The fly section of the SPIN Story Map Walking Tour Story Map

This is a PUBLIC Story Map Journal for sharing publicly available maps, apps, and resources.

PurposeThis job aid will lead the GIS analyst through the process of manually creating an incident map journal and how to create additional pages for the journal. This process should be used at the beginning of an incident and then the journal should be maintained to assure it remains viable. The incident map journal serves as a curated center to place maps, apps, and dashboards relevant to the incident.

This job aid assumes a working knowledge of how to create maps, apps, and dashboards on ArcGIS Online. For a tutorial, go to the Create apps from maps - ArcGIS Tutorial.Example workflow for the Geo-Enabled Plans Session at InSPIRE. Job Aid developed by FEMA GIS to enable GIS analysts to rapidly spin-up a standardized incident journal.

Background for the study Systematic reviews are an important type of evidence to inform clinical practice guidelines in health care. They synthesise all available primary research to provide a more reliable estimate of effectiveness and risk factors among others. Next to the transparent reporting of each phase of the review, the literature search to retrieve all evidence is a crucial component of a systematic review. When the search is of poor quality, the process might not identify all available data for analysis. The following phases such as screening, data extraction, assessing study quality and synthesising data depend on identifying relevant studies. As a result of a poorly executed search, the systematic review might be biased, might lack information and might misinform its users. There are several guiding documents to aid researchers to make systematic reviews of good quality, such as methodological handbooks for conducting the research, reporting guidelines for writing a systematic review and checklists for appraising the quality of systematic reviews. Reporting guidelines There are several checklists for evaluating the methodological quality of systematic reviews, such as Risk of Bias Assessment Tool for Systematic Reviews (ROBIS), A MeaSurement Tool to Assess systematic Reviews 2 (AMSTAR2) and Critical Appraisal Skills Programme (CASP) Checklists. These consist of several items for assessing the methodological aspects of a systematic review, including the literature search. Even though there are plenty of resources on how to conduct, report or assess systematic reviews, papers with poor quality searches are still being published. The low quality relates to both the execution (Faggion et al., 2013; Franco et al., 2018; Koffel & Rethlefsen, 2016; Mullins et al., 2014; Opheim et al., 2019; Salvador-Olivan et al., 2019; Sampson & McGowan, 2006; Yoshii et al., 2009) and reporting of the search (Faggion et al., 2013; Faggion et al., 2018; Koffel & Rethlefsen, 2016; Mullins et al., 2014). Other guiding documents document searches in general, such as publishing organisations’ ethical guidelines, e.g. the International Committee of Medical Journal Editors (ICMJE) and the Committee on Publication Ethics (COPE) guidelines. These committees encourage journals to instruct authors to follow established guidelines for research, and to state how research data is located, selected and analysed. Furthermore, research methods should be described so that it is possible to reproduce the results (International Committee of Medical Journal Editors, 2022). In addition to the general reporting guidelines, researchers should follow the author instructions as provided by the journals. There are several studies investigating the uptake of reporting guidelines (Page & Moher, 2017) and the expectations to report statistics in journals (Blann & Nation, 2009; Giofre et al., 2017). There are two surveys investigating the role of author instructions on reporting of systematic searches. Biocic et al. (2019) investigated, among other things, the systematic review search methods requirements listed in the author instructions from 26 journals. 46% of the journals mentioned reporting guidelines, and 19% gave additional instructions on reporting of search methods. Unfortunately, the details of these instructions have not been published. Goldberg et al. (2022) assessed author instructions in a sample of publications from one US university. Their sample consisted of 145 unique journals, of which they found 60% to be addressing reporting guidelines. Only 9% of author instructions mentioned searching more than two databases and 5% recommended working with a librarian when doing a systematic review. As the sample of journals was selected based on publications from only one university, the results may lack transferability to a more general medical and health context.

In this study, we want to further explore the role of academic journals – in the field of medicine and health – in guiding authors to report systematic review searches. We will do this by reviewing the information in the author instructions from a bigger and wider sample than the two studies previously mentioned. We also want to analyse in more detail the instructions on the information retrieval process, which is different to the research from Goldberg et al. (2022) and Biocic et al. (2019).

Aim of the study The aim of this study is to map to what degree the author instructions of medical and health related journals encourage reporting of systematic review searches. We aim to answer the following research questions: 1. Do journals’ author instructions include a section on reporting literature searches for systematic reviews? Are these instructions based on internationally accepted guidelines (e.g. Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement or Methodological Expectations of Cochrane Intervention Reviews (MECIR) standards) or in-house guidelines? Is it recommended or required to follow the guidelines when submitting a manuscript? 2. Do the author instructions include a procedure used by journal editors to verify if the prescribed reporting guidelines were followed (e.g. submitting a completed PRISMA checklist upon submission of the manuscript)? 3. Do the author instructions recommend involving an information specialist or medical librarian when creating the search strategy, or do they mention consulting one during peer review of systematic reviews? 4. Do the journals’ author instructions describe a procedure for registering a systematic review protocol? If so, is it recommended or mandatory to register a protocol?

This map was created for the US National Science Foundation Land-Atmosphere-Ice Interactions (LAII) Flux Study and the Arctic Transitions in the Land-Atmosphere System (ATLAS) Study (OPP-9318530 and OPP-9415554). The map covers all of northern Alaska, from the Brooks Range divide to the coast. It is a raster (tif) map, with 50 m pixels, and 9 land cover categories. It is based on an unsupervised classification of a Landsat Multispectral Scanner (MSS) composite created by the National Mapping Division, U.S. Geological Survey EROS data center, Anchorage, Alaska. Geobotanical maps and earlier Landsat-derived maps of the region were used to interpret the spectral classes. References Muller, S. V., A. E. Racoviteanu, and D. A. Walker. 1999. Landsat MSS-derived land-cover map of northern Alaska: Extrapolation methods and a comparison with photo-interpreted and AVHRR-derived maps. International Journal of Remote Sensing 20:2921-2946.

This article introduces work in progress to develop a new, open biomedical map of science (OBMS) using the PubMed citation database. The new science map represents bimodal network relationships between journals and medical subject heading (MeSH) descriptors, based on a journal's articles indexed in the MEDLINE component of PubMed. We review the current efforts to use PubMed data in science of science studies and science mapping. As part of the development process, we compare the journals indexed in PubMed with journals included in the 2011 UCSD map of science to establish a baseline of disciplinary coverage of PubMed for the period 2009–2019. Journal article frequency is analyzed to establish the minimum number of citations required by a journal for inclusion in a map of science. A prototype OBMS is presented, and we discuss the strengths and weaknesses of the OBMS, as well as the next steps for using and productizing this new open map for general and free usage.

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

Questionnaire for CaGIS Journal - Impact of the mini-map on interpretations of spatial situations in virtual geographical spaces of video game

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Welcome to the City of Oshawa's Open Data Catalogue Pilot project. The Catalogue allows you to explore and download open data, discover and build apps, and engage to solve important local issues. You can search for open datasets above, explore Open Data categories below, or view the list of all datasets. [City of Oshawa]

The User Guide V2.4 of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: L2HNet. A more detailed description of the data can be found in the paper. More related work can be found at my homepage.

Click to check all the data versions and download the data (点击查看/下载所有数据版本)

NOTE: If you have any data needs, questions, or technical issues, contact us at ashelee@whu.edu.cn (Zhuohong Li, 李卓鸿).

The land-cover mapping method with Python code is open-access at Code link. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (Paper link).

我们的最新制图算法被计算机视觉顶会CVPR2024接收(Paper link),代码开源在:Code link,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。

Citation format of the paper:
Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023.

Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, ISPRS Journal of Photogrammetry and Remote Sensing. 192, pp.244-267, 2022.

BibTex format of the paper:

@article{li2023sinolc,
 title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data},
 author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan},
 journal={Earth System Science Data},
 volume={15},
 number={11},
 pages={4749--4780},
 year={2023},
 publisher={Copernicus Publications G{\"o}ttingen, Germany}
}

@article{li2022breaking,
 title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels},
 author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei},
 journal={ISPRS Journal of Photogrammetry and Remote Sensing},
 volume={192},
 pages={244--267},
 year={2022},
 publisher={Elsevier}
}

This link provides information and additional metadata related to the USGS National Seismic Hazard Maps. A direct shapefile download is available at https://www.sciencebase.gov/catalog/item/5db9be62e4b06957974eb5caBackground on Hazard Explorer Tool:The Hazard Explorer Tool is a web mapping application available in FEMA's Preparedness Toolkit that allows exercise planners to identify hazards that exist in their community, where their population is most vulnerable, and where their critical infrastructure/key resources are at risk.The Hazard Explorer Tool was developed under the National Exercise Program, which serves as the principal mechanism for examining the preparedness and readiness of the United States across the entire homeland security and management exercise. Communities design, coordinate, conduct, and evaluate exercises across the US as a part of their preparedness efforts.The Map Journal serves as a tool to help you identify and evaluate potential exercise scenario locations, hazard exposure, and other risk-related factors to support exercise planning. In this tool, you will identify:Which hazards exist near your location;Where your population is most vulnerable; andWhat infrastructure and resources would be most impacted in your selected scenario location.The final output of this tool is a basic PDF map of your selected scenario location, as well as links to data sources that you can share with your GIS staff to conduct more in-depth analysis for use in planning and conducting your exercise.For more information on the Hazard Explorer Tool, please visit: https://preptoolkit.fema.gov/web/hazard-explorer/hazard-explorer-tool

Mapped polygons at 1:7.5 million scale contain many vegetation types. The map portrays the zonal vegetation within each mapped polygon. Zonal sites are areas where the vegetation develops under the prevailing climate, uninfluenced by extremes of soil moisture, snow, soil chemistry, or disturbance, and are generally flat or gently sloping, moderately drained sites, with fine-grained soils (Vysotsky 1927). Large areas of azonal vegetation that are dependent on specific soil or hydrological conditions, such as mountain ranges and large wetlands, were also mapped. The legend contains five broad physiognomic categories: B — barrens, G — graminoid-dominated tundras, P — prostrate-shrub-dominated tundras, S — erect-shrub-dominated tundras, and W — wetlands. These are subdivided into 15 vegetation mapping units with numeric codes added to the alphabetic codes. The mapping units are named according to dominant plant functional types except in the mountains where complexes of vegetation are named according to the dominant bedrock (carbonate and noncarbonate mountain complexes). The coloring scheme of the map is suggestive of the physiognomy of the vegetation. The plant functional types are based on a variety of criteria including growth form (e.g., graminoids, shrubs), size (e.g., dwarf and low shrubs), and taxonomical status (e.g., sedges, rushes, grasses). The legend takes into special consideration the stature of woody shrubs, which is a major diagnostic feature of zonal vegetation in the Arctic (Edlund and Alt 1989, Walker et al. 2002, Yurtsev 1994). Very steep bioclimate gradients occur in mountains, so these areas are mapped as complexes of elevation belts. Mountainous areas of the map are shown with hachures; the background color indicates the nature of the bedrock, and the color of the hachures indicate the bioclimate subzone at the base of the mountains. Back to Alaska Arctic Tundra Vegetation Map (Raynolds et al. 2006) Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes AVHRR NDVI , Bioclimate Subzone, Elevation, False Color-Infrared CIR, Floristic Province, Lake Cover, Landscape, Substrate Chemistry, Vegetation References Edlund, S. A. and B. T. Alt. 1989. Regional congruence of vegetation and summer climate patterns in the Queen Elizabeth Islands, Northwest Territories, Canada. Arctic 42:3-23. Vysotsky, G.N. 1927. Theses on soil and moisture (conspectus and terminology). Lesovedenie (eds.) Sbornik Lesnogo Obschestva v Leningrade. Leningrad. pp. 67-79 (In Russian). Walker, D. A., W. A. Gould, and M. K. Raynolds. 2002. The Circumpolar Arctic Vegetation Map: Environmental controls, AVHRR-derived base maps, and integrated mapping procedures. International Journal of Remote Sensing 23:2551-2570. Yurtsev, B. A. 1994. Floristic divisions of the Arctic. Journal of Vegetation Science 5:765-776.

Map packages for use in ArcGIS Pro or ArcMap containing three-tiered geomorphological data and geographical datasets such as rivers and hillshading. Datasets were used to generate figures for publication: Hierarchical geomorphological mapping in mountainous areas. 2021. Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Journal of Maps. All data is in MGI Austria GK West projected coordinate system (EPSG: 31254) and was clipped to the study area.

This dataset includes global soil salinity layers for the years 1986, 1992, 2000, 2002, 2005, 2009 and 2016. The maps were generated with a random forest classifier that was trained using seven soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. The validation accuracy of the resulting maps was in the range of 67–70%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Further details are provided in a peer-reviewed journal article (https://doi.org/10.1016/j.rse.2019.111260). The code and data used to produce the global soil salinity maps can be accessed by registered Google Earth Engine users at https://code.earthengine.google.com/d43e5a92ae1deed32a0929f57b572756.

For complete collection of data and models, see https://doi.org/10.21942/uva.c.5290546.Map package for use in ArcGIS Pro containing three-tiered geomorphological data and geographical datasets such as rivers and hillshading. Datasets were used to generate figures for publication: Hierarchical geomorphological mapping in mountainous areas. Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021. All data is in MGI Austria GK West projected coordinate system (EPSG: 31254) and was clipped to the study area.

For complete collection of data and models, see https://doi.org/10.21942/uva.c.5290546.Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results:Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.

This resource describes a dataset of gridded depth at horizontal resolution of 3 meters, published November 15, 2017, downloaded from FEMA [1] and hosted in this archive at the University of Texas Advanced Computing Center (TACC) [2].. The raster dataset is contained within an Esri ArcGIS geodatabase. This product utilized Triangulated Irregular Network (TIN) interpolation, four quality assurance measures (identifying dips, spikes, duplication, and inaccurate/unrealistic measurements). High Water Marks were obtained from the Harris County Flood Control District (HCFCD), US Geological Survey (USGS), and other inspection data. Elevation data comprised a mosaic of 3 meter resampled elevations from 1M & 3M LiDAR, and IFSAR data. One section of the IfSAR data was found to be erroneous, and replaced with a blended 10 meter section. [This description was in correspondence January 22, 2018, from Mark English, GeoSpatial Risk Analyst, FEMA Region VIII, Mitigation Division.]

A preliminary version of these depths dated September 10, 2017 can be viewed in a FEMA web map [3]. This web map shows a forecasted depth grid, based on National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasted water levels.

See FEMA's Natural Hazard Risk Assessment Program (NHRAP) ftp site [4] for additional HWM-based depth grids and inundation polygons: - Harris County AOIs and Inundation Boundaries [5] - Harris County Depth Grids [6] - Aransas, Nueces, and San Patricio Coastal Depth Grids and Boundaries [7] FEMA notes on these Modeled Preliminary Observations: o Based on observed Water Levels at stream gauges interpolated along rivers, downsampled to 5m resolution DEM o Depth grids updated with new observed peak crest as they become available o Will include High Water Mark information as it becomes available o Extents validated with remote sensing o Use for determining damage levels on specific structures

See also FEMA's journal of mitigation planning and actions related to Harvey [8].

References and related links: [1] FEMA_Depths_3m_v3.zip (39 gb ftp download) [https://data.femadata.com/Region8/Mitigation/Data_Share/] [2] TACC 39gb wget or ftp download [https://web.corral.tacc.utexas.edu/nfiedata/Harvey/flood_data/FEMA_Harvey_Depths_3m.gdb.zip] [3] FEMA map viewer for Hurricane Harvey resources (flood depths is bottom selection in layers list) [https://fema.maps.arcgis.com/apps/webappviewer/index.html?id=50f21538c7bf4e08b9faab430bc237c9] [4] FEMA NHRAP ftp [https://data.femadata.com/FIMA/NHRAP/Harvey/] [5] [https://data.femadata.com/FIMA/NHRAP/Harvey/Harris_AOIandBoundaries.zip] [6] [https://data.femadata.com/FIMA/NHRAP/Harvey/Harris_Mosaic_dgft.zip] [7] [https://data.femadata.com/FIMA/NHRAP/Harvey/Rockport_DG_unclipped.zip] [8] Hurricane Harvey Mitigation Portfolio - FEMA map journal [https://fema.maps.arcgis.com/apps/MapJournal/index.html?appid=70204cf2762d45409553fd9642700b7f]

Here we present the multispecies map of probable suitable habitat in the project area. The multispecies map of probable suitable habitat combines data from all 26 species for which probable suitable habitat was mapped and indicates the number of species for which probable suitable habitat is predicted at each location. Data are presented at a spatial resolution of 10 m pixels, which was required to harmonize the original model inputs. However, maps of suitable habitat should be used at a resolution no smaller than 360 m (i.e., 36 pixels x 36 pixels), which corresponds with the resolution of the coarsest model input. This product can be used to inform future conservation, planning, and management actions in the California desert. Complete methods and other additional information are provided in the journal article associated with this data release (Reese and others, 2019).