The map shows population density in Tioga County NY using a quantile classification with 5 data breaks each rounded to the nearest 10 people. The population data is census block level data from the 2010 U.S. Census.

U.S. Census population data for Kansas counties from 1890 through 2010. The choropleth map shows 2010 population based on a quantile classification. Click on any county to see additional information about historic maximums, population loss, and trend in population since 1890.

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 dataset is a subset of the Hunter Riverine landscapes classes to be shown as an augmentation to the modelled river impacts layer.

It contains non-ephemeral landscape classes (low to mod intermittent, mod to highly intermittent and perennial) which are deemed to be potentially subject to hydrological change due to having their headwaters in areas subject to ACRD induced drawdown.

Potential impact is flagged at Q05, Q50 and Q95 levels in the attribute table.

Purpose

for use in map reports

Dataset History

Non ephemeral stream landscape classes were compared with foot prints of 0.2m groundwater ACRD drawdown at the Q05 Q50 and Q95 levels. Streams rising out of and/or intersecting the footprints at the respective quantiles were tagged acoordingly were selected out and tagged accordingly in the attribute table

Dataset Citation

Bioregional Assessment Programme (2017) HUN SW Potentially Impacted Reaches by Quantile v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/55c568ce-ec90-40ca-9fd6-6c8fa58519e7.

Dataset Ancestors

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

• Derived From HUN River Perenniality v01

• Derived From HUN GW Model code v01

• Derived From HUN Landscape Classification v02

• Derived From Travelling Stock Route Conservation Values

• Derived From HUN GW Model v01

• Derived From NSW Wetlands

• Derived From Climate Change Corridors Coastal North East NSW

• Derived From NSW Office of Water Surface Water Licences Processed for Hunter v1 20140516

• Derived From Climate Change Corridors for Nandewar and New England Tablelands

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From HUN GW Quantiles Interpolation for IMIA Database v01

• Derived From BA ALL Assessment Units 1000m Reference 20160516_v01

• Derived From Asset database for the Hunter subregion on 27 August 2015

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

• Derived From Groundwater Economic Assets Hunter NSW 20150331 PersRem

• Derived From Geofabric Surface Network - V2.1.1

• Derived From Hunter CMA GDEs (DRAFT DPI pre-release)

• Derived From Camerons Gorge Grassy White Box Endangered Ecological Community (EEC) 2008

• Derived From Atlas of Living Australia NSW ALA Portal 20140613

• Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129

• Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004

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

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Gosford Council Endangered Ecological Communities (Umina woodlands) EEC3906

• Derived From NSW Office of Water Surface Water Offtakes - Hunter v1 24102013

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

• Derived From Australia - Species of National Environmental Significance Database

• 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 Northern Rivers CMA GDEs (DRAFT DPI pre-release)

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

• Derived From Ramsar Wetlands of Australia

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Hunter subregion boundary

• Derived From Commonwealth Heritage List Spatial Database (CHL)

• Derived From Groundwater Economic Elements Hunter NSW 20150520 PersRem v02

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

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

• Derived From Bioregional Assessment areas v03

• Derived From HUN Groundwater tables 20170421

• Derived From HUN Assessment Units 1000m 20160725 v02

• Derived From HUN Landscape Classification v03

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

• Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514

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

• Derived From Groundwater Entitlement Hunter NSW Office of Water 20150324

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

• Derived From Fauna Corridors for North East NSW

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

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

• Derived From NSW Office of Water GW licence extract linked to spatial locations for NorthandSouthSydney v3 13032014

• Derived From Asset database for the Hunter subregion on 16 June 2015

• Derived From Australia World Heritage Areas

• Derived From Asset database for the Hunter subregion on 12 February 2015

• Derived From [Lower Hunter

Overview: 142: Areas used for sports, leisure and recreation purposes. Traceability (lineage): This dataset was produced with a machine learning framework with several input datasets, specified in detail in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ) Scientific methodology: The single-class probability layers were generated with a spatiotemporal ensemble machine learning framework detailed in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). The single-class uncertainty layers were calculated by taking the standard deviation of the three single-class probabilities predicted by the three components of the ensemble. The HCL (hard class) layers represents the class with the highest probability as predicted by the ensemble. Usability: The HCL layers have a decreasing average accuracy (weighted F1-score) at each subsequent level in the CLC hierarchy. These metrics are 0.83 at level 1 (5 classes):, 0.63 at level 2 (14 classes), and 0.49 at level 3 (43 classes). This means that the hard-class maps are more reliable when aggregating classes to a higher level in the hierarchy (e.g. 'Discontinuous Urban Fabric' and 'Continuous Urban Fabric' to 'Urban Fabric'). Some single-class probabilities may more closely represent actual patterns for some classes that were overshadowed by unequal sample point distributions. Users are encouraged to set their own thresholds when postprocessing these datasets to optimize the accuracy for their specific use case. Uncertainty quantification: Uncertainty is quantified by taking the standard deviation of the probabilities predicted by the three components of the spatiotemporal ensemble model. Data validation approaches: The LULC classification was validated through spatial 5-fold cross-validation as detailed in the accompanying publication. Completeness: The dataset has chunks of empty predictions in regions with complex coast lines (e.g. the Zeeland province in the Netherlands and the Mar da Palha bay area in Portugal). These are artifacts that will be avoided in subsequent versions of the LULC product. Consistency: The accuracy of the predictions was compared per year and per 30km*30km tile across europe to derive temporal and spatial consistency by calculating the standard deviation. The standard deviation of annual weighted F1-score was 0.135, while the standard deviation of weighted F1-score per tile was 0.150. This means the dataset is more consistent through time than through space: Predictions are notably less accurate along the Mediterrranean coast. The accompanying publication contains additional information and visualisations. Positional accuracy: The raster layers have a resolution of 30m, identical to that of the Landsat data cube used as input features for the machine learning framework that predicted it. Temporal accuracy: The dataset contains predictions and uncertainty layers for each year between 2000 and 2019. Thematic accuracy: The maps reproduce the Corine Land Cover classification system, a hierarchical legend that consists of 5 classes at the highest level, 14 classes at the second level, and 44 classes at the third level. Class 523: Oceans was omitted due to computational constraints.

The zip file contains grid files in UTM 16S resulted from AUV mutlibeam data processing and a table with descriptions of these grid files. AUV bathymetry data resulted from interpolation of multibeam depth measurements using the IDW algorithm in SAGA GIS. The AUV bathymetric derivatives (Bathymetric Position Index, Concavity, LS factor, and Terrain Ruggedness Index were calculated in SAGA GIS. The slope derivative was calculated in ArcMap. The AUV backscatter statistics (10th quantile, 90th quantile, mean and mode) were calculated in FMGT Geocoder. The Bayesian classification map was created in SAGA GIS using data from Bayesian classification in Matlab. The ISODATA classification map was created in SAGA GIS using the the AUV backscatter statistics and the Random Forest predictive map was created using the MGET toolbox in ArcMap and the AUV bathymetry, bathymetric derivatives and backscatter statistics data.

Here we provide the data products from publications:

Clark, C.J.R., et al., The Quest for the Missing Dust: I – Restoring Large Scale Emission in Herschel Maps of Local Group Galaxies, ApJ 921 35

Clark, C.J.R., et al., The Quest for the Missing Dust: II – Two Orders of Magnitude of Evolution in the Dust-to-Gas Ratio Resolved Within Local Group Galaxies, accepted for publication in ApJ

This data concerns four Local Group galaxies: the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC), M31, and M33.

For each galaxy, we provide our new Herschel maps, as described in the above publications, which were combined in Fourier space ('feathered') with Planck, IRAS, and COBE data, in order to restore extended emission that was removed from previous Herschel reductions for these galaxies.

For each galaxy, we provide this new Herschel data for 5 Hershcel bands: the PACS 100 and 160 \(\mu\)m bands, and the SPIRE 250, 350, and 500 \(\mu\)m bands. This data is provided in FITS format, with one FITS file for each band for each galaxy. Each of these files contains 4 extensions. Extension 1 (IMAGE) provides the standard feathered map. Extension 2 (UNC) provides the uncertainty map. Extension 3 (MASK) provides a binary mask map indicating the portion of the data where reliable, fully-feathered high-resolution coverage is available. Extension 4 provides the foreground-subtracted version of the feathered map (FGND_SUB), the header of which also describes the uncertainty on that subtraction. All maps are in units of MJy/sr (except the MASK extension, which is boolean),

We also provide the outputs of our Spectral Energy Distribution (SED) fitting to this data, as described in the publications. For each galaxy, we provide FITS files giving the median value of each parameter in each pixel, and maps of the uncertainties on those medians (being the 68.3% quantile around the median). The parameters are dust mass surface density (Sigma_H.fits), dust temperature (Temp.fits), beta 1 (Beta-1.fits), beta 2 (Beta-2.fits), break wavelength (Break.fits), and 500 \(\mu\)m excess (Excess.fits). Each of these files contain 2 extensions. Extension 1 (median) provides the map of pixel parameter median values. Extension 2 (uncert) provides the map of uncertainties on those medians.

Additionally, we provide the full posterior probability distribution for all SED parameters, consisting of 1000 posterior samples, for all pixels, in the form of a FITS file containing a 4-dimensional hypercube, with axes corresponding to right ascension, declination, parameters (in order: dust mass surface density, dust temperature, beta 1, beta 2, break wavelength, and 500 \(\mu\)m excess), and samples. This is provided as a gzip compressed FITS file for each galaxy.

For each galaxy, we also provide our maps of the hydrogen surface density (Sigma_H.fits), and dust-to-gas ratio (DtG.fits).

Lastly, we provide Swift-UVOT data also used in our analysis. For the LMC and SMC, this is the data presented in Hagen et al. (2017). For M31 and M33, this data was processed in the same manner as the data in Hagen et al. (2017), and will be presented fully in Decleir (in prep.). We provide maps from Swift-UVOT bands W1, W2, and M2. For each band, we provide maps recording the count rate (in photons sec⁻¹), and maps giving the exposure time (in sec), and maps of the surface brightness in MJy sr⁻1 (converted using the Swift-UVOT zero points given in Breeveld et al., 2011).

View on Map View ArcGIS Service BTM Standard deviation – this mosaic dataset is part of a series of seafloor terrain datasets aimed at providing a consistent baseline to assist users in consistently characterizing Aotearoa New Zealand seafloor habitats. This series has been developed using the tools provided within the Benthic Terrain Model (BTM [v3.0]) across different multibeam echo-sounder datasets. The series includes derived outputs from 50 MBES survey sets conducted between 1999 and 2020 from throughout the New Zealand marine environment (where available) covering an area of approximately 52,000 km2. Consistency and compatibility of the benthic terrain datasets have been achieved by utilising a common projected coordinate system (WGS84 Web Mercator), resolution (10 m), and by using a standard classification dictionary (also utilised by previous BTM studies in NZ). However, we advise caution when comparing the classification between different survey areas.Derived BTM outputs include the Bathymetric Position Index (BPI); Surface Derivative; Rugosity; Depth Statistics; Terrain Classification. A standardised digital surface model, and derived hillshade and aspect datasets have also been made available. The index of the original MBES survey surface models used in this analysis can be accessed from https://data.linz.govt.nz/layer/95574-nz-bathymetric-surface-model-index/The full report and description of available output datasets are available at: https://www.doc.govt.nz/globalassets/documents/science-and-technical/drds367entire.pdf