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.

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.

City of Milpitas (CFDs) Community Financial Districts broken down by APNs. CFDs are classified to District 2005 and District 2008.Searchable Layers in Map:CFD Parcels Open & Closed - Subdivision Name

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

A handout for the presentation on finding small area data.

This statistic shows the results of a survey on the usage of the internet for route planning, maps and road maps (e.g. Google Maps) in Germany from 2013 to 2016. In 2016, there were about 13.67 million people among the German-speaking population aged 14 years and older, who frequently used the internet to plan routes or to access maps and road maps.

Context

Overwatch is a 6v6 FPS (first-person shooter) team game with great variety between heroes who can be played as. Overwatch League (OWL) is the professional esports league of Overwatch. When watching the OWL matches this year, I noticed the power-rankings and predictive statistics by IBM Watson extremely intriguing, so I determined to introduce the datasets into Kaggle. I, myself, really want to replicate the predictions and rankings, then testing with the stats lab.

Content

The datasets include players, head-to-head match-ups, and maps. The player historical statistics should contain OWL games from 2018 till now. It's centered around each player, and player's picked hero, its team name, performance, match IDs, etc.

Acknowledgements

Overwatch League Stats Lab has updated and downloadable csv files. And here are some interesting and inspiring questions to look at: https://overwatchleague.com/en-us/news/23303225.

Inspiration

Other than the power rankings and outcome predictions, I plan to look at teamfight stats, first elimination, and first death to compare the team's power.

For Players: 1. Match Report dashboard 2. Rate Ranks dashboard: Who led the league in solo kills/10 mins in 2018 as Junkrat? (min. 60 mins played) 3. Career Totals dashboard 4. Single Records dashboard

For Heroes: 1. Which 4 heroes did one play for 10% or more of his time on assault map attack rounds in the season? 2. Which hero increased in usage from 8% at the end of Stage 4 of 2018 to over 45% in the inaugural season playoffs?

For Matches: 1. Which team played the most matches that ended in a 3-2 score during the 2021 regular season? 2. Which team is entering the 2021 season on a 7-map loss streak? 3. Which team has the fastest completion time on Rialto?

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).

Description of columns in the ArcGIS point file "Points for Maps" which provides the final statistics used to make the maps of mean daily water levels and maps of the 25th, 50th, and 75th percentiles of daily water levels during 2000–2009 in Miami-Dade County; and maps showing the differences in the statistics of water levels between 1990–1999 and 2000–2009.

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.

This is a hub site that has been set up to display recent web applications and static maps created by the GIS Team of the Center for Health Statistics of the Texas Department of State Health Services. Additionally, the hub site includes a link to the Survey123 GIS service request which was created in 2022.

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

This dataset saves workflows related to the COVID-19 statistics anlaysis.

Risk Management Assistance (RMA) is designed to assist agency administrators/line officers and incident commanders by providing access to experienced line officers, personnel skilled in risk management, fire operations, and enhanced fire analytics to improve fire management responses through a risk informed process. RMA products and personnel strengthen the ability to examine alternative strategies that better consider the exposure tradeoffs, assess risk to highly valued resources and assets, and seek opportunities for realizing the beneficial effects of fire. The intent is to apply existing and emerging decision support tools coupled with risk management expertise to improve the overall effectiveness and efficiency of wildfire response.

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.

The map reliability calculator was developed to provide guidance for those mapping American Community Survey (ACS) data, with regards to data uncertainty and its impact on a map. ACS estimates are derived from a survey, and such statistics are subject to sampling error – divergence from the actual characteristics of the surveyed population. Sampling error can have a considerable impact on the representativeness of maps. One way to measure the impact of sampling error on quantitative choropleth maps is to calculate the probability that geographic units are misclassified. The calculator determines probable misclassification by examining published estimates, their associated Margins of Error (MOEs), and category break points. Using these numbers along with a standard probability density function, the calculator determines the likelihood that an estimate’s actual value falls in a different category. The cumulative probability of erroneously classed units is summed for all geographies in a category and averaged, to produce a relative reliability statistic. The same averaging of the cumulative probability of error is also calculated for the entire map. From these statistics, ACS data mappers can see the likelihood, on average, that any given geography in a map, or map category, actually falls in a different category and has therefore been misclassified. The map reliability calculator was developed to provide guidance for those mapping American Community Survey (ACS) data, with regards to data uncertainty and its impact on a map. ACS estimates are derived from a survey, and such statistics are subject to sampling error – divergence from the actual characteristics of the surveyed population. Sampling error can have a considerable impact on the representativeness of maps. One way to measure the impact of sampling error on quantitative choropleth maps is to calculate the probability that geographic units are misclassified. The calculator determines probable misclassification by examining published estimates, their associated Margins of Error (MOEs), and category break points. Using these numbers along with a standard probability density function, the calculator determines the likelihood that an estimate’s actual value falls in a different category. The cumulative probability of erroneously classed units is summed for all geographies in a category and averaged, to produce a relative reliability statistic. The same averaging of the cumulative probability of error is also calculated for the entire map. From these statistics, ACS data mappers can see the likelihood, on average, that any given geography in a map, or map category, actually falls in a different category and has therefore been misclassified. To provide further guidance, the map reliability calculator also tells ACS data mappers whether their proposed map passes a reliability test – suggesting that the map is suitable for general use. There are two criteria employed in this reliability threshold. First, a map must have less than a 10% chance of erroneously classed geographies. This matches the Census Bureau’s standard of publishing MOEs at a 90% confidence interval and using the 90% confidence level to determine statistically significant differences. Additionally, all individual categories must have reliability scores under 20%. This second criterion ensures that even categories with relatively few geographies, and therefore little impact on overall map reliability, still are reasonably trustworthy representations of reality.

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

Map shows the percentage change in number of occupied and unoccupied private dwellings between the 2018 and 2023 Censuses.Download lookup file from Stats NZ ArcGIS Online or Stats NZ geographic data service.FootnotesGeographical boundariesStatistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018. Caution using time series Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data). About the 2023 Census dataset For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings. Data quality The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.Quality rating of a variable The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable. Dwelling occupancy status quality rating Dwelling occupancy status is rated as high quality. Dwelling occupancy status – 2023 Census: Information by concept has more information, for example, definitions and data quality.Dwelling type quality rating Dwelling type is rated as moderate quality. Dwelling type – 2023 Census: Information by concept has more information, for example, definitions and data quality.Using data for good Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.Confidentiality The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.Symbol-998 Not applicable-999 Confidential

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