Abstract Arithmetic map operations are very common procedures used in GIS to combine raster maps resulting in a new and improved raster map. It is essential that this new map be accompanied by an assessment of uncertainty. This paper shows how we can calculate the uncertainty of the resulting map after performing some arithmetic operation. Actually, the propagation of uncertainty depends on a reliable measurement of the local accuracy and local covariance, as well. In this sense, the use of the interpolation variance is proposed because it takes into account both data configuration and data values. Taylor series expansion is used to derive the mean and variance of the function defined by an arithmetic operation. We show exact results for means and variances for arithmetic operations involving addition, subtraction and multiplication and that it is possible to get approximate mean and variance for the quotient of raster maps.

Raczynski, K., Babineaux, C., & Cartwright, J. H. (2025). GEO Tutorial: Nine Ways For Spatial Data Interpolation in ArcGIS Pro. Mississippi State University: Geosystems Research Institute. [View Document] GEO Tutorial Number of Pages: 8Publication Date: 06/2025This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

Sediment samples were collected in the summer of 2017 over the entire study area at 500 m spacing and in the channels. Grain size analysis of the collected sediment samples was conducted using a Malvern Mastersizer 2000© laser beam grain sizer. The median grain size (d50 in µm) at each sample location was then interpolated over the entire study area. Interpolation was made with the SAGA-GIS software (version 7.9.0). According to the cross-validation error, the best method for the D50mm interpolation was the Modified Quadratic Shepard. The 10-fold validation provided an R² of 0.93, an NMRSE of 24.5, an RMSE of 83.9, an MRE of 7039 with the fit set to “node”, the quadratic neighbours and weighting neighbours set to 50 and the spatial resolution was set to 10 m. The resultant interpolation map was then categorized following the nomenclature of Blott and Pye (2001) provided in the file style_sediment_map.qml.

Deoxygenation of the oceans is one of the most important issues in oceanography today. Using dissolved oxygen measurements taken at various depths in Monterey Bay, California, you'll perform a 3D geostatistical interpolation to predict the oxygen levels throughout the entire bay.In this lesson you will build skills in these areas: - Charting data with histograms and scatter plots in ArcGIS Pro- Interpolating 3D measurements- Comparing 2D interpolation- Exporting rasters - Creating a 3D animationLearn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

a, , and are reflectance of blue, red, and NIR band in the HJ-1A CCD optical image, respectively.Calculation for vegetation indices a.

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

This raster file represents land within the Mountain Home study boundary classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by use of Random Forest, a supervised machine learning algorithm. Classification models often employ Random Forest due to its accuracy and efficiency at labeling large spatial datasets. To build a Random Forest model and supervise the learning process, IDWR staff create pre-labeled data, or training points, which are used by the algorithm to construct decision trees that will be later used on unseen data. Model accuracy is determined using a subset of the training points, otherwise known as a validation dataset. Several satellite-based input datasets are made available to the Random Forest model, which aid in distinguishing characteristics of irrigated lands. These characteristics allow patterns to be established by the model, e.g., high NDVI during summer months for cultivated crops, or consistently low ET for dryland areas. Mountain Home Irrigated Lands 2023 employed the following input datasets: US Geological Survey (USGS) products, including Landsat 8/9 and 10-meter 3DEP DEM, and European Space Agency (ESA) Copernicus products, including Harmonized Sentinel-2 and Global 30m Height Above Nearest Drainage (HAND). For the creation of manually labeled training points, IDWR staff accessed the following datasets: NDVI derived from Landsat 8/9, Sentinel-2 CIR imagery, US Department of Agriculture National Agricultural Statistics Service (USDA NASS) Cropland Data Layer, Active Water Rights Place of Use data from IDWR, and USDA’s National Agriculture Imagery Program (NAIP) imagery. All datasets were available for the current year of interest (2023). The published Mountain Home Irrigated Lands 2023 land classification raster was generated after four model runs, where at each iteration, IDWR staff added or removed training points to help improve results. Early model runs showed poor results in riparian areas near the Snake River, concentrated animal feeding operations (CAFOs), and non-irrigated areas at higher elevations. These issues were resolved after several model runs in combination with post-processing masks. Masks used include Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data. These data were amended to exclude polygons overlying irrigated areas, and to expand riparian area in specific locations. A manually created mask was primarily used to fill in areas around the Snake River that the model did not uniformly designate as irrigated. Ground-truthing and a thorough review of IDWR’s water rights database provided further insight for class assignments near the town of Mayfield. Lastly, the Majority Filter tool in ArcGIS was applied using a kernel of 8 nearest neighbors to smooth out “speckling” within irrigated fields. The masking datasets and the final iteration of training points are available on request. Information regarding Sentinel and Landsat imagery:All satellite data products used within the Random Forest model were accessed via the Google Earth Engine API. To find more information on Sentinel data used, query the Earth Engine Data Catalog https://developers.google.com/earth-engine/datasets) using “COPERNICUS/S2_SR_HARMONIZED.” Information on Landsat datasets used can be found by querying “LANDSAT/LC08/C02/T1_L2” (for Landsat 8) and “LANDSAT/LC09/C02/T1_L2” (for Landsat 9).Each satellite product has several bands of available data. For our purposes, shortwave infrared 2 (SWIR2), blue, Normalized Difference Vegetation Index (NDVI), and near infrared (NIR) were extracted from both Sentinel and Landsat images. These images were later interpolated to the following dates: 2023-04-15, 2023-05-15, 2023-06-14, 2023-07-14, 2023-08-13, 2023-09-12. Interpolated values were taken from up to 45 days before and after each interpolated date. April-June interpolated Landsat images, as well as the April interpolated Sentinel image, were not used in the model given the extent of cloud cover overlying irrigated area. For more information on the pre-processing of satellite data used in the Random Forest model, please reach out to IDWR at gisinfo@idwr.idaho.gov.

The main objective of this study is to reconstruct the pre-Roman topography under the city of Aachen, Germany. Aachen has a 2000-year settlement history, and enormous amounts of anthropogenic deposits have accumulated during this time; these deposits are thus also visualized. The key data used are archeological excavation records and geological drilling documents that contain elevation and spatial data, and the Kriging algorithm was used to interpolate these data to produce two high-resolution raster datasets showing both the paleotopography and thickness of anthropogenic deposits. The paleo-DEM was then employed to reconstruct the course of former streams running through the study area. This research provides new insights into the topographic preconditions encountered by the first Roman settlers. As the accumulation of anthropogenic deposits in distinct areas of the city is linked to specific historical periods, the results also reveal the developmental stages of the ancient city.

The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classified based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

EPA Intelligent Dasymetric Mapping (IDM) ToolboxThe Intelligent Dasymetric Mapping (IDM) Toolbox is available to download based on the version of ArcGIS software implemented.The IDM Toolbox uses ArcPy and arcpy.da functionality. This version requires ArcGIS 10.3 or higher. The ArcPy toolbox contains a number of scripts that assist preparing vector population and raster ancillary datasets for intelligent dasymetric mapping, performs the dasymetric calculations, and then generates a floating point output raster of revised population density. Please see the documentation in the zip file for more information on the individual tools.You may find more information by the EPA about this data and the toolbox here: https://www.epa.gov/enviroatlas/dasymetric-toolboxTO DOWNLOAD: simply click on the "Open" button at the top right to start the 2GB download of the zip file. Or, you may go directly to the EPA's FTP download site here: https://edg.epa.gov/data/public/ORD/EnviroAtlas/National/ConterminousUS/ and download the "dasymetric_us_20160208.zip" file.

This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

The feature class MM_Tmax_Aprile, is a polygonal feature class that represents areas with maximum temperatures measured in the month of April homogeneous. Of the Maximum Temperatures are considered the maximum, the average and the minimum monthly. Each area has been calculated by interpolation from the data collected from the individual measuring stations.

This GIS dataset is the result of the interpolation of bathymetry from depth measurements made in Long and Tryne Fjords in the Vestfold Hills, Antarctica (see Entry: VH_bathy_99). The Topogrid command within the ArcInfo GIS software, version 8.0.2, was used to do the interpolation. Coastline and spot height (heights above sea level) data, extracted from the Australian Antarctic Data Centre's Vestfold Hills topographic GIS dataset (see Entry: vest_hills_gis), was also used as input data to optimise the interpolation close to the coastline. See related URLs for a map showing the interpolated bathymetry.

This resource is 3 GIS layers in a web service related to the geology of Merrimack Basin, New Hampshire. It consists of GIS geostatistical Interpolation of a surface that models the depth to bedrock. It is derived from known georeferenced locations where depths to bedrock have been observed. These primarily include bedrock outcrops and well or boring locations. Interpolation method was ordinary kriging, using a lag size of 347 ft, 18 lags, a nugget of 42.24, partial sill of 647.508, and a 4633 ft range. A maximum of 20 neighbors and a minimum of 8 neighbors in a single circular sector were used in interpolation. The data are provided in the following formats: a web map service, a web feature service, and an ERSI Service Endpoint. It was compiled by the New Hampshire Geological Survey and made available for distribution through the National Geothermal Data System.

1) Merrimack Basin Bedrock Depth: This layer displays a GIS geostistical Interpolation of a surface that models the depth to bedrock. It was derived from known georeferenced locations where depths to bedrock have been observed. These primarily include bedrock outcrops and well or boring locations. Interpolation method was ordinary kriging, using a lag size of 448.6 ft. A maximum of 20 neighbors and a minimum of 8 neighbors were used in interpolation. Layer in the web service is "merri_bdkd".

2) Merrimack Basin Bedrock Depth Standard Error: This layer displays the Predicted Standard Error of GIS geostatistical interpolation of a surface that models the depth to bedrock. The predicted standard error is the standard deviation of the predicted surface, and is a function of distance from the nearest data point. Layer in web service is "merri_error".

3) Merrimack Basin Bedrock Depth Contours: This layer displays10 foot contours of a GIS geostatistical interpolation of a surface that models the depth to bedrock. Derived from known georeferenced locations where depths to bedrock have been observed. Contours were smoothed and topology was enforced to ensure that contours did not cross. Features with length less than 100 ft were removed. Layer in web service is "merri_bdkd_contour".

The rapid growth of urban development, industrialization, mining, farming, and biological activities has resulted in potentially toxic metal pollution of the soil all over the world. This has caused degradation of soil quality, lower crop production, and risk to human health. For this work, two study sites were selected to evaluate metal concentrations in the agricultural as well as the recreational soil around the Cerrito Blanco in Matehuala, San Luis Potosi, Mexico. The concentrations of eight metals, namely As, Ca, Mg, Na, K, Sr, Mn, and Fe were analysed in order to determine the level of contamination risk as well as their spatial distributions. However, this study is mainly focused on toxic metals, e.g. As, Sr, Mn, and Fe. The contamination indices techniques were used to evaluate the risk assessment of soil. Additionally, the positive matrix factorization (PMF) model as well as the geostatistical analysis was used to identify the contamination sources based on 64 surface soil samples. After implementing PMF to analyze the soils, it was possible to differentiate the variations in factors linked to the contaminants, farming impacts, and the reference soil geochemistry. The soil in the two studied locations included high concentrations of As, Ca, Mg, K, Sr, Mn, and Fe, including variations in their spatial compositions, which were caused by direct mining activities, the movement and deposition of smelting waste, and the extensive use of irrigated contaminated groundwater for irrigation. The four possible factors were identified for soil pollution including industrial, transportation, agricultural, and naturogenic based on the PMF and geostatistical analysis. The spatial distribution of metal concentrations in the soil was also presented using a geographical information system (GIS) interpolation technique. The identification of metal sources and contamination risk mapping presents a significant role in minimizing pollution sources, and it may be performed in regions with high levels of soil contamination risk.

Price inflation has outbalanced the income of residents and buyers in major post-industrial city-regions, and real estate has become an important driver of these inequalities. In a context of a resilient inflation of home values during the last two decades in the greater Paris Region, it is critical to examine housing price dynamics to get a better understanding of socioeconomic segregation. This paper aims at presenting spatial analysis of the dynamics of segregation pertaining to inflation, analyzing price and sellers and buyers data. Using interpolation techniques and multivariate analysis, the paper presents a spatial analysis of property-level data from the Paris Chamber of Notaries (1996-2012) in a GIS (159,000 transactions in suburban areas, single family homes only). Multivariate analysis capture price change and local trajectories of occupational status, i.e. changes in balance between inward and outward flows of sellers and buyers. We adopt a method that fits the fragmented spatial patterns of suburbanization. To do so, we remove the spatial bias by means of a regular 1-km spatial grid, interpolating the variables within it, using a time-distance matrix. The main results are threefold. We document the spatial patterns of professionalization (a rise of executives, intermediate occupation and employees) to describe the main trends of inward mobility in property ownership in suburbs, offsetting the outward mobility of retired persons. Second, neighborhood trajectories are related the diverging patterns of appreciation, between local contexts of accumulation with a growth of residential prices, and suburbs with declining trends. The maturity of suburbanization yields a diversified structure of segregation between the social groups, that do not simply oppose executives vs. blue collar suburbs. A follow-up research agenda is finally outlined.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Valles Calders, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).