The ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) Project Package includes all of the layers that are in the NTIA Permitting and Environmental Information Application as well as the APPEIT Tool which will allow users to input a project area and determine what layers from the application overlap with it. An overview of the project package and the APPEIT tool is provided below.

User instructions on how to use the tool are available here. A video explaining how to use the Project Package is also available here.

Project Package Overview

This map package includes all of the layers from the NTIA Permitting and Environmental Information Application. The layers included are all feature services from various Federal and State agencies. The map package was created with ArcGIS Pro 3.4.0. The map package was created to allow users easy access to all feature services including symbology. The map package will allow users to avoid downloading datasets individually and easily incorporate into their own GIS system. The map package includes three maps.

1. Permitting and Environmental Information Application Layers for GIS Analysis - This map includes all of the map tabs shown in the application, except State Data which is provided in another tab. This map includes feature services that can be used for analysis with other project layers such as a route or project area.

2. Permitting and Environmental Information Application Layers – For Reference Only - This map includes layers that cannot be used for analysis since they are either imagery or tile layers.

3. State Data - Reference Only - This map includes all relevant state data that is shown in the application.

The NTIA Permitting and Environmental Information Application was created to help with your permitting planning and environmental review preparation efforts by providing access to multiple maps from publicly available sources, including federal review, permitting, and resource agencies. The application should be used for informational purposes only and is intended solely to assist users with preliminary identification of areas that may require permits or planning to avoid potentially significant impacts to environmental resources subject to the National Environmental Policy Act (NEPA) and other statutory requirements. Multiple maps are provided in the application which are created from public sources. This application does not have an exhaustive list of everything you need for permitting or environmental review for a project but is an initial starting point to see what might be required.

APPEIT Tool OverviewThe Department of Commerce’s National Telecommunications and Information Administration (NTIA) is providing the ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) to help federal broadband grant recipients and subgrantees identify permits and environmental factors as they plan routes for their broadband deployments. Identifying permit requirements early, initiating pre-application coordination with permitting agencies, and avoiding environmental impacts help drive successful infrastructure projects. NTIA’s public release of the APPEIT tool supports government-wide efforts to improve permitting and explore how online and digital technologies can promote efficient environmental reviews.

This Esri ArcGIS Pro tool is included in the map package and was created to support permitting, planning, and environmental review preparation efforts by providing access to data layers from publicly available sources, including federal review, permitting, and resource agencies. An SOP on how to use the tool is available here. For the full list of APPEIT layers, see Appendix Table 1 in the SOP. The tool is comprised of an ArcGIS Pro Project containing a custom ArcGIS Toolbox tool, linked web map shared by the NTIA’s National Broadband Map (NBAM), a report template, and a Tasks item to guide users through using the tool. This ArcGIS Pro project and its contents (maps and data) are consolidated into this (.ppkx) project file.

To use APPEIT, users will input a project area boundary or project route line in a shapefile or feature class format. The tool will return as a CSV and PDF report that lists any federal layers from the ArcGIS Pro Permitting and Environmental Information Web Map that intersect the project. Users may only input a single project area or line at a time; multiple projects or project segments will need to be screened separately. For project route lines, users are required to specify a buffer distance. The buffer distance that is used for broadband projects should be determined by the area of anticipated impact and should generally not exceed 500 feet. For example, the State of Maryland recommends a 100-foot buffer for broadband permitting. The tool restricts buffers to two miles to ensure relevant results.

Disclaimer

This document is intended solely to assist federal broadband grant recipients and subgrantees in better understanding Infrastructure Investment and Jobs Act (IIJA) broadband grant programs and the requirements set forth in the Notice of Funding Opportunity (NOFO) for this program. This document does not and is not intended to supersede, modify, or otherwise alter applicable statutory or regulatory requirements, the terms and conditions of the award, or the specific application requirements set forth in the NOFO. In all cases, statutory and regulatory mandates, the terms and conditions of the award, the requirements set forth in the NOFO, and follow-on policies and guidance, shall prevail over any inconsistencies contained in this document.

NTIA’s ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) should be used for informational purposes only and is intended solely to assist users with preliminary identification of broadband deployments that may require permits or planning to avoid potentially significant impacts to environmental resources subject to the National Environmental Policy Act (NEPA) and other statutory requirements.

The tool is not an exhaustive or complete resource and does not and is not intended to substitute for, supersede, modify, or otherwise alter any applicable statutory or regulatory requirements, or the specific application requirements set forth in any NTIA NOFO, Terms and Conditions, or Special Award Condition. In all cases, statutory and regulatory mandates, and the requirements set forth in NTIA grant documents, shall prevail over any inconsistencies contained in these templates.

The tool relies on publicly available data available on the websites of other federal, state, local, and Tribal agencies, and in some instances, private organizations and research institutions. Layers identified with a double asterisk include information relevant to determining if an “extraordinary circumstance” may warrant more detailed environmental review when a categorical exclusion may otherwise apply. While NTIA continues to make amendments to its websites to comply with Section 508, NTIA cannot ensure Section 508 compliance of federal and non-federal websites or resources users may access from links on NTIA websites.

All data is presented “as is,” “as available” for informational purposes. NTIA does not warrant the accuracy, adequacy, or completeness of this information and expressly disclaims liability for any errors or omissions.

Please e-mail NTIAanalytics@ntia.gov with any questions.

The Geodatabase to Shapefile Warning Tool examines feature classes in input file geodatabases for characteristics and data that would be lost or altered if it were transformed into a shapefile. Checks include:
1) large files (feature classes with more than 255 fields or over 2GB), 2) field names longer than 10 characters
string fields longer than 254 characters, 3) date fields with time values 4) NULL values, 5) BLOB, guid, global id, and raster field types, 6) attribute domains or subtypes, and 7) annotation or topology

The results of this inspection are written to a text file ("warning_report_[geodatabase_name]") in the directory where the geodatabase is located. A section at the top provides a list of feature classes and information about the geodatabase as a whole. The report has a section for each valid feature class that returned a warning, with a summary of possible warnings and then more details about issues found.

The tool can process multiple file geodatabases at once. A separate text file report will be created for each geodatabase. The toolbox was created using ArcGIS Pro 3.7.11.

For more information about this and other related tools, explore the Geospatial Data Curation toolkit

Deep-sea corals are colonies of tiny animals that build a common skeleton, feed on microscopic organisms, and live in water depths greater than 50m where there is little or no sunlight. The NOAA Deep Sea Coral Research & Technology Program manages an extensive database citing the locations of known corals. These coral locations have been documented over centuries and come from a variety of field collection methods. This database is updated frequently and is made available by the NOAA National Centers for Environmental Information to assist in marine spatial planning efforts and to aid in the overall preservation and education of deep-sea coral resources.In this lesson you will build skills in these areas:- Locate and download deep-sea coral data- Make charts in ArcGIS Pro- Calculate summary statistics- Run reports in ArcGIS ProLearn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

The protected areas include National Parks, Areas of Outstanding Natural Beauty and National Scenic Areas. The original datasets used to create this are listed and linked below: National Parks (England)Areas of Outstanding Natural Beauty (England)Areas of Outstanding Natural Beauty (Wales)Areas of Outstanding Natural Beauty (Northern Ireland)National Scenic AreasOtter Data filtered to the relevant years and then to only include open recordsLand Cover Map 2019 (25m rasterised land parcels, GB)Citation: Morton, R. D.; Marston, C.G.; O’Neil, A. W.; Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB). NERC Environmental Information Data Centre. https://doi.org/10.5285/f15289da-6424-4a5e-bd92-48c4d9c830ccLand Cover Map 2019 (25m rasterised land parcels, N. Ireland)Citation: Morton, R. D.; Marston, C.G.; O’Neil, A. W.; Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, N. Ireland). NERC Environmental Information Data Centre. https://doi.org/10.5285/2f711e25-8043-4a12-ab66-a52d4e649532OS Open RiversData was processed in ArcGIS Pro before being shared to ArcGIS Online before being used in Report Builder for ArcGIS.

Purpose:This feature layer describes water quality sampling data performed at several operating coal mines in the South Fork of Cherry watershed, West Virginia.Source & Data:Data was downloaded from WV Department of Environmental Protection's ApplicationXtender online database and EPA's ECHO online database between January and April, 2023.There are five data sets here: Surface Water Monitoring Sites, which contains basic information about monitoring sites (name, lat/long, etc.) and NPDES Outlet Monitoring Sites, which contains similar information about outfall discharges surrounding the active mines. Biological Assessment Stations (BAS) contain similar information for pre-project biological sampling. NOV Summary contains locations of Notices of Violation received by South Fork Coal Company from WV Department of Environmental Protection. The Quarterly Monitoring Reports table contains the sampling data for the Surface Water Monitoring Sites, which actually goes as far back as 2018 for some mines. Parameters of concern include iron, aluminum and selenium, among others.A relationship class between Surface Water Monitoring Sites and the Quarterly Monitoring Reports allows access to individual sample results.Processing:Notices of Violation were obtained from the WV DEP AppXtender database for Mining and Reclamation Article 3 (SMCRA) Permitting, and Mining and Reclamation NPDES Permitting. Violation data were entered into Excel and loaded into ArcGIS Pro as a CSV text file with Lat/Long coordinates for each Violation. The CSV file was converted to a point feature class.Water quality data were downloaded in PDF format from the WVDEP AppXtender website. Non-searchable PDFs were converted via Optical Character Recognition, so that data could be copied. Sample results were copied and pasted manually to Notepad++, and several columns were re-ordered. Data was grouped by sample station and sorted chronologically. Sample data, contained in the associated table (SW_QM_Reports) were linked back to the monitoring station locations using the Station_ID text field in a geodatabase relationship class.Water monitoring station locations were taken from published Drainage Maps and from water quality reports. A CSV table was created with station Lat/Long locations and loaded into ArcGIS Pro. It was then converted to a point feature class.Stream Crossings and Road Construction Areas were digitized as polygon feature classes from project Drainage and Progress maps that were converted to TIFF image format from PDF and georeferenced.The ArcGIS Pro map - South Fork Cherry River Water Quality, was published as a service definition to ArcGIS Online.Symbology:NOV Summary - dark blue, solid pointLost Flats Surface Water Monitoring Sites: Data Available - medium blue point, black outlineLost Flats Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineLost Flats NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Surface Water Monitoring Sites: Data Available - medium blue point, black outlineBlue Knob Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineBlue Knob NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Biological Assessment Stations: Data Available - medium green point, black outlineBlue Knob Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Surface Water Monitoring Sites: Data Available - medium blue point, black outlineRocky Run Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineRocky Run NPDES Outlet Monitoring Sites - orange point, black outlineRocky Run Biological Assessment Stations: Data Available - medium green point, black outlineRocky Run Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Stream Crossings: turquoise blue polygon with red outlineRocky Run Haul Road Construction Areas: dark red (40% transparent) polygon with black outlineHaul Road No 2 Surface Water Monitoring Sites: Data Available - medium blue point, black outlineHaul Road No 2 Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineHaul Road No 2 NPDES Outlet Monitoring Sites - orange point, black outline

The Israel geospatial analytics market is projected to grow from USD 1.69 million in 2025 to USD 2.69 million by 2033, at a CAGR of 5.93% during the forecast period. The growth of this market is attributed to increasing adoption of geospatial analytics in various end-user verticals, such as agriculture, utility and communication, defense and intelligence, government, mining and natural resources, automotive and transportation, healthcare, real estate and construction, and other end-user verticals. Geospatial analytics helps in better decision-making, improves operational efficiency, and enhances customer engagement. Key drivers of the Israel geospatial analytics market include increasing adoption of cloud-based geospatial platforms, rising demand for real-time insights, and growing investments in smart city development. However, factors such as high cost of implementation and skilled labor shortage may hinder the market growth. Major companies operating in the Israel geospatial analytics market include SAS Institute Inc., General Electrical Company, Esri Inc. (Environmental Systems Research Institute), Harris Corporation, Microsoft Corporation, Autodesk Inc., Oracle Corporation, Trimble Inc., Bentley Systems Inc., and Google Inc. The Israel geospatial analytics market is estimated to grow from $170 million in 2023 to $320 million by 2029, at a CAGR of 9.5%. The market growth is majorly driven by the increasing adoption of geospatial technologies in various end-user verticals, such as agriculture, utility and communication, defense and intelligence, government, mining and natural resources, automotive and transportation, healthcare, real estate and construction. Recent developments include: June 2023: Autodesk and Esri's partnership accelerates innovations in AEC. Autodesk's InfoWater Pro and Esri's ArcGIS Pro were integrated to make this possible, and there are many more examples of how their partnership with Esri enables BIM and GIS data to flow between respective solutions seamlessly. The result is that project stakeholders can now visualize, understand, and analyze infrastructure within its real-world context., February 2023: Mercedes-Benz and Google announced a long-term strategic partnership to accelerate auto innovation and create the industry's next-generation digital luxury car experience. With this partnership, Mercedes-Benz will be the first automaker to build its branded navigation experience based on new in-car data and navigation capabilities from the Google Maps Platform. This will give the luxury automaker access to Google's leading geospatial offering, including detailed information about places, real-time and predictive traffic information, automatic rerouting, and more.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: High Costs and Operational Concerns, Concerns related to Geoprivacy and Confidential Data. Notable trends are: Surface Analysis is Expected to Hold Significant Share of the Market.

The Middle East Geospatial Analytics market, valued at $1.16 billion in 2025, is projected to experience robust growth, driven by significant investments in infrastructure development, smart city initiatives, and the burgeoning need for precise location intelligence across various sectors. A Compound Annual Growth Rate (CAGR) of 8.15% from 2025 to 2033 indicates a substantial expansion, with the market expected to surpass $2 billion by 2033. Key drivers include increasing adoption of advanced technologies like AI and machine learning within geospatial analytics, coupled with growing government initiatives promoting digital transformation and data-driven decision-making. The demand for accurate land management, resource optimization, and efficient urban planning is further fueling market expansion. Segmentation reveals strong growth in surface analysis and network analysis within the ‘By Type’ category, while the ‘By End-user Vertical’ segment is witnessing significant contributions from the Agriculture, Utility & Communication, and Defense & Intelligence sectors. The presence of established players like Esri, Bentley Systems, and Autodesk, alongside emerging specialized firms, ensures a competitive and dynamic market landscape. However, challenges like data security concerns, high implementation costs, and the need for skilled professionals could potentially restrain market growth. The Middle East's unique geopolitical landscape and rapid urbanization present both opportunities and challenges. Government initiatives focused on national infrastructure projects and sustainable development are creating substantial demand for geospatial analytics solutions. The region's focus on diversification beyond oil and gas is further stimulating adoption across sectors like agriculture, tourism, and transportation. However, regulatory hurdles and data privacy concerns, especially within the defense and intelligence sectors, need careful consideration. The high cost of sophisticated geospatial analytics technology and the need for specialized expertise might limit penetration in certain segments. Nevertheless, the long-term outlook remains optimistic, driven by the region's commitment to technological advancement and the increasing recognition of the value of data-driven insights for improved decision-making. Recent developments include: June 2023: Autodesk and Esri's partnership accelerated innovations in AEC. Autodesk's InfoWater Pro and Esri's ArcGIS Pro were integrated to make this possible, and there are many more examples of how their partnership with Esri enables BIM and GIS data to flow between respective solutions seamlessly. The result is that project stakeholders can now visualize, understand, and analyze infrastructure within its real-world context., February 2023: Mercedes-Benz and Google announced a long-term strategic partnership to accelerate auto innovation and create the industry's next-generation digital luxury car experience. With this partnership, Mercedes-Benz will be the first automaker to build its branded navigation experience based on new in-car data and navigation capabilities from the Google Maps Platform. This will give the luxury automaker access to Google's leading geospatial offering, including detailed information about places, real-time and predictive traffic information, automatic rerouting, and more.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Notable trends are: Surface Analysis is Expected to Hold Significant Share of the Market.

The market for Geographic Information Systems (GIS) solutions is projected to reach a staggering XXX million by 2033, growing at a remarkable CAGR of XX% from 2025 to 2033. This growth is driven by the increasing adoption of GIS technology across various industries, including transportation, AEC, telecommunications, agriculture, and entertainment. GIS solutions provide valuable insights by overlaying data onto geographic maps, helping businesses make informed decisions, optimize operations, and enhance customer experiences. Moreover, the growing awareness of sustainability and the need for environmental conservation is further fueling the demand for GIS solutions in sectors such as utilities, environmental consulting, and urban planning. The GIS market is segmented based on type (software, service), application, and region. North America dominates the market, followed by Europe and Asia Pacific. Key players in the GIS industry include Esri, Pro GIS Solutions, GBS, Fugro, DataVoice, Pontech, ABPmer, VertiGIS, Tata Communications, GIS Solutions, Inc, CGIS Solutions, and Spectus. The market is characterized by intense competition, ongoing advancements in technology, and the emergence of specialized GIS solutions. As businesses recognize the transformative potential of GIS technology, the market is expected to continue to experience robust growth in the coming years.

Images were acquired from approximately 80 m above ground surface on the 12th of February 2021, using a Phantom 4 Advanced drone with an FC330 camera. The images are in file input_images.zip.

The mission planning software DJI GS Pro was used to automatically acquire images at suitable locations across the survey area to enable the reconstruction of a three dimensional model.

Images 422 to 531 were imported to the photogrammetry software Pix4D (version 4.6.4). The created Pix4D project is Station12Feb2021_limited.p4d, and the processing report is Station12Feb2021_limited_report.pdf.

Four three-dimensional ground control points were used to improve the positioning of the model. No two dimensional control points or check points were used.

These points were in ITRF 2000@2000 datum (UTM Zone 49S), with co-ordinates as per the table below:

Label, Type, X(m), Y(m), Z(m), Accuracy Horz(m), Accuracy Vert(M) BM05, 3D GCP, 478814.460, 2648561.910, 38.558, 0.050, 0.100 EW-05, 3D GCP, 478635.540, 2648617.260, 27.260, 0.050, 0.100 FuelFlange, 3D GCP, 478970.810, 2648642.250, 21.920, 0.050, 0.100 MeltbellFootingA, 3D GCP, 478680.270, 2648466.547, 35.850, 0.050, 0.100

BM-05 is a survey benchmark near the Casey flagpoles, see https://data.aad.gov.au/aadc/survey/display_station.cfm?station_id=600 EW-05 is a 44 gallon drum used as a groundwater extraction well by the remediation project Fuel Flange is the last fuel flange located on the elevated fuel line prior to the fuel line “dipping” under the wharf road. Meltbell footing A is a concrete footing for the Casey melt bell (surveyed in 2019/20).

No point cloud processing (e.g. removal of errant points) was done prior to orthomosaic and model generation.

The resulting orthomosaic (Station12Feb2021_limited_transparent_mosaic_group1.tif) has an average ground sampling distance of 2.9 cm, and covers an area of approximately 15.8 hectares, encompassing the majority of buildings along “main street” at Casey. The quarry, biopiles, helipad, and upper fuel farm area are all visible.

Contour lines were generated in Pix4D at 0.5 m intervals.

Due to the limited number of ground control points, and their imprecision, the estimated residual mean squared error across three dimensions is 0.17 m (17cm), and will be worse on the periphery of the imaged area.

The orthomosaic was exported from ArcGIS to a Google Earth file (CaseyStation Orthomosaic Feb 12 2021.kmz) using XTools Pro Version 17.2.

A map was created in ArcGIS showing the orthomosaic with a background showing contour lines obtained from the AADC data product windmill_is.mdb.

The map was exported in .jpg and .pdf format at 250 dpi. Casey Station Orthomosaic Feb 12 2021.pdf Casey Station Orthomosaic Feb 12 2021.jpg

The Pix4D folder structure has been copied across (with the exception of the temp folder) and is included in this dataset.

Pix4D Folder Structure:

Station12Feb2021_limited.zip 1_intitial • Contains Pix4D files created during the project • Contains the final processing report (as .pdf) 2_densification • Contains the 3D mesh as an .obj file • Contains the point cloud as a .LAS and .PLY file • Contains the point cloud as a .p4b file 3_dsm_ortho • Contains the digital surface model as a georeferenced .tif file • Contains the orthomosaic as a georeferenced .tif file

A text readable log file from the project processing is in the file Station12Feb2021_limited.log

The data here are large wood data collected in the Summer of 2022 in the Frying Pan River Basin in the Sawatch Mountains of Central Colorado. There are six datasets included or referenced here. The first is general geomorphic and watershed characteristics of the stream reaches surveyed. The second is data from field reports to the CAIC. The third is topographic data for the studied avalanche pathways. The fourth is summary data of the wood volumes within each surveyed reach. The fifth is the unprocessed raw data for all wood jams and individual pieces surveyed. The sixth is a table of literature-derived annual recruitment rates for mechanisms common to mountain streams. Data may also be accessed via the Dryad data repository as linked in the data accessibility statement. Raw data relate several wood jam and individual piece properties, including length, width, and depth of the former, and length and diameter of the latter. Data also indicate several other wood characteristics, such as piece orientation, stability and decay class, and the presence of a rootwad. Finally, data include information about the geomorphic impact of each surveyed piece and jam. Data were collected to examine research questions related to in-stream wood load volumes supplied by snow avalanches and the resultant geomorphic impacts. Methods Raw data were collected in the field by two trained observers. Wood loads were measured in the field using a census approach for all wood jams and individual wood pieces within the bankfull channel. Jams were identified as accumulations with three or more contiguous pieces; jam volume was quantified by measuring the length, width, and height of a rectangular prism fit to the dimensions of the jam and visually estimating porosity. Porosity was consistently estimated by two independent observers to minimize systematic bias. For individual wood pieces, diameter and length were measured and then used to compute volume via the formula of a cylinder. All measurements were made for wood at least partially contained within the bankfull channel, which was visually estimated in the field using topography (e.g., slope breaks). For wood pieces or jams that extended laterally beyond the bankfull dimensions, those portions outside were excluded from measurements. Measured wood loads were normalized by stream surface area (in ha) for comparisons between reaches with varied bankfull widths and lengths. These data have not been processed other than the above related volume calculations, which were then summarized across sites and watersheds (which have yielded the wood_volume_summary.csv data also presented here). Data regarding topographic and vegetation characteristics of the studied avalanche pathways were obtained via freely available remote datasets. These include a 1-m DEM for the study area available from USGS EarthExplorer (https://earthexplorer.usgs.gov/) and vegetation data from the LANDFIRE program (https://landfire.gov/). Canopy cover comes from the Existing Vegetation Cover raster in the LANDFIRE 2016 dataset. Planform curvature and slope angle were dervied from a 1-m DEM using the Curvature and Slope functions in ArcGIS Pro Version 2.8. Median was calculated for each pathway area using the Zonal Statistics function in ArcGIS Pro. Avalanche report data was gathered from Colorado Avalanche Information Center field reports (https://avalanche.state.co.us/observations/view-field-reports), restricting the "area" field to Sawatch and Aspen and the dates from 2019-03-01 to 2019-03-15. Literature data were gathered using a publication database/search engine (scholar.google.com) to find relevant sources via keyword sources. Data were then processed using the information in each publication to determine rates in units of m3/ha/yr. NAs in all datasets mean that the measure in question was inapplicable to the parameter under consideration. An open-source R code is provided to re-create data processing and figure creation.

This directory contains files related to the scientific research project of Luc van Dijk at the Department of Earth, Energy, and Environment, University of Calgary. The project title is "High-resolution, Decadal to Weekly Geomorphic Change Analysis of the Elbow River in Calgary, using Multi-temporal Lidar and Repeat Terrestrial Laser Scanning". This project in the field of geomorphology was a collaboration between the University of Calgary and Utrecht University in the Netherlands. The project was completed on October 27, 2023. Below is a description of the files in this directory.

DisplacementVolumeDistributions_TLS.xlsx

Excel file containing tabular data of the normalized sediment displacement volumes that were obtained using TLS. Each tab in the Excel file represents a period of interest in 2023. The data in this file were used to generate the 'histogram-like' figures in the report.

DoD_rasters.zip

Folder containing the aerial lidar DEMs of Difference (DoDs) for each period of interest. The DoDs are 'waterless', i.e. the water surface is masked. The suffix of the file name before the file extension (e.g., ..._10cm.tif) indicates the maximum REM value that was used for the automated masking of the water surface extent (see report section 3.1.2). If the file name contains "large", it refers to the upstream greater area (see report section 3.1.3).

Within this folder is another folder called 'Clipped2AOIs'. This folder contains the same DoDs, but covering only the extents of the sites of interest ('AOIs' = Areas Of Interest).

FilteredPointClouds_TLS.zip

Folder containing the processed and filtered point clouds that were acquired throughout the summer of 2023 using TLS. These point clouds have been pre-processed and filtered to remove vegetation (see report section 3.2). They are grouped in sub-folders per acquisition date. The filenames are numbered to location, i.e. 'elbow1', 'elbow2', 'elbow3' and 'elbow4'. These correspond to the sites of interest: Glenmore Dam, golf club, Sandy Beach and Riverdale, respectively.

PythonScripts_Discharge_Rainfall.zip

Folder containing the Python scripts that were made to process the discharge and rainfall data that were sourced from Environment Canada and The City of Calgary (see report section 3.3). The scripts themselves contain descriptions of their purpose.

PythonScripts_DisplacementVolumeAnalysis.zip

Folder containing the Python scripts that were made to process and analyze the aerial lidar DoDs and the TLS rasterized difference point clouds (M3C2 output). The 'convert2pickle' scripts converted the sizable rasters to smaller pickle files, which were easier and faster to work with. The 'chart' scripts load the data from the pickle files, analyze them and produce the 'histogram-like' figures in the report. The scripts themselves contain descriptions of their purpose.

RainfallDischargeData.xlsx

Excel file containing the discharge and rainfall data from Environment Canada and The City of Calgary. The data came from different sources in different formats and were combined into this single table.

RasterizedDifferencedPointClouds_M3C2.zip

Folder containing the rasterized results of the differenced TLS point clouds (M3C2 output) (see report section 3.2.4). The filenames are numbered to location, i.e. 'Elbow1', 'Elbow2', 'Elbow3' and 'Elbow4'. These correspond to the sites of interest: Glenmore Dam, golf club, Sandy Beach and Riverdale, respectively. The numeric sequence in the file name indicates the start and end date of the change analysis in a 'mm-dd' format. The suffixes '_dist', '_unc' and '_sig' refer to the three output layers of the M3C2 algorithm: distance, uncertainty and significance of change. The main files of interest are the '.tif' files. Files sharing the same name, but with different extensions (.tfw, .tif.aux.xml, .tif.xml) are supplementary/auxiliary files for the '.tif' file, generated by ArcGIS Pro.

ScarpsOfInterest_shapefile.zip

Folder containing a polygon shapefile describing the extents and locations of the sites of interest. The main file of interest is the '.shp' file. The other files with the same name, but different extensions (.cpg, .dbf, .prj, .sbn, .sbx, .shp.xml, .shx) are supplementary/auxiliary files for the '.shp' file, generated by ArcGIS Pro.

This downloadable zip file contains an ESRI File Geodatabase that is compatible with most versions of ArcGIS Pro, ArcMap, and AutoCAD Map 3D or Civil 3D. To view the geodatabase’s contents, please download the zip file to a local directory and extract its contents. This zipped geodatabase will require approximately 2.85 GB of disc space (3.09 GB extracted). Due to its size, the zip file may take some time to download.Once extracted, this file geodatabase contains a topographic contour layer that was derived from LiDAR collected in spring of 2020 by Dewberry Engineers in coordination with Tallahassee - Leon County GIS. The contours were extracted at a 2 foot interval with index contours every 10 feet.Lidar Acquisition Executive SummaryThe primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (lidar) technology for the Tallahassee Leon County Project Area. The lidar data were processed and classified according to project specifications. Detailed breaklines and bare-earth Digital Elevation Models (DEMs) were produced for the project area. Data was formatted according to tiles with each tile covering an area of 5000 ft by 5000 ft. A total of 876 tiles were produced for the project encompassing an area of approximately 785.55 sq. miles.The Project TeamDewberry served as the prime contractor for the project. In addition to project management, Dewberry was responsible for LAS classification, all lidar products, breakline production, Digital Elevation Model (DEM) production, and quality assurance. Dewberry’s Frederick C. Rankin completed ground surveying for the project and delivered surveyed checkpoints. His task was to acquire surveyed checkpoints for the project to use in independent testing of the vertical accuracy of the lidar-derived surface model. He also verified the GPS base station coordinates used during lidar data acquisition to ensure that the base station coordinates were accurate. Please see Appendix A to view the separate Survey Report that was created for this portion of the project. Digital Aerial Solutions, LLC completed lidar data acquisition and data calibration for the project area.SURVEY AREAThe project area addressed by this report falls within the Florida county of Leon.DATE OF SURVEYThe lidar aerial acquisition was conducted from TBDORIGINAL COORDINATE REFERENCE SYSTEMData produced for the project were delivered in the following reference system.Horizontal Datum: The horizontal datum for the project is North American Datum of 1983 with the 2011 Adjustment (NAD 83 (2011))Vertical Datum: The Vertical datum for the project is North American Vertical Datum of 1988 (NAVD88)Coordinate System: NAD83 (2011) State Plane Florida North (US survey feet)Units: Horizontal units are in U.S. Survey Feet, Vertical units are in U.S. Survey Feet.Geiod Model: Geoid12B (Geoid 12B) was used to convert ellipsoid heights to orthometric heights).

Web Soil Survey Web Soil Survey links with additional documents Other Documents to Reference:Web Soil Survey BrochureWeb Soil Survey Brochure in SpanishGetting Started in Web Soil SurveyUsing Web Soil Survey in 4 Basic StepsHow to Use Web Soil Survey 3.0Guide on downloading SSURGO from Web Soil SurveyWeb Soil Survey Tips and ShortcutsWeb Soil Survey Known Problems and WorkaroundsWeb Soil Survey Frequently Asked QuestionsWeb Soil Survey Help OnlineWeb Soil Survey Accessibility FeaturesDefining an AOI for Web Soil Survey on a Mobile DeviceWeb Soil Survey Adding a Multi-part AOI featureUsing Google Earth Pro to create multiple AOIs for Web Soil SurveyWeb Soil Survey Version Release History DocumentsWeb Soil Survey Guide to Maps, Reports, and TablesWeb Soil Survey - Soil Data Explorer TabUsing Web Soil Survey YouTube Videos

This topographic contour layer was derived from LiDAR collected in spring of 2020 by Dewberry Engineers in coordination with Tallahassee - Leon County GIS. The contours were extracted at a 2 foot interval with index contours every 10 feet. This tile layer was generated as a Map Tile Package (.mtpkx) in ArcGIS Pro and published to ArcGIS online as a hosted tile layer. For web mapping compatibility, this layer has been re-projected from its original coordinate system to the web standard used by ESRI, Google, and Bing (Web Mercator Auxiliary Sphere).The feature layer used to generate this tile layer can be downloaded as a zipped geodatabase from TLCGIS' geodatahub. Download LinkLidar Acquisition Executive SummaryThe primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (lidar) technology for the Tallahassee Leon County Project Area. The lidar data were processed and classified according to project specifications. Detailed breaklines and bare-earth Digital Elevation Models (DEMs) were produced for the project area. Data was formatted according to tiles with each tile covering an area of 5000 ft by 5000 ft. A total of 876 tiles were produced for the project encompassing an area of approximately 785.55 sq. miles.The Project TeamDewberry served as the prime contractor for the project. In addition to project management, Dewberry was responsible for LAS classification, all lidar products, breakline production, Digital Elevation Model (DEM) production, and quality assurance. Dewberry’s Frederick C. Rankin completed ground surveying for the project and delivered surveyed checkpoints. His task was to acquire surveyed checkpoints for the project to use in independent testing of the vertical accuracy of the lidar-derived surface model. He also verified the GPS base station coordinates used during lidar data acquisition to ensure that the base station coordinates were accurate. Please see Appendix A to view the separate Survey Report that was created for this portion of the project. Digital Aerial Solutions, LLC completed lidar data acquisition and data calibration for the project area.SURVEY AREAThe project area addressed by this report falls within the Florida county of Leon.DATE OF SURVEYThe lidar aerial acquisition was conducted from TBDORIGINAL COORDINATE REFERENCE SYSTEMData produced for the project were delivered in the following reference system.Horizontal Datum: The horizontal datum for the project is North American Datum of 1983 with the 2011 Adjustment (NAD 83 (2011))Vertical Datum: The Vertical datum for the project is North American Vertical Datum of 1988 (NAVD88)Coordinate System: NAD83 (2011) State Plane Florida North (US survey feet)Units: Horizontal units are in U.S. Survey Feet, Vertical units are in U.S. Survey Feet.Geiod Model: Geoid12B (Geoid 12B) was used to convert ellipsoid heights to orthometric heights).

This topographic contour layer was derived from LiDAR collected in spring of 2018 by Dewberry Engineers in coordination with Tallahassee - Leon County GIS. The contours were extracted at a 2 foot interval with index contours every 10 feet. This tile layer was generated as a Map Tile Package (.mtpkx) in ArcGIS Pro and published to ArcGIS online as a hosted tile layer. For web mapping compatibility, this layer has been re-projected from its original coordinate system to the web standard used by ESRI, Google, and Bing (Web Mercator Auxiliary Sphere).The feature layer used to generate this tile layer can be downloaded as a zipped geodatabase from TLCGIS' geodatahub. Download LinkLidar Acquisition Executive SummaryThe primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (lidar) technology for the Tallahassee Leon County Project Area. The lidar data were processed and classified according to project specifications. Detailed breaklines and bare-earth Digital Elevation Models (DEMs) were produced for the project area. Data was formatted according to tiles with each tile covering an area of 5000 ft by 5000 ft. A total of 876 tiles were produced for the project encompassing an area of approximately 785.55 sq. miles.THE PROJECT TEAMDewberry served as the prime contractor for the project. In addition to project management, Dewberry was responsible for LAS classification, all lidar products, breakline production, Digital Elevation Model (DEM) production, and quality assurance. Dewberry’s Frederick C. Rankin completed ground surveying for the project and delivered surveyed checkpoints. His task was to acquire surveyed checkpoints for the project to use in independent testing of the vertical accuracy of the lidar-derived surface model. He also verified the GPS base station coordinates used during lidar data acquisition to ensure that the base station coordinates were accurate. Please see Appendix A to view the separate Survey Report that was created for this portion of the project. Digital Aerial Solutions, LLC completed lidar data acquisition and data calibration for the project area.SURVEY AREAThe project area addressed by this report falls within the Florida county of Leon.DATE OF SURVEYThe lidar aerial acquisition was conducted from February 05, 2018 thru April 25, 2018.ORIGINAL COORDINATE REFERENCE SYSTEMData produced for the project were delivered in the following reference system. Horizontal Datum: The horizontal datum for the project is North American Datum of 1983 with the 2011 Adjustment (NAD 83 (2011)) Vertical Datum: The Vertical datum for the project is North American Vertical Datum of 1988 (NAVD88)Coordinate System: NAD83 (2011) State Plane Florida North (US survey feet)Units: Horizontal units are in U.S. Survey Feet, Vertical units are in U.S. Survey Feet.Geiod Model: Geoid12B (Geoid 12B) was used to convert ellipsoid heights to orthometric heights).

This downloadable zip file contains an ESRI File Geodatabase that is compatible with most versions of ArcGIS Pro, ArcMap, and AutoCAD Map 3D or Civil 3D. To view the geodatabase’s contents, please download the zip file to a local directory and extract its contents. This zipped geodatabase will require approximately 2.85 GB of disc space (3.09 GB extracted). Due to its size, the zip file may take some time to download.This topographic contour layer was derived from LiDAR collected in spring of 2018 by Dewberry Engineers in coordination with Tallahassee - Leon County GIS. The contours were extracted at a 2 foot interval with index contours every 10 feet. Lidar Acquisition Executive SummaryThe primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (lidar) technology for the Tallahassee Leon County Project Area. The lidar data were processed and classified according to project specifications. Detailed breaklines and bare-earth Digital Elevation Models (DEMs) were produced for the project area. Data was formatted according to tiles with each tile covering an area of 5000 ft by 5000 ft. A total of 876 tiles were produced for the project encompassing an area of approximately 785.55 sq. miles.THE PROJECT TEAMDewberry served as the prime contractor for the project. In addition to project management, Dewberry was responsible for LAS classification, all lidar products, breakline production, Digital Elevation Model (DEM) production, and quality assurance. Dewberry’s Frederick C. Rankin completed ground surveying for the project and delivered surveyed checkpoints. His task was to acquire surveyed checkpoints for the project to use in independent testing of the vertical accuracy of the lidar-derived surface model. He also verified the GPS base station coordinates used during lidar data acquisition to ensure that the base station coordinates were accurate. Please see Appendix A to view the separate Survey Report that was created for this portion of the project. Digital Aerial Solutions, LLC completed lidar data acquisition and data calibration for the project area.SURVEY AREAThe project area addressed by this report falls within the Florida county of Leon.DATE OF SURVEYThe lidar aerial acquisition was conducted from February 05, 2018 thru April 25, 2018.ORIGINAL COORDINATE REFERENCE SYSTEMData produced for the project were delivered in the following reference system.Horizontal Datum: The horizontal datum for the project is North American Datum of 1983 with the 2011 Adjustment (NAD 83 (2011))Vertical Datum: The Vertical datum for the project is North American Vertical Datum of 1988 (NAVD88)Coordinate System: NAD83 (2011) State Plane Florida North (US survey feet)Units: Horizontal units are in U.S. Survey Feet, Vertical units are in U.S. Survey Feet.Geiod Model: Geoid12B (Geoid 12B) was used to convert ellipsoid heights to orthometric heights).

Table from the American Community Survey (ACS) 5-year series on income and earning related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B19025 Aggregate Household Income, B19013 Median Household Income, B19001 Household Income, B19113 Median Family Household Income, B19101 Family Household Income, B19202 Median Nonfamily Household Income, B19201 Nonfamily Household Income, B19301 Per Capita Income/B19313 Aggregate Income/B01001 Sex by Age, C24010 Sex by Occupation of the Civilian Employed Population 16 years and Over, B20017 Median Earnings by Sex by Work Experience for the Population 16 years and over with Earnings, B20001 Sex by Earnings for the Population 16 years and over with Earnings. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B19013, B19001, B19113, B19101, B19202, B19201, B19301, B19313, B01001, C24010, B20017, B20001, B19025Data downloaded from: Census Bureau's Explore Census Data 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:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This dataset contains mining claim cases with the case disposition (status) of closed from US Bureau of Land Management's, BLM, Mineral and Land Record System(MLRS). The BLM only requires that mining claims be identified down to the affected quarter section(s)—as such, that is what the MLRS research map and public reports will reflect, most commonly. Claim boundaries, as staked and monumented, are found in the accepted Notice/Certificate of Location as part of the official case file, managed by the BLM State Office having jurisdiction over the claim. The geometries are created in multiple ways but are primarily derived from Legal Land Descriptions (LLD) for the case and geocoded (mapped) using the Public Land Survey System (PLSS) derived from the most accurate survey data available through BLM Cadastral Survey workforce. Geospatial representations might be missing for some cases that can not be geocoded using the MLRS algorithm. Each case is given a data quality score based on how well it mapped. These can be lumped into seven groups to provide a simplified way to understand the scores.Group 1: Direct PLSS Match. Scores “0”, “1”, “2”, “3” should all have a match to the PLSS data. There are slight differences, but the primary expectation is that these match the PLSS. Group 2: Calculated PLSS Match. Scores “4”, “4.1”, “5”, “6”, “7” and “8” were generated through a process of creating the geometry that is not a direct capture from the PLSS. They represent a best guess based on the underlining PLSS Group 3 – Mapped to Section. Score of “8.1”, “8.2”, “8.3”, “9” and “10” are mapped to the Section for various reasons (refer to log information in data quality field). Group 4- Combination of mapped and unmapped areas. Score of 15 represents a case that has some portions that would map and others that do not. Group 5 – No NLSDB Geometry, Only Attributes. Scores “11”, “12”, “20”, “21” and “22” do not have a match to the PLSS and no geometry is in the NLSDB, and only attributes exist in the data. Group 6 – Mapped to County. Scores of “25” map to the County. Group 7 – Improved Geometry. Scores of “100” are cases that have had their geometry edited by BLM staff using ArcGIS Pro or MLRS bulk upload tool.