As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

This statistic shows the percentage of search engine users in metropolitan and rural areas in China in 2020. At this time, about **** percent of the Chinese online search users lived in rural China.

In February 2025, Microsoft Sites handled **** percent of all search queries in the United States. During the same period, Verizon Media (formerly known as Yahoo and Oath) had a search market share of little less than ** percent. Market leader Google generated **** percent of all core search queries in the United States.

This map was developed to examine multi-scale spatial relationships between percentage of sagebrush and other response variables of interest. A map of sagebrush in the western United States was used as a base layer for a moving window analysis to calculate the percentage of the area classified as sagebrush within the given search radius.

This map shows the extent of land area equipped for irrigation -expressed as percentage- around the turn of the 20th century according to the Global Map of Irrigation Areas (version 4.0.1), together with areas of rainfed agriculture. Data are available from AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations.

As of March 2025, Google continued to dominate the global search engine industry by far, with an 89.62 percent market share. However, this stronghold may be showing signs of erosion, with its share across all devices dipping to its lowest point in over two decades. Bing, Google's closest competitor, currently holds a market share of 4.01 percent across, while Russia-based Yandex hikes to the third place with a share of around 2.51 percent. Competitive landscape and regional variations While Google's overall dominance persists, other search engines carve out niches in various markets and platforms. Bing holds a 12.21 percent market share across desktop devices worldwide, as Yandex and Baidu have found success inside and outside of their home markets. Yandex is used by over 63 percent of Russian internet users, but Baidu has seen its market share significantly in China As regional variations highlight the importance of local players in challenging Google's global supremacy, the company is likely to face more challenges with the AI-powered online search trend and increasing regulatory scrutiny. Search behavior and antitrust concerns Despite facing more competition, Google remains deeply ingrained in users' online habits. In 2024, "Google" itself was the most popular search query on its own platform, followed by "YouTube" - another Google-owned property. This self-reinforcing ecosystem has drawn scrutiny from regulators, with the European Commission imposing millionaire antitrust fines on the company. As its influence extends beyond search into various online services, the company's market position continues to be a subject of debate among industry watchdogs and authorities worldwide.

Number of environmental cars in traffic divided by total number of cars in traffic, multiplied by 100.Refers to 31/12 and the geographical municipality / region . From July 1, 2018, the data relates to climate bonus cars. Refers to cars that at the time of registration met the requirements for an environmental car. This means that cars registered before 1 January 2013 must meet the criteria for MB2007 and cars registered on 1 January 2013 or later must meet the criteria for MB2013. Source: SCB.This is stated according to the statistics previously available on Kolada.In Kolada everyone can search for data about Umeå municipality.

Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. The Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update. Two oceanfront shorelines for Massachusetts (approximately 1,800 km in total length) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS).

Analysis of ‘Parking Statistics in North America’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/terenceshin/searching-for-parking-statistics-in-north-america on 28 January 2022.

--- Dataset description provided by original source is as follows ---

ABOUT

This dataset identifies areas within a city where drivers are experiencing difficulty searching for parking. Cities can use this data to identify problem areas, adjust signage, and more. Only cities with a population of more than 100,000 are included.

Data

Some variables to highlight:

• AvgTimeToPark: The average time taken to search for parking (in minutes)
• AvgTimeToParkRatio: The ratio between the average time taken to search for parking and of those not searching for parking in the current geohash
• TotalSearching: The number of drivers searching for parking
• PercentSearching: The percentage of drivers that were searching for parking
• AvgUniqueGeohashes: The average number of unique geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking
• AvgTotalGeohashes: The average number of all geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking
• CirclingDistribution: JSON object representing the neighbouring geohashes at the 7 character level whereby vehicles searching for parking tend to spend their time. Each geohash will have the average percentage of time spent in that geohash prior to parking.
• HourlyDistribution: JSON object representing the average prevalence of searching for parking by hour of day (% distribution based on number of vehicles experiencing parking problems)
• SearchingByHour: JSON object representing the average percentage of vehicles searching for parking within the hour
• PercentCar: Percentage of vehicles with parking issues that were cars
• PercentMPV: Percentage of vehicles with parking issues that were multi purpose vehicles
• PercentLDT: Percentage of vehicles with parking issues that were light duty trucks
• PercentMDT: Percentage of vehicles with parking issues that were medium duty trucks
• PercentHDT: Percentage of vehicles with parking issues that were heavy duty trucks
• PercentOther: Percentage of vehicles with parking issues that were unknown classification

Content

This dataset is aggregated over the previous 6 months and is updated monthly. This data is publicly available from Geotab (geotab.com).

Inspiration

As some inspiration, here are some questions:

• Which cities are the hardest to find parking?
• By joining population data externally, can you determine a relationship between a region's population and the time that it takes to find parking?
• Similarly, by finding external data, is there a correlation between GDP and parking times? What about average household income?

--- Original source retains full ownership of the source dataset ---

A best-fit lookup between Output Areas (OA) to Nomenclature of Territorial Units for Statistics (NUTS) and Local Administrative Units (LAU) as at 31 December 2011 in England and Wales. The NUTS areas (Levels 1, 2 and 3) are operative as at January 2006 and the LAU areas (Levels 1 and 2) are operative as at January 2010. The methodology used to create the OA lookups is based on the distribution of 2011 Census population. A median average of each OA's household grid references, weighted by the population at each household has been used to create a population weighted centroid for each OA. This centroid has been used to allocate the OA and all its associated statistics to any higher geographies. Information on the percentage of an output areas population that falls into the higher geography has also been included in this best-fit lookup file. A ‘best-fit percentage’ indicator has been included in this lookup, to support an understanding of the actual relationship between the OA and the higher geography to which it has been best-fitted. This ‘best-fit percentage' indicator has been calculated by working out the percentage of the OA’s population that falls exactly within the output geography's boundary. This is calculated by plotting the grid references of the households into the output geography's digitised boundaries and aggregating the population that actually falls within the best-fitted geography's boundary. 100 indicates that all the OA's population actually fell within the best-fitted boundary. A value of 90 means that the OA's boundary is split across the output geography's boundary and that 90% of the OA's population actually fell within the geography the OA was best-fitted to. Only the higher geography to which the OA has been allocated will be given a percentage match. (File Size 26.5MB).Field Names – OA11CD, LAU210CD, LAU210NM, OA11PERCENT, LAU110CD, LAU110NM, OA11PERCENT1, NUTS306CD, NUTS306NM, OA11PERCENT2, NUTS206CD, NUTS206NM, OA11PERCENT3, NUTS106CD, NUTS106NM, OA11PERCENT4

Field Types – Text, Text, Text, Text, Text, Text, Text, Text, Text, Text, Text, Text, Text, Text, Text, Text

Field Lengths – 9, 10, 56, 3, 7, 28, 3, 5, 70, 3, 4, 48, 3, 3, 24, 3REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/OA11_LAU2_LAU1_NUTS3_NUTS2_NUTS1_EW_LU_25dc24c61a1b47d1834184ba01094dc3/FeatureServer

For more information and an overview of best-fitting follow this link - https://geoportal.statistics.gov.uk/datasets/f0aac7ccbfd04cda9eb03e353c613faa/about

Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. The Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update. Two oceanfront shorelines for Massachusetts (approximately 1,800 km in total length) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS).

This dataset tracks annual white student percentage from 2016 to 2023 for Project Search Mayo Clinic vs. Minnesota and Rochester Public School District

Historical Dataset of Project Search Mayo Clinic is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2016-2023),Total Classroom Teachers Trends Over Years (2021-2023),Student-Teacher Ratio Comparison Over Years (2021-2023),Black Student Percentage Comparison Over Years (2016-2020),White Student Percentage Comparison Over Years (2016-2023),Diversity Score Comparison Over Years (2016-2023)

This dataset tracks annual two or more races student percentage from 2013 to 2023 for Project Search vs. Minnesota and Centennial Public School District

This dataset tracks annual hispanic student percentage from 2014 to 2023 for Project Search vs. Minnesota and Centennial Public School District

The photosynthetic composition (C3 or C4) of vegetation on the land surface is essential for accurate simulations of biosphere-atmosphere exchanges of carbon, water, and energy. C3 and C4 plants have different responses to light, temperature, CO2, and nitrogen; they also differ in physiological functions like stomatal conductance and isotope fractionation. A fine-scale distribution of these plant types is essential for earth science modeling.The C4 percentage is determined from datasets that describe the continuous distribution of plant growth forms (i.e., the percent of a grid cell covered by herbaceous or woody vegetation), climate classifications, the fraction of a grid cell covered in croplands, and national crop type harvest area statistics. The staff from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II have made the original data set consistent with the ISLSCP-2 land/water mask. This data set contains a single file in ArcInfo ASCIIGRID format.This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.

This dataset tracks annual black student percentage from 2011 to 2022 for Project Search vs. Minnesota and Centennial Public School District

Projections of global changes in water scarcity with the current extent of maize, rice, wheat, vegetables, pulses and fruit production commodities were combined to identify the potential country level vulnerabilities of cropland land to water scarcity in 2050. The data relate to an analysis of the impact changes in water availability will have on maize, rice, wheat, vegetables, pulses and fruit production commodities availability in 2050. Full details about this dataset can be found at https://doi.org/10.5285/84b3b580-acbf-487d-bf44-c21bc2cf12ee

This dataset tracks annual asian student percentage from 2015 to 2023 for Project Search vs. Minnesota and Centennial Public School District