Dear Scientist!This database contains data collected due to conducting study: "Analysis of the route safety of abnormal vehicle from the perspective of traffic parameters and infrastructure characteristics with the use of web technologies and machine learning" funded by National Science Centre Poland (Grant reference 2021/05/X/ST8/01669). The structure of files is arising from the aims of the study and numerous of sources needed to tailor suitable data possible to use as an input layer for neural network. You can find a following folders and files:1. Road_Parameters_Data (.csv) - which is data colleced by author before the study (2021). Here you can find information about technical quality and types of main roads located in Mazovia province (Poland). The source of data was Polish General Directorate for National Roads and Motorways. 2. Google_Maps_Data (.json) - here you can find the data, which was collected using the authors’ webservice created using the Python language, which downloaded the said data in the Distance Matrix API service on Google Maps at two-hour intervals from 25 May 2022 to 22 June 2022. The application retrieved the TRAFFIC FACTOR parameter, which was a ratio of actual time of travel divided by historical time of travel for particular roads.3. Geocoding_Roads_Data (.json) - in this folder you can find data gained from reverse geocoding approach based on geographical coordinates and the request parameter latlng were employed. As a result, Google Maps returned a response containing the postal code for the field types defined as postal_code and the name of the lowest possible level of the territorial unit for the field administrative_area_level. 4. Population_Density_Data (.csv) - here you can find date for territorial units, which were assigned to individual records were used to search the database of the Polish Postal Service using the authors' original web service written in the Python programming language. The records which contained a postal code were assigned the name of the municipality which corresponded to it. Finally, postal codes and names of territorial units were compared with the database of the Statistics Poland (GUS) containing information on population density for individual municipalities and assigned to existing records from the database.5. Roads_Incidents_Data (.json) - in this folder you can find a data collected by a webservice, which was programmed in the Python language and used for analysing the reported obstructions available on the website of the General Directorate for National Roads and Motorways. In the event of traffic obstruction emergence in the Mazovia Province, the application, on the basis of the number and kilometre of the road on which it occurred, could associate it later with appropriate records based on the links parameters. The data was colleced from 26 May to 22 June 2022.6. Weather_For_Roads_Data (.json) - here you can find the data concerning the weather conditions on the roads occurring at days of the study. To make this feasible, a webservice was programmed in the Python language, by means of which the selected items from the response returned by the www.timeanddate.com server for the corresponding input parameters were retrieved – geographical coordinates of the midpoint between the nodes of the particular roads. The data was colleced for day between 27 May and 22 June 2022.7. data_v_1 (.csv) - collected only data for road parameters8. data_v_2 (.csv) - collected data for road parameters + population density9. data_v_3 (.json) - collected data for road parameters + population density + traffic10. data_v_4 (.json) - collected data for road parameters + population density + traffic + weather + road incidents11. data_v_5 (.csv) - collected VALIDATED and cleaned data for road parameters + population density + traffic + weather + road incidents. At this stage, the road sections for which the parameter traffic factor was assessed to have been estimated incorrectly were eliminated. These were combinations for which the value of the traffic factor remained the same regardless the time of day or which took several of the same values during the course of the whole study. Moreover, it was also assumed that the final database should consist of road sections for traffic factor less than 1.2 constitute at least 10% of all results. Thus, the sections with no tendency to become congested and characterized by a small number of road traffic users were eliminated.Good luck with your research!Igor Betkier, PhD

This data release includes two Wikipedia datasets related to the readership of the project as it relates to the early COVID-19 pandemic period. The first dataset is COVID-19 article page views by country, the second dataset is one hop navigation where one of the two pages are COVID-19 related. The data covers roughly the first six months of the pandemic, more specifically from January 1st 2020 to June 30th 2020. For more background on the pandemic in those months, see English Wikipedia's Timeline of the COVID-19 pandemic.Wikipedia articles are considered COVID-19 related according the methodology described here, the list of COVID-19 articles used for the released datasets is available in covid_articles.tsv. For simplicity and transparency, the same list of articles from 20 April 2020 was used for the entire dataset though in practice new COVID-19-relevant articles were constantly being created as the pandemic evolved.Privacy considerationsWhile this data is considered valuable for the insight that it can provide about information-seeking behaviors around the pandemic in its early months across diverse geographies, care must be taken to not inadvertently reveal information about the behavior of individual Wikipedia readers. We put in place a number of filters to release as much data as we can while minimizing the risk to readers.The Wikimedia foundation started to release most viewed articles by country from Jan 2021. At the beginning of the COVID-19 an exemption was made to store reader data about the pandemic with additional privacy protections:- exclude the page views from users engaged in an edit session- exclude reader data from specific countries (with a few exceptions)- the aggregated statistics are based on 50% of reader sessions that involve a pageview to a COVID-19-related article (see covid_pages.tsv). As a control, a 1% random sample of reader sessions that have no pageviews to COVID-19-related articles was kept. In aggregate, we make sure this 1% non-COVID-19 sample and 50% COVID-19 sample represents less than 10% of pageviews for a country for that day. The randomization and filters occurs on a daily cadence with all timestamps in UTC.- exclude power users - i.e. userhashes with greater than 500 pageviews in a day. This doubles as another form of likely bot removal, protects very heavy users of the project, and also in theory would help reduce the chance of a single user heavily skewing the data.- exclude readership from users of the iOS and Android Wikipedia apps. In effect, the view counts in this dataset represent comparable trends rather than the total amount of traffic from a given country. For more background on readership data per country data, and the COVID-19 privacy protections in particular, see this phabricator.To further minimize privacy risks, a k-anonymity threshold of 100 was applied to the aggregated counts. For example, a page needs to be viewed at least 100 times in a given country and week in order to be included in the dataset. In addition, the view counts are floored to a multiple of 100.DatasetsThe datasets published in this release are derived from a reader session dataset generated by the code in this notebook with the filtering described above. The raw reader session data itself will not be publicly available due to privacy considerations. The datasets described below are similar to the pageviews and clickstream data that the Wikimedia foundation publishes already, with the addition of the country specific counts.COVID-19 pageviewsThe file covid_pageviews.tsv contains:- pageview counts for COVID-19 related pages, aggregated by week and country- k-anonymity threshold of 100- example: In the 13th week of 2020 (23 March - 29 March 2020), the page 'Pandémie_de_Covid-19_en_Italie' on French Wikipedia was visited 11700 times from readers in Belgium- as a control bucket, we include pageview counts to all pages aggregated by week and country. Due to privacy considerations during the collection of the data, the control bucket was sampled at ~1% of all view traffic. The view counts for the control title are thus proportional to the total number of pageviews to all pages.The file is ~8 MB and contains ~134000 data points across the 27 weeks, 108 countries, and 168 projects.Covid reader session bigramsThe file covid_session_bigrams.tsv contains:- number of occurrences of visits to pages A -> B, where either A or B is a COVID-19 related article. Note that the bigrams are tuples (from, to) of articles viewed in succession, the underlying mechanism can be clicking on a link in an article, but it may also have been a new search or reading both articles based on links from third source articles. In contrast, the clickstream data is based on referral information only- aggregated by month and country- k-anonymity threshold of 100- example: In March of 2020, there were a 1000 occurences of readers accessing the page es.wikipedia/SARS-CoV-2 followed by es.wikipedia/Orthocoronavirinae from ChileThe file is ~10 MB and contains ~90000 bigrams across the 6 months, 96 countries, and 56 projects.ContactPlease reach out to research-feedback@wikimedia.org for any questions.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

The Brisbane City Council parking occupancy forecasting data is provided to be accessed by third party web or app developers to develop tools to provide Brisbane residents and visitors with likely parking availability within a paid parking area.

The parking occupancy forecasting data is compiled using advanced analytics and machine learning to estimate paid parking availability. The solution uses parking occupancy survey data, parking meter transaction data and other traffic and environmental data.

This dataset is linked to the open data called Parking — Meter locations. The field called MOBILE_ZONE is used to link the datasets. MOBILE_ZONE is a seven-digit mobile payment zone number that may include one or many parking meter numbers.

Additional information on parking meters can be found on the Brisbane City Council website.

The Brisbane City Council parking occupancy forecasting data includes parking data for all of Council’s parking meters. The data attributes used in this resource and their descriptions can be found in the Parking — Occupancy forecasting — metadata — CSV resource in this dataset.

The Data and resources section of this dataset contains further information for this dataset.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

The Brisbane City Council parking occupancy forecasting data is provided to be accessed by third party web or app developers to develop tools to provide Brisbane residents and visitors with likely parking availability within a paid parking area.

The parking occupancy forecasting data is compiled using advanced analytics and machine learning to estimate paid parking availability. The solution uses parking occupancy survey data, parking meter transaction data and other traffic and environmental data.

This dataset is linked to the open data called Parking — Meter locations. The field called MOBILE_ZONE is used to link the datasets. MOBILE_ZONE is a seven-digit mobile payment zone number that may include one or many parking meter numbers.

Additional information on parking meters can be found on the Brisbane City Council website.

The Brisbane City Council parking occupancy forecasting data includes parking data for all of Council’s parking meters. The data attributes used in this resource and their descriptions can be found in the Parking — Occupancy forecasting — metadata — CSV resource in this dataset.

The Data and resources section of this dataset contains further information for this dataset.

This interactive web application features both the on-road Maryland Level of Bicycle Stress (LTS) feature layer for all road centerlines in Maryland as well the Road-Separated feature layer of all road-separated bike routes throughout Maryland. An overview of the methodology and attribute data for the Maryland Level of Bicycle Stress (LTS) is provided below. For a detailed full report of the methodology, please view the PDF published by the Maryland Department of Transportation here. The Maryland Department of Transportation is transitioning from using the Bicycle Level of Comfort (BLOC) to using the Level of Traffic Stress (LTS) for measuring the “bikeability” of the roadway network. This transition is in coordination with the implementation of MDOT SHA’s Context Driven Design Guidelines and other national and departmental initiatives. LTS is preferred over BLOC as LTS requires fewer variables to calculate including: Average Annual Daily Traffic, Speed Limits, Presence of Bicycle Facilities, Shoulder, etc. Data LimitationsA principle of data governance MDOT strives to provide the best possible data products. While the initial LTS analysis of Maryland’s bicycle network has many uses, it should be used with a clear understanding of the current limitations the data presents.Assumptions - As noted earlier in this document, some of the metrics used to determine LTS score were estimated. Speed limits for many local roadways were not included in the original data and were assigned based on the functional classification of the roadway. Speed limits are also based on the posted speed limit, not the prevailing operating vehicle speeds which can vary greatly. Such discrepancies between actual and assumed conditions could introduce margins of error in some cases. As data quality improves with future iterations, the LTS scoring accuracy will also improve.Generalizations - MDOT’s LTS methodology follows industry standards but needs to account for varying roadway conditions and data reliability from various sources. The LTS methodology aims to accurately capture Maryland’s bicycle conditions and infrastructure but must consider data maintenance requirements. To limit data maintenance generalizations were made in the methodology so that a score could be assigned. Specifically, factors such as intersections, intersection approaches and bike lane blockages are not included in this initial analysis. LTS scores may be adjusted in the future based on MDOT review, updated industry standards, and additional LTS metrics being included in OMOC such as parking and buffer widths.Timestamped - As the LTS score is derived from a dynamic linear referencing system (LRS), any LTS analysis performed reflects the data available in OMOC. Each analysis must be considered ‘timestamped’ and becoming less reliable with age. As variables within OMOC change, whether through documented roadway construction, bikeway improvements or a speed limit reduction, LTS scores will also change. Fortunately, as this data is updated in the linear referencing system, the data becomes more reliable and LTS scores become more accurate.Presence and type of bicycle facilitySpeed limitNumber of Through Lanes/Traffic VolumeTraditionally, the Level of Traffic Stress (LTS) (scale “1” to “4”) is a measure for assessing the quality of the roadway network for its comfort with various bicycle users. The lower the LTS score, the more inviting the bicycle facility is for more audiences.LTS Methodology (Overview)MDOT’s LTS methodology is based on the metrics established by the Mineta Transportation Institute (MTI) Report 11-19 “Low-Stress Bicycling and Network Connectivity (May 2012) - additional criteria refined by Dr. Peter G. Furth (June 2017) below and Montgomery County's Revised Level of Traffic Stress.Shared-use Path Data Development and Complimentary Road Separated Bike Routes DatasetA complimentary dataset – Road Separated Bike Routes, was completed prior to the roadway dataset and is included in this application. It is also provided to the public via (https://maryland.maps.arcgis.com/home/item.html?id=1e12f2996e76447aba89099f41b14359). This first dataset is an inventory of all shared-use paths open to public, two-way bicycle access which contribute to the bicycle transportation network. Shared-use paths and sidepaths were assigned an LTS score of “0” to indicate minimal interaction with motor vehicle traffic. Many paved loop trails entirely within parks, which had no connection to the adjacent roadway network, were not included but may be included in future iterations. Sidepaths, where a shared-use path runs parallel to an adjacent roadway, are included in this complimentary Road Separated Bike Routes Dataset. Sidepaths do not have as an inviting biking environment as shared-use paths with an independent alignment due to the proximity of motor vehicle traffic in addition to greater likelihood of intersections with more roadways and driveways. Future iterations of the LTS will assign an LTS score of “1” to sidepaths. On-street Bicycle Facility Data DevelopmentThis second dataset includes all on-road bicycle facilities which have a designated roadway space for bicycle travel including bike lanes and protected bike lanes. Marked shared lanes in which bicycle and motor vehicle traffic share travel lanes were not included. Shared lanes, whether sharrows, bike boulevards or signed routes were inventoried but treated as mixed traffic for LTS analysis. The bicycle facilities included in the analysis include:Standard Bike Lanes – A roadway lane designated for bicycle travel at least 5-feet-wide. Bike lanes may be located against the curb or between a parking lane and a motor vehicle travel lane. Buffered bike lanes without vertical separation from motor vehicle traffic are included in this category. Following AASHTO and MDOT SHA design standards, bike lanes are assumed to be at least 5-feet-wide even through some existing bike lanes are less than 5-feet-wide.Protected Bike Lanes – lanes located within the street but are separated from motor vehicle travel lanes by a vertical buffer, whether by a row of parked cars, flex posts or concrete planters.Shoulders – Roadway shoulders are commonly used by bicycle traffic. As such, roadways with shoulders open to bicycle traffic were identified and rated for LTS in relation to adjacent traffic speeds and volumes as well as the shoulder width. Shoulders less than 5-feet-wide, the standard bike lane width, were excluded from analysis and these roadway segments were treated as mixed traffic.The Office of Highway Development at MDOT SHA provided the on-street bicycle facility inventory data for state roadways. The shared-use path inventory and on-street bicycle facility inventory was compiled from local jurisdiction’s open-source download or shared form the GIS/IT departments. Before integrating into OMOC, these datasets were verified by conducting desktop surveys and site visits, and by consulting with local officials and residents.-----------------------------------------------------------------------------------------------------------Inquiries? Contact Us!For Methodology: Contact Nate Evans (nevans1@mdot.maryland.gov)For GIS \ Data: Contact Andrew Bernish (abernish@mdot.maryland.gov)

Update Frequency: Daily

This data-set includes traffic crash information including case number, accident date and the location.

• Reportable crash reports can take up to 10 business days to appear after the date of the crash if there are no issues with the report.

• If you cannot find your crash report after 10 business days, please call the Milwaukee Police Department Open Records Section at (414) 935-7435 for further assistance.

• Non-reportable crash reports can only be obtained by contacting the Open Records Section and will not show up in a search on this site. A non-reportable crash is any accident that does not:

1) result in injury or death to any person

2) damage government-owned non-vehicle property to an apparent extent of $200 or more

3) result in total damage to property owned by any one person to an apparent extent of $1000 or more.

• All MV4000 crash reports, completed by MPD officers, will be available from the Wisconsin Department of Transportation (WisDOT) Division of Motor Vehicles (DMV) Accident Records Unit, generally 10 days after the incident.

Online Request: Request your Crash Report online at WisDOT-DMV website, https://app.wi.gov/crashreports.

Mail: Wisconsin Department of Transportation Crash Records Unit P.O. Box 7919 Madison, WI 53707-7919

Phone: (608) 266-8753

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

🇩🇪 독일 German We developed TomTom Intermediate Traffic to deliver detailed, real-time traffic content to business customers who integrate it into their own applications. Target customers for TomTom Intermediate Traffic include automotive OEMs, web and application developers, and governments. We deliver bulk traffic flow information that provides a comprehensive view of the entire road network. TomTom delivered our first live traffic product in 2007 and our experience has taught us how to continue delivering the best traffic products in the market. Our real-time traffic products are created by merging multiple data sources, including anonymized measurement data from over 650 million GPS-enabled devices. Using highly granular data, gathered on nearly every stretch of road, we can calculate travel times and speeds for virtually any day or time. We focus on our travel information so our customers can focus on their own business objectives.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

The Brisbane City Council parking occupancy forecasting data is provided to be accessed by third party web or app developers to develop tools to provide Brisbane residents and visitors with likely parking availability within a paid parking area.

The parking occupancy forecasting data is compiled using advanced analytics and machine learning to estimate paid parking availability. The solution uses parking occupancy survey data, parking meter transaction data and other traffic and environmental data.

This dataset is linked to the open data called Parking — Meter locations. The field called MOBILE_ZONE is used to link the datasets. MOBILE_ZONE is a seven-digit mobile payment zone number that may include one or many parking meter numbers.

Additional information on parking meters can be found on the Brisbane City Council website.

The Brisbane City Council parking occupancy forecasting data includes parking data for all of Council’s parking meters. The data attributes used in this resource and their descriptions can be found in the Parking — Occupancy forecasting — metadata — CSV resource in this dataset.

The Data and resources section of this dataset contains further information for this dataset.

The dataset on maritime transport of goods, passengers and vessels in the European main ports was created in 2014 by Eurofish and Cogea for the European Marine Observation and Data Network (EMODnet). It is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset is the result of the harmonization and aggregation on annual basis of the quarterly Eurostat Maritime transport data, provided by port in the EU Member States, Montenegro, Norway, Turkey and the UK. It is updated every year, and is available for viewing and download on EMODnet - Human Activities web portal (https://emodnet.ec.europa.eu/en/human-activities). EUROSTAT data have been related to the 'Ports 2013' EUROSTAT GISCO's points georeferenced dataset, when available, or to the ports locations coming from other sources, as UN/LOCODE, Lloyd’s List, Marine Traffic and VESSEL TRACKER. Goods traffic data are reported in thousands tonnes by type of cargo and direction. Passengers traffic data are reported in thousands passengers (excluding cruise passengers) by direction and traffic type. Vessels traffic data are reported in unit and gross tonnage (thousands) of vessels by vessel size class and vessel type. Where available, the latest update includes data from the 1997 up to the 2023.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2F186cf4f6172ca2c696819b7b09931bd3%2Fimage3.jpg?generation=1584955857130173&alt=media" alt="">

The presence of business in the digital space is a must now. Indeed, there’s hardly any company, be it a small startup or an international corporation, that wouldn’t be available online. For this, the company may use one of two options — to develop an app or a website, or both.

In the case of a limited budget, business owners often have to make a choice. Thus, considering that mobile traffic bypassed the desktop’s in 2016 and continues to grow, it becomes obvious that the business should become accessible and convenient for smartphone users. But what is better a responsive website or a mobile application?

Entrepreneurs often turn to development companies to ask this question. Lacking sufficient knowledge, they hope to get answers to their questions from people with experience in this field. So, we decided to compile a guide that will give you clear and understandable information.

Mobile app

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2F0541557795519f24d812f78dfb51867e%2Fimage4.png?generation=1584955894277647&alt=media" alt="">

Let's look at the stats. It will help you understand why a mobile app may be the obvious choice for your client.

In 2019, smartphone users installed about 204 billion(!) applications on their devices. On average, this is more than 26 applications per inhabitant of the planet Earth. And if this is not enough evidence, here’s one more point. The expected revenue of mobile applications will be $189 billion in 2020.

It sounds impressive, but this does not mean that a mobile application is something indispensable for every business. Not at all. Let's go through the pros and cons of a mobile application and try to understand when it is needed.

Pros

• A new level of interaction. Mobile applications are a more convenient method of interaction. They load and process content faster. One more useful feature is notifications. Perhaps, applications are the best way to inform users about new updates, promotions, and other news (who will read long letters in the mail?).
• Personalized targeting. Mobile applications are ideal for products or services that need to be used on an ongoing basis. The options like creating accounts, entering profile information, etc., make applications more personalized than websites. All this allows the business to target their audience more accurately without wasting money.
• Offline usage. That’s another major advantage. Applications can provide users with access to content without an internet connection.

Cons

• Development costs. In order to reach the maximum audience with a mobile app, it is necessary to cover two main operating systems — iOS and Android. Development for each OS can be too expensive for small business owners and they will have to make difficult choices. The way out of this situation is cross-platform development. Why? Because there’s no need to guess which platform targets prefer using — iOS or Android. Instead, you create just one app that runs seamlessly on both platforms.

• Maintenance. The application is a technical product that needs constant support. Upgrades should be carried out in a timely manner. Often, users need to personally update applications by downloading a new version, which is annoying. Regular bug-fixing for various devices (smartphones, tablets) and different operating systems might be a real problem. Plus, any update should be confirmed by the store where the application is placed.

• Suitable for businesses that provide interactive and personalized content (refers to all lifestyle and healthcare solutions), require regular app usage (for instance, to-do lists), rely on visual interaction and so on. For games, like Angry Birds, creating an app is also a wise choice.

Website

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2Fd4f5bf1fdd0d0e65fae38c7251f56f13%2Fimage1.jpg?generation=1584955919738648&alt=media" alt="">

In order to be convenient for users of mobile devices, a website should be responsive. We want to make an emphasis on this since it is critically important. Most of the traffic on the Internet comes from mobile devices, so your website should be adaptable, or in other words, mobile-friendly. If a mobile user needs to zoom in all the necessary elements and text to see something, they will immediately quit your website.

On the other hand, a responsive website has the following benefits.

Pros

• Maintenance. Maintaining a website is less costly. When compared to applications where the user mu...

These layers are used in the The U.S. Vessel Traffic application; a web-based visualization and data-access utility created by Esri. Explore U.S. maritime activity, look for patterns of vessel activity such as around ports and fishing grounds, or download manageable subsets of this massive data set. Vessel traffic data are an invaluable resource made available to our community by the US Coast Guard, NOAA and BOEM through Marine Cadastre. This information can help marine spatial planners better understand users of ocean space and identify potential space-use conflicts.To download this data for your own analysis, explore the Download Options, navigate to a NOAA Electronic Navigation Chart area of interest, and make your selection. This data was sourced from the Automatic Identification System (AIS) provided by USCG, NOAA, and BOEM through Marine Cadastre and aggregated for visualization and sharing in ArcGIS Pro. This application was built with the ArcGIS API for JavaScript.Access this data as an ArcGIS Online collection here. Learn more about AIS tracking here. Find more ocean and maritime resources in Living Atlas. Inquiries can be sent to Keith VanGraafeiland.

The Urban Observatory Compare app shows maps of the same subject for three cities, in a side by side comparison view. The app allows quick visual comparisons of the patterns at work in cities around the world.The app allows people to interact with rich datasets for each city. People can use the Urban Observatory web application to easily compare cities by using a simple web browser. As a user zooms in to one digital city map, other city maps will zoom in parallel, revealing similarities and differences in density and distribution. For instance, a person can simultaneously view traffic density for Abu Dhabi and Paris or simultaneously view vegetation in London and Tokyo.The Urban Observatory is brought to you by Richard Saul Wurman, creator of Technology/Entertainment/Design (TED) and 19.20.21; Jon Kamen of the Academy Award-, Emmy Award-, and Golden Globe Award-winning film company @radical.media; and Esri president Jack Dangermond. "A map is a pattern made understandable, and patterns must be compared to understand successes, failures, and opportunities of our global cities," says Wurman. "The Urban Observatory demonstrates this new paradigm, using cartographic language and constructive data display. People and cities can use maps as a common language," said Wurman. The application utilizes Esri's ArcGIS API for JavaScript. Once a web map is created, it is added to a group and tagged to indicated its city and subject information. Those tags are read by the application as it starts up in the browser.

Contains web visitations statistics for Ottawa.ca. Information consists of visits, views, time on pages, exit rates, entrances and bounce rates. Going forward, data will exclude use by City employees.

Accuracy: There are no known errors within the data set.

Update Frequency: Updated monthly

Attributes: Page-view: is the total number of pages viewed. Repeated views of a single page are counted.

Unique Page-view: is the number of visits during which the specified page was viewed at least once. A unique page-view is counted for each page URL + page Title combination. Avg. Time on Page: is the average amount of time visitors spent viewing a specified page or set of pages. Entrances: is the number of times visitors entered your site through a specified page or set of pages. Bounce Rate: is the percentage of single-page visits (i.e. visits in which the person left your site from the entrance page without interacting with the page).% Exit: is the percentage of site exits that occurred from a specified page or set of pages.

Contact: Kyla Weir

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

As of April 2024, Bahrain was the country with the highest Instagram audience reach with 95.6 percent. Kazakhstan also had a high Instagram audience penetration rate, with 90.8 percent of the population using the social network. In the United Arab Emirates, Turkey, and Brunei, the photo-sharing platform was used by more than 85 percent of each country's population.

As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.