Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Attributes of sites in Hamilton City which collect anonymised data from a sample of vehicles. Note: A Link is the section of the road between two sites

Column_InfoSite_Id, int : Unique identiferNumber, int : Asset number. Note: If the site is at a signalised intersection, Number will match 'Site_Number' in the table 'Traffic Signal Site Location'Is_Enabled, varchar : Site is currently enabledDisabled_Date, datetime : If currently disabled, the date at which the site was disabledSite_Name, varchar : Description of the site locationLatitude, numeric : North-south geographic coordinatesLongitude, numeric : East-west geographic coordinates

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Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. Historical traffic is based on the average of observed speeds over the past three years. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

In March 2024, close to 4.4 billion unique global visitors had visited Wikipedia.org, slightly down from 4.4 billion visitors since August of the same year. Wikipedia is a free online encyclopedia with articles generated by volunteers worldwide. The platform is hosted by the Wikimedia Foundation.

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows: Green (fast): 85 - 100% of free flow speeds Yellow (moderate): 65 - 85% Orange (slow); 45 - 65% Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

Average Annual Daily Traffic data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain data on the total volume of vehicle traffic on a highway or road for a year divided by 365 days.

Our web services allow you to view geographical and forest data or to perform geoprocessing in your applications and websites. For simplified use, our web services are freely and free of charge (with the exception of web services from SCAN 25/100/ICAO). Choose a use to use our web services through their open-access URLs: — “discovery” web services Free and free access to IGN Plan and Air Photographs in your website or app — “essential” web services Free and free access to our most popular web services — Expert web services Free and free access to a rich catalogue of web services based on our public data — INSPIRE web services Consultation services (WMS) and download services (WFS) in accordance with the European INSPIRE Directive — Web services in beta version Test new web services in beta — Web services from IGN scans Access services based on scan data 25, 100 and ICAO

The map layers in this service provide color-coded maps of the traffic conditions you can expect for the present time (the default). The map shows present traffic as a blend of live and typical information. Live speeds are used wherever available and are established from real-time sensor readings. Typical speeds come from a record of average speeds, which are collected over several weeks within the last year or so. Layers also show current incident locations where available. By changing the map time, the service can also provide past and future conditions. Live readings from sensors are saved for 12 hours, so setting the map time back within 12 hours allows you to see a actual recorded traffic speeds, supplemented with typical averages by default. You can choose to turn off the average speeds and see only the recorded live traffic speeds for any time within the 12-hour window. Predictive traffic conditions are shown for any time in the future.The color-coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation, and field operations. A color-coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes.The map also includes dynamic traffic incidents showing the location of accidents, construction, closures, and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis.Data sourceEsri’s typical speed records and live and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds.Data coverageThe service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. Look at the coverage map to learn whether a country currently supports traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, visit the directions and routing documentation and the ArcGIS Help.SymbologyTraffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%To view live traffic only—that is, excluding typical traffic conditions—enable the Live Traffic layer and disable the Traffic layer. (You can find these layers under World/Traffic > [region] > [region] Traffic). To view more comprehensive traffic information that includes live and typical conditions, disable the Live Traffic layer and enable the Traffic layer.ArcGIS Online organization subscriptionImportant Note:The World Traffic map service is available for users with an ArcGIS Online organizational subscription. To access this map service, you'll need to sign in with an account that is a member of an organizational subscription. If you don't have an organizational subscription, you can create a new account and then sign up for a 30-day trial of ArcGIS Online.

Our Web Data dataset includes such data points as company name, location, headcount, industry, and size, among others. It offers extensive fresh and historical data, including even companies that operate in stealth mode.

For lead generation

With millions of companies worldwide, Web Company Database helps you filter potential clients based on custom criteria and speed up the conversion process.

Use cases

1. Filter potential clients according to location, size, and other criteria
2. Enrich your existing database
3. Improve conversion rates
4. Use predictive models to identify potential leads
5. Group your leads in segments for more accurate targeting

For market and business analysis

Our Web Company Data provides information about millions of companies, allowing you to find your competitors and see their weaknesses and strengths.

Use cases

1. Pinpoint your competitors
2. Learn about your competitors' size, headcount, and revenue
3. Prepare a data-driven plan for the next quarter

For Investors

We recommend B2B Web Data for investors to discover and evaluate businesses with the highest potential.

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal’s global B2B Web Dataset.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies hungry for the next investment
3. Check if a startup is about to reach the next maturity phase
4. Identify and predict a startup's potential at the founding moment
5. Choose companies that fit you in terms of size and headcount

For sales prospecting

B2B Web Database saves time your employees would otherwise use to search for potential clients manually.

Use cases

1. Make a short list of the top prospects
2. Define which companies are large or small enough to buy your product
3. Based on the revenue, determine which companies are ready to convert
4. Sort the companies by their distance from your warehouse to draw a line where selling won't result in satisfactory profit

Vehicle travel time and delay data on sections of road in Hamilton City, based on Bluetooth sensor records. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_link_stats?Page=1&Start_Date=2021-06-02&End_Date=2021-06-03. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset.

Column_Info

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Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Recorded volume data at SCATS intersections or pedestrian crossings in Hamilton. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_signal_detector_count?Page=1&Start_Date=2020-10-01&End_Date=2020-10-02. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset.

Column_InfoSite_Number, int : SCATS ID - Unique identifierDetector_Number, int : Detector number that the count is recorded toDate, datetime : Start of the 15 minute time interval that the count was recorded forCount, int : Number of vehicles that passed over the detector

Relationship

This table reference to table Traffic_Signal_Detector

Analytics

For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here.

Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Locations of signalised intersections and signalised pedestrian crossings in Hamilton City

Column_InfoSite_Number, int : SCATS ID - Unique identifierRoad_1, varchar : First road descriptorRoad_2, varchar : Second road descriptor - 'Ped xing' means it is a mid-block pedestrian signalRoad_3, varchar : Third road descriptor if relevantSite_Type, varchar : Pedestrian crossing or intersection, and whether on a state highway or council roadIs_CBD, int : Site is within the CBD boundaryEasting, decimal : Eastward-measured distance in NZTM projectionNorthing, decimal : Northward-measured distance in NZTM projectionLatitude, decimal : North-south geographic coordinatesLongitude, decimal : East-west geographic coordinates

Relationship

This table is referenced by Traffic_Signal_Detector

Analytics

For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here.

Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Pedestrian count at counters in Hamilton City. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_Pedestrian_count?Page=1&Start_Date=2020-10-01&End_Date=2020-10-02. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset.

Column_InfoCounter_Id, int : Unique identifier of the counterCount_Datetime, varchar : Start of the time interval that the count was recorded forCount_Number, int : Volume of pedestrians recorded for the given time interval

Relationship

This table reference to table Pedestrian_Counter_Information

Analytics

For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here.

Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'