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.

Toolbox Use
License
Creative Commons-PDDC
Recommended Citation
Welty JL, Jeffries MI, Arkle RS, Pilliod DS, Kemp SK. 2021. GIS Clipping and Summarization Toolbox: U.S. Geological Survey Software Release. https://doi.org/10.5066/P99X8558

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.

Lesson 1. An Introduction to working with multispectral satellite data in ArcGIS Pro In which we learn: • How to unpack tar and gz files from USGS EROS • The basic map interface in ArcGIS • How to add image files • What each individual band of Landsat spectral data looks like • The difference between: o Analysis-ready data: surface reflectance and surface temperature o Landsat Collection 1 Level 3 data: burned area and dynamic surface water o Sentinel2data o ISRO AWiFS and LISS-3 data Lesson 2. Basic image preprocessing In which we learn: • How to composite using the composite band tool • How to represent composite images • All about band combinations • How to composite using raster functions • How to subset data into a rectangle • How to clip to a polygon Lesson 3. Working with mosaic datasets In which we learn: o How to prepare an empty mosaic dataset o How to add images to a mosaic dataset o How to change symbology in a mosaic dataset o How to add a time attribute o How to add a time dimension to the mosaic dataset o How to view time series data in a mosaic dataset Lesson 4. Working with and creating derived datasets In which we learn: • How to visualize Landsat ARD surface temperature • How to calculate F° from K° using ARD surface temperature • How to generate and apply .lyrx files • How to calculate an NDVI raster using ISRO LISS-3 data • How to visualize burned areas using Landsat Level 3 data • How to visualize dynamic surface water extent using Landsat Level 3 data

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

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.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the GEODATA TOPO 250K Series 3 dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

A clip to the boundary of the NIC bioregion of the original Geodata Topo 250k Series 3 data.

Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-ecbd-7506-e044-00144fdd4fa6/GEODATA+TOPO+250K+Series+3+%28Packaged+-+Shape+file+format%29

Dataset History

These data have been derived from the TOPO 250K Series 3 data by clipping the data to the external boundary of the Northern Inland Catchments bioregion using the ESRI ArcGIS Analysis Clip tool.

Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-ecbd-7506-e044-00144fdd4fa6/GEODATA+TOPO+250K+Series+3+%28Packaged+-+Shape+file+format%29

Dataset Citation

Bioregional Assessment Programme (XXXX) Northern Inland Catchments bioregion GEODATA TOPO 250K Series 3. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/3bd6389b-be36-4e91-bc20-131e18f209c2.

Dataset Ancestors

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

ArcGIS Pro/QGIS to modify layers R for scripts

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 in 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 Vegetation 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)

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.

🇺🇸 미국 English The Watershed Boundary Dataset (WBD) is a seamless, national hydrologic unit dataset. Hydrologic units represent the area of the landscape that drains to a portion of the stream network. (https://www.usgs.gov/national-hydrography/watershed-boundary-dataset) It is maintained by the U.S. Geological Survey (USGS) in partnership with the states. The Department of Water Resources is the steward for the California portion of this dataset.The hydrologic units (HU) in the WBD form a standardized system for organizing, collecting, managing, and reporting hydrologic information for the nation. The HUs in the WBD are arranged in a nested, hierarchical system with each HU in the system identified using a unique code. Hydrologic unit codes (HUC) are developed using a progressive two-digit system where each successively smaller areal unit is identified by adding two digits to the identifying code the smaller unit is nested within. WBD contains eight levels of progressive hydrologic units identified by unique 2- to 16-digit codes. The dataset is complete for the United States to the 12-digit hydrologic unit. The 8-digit level unit is often referred to as HUC8 and is a commonly used reference framework for planning and environmental assessment. This particular version of the dataset was created by downloading the CA State extract of the National Hydrography Dataset from the USGS website https://www.usgs.gov/national-hydrography/access-national-hydrography-products and then performing a geoprocessing operation in ArcGIS Pro software to clip the HUC8s at the state of California political boundary. (https://data.cnra.ca.gov/dataset/california-county-boundaries2). A web map service was created with this dataset, but at it's original digitized resolution it can take a long time to render in a web map application. This dataset is a simplified version, created by use of the ArcGIS Simplify Polygon tool with the Douglas-Peucker Line simplification algorithm, reducing the vertex count from 1,095,449 to 9108. This dataset was reprojected from the original NAD 83 Geographic Coordinate System to WGS 1984 Web Mercator auxiliary sphere for use in web map applications. Any questions about this dataset may be sent to jane.schafer-kramer@water.ca.gov

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

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.

Notice: this is not the latest Heat 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. The Heat Anomalies is also reclassified into a Heat Severity raster also published on this site. This 30-meter raster was derived from 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:Full Range Heat Anomalies - USA 2022Full Range Heat Anomalies - USA 2021Full Range Heat Anomalies - USA 2020Federal 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.

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.

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.

This layer is based on the Vermont E911 Road Centerline layer and depicts associated broadband deployment for each road feature. For each road feature the BB_TYPE field depicts the presence of Cable or FTTH deployment. The layer consists of three component parts: the 2021 FTTH layer, the 2021 Cable layer, and the E-911 Road Centerline layer. The FTTH and Cable layers themselves are also comprised primarily of features copied from the E-911 Road Centerline layer. Note that deployment does not always extend to to the complete extent of road segments so the layers include many clipped road segments and thus care must be taken in performing analysis using just road segment IDs in this layer as it includes duplicate segment IDs.The following procedures were employed in the creation of this layer.Step 1: The FTTH and Cable layers are primarily comprised of road segments with the ten (10) AOT CLASS entries listed below. However, the layers also include some segments from other AOT classes where service was reported and some features from the E-911 Driveways layer where service was reported. These features were removed from this layer, which only includes the features with the 10 AOTCLASS entries.Town Highway Class 1 (code 1)Town Highway Class 2 (code 2)Town Highway Class 3 (code 3)Town Highway Class 4 (code 4)State Forest Highway (code 5)National Forest Highway (code 6)Private Road (code 8)Private Road (code 9)State Highway (code 30)US Highway (Code 40)Step 2: Use the Erase tool (the opposite of the Clip tool) to remove from the Cable layer lines that overlap with the FTTH layer.Step 3. Combine the FTTH and Erased Cable layersStep 4: Use the Erase tool to remove from the E-911 RDS layer the lines that overlap with the combined Cable and FTTH layer.Step 5: Add BB_Miles field, calculate milesStep 6: Add BB_TYPE field, populate Cable, FTTH, NoCableOrFTTHStep 7: Merge FTTH, Erased Cable, and Erased E-911 Centerline layer to create Broadband_RDS layer Step 8: Run statistics on Broadband Status layer, Count ESITEID by SegmentIDStep 9: Join statistics results to Broadband RDS layerStep 10: Select features where BB_TYPE= NoCableOrFTTH and Count of ESITEID is null, populate BB_TYPE= NoCableOrFTTH_NoLocStep 11: Join based on MCODE to add fields from BNDHASH, including TOWNNAME, TOWNGEOID and CUD, as MCODE is a naming convention used by Vermont E-911 and does not completely align with naming convention in BNDHASH