The goal of this project is to create a map of the planet Mars, by using ESRI software. For this, a 3D project was developed using ArcGIS Pro, considering a global scene, to be published in an online platform. All the various data from Mars will be available in a single website, where everyone can visualize and interact. The Red Planet has been studied for many decades and this year marks the launch of a new rover, Mars2020, which will happen on the 17th of July. This new rover will be continuing the on-going work of the Curiosity Rover, launched in 2012. The main objective for these rovers is to determine if Mars could have supported life, by studying its water, climate and geology. Currently, the only operational rover in Mars is Curiosity and with that in mind, this project will have a strong focus on the path taken by this rover, during almost 8 years of exploration. In the web application, the user will be able to see the course taken by Curiosity in Mars’ Gale Crater, from its landing until January 2020. The map highlights several points of interest, such as the location after each year passed on MarsEarth year and every kilometer, which can be interacted with as well as browse through photos taken at each of the locations, through a pop-up window. Additionally, the application also supports global data of Mars. The two main pieces, used as basemaps, are the global imagery, with a pixel size of 925 meters and the Digital Elevation Model (DEM), with 200 meters per pixel. The DEM represents the topography of Mars and was also used to develop Relief and Slope Maps. Furthermore, the application also includes data regarding the geology of the planet and nomenclature to identify regions, areas of interest and craters of Mars. This project wouldn’t have been possible without NASA’s open-source philosophy, working alongside other entities, such as the European Space Agency, the International Astronomical Union and the Working Group for Planetary System Nomenclature. All the data related to Imagery, DEM raster files, Mars geology and nomenclature was obtained on USGS Astrogeology Science Center database. Finally, the data related to the Curiosity Rover was obtained on the portal of The Planetary Society. Working with global datasets means working with very large files, so selecting the right approach is crucial and there isn’t much margin for experiments. In fact, a wrong step means losing several hours of computing time. All the data that was downloaded came in Mars Coordinate Reference Systems (CRS) and luckily, ESRI handles that format well. This not only allowed the development of accurate analysis of the planet, but also modelling the data around a globe. One limitation, however, is that ESRI only has the celestial body for planet Earth, so this meant that the Mars imagery and elevation was wrapped around Earth. ArcGIS Pro allows CRS transformation on the fly, but rendering times were not efficient, so the workaround was to project all data into WGS84. The slope map and respective reclassification and hillshading was developed in the original CRS. This process was done twice: one globally and another considering the Gale Crater. The results show that the crater’s slope characteristics are quite different from the global panorama of Mars. The crater has a depression that is approximately 5000 meters deep, but at the top it’s possible to identify an elevation of 750 meters, according to the altitude system of Mars. These discrepancies in a relatively small area result in very high slope values. Globally, 88% of the area has slopes less than 2 degrees, while in the Gale Crater this value is only 36%. Slopes between 2 and 10 degrees represent almost 60% of the area of the crater. On the other hand, they only represent 10% of the area globally. A considerable area with more than 10 degrees of slope can also be found within the crater, but globally the value is less than 1%. By combining Curiosity’s track path with the DEM, a profile graph of the path was obtained. It is possible to observe that Curiosity landed in a flat area and has been exploring in a “steady path”. However, in the last few years (since the 12th km), the rover has been more adventurous and is starting to climb the crater. In the last 10 km of its journey, Curiosity “climbed” around 300 meters, whereas in the first 11 km it never went above 100 meters. With the data processed in the WGS84 system, all was ready to start modelling Mars, which was firstly done in ArcGIS Pro. When the data was loaded, symbology and pop-ups configured, the project was exported to ArcGIS Online. Both the imagery and elevation layer were exported as “hosted tile service”. This was a key step, since keeping the same level of detail online and offline would have a steep increase in imagery size, to hundreds of Terabytes, thus a lot of work was put into balancing tile cache size and the intended quality of imagery. For the remaining data, it was a straight-forward step, exporting these files as vectors. Once all the data was in the Online Portal, a Global Web Scene was developed. This is an on-going project with an outlook to develop the global scene into an application with ESRI’s AppBuilder, allowing the addition of more information. In the future, there is also interest to increment the displayed data, like adding the paths taken by other rovers in the past, alongside detailed imagery of other areas beyond the Gale Crater. Finally, with 2021 being the year when the new rover Mars2020 will land on the Red Planet, we might be looking into adding it to this project.https://arcg.is/KuS4r

This layer uses sand, silt, and clay most likely values from soilgrids.org to create texture classes. Soilgrids.org sand, silt, and clay datasets are integers that give a weight in grams in each particle class. The weight we are converting directly into percent, for example soilgrids value of 500g of sand means 50% sand ((500g/1kg) * 100 = 50%).A 100cm depth was chosen because it matches many of the world's most important crops' rooting depths. A 0 to 60cm version of this is also available.Variable mapped: Predominant USDA texture class as derived from predicted percent sand, silt, and clay.Data Projection: Goode's Homolosine (land) WKID 54052Mosaic Projection: Goode's Homolosine (land) WKID 54052Extent: World, except AntarcticaCell Size: 250 mSource Type: ThematicVisible Scale: All scales are visibleSource: SoilGrids.orgPublication Date: June 14, 2021NOTE: This layer uses the USDA texture classification system with international soil datasets, which use different particle size definitions than the USDA. Very little silt shows up in this layer, this could be a reason why.To determine the predominant soil texture we first classified texture for the following layer depths:0-5cm5-15cm15-30cm30-60cm60-100cmThen we used focal statistics with the majority option to find the majority texture class of each pixel from the five layers, weighted as follows:0-5cm * 15-15cm * 215-30cm * 330-60cm * 660-100cm * 7 (not 8, something had to break the tie and I reduced the multiplier by 1 to break ties, thinking of all soil depths the depth from 95-100cm may be the least significant in the stack overall.)-----------------------------------------------------------------Raster functions were created to classify sand, silt, and clay using the following statements in raster calculator:Sand Con((( Silt + ( 1.5 * Clay )) < 150 ), 1, 0)Loamy Sand Con(((Silt + (1.5 * Clay)) >= 150) & ((Silt + (2 * Clay)) < 300),2, 0)Sandy Loam Con(((Clay

=70)&(Clay<200)&(Sand>520)&((Silt + (2 * Clay)) = 300))|((Clay<70)&(Silt<500)&((Silt + (2 * Clay)) = 300)),4, 0)Loam Con(((Clay>=70) & (Clay<270) & (Silt>=280) & (Silt<500) & (Sand<=520)),8 ,0)Silt LoamCon((((Silt>=500) & (Clay>=120) & (Clay<270)) | ((Silt>=500) & (Silt<800) & (Clay<120))),16 , 0)SiltCon(((Silt >= 800)&(Clay<120)),32 ,0)Sandy Clay LoamCon(((Clay>=200) & (Clay < 350) & (Silt < 280) & (Sand > 450)),64 ,0)Clay LoamCon(((Clay >= 270) & (Clay<400) & (Sand > 200) & (Sand <= 450)), 128, 0)Silty Clay LoamCon(((Clay >= 270) & (Clay < 400) & (Sand <= 200)),256 ,0)Sandy ClayCon(((Clay >= 350) & (Sand > 450)) ,512 , 0)Silty Clay Con(((Clay >= 400) & (Silt >= 400)), 1024, 0)Clay Con(((Clay>=400) & (Sand <= 450) & (Silt < 400)) , 2048 , 0 )These conditionals were used on the "mean" soilgrids.org rasters for silt, sand, and clay on rasters representing the following depths:0-5 cm below the land surface5-15cm below the land surface15-30cm below the land surface30-60cm below the land surface60-100cm below the land surfaceThe conditionals were just summed together to create check rasters for each depth. All analysis was done in soilgrids.org own Goode's Homolosine projection (land) in ArcGIS Pro. The data were served in this same projection in ArcGIS Image for ArcGIS Online.---------------------------------------------------------------------------------------------------At first, the classes were given a value of 1, 2, 4, 8, 16, 32 and so on, then were added together. This is so we could see if some classes were overlapping others. We continued to troubleshoot the above definitions until there were no overlaps and as few values of 0 as possible. Once the overlaps and misses were fixed, the dataset was reclassed into values of 1-13. An attribute table was built to drive popups and a legend.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at https://goto.arcgisonline.com/landscape11/USA_Soils_Drainage_Class.Soils vary widely in their ability to retain or drain water. The rate at which water drains into the soil has a direct effect on the amount and timing of runoff, what crops can be grown, and where wetlands form. In soils with low drainage rates water will pond on the soil's surface. Poorly drained soils are desirable when growing crops like rice where the fields are flooded for cultivation but other crops need better drained soils.Dataset SummaryPhenomenon Mapped: Drainage Class of SoilsUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: July 2020ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for drainage class is derived from the gSSURGO map unit aggregated attribute table field Drainage Class - Dominant Condition (drclassdcd).The layer has an attribute field for Drainage Class and a description field for use in pop-ups. The eight values of drainage class with corresponding attribute table index value are defined by the NRCS Soil Survey Manual as:0. Excessively drained: Water is removed very rapidly. The occurrence of internal free water commonly is very rare or very deep. The soils are commonly coarse-textured and have very high hydraulic conductivity or are very shallow.1. Somewhat excessively drained: Water is removed from the soil rapidly. Internal free water occurrence commonly is very rare or very deep. The soils are commonly coarse-textured and have high saturated hydraulic conductivity or are very shallow.2. Well drained: Water is removed from the soil readily but not rapidly. Internal free water occurrence commonly is deep or very deep; annual duration is not specified. Water is available to plants throughout most of the growing season in humid regions. Wetness does not inhibit growth of roots for significant periods during most growing seasons. The soils are mainly free of the deep to redoximorphic features that are related to wetness.3. Moderately well drained: Water is removed from the soil somewhat slowly during some periods of the year. Internal free water occurrence commonly is moderately deep and transitory through permanent. The soils are wet for only a short time within the rooting depth during the growing season, but long enough that most mesophytic crops are affected. They commonly have a moderately low or lower saturated hydraulic conductivity in a layer within the upper 1 m, periodically receive high rainfall, or both.4. Somewhat poorly drained: Water is removed slowly so that the soil is wet at a shallow depth for significant periods during the growing season. The occurrence of internal free water commonly is shallow to moderately deep and transitory to permanent. Wetness markedly restricts the growth of mesophytic crops, unless artificial drainage is provided. The soils commonly have one or more of the following characteristics: low or very low saturated hydraulic conductivity, a high water table, additional water from seepage, or nearly continuous rainfall.5. Poorly drained: Water is removed so slowly that the soil is wet at shallow depths periodically during the growing season or remains wet for long periods. The occurrence of internal free water is shallow or very shallow and common or persistent. Free water is commonly at or near the surface long enough during the growing season so that most mesophytic crops cannot be grown, unless the soil is artificially drained. The soil, however, is not continuously wet directly below plow-depth. Free water at shallow depth is usually present. This water table is commonly the result of low or very low saturated hydraulic conductivity of nearly continuous rainfall, or of a combination of these.6. Very poorly drained: Water is removed from the soil so slowly that free water remains at or very near the ground surface during much of the growing season. The occurrence of internal free water is very shallow and persistent or permanent. Unless the soil is artificially drained, most mesophytic crops cannot be grown. The soils are commonly level or depressed and frequently ponded. If rainfall is high or nearly continuous, slope gradients may be greater.7. Subaqueous Soils: These soils are under the surface of a body of water. (There are only a few of these in the entire dataset.)What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "drainage class" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "drainage class" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

Dataset description: This repository contains data pertaining to the manuscript "Mawrth Vallis, Mars, classified using the NOAH-H deep-learning terrain classification system." submitted to Journal of Maps. NOAH-H Mosaics: Mawrth_Vallis_NOAHH_Mosaic_DC_IG_25cm4bit_20230121_reclass.zip This folder contain mosaics of terrain classifications for Mawrth Vallis, Mars, made by the Novelty or Anomaly Hunter - HiRISE (NOAH-H) deep learning convolutional neural network developed for the European Space Agency (ESA) by SCISYS Ltd. In coordination with the Open University Planetary Environments Group. These folders contain the NOAH-H mosaics, as well as ancillary files needed to display the NOAH-H products in geographic information software (GIS). Included are two large raster datasets, containing the NOAH-H classification for the entire study area. One uses the 14 descriptive classes of the terrain, and the other with the five interpretative groups (Barrett et al., 2022). · Mawrth_Vallis_NOAHH_Mosaic_DC_25cm4bit_20230121_reclass.tif Contains the full 14 class “Descriptive Classes” (DC) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. · Mawrth_Vallis_NOAHH_Mosaic_IG_25cm4bit_20230121_reclass.tif Contains the 5 class “Interpretive Groups” (IG) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. Symbology layer files: NOAH-H_Symbology.zip This folder contains GIS layer file and colour map files for both the Descriptive Classes (DC) and interpretive Groups (IG) versions of the classification. These can be applied to the data using the symbology options in GIS. Georeferencing Control points: Mawrth_Vallis_Final_Control_Points.zip This file contains the control points used to georeferenced the 26 individual HiRISE images which make up the mosaic. These allow publicly available HiRISE images to be aligned to the terrain in Mawrth Vallis, and thus the NOAH-H Mosaic. Twenty-six 25 cm/pixel HiRISE images of Mawrth Vallis were used as input for NOAH-H. These are:

PSP_002140_2025_RED

PSP_002074_2025_RED

ESP_057351_2020_RED

ESP_053909_2025_RED

ESP_053698_2025_RED

ESP_052274_2025_RED

ESP_051931_2025_RED

ESP_051351_2025_RED

ESP_051219_2030_RED

ESP_050217_2025_RED

ESP_046960_2025_RED

ESP_046670_2025_RED

ESP_046525_2025_RED

ESP_046459_2025_RED

ESP_046314_2025_RED

ESP_045536_2025_RED

ESP_045114_2025_RED

ESP_044903_2025_RED

ESP_043782_2025_RED

ESP_043637_2025_RED

ESP_038758_2025_RED

ESP_037795_2025_RED

ESP_037294_2025_RED

ESP_036872_2025_RED

ESP_036582_2025_RED

ESP_035804_2025_RED NOAH-H produced corresponding 25 cm/pixel rasters where each pixel is assigned a terrain class based on the corresponding pixels in the input HiRISE image. To mosaic the NOAH-H rasters together, first the input HiRISE images were georeferenced to the HRSC basemap (HMC_11E10_co5) tile, using CTX images as an intermediate step. High order (spline, in ArcGIS Pro 3.0) transformations were used to make the HiRISE images georeference closely onto the target layers. Once the HiRISE images were georeferenced, the same control points and transformations were applied to the corresponding NOAH-H rasters. To mosaic the georeferenced NOAH-H rasters the pixel values for the classes needed to be changed so that more confidently identified, and more dangerous, classes made it into the mosaic (see dataset manuscript for details. To produce a HiRISE layer which fits the NOAH-H classification, download one of the listed HiRISE images from https://www.uahirise.org/, Select the corresponding control point file from this archive and apply a spline transformation through the GIS georeferencing toolbar. Manually Mapped Aeolian Bedforms: Mawrth_Manual_TARs.zip The manually mapped data was produced by Fawdon, independently of the NOAH-H project, as an assessment of “Aeolian Hazard” at Mawrth Vallis. This was done to inform the ExoMars landing site selection process. This file contains two GIS shape files, containing the manually mapped bedforms for both the entire mapping area, and the HiRISE image ESP_046459_2025_RED where the two datasets were compared on a pixel scale. The full manual map is offset slightly from the NOAH-H, since it was digitised from bespoke HiRISE orthomosaics, rather than from the publicly available HiRISE Red band images. It is suitable for comparison to the NOAH-H data with 100m-1km aggregation as in figure 8 of the associated paper. It is not suitable for pixel scale comparison. The map of ESP_046459_2025_RED was manually georeferenced to the NOAH-H mosaic, allowing for direct pixel to pixel comparisons, as presented in figure 6 of the associated paper. Two GIS shape files are included: · Mawrth_Manual_TARs_ESP_046459_2025.shp · Mawrth_Manual_TARs_all.shp Containing the high fidelity data for ESP_046459_2025, and the medium fidelity data for the entire area respectively. The are accompanied by ancillary files needed to view them in GIS. Gridded Density Statistics This dataset contains gridded density maps of Transverse Aeolian Ridges and Boulders, as classified by the Novelty or Anomaly Hunter – HiRISE (NOAH-H). The area covered is the runner up candidate ExoMars landing site in Mawrth Vallis, Mars. These are the data shown in figures; 7, 8, and S1. Files are presented for every classified ripple and boulder class, as well as for thematic groups. These are presented as .shp GIS shapefiles, along with all auxiliary files required to view them in GIS. Gridded Density stats are available in two zip folders, one for NOAH-H predicted density, and one for manually mapped density. NOAH-H Predicted Density: Mawrth_NOAHH_1km_Grid_TAR_Boulder_Density.zip Individual classes are found in the files: · Mawrth_NOAHH_1km_Grid_8TARs.shp · Mawrth_NOAHH_1km_Grid_9TARs.shp · Mawrth_NOAHH_1km_Grid_11TARs.shp · Mawrth_NOAHH_1km_Grid_12TARs.shp · Mawrth_NOAHH_1km_Grid_13TARs.shp · Mawrth_NOAHH_1km_Grid_Boulders.shp Where the text following Grid denotes the NOAH-H classes represented, and the landform classified. E.g. 8TARs = NOAH-H TAR class 8. The following thematic groups are also included: · Mawrth_NOAHH_1km_Grid_8_11continuousTARs.shp · Mawrth_NOAHH_1km_Grid_12_13discontinuousTARs · Mawrth_NOAHH_1km_Grid_8_10largeTARs.shp · Mawrth_NOAHH_1km_Grid_11_13smallTARs.shp · Mawrth_NOAHH_1km_Grid_8_13AllTARs.shp When the numbers denote the range of NOAH-H classes which were aggregated to produce the map, followed by a description of the thematic group: “continuous”, “discontinuous”, “large”, “small”, “all”. Manually Mapped Density Plots: Mawrth_Manual_1km_Grid.zip These GIS shapefiles have the same format as the NOAH-H classified ones. Three datasets are presented for all TARs (“_allTARs”), Continuous TARs (“_con”) and Discontinuous TARs (“_dis”) · Mawrth_Manual_1km_Grid_AllTARs.shp · Mawrth_Manual_1km_Grid_Con.shp · Mawrth_Manual_1km_Grid_Dis.shp Related public datasets: The HiRISE images discussed in this work are publicly available from https://www.uahirise.org/. and are credited to NASA/JPL/University of Arizona. HRSC images are credited to the European Space Agency; Mars Express mission team, German Aerospace Center (DLR), and the Freie Universität Berlin (FUB). They are available at the ESA Planetary Science Archive (PSA) https://www.cosmos.esa.int/web/psa/mars-express and are used under the Creative Commons CC BY-SA 3.0 IGO licence. SPATIAL DATA COORDINATE SYSTEM INFORMATION All NOAH-H files and derivative density plots have the same projected coordinate system: “Equirectangular Mars” - Projection: Plate Carree - Sphere radius: 3393833.2607584 m SOFTWARE INFORMATION All GIS workflows (georeferencing, mosaicking) were conducted in ArcGIS Pro 3.0. NOAH-H is a deep learning semantic segmentation software developed by SciSys Ltd for the European Space Agency to aid preparation for the ExoMars rover mission.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

This links to a .ZIP file contains Montana Spatial Data Infrastructure (MSDI) and other pertinent data layers clipped to the Montana Yellowstone River 2022 Spring Flood Disaster Subset Area of Interest polygon. The Area of Interest includes areas immediately adjacent to the flooded tributaries of the Yellowstone River in Carbon, Park, Stillwater, Sweet Grass, Treasure and Yellowstone Counties. The data layers are current as of July 2022. The .ZIP file also contains ArcMap layer files, map templates, and metadata for the source geodatabase data.For datasets clipped to the county or statewide use the Montana Data Bundler: https://msl.mt.gov/GIS/BundlerInside the zip are: A 2022MontanaFlood_DataList.docx that lists all GIS data included in this archive.A ReadMe.docx that details the data organization, instructions on how to set he map file paths, how to change the display map extents, and how to connect to web GIS services.ArcMap Layer Symbology Files (.lyr)GIS Layer MetadataMap Project Templates (ArcMap 10.7 and ArcGIS Pro 2.9 are included; other versions available upon request)File Geodatabase with data layers clipped to the Spring 2022 Flood Yellowstone River Area of InterestData Included:Montana Spatial Data Infrastructure (MSDI) DataAdministrative Boundaries - County Boundaries - Municipalities-Cities, TownsCadastral - Ownership - Public Lands - Conservation Easements Geographic Names - MT_NamesNational Hydrography Dataset - WBDHUC8-HUC8SubBasin - WBDHUC10-HUC10Watershed - WBDHUC12-HUC12Subwatershed - NHDFlowline - NHDWaterbody - NHDAreaCADNSDI (Public Land Survey database) - PLSSFirstDivision-Sections - PLSSTownship-TownshipsStructure/Address PointsTransportation - Bridges - Railroads - Roads Wetland and RiparianMTNHP Landcover - Landcover 2017 - Landcover 2021 (version 1)Elevation - NED 10 meter digital elevation model (DEM) - NED-Continuous, Integer rasters - Aspect-Continuous, Integer rasters (10 meter) - Slope-Continuous, Integer rasters (10 meter) - LiDAR-Derived Building Footprints - LiDAR Building Footprint Boundary - LiDAR ProjectsSoils (NRCS SSURGO) - Soils Map units - Soils Points - Soils LinesUSDA Forest ServiceLandfire – Existing Vegetation Type (EVT)Landfire – Existing Vegetation Height (EVH)Landfire – Existing Vegetation Cover (EVC)USDA NASS DataCropLand Data Layer 2021Department of Revenue Data2020 DOR Final Land Units (FLU)MiscellaneousBuilding Footprints (Microsoft)USGS 24k Topo Quads

Soils vary widely in their ability to retain or drain water. The rate at which water drains into the soil has a direct effect on the amount and timing of runoff, what crops can be grown, and where wetlands form. In soils with low drainage rates water will pond on the soil"s surface. This layer summarizes soil drainage rates in eight classes:Excessively drained:Water is removed very rapidly. The occurrence of internal free water commonly is very rare or very deep. The soils are commonly coarse-textured and have very high hydraulic conductivity or are very shallow.Somewhat excessively drained:Water is removed from the soil rapidly. Internal free water occurrence commonly is very rare or very deep. The soils are commonly coarse-textured and have high saturated hydraulic conductivity or are very shallow.Well drained:Water is removed from the soil readily but not rapidly. Internal free water occurrence commonly is deep or very deep; annual duration is not specified. Water is available to plants throughout most of the growing season in humid regions. Wetness does not inhibit growth of roots for significant periods during most growing seasons. The soils are mainly free of the deep to redoximorphic features that are related to wetness.Moderately well drained:Water is removed from the soil somewhat slowly during some periods of the year. Internal free water occurrence commonly is moderately deep and transitory through permanent. The soils are wet for only a short time within the rooting depth during the growing season, but long enough that most mesophytic crops are affected. They commonly have a moderately low or lower saturated hydraulic conductivity in a layer within the upper 1 m, periodically receive high rainfall, or both.Somewhat poorly drained:Water is removed slowly so that the soil is wet at a shallow depth for significant periods during the growing season. The occurrence of internal free water commonly is shallow to moderately deep and transitory to permanent. Wetness markedly restricts the growth of mesophytic crops, unless artificial drainage is provided. The soils commonly have one or more of the following characteristics: low or very low saturated hydraulic conductivity, a high water table, additional water from seepage, or nearly continuous rainfall.Poorly drained:Water is removed so slowly that the soil is wet at shallow depths periodically during the growing season or remains wet for long periods. The occurrence of internal free water is shallow or very shallow and common or persistent. Free water is commonly at or near the surface long enough during the growing season so that most mesophytic crops cannot be grown, unless the soil is artificially drained. The soil, however, is not continuously wet directly below plow-depth. Free water at shallow depth is usually present. This water table is commonly the result of low or very low saturated hydraulic conductivity of nearly continuous rainfall, or of a combination of these.Very poorly drained:Water is removed from the soil so slowly that free water remains at or very near the ground surface during much of the growing season. The occurrence of internal free water is very shallow and persistent or permanent. Unless the soil is artificially drained, most mesophytic crops cannot be grown. The soils are commonly level or depressed and frequently ponded. If rainfall is high or nearly continuous, slope gradients may be greater.Subaqueous Soils:Free water is above the soil surface. Internal free water occurrence is permanent, and there is a positive water potential at the soil surface for more than 21 hours of each day. The soils have a peraquic soil moisture regime.For more information on the classifications see the Soil Survey Manual section on Soil Water. Dataset SummaryPhenomenon Mapped: Drainage Class of SoilsGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service(NRCS). The value for drainage class is derived from the gSSURGO map unit aggregated attribute table field Drainage Class - Dominant Condition (drclassdcd). What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "drainage class" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expandPortalif necessary, then select Living Atlas. Type "drainage class" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.