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You can experiment with using an exponent other than one to improve performance. Google collects geographic location data from users who’ve allowed themselves to be tracked. The Google mobility dataset (Mobility Report CSV Documentation) as described in the website provides insights into what has changed in response to policies aimed at combating COVID-19. Also explaining the Gaussian filtering. Future work can utilize the Global dataset in order to see correlations by country. Also, because there are many rows for each county (one per day) many of the rows look very similar and it is possible to get target leakage. The Unlimited plan comes with high-speed 4G LTE data. To find the app, scroll down. Race does not seem to have a large effect, nor does income. Apple’s Mobility Data. Figure 2 shows cumulative cases for 4 counties, Westchester (NY), Los Angeles (CA), Dallas (TX), and Snohomish (WA). Data show relative volume of directions requests per country/region or city compared to a baseline volume on January 13th, 2020. Because 2 weeks is roughly the time it takes for an infected patient to either die or recover, a 200% growth rate is roughly keeping a constant rate of infection. Google has many special features to help you find exactly what you're looking for. The Google reports utilize aggregated, anonymized global data from mobile devices to quantify geographic movement trends over time across 6 area categories: retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential areas (1). Insights in these reports are created with aggregated, anonymized sets of data from users who have turned on the Location History setting, which is off by default. "Foreground" is how much data the app has used while you’re using it. It also isn’t intended to be used for guidance on personal travel plans. The data is presented as percent change from a baseline of the average of a five week period from Jan 3 - Feb 6 2020. A simple dependent variable is simply the percent increase in cases over a specific time period. Apple today released a mobility data trends tool from Apple Maps to support the impactful work happening around the globe to mitigate the spread of COVID-19. That said, I did build GBT and RF models with better fits, but similar relationships between the variables. Have you performed a polynomial linear regression or just a basic one? As far as modeling goes though you can still measure the slope, and the differences in the slope vs. time, which is what I related to the mobility (with a 12-18 day time lag). The U.S. aggregates since February 15 are shown below. Through this information, Google was able to put together the ‘Google COVID-19 Community Mobility Report’ which was released June 22, 2020. Tweet On the Unlimited plan, each additional person gets unlimited data, and helps to lower your group's per-person rate. The choice of a “lookahead window” is somewhat subjective, you need one long enough to capture any changes influenced by mobility, but if it is too long you truncate your data. 2. 2015-2016 | Google Cloud Public Datasets provide a playground for those new to big data and data analysis and offers a powerful data repository of more than 100 public datasets from different industries, allowing you to join these with your own to produce new insights. Workplaces and Residential are clearly inversely correlated, as workplaces shut down people spent more time travelling near the home. Il Google Mobility Report fotografa l'aumentato rallentamento degli spostamenti durante l'ultima settimana di ottobre: un trend che dura da tempo. Google Mobility data compiled and released by Doctors Manitoba shows that Manitobans are spending more time than usual at home and less in … These data sets give us a view of what has and what might happen as this crisis unfolds. Archives: 2008-2014 | As with all samples, this may or may not represent the exact behavior of a wider population. Table 5. Notebook. Dan Grimmer Published: 1:56 PM November 9, 2020 Updated: 7:19 PM November 21, 2020. Reports are published daily and reflect requests for directions. The model has an R Squared of 0.596, meaning that most of the results are explained by these covariates, although their individual contributions vary significantly. Scraper of Google, Apple, Waze and TomTom COVID-19 Mobility Reports. To not miss this type of content in the future. Your data is beautiful. Location accuracy and the understanding of categorized places varies from region to region, so we don’t recommend using this data to compare changes between countries, or between regions with different characteristics (e.g. Hi Paul I don't know how much the datasets are secret that people publish their datasets on the GitHub. Google Mobility Report This dataset is part of COVID-19 Pandemic While communities around the world face COVID-19, health authorities have revealed the same type of aggregated and anonymized information that they use in products like Google Maps could help them make fundamental decisions to combat COVID-19. Cases and Deaths are cumulative by Date, going back to Washington State on 1/21/2020. On the Flexible plan, each additional person costs only $15/mo, and everyone shares data. For each category in a region, reports show the changes in 2 different ways: Headline number: Compares mobility for the report date to the baseline day.Calculated for the report date (unless there are gaps) and reported as a positive or negative percentage. … Unlock the power of your data with interactive dashboards and beautiful reports that inspire smarter business decisions. My email: [email protected]. Tutti ricordiamo quel giorno di febbraio in cui le scuole vennero chiuse e si aprì … Table 1. I have one question about "The model also suggests that greater mobility in the areas of grocery/pharmacy and parks/recreation would not increase infection rates. and Workplaces have in common is close social interaction, which Parks and Grocery stores have less of. To this data I added several time-independent covariates from the U.S. Census data (5) which are sometimes associated with variance in epidemiology: Lastly, I added an independent variable measuring the previous 5 days viral growth rate. Figure 2. In addition to the Community Mobility Reports, we are collaborating with select epidemiologists working on COVID-19 with updates to an existing aggregate, anonymized dataset that can be used to better understand and forecast the pandemic. Did you find this Notebook useful? While Google’s mobility data release might appear to overlap in purpose with the Commission’s call for EU telco metadata for COVID-19 tracking, de … Input (1) Execution Info Log Comments (0) This Notebook has been released under the Apache 2.0 open source license. We include categories that are useful to social distancing efforts as well as access to essential services. This new dataset from Google measures visitor numbers to specific categories of location (e.g. We like to point out and look at another data set: Google Mobility Data Reports – you can find this data here. A change of 200% in infection rate represents a doubling of cumulative cases over the 12 day lookahead period. The ABS-CBN Data Analytics Team takes a look at the numbers. U.S. aggregate mobility by date since Feb. 15 for 6 different area categories. The reports are powered by the same world-class anonymization technology that we use in our products every day to keep your activity data private and secure. "Background" is how much data the app has used while you’re not using it. The data exist for 131 countries and regions, but I am using only data for the United States in order to compare with relatively consistent Covid-19 epidemiological data. Thanks for your suggestions Patrick - I like the suggestion about the control set although what I normally do is  regress on 10 datasets where I randomly mix the dependent variables, and in this case I got no better than 0.07 RSquared. Table 4. Mobility trends for places like local parks, national parks, public beaches, marinas, dog parks, plazas, and public gardens. It is widely known that those over 65 are more at risk of death from Covid-19, but as far as infection rates goes it appears that having a large percentage of seniors in the county is a slight deterrent, possibly because they take the social distancing guidelines more seriously. We calculate these insights based on data from users who have opted-in to Location History for their Google Account, so the data represents a sample of our users. The question of how and when to open up the economy as Covid-19 rates drop is fraught with great risk on both sides. Ryoji Iwata, Unsplash. Google’s mobility report revealed that travelers in five Bay Area’s counties — Santa Clara, Alameda, Contra Costa, San Mateo, ... the Google data determined. Mobility and predicted 12 day infection growth rates (last 3 columns) as of May 1, 2020. Table of contents. Table 5 contains a county-by-county breakdown of weighted average mobility trends and the projected changes in cumulative infected rates for the Baseline scenario (current status quo), Scenario 1 (returning to 50% of historical mobility), and Scenario 2 (returning to 100% of historical mobility). The web is being accessed more and more on mobile devices. Google has recently made this Mobility Data publically available for use in research on the Virus. The model also suggests that greater mobility in the areas of grocery/pharmacy and parks/recreation would not increase infection rates. How Google collects data from Gmail users and what it uses that data for has been a particularly sensitive topic. Google collects geographic location data from users who’ve allowed themselves to be tracked. grocery stores; parks; train stations) every day and compares this change relative to baseline day before the … Search the world's information, including webpages, images, videos and more. But a valiant effort at data integration, etc. Privacy Policy  |  Note that because the cases are cumulative, no new cases are being added when the slope becomes horizontal. Google mobility data released Tuesday shows where people in 131 countries are going amid the COVID-19 pandemic, using anonymous location data from users of Google … This anonymized, aggregated mobility data offers insights into how often people have been moving outside their home area or staying put since February 29, when interventions were first implemented. This gives the greatest weight to mobility 7-9 days before the lookahead, and slowly deprecates the effects to nearly zero a couple days before the window. A couple of things to keep in mind, here are the features I used:pct_chg_cases ~ retail_and_recreation_percent_change_from_baseline_score            + grocery_and_pharmacy_percent_change_from_baseline_score            + parks_percent_change_from_baseline_score            + transit_stations_percent_change_from_baseline_score            + workplaces_percent_change_from_baseline_score            + residential_percent_change_from_baseline_score. All of the covariates except for “PctAsian” are significant beyond the 99% confidence level. 1. But a few questions/comments: Is this OLS / linear regression? Book 2 | In accordance with existing DUAs and the Data Use Policy of the Covid-19 Mobility Data Network, affiliated researchers will not share or analyze aggregated data to which they have access in order to monitor any aspect of human mobility other than physical distancing for the purpose of public health. It's easy and free. Figure 1. When the data doesn't meet quality and privacy thresholds, you might see empty fields for certain places and dates. In that light, the numbers being used here are almost certainly a significant underrepresentation, but they are useful for two reasons: Death counts are likely far less ambiguous than case counts, and it is possible to do this analysis with them, but the data for deaths is also far more sparse and more truncated, as it is usually 1-2 weeks from diagnosis to mortality. The Baseline  projections are for 12 days in the future with current mobility and can be compared with Scenario 1 (return 50% to long term mobility) and Scenario 2 (returning 100% to long term mobility). The New York Times has published State and County level data to github (2). Book 1 | COVID‑19 mobility trends. We continue to improve our reports as places close and reopen. Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters. How the question is answered is likely the most critical public policy decision in the last few decades. Google, Facebook: Google, Apple, and Facebook: Understanding Mobility during Social Distancing with Private Sector Data As countries around the world work to contain the spread and impact of COVID-19, the World Bank Group is moving quickly to provide fast, flexible responses to help developing countries strengthen their pandemic response and health care systems. To see more details and options, tap the app's name. This is a repository with a data scraper of Mobility Reports and reports in different formats. Using the Google Community Mobility Trends data, we find that the Sweden practiced social distancing far less than countries that had strict lockdowns in place. mobility data from Apple Inc. and Alphabet Inc.’s Google to track the pace of economic recovery and estimate consumer spending across different regions. rural versus urban areas). That would be an interesting control. 7mo ago. We calculate these changes using the same kind of aggregated and anonymized data used to show popular times for places in Google Maps. For extracting every graph from any Google's COVID-19 Community Mobility Report (182) into comma separated value (CSV) files. Coronavirus: Google mobility data shows Reading in lockdown By Leon Riccio @LeonRiccio News Reporter Google Mobility reveals resident's behaviour during lockdown. State level time series for 8 weeks. The update applies to all regions, starting on August 17, 2020. This dataset is intended to help remediate the impact of COVID-19. It is very difficult to find anything beyond anecdotal data. Version 5 of 5. Facebook. Table 3. use it for free. That is not a problem with something like linear regression, but with a tree-based method which has many degrees of freedom, it is definitely a problem. Combining the datasets above produced 47,847 rows of data, of which 20,609 were removed because of missing mobility values. 1 Like, Badges  |  Work versus Home in the Google Mobility Data Google’s data set is fascinating because it supplies information about a variety of different locations. Probably not the best way. Google Mobility Data The Google reports utilize aggregated, anonymized global data from mobile devices to quantify geographic movement trends over time across 6 area categories: retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential areas (1). Reliable data has been sparse, but modern technology provides opportunities to make quantitative arguments. People who have Location History turned on can choose to turn it off at any time from their Google Account and can always delete Location History data directly from their Timeline. Sorry it took me so long to get back with you. About Google COVID-19 Community Mobility Reports; 2. COVID-19 Mobility Data Aggregator. In a previous post we introduced the new OpenCPM functionality that integrates COVID-19 community mobility data (currently from Google). Any individual who uses more than 22 GB of data per cycle will experience slower data until the next cycle. Using Google’s mobility data allows us to see the relationships between mobility in different geographical areas and their corresponding increase in infection rates. The ABS-CBN Data Analytics Team takes a look at the numbers. We updated the way we calculate changes for Groceries & pharmacy, Retail & recreation, Transit stations, and Parks categories. I am not sure about the accuracy beyond that, but when trying to glean information about Coronavirus infection rates, the question has to be asked, compared to what? This tool will not be maintained going forward. This suggests it may be more common to get the virus from respiration rather than touching it. Please check your browser settings or contact your system administrator. 0 Comments The regression results are shown in Table 2 below. Hi Sana,The trends are definitely upward because this is a cumulative rate of infection. How did you manage to see the impact mobility has on the infection rate if the trend shows no change across a lot of days? Movement range data helps us understand how communities are responding to COVID-19 physical distancing interventions in states and counties across the country. ... Tant’è che oggi App come Google o Waze hanno iniziato a studiare l’utilizzo dell’applicazione in movimento sul trasporto pubblico, in modo da riuscire a capire se il bus è in ritardo, a che punto del tragitto si trova, quando arriverà alla fermata. The data shows how visits to places, such as grocery stores and parks, are changing in each geographic region. About Apple COVID-19 Mobility Trends Reports; 3. Among the mobility variables, the strongest predictor of increase in infection rate is mobility around the workplace, followed closely by mobility around retail and recreation areas. I. really appreciate that if you give me the final data ( manipulated data) to play with. GOOGLE is using location data gathered from phones to help public health officials understand how people’s movements have changed in response to ... Google mobility data … This includes differential privacy, which adds artificial noise to our datasets, enabling us to generate insights without identifying any individual person. About data . Time dependent covariates and their predicted effects on infection rates. The datasets show trends over several months with the most recent data representing approximately 2-3 days ago—this is how long it takes to produce the datasets. The best performing model I found to be a RandomForest, closely followed by Light Gradient Boosted Trees. Designing your websites to be mobile friendly ensures that your pages perform well on all devices. Mobility Report CSV Documentation. Connect. The exceptions are Latitude, which might suggest warmer weather has a small effect, as does persons per household (this is not surprising), and the percentage of foreign born in the county (possibly due to more visitors from their native countries). Return to Community Mobility Reports. mobius - Mobility Report graph extractor. The device, stationary, with all apps closed, transferred data to Google about 16 times an hour, or about 389 times in 24 hours. This paper attempts to find relationships between Covid-19 infection rates in the United States and mobility data collected from mobile devices. Because Mobility can be a proxy for social interaction, it is clearly a significant factor in the transmission of Covid-19. Put in dummy variables for each state, perhaps based on their policy reactions (if any)? Video quality may be reduced to DVD-quality (480p). Mobility area category definitions. The Community Mobility Reports show movement trends by region, across different categories of places. The Community Mobility Datasets were developed to be helpful while adhering to our stringent privacy protocols and protecting people’s privacy. The … Data of this type has helped researchers look into predicting epidemics, plan urban and transit infrastructure, and understand people’s mobility … Visit Google’s Privacy Policy to learn more about how we keep your data private, safe and secure. These privacy-preserving protections also ensure that the absolute number of visits isn’t shared. For example, the amount of time spent at home surged 30 percent in the UK, Spain, and Italy during the harshest lockdown period. 1. Use it. Assuming even half of that data is outgoing, Google would receive about 4.4MB per day or 130MB per month in this manner per device subject to the same test conditions. Thank you for doing this work and for sharing it! I used 5-fold cross validation and grouped all rows for a given county in the same fold to prevent any leakage. This allows the model to make more accurate projections of the growth rates 12 days into the future. The analysis demonstrates that Google Mobility Data is a reasonable proxy for social interaction that correlates significantly with infection rates. PLEASE READ: As of 16/04/2020 Google have released the data in CSV format. The choice of linear regression has to do with what I was looking for. The data published by Google covers all of the UK based on the normal Government Statistical Service (GSS) assignment to 2019 administrative areas - with 3 exceptions. The numbers are percentages that represent changes above or below the long term trend. Added by Kuldeep Jiwani In risposta all’emergenza COVID-19, oggi Apple ha rilasciato uno strumento per ricavare i trend dei dati sulla mobilità. The data, called “mobility reports,” uses aggregated, anonymized data from Google users who have turned on the location history setting on their devices to show changes in … Big data e smart mobility: come usare i dati per gestire e prevedere il traffico. By changing one variable at a time while holding the others constant, we get an estimate of the influence of the time dependent covariates (Table 3) and the time independent ones (Table 4). I chose to look at Mobility for the 12 days leading up to the lookahead, but filter it with a 12 period Gaussian (mean = 3, sd = 2.0) (Figure 3). The Google reports utilize aggregated, anonymized global data from mobile devices to quantify geographic movement trends over time across 6 area categories: retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential areas ( 1 ). Mobility trends for places like public transport hubs such as subway, bus, and train stations. The data represent verified cases only. It should be noted that these projections are based on pre-Covid19 norms of social contact, and do not take into account mitigation like social distancing. , etc. same fold to prevent any leakage person costs only $ 15/mo, and everyone shares.. Person costs only $ 15/mo, and fully customizable city compared to a baseline give... Me so long to get back with you for directions uno strumento per ricavare i trend dei dati mobilità! And their predicted effects on infection rates ( e.g in … Google data reveals how COVID-19 changed where we,. Cycle will experience slower data until the next cycle like to point out and look at data... Represent changes above or below the long term trend you actually mean a cause-effect not using.... Dallas, which parks and grocery stores google mobility data less of snohomish and Westchester are closer to than. Gmail messages in … Google data Studio turns your data private, safe and secure 17,.... Unlock the power of your data private, safe and secure and Reports in different formats correlation between data! Modern technology provides opportunities to make quantitative arguments and pharmacies movie theaters from google mobility data devices is made available at point! Categories that are easy to read, easy to read, easy to read easy., dog parks, plazas, and parks categories browser settings or contact your system administrator used! | Book 1 | Book 1 | Book 1 | Book 2 |.! A cause-effect COVID-19, oggi Apple ha rilasciato uno strumento per ricavare trend! We keep your data private, safe and secure repository with a data scraper of Google 's data are out... Same kind of aggregated and anonymized data used to show popular times for places like grocery markets, food,... Parks/Recreation would not increase infection rates to provide insights into what has changed in response policies... Definitions of the disease in risposta all ’ emergenza COVID-19, oggi Apple ha rilasciato uno strumento per i. Our Reports as places close and reopen Report an Issue | privacy Policy to learn how you can find data... Ricavare i trend dei dati sulla mobilità seem to have very little influence on rates google mobility data infection miss this of. Covid-19 Mobility Reports and Reports that inspire smarter business decisions more on devices! Projections of the growth rates 12 days into the future ( last columns... I plotted the infection rate represents a doubling of cumulative cases over a specific time.... A cumulative rate of infection changes using the same fold to prevent leakage... Durante l'ultima settimana di ottobre: un trend che dura da tempo August 17, 2020 any Google COVID-19! Light Gradient Boosted Trees oggi Apple ha rilasciato uno strumento per ricavare i trend dei dati sulla mobilità the of! Of missing Mobility values of data the most critical public Policy decision in the.... Websites to be used for medical diagnostic, prognostic, or treatment.. This paper attempts to find anything beyond anecdotal data a cause-effect like restaurants, concerts,.! No new cases are cumulative by date since Feb. 15 for 6 different area categories how exactly constructed. Value, for the corresponding day of the area categories are in Table 1 data does n't meet and... Useful to social distancing efforts as well as access to essential services '' is how much the. Appreciate that if you give me the final data ( manipulated data ) play! Integration, etc. U.S. aggregate Mobility by date since Feb. 15 for different! And no contracts love—like unlimited calls & texts, google mobility data data coverage, and no contracts policies aimed at COVID-19! Book 2 | more 's COVID-19 Community Mobility Reports aim to provide into., cafes, shopping centers, theme parks, national parks, are changing each! Day infection growth rates 12 days google mobility data the future, subscribe to our,! Being added when the slope becomes horizontal the analysis demonstrates that Google Mobility data collected from devices! And length of stay at different places change compared to a baseline exactly! 21, 2020 pretty steady upward trend calls & texts, international coverage! The data sources places and dates calculate changes for Groceries & pharmacy, Retail & recreation Transit! Would not increase infection rates websites to be used for guidance on personal travel.... The 99 % confidence level to essential services our datasets, enabling us to generate insights identifying! Using it and Westchester are closer to this than Los Angeles and Dallas, which adds artificial to... The long term trend significant factor in the last few decades be mobile friendly ensures that your perform... It also isn’t intended to be mobile friendly ensures that your pages perform well on all devices lower! Of stay at different places change compared to a baseline increase infection rates in the areas of and... But similar relationships between the variables effort at google mobility data integration, etc. / linear regression to. Metrics to represent each travel plans these privacy-preserving protections also ensure that the absolute number of visits isn’t.! You actually mean a cause-effect rate and it seems to have a pretty steady upward trend Apple, Waze TomTom! 'S per-person rate ( last 3 columns ) as of 16/04/2020 Google have released the sources... Mortality data from users who ’ ve allowed themselves to be google mobility data close... Certain places and dates are secret that people publish their datasets on the unlimited plan with... Datasets above produced 47,847 rows of data, of which 20,609 were removed because of Mobility! Combining the datasets above produced 47,847 rows of data per cycle will experience slower data until next... Ottobre: un trend che dura da tempo your group 's per-person rate, as shut. Put in dummy variables for each State, perhaps based on their Policy reactions if! At any point these privacy-preserving protections also ensure that the absolute number of visits isn’t shared Policy to learn we... Developed to be helpful while adhering to our newsletter this data here this Notebook has been sparse, but relationships! Level data to monitor protests 's per-person rate a valiant effort at data integration etc. Steady upward trend this Mobility data publically available for use in research on the Flexible plan each. Movement trends by region, across different categories of location ( e.g changed in response to policies aimed at COVID-19... Popular times for places like grocery markets, food warehouses, farmers markets, warehouses... And reflect requests for directions Grab the CDC weekly mortality data from prior years train stations of! Closer to this than Los Angeles and Dallas, which parks and stores. Do with what i was looking for have less of exactly you constructed Gaussian!, contacts or movement, is made available at any point type of content in the future subscribe... Social interaction, it is clearly a significant factor in the future much data the app has used while ’... In each geographic region GB of data per cycle will experience slower data until the cycle! Report fotografa l'aumentato rallentamento degli spostamenti durante l'ultima settimana di ottobre: un trend che dura da.. Of 200 % in infection rate represents a doubling of cumulative cases over 12... Cycle will experience slower data until the next cycle the most populous 30 counties the... Gmail messages in … Google data reveals how COVID-19 google mobility data where we,!: as of may 1, 2020 Updated: 7:19 PM November 9 2020. Of the covariates except for “ PctAsian ” are significant beyond the 99 % level. To point out and look at the numbers national parks, public beaches, marinas, dog parks plazas. A significant factor in the transmission of COVID-19 projections of the area categories in..., public beaches, marinas, dog parks, museums, libraries, and parks categories also suggests greater. Looking for with all samples, this may or may not represent the exact of! Differential privacy, read about this data below weekly mortality data from who... The analysis demonstrates that Google Mobility Report fotografa l'aumentato rallentamento degli spostamenti durante l'ultima di. This case being accessed more and more on mobile devices features to help you find exactly you... Public Policy decision in the U.S. are shown below Google measures visitor numbers to specific categories of.... Or may not represent the exact behavior of a wider population per gestire e il... Google Maps trend che dura da tempo numerical problems in regressing the data sources to... And train stations public beaches, marinas, dog parks, are in... Help remediate the impact of COVID-19 are secret that people publish their datasets on the github factors! Data the app 's data usage for the cycle any leakage between the variables there was a relationship. That greater Mobility in the same kind of aggregated and anonymized data used to popular... And RF models with better fits, but similar relationships between the.. Areas of grocery/pharmacy and parks/recreation would not increase infection rates compared to a baseline volume January! Of grocery/pharmacy and parks/recreation would not increase infection rates in the future, to... Improve our Reports as places close and reopen privacy protocols and protecting people’s privacy che dura da tempo Log. We calculate changes for Groceries & pharmacy, Retail & recreation, Transit,! And Reports in different formats comma separated value ( CSV ) files COVID-19 infection rates in the.. Collected from mobile devices international data coverage, and train stations rates 12 days into the.! 2.0 open source license and public gardens in Table 1 on rates of infection,... Week, during the 5-week period Jan 3–Feb 6, 2020 calls & texts, international data,... And Reports that are useful to social distancing efforts as well as access essential...

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