Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

The Counties clipped layer was created using ArcGIS's Clip (Analysis) Tool to extract the DRCOG County Boundaries that overlay the MHFD District Boundary.

The USA NLCD Land Cover 2021 dataset provides 30-meter resolution land-cover data compiled by the U.S. Geological Survey (USGS) in cooperation with the Multi-Resolution Land Characteristics (MRLC) Consortium.The Idaho subset was created in ArcGIS Pro using the Pairwise Clip tool, with the input features being the USA NLCD Land Cover 2021 layer and the Idaho state boundary polygon.This clipped dataset supports environmental and spatial analyses for land-cover classification and change detection studies within Idaho.The dataset was prepared as part of the GEOG 390 Finding and Preparing Data assignment to practice geospatial data acquisition, processing, and documentation.

ArcGIS Pro/QGIS to modify layers R for scripts

Notice: this is not the latest Heat Island Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, with patching from summer of 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Ce modèle numérique d'altitude (MNT) a été créé à partir du jeu de données de terrain principal (MTD) de Hakai au moyen de l'outil « MNT to raster » dans ArcGIS for Desktop d'ESRI à l'aide d'une méthode d'échantillonnage Natural Neighbour. Le DEM a été créé en mode natif à une résolution de 3 m. Ce DEM a été fixé à une zone tampon à 10 m du rivage. Une combinaison de différentes altitudes autour de l'île a été utilisée pour créer le rivage.

Le MNT qui en résulte est un modèle d'élévation hydroaplati en terre nue et donc considéré comme « topographiquement complet ». Chaque pixel représente l'altitude en mètres au-dessus du niveau moyen de la mer de la terre nue à cet endroit. Le système de référence vertical est le « Système de référence géodésique vertical canadien 1928 » (CGVD28).

Hakai a produit des DEM à différentes résolutions de manière native directement à partir du MTD des données LiDAR. Pour vos recherches, veuillez utiliser le produit de résolution approprié parmi ceux produits par Hakai. Afin de maintenir l'homogénéité, il n'est pas recommandé de procéder à un suréchantillonnage ou à une mise à l'échelle supérieure à partir de produits de résolution supérieure car cela pourrait introduire et propager des erreurs de différentes grandeurs dans les analyses en cours ; veuillez utiliser des produits déjà disponibles, et si vous avez besoin d'une résolution non disponible, contactez data@hakai.org afin d'obtenir un DEM produit directement à partir du MTD.

Les DEM topographiquement complets suivants ont été produits en mode natif à partir du DTM par Hakai :

MNE topographiquement complète de 3 m. Ce produit a été utilisé pour produire les ensembles de données hydrologiques de Hakai (cours d'eau et bassins versants) DEM Topographiquement complet de 20 m. Compatible avec les mesures du couvert végétal de Hakai et les rasters associés. MNT topographiquement complet de 25 m. Compatible avec les produits de données TRIM BCGov. DEM Topographiquement complet de 30 m. Compatible avec les produits STRM.

Création du jeu de données de terrain principal

Nuages de points LiDAR issus de missions effectuées en 2012 et 2014 au-dessus de l'île Calvert où ils ont été chargés (XYZ uniquement) dans une classe d'entités ponctuelles d'une géodatabase ESRI.

Seul le sol (classe 2) renvoie l'endroit où il est chargé dans la géodatabase.

Le « jeu de données de MNT » ESRI a été créé dans la même géodatabase à l'aide des points LiDAR en tant que points de masse intégrés.

Les lacs et les étangs TEM Plus avec des valeurs d'altitude moyennes au-dessus des miroirs des plans d'eau ont été utilisés comme lignes de rupture de remplacement dur pour obtenir un hydroaplatissement.

La géométrie d'emprise minimale de toutes les étendues de fichiers LAS contigus a été utilisée comme masque de découpe souple lors de la création du jeu de données de MNT en tant que limite de projet.

Le système de coordonnées horizontales et le datum utilisés pour le jeu de données de MNT sont : UTM Zone 9 NAD1983 ; le système de référence vertical a été défini sur CGVD28. Les deux systèmes de référence correspondent au système de référence natif des nuages de points LiDAR.

L'espacement minimal des points défini pendant la création du jeu de données de MNT a été défini sur 1.

Dataset description-br /- This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following: * a) Barcelona city boundaries * b) Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB) * c) Barcelona greater city (Urban Atlas) * d) Barcelona functional urban area (Urban Atlas) * e) Milan city boundaries * f) Milan metropolitan area, Piano Intercomunale Milanese (PIM) * g) Milan greater city (Urban Atlas) * h) Milan functional urban area (Urban Atlas)-br /- In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2). -br /- -br /- -br /- Dataset composition-br /- The dataset is provided in .csv format and is composed of: -br /- _IMD15_BCN_MI_Sources.csv_: Information on data sources -br /- _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona: * a) Barcelona city boundaries (label: bcn_city) * b) Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb) * c) Barcelona greater city (Urban Atlas) (label: bcn_grc) * d) Barcelona functional urban area (Urban Atlas) (label: bcn_fua)-br /- _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan: * e) Milan city boundaries (label: mi_city) * f) Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim) * g) Milan greater city (Urban Atlas) (label: mi_grc) * h) Milan functional urban area (Urban Atlas) (label: mi_fua)-br /- _IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h). -br /- Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM. -br /- In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included: * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan); * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area); * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness; * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.-br /- -br /- -br /- Further information on the Dataset-br /- This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.-br /- 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster. 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA. 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.-br /- Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h). -br /- Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following: * Clipping: Arctoolbox - Data management tools - Raster - Raster Processing - Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping. * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox - Data management tools - Raster - Raster properties - Delete Raster Attribute Table and then through Arctoolbox - Data management tools - Raster - Raster properties - Build Raster Attribute Table; the "overwrite" option has been selected. -br /- -br /- Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal - Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.-br /- The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

This web application highlights some of the capabilities for accessing Landsat imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Landsat images on a daily basis.

Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Landsat Explorer app provides the power of Landsat satellites, which gather data beyond what the eye can see. Use this app to draw on Landsat's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the entire Landsat archive to visualize how the Earth's surface has changed over the last forty years.

Quick access to the following band combinations and indices is provided:

• Agriculture : Highlights agriculture is bright green; Bands 6, 5, 2
• Natural Color : Sharpened with 15m panchromatic band; Bands 4, 3, 2 +8
• Color Infrared : Healthy vegetation is bright red; Bands 5, 4 ,3
• SWIR (Short Wave Infrared) : Highlights rock formations; Bands 7, 6, 4
• Geology : Highlights geologic features; Bands 7, 6, 2
• Bathymetric : Highlights underwater features; Bands 4, 3, 1
• Panchromatic : Panchromatic images at 15m; Band 8
• Vegetation Index : Normalized Difference Snow Index(NDVI); (Band 5 - Band 4)/(Band 5 + Band 4)
• Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 5 - Band 6)/(Band 5 + Band 6)
• SAVI : Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 5 - Band 4)/(Band 5 + Band 4 + 0.5))
• Water Index : Offset + Scale*(Band 3 - Band 6)/(Band 3 + Band 6)
• Burn Index : Offset + Scale*(Band 5 - Band 7)/(Band 5 + Band 7)
• Urban Index : Offset + Scale*(Band 5 - Band 6)/(Band 5 + Band 6)

The application is written using Web AppBuilder for ArcGIS accessing imagery layers using the ArcGIS API for JavaScript.

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB, DOE Project DE-EE0006726). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS).

This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations.

Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015.

Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted.

UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below) This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains an ArcGIS Toolbox with 6 ArcGIS Models: WellClipsToWormsSections, BufferedRasterToClippedRaster, ExtractThermalPropertiesToCrossSection, AddExtraInfoToCrossSection, and CrossSectionExtraction.

The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in “pred” are the predicted mean for that resource, and files ending in “err” are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource.

Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report.

The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource.

A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource.

Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model.

In 2012, the CPUC ordered the development of a statewide map that is designed specifically for the purpose of identifying areas where there is an increased risk for utility associated wildfires. The development of the CPUC -sponsored fire-threat map, herein "CPUC Fire-Threat Map," started in R.08-11-005 and continued in R.15-05-006.

A multistep process was used to develop the statewide CPUC Fire-Threat Map. The first step was to develop Fire Map 1 (FM 1), an agnostic map which depicts areas of California where there is an elevated hazard for the ignition and rapid spread of powerline fires due to strong winds, abundant dry vegetation, and other environmental conditions. These are the environmental conditions associated with the catastrophic powerline fires that burned 334 square miles of Southern California in October 2007. FM 1 was developed by CAL FIRE and adopted by the CPUC in Decision 16-05-036.

FM 1 served as the foundation for the development of the final CPUC Fire-Threat Map. The CPUC Fire-Threat Map delineates, in part, the boundaries of a new High Fire-Threat District (HFTD) where utility infrastructure and operations will be subject to stricter fire‑safety regulations. Importantly, the CPUC Fire-Threat Map (1) incorporates the fire hazards associated with historical powerline wildfires besides the October 2007 fires in Southern California (e.g., the Butte Fire that burned 71,000 acres in Amador and Calaveras Counties in September 2015), and (2) ranks fire-threat areas based on the risks that utility-associated wildfires pose to people and property.

Primary responsibility for the development of the CPUC Fire-Threat Map was delegated to a group of utility mapping experts known as the Peer Development Panel (PDP), with oversight from a team of independent experts known as the Independent Review Team (IRT). The members of the IRT were selected by CAL FIRE and CAL FIRE served as the Chair of the IRT. The development of CPUC Fire-Threat Map includes input from many stakeholders, including investor-owned and publicly owned electric utilities, communications infrastructure providers, public interest groups, and local public safety agencies.

The PDP served a draft statewide CPUC Fire-Threat Map on July 31, 2017, which was subsequently reviewed by the IRT. On October 2 and October 5, 2017, the PDP filed an Initial CPUC Fire-Threat Map that reflected the results of the IRT's review through September 25, 2017. The final IRT-approved CPUC Fire-Threat Map was filed on November 17, 2017. On November 21, 2017, SED filed on behalf of the IRT a summary report detailing the production of the CPUC Fire-Threat Map(referenced at the time as Fire Map 2). Interested parties were provided opportunity to submit alternate maps, written comments on the IRT-approved map and alternate maps (if any), and motions for Evidentiary Hearings. No motions for Evidentiary Hearings or alternate map proposals were received. As such, on January 19, 2018 the CPUC adopted, via Safety and Enforcement Division's (SED) disposition of a Tier 1 Advice Letter, the final CPUC Fire-Threat Map.

Additional information can be found here.

Feature layer generated from running the Overlay Layers analysis tool.

In 2012, the CPUC ordered the development of a statewide map that is designed specifically for the purpose of identifying areas where there is an increased risk for utility associated wildfires. The development of the CPUC -sponsored fire-threat map, herein "CPUC Fire-Threat Map," started in R.08-11-005 and continued in R.15-05-006.

A multistep process was used to develop the statewide CPUC Fire-Threat Map. The first step was to develop Fire Map 1 (FM 1), an agnostic map which depicts areas of California where there is an elevated hazard for the ignition and rapid spread of powerline fires due to strong winds, abundant dry vegetation, and other environmental conditions. These are the environmental conditions associated with the catastrophic powerline fires that burned 334 square miles of Southern California in October 2007. FM 1 was developed by CAL FIRE and adopted by the CPUC in Decision 16-05-036.

FM 1 served as the foundation for the development of the final CPUC Fire-Threat Map. The CPUC Fire-Threat Map delineates, in part, the boundaries of a new High Fire-Threat District (HFTD) where utility infrastructure and operations will be subject to stricter fire‑safety regulations. Importantly, the CPUC Fire-Threat Map (1) incorporates the fire hazards associated with historical powerline wildfires besides the October 2007 fires in Southern California (e.g., the Butte Fire that burned 71,000 acres in Amador and Calaveras Counties in September 2015), and (2) ranks fire-threat areas based on the risks that utility-associated wildfires pose to people and property.

Primary responsibility for the development of the CPUC Fire-Threat Map was delegated to a group of utility mapping experts known as the Peer Development Panel (PDP), with oversight from a team of independent experts known as the Independent Review Team (IRT). The members of the IRT were selected by CAL FIRE and CAL FIRE served as the Chair of the IRT. The development of CPUC Fire-Threat Map includes input from many stakeholders, including investor-owned and publicly owned electric utilities, communications infrastructure providers, public interest groups, and local public safety agencies.

The PDP served a draft statewide CPUC Fire-Threat Map on July 31, 2017, which was subsequently reviewed by the IRT. On October 2 and October 5, 2017, the PDP filed an Initial CPUC Fire-Threat Map that reflected the results of the IRT's review through September 25, 2017. The final IRT-approved CPUC Fire-Threat Map was filed on November 17, 2017. On November 21, 2017, SED filed on behalf of the IRT a summary report detailing the production of the CPUC Fire-Threat Map(referenced at the time as Fire Map 2). Interested parties were provided opportunity to submit alternate maps, written comments on the IRT-approved map and alternate maps (if any), and motions for Evidentiary Hearings. No motions for Evidentiary Hearings or alternate map proposals were received. As such, on January 19, 2018 the CPUC adopted, via Safety and Enforcement Division's (SED) disposition of a Tier 1 Advice Letter, the final CPUC Fire-Threat Map.

Additional information can be found here.

Feature layer generated from running the Overlay Layers analysis tool.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, patched with data from 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

This 3D model of Mount Saint Helens shows the topography using wood-textured contours set at 50m vertical spacing, with the darker wood grain color indicating the major contours at 1000, 1500, 2000, and 2500 meters above sea level. The state of the mountain before the eruption of May 13, 1980 is shown with thinner contours, allowing you to see the volume of rock that was ejected via the lateral blast.The process to create the contours uses CityEngine and ArcGIS Pro for data processing, symbolization, and publishing. The steps:Create a rectangular AOI polygon and use the Clip Raster tool on your local terrain raster. A 30m DEM was used for before, 10m for after.Run the Contour tool on the clipped raster, using the polygon output option - 50m was used for this scene.Run the Smooth Polygon tool on the contours. For Mount St. Helens, I used the PAEK algorithm, with a 200m smoothing tolerance. Depending on the resolution of the elevation raster and the extent of the AOI, a larger or smaller value may be needed. Write a CityEngine rule (see below) that extrudes and textures each contour polygon to create a stair-stepped 3D contour map. Provide multiple wood texture options with parameters for: grain size, grain rotation, extrusion height (to account for different contour depths if values other than 100m are used), and a hook for the rule to read the ContourMax attribute that is created by the Contour tool. Export CityEngine rule as a Rule Package (*.rpk).Add some extra features for context - a wooden planter box to hide some of the edges of the model, and water bodies.Apply the CityEngine-authored RPK to the contour polygons in ArcGIS Pro as a procedural fill symbol, adjust parameters for desired look & feel.Run Layer 3D to Feature Class tool to convert the procedural fill to multipatch features. Share Web SceneRather than create a more complicated CityEngine rule that applied textures for light/dark wood colors for minor/major contours, I just created a complete light- and dark-wood version of the mountain (and one with just the water), then shuffled them together.Depending on where this methodology is applied, you may want to clip out other areas - for example, glaciers, roads, or rivers. Or add annotation by inlaying a small north arrow in the corner of the map. I like the challenge of representing any feature in this scene in terms of wood colors and grains - some extruded, some recessed, some inlaid flat.

Public transit routes in San Diego County managed by the San Diego County Metropolitan Transit System (MTS) and the North County Transit District (NCTD). Bus, commuter and light rail, and trolley routes managed and developed from the General Transit Feed Specification (GTFS) data available from the transitland feed registry (formerly from GTFS Data Exchange). Routes are developed from the GTFS data available through the transitland feed registry (https://transit.land/feed-registry/) or transitfeed (http://transitfeeds.com) depending on which is most current (formerly from the GTFS Data Exchange). GTFS data is provided to the exchange by the transit agencies and processed by SanGIS to create a consolidated GIS layer containing routes from both systems. SanGIS uses a publicly available ESRI ArcToolbox tool to create the GIS data layer. The toolbox can be found at http://www.arcgis.com/home/item.html?id=14189102b795412a85bc5e1e09a0bafa. This data set is created using the ROUTES.txt and SHAPES.txt GTFS data files.Routes layers for MTS and NCTD are created separately and combined into a single layer using ArcGIS tools.

Feature layer generated from running the Overlay Layers analysis tool. This Ocean Climate Index is a clip for Southern California of a composite index of change in ocean climate conditions from 1985 to 2055 based on CMIP6 model projections provided by NOAA. The original title of the layer is H3 Hexagon Level 3 Ocean Climate Index and the index/analysis is by Josh Grail. https://services.arcgis.com/bDAhvQYMG4WL8O5o/arcgis/rest/services/H3_L3_Ocean_Climate_Index_/FeatureServer

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks.Input DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0 national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator.Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”.Save merged raster to .tif and import into ArcPro.Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0.Convert the raster produced above to vector using the “RasterToPolygon” tool.Clip to 2024 subregions using “Pairwise Clip” tool.Break apart multipart polygons using “Multipart to single parts” tool.Hand-edit to remove deep marine polygon.Dissolve the resulting data layer.This produces a seamless polygon defining land and shallow marine areas.Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks:All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public.All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use.All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM).All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status.Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes.Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset.The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function.Clip the resulting data layer to the inland and nearshore extent.Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”).Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers.Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster.Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park.Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC).Extract again to the inland and nearshore extent.Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field.Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator.Add color and legend to raster attribute table.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code. Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources.This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly

ssurgoOnDemandThe purpose of these tools are to give users the ability to get Soil Survey Geographic Database (SSURGO) properties and interpretations in an efficient manner. They are very similiar to the United States Department of Agriculture - Natural Resource Conservation Service's distributed Soil Data Viewer (SDV), although there are distinct differences. The most important difference is the data collected with the SSURGO On-Demand (SOD) tools are collected in real-time via web requests to Soil Data Access (https://sdmdataaccess.nrcs.usda.gov/). SOD tools do not require users to have the data found in a traditional SSURGO download from the NRCS's official repository, Web Soil Survey (https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm). The main intent of both SOD and SDV are to hide the complex relationships of the SSURGO tables and allow the users to focus on asking the question they need to get the information they want. This is accomplished in the user interface of the tools and the subsequent SQL is built and executed for the user. Currently, the tools packaged here are designed to run within the ESRI ArcGIS Desktop Application - ArcMap, version 10.1 or greater. However, much of the Python code is recyclable and could run within a Python intepreter or other GIS applications such as Quantum GIS with some modification.NOTE: The queries in these tools only consider the major components of soil map units.Within the SOD tools are 2 primary toolsets, descibed as follows:<1. AreasymbolThe Areasymbol tools collect SSURGO properties and interpretations based on a user supplied list of Soil Survey areasymbols (e.g. NC123). After the areasymbols have been collected, an aggregation method (see below) is selected . Tee aggregation method has no affect on interpretations other than how the SSURGO data aggregated. For soil properties, the aggregation method drives what properties can be run. For example, you can't run the weighted average aggregation method on Taxonomic Order. Similarly, for the same soil property, you wouldn't specify a depth range. The point here is the aggregation method affects what parameters need to be supplied for the SQL generation. It is important to note the user can specify any number of areasymbols and any number of interpretations. This is another distinct advantage of these tools. You could collect all of the SSURGO interpretations for every soil survey area (areasymbol) by executing the tool 1 time. This also demonstrates the flexibility SOD has in defining the geographic extent over which information is collected. The only constraint is the extent of soil survey areas selected to run (and these can be discontinuous).As SOD Areasymbol tools execute, 2 lists are collected from the tool dialog, a list of interpretations/properties and a list of areasymbols. As each interpretation/property is run, every areasymbol is run against the interpretation/property requested. For instance, suppose you wanted to collect the weighted average of sand, silt and clay for 5 soil survey areas. The sand property would run for all 5 soil survey areas and built into a table. Next the silt would run for all 5 soil survey areas and built into a table, and so on. In this example a total of 15 web request would have been sent and 3 tables are built. Two VERY IMPORTANT things here...A. All the areasymbol tools do is generate tables. They are not collecting spatial data.B. They are collecting stored information. They are not making calculations(with the exception of the weighted average aggregation method).<2. ExpressThe Express toolset is nearly identical to the Areasymbol toolset, with 2 exceptions.A. The area to collect SSURGO information over is defined by the user. The user digitizes coordinates into a 'feature set' after the tool is open. The points in the feature set are closed (first point is also the last) into a polygon. The polygon is sent to Soil Data Access and the features set points (polygon) are used to clip SSURGO spatial data. The geomotries of the clip operation are returned, along with the mapunit keys (unique identifier). It is best to keep the points in the feature set simple and beware of self intersections as they are fatal.B. Instead of running on a list of areasymbols, the SQL queries on a list of mapunit keys.The properties and interpretations options are identical to what was discussed for the Areasymbol toolset.The Express tools present the user the option of creating layer files (.lyr) where the the resultant interpretation/property are joined to the geometry and saved to disk as a virtual join. Additionally, for soil properties, an option exists to append all of the selected soil properties to a single table. In this case, if the user ran sand, silt, and clay properties, instead of 3 output tables, there is only 1 table with a sand column, a silt column, and a clay column.<Supplemental Information<sAggregation MethodAggregation is the process by which a set of component attribute values is reduced to a single value to represent the map unit as a whole.A map unit is typically composed of one or more "components". A component is either some type of soil or some nonsoil entity, e.g., rock outcrop. The components in the map unit name represent the major soils within a map unit delineation. Minor components make up the balance of the map unit. Great differences in soil properties can occur between map unit components and within short distances. Minor components may be very different from the major components. Such differences could significantly affect use and management of the map unit. Minor components may or may not be documented in the database. The results of aggregation do not reflect the presence or absence of limitations of the components which are not listed in the database. An on-site investigation is required to identify the location of individual map unit components. For queries of soil properties, only major components are considered for Dominant Component (numeric) and Weighted Average aggregation methods (see below). Additionally, the aggregation method selected drives the available properties to be queried. For queries of soil interpretations, all components are condisered.For each of a map unit's components, a corresponding percent composition is recorded. A percent composition of 60 indicates that the corresponding component typically makes up approximately 60% of the map unit. Percent composition is a critical factor in some, but not all, aggregation methods.For the attribute being aggregated, the first step of the aggregation process is to derive one attribute value for each of a map unit's components. From this set of component attributes, the next step of the aggregation process derives a single value that represents the map unit as a whole. Once a single value for each map unit is derived, a thematic map for soil map units can be generated. Aggregation must be done because, on any soil map, map units are delineated but components are not.The aggregation method "Dominant Component" returns the attribute value associated with the component with the highest percent composition in the map unit. If more than one component shares the highest percent composition, the value of the first named component is returned.The aggregation method "Dominant Condition" first groups like attribute values for the components in a map unit. For each group, percent composition is set to the sum of the percent composition of all components participating in that group. These groups now represent "conditions" rather than components. The attribute value associated with the group with the highest cumulative percent composition is returned. If more than one group shares the highest cumulative percent composition, the value of the group having the first named component of the mapunit is returned.The aggregation method "Weighted Average" computes a weighted average value for all components in the map unit. Percent composition is the weighting factor. The result returned by this aggregation method represents a weighted average value of the corresponding attribute throughout the map unit.The aggregation method "Minimum or Maximum" returns either the lowest or highest attribute value among all components of the map unit, depending on the corresponding "tie-break" rule. In this case, the "tie-break" rule indicates whether the lowest or highest value among all components should be returned. For this aggregation method, percent composition ties cannot occur. The result may correspond to a map unit component of very minor extent. This aggregation method is appropriate for either numeric attributes or attributes with a ranked or logically ordered domain.