Literature review dataset

This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1^st, 2020 to December 15^th, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.

This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.

The reference to cite the related paper is the following:

Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y

To read the manuscript please follow this link: https://doi.org/10.1007/s10708-022-10601-y

Overview: FEMA and Argonne National Laboratory completed the first analysis of community resilience indicators in 2018 and repeated the process in 2022. The analysis process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the research team to ensure the methodologies are:

Quantitative, Data and methodology are publicly available, Calculated at the county level or lower, Examine generalized hazard risk (rather than a singular hazard), and Focused on pre-disaster community conditions.

After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visualization, conducts a correlation analysis, and creates a composite index called the "FEMA Community Resilience Challenges Index (CRCI)".

In 2022, the FEMA and Argonne research team updated the 2018 literature review and examined 14 methodologies published between 2003 and 2021. Examining the indicators used in these methodologies, the research team identified 22 indicators as commonly used (indicators used in five or more of the 14 methodologies). The research team produced the FEMA CRCI at the county and the census tract levels. More details on these indicators and the research process can be found in the FEMA CRCI Storymap. Data last updated on July 24, 2024. This is the latest available version of the CRCI. Questions or comments about this layer? Email the RAPT team at FEMA-TARequest@fema.dhs.gov

Transformations that occur in the coastal territory often have an important link with the construction of port infrastructures, although establishing a direct correlation between causes and effects is rarely straightforward as they are phenomena that emerge over decades. Moreover, this phenomenon is fundamentally observed in developed countries, where we also find the added difficulty that a high number of variables intervene since the coast is usually an environment that is strongly anthropized by human action whilst being an important tourist asset. This study analyzes, from a different perspective than traditional coastal engineering approaches, the existing correlation between the construction of various marinas and coastal infrastructures along the southeast of the Spanish Mediterranean coast. The existing geostatistical correlation between the configuration of port areas and the coastal and socioeconomic impacts that occurred during the decades following the construction of these infrastructures was evaluated using spatiotemporal GIS indicators. The results obtained show that there are different patterns of behavior in the impact generated by port infrastructures depending on the spatial configuration of their boundary conditions, beyond the behavior of sedimentary dynamics usually studied in civil engineering.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

Collinearity diagnostics.

Descriptive statistics for the non-standardised and standardised dependent and independent variables used as proxies for social disorganisation characteristics in Khayelitsha and Fort Lauderdale. The statistics are presented as raw variables prior to transformations. The spatial statistical techniques used to examine spatial patterns of violent crime and the associations with social disorganisation in Khayelitsha include: - exploratory spatial data analysis (ESDA) to explore the spatial distribution of violent crime in Khayelitsha; - bivariate correlation analysis using Pearson product-moment correlation; - a series of spatial regression models to examine the association between crime and a selection of structural neighbourhood characteristics in Khayelitsha.

This study subdivides the Weddell Sea, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis uses 28 environmental variables for the sea surface, 25 variables for the seabed and 9 variables for the analysis between surface and bottom variables. The data were taken during the years 1983-2013. Some data were interpolated. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared for the identification of the most reasonable method. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested. For the seabed 8 and 12 clusters were identified as reasonable numbers for clustering the Weddell Sea. For the sea surface the numbers 8 and 13 and for the top/bottom analysis 8 and 3 were identified, respectively. Additionally, the results of 20 clusters are presented for the three alternatives offering the first small scale environmental regionalization of the Weddell Sea. Especially the results of 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.

Neal Hot Springs-ESRI Geodatabase (ArcGeology v1.3): - Contains all the geologic map data, including faults, contacts, folds, unit polygons, and attitudes of strata and faults. - List of stratigraphic units and stratigraphic correlation diagram. - Three cross-sections. - Locations of production, injection, and exploration wells. - Locations of 40Ar/39Ar samples. - Location of XRF geochemical samples. - 3D model constructed with EarthVision using geologic map data, cross-sections, drill-hole data, and geophysics (model not in the ESRI geodatabase).

Existing methods for calculating directional relations in polygons (i.e. the directional similarity model, the cone-based model, and the modified cone-based model) were compared to human perceptions of change through an online survey. The results from this survey provide the first empirical validation of computational approaches to calculating directional relations in polygonal spatial data. We have found that while the evaluated methods generally agreed with each other, they varied in their alignment with human perceptions of directional relations. Specifically, translation transformations of the target and reference polygons showed greatest discrepancy to human perceptions and across methods. The online survey was developed using Qualtrics Survey Software, and participants were recruited via online messaging on social media (i.e., Twitter) with hashtags related to geographic information science. In total sixty-one individuals responded to the survey. This survey consisted of nine questions. For the first question, participants indicated how many years they have worked with GIS and/or spatial data. For the remaining eight questions, participants ranked pictorial database scenes according to degrees of their match to query scenes. Each of these questions represented a test case that Goyal and Egenhofer (2001) used to empirically evaluate the directional similarity model; participants were randomly presented with four of these questions. The query scenes were created using ArcMap and contained a pair of reference and target polygons. The database scenes were generated by gradually changing the geometry of the target polygon within each query scene. The relations between the target and reference polygon varied by the type of movement, the scaling change of the polygon, and changes in rotation. The scenarios were varied in order to capture a representative range of variability in polygon movements and changes in real world data. The R statistical computing environment was used to determine the similarity value that corresponds with each database scene based on the directional similarity model, the cone-based model, and the modified cone-based model. Using the survey responses, the frequency of first, second, third, etc. ranks were calculated for each database scene. Weight variables were multiplied by the frequencies to create an overall rank based on participant responses. A rank of one was weighted as a five, a rank of two was weighted as a four, and so on. Spearman’s rank-order correlation was used to measure the strength and direction of association between the rank determined using the three models and the rank determined using participant responses.

MonitoredRainGardens_PaperTable.xlsx: Excel spreadsheet where the "Garden" sheet contains the 14 monitored green infrastructure (GI) sites with their design and physiographic features and the "Field Log" sheet contains the installation and field maintenance trips.

WaterWells.xlsx: an Excel file containing all of the water wells in the Detroit region uses for interpolating groundwater levels.

GI_GIS_Analysis.aparx: ArcGIS Pro project file which includes the 14 monitored GI sites and the GIS data for Detroit (percent imperviousness, elevation, slope, land use type, wells, interpolated groundwater levels, hydrologic soil group).

Code.zip: Zip folder containing another folder titled "Code" which holds: (1) a folder titled "SensorData" containing 16 csv files with the raw pressure transducer data for the 16 monitored GI sites during the measurement period (including the two excluded sites); (2) a csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features used in the correlation analysis; (3) a csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period; (4) a csv file titled "GLWA_RainGaugesforStudy.csv" that contains rainfall from 9 rain gauges during the measurement period; (5) a Jupyter notebook titled "storm_constants_analysis.ipynb" which provides the code for calculating the decay constants for the monitored GI; (6) a Jupyter notebook titled "storm_constants.ipynb" which provides the code for analyzing the decay constants including the correlation analysis and surface plots; and (7) a Jupiter notebook titled "modeled_response.ipynb" which provides the code for plotting the drawdown curves based on the decay constant.

Effective pavement maintenance is vital for the economy, optimal performance, and safety, necessitating a thorough evaluation of pavement conditions such as strength, roughness, and surface distress. Pavement performance indicators significantly influence vehicle safety and ride quality. Recent advancements have focused on leveraging data-driven models to predict pavement performance, aiming to optimize fund allocation and enhance Maintenance and Rehabilitation (M&R) strategies through precise assessment of pavement conditions and defects. A critical prerequisite for these models is access to standardized, high-quality datasets to enhance prediction accuracy in pavement infrastructure management. This data article presents a comprehensive dataset compiled to support pavement performance prediction research, focusing on Southeast Texas, particularly the flood-prone region of Beaumont. The dataset includes pavement and traffic data, meteorological records, flood maps, ground deformation, and topographic indices to assess the impact of load-associated and non-load-associated pavement degradation. Data preprocessing was conducted using ArcGIS Pro, Microsoft Excel, and Python, ensuring the dataset is formatted for direct application in data-driven modeling approaches, including Machine Learning methods. Key contributions of this dataset include facilitating the analysis of climatic and environmental impacts on pavement conditions, enabling the identification of critical features influencing pavement performance, and allowing comprehensive data analysis to explore correlations and trends among input variables. By addressing gaps in input variable selection studies, this dataset supports the development of predictive tools for estimating future maintenance needs and improving the resilience of pavement infrastructure in flood-affected areas. This work highlights the importance of standardized datasets in advancing pavement management systems and provides a foundation for future research to enhance pavement performance prediction accuracy.

Tuscarora-ESRI Geodatabase (ArcGeology v1.3): - Contains all the geologic map data, including faults, contacts, folds, unit polygons, and attitudes of strata and faults. - List of stratigraphic units and stratigraphic correlation diagram. - Detailed unit descriptions of stratigraphic units. - Five cross-sections. - Locations of production, injection, and monitor wells. - 3D model constructed with EarthVision using geologic map data, cross-sections, drill-hole data, and geophysics (model not in the ESRI geodatabase).

This data set is a series of polylines denoting fault lines mapped in USGS Publication MF-1790, "Geologic Map of the Late Cenozoic Deposits of the Sacramento Valley and Northern Sierran Foothills, California" (Helley and Harwood,1985). This data set was created by scanning the five- original sheets from USGS Publication MF-1790 (Helley and Harwood, 1985), the five sheets were georeferenced individually and the geologic information was digitized using AutoCAD 2006. The accuracy of the digitized lines was deemed to be within acceptable error tolerances, with the digitized lines accurately matching the original drafted lines in USGS Publication MF-1790 (Helley and Harwood, 1985). In general, the width of the contact lines on the paper copy, accounting for scale, ranged up to about 20 meters (66 feet). During the original digitization, minor topological mistakes (such as identical rock units on both sides of a lithologic contact or unclosed polygons) and omissions (such as unidentified lithologic units) were applied according to the best available knowledge. Comparisons were made between the original mylar and colorized field sheets (as available), in addition to the Geologic Map of the Battle Creek Fault Zone, Northern Sacramento Valley, California (USGS Map MF-1298, 1981), the Geologic Map of the Chico Monocline and Northeastern Part of the Sacramento Valley, California (USGS Miscellaneous Investigations Series Map I-1238, 1981), and the Geologic Map of the Red Bluff 30' X 60' Quadrangle, California (USGS Geologic Investigation Series Map I-2542, 1995). The correlation and description of geologic units were excerpted from USGS Publication MF-1790m (Helley and Harwood, 1985).

This data set is a series of polylines denoting the symbology for landslides; generally showing direction of movement downhill. These features were added to the landslide deposits mapped in USGS Publication MF-1790, "Geologic Map of the Late Cenozoic Deposits of the Sacramento Valley and Northern Sierran Foothills, California" (Helley and Harwood,1985). This data set was created by scanning the five- original sheets from USGS Publication MF-1790 (Helley and Harwood, 1985), the five sheets were georeferenced individually and the geologic information was digitized using AutoCAD 2006. The accuracy of the digitized lines was deemed to be within acceptable error tolerances, with the digitized lines accurately matching the original drafted lines in USGS Publication MF-1790 (Helley and Harwood, 1985). In general, the width of the contact lines on the paper copy, accounting for scale, ranged up to about 20 meters (66 feet). During the original digitization, minor topological mistakes (such as identical rock units on both sides of a lithologic contact or unclosed polygons) and omissions (such as unidentified lithologic units) were applied according to the best available knowledge. Comparisons were made between the original mylar and colorized field sheets (as available), in addition to the Geologic Map of the Battle Creek Fault Zone, Northern Sacramento Valley, California (USGS Map MF-1298, 1981), the Geologic Map of the Chico Monocline and Northeastern Part of the Sacramento Valley, California (USGS Miscellaneous Investigations Series Map I-1238, 1981), and the Geologic Map of the Red Bluff 30' X 60' Quadrangle, California (USGS Geologic Investigation Series Map I-2542, 1995). The correlation and description of geologic units were excerpted from USGS Publication MF-1790m (Helley and Harwood, 1985).

This data set is a series of polygons denoting the geologic units mapped in USGS Publication MF-1790, "Geologic Map of the Late Cenozoic Deposits of the Sacramento Valley and Northern Sierran Foothills, California" (Helley and Harwood,1985). This data set was created by scanning the five- original sheets from USGS Publication MF-1790 (Helley and Harwood, 1985), the five sheets were georeferenced individually and the geologic information was digitized using AutoCAD 2006. The accuracy of the digitized lines was deemed to be within acceptable error tolerances, with the digitized lines accurately matching the original drafted lines in USGS Publication MF-1790 (Helley and Harwood, 1985). In general, the width of the contact lines on the paper copy, accounting for scale, ranged up to about 20 meters (66 feet). During the original digitization, minor topological mistakes (such as identical rock units on both sides of a lithologic contact or unclosed polygons) and omissions (such as unidentified lithologic units) were applied according to the best available knowledge. Comparisons were made between the original mylar and colorized field sheets (as available), in addition to the Geologic Map of the Battle Creek Fault Zone, Northern Sacramento Valley, California (USGS Map MF-1298, 1981), the Geologic Map of the Chico Monocline and Northeastern Part of the Sacramento Valley, California (USGS Miscellaneous Investigations Series Map I-1238, 1981), the Geologic Map of the Red Bluff 30' X 60' Quadrangle, California (USGS Geologic Investigation Series Map I-2542, 1995), and the Geologic Map of the Whitmore Quadrangle, California (Geologic Quadrangle Map GQ-993) . The correlation and description of geologic units were excerpted from USGS Publication MF-1790m (Helley and Harwood, 1985).

This study analyzed the heavy metals contents of copper (Cu), zinc (Zn), cadmium (Cd), mercury (Hg), arsenic (As), chromium (Cr), nickel (Ni), and lead (Pb) in sediments of the Chaohu Lake using inductively coupled plasma mass spectrometry. All the data processes were conducted using Microsoft Office 2019, and the statistical analyses using the Sigma-Plot version 12.0 (Systat Software, Inc., San Jose, CA, USA) and SPSS (Version 19.0, Chicago, IL, USA). The geospatial map was accomplished using ArcGIS 10.2 (Esri, Redlands, CA, USA). Pearson’s correlation coefficient evaluated the occurrence of a linear correlation between analyzed variables. Data analysis also included mean and standard deviations.

The North Pilbara project's main objective is to assist industry in their development off exploration strategies. In order to do this, we provide high-quality data sets such as this GIS, which provides different views of the same area, allowing correlation, comparison, and analysis at a broad scale across the entire North Pilbara. The advantage of this GIS is that it packages AGSO's primary data holdings for the entire region into a convenient digital package that can be manipulated and integrated with proprietary data in standard mapping applications. The North Pilbara GIS provides industry with a decision-making context, or wide-spaced framework. The lack of context is due the fact that industry commonly only have restricted data holdings over their leases. Therefore, regional synthesis data sets provide a context and framework for exploration decisions made on more spatially limited data. The North Pilbara GIS provides many new digital data sets, including a number of variations of the magnetics, gravity, and gamma-ray spectrometry. A solid geology map, and derivative maps, mineral deposits, geological events, and Landsat 5-TM provide additional views. This data set complements the 1:1.5 Million scale colour atlas (announced in June-July issue 58 of AusGeoNews). This provision of a regional digital data set will be an invaluable tool for exploration companies making comparative, correlative, and analytical decisions on the prospectivity of the North Pilbara. Just a few of the new aspects of the GIS include: -the under cover shape of prospective rocks with a new digital solid geology map; -all the images generated by the project (magnetics, gravity, Landsat, and radiometrics); -the imaging of several large shear zones, and complexity in granites; -compilation of geochemistry and geochronology; -a new chemical map based on radiometrics; -identification of the source regions of transported regolith

Notice: this is not the latest Heat Island Severity image service. For 2023 data, visit https://tpl.maps.arcgis.com/home/item.html?id=db5bdb0f0c8c4b85b8270ec67448a0b6. This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.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.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): 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 Arizona Dr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAADaphne 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.

Wabuska-ESRI geodatabase (ArcGeology v1.3): - Contains all the geologic map data, including faults, contacts, folds, veins, dikes, unit polygons, and attitudes of strata. - List of stratigraphic units and stratigraphic correlation diagram. - One cross-section.

Salt Wells-ESRI Geodatabase (ArcGeology v1.3): - Contains all the geologic map data, including faults, contacts, folds, dikes, unit polygons, and attitudes of strata and faults. - List of stratigraphic units and stratigraphic correlation diagram. - Locations of 40Ar/39Ar samples.