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.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

The number of social media users in Saudi Arabia was forecast to continuously increase between 2024 and 2029 by in total six million users (+28.05 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 27.42 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users 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 social media users in countries like Israel and Kuwait.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

ICA250 - Individuals aged 16 years and over who use free apps and issues encountered when deleting/closing free apps. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Individuals aged 16 years and over who use free apps and issues encountered when deleting/closing free apps...

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Abstract copyright UK Data Service and data collection copyright owner.Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991. The Wellbeing app study was conducted in 2020 as part of the annual Innovation Panel (IP) Wave 13 interview. All adult respondents who had completed at least one previous IP interview were invited to download an app onto their smartphone or tablet. They were asked to use the app every evening for 14 days to report on their emotional state and self-regulation, external stressors, attachment, and interactions with loved ones. Participants were incentivised throughout the fieldwork period, with incentives being paid at the end of their two-week participation period. Of the 2,152 respondents who were invited to the app study, 967 completed the daily app questionnaire at least once. The Wellbeing app data were collected between 14 July and 26 November 2020. The protocols for the mobile app data collection included three experiments: i) varying the value of incentives for completing the study, ii) varying the length of the daily questionnaire, and iii) varying the placement of the invitation to the app study within the annual IP interview. The data deposited for the Wellbeing App Study include the survey and paradata collected with the app. The data can be linked to data on the same individuals from previous and future waves of the annual IP interviews (SN 6849) using the personal identifier, pidp. The Wellbeing app was developed and implemented by Connect Internet Solutions Ltd. For more information about the main IP study, see SN 6849. Suitable data analysis software These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Main Topics: The Wellbeing app study contains data collected every evening for 14 days on emotional state and self-regulation, external stressors, attachment, and interactions with loved ones. Sub-sample of the IP study, which uses multi-stage sampling. See the User Guide for details. Multi-stage stratified random sample Questionnaire implemented in a mobile application

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

This dataset is associated with the article "Mobile Augmented Reality Apps in Education: Exploring the User Experience through Large-Scale Public Reviews". It contains a static collection of all the scripts needed to reproduce the methodology of the paper (methodology folder) and the results obtained and reported in the paper (results folder). The latter include the metadata of all applications scraped, the complete information of the reviews, the results after classification, the file containing the manual classification, and the IRR test. Along with the methodology, the next 3 external references were used, all the files used are linked in this repository and refereed in the corresponding step. 1. Google-Play Scraper: Olano, F. (2020). Google-Play Scraper, version 7.1.2. Retrieved from https://github.com/facundoolano/google-play-scraper#search 2. google-play-scraper: Jo, M. (2020). google-play-scraper, version 0.0.2.2. Retrieved from https://github.com/JoMingyu/google-play-scraper. 3. AR doc tool: Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C. A., Canfora, G., & Gall, H. C. (2016). ARdoc: app reviews development-oriented classifier. Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2016, (November), 1023–1027. https://doi.org/10.1145/2950290.2983938.

This dataset is associated with the article "Augmented Reality Apps in Education: Exploring the User Experience through Data Mining". It contains a static collection of all the scripts needed to reproduce the methodology of the paper (methodology folder) and the results obtained and reported in the paper (results folder). The latter include the metadata of all applications scraped, the complete information of the reviews, the results after classification, the file containing the manual classification, and the IRR test. Along with the methodology, the next 3 external references were used, all the files used are linked in this repository and refereed in the corresponding step. 1. Google-Play Scraper: Olano, F. (2020). Google-Play Scraper, version 7.1.2. Retrieved from https://github.com/facundoolano/google-play-scraper#search 2. google-play-scraper: Jo, M. (2020). google-play-scraper, version 0.0.2.2. Retrieved from https://github.com/JoMingyu/google-play-scraper. 3. ARdoc tool: Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C. A., Canfora, G., & Gall, H. C. (2016). ARdoc: app reviews development oriented classifier. Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2016, (November), 1023–1027. https://doi.org/10.1145/2950290.2983938.

This case surveillance publicly available dataset has 32 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors. This dataset requires a registration process and a data use agreement.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
  
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
  
• COVID-19 Case Surveillance Restricted Access Data: Restricted access, patient-level dataset with clinical (including symptoms), demographics, and county and state of residence. Access requires a registration process and a data use agreement. (32 data elements)
  

Requesting Access to the COVID-19 Case Surveillance Restricted Access Detailed Data

Please review the following documents to determine your interest in accessing the COVID-19 Case Surveillance Restricted Access Detailed Data file:

1) CDC COVID-19 Case Surveillance Restricted Access Detailed Data: Summary, Guidance, Limitations Information, and Restricted Access Data Use Agreement Information

2) Data Dictionary for the COVID-19 Case Surveillance Restricted Access Detailed Data

The next step is to complete the Registration Information and Data Use Restrictions Agreement (RIDURA). Once complete, CDC will review your agreement. After access is granted, Ask SRRG (eocevent394@cdc.gov) will email you information about how to access the data through GitHub. If you have questions about obtaining access, email eocevent394@cdc.gov.

Overview

The COVID-19 case surveillance database includes patient-level data reported by U.S. states and autonomous reporting entities, including New York City, the District of Columbia, as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification. The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC.

COVID-19 case surveillance data are collected by jurisdictions and are shared voluntarily with CDC. For more information, visit: <a href="https://wwwn.cdc.gov/nndss/conditions/coronavirus-disease-2019-c

What is SEPAL?

SEPAL (https://sepal.io/) is a free and open source cloud computing platform for geo-spatial data access and processing. It empowers users to quickly process large amounts of data on their computer or mobile device. Users can create custom analysis ready data using freely available satellite imagery, generate and improve land use maps, analyze time series, run change detection and perform accuracy assessment and area estimation, among many other functionalities in the platform. Data can be created and analyzed for any place on Earth using SEPAL.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/63a3efa0-08ab-4ad6-9d4a-96af7b6a99ec/download/cambodia_mosaic_2020.png" alt="alt text" title="Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia">

Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia

SEPAL reaches over 5000 users in 180 countries for the creation of custom data products from freely available satellite data. SEPAL was developed as a part of the Open Foris suite, a set of free and open source software platforms and tools that facilitate flexible and efficient data collection, analysis and reporting. SEPAL combines and integrates modern geospatial data infrastructures and supercomputing power available through Google Earth Engine and Amazon Web Services with powerful open-source data processing software, such as R, ORFEO, GDAL, Python and Jupiter Notebooks. Users can easily access the archive of satellite imagery from NASA, the European Space Agency (ESA) as well as high spatial and temporal resolution data from Planet Labs and turn such images into data that can be used for reporting and better decision making.

National Forest Monitoring Systems in many countries have been strengthened by SEPAL, which provides technical government staff with computing resources and cutting edge technology to accurately map and monitor their forests. The platform was originally developed for monitoring forest carbon stock and stock changes for reducing emissions from deforestation and forest degradation (REDD+). The application of the tools on the platform now reach far beyond forest monitoring by providing different stakeholders access to cloud based image processing tools, remote sensing and machine learning for any application. Presently, users work on SEPAL for various applications related to land monitoring, land cover/use, land productivity, ecological zoning, ecosystem restoration monitoring, forest monitoring, near real time alerts for forest disturbances and fire, flood mapping, mapping impact of disasters, peatland rewetting status, and many others.

The Hand-in-Hand initiative enables countries that generate data through SEPAL to disseminate their data widely through the platform and to combine their data with the numerous other datasets available through Hand-in-Hand.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/868e59da-47b9-4736-93a9-f8d83f5731aa/download/probability_classification_over_zambia.png" alt="alt text" title="Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia">

Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

This dataset contains the sentiment analysis (SA) and topic classification (supervised) of the comments posted with pictures by LastQuake app users related to the 22nd March 2020 Zagreb earthquake. LastQuake app is a crowdsource-based earthquake information app that allows eyewitnesses to share information about the earthquakes they felt, combined with seismic data. This app was developed by the European-Mediterranean Seismological Centre (EMSC). Attributes and data contained in the database are:

• eq_evid : Number of the earthquake the comment is associated with
• eq_mag : magnitude of the event
• eq_t0 : Origin time (UTC)
• intensity: felt report intensity (as before leaving a comment users must leave a felt report)
• epidist : distance from the event of the comment, in km
• dt : response time from the origin time of the associated event, in seconds
• rate_pos : number of positive rates *
• rate_neg : number of negative rates*
• device : device from which the comment was left, i.e.desktop, mobile or app
• comm_valid : 0 or 1 depending on if we validated the comment or not. We invalidate comments when we consider them inappropriate (violence, insults,...)
• language: Original language on which the comment was written
• polarity: Polarity on which the comment is classified, i.e. positive, negative or neutral
• topic: Building damages or intensity
• comment: comment posted by LastQuake app user translated to English

LastQuake app obtained 31,911intensity reports from its users with comments, considered as text data, from which it has been possible to translate 31,403 (98%). The citizens included in their comments 361 pictures. After data processing, 314 (87%) pictures were selected for damage assessment. However, this database contains the classification of only those intensity reports that include pictures and comments: 45. This clarification is because some intensity reports from LasQuake app users include only comments or only pictures and some of them include both, and these are the intensity reports contained in this database. The supervised or unsupervised classification of the total number of comments posted by the LastQuake app users' with respect to the 22nd March 2020 Zagreb earthquake will be displayed in another database in the future.

There are multiple well-recognized and peer-reviewed global datasets that can be used to assess water availability and water pollution. Each of these datasets are based on different inputs, modeling approaches, and assumptions. Therefore, in SBTN Step 1: Assess and Step 2: Interpret & Prioritize, companies are required to consult different global datasets for a robust and comprehensive State of Nature (SoN) assessment for water availability and water pollution.

To streamline this process, WWF, the World Resources Institute (WRI), and SBTN worked together to develop two ready-to-use unified layers of SoN – one for water availability and one for water pollution – in line with the Technical Guidance for Steps 1: Assess and Step 2: Interpret & Prioritize. The result is a single file (shapefile) containing the maximum value both for water availability and for water pollution, as well as the datasets’ raw values (as references). This data is publicly available for download from this repository.

These unified layers will make it easier for companies to implement a robust approach, and they will lead to more aligned and comparable results between companies. A temporary App is available at https://arcg.is/0z9mOD0 to help companies assess the SoN for water availability and water pollution around their operations and supply chain locations. In the future, these layers will become available both in the WRI’s Aqueduct and in the WWF Risk Filter Suite.

For the SoN for water availability, the following datasets were considered:

• Baseline water stress (Hofste et al. 2019), data available here

• Water depletion (Brauman et al. 2016), data available here

• Blue water scarcity (Mekonnen & Hoekstra 2016), data upon request to the authors

For the SoN for water pollution, the following datasets were considered:

• Coastal Eutrophication Potential (Hofste et al. 2019), data available here

• Nitrate-Nitrite Concentration (Damania et al. 2019), data available here

• Periphyton Growth Potential (McDowell et al. 2020), data available here

In general, the same processing steps were performed for all datasets:

1. Compute the area-weighted median of each dataset at a common spatial resolution, i.e. HydroSHEDS HydroBasins Level 6 in this case.

2. Classify datasets to a common range as reclassifying raw values to 1-5 values, where 0 (zero) was used for cells or features with no data. See the documentation for more details.

3. Identify the maximum value between the classified datasets, separately, for Water Availability and for Water Pollution.

For transparency and reproducibility, the code is publicly available at https://github.com/rafaexx/sbtn-SoN-water

Researchers have developed many experimental tasks for studying memory for intentions in the laboratory. Virtually all of these tasks prevent participants from using external tools and reminders. However, in everyday life we often use diaries or to-do lists, or we set up reminders in our environment. How do we decide whether or not to use these strategies? What consequences do they have for our behaviour? How much do individuals differ in whether or not they use reminders, and what can we do to influence this? While some studies have investigated these questions in brain-injured individuals, we know very little about healthy people. Yet, with new technologies such as smartphone reminder apps and wearable devices, we increasingly 'offload' intentions into the world around us. This proposal describes a systematic and detailed investigation of 'intention offloading'. Data from Gilbert, S.J., Bird, A., Carpenter, J., Fleming, S.M., Sachdeva, C., & Tsai, P.C. (2020). Optimal use of reminders: Metacognition, effort, and cognitive offloading. Journal of Experimental Psychology: General, 149, 501-517Every day, we form many intentions for behaviours that we plan to execute after a delay. These intentions might be postponed for just a few seconds (e.g. intending to add an attachment to an email before clicking 'send'), or minutes, days, or longer (e.g. intending to attend a planned hospital appointment). If we are to live independent, purposeful lives, it is essential that we are able to fulfil such intentions. Yet we all know how easily forgotten they are. This can have catastrophic effects in safety-critical fields such as nursing or aviation, and serious consequences for health-related behaviours such as remembering to take medication. Understanding how we fulfil delayed intentions will allow us to develop techniques to optimise this aspect of memory, and potentially compensate for any difficulties. Researchers have developed many experimental tasks for studying memory for intentions in the laboratory. Virtually all of these tasks prevent participants from using external tools and reminders. However, in everyday life we often use diaries or to-do lists, or we set up reminders in our environment. How do we decide whether or not to use these strategies? What consequences do they have for our behaviour? How much do individuals differ in whether or not they use reminders, and what can we do to influence this? While some studies have investigated these questions in brain-injured individuals, we know very little about healthy people. Yet, with new technologies such as smartphone reminder apps and wearable devices, we increasingly 'offload' intentions into the world around us. This proposal describes a systematic and detailed investigation of 'intention offloading'.

This dataset includes all auto theft occurrences by reported date and related offences since 2014.Auto Theft DashboardDownload DocumentationThis data is provided at the offence and/or vehicle level, therefore one occurrence number may have several rows of data associated to the various MCIs used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.