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.

It’s a crisp fall morning in Portland. A local barista opens her shop and pulls out her phone to check delivery routes for fresh beans. She taps the familiar red-and-white pin icon, Google Maps. Across the globe in Tokyo, a student uses Street View to navigate to his university. Meanwhile,...

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.

A story map on how and why the boundaries were made, and a guide to their use for statistics

Mapping spatiotemporal dynamics of crop-specific areas is of great significance in addressing challenges faced by agricultural systems. But comparable multi-phase crop maps in year series have not yet been developed in most regions of the global. In this study, we developed a framework for updating annual crop-specific area maps at 10km resolution based on crop statistics disaggregating, multi-source data integrating and machine learning. In our framework, we collected related spatial indicator used in previous studies and trained random forest regression models to predict spatiotemporal dynamics of crop-specific areas based on them. Annual crop statistics were further disaggregated based on probabilistic layer and harmonized based on multiple constraints. Finally, our results include maps of crop-specific areas covering 42 types from 1961-2022 in Africa, maps of crop-specific areas covering 14 types from 1980-2022 in China and maps of crop-specific areas covering 15 types from 2008-2022 in USA. Results show that our products have a reasonable level of consistency with independent reference map or statistics. Our products could be used as data basis for food security and environmental impact assessments.

The map of the smallest statistical areas in the whole country and each county and city (the Ministry of the Interior Statistics Division establishes a "Statistical Area Classification System," including the smallest statistical area, first-level release area, and second-level release area, which is a concept of a small statistical area. Several of the smallest statistical areas form a first-level release area, and several first-level release areas then form a second-level release area, and so on, layer by layer, to establish a spatial unit system for the dissemination of Taiwan's socio-economic data statistics.) *Coordinate supply: Main island - 121 zone, off-island (Penghu County, Kinmen County, Matsu County) - 119 zone.

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.

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.

ESRI Demographics and Statistics Atlas - The maps show the entire United States by county, using data from the U.S. Census Bureau's 2010 Census and Esri.

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.

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.

Rock Lobster Statistical AreasLegal definitions for all Rock Lobster statistical areas were sourced from the Certified Statistical area maps held in Ministry for Primary Industries legal document safe. The Statistical area map for Rock Lobster is Map 6: Rock Lobster Statistical Areas. The seaward boundary of these areas is ambiguously defined, and for the purposes of mapping, has been appriximated to 30 km from the coast.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.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).

ACS 2022 Legal and Statistical Entities Web Map Service; January 1, 2022 vintage

The Land Cover Map 2024 (UK Land Cover Statistics) dataset summarises the coverage of different land cover types across Great Britain and Northern Ireland, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. This data is provided in both .csv and geopackage (vector) formats. Statistics are calculated at country, county, and regional (England only) levels from the Land Cover Map 2024 (10 m classified pixels) datasets for Great Britain and Northern Ireland. A full description of this and all UKCEH LCM2024 products are available from the LCM2024 product documentation. In addition to UKCEH as copyright holders, the Land Cover Map 2024 (UK Land Cover Statistics) products use digital boundary products and reference maps. The source of the data is the Office for National Statistics and they are licensed under the Open Government Licence v.3.0. They contain OS data © Crown copyright and database right [2024].

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’.

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).

National and second-level release area map for each county and city (The Ministry of the Interior's Statistics Department has established a "Statistical Area Classification System," which includes the smallest statistical area, first-level release area, and second-level release area, as a concept of a small statistical area. Several smallest statistical areas make up a first-level release area, and several first-level release areas further make up a second-level release area, and so on, to establish a spatial unit system for the statistical release of Taiwan's socio-economic data.)

These 28 tiff files represent 2015 population estimates. However, please note that many of the country-level files include 2020 population estimates including: Angola, Benin, Botswana, Burundi, Cameroon, Cabo Verde, Cote d'Ivoire, Djibouti, Eritrea, Eswatini, The Gambia, Ghana, Lesotho, Liberia, Mozambique, Namibia, Sao Tome & Principe, Sierra Leone, South Africa, Togo, Zambia, and Zimbabwe. South Sudan, Sudan, Somalia and Ethiopia are intentionally omitted from this dataset. However, a country-level dataset for Ethiopia can be found at https://data.humdata.org/dataset/ethiopia-high-resolution-population-density-maps-demographic-estimates.

These summary statistics list the number of molecules assembled to maps and the number of map assemblies generated during the processing of data from a single mapcard. The mixed mapcard bore DNA from both Escherichia and Shigella. The maps which were generated were circular and passed standard quality control.