each ASPA can be found in the README file accompanying the updated layer.

The downloadable dataset contains a folder with a points dataset, a folder with a polygon dataset, and a word document with further information.

For ASPAS and ASMAs within the AAT please see: https://data.aad.gov.au/metadata/aspas_asmas_aat - doi:10.4225/15/5a963cbd74a3a.

For the 2018 data update please see: https://data.aad.gov.au/metadata/AAS_4296_Updated_ASPAs_2018 - doi:10.26179/5c1b10c534c19.

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

The Multi-Temporal Landslide Inventory for the Far-Western region of Nepal datasets comprises 26350 different landslide events digitize in form of polygons from Google Earth satellite imagery interpretation. In Google earth has been used for interpretation 93 different sources for 79 different time slices between 2002 and 2018. The maximum scale of interpretation used is 1:1000, meanwhile the scale of digitalization was constant between 1:800 and 1:2000, resulting in a final visualization scale of 1:1000. All landslides in the inventory have been classified between deep-seated and shallow types (attribute field "Depth") by visual interpretation which have been later corroborated with calculations of the elevation differences within the surface of rupture area of the landslides

The dataset comprises 4 different shapefiles:

"LandslideInventory_FarWesternNepal_Pol.shp": Shapefile with 26350 Polygon features that bound completely the “zone of depletion” and partially the “zone of accumulation” of each identified landslide. Including completely the surface of rupture and more or less partially the depositional zone of the landslides. Landslide

"LandslideInventory_FarWesternNepal_Points.shp": Shapefile with 25639 Point features that approximately correspond with the center of the surface of rupture area, the point location within each landslide has ben extracted automatically with GIS tools using ALOS PALSAR (12.5 m) DEM.

"LandslideInventory_FarWesternNepal_Points_Dated1992_2018.shp": Shapefile with 8778 Point features for landslides in the inventory that have been dated within the period 1992-2018 (attribute field "Year". The dating of the landslides has been perform automatically by an own new toolbox in ArcGIS that compare annual Landsat (4-5, 7 and 8), to find sudden vegetation changes within the areas of the digitized landsldies. The tool has an accuracy of 83% to detect annual dates of activation or reactivations of the inventoried landslides.

"LandslideInventory_FarWesternNepal_AOI.shp": Shapefile with the Polygon boundary of the landslide inventory Area of Interpretation.

All shapefiles are in a UTM projected coordinate system UTM44N (WGS84).

This research was funded by the UK Natural Environment Research Council (NERC) and Department for International Development (DFID) as project NE/P000452/1 (LandslideEVO) under the Science for Humanitarian Emergencies and Resilience (SHEAR) program.

This dataset contains documentation on the 146 global regions used to organize responses to the ArchaeGLOBE land use questionnaire between May 18 and July 31, 2018. The regions were formed from modern administrative regions (Natural Earth 1:50m Admin1 - states and provinces, https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-1-states-provinces/). The boundaries of the polygons represent rough geographic areas that serve as analytical units useful in two respects - for the history of land use over the past 10,000 years (a moving target) and for the history of archaeological research. Some consideration was also given to creating regions that were relatively equal in size. The regionalization process went through several rounds of feedback and redrawing before arriving at the 146 regions used in the survey. No bounded regional system could ever truly reflect the complex spatial distribution of archaeological knowledge on past human land use, but operating at a regional scale was necessary to facilitate timely collaboration while achieving global coverage. Map in Google Earth Format: ArchaeGLOBE_Regions_kml.kmz Map in ArcGIS Shapefile Format: ArchaeGLOBE_Regions.zip (multiple files in zip file) The shapefile format is a digital vector file that stores geographic location and associated attribute information. It is actually a collection of several different file types: .shp — shape format: the feature geometry .shx — shape index format: a positional index of the feature geometry .dbf — attribute format: columnar attributes for each shape .prj — projection format: the coordinate system and projection information .sbn and .sbx — a spatial index of the features .shp.xml — geospatial metadata in XML format .cpg — specifies the code page for identifying character encoding Attributes: FID - a unique identifier for every object in a shapefile table (0-145) Shape - the type of object (polygon) World_ID - coded value assigned to each feature according to its division into one of seventeen ‘World Regions’ based on the geographic regions used by the Statistics Division of the United Nations (https://unstats.un.org/unsd/methodology/m49/), with small changes to better reflect archaeological scholarly communities. These large regions provide organizational structure, but are not analytical units for the study. World_RG - text description of each ‘World Region’ Archaeo_ID - unique identifier (1-146) corresponding to the region code used in the ArchaeoGLOBE land use questionnaire and all ArchaeoGLOBE datasets Archaeo_RG - text description of each region Total_Area - the total area, in square kilometers, of each region Land-Area - the total area minus the area of all lakes and reservoirs found within each region (source: https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-lakes/) PDF of Region Attribute Table: ArchaeoGLOBE Regions Attributes.pdf Excel file of Region Attribute Table: ArchaeoGLOBE Regions Attributes.xls Printed Maps in PDF Format: ArchaeoGLOBE Regions.pdf Documentation of the ArchaeoGLOBE Regional Map: ArchaeoGLOBE Regions README.doc

Starting with the most recently updated polygon shapefile of ASPAs (Terauds and Lee 2016), which contained some minor improvement on the original ASPA spatial layer first made publicly available in 2011, we first cross-checked the location of ASPA polygons with the spatially explicit locations provided in the ASPAs Management Plans. Once polygons were aligned with the Management Plans, we then georeferenced the maps provided in the management plans to check the ASPA boundaries in relation to known landscape features, In some cases, there was a lack of concurrence between co-ordinates, PDF map, coastline, rock layer or Google Earth. In these cases the following protocol was followed: snap to coordinates (unless clearly wrong), otherwise align to rock outcrop layer based on the PDF map, otherwise align to coastline. Full details of the updates made to each ASPA can be found in the README file accompanying the updated layer.

Abstract This is a source dataset created by the Bioregional Assessment Programme without the use of source data. This dataset contains all of the surface water footprint polygons that were created from mining reports that were used in the surface water modelling. There is also a document with the source references for all of the footprints included in the dataset. ## Dataset History Environmental impact statements and similar documents were downloaded from New South Wales Department of Planning and Environment Major Projects website, and from mining companies' websites. To obtain mine footprints for surface water modelling, the mining reports were searched for past and future projected mine layouts and surface water contributing areas. Each figure was digitised and georeferenced using one of four methods: 1. The preferred method was to use maps or plans with coordinates already on them. 2. If there were no coordinates, then three point locations were matched with points on Google Earth and the latitude and longitude from Google Earth were used to georeference the image. 3. If there were not three clearly identifiable point locations in the image, then supplementary points were found by matching contour information to the Shuttle Radar Topography Mission Smoothed Digital Elevation Model (SRTM DEM-S) grid Dataset GUID - 12e0731d-96dd-49cc-aa21-ebfd65a3f67a 4. Other site-specific approaches: a. Mangoola Coal Mine did not have adequate georeferencing points in the Year 10 and Final Landform images so these images were georeferenced to the matching project boundary in the other Mangoola Coal Mine images. b. The West Wallsend Colliery existing pit top surface facilities image, containing a satellite photo background, was georeferenced using Google Earth. The West Wallsend Colliery pit top facility outline was used to georeference the water management system image as they both contained the same outline. These areas were exported as polygon files (*.poly) using Geosoft Oasis Montaj software. A list of documents used for creating these polygon files are also included in the dataset ## Dataset Citation Bioregional Assessment Programme (2016) HUN SW footprint shapefiles v01. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/2a9520c8-1569-4e0e-8bd8-26e2c7b9e9e0.

The Multi-temporal landslide inventory for southern Sikkim State, India, is based on two data-sources (mapped extent given in Shapefile A): Google Earth images (Shapefiles B–C) and stereoscopic Cartosat-1 satellite images (Shapefiles D–F). The landslide inventories were collected for the purpose of mapping landslide domains (regions with similar physical and environmental characteristics that specifically drive landslide style) and the data was used to give a general idea of landslides occurring in the region rather than a detailed overview. The landslide inventories are given as shapefiles with two sources of data described separately, after which a summary of all shapefiles is given.

Google Earth landslides are mapped using images from 2002 to 2019 with a mapped extent of approximately 3000 km² and was ground-truthed during a 12-day field visit from 23 February to 6 March 2019. The resultant landslide inventory contains 440 landslides with three main landslide types identified: translational slides, debris flows, and rockfalls. Translational slides include debris slides, rock slides, and unclassified translational slides. In the landslide inventory, debris flows and rockfalls are mapped as points representing their source area and translational slides are mapped as polygons representing both the source and depositional area. A complete description of the landslide types and mapping is given in Heijenk (2022, Chapter 3, section 3.4.2) The final landslide inventory (refer to how they would access it here, so a reference, or shapefile) includes the following:

• Year, the year of the first image that the landslide appears in is taken,
• Geology, the geological unit that the landslide occurs in is taken from Mottram et al. (2004),
• Area, for translational slides the area is given,
• Topographic data (elevation, aspect, slope, and curvature), which is taken from ASTER GDEM (Version 3.0, 2018, 30 m horizontal resolution, 30 m vertical resolution).

The Cartosat landslide inventory contains 44 features mapped from one pair of stereoscopic Cartosat-1 images (National Remote Sensing Centre, Cartosat-1 ID 197823411, https://www.nrsc.gov.in/, 2.5 m x 2.5 m) captured on 30 September 2011 with extents of 851 km² and 957 km². Three main landslide types have been mapped: deep-seated landslides, multi-temporal landslide areas, and rockfall areas. For deep-seated landslides, the scarp is mapped separately from the depositional area. A complete description of the methodology is given in Heijenk (2022, Chapter 3, section 3.4.3).

The following shapefiles are included in this dataset:

1. Google_Earth_mapped_extent_21Dec2021.shp: Shapefile with a polygon that denotes the mapped extent of southern Sikkim State.
2. Google_Earth_landslides_polygon_21Dec2021.shp: Shapefile with 255 polygon features, where the polygon includes the source and depositional area of translational landslides.
3. Google_Earth_landslides_point_21Dec2021.shp: Shapefile with 185 point features that denote the source area of both debris flows and rockfalls.
4. Cartosat_197823411_extents.shp: Shapefile with 2 polygon features that denote the extent of the Cartosat-1 image pair captured on 30 September 2011.
5. Cartosat_landslides_21Dec2021.shp: Shapefile with 67 polygon features that describe 44 landslide features. Some landslide features have been mapped with separate polygons for the scarp and the depositional area.
6. Cartosat_197823411clouds.shp: Shapefile with 5 polygon features that show an estimated area of the clouds that block landslide mapping in the 30 September 2011 Cartosat-1 image pair.

All shapefiles are in an WGS 84 EPSG:3857 projection.

This research was funded by the UK Natural Environment Research Council (NERC, Grant # NE/R012148/1) and the British Geological Survey (BGS, BUFI).

References:

Heijenk, R.A. (2022). Landslide Variables, Inventories, and Domains in Data-Poor Regions: A Case Study in East Sikkim, India. [PhD thesis]. King’s College London.

This dataset consists of a shapefile of the reefs, islands, sand banks, cays and rocks of the whole Great Barrier Reef (GBR) including Torres Strait. This dataset is an extension of the mapping in …Show full descriptionThis dataset consists of a shapefile of the reefs, islands, sand banks, cays and rocks of the whole Great Barrier Reef (GBR) including Torres Strait. This dataset is an extension of the mapping in the GBR Marine Park to include Torres Strait. The Torres Strait region was mapped at a scale of 1:50,000 (Lawrey, E. P., Stewart M., 2016) and these new features are referred to as the "Torres Strait Reef and Island Features" dataset. The Complete GBR Reef and Island Features dataset integrates the "Torres Strait Reef and Island Features" dataset with the existing "GBR Features" (Great Barrier Reef Marine Park Authority, 2007) to create a single composite dataset of the whole Great Barrier Reef. This dataset includes 9600 features overall with 5685 from the "GBR Features" dataset and 3927 from the "Torres Strait Reef and Island Features" dataset. These two datasets can be easily separated if necessary based on the "DATASET" attribute. All new mapped features in Torres Strait were allocated permanent IDs (such as 10-479 for Thursday Island and 09-246 for Mabuiag Reef). These IDs are for easy unambiguous communication of features, especially for unnamed features. The reference imagery used for the mapping of the reefs is available on request as it is large (~45 GB). These files are saved in the eAtlas enduring repository. Methods: This project mapped Torres Strait using a combination of existing island datasets as well as a semi-automated and manual digitising of marine features (reefs and sand banks) from the latest aerial and satellite imagery. No features were added to the dataset without confirmed evidence of their existence and position from at least two satellite image sources. The Torres Strait Reef and Island Feature mapping was integrated with the existing "GBR Features" dataset by GBRMPA to ensure that there were no duplicate feature ID allocations and to create a single dataset of the whole GBR. The overall dataset development was as follows: Dataset collation and image preparation: Collation of existing maps and datasets. Download and preparation of the Landsat 5, 7, and 8 satellite image archive for Torres Strait. Spatial position correction of Landsat imagery against a known reference image. Sand Bank features: Manual digitisation of sand banks from Landsat 5 imagery. Conversion to a polygon shapefile for integration with the reef features. Reef features: Semi-automated digitisation of the marine features from Landsat 5 imagery. Manual trimming, cleaning and checking of marine features against available aerial and satellite imagery. Island features: Compilation of island features from existing datasets (DNRM 1:25k Queensland Coastline, and Geoscience Australia Geodata Coast 100k 2004) Correction of the island features from available aerial and Landsat imagery. Merging: of marine and island features into one dataset. Classification: of mapped features, including splitting fringing reefs based on changes in classification. ID allocation: Clustering to make groups of related features (i.e. an island, plus its fringing reefs and related sand banks; a reef plus its neighbouring patch reefs, etc.). Merging with the GBR Features dataset. This was to ensure that there were no duplicate allocations of feature IDs. This involved removing any overlapping features above the Great Barrier Reef Marine Park from the GBR Feature dataset. Allocation of group IDs (i.e. 10-362) following the scheme used in the GBR Features dataset. Using R scripting. Allocation of subgroup IDs (10-362b) to each feature in the dataset. Using R scripting. Allocation of names: Names of features were copied from some existing maps (Nautical Charts, 250k, 100k Topographic maps, CSIRO Torres Strait Atlas). For more information about the methods used in the development of this dataset see the associated technical report (Lawrey, E. P., Stewart M., 2016) Limitations: This dataset has mapped features from remote sensing and thus in some parts of Torres Strait where it is very turbid this may result in an underestimate of boundary of features. It also means that some features may be missing from the dataset. This dataset is NOT SUITABLE FOR NAVIGATION. The classification of features in this dataset was determined from remote sensing and not in-situ surveys. Each feature has a confidence rating associated with this classification. Features with a 'Low' confidence should be considered only as guidance. This project only digitised reefs in Torres Strait, no modifications were made to the features from the integrated GBR Features dataset. Format: This dataset is available as a shapefile, a set of associated A1 preview maps of the Torres Strait region, ArcMap MXD file with map styling and ArcMap map layer file. The shapefile is also available in KMZ format suitable for viewing in Google Earth. TS_AIMS_NESP_Torres_Strait_Features_V1b_with_GBR_Features.shp (26 MB), TS_AIMS_NESP_Torres_Strait_Features_V1b_with_GBR_Features.kmz: Torres Strait features (3927 polygon features) integrated with the (GBRMPA) GBR Features dataset (5685 polygon features). This dataset covers the entire GBR. Data Dictionary: DATASET: (TS Features, GBR Features) Which dataset this feature belongs to. This attribute is used when the Torres Strait Reef and Island Features dataset is merged with the GBRMPA GBR Features dataset. LOC_NAME_S: (e.g. Tobin (Zagarsum) Island (10-147a)) Location Name: Name of the feature and its ID GBR_NAME: (e.g. Tobin (Zagarsum) Island) Name of the features with no ID CHART_NAME: (e.g. Tobin Island) Name of the feature on the Australian Nautical Charts TRAD_NAME: (Zagarsum) Traditional name. From various sources. UN_FEATURE: (TRUE, FALSE) Unnamed Feature: If TRUE then the feature is unnamed. Useful for limiting labels in maps to features with names. LABEL_ID: (10-147a) ID of the feature SORT_GBR_I: (10147) ID of each feature cluster made up from the Latitude ID and Group ID. Used for sorting the features. FEAT_NAME: (Island, Rock, Reef, Cay, Mainland, Bank, Terrestrial Reef, Other ) Classification of the feature that is used in the GBR Features dataset. See 3.6 Classification scheme for more information. LEVEL_1, LEVEL_2, LEVEL_3: Hierarchical classification of the features. See Appendix 3: Feature Classification Descriptions. Checked: (TRUE, FALSE) Flag to record if the feature was reviewed in detail (at a scale of approximately 1:5000) after the initial digitisation. Unchecked features were only reviewed at a coarser scale (1:25000) to spot significant problems. IMG_SOURCE: (Aerial, AGRI, Landsat, ESRI) Imagery type used for the final digitisation checking and correction. (AGRI - AGRI PRISM by GA, Landsat is Landsat 8 or Landsat 5, ESRI - ArcMap satellite basemap) CLASS_SRC: (Aerial, AGRI, Landsat, Google, Marine Chart) Imagery type used to determine the classification of the feature. Often the classification will be an aggregation of information from multiple image sources. This field will record the highest resolution source used. For some small features the classification was obtained from the Marine Chart, generally for Rocky Reefs. CLASS_CONF: (High, Medium, Low) Confidence of the classification applied to the feature. The confidence is dependent on the clarity and range of the imagery available for classification. High - Clear high resolution imagery available (Aerial, Google) with good water visibility. Key characteristics of the classification clear visible. Feature classification fits the context for the neighbouring region. For unconsolidated features (such as sand banks) a High confidence classification would be applied if the shape, colour and context fit and in particular if movement is visible over time-lapse Landsat imagery. Medium - Moderate imagery available (Landsat 8 pan sharpened, some high resolution imagery) that shows key characteristics of the feature and the classification fits the context for the neighbouring region. Low - Only Landsat 5 imagery is available, the feature is small and its origin is unclear from the neighbouring context. This is the default confidence rating for any features that were not individually checked. POLY_ORIG: (QLD_DNRM_Coastline_25k, New, GBR_Features, AU_GA_Coast100k_2004) Original source of the polygon prior to any modifications. New features correspond to all the mapped marine features. Most features from the other source would have been modified as part of the checking and trimming of the dataset. SUB_NO: (100, 101, ¿) Subgroup number. Numeric count, starting at 100 of each feature in a group. Matches the subgroup ID i.e. 100 -> blank, 101 -> a, 102 -> b, etc. CODE: (e.g. 10-147-102-101) Unique code made from the various IDs. This is a GBR Feature attribute. UNIQUE_ID: (10147102101) Same as the CODE but without the hyphens, This is a GBR Feature attribute. Note: Version 1b, this attribution is currently out of date. FEATURE_C: (100 - 110) Code applied to each of the FEAT_NAMEs. QLD_NAME: (Tobin Island) Same as the GBR_NAME X_COORD: Longitude in decimal degrees east, in GDA94. Y_COORD: Latitude in decimal degrees north, in GDA94. SHAPE_AREA: Shape Area in km2 SHAPE_LEN: Shape perimeter length in km CHECKED: (TRUE, FALSE) Whether the features was carefully checked (at a scale of better than ~1:5000) and manually corrected to this level of precision. If FALSE then the feature was only checked to approximately a1:25000 scale. PriorityLn: (TRUE, FALSE) Priority Label - If TRUE then this feature's label should be included in a map. Usually correspond to features with names. Use to reduce near duplicate labels of the islands and their surrounding fringing reefs. COUNTRY: (Australia, Papua-New Guinea) Sovereignty of the feature. This is based on a spatial join with

The framework of the Cordilleran orogen of northwestern North America is commonly depicted as a 'collage' of terranes - crustal blocks containing records of a variety of geodynamic environments including continental fragments, pieces of island arc crust and oceanic crust. The series of maps available here are derived from a GIS compilation of terranes based on the map first published by Colpron et al. (2007) and more recently revised by Nelson et al. (2013). These maps are presented here in digital formats including ArcGIS file geodatabase (.gdb), shapefiles (.shp and related files), Google Earth (.kmz), as well as graphic files (.pdf). The GIS data includes terrane polygons and selected major Late Cretaceous and Tertiary strike-slip faults. Graphic PDF files derived from the GIS compilation were prepared for the Northern Cordillera (Alaska, Yukon and BC), the Canadian Cordillera (BC and Yukon), Yukon, and British Columbia. These maps are intended for page-size display (~1:5,000,000 and smaller). Polygons are accurate to ~1 km for Yukon and BC, ~5 km for Alaska. More detailed geological data are available from both BCGC, USGS and YGS websites. Descriptions of the terranes, their tectonic evolution and metallogeny can be found in Colpron et al. (2007), Nelson and Colpron (2007), Colpron and Nelson (2009), Nelson et al. (2013) and references therein. The terrane map project is a collaborative effort of the BC Geological Survey and the Yukon Geological Survey. Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca

This dataset is a shapefile of 767 polygons describing the contours of Juniperus communis L. and Juniperus sabina L. shrubs for the year 2021 in rectangular plots across Sierra Nevada. The coordinates of the polygons were obtained from a field work campaign with a differential centimetric GPS, and their contours were drawn manually in QGIS using the Google Earth satellite image for 2020 and the PNOA aerial image for the 2020. This dataset also contains an excel file describing the features of each polygon: the polygon centroid coordinates, the type of species, the sexgender, the morphotype, the damage in the vegetation cover estimated in the field and telematically, certainty of the digitalization with QGIS and also if the differential centimetric GPS used belongs to the University of Granada or the University of Almeria.

This data set provides a polygon shapefile delineating relatively large, slow-moving (4-17 cm/year in the radar line-of-sight direction) landslides in the continental U.S. western coastal states (California, Oregon, and Washington). The polygons also are provided in a Google Earth .kmz file. Delineated landslides were identified from displacement signals captured by InSAR (Interferometric Synthetic Aperture Radar) interferograms of ALOS PALSAR (Advanced Land Observing Satellite; Phased Array type L-band Synthetic Aperture Radar) images between 2007 and 2011, and ALOS-2 PALSAR-2 images between 2015 and 2019. The ALOS PALSAR images utilized cover the three states entirely; the ALOS-2 PALSAR images utilized cover primarily the western half of the study area where 97.6% of the identified landslides are located. The Scene IDs of the used ALOS and ALOS-2 images are provided in text files. The 1/3 arc-second National Elevation Datasets from the U.S. Geological Survey (https://apps.nationalmap.gov/downloader/, last accessed November 12, 2020), and optical images available from Google Earth were utilized to assist in landslide identification. Each polygon in the shapefile outlines the active area of a landslide. The active areas identified for a given landslide using the ALOS PALSAR and ALOS-2 PALSAR-2 interferograms differ slightly in some cases. For these, we used the larger polygon as the landslide boundary. The shapefile attribute table indicates which data were used to identify the landslide (“Comments”), and this is also indicated by the “Flag” field of the table, where values of 1, 2, and 3 indicate ALOS, ALOS2, and both datasets, respectively; a flag value of 4 was assigned for rock glaciers, which were only identified using ALOS data. The attribute table also provides areas of each polygon in square meters. These data support a study described in: Xu, Y., Schulz, W.H., Lu, Z., Kim, J., and Baxstrom, K., 2021, Geologic controls of slow-moving landslides near the U.S. west coast: Landslides, doi:10.1007/s10346-021-01732-3

This data set provides GIS shapefiles and Google Earth kmz files containing polygons delineating slow-moving (0.5-6 cm/year in the radar line-of-sight direction) landslides and subsiding fan deltas in the Glacier Bay region of Alaska and British Columbia. Landslides and fan deltas were identified from displacement signals captured by Interferometric Synthetic Aperture Radar (InSAR) interferograms of Sentinel-1 C-band Synthetic Aperture Radar images. The images were acquired at 12-day intervals from June to October from 2018 to 2020. We applied the persistent scatterer InSAR (PSInSAR) methods to images from both descending (scene P145) and ascending (scene P50) satellite tracks. We used PSInSAR results from the descending track as a primary means to identify ground movement and then used results from the ascending track to confirm the ground movement. The overlapping area covered by both images is 14,780 sq. km. Each polygon in the shapefile and .kmz file outlines an area of moving ground from 2018 to 2020. We categorized each area of moving ground into one of three categories: 1) slow-moving landslides on steep rocky slopes not near (> 2 km away from) present-day glacier termini, 2) slow moving landslides directly adjacent to (< 2 km away from) and associated with glacier thinning and retreat; and 3) subsidence of outwash fan deltas near glacier termini. These three categories are differentiated in the shapefile attribute table and in an explanation box in the kmz file. The attribute table also provides the area of each polygon in sq. meters. Overall, we detected 4 landslides distal to glacier termini, 22 adjacent to termini, and 5 subsiding fan deltas. We have also included shapefiles for the boundary of Glacier Bay National Park and Preserve; the coverage area for scenes P145 and P50, and the overlap between the two; and points and labels for each polygon of moving ground. These data were used in the following interpretive paper: Kim, J., Coe, J.A., Lu, Z., Avdievitch, N.N., and Hults, C.P., in review, Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia: Remote Sensing of Environment.

In the late evening of November 27, 2022, an effusive eruption began inside Moku'aweoweo caldera at the summit of Mauna Loa volcano. Within a few hours, lava had covered most of the caldera floor, and several fissures just outside caldera sent short lava flows up to 3 kilometers (2 miles) to the southwest. Later in the morning of November 28, summit effusion ceased and the eruption moved into the volcano's Northeast Rift Zone. Several rift zone fissures were initially active, but by November 30 effusion had focused at a vent known as fissure 3. For another 10 days, fissure 3 fountained and fed lava flows that eventually stretched 18 kilometers (11 miles) to the north, threatening but not reaching the Daniel K. Inouye Highway across the island's interior. Effusion from fissure 3 began declining overnight December 7–8 and ceased on December 10, by which time the eruption had covered approximately 36 square kilometers (14 square miles) of Mauna Loa with new lava. In this report, the authors have sought to chronicle this sequence of events using geospatial data in the form of an Esri file geodatabase, Esri shapefiles, and Google Earth KMZs, as well as rapid-response orthomosaic and thermal map rasters.

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 …Show full descriptionAbstract 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. Hunter Zone of Potential Hydrological change including input and derived layers. The final Zone of Potential Hydrological Change (ZPHC) is a union of the groundwater ZPHC and suface water ZPHC, which in turn were derived from groundwater and surface water impact modelling. The groundwater component of the ZPHC is where the the probability 5% or greater of equalling or exceeding 0.2m drawdown and is derived from the 95th Quantile layer. The surface water component of the ZoPHC is derived from the reaches that are deemed impacted from the surface water modelled (sometimes referred to as Step 1 reaches) as well as additional non modelled reaches that are deemed to be potentially impacted due to interactions with the GW drawdown layer (aka Step 2) or proposed mine operations at the surface (aka Step 3). How these are derived are expanded on in the History section of the metadata. The SW ZoPHC is created from AU cells that intersect any of the impacted stream lines (from steps 1-3 above) as well as those which intersect GDE landscapeclass singlepart polygons withing 150m of the impacted stream lines. Some manual post processing edits were undertaken to remove anomalous AU cells. Mostly these occurred in the Macquarie-Tuggerah region of the ZoPHC to exclude AU cells selected due to intersecting upslope rainforest and wet schlerophyll forest GDE polygons in narrow valley areas. Dataset History GW ZoPHC A CON statement in ArcGIS Spatial Analyst was used to extract the area of the 95th quantile (HUN_dmax_acrd_quantile_95.asc from the input dataset) raster layer where drawdown was >= 0.2m. The resulting integer zone grid was vectorised into a shapefile. An anomalous zone on the SW boundary of the subregion that was an artefact of the modelling was deleted. The result is the GW ZoPHC SW ZoPHC the outputs of the processes below can be found in the "Input_Component_Layers" folder of this dataset. Step 1 Potentially impacted reaches were extracted from HUN_SW_Modelling_InterpolatedReaches_Network_20170220_v02.shp (source dataset: HUN_SW_Modelling_Reaches_and_HRV_lookup_20170221_v02). These are line features where "SW_ZoPHC" = 'yes' or 'part'. From these extracted "impacted" reaches, the line features classified as "part" were manually edited (cut) according to the description in the HRV (Hydrological Response Variables) LUT spreadsheet (HUN_SW_Modelling_Reaches_HRV_lookup_20170221_v02.xlsx in source dataset: HUN_SW_Modelling_Reaches_and_HRV_lookup_20170221_v02). This typically involved trimming the line back only to that in or downstream of the GW ZoPHC extent. Also some "part" impacted line features were cut where they intersected existing (i.e. baseline) open cut pits (OC). These excisions were done onscreen by eye, with reference to OC pit polygons used for modelling and existing mine workings shown in Google Earth imagery. The result after this trimming of selected features is the shapefile Step_1_SW_Model_ImpactedReaches_modified_for_SWZoPHC_defiition.shp Step 2 Streams other than "highly intermittent ephemeral" in the Hunter Perenniality layer, that where inside or downstream of the GW ZoPHC were selected and underwent the same Baseline open pit excision process as above. This became the Step_2_NonEphemeral_Streams_affected_by_GW_drawdown_modified_for_SWZoPHC_defiition.shp shapefile. Note that some streams identified by Step 2 are already described in SW model (i.e. are included in the Step 1 features) Step 3 "highly intermittent ephemeral" streams from the Hunter Perenniality layer that were inside or downstream of ACRD open cut pits (based on the GW model ACRD OC footprint polygons) were selected. This became the Step_3_Ephemeral_Streams_crossing_ACRD_pits_modified_for_SWZoPHC_defiition.shp shapefile. Using the three "impacted" stream layers derived above in conjunction with a singlepart shapefile The final Zone of Potential Hydrological Change (ZPHC) is a union of the groundwater ZPHC and surface water ZPHC, which in turn were derived from groundwater and surface water impact modelling. Dataset Citation Bioregional Assessment Programme (2017) HUN ZoPHC and component layers 20170220. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/eb09503b-26ad-4ef5-9056-5672412aac67. Dataset Ancestors Derived From HUN SW Modelling Reaches and HRV lookup 20170221 v02 Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014 Derived From NSW Office of Water - National Groundwater Information System 20140701 Derived From HUN Alluvium (1:1m Geology) Derived From NSW Wetlands 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 Asset database for the Hunter subregion on 24 February 2016 Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 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 Greater Hunter Native Vegetation Mapping with Classification for Mapping Derived From Atlas of Living Australia NSW ALA Portal 20140613 Derived From Bioregional Assessment areas v03 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 NSW Office of Water Groundwater Licence Extract, North and South Sydney - Oct 2013 Derived From HUN River Perenniality v01 Derived From Mean Annual Climate Data of Australia 1981 to 2012 Derived From Climate Change Corridors (Moist Habitat) for North East NSW Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From Victoria - Seamless Geology 2014 Derived From Climate model 0.05x0.05 cells and cell centroids Derived From HUN Landscape Classification v02 Derived From Historical Mining footprints DTIRIS HUN 20150707 Derived From Darling River Hardyhead Predicted Distribution in Hunter River Catchment NSW 2015 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Geofabric Surface Network - V2.1.1 Derived From R-scripts for uncertainty analysis v01 Derived From NSW Office of Water Surface Water Offtakes - Hunter v1 24102013 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From Bioregional Assessment areas v05 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 Derived From HUN Landscape Classification v03 Derived From National Surface Water sites Hydstra Derived From HUN Mine footprints for timeseries Derived From Asset database for the Hunter subregion on 22 September 2015 Derived From BA ALL Assessment Units 1000m Reference 20160516_v01 Derived From NSW Office of Water GW licence extract linked to spatial locations for NorthandSouthSydney v3 13032014 Derived From HUN Groundwater footprint kmz files v01 Derived From Threatened migratory shorebird habitat mapping DECCW May 2006 Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From HUN AssetList Database v1p2 20150128 Derived From Australia - Species of National Environmental Significance Database Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From HUN GW Model code v01 Derived From Travelling Stock Route Conservation Values Derived From HUN GW Model v01 Derived From HUN SW GW Mine Footprints for IMIA 20160908 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004 Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129 Derived From Gippsland Project boundary Derived From Natural Resource Management (NRM) Regions 2010 Derived From Asset list for Hunter - CURRENT 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 Geological Provinces - Full Extent Derived From HUN GW Quantiles Interpolation v01 Derived From NSW Office of Water Surface Water Licences Processed for Hunter v1 20140516 Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514 Derived From HUN SW Model nodes 20170110 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From Asset database for the Hunter subregion on 16 June 2015 Derived From Australia World Heritage Areas Derived From Lower Hunter Spotted Gum Forest EEC 2010 Derived

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.

Hunter Zone of Potential Hydrological change including input and derived layers.

The final Zone of Potential Hydrological Change (ZPHC) is a union of the groundwater ZPHC and suface water ZPHC, which in turn were derived from groundwater and surface water impact modelling. The groundwater component of the ZPHC is where the the probability 5% or greater of equalling or exceeding 0.2m drawdown and is derived from the 95th Quantile layer.

The surface water component of the ZoPHC is derived from the reaches that are deemed impacted from the surface water modelled (sometimes referred to as Step 1 reaches) as well as additional non modelled reaches that are deemed to be potentially impacted due to interactions with the GW drawdown layer (aka Step 2) or proposed mine operations at the surface (aka Step 3). How these are derived is expanded on in the History section of the metadata.

The SW ZoPHC is created from AU cells that intersect any of the impacted stream lines (from steps 1-3 above) as well as those which intersect GDE landscapeclass singlepart polygons withing 150m of the impacted stream lines.

Some manual post processing edits were undertaken to remove anomalous AU cells. Mostly these occurred in the Macquarie-Tuggerah region of the ZoPHC to exclude AU cells selected due to intersecting upslope rainforest and wet schlerophyll forest GDE polygons in narrow valley areas.

The dataset contains the HUN_ZoPHC_source_AU_master_20171115.shp. It is the same as the previous one except that it has the additional field "RIM_reason" which identifies an AU as to what RIM analysis is subject to (eg "forested wetlands"). As before, it also contains the input component layers that were used to define the SW ZoPHC. They being

1) the bits of SW_Modelling reaches (above) that showed modelled or assumed change plus the additional rch_200 and rch_300 reaches

2) GDEs used to identify riparian AUs not intersected by the streamlines above but to be included in the SW ZoPHC.

Importantly the ZoPHC_source_AU_master contains the reach id to which an Assessment Unit (AU) is allocated.

Important note: an AU is only allocated to a reach if it is within the SW ZoPHC

This accounts for why there are some AUs that have a modelled reach passing through them but have a NULL "allocreach" value. It is because they are not in the SW ZoPHC. This will usually be because the reach shows modelled "no change" or there is presumed no change due to the reach not being hydrologically connected to any ACRD activity. However there are some AUs that have been deemed by expert judgement to be not in the SW Zone because existing Baseline activity has nullified the potential ACRD impacts.

Dataset History

GW ZoPHC

A CON statement in ArcGIS Spatial Analyst was used to extract the area of the 95th quantile (HUN_dmax_acrd_quantile_95.asc from the input dataset) raster layer where drawdown was >= 0.2m. The resulting integer zone grid was vectorised into a shapefile. An anomalous zone on the SW boundary of the subregion that was an artefact of the modelling was deleted. The result is the GW ZoPHC

SW ZoPHC

the outputs of the processes below can be found in the "Input_Component_Layers" folder of this dataset.

Step 1

Potentially impacted reaches were extracted from HUN_SW_Modelling_InterpolatedReaches_Network_20170220_v02.shp (source dataset: HUN_SW_Modelling_Reaches_and_HRV_lookup_20170221_v02). These are line features where "SW_ZoPHC" = 'yes' or 'part'. From these extracted "impacted" reaches, the line features classified as "part" were manually edited (cut) according to the description in the HRV (Hydrological Response Variables) LUT spreadsheet (HUN_SW_Modelling_Reaches_HRV_lookup_20170221_v02.xlsx in source dataset: HUN_SW_Modelling_Reaches_and_HRV_lookup_20170221_v02). This typically involved trimming the line back only to that in or downstream of the GW ZoPHC extent. Also some "part" impacted line features were cut where they intersected existing (i.e. baseline) open cut pits (OC). These excisions were done onscreen by eye, with reference to OC pit polygons used for modelling and existing mine workings shown in Google Earth imagery. The result after this trimming of selected features is the shapefile Step_1_SW_Model_ImpactedReaches_modified_for_SWZoPHC_defiition.shp

Step 2

Streams other than "highly intermittent ephemeral" in the Hunter Perenniality layer, that where inside or downstream of the GW ZoPHC were selected and underwent the same Baseline open pit excision process as above. This became the Step_2_NonEphemeral_Streams_affected_by_GW_drawdown_modified_for_SWZoPHC_defiition.shp shapefile. Note that some streams identified by Step 2 are already described in SW model (i.e. are included in the Step 1 features)

Step 3

"highly intermittent ephemeral" streams from the Hunter Perenniality layer that were inside or downstream of ACRD open cut pits (based on the GW model ACRD OC footprint polygons) were selected. This became the Step_3_Ephemeral_Streams_crossing_ACRD_pits_modified_for_SWZoPHC_defiition.shp shapefile.

Using the three "impacted" stream layers derived above in conjunction with a singlepart shapefile, the final Zone of Potential Hydrological Change (ZPHC) is a union of the groundwater ZPHC and surface water ZPHC, which in turn were derived from groundwater and surface water impact modelling.

Dataset Citation

Bioregional Assessment Programme (2017) HUN ZoPHC and component layers 20171115. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/d839f3b4-b6b6-438b-acc4-f909067e4135.

Dataset Ancestors

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

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

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

• Derived From NSW Wetlands

• 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 Asset database for the Hunter subregion on 24 February 2016

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

• 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 Greater Hunter Native Vegetation Mapping with Classification for Mapping

• Derived From Atlas of Living Australia NSW ALA Portal 20140613

• Derived From Bioregional Assessment areas v03

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

• Derived From HUN River Perenniality v01

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

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

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From Victoria - Seamless Geology 2014

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

These files give locations of Australian Aboriginal and Torres Strait Islander languages, as far as can be determined, as of European settlement. Subgroup polygons for Pama-Nyungan, family polygons for Australian language families, language polygons, and centroid coordinates. List of languages with subgroup information, glottocodes, ISO-639 codes, AIATSIS codes, and chirila holdings.

Funded by NSF grants BCS-0844551 and BCS-1423711.

Boundaries are approximate and the maps are not suitable for use as evidence in Native Title claims.

This work is part of the Chirila project (pamanyungan.net).

The files are in QGIS format with .kml export (suitable for viewing in google earth).

Vector polygon map data of city limits from Houston, Texas containing 731 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

The 2018 lower East Rift Zone eruption of Kilauea Volcano began in the late afternoon of 3 May, with fissure 1 opening and erupting lava onto Mohala Street in the Leilani Estates subdivision, part of the lower Puna District of the Island of Hawai'i. For the first week of the eruption, relatively viscous lava flowed only within a kilometer (0.6 miles) of the fissures within Leilani Estates, before activity shifted downrift (east-northeast) and out of the subdivision during mid-May. Around 18 May, activity along the lower East Rift Zone intensified, and fluid lava erupting at higher effusion rates from the downrift fissures reached the ocean within two days. Near the end of May, this more vigorous activity shifted back uprift into Leilani Estates, where fissure 8 reactivated with lava fountains feeding several 'a'a flows. The southernmost flow lobe developed into a well-defined lava channel and reached the ocean at Kapoho Bay - 11 kilometers (7 miles) away - on 3 June. Fissure 8 continued supplying this lava channel for more than two months, constructing an approximately 3.5-square-kilometer (1.4-square-mile) lava delta along the coastline. Over 4 and 5 August, activity at fissure 8 waned and flow in the lava channel came to a halt, only to be followed by weak activity within the vent in late August and early September. By then, the eruption had covered 35.5 square kilometers (13.7 square miles) of the lower Puna District with lava. In this report, the authors have sought to chronicle this sequence of events using geospatial data in the form of an Esri file geodatabase, Esri shapefiles, and Google Earth KMZs.

The framework of the Cordilleran orogen of northwestern North America is commonly depicted as a ‘collage’ of terranes – crustal blocks containing records of a variety of geodynamic environments including continental fragments, pieces of island arc crust and oceanic crust. The series of maps available here are derived from a GIS compilation of terranes based on the map first published by Colpron et al. (2007) and more recently revised by Nelson et al. (2013). These maps are presented here in digital formats including ArcGIS file geodatabase (.gdb), shapefiles (.shp and related files), Google Earth (.kmz), as well as graphic files (.pdf). The GIS dataincludes terrane polygons and selected major Late Cretaceous and Tertiary strike-slip faults. Graphic PDF files derived from the GIS compilation were prepared for the Northern Cordillera (Alaska, Yukon and BC), the Canadian Cordillera (BC and Yukon), Yukon, and British Columbia. These maps are intended for page-size display (~1:5,000,000 and smaller). Polygons are accurate to ~1 km for Yukon and BC, ~5 km for Alaska. More detailed geological data are available from both BCGC, USGS and YGS websites. Descriptions of the terranes, their tectonic evolution and metallogeny can be found in Colpron et al. (2007), Nelson and Colpron (2007), Colpron and Nelson (2009), Nelson et al. (2013) and references therein.The terrane map project is a collaborative effort of the BC Geological Survey and the Yukon Geological Survey.Distributed from GeoYukon by the Government of Yukon. Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: geomatics.help@gov.yk.ca