Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

This layer features special areas of interest (AOIs) that have been contributed to Esri Community Maps using the new Community Maps Editor app. The data that is accepted by Esri will be included in selected Esri basemaps, including our suite of Esri Vector Basemaps, and made available through this layer to export and use offline. Export DataThe contributed data is also available for contributors and other users to export (or extract) and re-use for their own purposes. Users can export the full layer from the ArcGIS Online item details page by clicking the Export Data button and selecting one of the supported formats (e.g. shapefile, or file geodatabase (FGDB)). User can extract selected layers for an area of interest by opening in Map Viewer, clicking the Analysis button, viewing the Manage Data tools, and using the Extract Data tool. To display this data with proper symbology and metadata in ArcGIS Pro, you can download and use this layer file.Data UsageThe data contributed through the Community Maps Editor app is primarily intended for use in the Esri Basemaps. Esri staff will periodically (e.g. weekly) review the contents of the contributed data and either accept or reject the data for use in the basemaps. Accepted features will be added to the Esri basemaps in a subsequent update and will remain in the app for the contributor or others to edit over time. Rejected features will be removed from the app.Esri Community Maps Contributors and other ArcGIS Online users can download accepted features from this layer for their internal use or map publishing, subject to the terms of use below.

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) 📍

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

Raw DEM and Soils data

Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)

DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.

DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.

Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)

Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).

Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).

ArcGIS Map Packages

Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).

Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.

Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).

Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019 Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA Department of Anthropology, Washington State University andrew.brown1234@gmail.com – Email andrewgillreathbrown.wordpress.com – Web

This map data product delivers high-precision, real-time, and historical GPS event records across North America. It is designed for organizations that require granular spatial data for applications such as mapping, movement tracking, retail analytics, and infrastructure planning.

Data Contents: Latitude & longitude coordinates Timestamp (epoch & human-readable date) Device ID (MAID: IDFA/GAID) Country code (ISO3) Horizontal accuracy (85% fill rate) Optional metadata: IP address, mobile carrier, device model

Access & Delivery: Available via API with custom polygon queries (up to 10,000 tiles) for targeted location insights. Data can be delivered hourly or daily in JSON, CSV, or Parquet formats, through AWS S3, Google Cloud Storage, or direct API access. Historical coverage extends back to September 2024, with 95% of events delivered within 3 days for near-real-time analysis.

Compliance & Flexibility: GDPR and CCPA compliant Credit-based query pricing for scalability Custom schema mapping and folder structure available

Applications: Map creation and enhancement POI visitation analytics Urban mobility and transit modeling Retail site selection and catchment area mapping Real estate and zoning analysis Geospatial risk and environmental planning

Recommendations for the suitable contents of the geospatial datasets presenting the distribution of languages including the benefits of each, and our solutions (selected in the case study) concerning the Uralic languages.

The Digital Topographic Map 1:50 000 (DTK50) is a 1:50 000 scale topographic map. The contents of the DTK50 are: Roads, paths, railways, waters, vegetation areas, borders, individual buildings (in industrial areas only), elevation lines, fonts, etc. The map contents are no longer fully represented due to the smaller scale. The graphics of the DTK50 are based on the signature catalogue SK50 of the AdV. The DTK50 is suitable as a basis for technical planning for areas with a larger extent, such as for districts. The retrievable map section per WMS request is limited to a maximum of 4,000x4,000 pixels. For more information on the DTK50 visit: http://www.ldbv.bayern.de/produkte/topo/digi.html

The documents included in this dataset provide information on:a) personal questions given to survey participants (DemographicsQuestionnaire.pdf)b) spatial questions given to participants (SpatialQuestions.pdf)c) the adapted SUS questionnaire (MapUsabilityScale.pdf)d) The dataset of collected participants responses, in the form of a zip archive (3D_printed_map.7z). e) a document with brief guidelines for conducting the survey (Guidelines.docx).f) Finally, the R script (experiment.R) to run the statistical analysis detailed in the paper and to generate Tables 1-4 and the contents of Figure 9 are also included. The R script needs calling the above-mentioned dataset of participants' responses (d), to run effectively.

The ArcGIS Online US Geological Survey (USGS) topographic map collection now contains over 177,000 historical quadrangle maps dating from 1882 to 2006. The USGS Historical Topographic Map Explorer app brings these maps to life through an interface that guides users through the steps for exploring the map collection:Find a location of interest.View the maps.Compare the maps.Download and share the maps or open them in ArcGIS Desktop (ArcGIS Pro or ArcMap) where places will appear in their correct geographic location. Save the maps in an ArcGIS Online web map.

Finding the maps of interest is simple. Users can see a footprint of the map in the map view before they decide to add it to the display, and thumbnails of the maps are shown in pop-ups on the timeline. The timeline also helps users find maps because they can zoom and pan, and maps at select scales can be turned on or off by using the legend boxes to the left of the timeline. Once maps have been added to the display, users can reorder them by dragging them. Users can also download maps as zipped GeoTIFF images. Users can also share the current state of the app through a hyperlink or social media. This ArcWatch article guides you through each of these steps: https://www.esri.com/esri-news/arcwatch/1014/envisioning-the-past.Once signed in, users can create a web map with the current map view and any maps they have selected. The web map will open in ArcGIS Online. The title of the web map will be the same as the top map on the side panel of the app. All historical maps that were selected in the app will appear in the Contents section of the web map with the earliest at the top and the latest at the bottom. Turning the historical maps on and off or setting the transparency on the layers allows users to compare the historical maps over time. Also, the web map can be opened in ArcGIS Desktop (ArcGIS Pro or ArcMap) and used for exploration or data capture.Users can find out more about the USGS topograhic map collection and the app by clicking on the information button at the upper right. This opens a pop-up with information about the maps and app. The pop-up includes a useful link to a USGS web page that provides access to documents with keys explaining the symbols on historic and current USGS topographic maps. The pop-up also has a link to send Esri questions or comments about the map collection or the app.We have shared the updated app on GitHub, so users can download it and configure it to work with their own map collections.

The Map of the Czech Republic 1:1,000,000 (MČR 1M) relates with the contents of the map of the Czech Republic 1:500,000 and is conceived as a general geographic map. It shows the entire territory of the Czech Republic on a single map sheet. The MČR 1M contains planimetry, altimetry, geographic coordinate grid, map lettering and the map legend. Planimetry consists of settlements, transportation (highways, roads, railways), hydrography (significant water courses and reservoirs), state and regional boundaries, vegetation and land surface (forests). Subject of the altimetry are elevation points. Map lettering and marginal notes consist of standard geographic names, map name and its scale with imprint data and the graphic scale, textual part of the legend geographic coordinates on the frame edges. The subjects of the map contents are coherently displayed also on the adjacent areas of neighbour states.

This data release presents geologic map data for the surficial geology of the Aztec 1-degree by 2-degree quadrangle. The map area lies within two physiographic provinces of Fenneman (1928): the Southern Rocky Mountains province, and the Colorado Plateau province, Navajo section. Geologic mapping is mostly compiled from published geologic map data sources ranging from 1:24,000 to 1:250,000 scale, with limited new interpretive contributions. Gaps in map compilation are related to a lack of published geologic mapping at the time of compilation, and not necessarily a lack of surficial deposits. Much of the geology incorporated from published geologic maps is adjusted based on digital elevation model and natural-color image data sources to improve spatial resolution of the data. Spatial adjustments and new interpretations also eliminate mismatches at source map boundaries. This data set represents only the surficial geology, defined as generally unconsolidated to moderately consolidated sedimentary deposits that are Quaternary or partly Quaternary in age, and faults that have documented Quaternary offset. Bedrock and sedimentary material directly deposited as a result of volcanic activity are not included in this database, nor are faults that are not known to have moved during the Quaternary. Map units in the Aztec quadrangle include alluvium, glacial, eolian, mass-wasting, colluvium, and alluvium/colluvium deposit types. Alluvium map units, present throughout the map area, range in age from Quaternary-Tertiary to Holocene and form stream-channel, floodplain, terrace, alluvial-fan, and pediment deposits. Along glaciated drainages terraces are commonly made up of glacial outwash. Glacial map units are concentrated in the northeast corner of the map area and are mostly undifferentiated till deposited in mountain valleys during Pleistocene glaciations. Eolian map units are mostly middle Pleistocene to Holocene eolian sand deposits forming sand sheets and dunes. Mass-wasting map units are concentrated in the eastern part of the map area, and include deposits formed primarily by slide, slump, earthflow, and rock-fall processes. Colluvium and alluvium/colluvium map units form hillslope and undifferentiated valley floor/hillslope deposits, respectively. The detail of geologic mapping varies from about 1:50,000- to 1:250,000-scale depending on the scale of published geologic maps available at the time of compilation, and for new mapping, the resolution of geologic features on available basemap data. Map units are organized within geologic provinces as described by the Seamless Integrated Geologic Mapping (SIGMa) (Turner and others, 2022) extension to the Geologic Map Schema (GeMS) (USGS, 2020). For this data release, first order geologic provinces are the physiographic provinces of Fenneman (1928), which reflect the major geomorphological setting affecting depositional processes. Second order provinces are physiographic sections of Fenneman (1928) if present. Third and fourth order provinces are defined by deposit type. Attributes derived from published source maps are recorded in the map unit polygons to preserve detail and allow database users the flexibility to create derivative map units. Map units constructed by the authors are based on geologic province, general deposit type and generalized groupings of minimum and maximum age to create a number of units typical for geologic maps of this scale. Polygons representing map units were assigned a host of attributes to make that geology easily searchable. Each polygon contains a general depositional process (‘DepositGeneral’) as well as three fields that describe more detailed depositional processes responsible for some deposition in that polygon (‘LocalGeneticType1’ – ‘LocalGeneticType3’). Three fields describe the materials that make up the deposit (‘LocalMaterial1’ – ‘LocalMaterial3’) and the minimum and maximum chronostratigraphic age of a deposit is stored in the ‘LocalAgeMin’ and ‘LocalAgeMax’ fields, respectively. Where a polygon is associated with a prominent landform or a formal stratigraphic name the ‘LocalLandform’ and ‘LocalStratName’ fields are populated. The field ‘LocalThickness’ provides a textual summary of how thick a source publication described a deposit to be. Where three fields are used to describe the contents of a deposit, we attempt to place descriptors in a relative ordering such that the first field is most prominent, however for remotely interpreted deposits and some sources that provide generalized descriptions this was not possible. Values within these searchable fields are generally taken directly from source maps, however we do perform some conservative adjustments of values based on observations from the landscape and/or adjacent source maps. Where new features were interpreted from remote observations, we derive polygon attributes based on a conservative correlation to neighboring maps. Detail provided at the polygon level is simplified into a map unit by matching its values to the DescriptionOfMapUnits_Surficial table. Specifically, we construct map units within each province based on values of ‘DepositGeneral’ and a set of chronostratigraphic age bins that attempt to capture important aspects of Quaternary landscape evolution. Polygons are assigned to the mapunit with a corresponding ‘DepositGeneral’ and the narrowest chronostratigraphic age bin that entirely contains the ‘LocalAgeMin’ and ‘LocalAgeMax’ values of that polygon. Therefore, users may notice some mismatch between the age range of a polygon and the age range of the assigned map unit, where ‘LocalAgeMin’ and ‘LocalAgeMax’ (e.g., Holocene – Holocene) may define a shorter temporal range than suggested by the map unit (e.g., Holocene – late Pleistocene). This apparent discrepancy allows for detailed information to be preserved in the polygons, while also allowing for an integrated suite of map units that facilitate visualization over a large region.

Contents: This is an ArcGIS Pro zip file that you can download and use for creating map books based on United States National Grid (USNG). It contains a geodatabase, layouts, and tasks designed to teach you how to create a basic map book.Version 1.0.0 Uploaded on May 24th and created with ArcGIS Pro 2.1.3 - Please see the README below before getting started!Updated to 1.1.0 on August 20thUpdated to 1.2.0 on September 7thUpdated to 2.0.0 on October 12thUpdate to 2.1.0 on December 29thBack to 1.2.0 due to breaking changes in the templateBack to 1.0.0 due to breaking changes in the template as of June 11th 2019Updated to 2.1.1 on October 8th 2019Audience: GIS Professionals and new users of ArcGIS Pro who support Public Safety agencies with map books. If you are looking for apps that can be used by any public safety professional, see the USNG Lookup Viewer.Purpose: To teach you how to make a map book with critical infrastructure and a basemap, based on USNG. You NEED to follow the steps in the task and not try to take shortcuts the first time you use this task in order to receive the full benefits. Background: This ArcGIS Pro template is meant to be a starting point for your map book projects and is based on best practices by the USNG National Implementation Center (TUNIC) at Delta State University and is hosted by the NAPSG Foundation. This does not replace previous templates created in ArcMap, but is a new experimental approach to making map books. We will continue to refine this template and work with other organizations to make improvements over time. So please send us your feedback admin@publicsafetygis.org and comments below. Instructions: Download the zip file by clicking on the thumbnail or the Download button.Unzip the file to an appropriate location on your computer (C:\Users\YourUsername\Documents\ArcGIS\Projects is a common location for ArcGIS Pro Projects).Open the USNG Map book Project File (APRX).If the Task is not already open by default, navigate to Catalog > Tasks > and open 'Create a US National Grid Map Book' Follow the instructions! This task will have some automated processes and models that run in the background but you should pay close attention to the instructions so you also learn all of the steps. This will allow you to innovate and customize the template for your own use.FAQsWhat is US National Grid? The US National Grid (USNG) is a point and area reference system that provides for actionable location information in a uniform format. Its use helps achieve consistent situational awareness across all levels of government, disciplines, and threats & hazards – regardless of your role in an incident.One of the key resources NAPSG makes available to support emergency responders is a basic USNG situational awareness application. See the NAPSG Foundation and USNG Center websites for more information.What is an ArcGIS Pro Task? A task is a set of preconfigured steps that guide you and others through a workflow or business process. A task can be used to implement a best-practice workflow, improve the efficiency of a workflow, or create a series of interactive tutorial steps. See "What is a Task?" for more information.Do I need to be proficient in ArcGIS Pro to use this template? We feel that this is a good starting point if you have already taken the ArcGIS Pro QuickStart Tutorials. While the task will automate many steps, you will want to get comfortable with the map layouts and other new features in ArcGIS Pro.Is this template free? This resources is provided at no-cost, but also with no guarantees of quality assurance or support at this time. Can't I just use ArcMap? Ok - here you go. USNG 1:24K Map Template for ArcMapKnown Limitations and BugsZoom To: It appears there may be a bug or limitation with automatically zooming the map to the proper extent, so get comfortable with navigation or zoom to feature via the attribute table.FGDC Compliance: We are seeking feedback from experts in the field to make sure that this meets minimum requirements. At this point in time we do not claim to have any official endorsement of standardization. File Size: Highly detailed basemaps can really add up and contribute to your overall file size, especially over a large area / many pages. Consider making a simple "Basemap" of street centerlines and building footprints.We will do the best we can to address limitations and are very open to feedback!

The map of the Czech Republic 1:1 000 000 (MCR 1M) is a content link to the Map of the Czech Republic 1:500 000. It displays the entire territory of the Czech Republic on one map sheet, i.e. an area of 78 886 km². The dimensions of the paper are 55 x 37 cm, the dimensions of the map field are 49,5 x 31 cm. The map is derived from the map of the Czech Republic 1:500 000. It contains location, altimeter, geographical network, description and explanatory notes to the map. The subject of the location is settlements, roads (motorways, roads, railways), water (significant watercourses and reservoirs), borders (state and regional), vegetation and soil surface (forests). The height points are the subject of the altimeter. The description includes standardised geographical nomenclature (names of settlements, waters and orographic units), the name and scale of the map with printed data and the graphical scale, the text part of the explanatory notes and the frame data (geographical coordinates). The objects of the map’s contents are also continuously depicted on adjacent parts of neighbouring states.

Dataset

This dataset was created by Takashi Someya

Released under Apache 2.0

Contents

The base map consists of the city’s basic geographical information and has the highest level of detail used in the urban development area as a whole. The map is also used outside the city’s activities in areas such as planning and planning. By providing the base map as open data, the city opens up for wider use and the possibility of new innovative applications.ContentBasic map includes:

BuildingsCommunicationMarket useAddressesRegistermap (property limits and rights, etc.) The information in the register map has no legal effect and may be poorly accurate. In case of exact information requirements, verification should be carried out on the basis of decision documents.AtkomstBaskartan is downloaded via http://kartor.helsingborg.se/oppnageodata/baskarta.phpFormat and object modelThe map is delivered as a zip file containing one GeoJSON file per object type. Coordinate system is SWEREF99 13 30. The files are a direct export from the Helsingborg City Planning Administration’s database and are named as follows:

Object types sometimes have attributes that come from domains. Then a value can be represented in a digit instead of saving a string over and over again. During export we have exploded the domains with the suffix “_resolved” so that they can still be seen in plain text.“PURPOSE”:10, “PURPOSE_resolved”:“Småhus — detached”

The tables in the theme “Registration map” have a specific title in two letters. Exempel:Registerkarta AQIn order to understand the contents of those tables, it may help to examine the attribute “dep” where a more readable description is given. Complete documentation on the registry map is currently missing. However, Lantmäteriet provides similar products where table names exist. Please see exempel:https://www.lantmateriet.se/globalassets/kartor-oc...MetadataEn mapping to translate table names into English can be found here. Structure:[{“Geo object class”:“Facility, point”, “Geo object class English”:“MAPCONSTRUCTIONP”},... ]

Refresh rate The zip file is updated weekly, the night between Saturday and Sunday. In the zip file there is a folder metadata. In it is readme.txt which contains a date stamp that tells you when the actual export was made.

FAQ base map

How can I look at the map without any specific program? Download the zip file and unpack it. Search “GeoJSON viewer” in your browser. For example, http://www.mapshaper.org/. Drag in and drop a GeoJSON file to view it.

Can I use the base map in my CAD system?Plugin/app is available to Autodesk. https://apps.autodesk.com/ACD/en/Detail/Index?id=5...

Can I use the base map in my GIS? QGIS has good support for GeoJSON. ArcMap requires Data Interopability add-on. FME can read and convert.

Can I convert GeoJSON to shape? Several free services are available to convert to shape. Among others, http://www.mapshaper.org/.

Terasaki Hart et al. 2025, "Global phenology ..." This archive contains all data and results generated by this study, as well as some input data derived from publicly accessible resources. All contents are covered by the CC-BY-NC-SA license (in short: please use, but with attribution, without commercial gain, and only if you share too). Contents NOTE: see Filename abbreviation glossary at bottom to decipher shorthand in filenames.

./rasters/:

./main/: main rasters resulting from harmonic regressions and asynchrony calculations, including:

*_coeffs.tif: harmonic regression coefficients (5 coefficient bands: intercept, sine and cosine of annual cyclical time in radians, then sine and cosine of semiannual cyclical time in radians) (global, 0.05 degree, EPSG:4326) *_harm_reg_R2.tif: harmonic regression R^2s (1 band) (global, 0.05 degree, EPSG:4326) *_asynch_*km.tif: asynchrony results (including maps in Fig. 2A and Extended Data Fig. 7B) for all three neighborhood radii (50, 100, and 150 km) (each raster has 4 bands: asynchrony value, then R^2 value, P-value, and n of the OLS regression from which the asynchrony value (i.e., slope) was derived) (NOTE: the _STRICT qualifier in NIRv_STRICT and SIF_STRICT indicates the additional land cover filtering that masked all agricultural land before calculating LSP asynchrony) (global, 0.05 degree, EPSG:4326)

./masks/: data for all 5 masking maps presented in Extended Data Fig. 1B (lcMask_DEFAULT.tif includes only the red masked areas in the land cover map in the figure, whereas lcMask_STRICT.tif includes also the black masked (agricultural) areas) (global, 0.05 degree, EPSG:4326) ./drivers/: rasters specific to the LSP asynchrony drivers analysis, including:

hansen_lulcc_pct_neigh_mean.tif: neighborhood (100 km radius) mean of land use and cover change, derived from GLAD Lab Hansen et al. 2019 global harmonized land use and land cover change (global, 0.05 degree, EPSG:4326) MODIS_fire_freq_mean.tif: neigborhood mean (100 km radius) burn frequency, derived from MODIS Burned Area (MCD64A1.061) (global, 0.05 degree, EPSG:4326) MODIS_IGBP_veg_entropy.tif: neighborhood (100 km radius) entropy of vegetation structure, derived from MODIS annual land cover(MCD12C1.061) (global, 0.05 degree, EPSG:4326) err_map_*COORDS_*_*km.tif: mapped prediction errors from all LSP asynchrony drivers models ('yCOORDS' or 'nCOORDS' = whether geographic coordinates were included or excluded as covariates; 'NIRv' of 'SIF' = whether NIRv- or SIF-based LSP asynchrony was modeled; '50km','100km', or '150km' = whether a 50, 100, or 150 km neighborhood radius was used) (global, 0.05 degree, EPSG:4326) SHAP_map_*COORDS_*_*_*km.tif: maps of SHAP values for all 11 covariates (if 'yCOORDS', in which case longitude ('x') and latitude ('y') are included as covariates) or 9 covariates (if 'nCOORDS') for all LSP asynchrony drivers models ('NIRv' of 'SIF' = NIRv- or SIF-based LSP asynchrony; '50km','100km', or '150km' = 50, 100, 150 km neighborhood radius used) (global, 0.05 degree, EPSG:4326) SHAP_predom_top.tif: map of normalized-difference of SHAP values of the top two LSP asynchrony drivers from the main (NIRv-based, 100 km neighborhood, with geographic coordinates included) drivers model (asynchrony of minimum temperature and precipitation seasonality) (plotted in Fig. 2B) (global, 0.05 degree, EPSG:4326) SHAP_predom_all.tif: map of the index of the predominant driver of LSP asynchrony at each pixel, according to SHAP values for the main (NIRv-based, 100 km neighborhood, with geographic coordinates included) model (0: ppt.asy, 1: tmp.min.asy, 2: brn.frq.mea, 3: tmp.max.masy, 4: def.asy, 5: cld.asy, 6: veg.ent, 7: vrm.med, 8: luc.pct.mea) (plotted in Extended Data Fig. 9C) (global, 0.05 degree, EPSG:4326)

./etc/: other rasters generated as part of analysis, including:

NIRv_4_EOFs_sqrt_coswts_standts.tif: 4 EOFs calculated from the global map of fitted annual average NIRv LSP phenocycles (calculated using square-root-cosine weighting on latitude and using standardized phenocycle time series) (global, 0.05 degree,EPSG:4326) NIRv_4_EOFs_sqrt_coswts_standts.tif: the same EOFs as the previously listed filed, but transformed for RGB visual display in Fig. 1 (scaled to the [0, 1] interval, 'folded' over the ITCZ using a latitudinally varying weighted sum of EOF and 1-EOF, and reprojected to the Greenwich-centered Equal Earth projection) (global, EPSG:8857) NIRv_LSP_modality_EPSG8857.tif: LSP modality (from perfect annual to perfect semiannual), plotted in Extended Data Fig. 6A (global, EPSG:8857) NIRv_SIF_phen_R2s.tif: R^2s between all NIRv and SIF LSP phenocycles, plotted in Extended Data Fig. 6B (global, 0.05 degree, EPSG:4326)

./tables/:

./LSP_fitting_examples/: GeoJSONs of raw data extracted at the example sites plotted in Extended Data Fig. 2A-D ./itcz/: Shapefiles of the boreal summer and winter average ITCZ lines, digitized from Zhisheng et al. and used to calculate the annual-mean ITCZ map that was used in visualization in Fig. 1 and Extended Data Fig. 4 ./isoclim/: Shapefile of results of the isoclimatic phenological asynchrony analysis (Fig. 3) ./phen/inat/: data and results for iNaturalist flowering phenology analysis, including:

TID_*.json: GeoJSONs of the flowering observation data used to plot the two examples shown in Fig. 4A all_inat_plant_phen_taxa.csv: all iNat taxa that had available plant phenology data, as of date of download inat_hex_results.json: GeoJSON of hextile-summarized results of flowering modality (Extended Data Fig. 10) iNat_MMRR_results_ALL.csv: unformatted version of SUPP_TAB_4_iNat_MMRR_results.csv, containing results for all tested taxa (Extended Data Table 4 shows only taxa with significant results for the LSP-distance coefficient)

./phen/coffea_arabica/: four CSVs of sampling points digitized within polygons shown in the Bacca et al. Fig. 2 version of the Colombian coffee harvest map, for analysis of harvest asynchrony analysis (analyzed in Fig. 4C) ./gen/rhinella_granulosa/: tables of geographic sampling locations and pairwise genetic distances (derived from Thomé et al. data), for analysis of genetic isolation by phenological asynchrony in Rhinella granulosa (Fig. 4B) ./gen/xiphorhnychus_fuscus/: tables of geographic sampling locations and pairwise genetic distances (derived from Quintero et al. data), for analysis of genetic isolation by phenological asynchrony in Xiphorhynchus fuscus (Fig. 4B) ./SI/SUPP_TAB_*: tables from the Supplementary Information ./ED/TAB_*: tables containing content that is visualized in the Extended Data figures ./etc/: additional tabular results that are reported but are not presented in full within the paper, including:

EXTRA_TAB_landgen_MMRR_results.csv: a table of the full landscape genetic MMRR results EXTRA_TAB_drivers_model_tuning_results_subset_frac_*_NIRv_100km.csv: tables of the hyperparameter tuning results for the phenological asynchrony drivers model, for both of the subsetting fractions of the full raster dataset that were tested (0.05, and 0.005); tuning was done using a 100 km neighborhood and the NIRv-based LSP asynchrony map

./figures/:

./main/FIG_*: the main figures ./ED/ED_FIG_*: the Extended Data figures

./videos/:

./SI/SUPP_VID_1_normalized_NIRv_LSP_300dpi.mp4: Supplementary Information Video 1, animating the average annual LSP phenocycles for all global pixels

./logs/: files logging additional metrics and results that are printed to STDOUT by some analyses, including:

NIRv_SIF_LSP_R2_median_stats_result.log.png: NIRv-SIF LSP correlation results (Extended Data Fig. 6B) cheatgrass_stats_results.log.png: the cheatgrass analysis (Fig. 2B) ./drivers/: short summary info for all phenological asynchrony random forest models (Extended Data Fig. 9A) isoclim_stats_results.log.png: isoclimatic phenology asynchrony analysis (Fig. 3) inat_phen_MMRR.log: iNat phenology MMRR analysis (Fig. 4A) plot_flowphen_landgen_cafe_results.log: log produced by the script that runs all analyses plotted in Fig. 4

Filename abbreviation glossary

*: 'wildcard' (indicates that multiple files exist with different filename patterns in this position) NIRv: NIRv-derived LSP SIF: SIF-derived LSP tmmn and tmmx: minimum and maximum temperature tmp.min.asy and tmp.max.asy: asynchrony in seasonality of minimum and maximum temperature pr: precipitation ppt.asy: asynchrony in seasonality of precipitation def: climate water deficit def.asy: asynchrony in seasonality of climate water deficit cloud: fractional cloud cover cld.asy: asynchrony in seasonality of fractional cloud cover brn.frq.mea: 100 km neighborhood mean burn frequency veg.ent: 100 km neighborhood entropy in vegetation structure vrm.med: 100 km median vector ruggedness metric luc.pct.mea: 100 km neighborhood mean percent land use and land cover change EOF: empirical orthogonal functions

Questions? Please reach out! drew DOT terasaki DOT hart AT gmail DOT com

This map presents multiple levels of land cover classifications for the continental United States based on Landsat TM 2001 satellite imagery. The map combines three levels of land cover from the Gap Analysis Program (GAP). The three levels are hierarchical and allow you to interactively select the level of detail needed for a project. You can switch between the levels in the map contents.Level 1: contains 8 classes generalized to the level of vegetative physiognomy i.e. grassland, shrubland, forestLevel 2: contains 43 classes, and incorporates information on elevation and climateLevel 3: contains the full 590 Ecological Systems classification The Gap Analysis Program (GAP) national land cover viewer displays data on the vegetation and land use patterns of the continental United States. It combines land cover data generated for the Southwest Regional Gap Analysis project completed in 2004, the Southeast Regional Gap Analysis Project completed in 2007, the Northwest Regional Gap project, and the updated California Gap project completed in 2009. For areas of the country without an Ecological System level Gap project, data created by the Landfire Project was used. All these projects use consistent base satellite imagery, the same classification systems and similar mapping methodology allowing for the creation of a seamless national land cover map. For more information or to download the data, please visit http://www.gap.uidaho.edu/landcoverviewer.html.

Ephedra sinica Stapf. is a shrubby plant widely used in traditional Chinese medicine due to its high level of medicinal value, thus, it is in high demand. Ephedrine (E) and pseudoephedrine (PE) are key medicinal components and quality indicators for E. sinica. These two ephedrine-type alkaloids are basic elements that exert the medicinal effect of E. sinica. Recently, indiscriminate destruction and grassland desertification have caused the quantity and quality of these pharmacological plants to degenerate. Predicting potentially suitable habitat for high-quality E. sinica is essential for its future conservation and domestication. In this study, MaxEnt software was utilized to map suitable habitats for E. sinica in Inner Mongolia based on occurrence data and a set of variables related to climate, soil, topography and human impact. The model parametrization was optimized by evaluating alternative combinations of feature classes and values of the regularization multiplier. Second, a geospatial quality model was fitted to relate E and PE contents to the same environmental variables and to predict their spatial patterns across the study area. Outputs from the two models were finally coupled to map areas predicted to have both suitable conditions for E. sinica and high alkaloid content. Our results indicate that E. sinica with high-quality E content was mainly distributed in the Horqin, Ulan Butong and Wulanchabu grasslands. E. sinica with high-quality PE content was primarily found in the Ordos, Wulanchabu and Ulan Butong grasslands. This study provides scientific information for the protection and sustainable utilization of E. sinica. It can also help to control and prevent desertification in Inner Mongolia.

Full code and dataset for the NBP 2202 map website. Data were collected during Jan-Feb 2022 in the Amundsen sea from the Nathaniel B. Palmer. This is a Python-flask app which displays data in a javascript leaflet map. The contents of this dataset should be all you need to host the website yourself, for local viewing or to make publicly available

This upload is a copy of the GitHub repo taken on 24/03/22 with additional satellite data that was too large for git.

The github repo can be found here https://github.com/callumrollo/itgc-2022-map/

The website is currently maintained at https://nbp2202map.com/

All data are publicly available. Locations and information displayed in the map are for convenience purposes only and are not authoritative. Contact the PIS of the International Thwaites Glacier Collaboration (ITGC) for full datasets. This website is the author's personal work and does not reflect the views of the ITGC group. The author has no official affiliation with ITGC.

Dataset

This dataset was created by Kawsar Hossain

Released under Apache 2.0

Contents