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.

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.

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.

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 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

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

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’.

Reference maps illustrate the location of census standard geographic areas for which census statistical data are tabulated and disseminated. The maps display the boundaries, names and unique identifiers of standard geographic areas, as well as physical features such as streets, railroads, coastlines, rivers and lakes. Reference maps include: Standard Geographical Classification (SGC) Census tracts Federal electoral districts

Introduction

Google Maps statistics:Â Google Maps, launched in 2005, has evolved from a basic navigation tool into a comprehensive platform integral to daily life. As of October 2024, it surpassed 2 billion monthly active users, making it one of the most widely used applications globally. The platform hosts over 200 million businesses and places, with more than 120 million Local Guides contributing daily through reviews, photos, and updates.

Users collectively contribute over 20 million pieces of information daily, enhancing the map's accuracy and utility. In 2023, Google Maps generated approximately USD 11.1 billion in revenue, primarily from advertising and API services. The platform's extensive reach and user engagement underscore its pivotal role in modern navigation and local discovery.

In the following article, we shall study the essential Google Maps statistics related to the application, which will help illustrate the immensity of its operations.

A thematic map shows the spatial distribution of one or more specific data themes for standard geographic areas. Thematic maps include: Population Age Income Language of work Instruction in the official minority language

Metropolitan Statistical Areas are CBSAs associated with at least one urbanized area that has a population of at least 50,000. The metropolitan statistical area comprises the central county or counties or equivalent entities containing the core, plus adjacent outlying counties having a high degree of social and economic integration with the central county or counties as measured through commuting.Download: https://www2.census.gov/geo/tiger/TGRGDB24/tlgdb_2024_a_us_nationgeo.gdb.zip Layer: Core_Based_Statistical_Area where [MEMI] = "1"Metadata: https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19115/series_tl_2023_cbsa.shp.iso.xml

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

Collection description

F map, T maps and six ROIs

Subject species

homo sapiens

Map type

Other

Paua Statistical AreasLegal definitions for all Paua statistical areas were sourced from the Certified Statistical area maps held in Ministry for Primary Industries legal document safe. The Statistical area maps for Paua are Maps 11, 11a – k, m – n, p – v: Paua Statistical Areas. The seaward boundary of these areas is ambiguously defined, and for the purposes of mapping, has been assessed as being 30 km from the coast or at the intersection with another boundary statistical area boundary.All boundaries have been generalised inland where they reach the coastline. An authoritative coastal boundary of these statistical areas is dependent on the "mean high water mark". An accurate digital version of the mean high water mark for New Zealand does not exist at this stage. This information layer is considered reasonably accurate but not authoritative.The outer New Zealand’s Exclusive Economic Zone (EEZ) boundary used to created these statistical areas was sourced from Land Information New Zealand (LINZ).

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan and Micropolitan Statistical Areas are together termed Core Based Statistical Areas (CBSAs) and are defined by the Office of Management and Budget (OMB) and consist of the county or counties or equivalent entities associated with at least one urban core (urbanized area or urban cluster) of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. Categories of CBSAs are: Metropolitan Statistical Areas, based on urbanized areas of 50,000 or more population, and Micropolitan Statistical Areas, based on urban clusters of at least 10,000 population but less than 50,000 population. The CBSAs for the 2010 Census are those defined by OMB and published in December 2009.

© The United States CBSA Boundaries files were compiled from a variety of sources including the US Bureau of the Census, and data supplied by individual states. This layer is sourced from maps.bts.dot.gov.

It is classified into map API, data API, and mobile SDK, and it is a service that provides data and map service of population, household, housing, and business owned by Statistics Korea so that other organizations and services can use it. ○ Map API: Provides API for map service provided by SGIS Open Platform ○ Data API: Provides API to use data on population, household, housing, business, etc. owned by Statistics Korea ○ Mobile SDK: Map based on Android and iOS SDK provided in native language to develop services

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).The TIGER/Line Shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System. The MAF/TIGER System represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line Shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGERweb REST Services allows users to integrate the Census Bureau's Topologically Integrated Geographic Encoding and Referencing database (TIGER) data into their own GIS or custom web-based applications.For a more detailed description of the areas listed or terms below, refer to TIGER/Line documentation or the Geographic Areas Reference Manual, (GARM).Metadata

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

Data used in the policy statistics map as service-specific data for configuring the utilization service

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.

Mapping plan parcel - statistical sector corresponds to the dataset associating a plan parcel such as defined in article 2 of the Royal Decree of July 30th 2018 with the statistical sector including it. This dataset is composed of a single class mentioning the identifier of the plan parcels as well as the identifier of the statistical sectors. The dataset can be freely downloaded as a zipped CSV.