This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This Tutorial consists of four tutorials that deal with integrating the statistical programming language R with ArcGIS for Desktop. Several concepts are covered which include configuring ArcGIS with R, writing basic R scripts, writing R scripts that work with ArcGIS data, and constructing R Tools for use within ArcGIS Pro. It is recommended that the tutorials are completed in sequential order. Each of the four tutorials (as well as a version of this document), can viewed directly from your Web browser by following the links below. However, you must obtain a complete copy of the tutorial files by downloading the latest release (or by cloning the tutorial repository on GitHub) if you wish to follow the tutorials interactively using ArcGIS and R software, along with pre-configured sample data.To download the tutorial documents and datasets, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/r-arcgis-tutorials.gitSoftware & Solutions Used: ArcGIS Pro 3.4 Internet browser (e.g., Mozilla Firefox, Google Chrome, Safari) R Statistical Computing Language – version 4.3.3 R-ArcGIS Bindings – version 1.0.1.311RStudio Desktop – version 2024.09.0+375Time to Complete: 2.5 h (excludes installation time)File Size: 115 MBDate Created: November 2017Last Updated: December 2024

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification.

The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively.

After completing this seminar you will be able to:

1. explain how ANNs work including weights, bias, activation, and optimization.
2. describe and explain different loss and assessment metrics and determine appropriate use cases.
3. use the tensor data model to represent data as input for deep learning.
4. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization.
5. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data.
6. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs.
7. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.
8. explain how object and instance segmentation are different from traditional CNNs and semantic segmentation and how they can be used to generate bounding boxes and feature masks for each instance of a class.
9. use ArcGIS Pro to perform object detection (to obtain bounding boxes) and instance segmentation (to obtain pixel-level instance masks).

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

Aligning rasters such that their bounding extent and cell sizes match precisely is a tedious, time consuming, and challenging task. East-to-use tools have been lacking up until now. Many modeling approaches require rasters to be perfectly aligned. For example, a common workflow using R would be to stack rasters and then do subsequent predictive modeling using the stacked rasters as covariates. The Align Rasters Toolbox allows users to quickly and easily align rasters. It has options for working with rasters of differing cell sizes and extents. The Align Rasters without Expansion tool is suitable for situations in which the template raster is smaller than all inputs.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

Although there are a large number of software products available for calculating landscape metrics (e.g. FRAGSTATS, landscapemetrics package in R) no tools are currently available (to my knowledge) that calculate landscape metrics directly in ArcGIS Pro. Moreover, many, if not most, landscape metrics were designed with vector data in mind, but most software calculates landscape metrics from raster data due to processing time and complexity. Scaling landscape metrics can also be tedious in some instances. This toolbox was designed to calculate attributes of patches that are easily calculated on polygons in ArcGIS (i.e. area, number of patches, Landscape Shape Index, edge density, patch size, distance to the nearest patch) and scales those calculations to coarser resolutions using Block Statistics. The tool also summarizes the relationships among metrics by using Principal Component Analysis and correlation matrices to assess relationships among variables. All variables are output to a single folder.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

vfillDL.zip

dems: LiDAR DTM data partitioned into training, three testing, and two validation datasets. Original DTM data were obtained from 3DEP (https://www.usgs.gov/3d-elevation-program) and the WV GIS Technical Center (https://wvgis.wvu.edu/) . extents: extents of the training, testing, and validation areas. These extents were defined by the researchers. vectors: vector features representing valley fills and partitioned into separate training, testing, and validation datasets. Extents were created by the researchers.

Predator-prey interactions can be profoundly influenced by vegetation conditions, particularly when predator and prey prefer different habitats. Although such interactions have proven challenging to study for small and cryptic predators, recent methodological advances substantially improve opportunities for understanding how vegetation influences prey acquisition and strengthen conservation planning for this group. The California Spotted Owl (Strix occidentalis occidentalis) is well-known as an old-forest species of conservation concern, but whose primary prey in many regions – woodrats (Neotoma spp.) – occurs in a broad range of vegetation conditions. Here, we used high-resolution GPS tracking coupled with nest video monitoring to test the hypothesis that prey capture rates vary as a function of vegetation structure and heterogeneity, with emergent, reproductive consequences for Spotted Owls in Southern California. Foraging owls were more successful capturing prey, including woodrats, in taller multilayered forests, in areas with higher heterogeneity in vegetation types, and near forest-chaparral edges. Consistent with these findings, Spotted Owls delivered prey items more frequently to nests in territories with greater heterogeneity in vegetation types and delivered prey biomass at a higher rate in territories with more forest-chaparral edge. Spotted Owls had higher reproductive success in territories with higher mean canopy cover, taller trees, and more shrubby vegetation. Collectively, our results provide additional and compelling evidence that a mosaic of large tree forests with complex canopy and shrubby vegetation increases access to prey with potential reproductive benefits to Spotted Owls in landscapes where woodrats are a primary prey item. We suggest that forest management activities that enhance forest structure and vegetation heterogeneity could help curb declining Spotted Owl populations while promoting resilient ecosystems in some regions. Methods See README DOCUMENT Naming conventions *RSF or prey refers to prey capture analysis *delivery in a file name refers to delivery rate analysis *repro in a filename means that file is for the delivery rate analysis

Setup *files with vegetation data should work with minimal alteration(will need to specify working directory) with associated R code for each analysis *Shapefiles were made in ArcGIS pro but they can be opened with any GIS software such as QGIS.

Locational data files

NOTE LOCATIONAL DATA IS SHIFTED AND ROTATED FROM THE ORIGINAL -due to the sensitive nature of this species. The locational_data includes: * All_2021_owls_shifted * Point file showing all GPS tag locations for prey capture analysis * Attributes include: * TERRITORY ID: Numerical identifier for each bird * Year: year GPS tag was recorded * Month: month GPS tag was recorded * Day: Day GPS tag was recorded * Hour: Hour GPS tag was recorded * Minute: minute GPS tag was recorded * All_linked_polygons_shifted * Polygon file showing capture polygons for prey capture analysis * Attributes include * Territory ID: numerical identifier for each bird * Polygon id: numerical identifier for each capture polygon for each bird * Shape area: area of each polygon * SBNF_camera_nests_shifted * Point file showing spotted owl nests for prey capture analysis * Attributes include * Territory id: numerical identifier for each bird * C95_KDE_2021_socal_shifted * Polygon file of owls 95% kernel density estimate for prey delivery rate analysis * Attributes include * Id: numerical identifier for each territory(bird) * Area: area of each polygon * San_bernardino_territory_centers * Point file showing Territory centers for historical SBNF territories – shifted for repro success analysis * Attributes include * Repro Territory id: unique identifier for each territory in broader set of territories

Besides the sifted locational data we have included - For the Resource selection function vegetation data, for the delivery analysis we have included an overview of prey deliveries by territory and vegetation data used, and for the reproductive analysis we have again included vegetation data as well as an overview of reproductive success. these are labled as follows:

Files for the prey capture analysis

Socal_RSF_data.txt

*description: Text file with vegetation data paired with capture locations both buffered polygons used in prey capture analysis and the unbuffered ones which were not used.(Pair with Socal_rsf_code R script) *format: .txt *Dimensions: 2641 X 35

*Variables: *ORIG_fid: completely unique identifier for each row *unique_id: unique identifier for each capture polygon(shared between a buffered capture location and its unbuffered pair) *territory_id: unique numerical idenifier of territory *Polygon_id: within territory unique prey capture polygon id *buff: bianary buffered or unbuffered (1=buffered, 0=unbuffered) *used: bianary used=1 available=0 *prey_type: prey species associated with polygon unkn:unknown, flsq:flying squirel, wora:woodrat, umou:mouse, pogo:pocketgopher, grsq: grey squirel, ubrd: unknown bird, umol:unknown mole, uvol, unknown vole. *area_sqm: area of polygon in square meters *CanCov_2020_buff: average canopy cover in polygon *CanHeight_2020_buff: average canopy height in polygon *Canlayer_2020_buff: average number of canopy layers in polygon *Understory_density_2020_buff: average brushy vegetation density in polygon *pix_COUNT: count of pixels in polygon (not needed for analysis) *p_chaparral: percent of polygon comprised of chaparral habitat
*p_conifer: percent of polygon comprised of conifer habitat *p_hardwood: percent of polygon comprised of hardwood habitat *p_other: percent of polygon comprised of other habitat types *Calveg_cap_CHt_gt10_CC_30to70_intersect_buff: percent of polygon comprised of trees taller than 10m with 30-70percent canopy cover (used to check data) *Calveg_cap_CHt_gt10_CCgt70_intersect_buff: percent of polygon comprised of trees taller than 10m with greater than 70percent canopy cover (used to check data) *Calveg_cap_CHt_lt10_intersect_buff:percent of polygon comprised of trees less than 10m (used to check data)
*p_sm_conifer: percent of polygon comprised of conifer trees less than 10m (used to calculate diversity)
*p_lrg_conifer_sc: percent of polygon comprised of conifer forests >10m tall with sparse canopy(used to calculate diversity) *p_large_conifer_dc: percent of polygon comprised of conifer forests greater than 10m tall with dense canopy (used to calculate diversity) *p_sm_hard: percent of polygon comprised of hardwood trees less than 10m (used to calculate diversity) *p_lrg_hard_sc: percent of polygon comprised of hardwood forests greater than 10m with sparse canopy(used to calculate diversity)
*p_lrg_hard_dc: percent of polygon comprised of hardwood forests greater than 10m dense canopy (used to calculate diversity) *p_forests_gt10_verysparse_CC: percent of polygon comprised of trees less than 10m with very sparse canopies (used to calculate diversity) *primary_edge: total distance in meters of primary edge in a polygon
*normalized_by_area_primary_edge: total distance in m of primary edge in a polygon divided by the area of the polygon
*secondary_edge: total distance in meters of secondary edge in a polygon *normalized_by_area_secondary_edge:total distance in m of secondary edge in a polygon divided by the area of the polygon *coarse_diversity: shannon diversity in each polygon (see methods below) *fine_diversity: shannon diversity in each polygon (see methods below) *nest_distance: distance from polygon center to nest for each polygon in meters

For the Delivery analysis

note: For information on determining average prey biomass see methods as well as zulla et al 2022 for flying squirels and woodrat masses Zulla CJ, Jones GM, Kramer HA, Keane JJ, Roberts KN, Dotters BP, Sawyer SC, Whitmore SA, Berigan WJ, Kelly KG, Gutiérrez RJ, Peery MZ. Forest heterogeneity outweighs movement costs by enhancing hunting success and fitness in spotted owls. doi:10.21203/rs.3.rs-1370884/v1. PPR:PPR470028.

prey_deliveries_byterritory.csv *Description: overview file of prey delivered to each nest *format: .csv *dimensions:332 x 8

*Variables: *SITE: Unique numerical identifier for each territory *DATE: date prey was delivered (in UTC) *CAMERA TIME: time in UTC prey was delivered *VIDEO TIME: time on video prey was delivered - unrelated to real time just original file
*PREY ITEM: prey species delivered to nest unkn:unknown, uncr: unknown if delivery(removed from eventual analysis due to

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Dataset contains building locations in Poland in 1970-80s. The source information were polish archival 1:10 000 topographical maps. Buildings were extracted from maps using Mask R-CNN model implemented in Esri ArcGIS Pro software. In post processing we have removed most of the false possitives. The dataset of building locations covers the entire country and contains approximately 11 million buildings. The accuracy of the dataset was assessed manually on randomly selected map sheets. The overall accuracy is 95% (F1 0.98).

Named Landforms of the World version 2 (NLWv2) contains four sub-layers representing geomorphological landforms, provinces, divisions, and their respective cartographic boundaries. The latter supports map making, while the first three represent basic units, such as landforms, which comprise provinces, and provinces comprise divisions. NLW is a substantial update to World Named Landforms in both compilation method and the attributes that describe each landform. For more details, please refer to our paper, Named Landforms of the World: A Geomorphological and Physiographic Compilation, in Annals of the American Association of Geographers. July 2, 2025: We have made Named Landforms of the World v3 (NLWv3) available. Please explore this group containing all of the layers and data. NLWv2 will remain available. Landforms are commonly defined as natural features on the surface of the Earth. The National Geographic Society specifies terrain as the basis for landforms and lists four major types: mountains, hills, plateaus, and plains. Here, however, we define landforms in a richer way that includes properties relating to underlying geologic structure, erosional and depositional character, and tectonic setting and processes. These characteristics were asserted by Dr. Richard E. Murphy in 1968 in his map, titled Landforms of the World. We blended Murphy"s definition for landforms with the work E.M. Bridges, who in his 1990 book, World Geomorphology, provided a globally consistent description of geomorphological divisions, provinces, and sections to give names to the landform regions of the world. AttributeDescriptionBridges Full NameFull name from E.M. Bridges" 1990 "World Geomorphology" Division and if present province and section - intended for labeling print maps of small extents. Bridges DivisionGeomorphological Division as described in E.M. Bridges" 1990 "World Geomorphology" - All Landforms have a division assigned, i.e., no nulls. Bridges ProvinceGeomorphological Province as described in E.M. Bridges" 1990 "World Geomorphology" - Not all divisions are subdivided into provinces. Bridges SectionGeomorphological Section as described in E.M. Bridges" 1990 "World Geomorphology" - Not all provinces are subdivided into sections.StructureLandform Structure as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Alpine Systems: Area of mountains formed by orogenic (collisions of tectonic plates) processes in the past 350 to 500 million years. - Caledonian/Hercynian Shield Remnants: Area of mountains formed by orogenic (collisions of tectonic plates) processes 350 to 500 million years ago. - Gondwana or Laurasian Shields: Area underlaid by mostly crystalline rock formations fromed one billion or more years ago and unbroken by tectonic processes. - Rifted Shield Areas: fractures or spreading along or adjacent to tectonic plate edges. - Isolated Volcanic Areas: volcanic activity occurring outside of Alpine Systems and Rifted Shields. - Sedimentary: Areas of deposition occurring within the past 2.5 million years Moist or DryLandform Erosional/Depositional variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Moist: where annual aridity index is 1.0 or higher, which implies precipitation is absorbed or lost via runoff. - Dry: where annual aridity index is less than 1.0, which implies more precipitation evaporates before it can be absorbed or lost via runoff. TopographicLandform Topographic type variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Karagulle et. al. 2017 - based on rich morphometric characteristics. Coded Value Domain. Values include: - Plains: Areas with less than 90-meters of relief and slopes under 20%. - Hills: Areas with 90- to 300-meters of local relief. - Mountains: Areas with over 300-meters of relief - High Tablelands: Areas with over 300-meters of relief and 50% of highest elevation areas are of gentle slope. - Depressions or Basins: Areas of land surrounded land of higher elevation. Glaciation TypeLandform Erosional/Depositional variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Values include: - Wisconsin/Wurm Glacial Extent: Areas of most recent glaciation which formed 115,000 years ago and ended 11,000 years ago. - Pre-Wisconsin/Wurm Glacial Extent: Areas subjected only to glaciation prior to 140,000 years ago. ContinentAssigned by Author during data compilation. Bridges Short NameThe name of the smallest of Division, Province, or Section containing this landform feature. Murphy Landform CodeCombination of Richard E. Murphy"s 1968 "Landforms of the World" variables expressed as a 3- or 4- letter notation. Used to label medium scale maps. Area_GeoGeodesic area in km2. Primary PlateName of tectonic plate that either completely underlays this landform feature or underlays the largest portion of the landform"s area.Secondary PlateWhen a landform is underlaid by two or more tectonic plates, this is the plate that underlays the second largest area.3rd PlateWhen a landform is underlaid by three or more tectonic plates, this is the plate that underlays the third largest area.4th PlateWhen a landform is underlaid by four or more tectonic plates, this is the plate that underlays the fourth largest area.5th PlateWhen a landform is underlaid by five tectonic plates, this is the plate that underlays the fifth largest area.NotesContains standard text to convey additional tectonic process characteristics. Tectonic ProcessAssigns values of orogenic, rift zone, or above subducting plate. These data are also available as an ArcGIS Pro Map Package: Named_Landforms_of_the_World_v2.0.mpkx.These data supersede the earlier v1.0: World Named Landforms. Change Log:DateDescription of ChangeJuly 20, 2022Corrected spelling of Guiana from incorrect representation, "Guyana", used by Bridges.July 27, 2022Corrected Structure coded value domain value, changing "Caledonian/Hercynian Shield" to "Caledonian , Hercynian, or Appalachian Remnants". Cite as: Frye, C., Sayre R., Pippi, M., Karagulle, Murphy, A., D. Soller, D.R., Gilbert, M., and Richards, J., 2022. Named Landforms of the World. DOI: 10.13140/RG.2.2.33178.93129. Accessed on:

Created using ArcGIS Pro Geoprocessing tools (Create Space Time Cube, Emerging Hot Spot Analysis, and Enrich Layer) and the ArcGIS R Bridge. The EBest function, part of the spdep package was used to calculate an Empirical Bayes smoothed crime rate with 2016 population estimates. This procedure is presented as part of the R-ArcGIS Workflow Demo on GeoNet.Relative Burglary Risk is the natural log (Ln) of the kernel density of burglaries g(x) divided by the kernel density of households g(y) calculated using CrimeStat. Note: Ten months of burglary data (the minimum required) were used for this initial analysis. Also Note: These locations are one-half kilometer square polygons. It will be updated in the future as more data from the Albuquerque Police Department is obtained (see ABQ Data).Please see the web map for another similar way to present these results.More information at (http://www.unm.edu/~lspear/other_nm.html).

Homeowners in coastal environments often augment their access to estuarine ecosystems by building private docks on their personal property. Despite the commonality of docks, particularly in the Southeastern United States, few works have investigated their historical development, their distribution across the landscape, or the environmental justice dimensions of this distribution. In this study, we used historic aerial photography to track the abundance and size of docks across six South Carolina counties from the 1950s to 2016. Across our roughly 60-year study period, dock abundance grew by two orders of magnitude, the mean length of newly constructed docks doubled, and the cumulative length of docks ballooned from 34 to 560 km. Additionally, we drew on census data interpolated into consistent 2010 tract boundaries to analyze the racial and economic distribution of docks in 1994, 1999, 2011, and 2016. Racial composition, measured as the percentage of a tract’s population that was White,..., Dock data was collected via historic aerial imagery of the South Carolina coast. Pre-1990 imagery was obtained from the University of South Carolina library, 1994 and 1999 imagery was obtained from the South Carolina Department of Natural Resources, and 2011 imagery was obtained from the US Department of Agriculture National Agriculture Imagery Program’s Geospatial Data Gateway (https://nrcs.app.box.com/v/gateway/folder/19350726983). Census data was obtained from the NHGIS and Historical Housing Unit and Urbanization Database at the tract level using their crosswalk files to interpolate 1990 and 2000 data to 2010 tract geographies. Docks were tracked across decades in ArcGIS Pro and statistical models were run using R. Greater methodological detail is provided in the "Historic Infrastructure Methodology" file in the "Historic_Dock_Supplemental" folder on Zenodo. All pre-1990 images therein are reproduced with permission of the University of South Carolina library., R, ArcGIS Pro Version 2.9 or greater., # Exponential growth of private coastal infrastructure influenced by geography and race in South Carolina, USA

This data set contains ArcGIS Pro files and a CSV of every structure identified in the study. The code used to run the models can be found on the associated Zenodo page in the "Historic_Dock_Code" folder. Additional methodological information and model diagnostics can be found in the "Historic_Dock_Supplemental" folder on the associated Zenodo page. If you have any questions or requests please email Jeffrey Beauvais (he/him) at beauvais.work@gmail.com

Description of the Data and file structure

Parent folder: Historic_Dock_ArcPro_Files

Contains a geodatabase (.gdb file) with final point layers used in the analysis for dock counts, lengths, and geographic boundaries. Intermediate files were redundant and excluded but available upon request. Some folders within are empty and automatically generated by ArcGIS Pro when loading the p...

Data was collected from state agencies in South Carolina and the American Community Survey. All analyses were conducted in ArcMap 10.6.1 and R Version 4.0.4. A complete methodology can be found in the "Infrastructure Protocols and Guide.docx" file included.

surficialDL: A geomorpholgy deep learning dataset of alluvium and thick glacial till derived form 1:24,000 scale surficial geology data for the western portion of Massachusetts, USA

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

surficialDL The digital terrain model associated with these data/project is available here: https://s3.us-east-1.amazonaws.com/download.massgis.digital.mass.gov/lidar/LIDAR_DEM_32BIT_FP.gdb.zip. alluvDL: polygons (vectors folder) and extents (extents folder) for alluvium features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology.
tillDL: polygons (vector folder) and extents (extents folder) for thick till features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology.

Occupancy status, Units, Year built, Owner/Renter (Tenure), Mortgage/Rent costs variables from 1-Year ACS.Contact: District of Columbia, Office of Planning. Email: planning@dc.govGeography: District of ColumbiaCurrent Vintage: 2023ACS Table(s): DP04Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 2, 2025National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data. Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Data processed using R statistical package and ArcGIS Pro.Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 1-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.govGeography: District of ColumbiaCurrent Vintage: 2019ACS Table(s): DP03Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 19, 2019National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data. Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in September. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Data processed using R statistical package and ArcGIS Desktop.Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

The desire to stabilize coastlines has led to widespread use of hard armoring infrastructure across the globe, however, ecologists and coastal managers have increasingly documented the deleterious effects of armoring on ecological communities. Although many studies have assessed economic and landscape correlates of armoring, few studies incorporate race as a predictor of armoring. Race may be an important force structuring the placement of armoring due to the long history of Black land loss in the US Southeast. Here, we assessed the distribution of armoring in the US state of South Carolina with respect to demographic and housing characteristics using a high spatial resolution data set and a combination of spatial statistics and generalized linear mixed models. We found clusters of high armoring counts in the more urbanized Beaufort and Charleston counties, with these clusters frequently occurring in large-scale, planned communities. We found a positive correlation between armor count a..., The data analyzed in this study were originally collected by Jackson (2017). Armoring data was associated with census block groups in ArcPro and then analyzed with respect to demographic data gathered from the Census Bureau via GLMMs in R. A copy of the Jackson 2017 is provided in the "Armoring_Supplemental" folder. Jackson, C. 2017. Mapping Coastal Erosion Hazards Along Sheltered Coastlines in South Carolina 1849 to 2015. South Carolina Department of Health and Environmental Control Office of Ocean and Coastal Resource Management., ArcGIS Pro and R were used for analyses., # Racial composition and homeownership influence the distribution of coastal armoring in South Carolina, United States

This text document contains a brief description of all the files in the "Armoring_Data" folder. All R scripts used to run the analysis are hosted on the associated Zenodo link and contain a separate README therein ("0_README.R"). If you have any questions or requests, please email me (Jeffrey Beauvais, he/him, beauvais.work@gmail.com).

Description of the Data and file structure

Parent folder: Armoring_ArcPro_Files

Some folders ("BeauvaisByers_Armoring_Index", "GpMessages", and "ImportLog") are empty and automatically generated by ArcGIS Pro when loading the program.

Sub-folder: BeauvaisByers_Armoring.gdbÂ

Contains a geodatabase (.gdb file) with final point layers used in the analysis for dock counts, lengths, and geographic boundaries. Intermediate files were redundant and excluded, but are available ...,

Household type, Education, Disability, Language, Computer/Internet Use, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.govGeography: Census TractsCurrent Vintage: 2019-2023ACS Table(s): DP02Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 2, 2025National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data. Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Data processed using R statistical package and ArcGIS Pro.Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.