The product data are six statistics that were estimated for the chemical concentration of lithium in the soil C horizon of the conterminous United States. The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 55 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of lithium concentration.

**Activation co-ordinates (x, y, z) are given in MNI space (Montreal Neurological Institute). Numbers given in parentheses reflect approximate Brodmann Area (BA) locations.¶Magnitude and extent statistics correspond to a minimum p FDR

The product data are six statistics that were estimated for the chemical concentration of lanthanum in the soil C horizon of the conterminous United States (Smith and others, 2013). The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 48.8 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of lanthanum concentration.

Introduction

Google Maps Statistics: Google Maps has changed how we used to navigate or explore the world. In 2024, it will most certainly become the ultimate mapping service, getting so much more than most other services and boasting so many more users. This article will discuss some of the Google Maps statistics its global coverage, technology achievements, and downloads.

A collection of 13 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

F map, T maps and six ROIs

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.

Per-and polyfluoroalkyl substances (PFAS) are synthetic chemicals that are increasingly being detected in groundwater. The negative health consequences associated with human exposure to PFAS make it essential to quantify the distribution of PFAS in groundwater systems. Mapping PFAS distributions is particularly challenging because a national patchwork of testing and reporting requirements has resulted in sparse and spatially biased data. In this analysis, an inhomogeneous Poisson process (IPP) modeling approach is adopted from ecological statistics to continuously map PFAS distributions in groundwater across the contiguous United States. The model is trained on a unique data set of 8910 PFAS groundwater measurements, using combined concentrations of two PFAS analytes. The IPP model predictions are compared with results from random forest models to highlight the robustness of this statistical modeling approach on sparse data sets. This analysis provides a new approach to not only map PFAS contamination in groundwater but also prioritize future sampling efforts.

SPM{F}-filtered: u = 5.631, k = 10

Collection description

F map, T maps and six ROIs

Subject species

homo sapiens

Map type

Other

The hereby provided zipped folder contains the group-level statistical maps underlying the corresponding fMRI figures in Winkelmeier et al. (2022). - For the univariate analyses (Figure 3 and Supplementary Figures 3 and 4), unthresholded Z statistical maps are provided. These maps are derived from the fMRI group analysis employing the Sandwich Estimator Toolbox (SwE, Guillaume et al., NeuroImage 94, 287-302 (2014)). The Z statistical maps are in Paxinos space as described in the methods section of the manuscript. Image resolution corresponds to the original resolution of the EPI acquisition matrix. - For the task-related functional connectivity analyses (Figure 4 and Supplementary Figure 5), we provide unthresholded T statistical maps, derived from the group-level analysis using the BASCO toolbox (Gottlich et al., Front Syst Neurosci 9, 126 (2015)). As above, maps are in Paxinos space, with the resolution again corresponding to the original EPI resolution. For more details please consult the original work ‘Winkelmeier et al., Nature Communications, 2022’ or contact ‘wokelsch@uni-mainz.de’.

The product data are six statistics that were estimated for the chemical concentration of cobalt in the soil C horizon of the conterminous United States (Smith and others, 2013). The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 24.4 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of cobalt concentration.

A collection of 1 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Esri ArcGIS Online (AGOL) Feature Layer which provides access to the MDOT SHA County Flood Statistics MapsMDOT SHA County Flood Statistics Maps data consists of polygon geometric features which represent the geographic extent of each Maryland County with an available MDOT SHA County Flood Statistics Map. Users of this layer should consume the URL contained within each pop-up to access the MDOT SHA County Flood Statistics Map.MDOT SHA County Flood Statistics Maps data is owned & maintained by the MDOT SHA OPPE Innovative Planning & Performance Division (IPPD).For more information related to the maps, contact MDOT SHA OPPE Innovative Planning & Performance Division (IPPD):Email: IPPD@mdot.maryland.govFor more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

This dataset lists all the information related to the area of housing by municipality in the Provence Alpes-Côte d'Azur region in 2015. The data are sourced from INSEE, RP main farm and they are extracted from "Our Territory", an interactive statistical mapping tool operated by the Provence Alpes-Côte d'Azur Region. The suf_lgt.xls dataset includes the following indicators: - surface area of dwellings of less than 25 m2 - surface area of dwellings of 25 to less than 40 m2 - surface area of dwellings of 40 to less than 70 m2 - surface area of dwellings of 70 to less than 100 m2 - surface area of dwellings of 100 to less than 150 m2 - surface area of dwellings of 150 m2 or more About the indicators: * The meaning, source and vintage of each indicator are detailed in a spreadsheet of the dataset, one tab per indicator. * The "Data" tab contains the data itself. The data from this tab is available in the preview and by API. These data can be viewed on the Our Territory application, an interactive statistical mapping tool operated by the Provence Alpes-Côte d'Azur Region. It provides the institution’s partners and the general public with a set of resources for knowledge of the territory and makes it possible to obtain figures and personalise its own maps. It contains the essential data to understand territorial dynamics (more than 3000 indicators) https://ourreterritoire.maregionsud.fr/

This dataset lists the population by municipality and age group in the Provence Alpes-Côte d'Azur Region in 2015. The data are sourced from INSEE, RP main farm and they are extracted from "Our Territory", an interactive statistical mapping tool operated by the Provence Alpes-Côte d'Azur Region. The pop_age.xls dataset includes the following indicators: * Municipal population * Number of people aged 0 to 14 * Number of people aged 15 to 29 * Number of people aged 15 to 29 * Number of people aged 30 to 44 * Number of people aged 45 to 59 * Number of people aged 60 to 74 About the indicators: - The meaning, source and vintage of each indicator are detailed in a spreadsheet of the dataset. - The "Data" tab contains the data itself. The data from this tab is available in the preview and by API. These data can be viewed on the Our Territory application, an interactive statistical mapping tool operated by the Provence Alpes-Côte d'Azur Region. It provides the institution’s partners and the general public with a set of resources for knowledge of the territory and makes it possible to obtain figures and personalise its own maps. It contains the essential data to understand territorial dynamics (more than 3000 indicators) https://ourreterritoire.maregionsud.fr/

This dataset is from a study "Role of interoceptive accuracy in topographical changes in emotion-induced bodily sensations". This dataset does not include individual sensation map because participants did not consent to have their sensation map made publicly available. The present study investigated whether or not interoceptive accuracy was associated with topographical changes in this map following emotion-induced bodily sensations. This study included 31 participants who observed short video clips containing emotional stimuli and then reported their sensations on the body map. Interoceptive accuracy was evaluated with a heartbeat detection task and the spatial patterns of bodily sensations to specific emotions, including anger, fear, disgust, happiness, sadness, and neutral, were visualized using Statistical Parametric Mapping (SPM) analyses. Distinct patterns of bodily sensations were identified for different emotional states. In addition, positive correlations were found between the magnitude of sensation in emotion-specific regions and interoceptive accuracy across individuals. A greater degree of interoceptive accuracy was associated with more specific topographical changes after emotional stimuli.

SPM{T}-filtered: u = 3.131, k = 0

Collection description

F map, T maps and six ROIs

Subject species

homo sapiens

Map type

R

This dataset represents climate observations within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. PRISM is a set of monthly, yearly, and single-event gridded data products of mean temperature and precipitation, max/min temperatures, and dewpoints, primarily for the United States. In-situ point measurements are ingested into the PRISM (Parameter elevation Regression on Independent Slopes Model) statistical mapping system. The PRISM products use a weighted regression scheme to account for complex climate regimes associated with orography, rain shadows, temperature inversions, slope aspect, coastal proximity, and other factors. These data are summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

Locations and border maps for cities of the Holy Roman Empire as listed in the Deutsches Städtebuch (Keyser et al., eds., 1939-2003).

Abstract copyright UK Data Service and data collection copyright owner.

These digital boundaries were created by the Great Britain Historical GIS Project and form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

They represent the boundaries of Registration Districts in England and Wales as in use at the date of each Census of Population between 1851 and 1911, 1911 being the last census to report extensively on these units.

These digital boundaries can be used to map economic, social and demographic statistics from the Censuses of Population, 1851 to 1911, the Registrar-General's reports from the same period, and other relevant statistical sources. They can also be used as reference maps for these administrative units.

Note that these Registration Districts were mostly identical to the Poor Law Unions which existed in the same period, but there are significant exceptions, most often where one Registration District was divided into multiple Poor Law Unions. These differences have been recorded by the Great Britain Historical GIS.

The boundary data contain the same numerical identifiers as are included in the GBHD transcriptions of census and vital registration statistics for Registration Districts, making statistical mapping straightforward.