A map used in the Road Assignments ArcGIS Workforce project to assign field work activity.

An ArcGIS Field Maps map used by field staff to conduct a tree inventory and record inspection and maintenance activities.

This dataset contsin the raw fMRI data of a preregistered study. Dataset includes:

session pre 1. anat/ anatomical scans (T1-weighted images) for each subject 2. func/ whole-brain EPI data from all task runs (8x single task, 2x dual task, 1x resting state and 2x localizer task) 3. fmap/ fieldmaps with magnitude1, magnitude2 and phasediff

session post 2. func/ whole-brain EPI data from all task runs (8x single task, 2x dual task) 3. fmap/ fieldmaps with magnitude1, magnitude2 and phasediff

Please note, some participants did not complete the post session. We updated our consent form to get explicit permission to publish the individual data, although not all participants resigned the new version. Those participants are excluded here but part of the t-maps on neurovault (compare participants.tsv).

Tasks were always included either visual or/and auditory input and required either manual or/and vocal responses (visual+manual and auditory+vocal are modality compatible and visual+vocal and auditory+manual are modality incompatible). Tasks were presented as either single task, or dual task. Participants completed a practice intervention prior to session post in which one group worked for 80 minutes outside the scanner on modality incompatible dual-tasks, one on modality compatible dual-task and the third one paused for 80 min.

For exact tasks description and material and scripts, please see the preregistration: https://osf.io/whpz8

Single-echo/multi-echo comparison pilot

This dataset contains ABCD-protocol single-echo BOLD scans, along with complex-valued, multi-echo BOLD scans for comparison. The multi-echo BOLD protocol uses the CMRR MB-EPI sequence, and comes from collaborators at UMinn. These scans include five echoes with both magnitude and phase reconstruction.

The primary goal of this dataset was to evaluate the usability of the multi-echo fMRI protocol in a larger study, via direct comparison to the ABCD fMRI protocol, as well as via test-retest reliability analyses. However, these data may be useful to others (e.g., for testing complex-valued models, applying phase regression to multi-echo data, testing multi-echo denoising methods).

Dataset contents

This dataset includes 8 participants, each with between 1 and 3 sessions. MR data were acquired using a 3-Tesla Siemens Prisma MRI scanner.

The imaging data were converted to NIfTI-1 format with dcm2niix v1.0.20220505, using heudiconv 0.13.1.

In each session, the following scans were acquired:

Structural data

A T1-weighted anatomical scan (256 slices; repetition time, TR=1900 ms; echo time, TE=2.93 ms; flip angle, FA=9 degrees; field of view, FOV=176x262.144 mm, matrix size=176x256; voxel size=1x0.977x0.977 mm).

Functional data

One run of Penn fractal n-back task five-echo fMRI data (72 slices; repetition time, TR=1761 ms; echo times, TE=14.2, 38.93, 63.66, 88.39, 113.12 ms; flip angle, FA=68 degrees; field of view, FOV=220x220 mm, matrix size=110x110; voxel size=2x2x2 mm; multiband acceleration factor=6). Both magnitude and phase data were reconstructed for this run. The run was 7:03 minutes in length, including the three no-radiofrequency-excitation volumes at the end. After the _noRF volumes were split into separate files, each run was 6:58 minutes long.

Two runs of open-eye resting-state five-echo fMRI data (72 slices; repetition time, TR=1761 ms; echo times, TE=14.2, 38.93, 63.66, 88.39, 113.12 ms; flip angle, FA=68 degrees; field of view, FOV=220x220 mm, matrix size=110x110; voxel size=2x2x2 mm; multiband acceleration factor=6). Both magnitude and phase data were reconstructed for these runs. Each run was 5:59 minutes in length, including the three no-radiofrequency-excitation volumes at the end. After the _noRF volumes were split into separate files, each run was 5:54 minutes long.

Two runs of open-eye resting-state single-echo fMRI data acquired according to the ABCD protocol (60 slices; repetition time, TR=800 ms; echo time, TE=30 ms; flip angle, FA=52 degrees; field of view, FOV=216x216 mm, matrix size=90x90; voxel size=2.4x2.4x2.4 mm; multiband acceleration factor=6). Only magnitude data were reconstructed for these runs. Each run was 6:00 minutes in length.

Field maps

Two sets of field maps were acquired for the multi-echo fMRI scans.

One set was a multiband, multi-echo gradient echo PEpolar-type field map (acq-MEGE), acquired with the same parameters as the multi-echo fMRI scans (except without magnitude+phase reconstruction). For each acquisition, we have created a copy of the single-band reference image from the first echo as the primary field map.

The other set was a multi-echo spin-echo PEpolar-type field map (acq-MESE). We have also created a copy of the first echo for each direction as a standard field map.

The single-echo copies of both the acq-MEGE and the acq-MESE field maps have B0FieldIdentifier fields and IntendedFor fields, though we used the acq-MESE field maps for the B0FieldSource fields of the multi-echo fMRI scans. Therefore, tools which leverage the B0* fields, such as fMRIPrep, should use the single-echo acq-MESE scans for distortion correction.

Single-echo PEpolar-type EPI field maps (acq-SESE) with parameters matching the single-echo fMRI data were also acquired for distortion correction.

Dataset idiosyncrasies

Multi-echo field maps

There are two sets of PEpolar-style field maps for the multi-echo BOLD scans: one gradient echo and one spin echo. Each field map set contains five echoes, like the BOLD scans. However, because distortion shouldn't vary across echoes (at least not at 3T), there is no need for multi-echo PEpolar-style field maps, and tools like fMRIPrep can't use them. As such, we have made a copy of the spin echo field map's first echo without the echo entity for BIDS compliance, as well as a copy of the gradient echo field map's first echo's single-band reference image.

No radio frequency excitation scans

The multi-echo BOLD scans included three no-radio-frequency noise scans acquired at the end of the scan, which have been split off into files with the _noRF suffix. These noise scans can be used to suppress thermal noise with NORDIC denoising. BOLD runs that were stopped early or failed to fully reconstruct may be missing these noise scans.

The _noRF suffix is will be supported in BIDS version 1.10.0, but until that version is released they will be ignored by the validator.

Penn Fractal N-Back events files

The events files for the fractal n-back task were added in version 1.1.0 of the dataset. It looks like the task software only logged the last response in each trial's response window, so there are some trials with very long response times from responses that occurred during the next block's instruction screen.

sub-08 ses-noHC

Subject 08's ses-noHC was accidentally acquired without the head coil plugged in. We included the session in the dataset in case anyone might find it useful, but do not recommend using the data for analyses.

sub-04 ses-2 and ses-3

Subject 04 had to stop session 2 early, so a separate session was acquired to finish acquiring the remaining scans.

Excluded data

Physio (PPG + chest belt) data were acquired for a subset of the scans, but, due to equipment issues, the data were unusable and have been excluded from the dataset.

There was also an MEGRE field map sequence in the protocol, provided by Dr. Andrew Van, but there were reconstruction errors at the scanner, so these field maps were not usable. We've chosen to exclude the remaining files from the dataset.

In some cases, we noticed reconstruction errors on final volumes in the multi-echo BOLD runs. When that happened, we dropped any trailing volumes, so that all files from a given run are the same length. For some runs, this involved entirely removing the noRF scans.

NORDIC-denoised BOLD runs

We have run NORDIC on the multi-echo scans, using the noRF files when available. The NORDIC-denoised data have the rec-nordic entity in the filenames.

We have made copies of the associated single-band reference images as well. We may add these files at some point in the future.

Notes about acquisition

The multi-echo BOLD scans were acquired on a 3T Siemens Prisma scanner running VE11C. The same protocol has exhibited consistent reconstruction errors on XA30.

Animations have become a frequently utilized illustration technique on maps but changes in their graphical loading remain understudied in empirical geovisualization and cartographic research. Animated streamlets have gained attention as an illustrative animation technique and have become popular on widely viewed maps. We conducted an experiment to investigate how altering four major animation parameters of animated streamlets affects people’s reading performance of field maxima on vector fields. The study involved 73 participants who performed reaction-time tasks on pointing maxima on vector field stimuli. Reaction times and correctness of answers changed surprisingly little between visually different animations, with only a few occasional statistical significances. The results suggest that motion of animated streamlets is such a strong visual cue that altering graphical parameters makes only little difference when searching for the maxima. This leads to the conclusion that, for this kind of a task, animated streamlets on maps can be designed relatively freely in graphical terms and their style fitted to other contents of the map. In the broader visual and geovisual analytics context, the results can lead to more generally hypothesizing that graphical loading of animations with continuous motion flux could be altered without severely affecting their communicative power.

NCED is currently involved in researching the effectiveness of anaglyph maps in the classroom and are working with educators and scientists to interpret various Earth-surface processes. Based on the findings of the research, various activities and interpretive information will be developed and available for educators to use in their classrooms. Keep checking back with this website because activities and maps are always being updated. We believe that anaglyph maps are an important tool in helping students see the world and are working to further develop materials and activities to support educators in their use of the maps.

This website has various 3-D maps and supporting materials that are available for download. Maps can be printed, viewed on computer monitors, or projected on to screens for larger audiences. Keep an eye on our website for more maps, activities and new information. Let us know how you use anaglyph maps in your classroom. Email any ideas or activities you have to ncedmaps@umn.edu

Anaglyph paper maps are a cost effective offshoot of the GeoWall Project. Geowall is a high end visualization tool developed for use in the University of Minnesota's Geology and Geophysics Department. Because of its effectiveness it has been expanded to 300 institutions across the United States. GeoWall projects 3-D images and allows students to see 3-D representations but is limited because of the technology. Paper maps are a cost effective solution that allows anaglyph technology to be used in classroom and field-based applications.

Maps are best when viewed with RED/CYAN anaglyph glasses!

A note on downloading: "viewable" maps are .jpg files; "high-quality downloads" are .tif files. While it is possible to view the latter in a web-browser in most cases, the download may be slow. As an alternative, try right-clicking on the link to the high-quality download and choosing "save" from the pop-up menu that results. Save the file to your own machine, then try opening the saved copy. This may be faster than clicking directly on the link to open it in the browser.

World Map: 3-D map that highlights oceanic bathymetry and plate boundaries.

Continental United States: 3-D grayscale map of the Lower 48.

Western United States: 3-D grayscale map of the Western United States with state boundaries.

Regional Map: 3-D greyscale map stretching from Hudson Bay to the Central Great Plains. This map includes the Western Great Lakes and the Canadian Shield.

Minnesota Map: 3-D greyscale map of Minnesota with county and state boundaries.

Twin Cities: 3-D map extending beyond Minneapolis and St. Paul.

Twin Cities Confluence Map: 3-D map highlighting the confluence of the Mississippi and Minnesota Rivers. This map includes most of Minneapolis and St. Paul.

Minneapolis, MN: 3-D topographical map of South Minneapolis.

Bassets Creek, Minneapolis: 3-D topographical map of the Bassets Creek watershed.

North Minneapolis: 3-D topographical map highlighting North Minneapolis and the Mississippi River.

St. Paul, MN: 3-D topographical map of St. Paul.

Western Suburbs, Twin Cities: 3-D topographical map of St. Louis Park, Hopkins and Minnetonka area.

Minnesota River Valley Suburbs, Twin Cities: 3-D topographical map of Bloomington, Eden Prairie and Edina area.

Southern Suburbs, Twin Cities: 3-D topographical map of Burnsville, Lakeville and Prior Lake area.

Southeast Suburbs, Twin Cities: 3-D topographical map of South St. Paul, Mendota Heights, Apple Valley and Eagan area.

Northeast Suburbs, Twin Cities: 3-D topographical map of White Bear Lake, Maplewood and Roseville area.

Northwest Suburbs, Mississippi River, Twin Cities: 3-D topographical map of North Minneapolis, Brooklyn Center and Maple Grove area.

Blaine, MN: 3-D map of Blaine and the Mississippi River.

White Bear Lake, MN: 3-D topographical map of White Bear Lake and the surrounding area.

Maple Grove, MN: 3-D topographical mmap of the NW suburbs of the Twin Cities.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryPhenomenon Mapped: Ready-to-use project packages with over 170 attributes derived from the SSURGO dataset, split up by HUC8s. Geographic Extent: The dataset covers the 48 contiguous United States plus Hawaii and portions of Alaska. Map packages are available for Puerto Rico and the US Virgin Islands. A project package for US Island Territories and associated states of the Pacific Ocean can be downloaded by clicking one of the included areas in the map. The Pacific Project Package includes: Guam, the Marshall Islands, the Northern Marianas Islands, Palau, the Federated States of Micronesia, and American Samoa.Source: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024Link to source metadata*Not all areas within SSURGO have completed soil surveys and many attributes have areas with no data.The soil data in the packages is also available as a feature layer in the ArcGIS Living Atlas of the World.AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Map Unit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Map Unit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some map units have a null value for soil order, a custom script was used to populate this field using the Component Name (compname) and Map Unit Name (muname) fields. This field was created using the dominant soil order of each map unit.Esri SymbologyHorizon TableEach map unit polygon has one or more components and each component has one or more layers known as horizons. To incorporate this field from the Horizon table into the attributes for this layer, a custom script was used to first calculate the mean value weighted by thickness of the horizon for each component and then a mean value of components weighted by the Component Percentage Representative Value field for each map unit. K-Factor Rock FreeEsri Soil OrderThese fields were calculated from the Component table using a model that included the Pivot Table Tool, the Summarize Tool and a custom script. The first 11 fields provide the sum of Component Percentage Representative Value for each soil order for each map unit. The Soil Order Dominant Condition field was calculated by selecting the highest value in the preceding 11 soil order fields. In the case of tied values the component with the lowest average slope value (slope_r) was selected. If both soil order and slope were tied

Purpose: This is an ArcGIS Pro template that GIS Specialists can use to identify vulnerable populations and special needs infrastructure most at risk to flooding events.How does it work?Determine and understand the Place Vulnerability (based on Cutter et al. 1997) and the Special Needs Infrastructure for an area of interest based on Special Flood Hazard Zones, Social Vulnerability Index, and the distribution of its Population and Housing units. The final product will be charts of the data distribution and a Hosted Feature Layer. See this Story Map example for a more detailed explanation.This uses the FEMA National Flood Hazard Layer as an input (although you can substitute your own flood hazard data), check availability for your County before beginning the Task: FEMA NFHL ViewerThe solution consists of several tasks that allow you to:Select an area of interest for your Place Vulnerability Analysis. Select a Hazard that may occur within your area of interest.Select the Social Vulnerability Index (SVI) features contained within your area of interest using the CDC’s Social Vulnerability Index (SVI) – 2016 overall SVI layer at the census tract level in the map.Determine and understand the Social Vulnerability Index for the hazard zones identified within you area of interest.Identify the Special Needs Infrastructure features located within the hazard zones identified within you area of interest.Share your data to ArcGIS Online as a Hosted Feature Layer.FIRST STEPS:Create a folder C:\GIS\ if you do not already have this folder created. (This is a suggested step as the ArcGIS Pro Tasks does not appear to keep relative paths)Download the ZIP file.Extract the ZIP file and save it to the C:\GIS\ location on your computer. Open the PlaceVulnerabilityAnalysis.aprx file.Once the Project file (.aprx) opens, we suggest the following setup to easily view the Tasks instructions, the Map and its Contents, and the Databases (.gdb) from the Catalog pane.The following public web map is included as a Template in the ArcGIS Pro solution file: Place Vulnerability Template Web MapNote 1:As this is a beta version, please take note of some pain points:Data input and output locations may need to be manually populated from the related workspaces (.gdb) or the tools may fail to run. Make sure to unzip/extract the file to the C:\GIS\ location on your computer to avoid issues.Switching from one step to the next may not be totally seamless yet.If you are experiencing any issues with the Flood Hazard Zones service provided, or if the data is not available for your area of interest, you can also download your Flood Hazard Zones data from the FEMA Flood Map Service Center. In the search, use the FEMA ID. Once downloaded, save the data in your project folder and use it as an input.Note 2:In this task, the default hazard being used are the National Flood Hazard Zones. If you would like to use a different hazard, you will need to add the new hazard layer to the map and update all query expressions accordingly.For questions, bug reports, or new requirements contact pdoherty@publicsafetygis.org

For a detailed view of Alaska, please reference the BIA AIAN LAR (Alaska Detail) Map here: https://bia-geospatial.maps.arcgis.com/sharing/rest/content/items/c914b3914c97440f9e90d142be55a683/data. The purpose of the American Indian and Alaska Native Land Area Representation (AIAN-LAR) Geographic Information System (GIS) dataset is to depict the exterior extent of land held in “trust” or “restricted fee” status by the United States for a tribe(s) and individual Indians of federally recognized Tribes. A tribe is a tribe, band, pueblo, community or other federally acknowledged group of Indians. A federally recognized tribe is an American Indian or Alaska Native tribal entity that is recognized as having a government-to-government relationship with the United States, with the responsibilities, powers, limitations, and obligations attached to that designation, and are eligible for funding and services from the BIA. Furthermore, federally recognized tribes are recognized as possessing certain inherent rights of self-government (i.e., tribal sovereignty) and are entitled to receive certain federal benefits, services, and protections because of their special relationship with the United States. At present, there are 574 federally recognized American Indian and Alaska Native tribes and villages. Not all federally recognized Tribes have a designated land area, land in trust or restricted status and therefore may not have an associated land area represented in the AIAN-LAR. Not all land areas such as public domain allotments are under the jurisdiction or associated with any particular federally recognized tribe. The BIA publishes an updated list of federally recognized tribes in a federal register notice. These data are public information and may be used and interpreted by organizations, agencies, units of government, or other entities. The user, agency or organization has sole responsibility for ensuring the appropriate use, application, integration and republication of these data. The most recent federal register notice is located at: https://www.federalregister.gov/documents/2023/01/12/2023-00504/indian-entities-recognized-by-and-eligible-to-receive-services-from-the-united-states-bureau-of

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!

WRA, Inc (WRA) created a fine-scale vegetation map of portions of the Cojo-Jalama Ranches. WRA conducted field reconnaissance assistance for this project, as well as accuracy assessment (AA) field data collection. The primary purpose of the project was to provide a comprehensive overview of habitats, plants, and wildlife to inform planning for land-use, conservation, and ranch-specific activities.The mapping study area, consists of approximately 24,400 acres of the Cojo-Jalama ranches in unicoporated coastal Santa Barbara County, California. Work was performed on the project between 2012 and 2017. Ranch-wide floristic surveying was conducted from April 2012 to October 2014 to address natural communities and sensitive plant and wildlife species. WRA botanists documented vegetation alliances while on-foot. Site-specific surveys were conducted between 2015 and 2017 collected natural community and sensitive species data to be incorporated into WRA’s long-term geodatabase as supplementary data. WRA botanists then further refined vegetation alliance mapping by conducting accuracy assessment where site-specific surveys were conducted; when terrain made sites inaccessible, field botanists used binoculars to observe plant communities from an appropriate vantage point.Field maps generated by WRA used high-quality aerial photographs from 2010 to 2012 overlain with 10-foot contour lines. vegetation polygons were then hand-drawn by field biologists and later digitized using ArcGIS software. Trimble Geo XH GPS units with sub-meter accuracy were used by the biologists to map especially small-scale landscape features and rare plant point-occurrences. The minimum mapping unit (MMU) is variable from 1.0 acres to point depending on the map feature type. There was a total of 50 mapping classes. The overall Fuzzy Accuracy Assessment rating for the final vegetation map was not calculated, but much of the map was field checked.

Several regions are represented in this unique collection of earth surface measurements of magnetic field parameters and their related anomalies. The DNAG Magnetics "Super grid" of Magnetic Anomaly Map of North America was created from the four "Original" DNAG Magnetic data sets distributed by The Committee for the Magnetic Anomaly Map of North America, 1987. This development of a super grid involved an extensive task of matching original quadrant information and eliminating overlap. The resulting grid, with x and y step intervals of 2.0 kilometers yields a grid with dimensions (4451 x 4273) containing 19,019,123 values. This process can be thought of as "stitching the grids." The data in this grid are in a Spherical Transverse Mercator projection, the kilometer coordinates of which can be recovered from the indices of a grid point. The Ministry of Geology of the USSR published a mosaic series of 18 maps in 1974, at a scale of 1:2,500,000 showing the residual magnetic intensity over the land mass of the USSR. Much of the source material originated from data collected between 1949-1962, during which time the entire territory of the USSR was surveyed using aerial magnetic survey techniques. These surveys wereadjusted based on many methods including secular variation linked to magnetic observatories. Anomalies were computed with reference to a normal field map for 1964-65 constructed from equally accurate total field measurements along control network strips. Digitization was accomplished in 1982 by the U.S. Naval Oceanographic Office. The "BRIGGS cubic spline" method was used to compute grid values. A one-minute grid was created by properly matching the boundaries of the digitized sub-sections. The units of the original map aremilli-Oersteds and the units of the resulting digital grid are milli-Oersted/100. Corrections to the digital contour file were made by Conoco Inc.in 1993. New Grid files at 2.5 Km and 5.0 Km spacing were created and re-archived by NGDC. These data are available on CD-ROM. World Data Center-A (WDC-A) for Solid Earth Geophysics presently holds Grid data from many U.S. and other regions. These data were contributed by: USGS, MINN G.S. and other Worldwide organizations. Grid intervals vary but are as fine as 213.36m for the NGS Super Grid of the state of Minnesota. Other grids were recreated indigital form from previously published maps and charts. The bulk of these grid data files were contributed to NGDC after 1985. A detailed list of the specific regions is available upon request.

A benthic habitat polygon coverage has been created of the coral reef ecosystem within the U.S. Coral Reef Task Force Watershed Partnership Initiative Kaanapali priority study area and the State of Hawaii Kahekili Herbivore Fisheries Management Area, West-Central Maui, Hawaii. Polygons were hand-digitized from visual interpretation of QuickBird-2 satellite imagery (2005), and SHOALS bathymetry data. We also utilized in situ knowledge from underwater photography and videography (2002-2011), side-scan sonar data, and diver and snorkeler observations. The polygons have attributes for Main Structure/Substrate, Dominant Structure/Substrate, Major Biological Cover, Percent of Major Biological Cover, Reef Zone, Unique ID, and measurements of Area (in square meters) of each polygon.

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Rice (Oryza sativaandO. glaberrima) is one of the world"s most important staple food crops. Over half of the world"s population relies on rice. The people in some parts of Africa have been cultivating rice for over 3,500 years. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofrice harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of rice as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Cassava Groundnut (Peanut) Maize (Corn) Millet PotatoSorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links: Landscape Layers - a reintroductionLiving Atlas Discussion Group

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Mapping and interpretation of JODA involved a five step process including: (1) field reconnaissance, (2) map class development, (3) image processing and interpretation, (4) draft map validation, and (5) spatial database development. Field reconnaissance was initiated by CTI and NMI staff in 2008 to quickly familiarize the mappers with the vegetation patterns and distribution at JODA. As the classification plot data were acquired later in 2008, feedback on the dominant and characteristic plant species was solicited from ORNHIC ecologists. boundary placement and labeling. Field notes were made directly on vegetation map copies and an additional 70 observation points were sampled to support the notations. Confusing sites were visited including the Picture Gorge area where shadows on the NAIP imagery prevented viewing the distribution of vegetation types. Ground data and ground photographs were collected to insure consistent mapping of confusing sites. Upon return to the office, minor updates of the draft vegetation map were completed prior to the AA task.

Taken from sections of the report:

1. Introduction

This report details the survey work carried out on Macquarie Island during November 1997 by LANDINFO staff on behalf of the Australian Antarctic Division's Mapping Program. The main task of the survey team was to acquire aerial photography of the island to enable the production of a new topographic map of the island. Other tasks involved field checking the digital station area map (DSAM) and providing support to the tide gauge maintenance team.

The following team carried out the survey-mapping work: Tom Gordon LANDINFO Surveyor Roger Handsworth Antarctic Division Engineer

Although this report touches on the work carried out by Roger Handsworth and Rupert Summerson, it does not cover the specifics of their work.

1. Project Brief

The survey-mapping brief lists the following tasks:

1. Aerial Photography of the Island and station area.
2. Aerial Photography of Judge and Clerk Island to the south and Bishop and Clerk Island to the north of Macquarie Island.
3. Second order levelling from the tide gauge bench marks to AUS 211
4. Updating the Digital Station Area map.

These tasks are listed in order of priority. A copy of the survey brief for Macquarie Island is included in Appendix A.

A data processing task was needed by National Park Service to transpose field tabular and spatial data into various digital formats of the 1994 Aquatic Plant Survey Report. A survey of aquatic vascular plants was conducted in a 122 mile stretch of the Upper Delaware River between Hancock, New York and the Delaware Water Gap in 1991 and 1992. A total of 196 sites were inventoried and twenty-eight species of plants were recorded. The aquatic vascular plant flora in this section of the Delaware River appeared to be thriving, due in large part to good water quality and moderate impacts by man. The purpose of this task was the digitizing of hand-drawn field mapping data from hardcopy paper maps into digital map products. These digital mapping files are in the NPS standard GIS format and in National Park Service's accepted coordinate and projection systems. This is a data set composed of field survey of aquatic vegetation in the Delaware Water Gap and the Upper Delaware Scenic and Recreational Area National Parks. Data were compiled by field sampling with GPS coordinates and underwater imaging and bathymetry. Data were compiled by teams composed of personnel from the National Park Service, the U.S. Geological Survey and the Western Pennsylvania Conservancy. The data were used as training and ground truth for remote sensing image analysis of hyperspectral collected by the Civil Air Patrol with the Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance, also known by the acronym ARCHER. Archer is an aerial imaging system that produces ground images far more detailed than plain sight or ordinary aerial photography can. It is the most sophisticated unclassified hyperspectral imaging system available, according to U.S. Government officials. ARCHER can automatically scan detailed imaging for a given signature of the object being sought (such as a missing aircraft), for abnormalities in the surrounding area, or for changes from previously recorded spectral signatures. ARCHER was used in this project, to evaluate its potential for mapping submerged aquatic vegetation.

This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of San Francisco map area, California. The vector data file is included in "Folds_OffshoreSanFrancisco.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSanFrancisco/data_catalog_OffshoreSanFrancisco.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D., Golden, N.E., Hartwell, S.R., Endris, C.A., Manson, M.W., Sliter, R.W., Kvitek, R.G., Watt, J.T., Ross, S.L., and Bruns, T.R. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of San Francisco, California (ver. 1.1, June 2015): U.S. Geological Survey Open-File Report 2015–1068, pamphlet 39 p., 10 sheets, scale 1:24,000, https://dx.doi.org/10.3133/ofr20151068. Folds were primarily mapped by interpretation of seismic reflection profile data (see field activities S-15-10-NC and F-2-07-NC). The seismic reflection profiles were collected between 2007 and 2010.

This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Bolinas map area, California. The vector data file is included in "Faults_OffshoreBolinas.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreBolinas/data_catalog_OffshoreBolinas.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Golden, N.E., Hartwell, S.R., Manson, M.W., Sliter, R.W., Endris, C.A., Watt, J.T., Ross, S.L., Kvitek, R.G., Phillips, E.L., Bruns, T.R., and Chin, J.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series — Offshore of Bolinas, California: U.S. Geological Survey Open-File Report 2015–1135, pamphlet 36 p., 10 sheets, https://doi.org/10.3133/ofr20151135. Faults in the Offshore of Bolinas map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activities S-8-09-NC and L-1-06-SF. The seismic reflection profiles were collected between 2006 and 2009.

This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Pacifica map area, California. The vector data file is included in "Faults_OffshorePacifica.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshorePacifica/data_catalog_OffshorePacifica.html. These data accompany the pamphlet and map sheets of Edwards, B.D., Phillips, E.L., Dartnell, P., Greene, H.G., Bretz, C.K., Kvitek, R.G., Hartwell, S.R., Johnson, S.Y., Cochrane, G.R., Dieter, B.E., Sliter, R.W., Ross, S.L., Golden, N.E., Watt, J.T., Chin, J.L., Erdey, M.D., Krigsman, L.M., Manson, M.W., and Endris, C.A. (S.A. Cochran and B.D. Edwards, eds.), 2014, California State Waters Map Series—Offshore of Pacifica, California: U.S. Geological Survey Open-File Report 2014–1260, pamphlet 38 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20141260. Faults in the Offshore of Pacifica map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activities S-15-10-NC and F-2-07-NC. The seismic reflection profiles were collected between 2007 and 2010.