Guide to Publicly Available Demographic Data This data source guide is a reference tool describing data important to workforce professionals. We created the guide because multiple federal and state organizations provide data relevant to workforce professionals; and skillful data use requires understanding: the sources of data how often it is collected, for what years it is available, and a link to the data release dates the geographic level of analysis (state, county, etc.) the variables included in the data how to access and use the data

Reference map

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

SLIDO-4.5 is an Esri ArcGIS version 10.7 file geodatabase which can be downloaded here: https://www.oregon.gov/dogami/slido/Pages/data.aspx The geodatabase contains two feature datasets (a group of datasets within the geodatabase) containing six feature classes total, as well as two raster data sets, one individual table, and two individual feature classes. The original studies vary widely in scale, scope and focus which is reflected in the wide range of accuracy, detail, and completeness with which landslides are mapped. In the future, we propose a continuous update of SLIDO. These updates should take place: 1) each time DOGAMI publishes a new GIS dataset that contains landslide inventory or susceptibility data or 2) at the end of each winter season, a common time for landslide occurrences in Oregon, which will include recent historic landslide point data. In order to keep track of the updates, we will use a primary release number such as Release 4.0 along with a decimal number identifying the update such as 4.5.

Dataset

This dataset was created by Immulu

Released under Community Data License Agreement - Permissive - Version 1.0

Contents

USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Ranger Station, White House, and City/Town Hall. Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. Included is a feature class of preliminary building polygons provided by FEMA, USA Structures. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain structures data in either Esri File Geodatabase or Shapefile formats. For additional information on the structures data model, go to https://www.usgs.gov/ngp-standards-and-specifications/national-map-structures-content.

The USGS National Hydrography Dataset (NHD) service from The National Map is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000 (or larger) scale and referred to as high resolution NHD, and the other based on 1:100,000 scale and referred to as medium resolution NHD. The NHD from The National Map supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. The NHD is commonly combined with other data themes, such as boundaries, elevation, structures, and transportation, to produce general reference base maps. The National Map download client allows free downloads of public domain NHD data in either Esri File Geodatabase or Shapefile formats. For additional information on the NHD, go to https://nhd.usgs.gov/index.html.

The USGS Transportation downloadable data from The National Map (TNM) is based on TIGER/Line data provided through U.S. Census Bureau and supplemented with HERE road data to create tile cache base maps. Some of the TIGER/Line data includes limited corrections done by USGS. Transportation data consists of roads, railroads, trails, airports, and other features associated with the transport of people or commerce. The data include the name or route designator, classification, and location. Transportation data support general mapping and geographic information system technology analysis for applications such as traffic safety, congestion mitigation, disaster planning, and emergency response. The National Map transportation data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and structures, to produce general reference base maps. The National Map viewer allows free downloads of public domain transportation data in either Esri File Geodatabase or Shapefile formats. For additional information on the transportation data model, go to https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map.

For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). Methods

1. INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limited human intervention. Sloan et al. (2023) present a map indicating the various areas of Equatorial Asia from which these images were sourced.
IMAGE NAMING CONVENTION A common naming convention applies to satellite images’ file names: XX##.png where:

XX – denotes the geographical region / major island of Equatorial Asia of the image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

1. INTERPRETING ROAD FEATURES IN THE IMAGES For each of the 200 input satellite images, its road was visually interpreted and manually digitized to create a reference image dataset by which to train, validate, and test AI road-mapping models, as detailed in Sloan et al. (2023). The reference dataset of road features was digitized using the ‘pen tool’ in Adobe Photoshop. The pen’s ‘width’ was held constant over varying scales of observation (i.e., image ‘zoom’) during digitization. Consequently, at relatively small scales at least, digitized road features likely incorporate vegetation immediately bordering roads. The resultant binary (Road / Not Road) reference images were saved as PNG images with the same image dimensions as the original 200 images.

2. IMAGE TILES AND REFERENCE DATA FOR MODEL DEVELOPMENT

The 200 satellite images and the corresponding 200 road-reference images were both subdivided (aka ‘sliced’) into thousands of smaller image ‘tiles’ of 256x256 pixels each. Subsequent to image subdivision, subdivided images were also rotated by 90, 180, or 270 degrees to create additional, complementary image tiles for model development. In total, 8904 image tiles resulted from image subdivision and rotation. These 8904 image tiles are the main data of interest disseminated here. Each image tile entails the true-colour satellite image (256x256 pixels) and a corresponding binary road reference image (Road / Not Road).
Of these 8904 image tiles, Sloan et al. (2023) randomly selected 80% for model training (during which a model ‘learns’ to recognize road features in the input imagery), 10% for model validation (during which model parameters are iteratively refined), and 10% for final model testing (during which the final accuracy of the output road map is assessed). Here we present these data in two folders accordingly:

'Training’ – contains 7124 image tiles used for model training in Sloan et al. (2023), i.e., 80% of the original pool of 8904 image tiles. ‘Testing’– contains 1780 image tiles used for model validation and model testing in Sloan et al. (2023), i.e., 20% of the original pool of 8904 image tiles, being the combined set of image tiles for model validation and testing in Sloan et al. (2023).

IMAGE TILE NAMING CONVENTION A common naming convention applies to image tiles’ directories and file names, in both the ‘training’ and ‘testing’ folders: XX##_A_B_C_DrotDDD where

XX – denotes the geographical region / major island of Equatorial Asia of the original input 1920x886 pixel image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

A, B, C and D – can all be ignored. These values, which are one of 0, 256, 512, 768, 1024, 1280, 1536, and 1792, are effectively ‘pixel coordinates’ in the corresponding original 1920x886-pixel input image. They were recorded within the names of image tiles’ sub-directories and file names merely to ensure that names/directory were uniquely named)

rot – implies an image rotation. Not all image tiles are rotated, so ‘rot’ will appear only occasionally.

DDD – denotes the degree of image-tile rotation, e.g., 90, 180, 270. Not all image tiles are rotated, so ‘DD’ will appear only occasionally.

Note that the designator ‘XX##’ is directly equivalent to the filenames of the corresponding 1920x886-pixel input satellite images, detailed above. Therefore, each image tiles can be ‘matched’ with its parent full-scale satellite image. For example, in the ‘training’ folder, the subdirectory ‘Bo12_0_0_256_256’ indicates that its image tile therein (also named ‘Bo12_0_0_256_256’) would have been sourced from the full-scale image ‘Bo12.png’.

Libraries data contains name and location data for selected manmade facilities. These data are designed to be used in general mapping and analysis of structure related activities using a geographic information system (GIS) For mapping purposes, structures can be used with other GIS data themes to produce general reference maps as a base map dataset.

Libraries data were updated for Maine as part of a data stewardship agreement with the USGS to provide infrastructure data for the National Structures Dataset. These data are intended for use in general mapping alone or with other data themes to produce general reference maps. Maps may serve as base maps for special purpose mapping

Dataset

This dataset was created by Nazmus Sadat013

Released under Apache 2.0

Contents

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

The INDIGO Change Detection Reference Dataset

Description

This graffiti-centred change detection dataset was developed in the context of INDIGO, a research project focusing on the documentation, analysis and dissemination of graffiti along Vienna's Donaukanal. The dataset aims to support the development and assessment of change detection algorithms.

The dataset was collected from a test site approximately 50 meters in length along Vienna's Donaukanal during 11 days between 2022/10/21 and 2022/12/01. Various cameras with different settings were used, resulting in a total of 29 data collection sessions or "epochs" (see "EpochIDs.jpg" for details). Each epoch contains 17 images generated from 29 distinct 3D models with different textures. In total, the dataset comprises 6,902 unique image pairs, along with corresponding reference change maps. Additionally, exclusion masks are provided to ignore parts of the scene that might be irrelevant, such as the background.

To summarise, the dataset, labelled as "Data.zip," includes the following:

• Synthetic Images: These are colour images created within Agisoft Metashape Professional 1.8.4, generated by rendering views from 17 artificial cameras observing 29 differently textured versions of the same 3D surface model.
• Change Maps: Binary images that were manually and programmatically generated, using a Python script, from two synthetic graffiti images. These maps highlight the areas where changes have occurred.
• Exclusion Masks: Binary images are manually created from synthetic graffiti images to identify "no data" areas or irrelevant ground pixels.

Image Acquisition

Image acquisition involved the use of two different camera setups. The first two datasets (ID 1 and 2; cf. "EpochIDs.jpg") were obtained using a Nikon Z 7II camera with a pixel count of 45.4 MP, paired with a Nikon NIKKOR Z 20 mm lens. For the remaining image datasets (ID 3-29), a triple GoPro setup was employed. This triple setup featured three GoPro cameras, comprising two GoPro HERO 10 cameras and one GoPro HERO 11, all securely mounted within a frame. This triple-camera setup was utilised on nine different days with varying camera settings, resulting in the acquisition of 27 image datasets in total (nine days with three datasets each).

Data Structure

The "Data.zip" file contains two subfolders:

• 1_ImagesAndChangeMaps: This folder contains the primary dataset. Each subfolder corresponds to a specific epoch. Within each epoch folder resides a subfolder for every other epoch with which a distinct epoch pair can be created. It is important to note that the pairs "Epoch Y and Epoch Z" are equivalent to "Epoch Z and Epoch Y", so the latter combinations are not included in this dataset. Each sub-subfolder, organised by epoch, contains 17 more subfolders, which hold the image data. These subfolders consist of:
  • Two synthetic images rendered from the same synthetic camera ("X_Y.jpg" and "X_Z.jpg")
  • The corresponding binary reference change map depicting the graffiti-related differences between the two images ("X_YZ.png"). Black areas denote new graffiti (i.e. "change"), and white denotes "no change". "DataStructure.png" provides a visual explanation concerning the creation of the dataset.
    
    The filenames follow the following pattern:
    • X - Is the ID number of the synthetic camera. In total, 17 synthetic cameras were placed along the test site
    • Y - Corresponds to the reference epoch (i.e. the "older epoch")
    • Z - Corresponds to the "new epoch"
      
• 2_ExclusionMasks: This folder contains the binary exclusion masks. They were manually created from synthetic graffiti images and identify "no data" areas or areas considered irrelevant, such as "ground pixels". Two exclusion masks were generated for each of the 17 synthetic cameras:
  • "groundMasks": depict ground pixels which are usually irrelevant for the detection of graffiti
  • "noDataMasks": depict "background" for which no data is available.
    

A detailed dataset description (including detailed explanations of the data creation) is part of a journal paper currently in preparation. The paper will be linked here for further clarification as soon as it is available.

Licensing

Due to the nature of the three image types, this dataset comes with two licenses:

• Synthetic images:
  • These come with an In Copyright license (for the rights usage terms, see https://rightsstatements.org/page/InC/1.0/?language=en).
  • The copyright lies with:
    • the Ludwig Boltzmann Gesellschaft (https://d-nb.info/gnd/1024204324)
    • the TU Wien (https://d-nb.info/gnd/55426-1)
    • One or more anonymous graffiti creator(s) upon whose work these images are based.
  • The first two entities are also the licensor of these images.
    
• Change maps and masks:
  • These are openly licensed via CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)
  • In this case, the copyright lies with:
    • the Ludwig Boltzmann Gesellschaft (https://d-nb.info/gnd/1024204324)
    • the TU Wien (https://d-nb.info/gnd/55426-1)
  • Both institutes are also the licensor of these images.

Every synthetic image, change map and mask has this licensing information embedded as IPTC photo metadata. In addition, the images' IPTC metadata also provide a short image description, the image creator and the creator's identity (in the form of an ORCiD).

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If there are any questions, problems or suggestions for the dataset or the description, please do not hesitate to contact the corresponding author, Benjamin Wild.

This is a general reference map of Canada and surrounding countries. The representation of political features on this map does not necessarily reflect the position of the Government of Canada on international issues of recognition, sovereignty or jurisdiction. Political status is as of 2009.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Dataset contains framework layers compiled for representation on state reference map, scale 1:1.5 million. Line and polygon features only. Road, rail, waterbody and watercourse themes included. State coastline not included.

Purpose

Can be used as a framework layer for whole of state mapping or for a generalised framework for regional mapping. Not suitable for analysis.

Dataset History

Information was compiled and digitised in generalised form from 1:250 000 scale hard copy maps. The individual CAD files were combined into seamless form and converted to Lambert Conformal Conic projection, standard parallels 29 degrees and 35 degrees S, central meridian 135 degrees E. Subsequently the information was converted to GIS format and re-projected to the state standard LCC projection.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) Topography - State Refence Map - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/b6f2d7af-7fbb-4bf5-9051-b725d51b270a.

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

This digital data set is a compilation of reference points representing surface-water features, ground-water levels, and topographic settings in Nevada that were used for the regional ground-water potential map by Bedinger and Harrill (2004). The regional ground-water- potential map was developed to assess potential interbasin flow in the Death Valley regional ground-water flow system (DVRFS), a 100,000-square-kilometer region of southern Nevada and California. To obtain an adequate network of control points, Bedinger and Harrill (2004) also used regional potential altitudes derived from springs and deep well data. A set of general guidelines was developed to relate regional ground-water potential to these more readily observed surface and near-surface ground-water levels and to hydrologic characteristics of ground-water basins in the DVRFS (see "Larger Work Citation", Appendix 1).

This dataset contains raw eye-tracking data collected during a study "Students’ strategies of familiarisation with a general-reference map of an unknown area" investigating how upper-secondary students familiarise themselves with a general-reference map of an unfamiliar area. The study involved 20 participants (aged 18–20), who were given 60 seconds to explore a specially prepared map using a screen-based eye tracker (SMI RED 250, 250 Hz).The dataset includes number of fixations as well as fixation in predefined Areas of Interest (AOIs), such as the map face, legend, hypsometric tints, and the graphic scale. The data were exported from SMI BeGaze and converted using the SMI2OGAMA converter for further processing. Format: txt.

These boundaries define the regions based on terrestrial and marine areas. These are intended to be used in by CA Nature to support activities related to Executive Order N-82-20. These include California's 30x30 effort, Climate Smart Land Strategies, and equitable access to open space. This layer is derived from the 4th California Climate Assessment regions, and enhanced using the California County Boundaries dataset (version 19.1) maintained by the California Department of Forestry and Fire Protection's Fire Resource Assessment Program, and the 3 Nautical Mile marine boundary for California sourced from the California Department of Fish and Wildlife.

A dataset of global land cover validation samples in 2015. In order to guarantee the confidence and objective of the validation samples, several existing reference datasets such as GLCNMO2008 training dataset, VIIRS reference dataset, STEP reference dataset, Global cropland reference data and so on, high resolution imagery in the Google earth and time-series NDVI,NDSI values of each related point are integrated to derive the global validation datasets. The dataset is provided in .shp format.