Analysis of ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/vehicle-miles-travelede on 13 February 2022.

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

About this dataset

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least.

About This Data

The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

This dataset was created by Angeliki Kastanis and contains around 0 samples along with Date At Low, Mean7 County Vmt At Low, technical information and other features such as: - County Name - County Fips - and more.

How to use this dataset

• Analyze State Name in relation to Baseline Jan Vmt
• Study the influence of Date At Low on Mean7 County Vmt At Low
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Angeliki Kastanis

Start A New Notebook!

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

Birthday Paradox Visitor Data

Exploring Probability and Patterns of Day of the Week Birthdays

By data.world's Admin [source]

About this dataset

This dataset contains daily visitor-submitted birthdays and associated data from an ongoing experimentation known as the Birthday Paradox. Be enlightened as you learn how many people have chosen the same day of their birthday as yours. Get a better perspective on how this phenomenon varies day-to-day, including recent submissions within the last 24 hours. This experiment is published under the MIT License, giving you access to detailed information behind this perplexing cognitive illusion. Find out now why the probability of two people in the same room having birthday matches is much higher than one might expect!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides data on the Birthday Paradox Visitor Experiments. It contains information such as daily visitor-submitted birthdays, the total number of visitors who have submitted birthdays, the total number of visitors who guessed the same day as their birthday, and more. This dataset can be used to analyze patterns in visitor behavior related to the Birthday Paradox Experiment.

In order to use this dataset effectively and efficiently, it is important to understand its fields and variables:
- Updated: The date when this data was last updated
- Count: The total number of visitors who have submitted birthdays
- Recent: The number of visitors who have submitted birthdays in the last 24 hours
- binnedDay: The day of the week for a given visitor's birthday submission
- binnedGuess: The day of week that a given visitor guessed their birthday would fall on 6) Tally: Total number of visitors who guessed same day as their birthday 7) binnedTally: Total number of visitors grouped by guess day

To begin using this dataset you should first filter your data based on desired criteria such as date range or binnedDay. For instance, if you are interested in analyzing Birthady Paradox Experiment results for Monday submissions only then you can filter your data by binnedDay = 'Monday'. Then further analyze your filtered query by examining other fields such as binnedGuess and comparing it with tally or binnedTally results accordingly. For example if we look at Monday entries above we should compare 'Monday' tallies with 'Tuesday' guesses (or any other weekday). ` Furthermore understanding updates from recent field can also provide interesting insights into user behavior related to Birthady Paradox Experiment -- trackingt recent entries may yield valuable trends over time.

By exploring various combinations offields available in this dataset users will be ableto gain a better understandingof how user behaviordiffers across different daysofweek both within a singledayandover periodsoftimeaccordingtodifferent criteria providedbythisdataset

Research Ideas

• Analyzing the likelihood of whether a person will guess their own birthday correctly.
• Estimating which day of the week is seeing the most number of visitors submitting their birthdays each day and analyzing how this varies over time.
• Investigating how likely it is for two people from different regions to have the same birthday by comparing their respective submission rates on each day of the week

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: data.csv | Column name | Description | |:----------------|:-----------------------------------------------------------------------------------| | updated | The date and time the data was last updated. (DateTime) | | count | The total number of visitor submissions. (Integer) | | recent | The number of visitor submissions in the last 24 hours. (Integer) | | binnedDay | The day of the week the visitor submitted their birthday. (String) | | binnedGuess | The day of the week the visitor guessed their birthday. (String) | | tally | The total number of visitor guesses that matched their actual birthdays. (Integer) | | binnedTally | The day of the week the visitor guessed their birthday correctly. (String) |

Acknowledgement...

Context

Approximately 10 people are shot on an average day in Chicago.

http://www.chicagotribune.com/news/data/ct-shooting-victims-map-charts-htmlstory.html http://www.chicagotribune.com/news/local/breaking/ct-chicago-homicides-data-tracker-htmlstory.html http://www.chicagotribune.com/news/local/breaking/ct-homicide-victims-2017-htmlstory.html

Content

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. This data includes unverified reports supplied to the Police Department. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time.

Update Frequency: Daily

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:chicago_crime

https://cloud.google.com/bigquery/public-data/chicago-crime-data

Dataset Source: City of Chicago

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source —https://data.cityofchicago.org — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Ferdinand Stohr from Unplash.

Inspiration

What categories of crime exhibited the greatest year-over-year increase between 2015 and 2016?

Which month generally has the greatest number of motor vehicle thefts?

How does temperature affect the incident rate of violent crime (assault or battery)?

https://cloud.google.com/bigquery/images/chicago-scatter.png" alt=""> https://cloud.google.com/bigquery/images/chicago-scatter.png

Overview This Extended Golf Play Dataset is a rich and detailed collection designed to extend the classic golf dataset. It includes a variety of features to cover many aspects of data science. This dataset is especially useful for teaching because it offers many small datasets within it, each one created for a different learning purpose.

Core Features: Outlook: Type of weather (sunny, cloudy, rainy, snowy). Temperature: How hot or cold it is, in Celsius. Humidity: How much moisture is in the air, as a percent. Windy: If it is windy or not (True or False). Play: If golf was played or not (Yes or No). Extra Features: ID: Each player's unique number. Date: The day the data was recorded. Weekday: What day of the week it is. Holiday: If the day is a special holiday (Yes or No). Season: Time of the year (spring, summer, autumn, winter). Crowded-ness: How crowded the golf course is. PlayTime-Hour: How long people played golf, in hours. Text Features: Review: What players said about their day at golf. EmailCampaign: Emails the golf place sent every day. MaintenanceTasks: Work done to take care of the golf course. Mini Datasets Collection This dataset includes a special set of mini datasets:

Each mini dataset focuses on a specific teaching point, like how to clean data or how to combine datasets. They're perfect for beginners to practice with real examples. Along with these datasets, you'll find notebooks with step-by-step guides that show you how to use the data. Learning With This Dataset Students can use this dataset to learn many skills:

Seeing Data: Learn how to make graphs and see patterns. Sorting Data: Find out which data helps to predict if golf will be played. Finding Odd Data: Spot data that doesn't look right. Understanding Data Over Time: Look at how things change day by day or month by month. Grouping Data: Learn how to put similar days together. Learning From Text: Use players' reviews to get more insights. Making Recommendations: Suggest the best time to play golf based on past data. Who Can Use This Dataset This dataset is for everyone:

New Learners: It's easy to understand and has guides to help you learn. Teachers: Great for classes on how to see and understand data. Researchers: Good for testing new ways to analyze data.

Original Data Source: ⛳️ Golf Play Dataset Extended

This data set is supplement to this Scientific Reports article.

The data set provides estimates of country-level daily mobility metrics (uncertainty included) for 17 countries from March 11, 2020 to present. Estimates are based on more than 3.8 million smartphone trajectories.

Metrics:

Estimated daily average travelled distance by people.

Estimated percentage of people who did not move during the 24 hours of the day.

Countries: Argentina (ARG), Chile (CHL), Colombia (COL), Costa Rica (CRI), Ecuador (ECU), Greece (GRC), Guatemala (GTM), Italy (ITA), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Peru (PER), Philippines (PHL), Slovenia (SVN), Turkey (TUR), United States (USA) and Venezuela (VEN).

Covered period: from March 11, 2020 to present.

Temporal resolution: daily.

Temporal smoothing:

No smoothing.

7-day moving average.

14-day moving average.

21-day moving average.

28-day moving average.

Uncertainty: 95% bootstrap confidence interval.

Data ownership

Anonymized data on smartphone trajectories are collected, owned and managed by Futura Innovation SRL. Smartphone trajectories are stored and analyzed on servers owned by Futura Innovation SRL and not shared with third parties, including the author of this repository and his organization (University of Bergamo).

Contribution

Ilaria Cremonesi of Futura Innovation SRL is the data owner and data manager.

Francesco Finazzi of University of Bergamo developed the statistical methodology for the data analysis and the algorithms implemented on Futura Innovation SRL servers.

Repository update

CSV files of this repository are regularly produced by Futura Innovation SRL and published by the repository's author after validation.

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DuckDuckGo Privacy Tools Without a Login DuckDuckGo is more than just a search engine. It also offers a range of privacy tools—all without needing a DuckDuckGo login account:

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Email Protection: DuckDuckGo recently launched a service that allows users to create "@duck.com" email addresses that forward to their real email—removing trackers in the process. Users sign up for this using a token or limited identifier, but it still doesn’t constitute a full DuckDuckGo login account.

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Final Thoughts While the term DuckDuckGo login account is frequently searched, it represents a misunderstanding of how the platform operates . Unlike other tech companies that monetize personal data, DuckDuckGo has stayed true to its promise of privacy .

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The DailyDialog dataset is a meticulously curated collection of multi-turn dialogues that aims to accurately represent the way we communicate in our daily lives. It covers a wide range of topics that are relevant to our everyday experiences. What sets this dataset apart is that it includes human-written conversations, which means the language used is more natural and realistic, resulting in less noise and higher quality data.

Each dialogue in the dataset consists of two or more participants engaging in a conversation. The conversations are provided in textual form, allowing for easy analysis and processing. Alongside the dialogues, there are also corresponding labels for communication intention and emotion attached to each utterance.

The communication intention labels categorize each utterance based on its intended purpose or goal within the conversation. These categories provide valuable insights into how different participants express their intentions through speech.

In addition to the communication intention labels, there are also emotion labels assigned to each utterance in the dialogues. These emotion labels capture the emotional state or sentiment expressed by participants during various points in the conversation.

To facilitate model evaluation and testing, DailyDialog provides three separate files: validation.csv, train.csv, and test.csv. The validation set (validation.csv) contains dialogues with their respective communication intention and emotion labels for assessing model performance during development stages. The train set (train.csv) includes dialogues paired with corresponding communication intention and emotion labels for training purposes. Lastly, test.csv serves as an independent test set that enables evaluating models' proficiency by providing unseen dialogues along with their associated communication intention and emotion labels.

Overall, DailyDialog stands out as a high-quality dataset due to its accurate representation of daily life conversations paired with comprehensive labeling of both communication intentions and emotions expressed throughout these dialogues. This makes it an invaluable resource for developing robust dialogue systems capable of understanding human interactions on a deeper level while being able to identify diverse intentions behind speech acts alongside various emotional states encountered during daily life exchanges

How to use the dataset Welcome to the DailyDialog dataset! This high-quality multi-turn dialog dataset has been curated to reflect our daily communication style and covers a wide range of topics related to our everyday lives. The dataset consists of human-written conversations, making it less noisy and more realistic. Each conversation in the dataset has been manually labeled with communication intention and emotion information, providing valuable insights into the dialogues.

To make the most of this dataset, here is a step-by-step guide on how you can use it effectively:

Understanding the columns:

dialog: This column contains the actual conversation between two or more participants. It is in text format. act: The act column represents the communication intention labels for each utterance in the dialogue. These labels categorize each utterance based on its intention. emotion: The emotion column contains emotion labels for each utterance in the dialogue. These labels represent the emotions expressed during that particular utterance. Familiarize yourself with validation.csv:

The validation.csv file serves as a validation set for evaluating your model's performance. It contains pre-labeled conversations along with their corresponding communication intentions and emotion labels. Explore train.csv for training purposes:

The train.csv file is meant for training purposes and provides conversations along with their communication intentions and emotion labels. Test your model using test.csv:

Test.csv file has conversation along ithentensions or emotional label which can be addressed once program is recreated. Finally, remember that this DailyDialog dataset offers an excellent opportunity to develop models capable of understanding multi-turn dialogues in a wide range of everyday scenarios. By utilizing both communication intention and emotion information provided, you can gain valuable insights into analyzing human conversations.

So dive into this rich resource, experiment with different techniques such as natural language processing and machine learning, and discover new ways of understanding and modeling human dialogues!

Research Ideas Natural Language Processing: This dataset can be used for training NLP models to understand and generate more realistic and human-like dialogues. The communication intention labels can help in identifying the purpose or goal of each utterance, while the emotion labels can add emotional context to the conversations. Sentiment Analysis: With the emotion labels, this dataset can be used for sentiment analysis tasks t

Introduction

The ComplexVAD dataset consists of 104 training and 113 testing video sequences taken from a static camera looking at a scene of a two-lane street with sidewalks on either side of the street and another sidewalk going across the street at a crosswalk. The videos were collected over a period of a few months on the campus of the University of South Florida using a camcorder with 1920 x 1080 pixel resolution. Videos were collected at various times during the day and on each day of the week. Videos vary in duration with most being about 12 minutes long. The total duration of all training and testing videos is a little over 34 hours. The scene includes cars, buses and golf carts driving in two directions on the street, pedestrians walking and jogging on the sidewalks and crossing the street, people on scooters, skateboards and bicycles on the street and sidewalks, and cars moving in the parking lot in the background. Branches of a tree also move at the top of many frames.

The 113 testing videos have a total of 118 anomalous events consisting of 40 different anomaly types.

Ground truth annotations are provided for each testing video in the form of bounding boxes around each anomalous event in each frame. Each bounding box is also labeled with a track number, meaning each anomalous event is labeled as a track of bounding boxes. A single frame can have more than one anomaly labeled.

At a Glance

The size of the unzipped dataset is ~39GB

The dataset consists of Train sequences (containing only videos with normal activity), Test sequences (containing some anomalous activity), a ground truth annotation file for each Test sequence, and a README.md file describing the data organization and ground truth annotation format.

The zip files contain a Train directory, a Test directory, an annotations directory, and a README.md file.

License

The ComplexVAD dataset is released under CC-BY-SA-4.0 license.

All data:

Created by Mitsubishi Electric Research Laboratories (MERL), 2024

SPDX-License-Identifier: CC-BY-SA-4.0

The dataset includes information about how populations are responding to physical distancing measures. The metrics used in this dataset include Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day.

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The dataset is password-protected until the article is accepted by the Scientific Data Journal. After acceptance, the dataset will be available to everyone. For any questions, comments or other issues please contact Piotr Woźniak, email: p.wozniak@prz.edu.pl.

License

The MDDRobots dataset is made available under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/.

Summary

The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions.

It is obligatory to cite the following paper in every work that uses the dataset:

Wozniak, P.; Krzeszowski T.; Kwolek B.: Multi-Domain Dataset for Indoor Place Recognition and Anomaly Detection by Mobile Robots, Scientific Data, ISSN: 2052-4463, 2024.

Data description

The data is divided into five sets, each containing data for different cameras, which have further subsets. Each subset (Training, Test 1, Test 2, and Test 3) consists of nine sequences. There are a total of 87,750 three-channel RGB color images in PNG format organized into 19 zip folders. Each image in the sequence is labeled to represent a room. The number of images for each subset differs due to the division into training and testing data, as well as different methods of recording the sequences. To ensure a balanced dataset, each room in the sequence has the same number of images. Different environmental changes are introduced in each subset, mainly due to changes in the route, robot, and recording equipment. The rooms are well-lit but not overexposed. Test 1 data are closest to those from the training set. Test 3 sequences present changed conditions, such as a different time of day, a changed lighting system, and intensive equipment changes. Test 2 sequences pose the most significant challenge as they contain various recorded activities performed by people moving around rooms. This sequence data does not appear in the Xtion subset.

Dataset structure

• RobotPiCamera_DataSet
  • DataSet_RobotPiCamera_RGB_train
  • DataSet_RobotPiCamera_RGB_test1
  • DataSet_RobotPiCamera_RGB_test2
  • DataSet_RobotPiCamera_RGB_test3
• Xtion_DataSet
  • DataSet_XTION_RGB_train
  • DataSet_XTION_RGB_test1
  • DataSet_XTION_RGB_test3
• GOPRO_DataSet
  • DataSet_GOPRO_RGB_train
  • DataSet_GOPRO_RGB_test1
  • DataSet_GOPRO_RGB_test2
  • DataSet_GOPRO_RGB_test3
• iPhone_DataSet
  • DataSet_IPHONE_RGB_train
  • DataSet_IPHONE_RGB_test1
  • DataSet_IPHONE_RGB_test2
  • DataSet_IPHONE_RGB_test3
• P40PRO_DataSet
  • DataSet_P40PRO_RGB_train
  • DataSet_P40PRO_RGB_test1
  • DataSet_P40PRO_RGB_test2
  • DataSet_P40PRO_RGB_test3

Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.

Total Images (Images per Place)

Further information

For any questions, comments or other issues please contact Piotr Woźniak

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least. ## About This Data The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

@(https://interactives.ap.org/vmt-map/)

Interactive Embed Code

Using the Data

This data can help put your county's mobility in context with your state and over time. The data set contains different measures of change - daily comparisons and seven day rolling averages. The rolling average allows for a smoother trend line for comparison across counties and states. To get the full picture, there are also two available baselines - vehicle miles traveled in January 2020 (pre-pandemic) and vehicle miles traveled at each geography's low point during the pandemic.

Caveats

• The data from StreetLight Data, Inc does not include data for some low-population counties with low VMT (<5,000 miles/day in their baseline month of January 2020). In our analyses, we only include the 2,779 counties that have daily data for the entire period (March 1, 2020 to current).
• In some cases, a lack of decline in mobility from March to April can indicate that movement in the county is essential to keeping the larger economy going or that residents need to drive further to reach essentials businesses like grocery stores compared to other counties.
• The VMT includes both passenger and commercial miles, so truck traffic is included. However, the proxy is based on the "total number of trip starts and ends for all devices whose most frequent location is in this county". It does not count the VMT of trucks cutting through a county.
• For those instances where travel begins in one county and ends in another, the county where the miles are recorded is always the vehicle’s home county. ###### Contact reporter Angeliki Kastanis at akastanis@ap.org.

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema 

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
  _id: 

Overview

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries.

Content

Over 9000 stations' data are typically available.

The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches)

Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

This public dataset was created by the National Oceanic and Atmospheric Administration (NOAA) and includes global data obtained from the USAF Climatology Center. This dataset covers GSOD data between 1929 and present, collected from over 9000 stations. Dataset Source: NOAA

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Photo by Allan Nygren on Unsplash

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

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Analysis of ‘Pakistan Corona Virus Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zusmani/pakistan-corona-virus-citywise-data on 30 September 2021.

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

Context

Pakistan witnessed its first Corona virus patient on February 26th 2020. It's a bumpy ride since then. The cases are increasing gradually and we haven't seen the worst yet. While, there are few government resources for cumulative updates, there is no place where you can find the city level patients data. It's also not possible to find the running chronological tally of patients as they test positive. We have decided to create our own dataset for all the researchers out there with such details so we can model the infection spread and forecast the situation in coming days. We hope, by doing so, we will be able to inform policy makers on various intervention models, and healthcare professionals to be ready for the influx of new patients. We certainly hope, that this little contribution will go a long way for saving lives in Pakistan

Content

The dataset contains seven columns for date, number of cases, number of deaths, number of people recovered, travel history of those cases, and location of the cases (province and city).

The first version has the data from first case of February 26 2020 to April 19, 2020. We intend to publish weekly updates

Acknowledgements

Users are allowed to use, copy, distribute and cite the dataset as follows: “Zeeshan-ul-hassan Usmani, Sana Rasheed, Pakistan Corona Virus Data, Kaggle Dataset Repository, April 19, 2020.”

Inspiration

Some ideas worth exploring:

Can we find the spread factor for the Corona virus in Pakistan?

How long it takes for a positive case to infect another in Pakistan?

How we can use this data to simulate lock down scenarios and find its impact on country's economy? Here is a good
read to get started - http://zeeshanusmani.com/urdu/corona-economic-impact/

How does Pakistan Corona virus spread compare against its neighbors and other developed counties?

What would be the impact of this infection spread on country's economy and people living under poverty? Here are two briefs to get you started

http://zeeshanusmani.com/urdu/corona/ http://zeeshanusmani.com/urdu/corona-what-to-learn/

How do we visualize this dataset to inform policy makers? Here is one example https://zeeshanusmani.com/corona/

Can we predict the number of cases in next 10 days and a month?

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

Energy Consumption of United States Over Time

Building Energy Data Book

By Department of Energy [source]

About this dataset

The Building Energy Data Book (2011) is an invaluable resource for gaining insight into the current state of energy consumption in the buildings sector. This dataset provides comprehensive data on residential, commercial and industrial building energy consumption, construction techniques, building technologies and characteristics. With this resource, you can get an in-depth understanding of how energy is used in various types of buildings - from single family homes to large office complexes - as well as its impact on the environment. The BTO within the U.S Department of Energy's Office of Energy Efficiency and Renewable Energy developed this dataset to provide a wealth of knowledge for researchers, policy makers, engineers and even everyday observers who are interested in learning more about our built environment and its energy usage patterns

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides comprehensive information regarding energy consumption in the buildings sector of the United States. It contains a number of key variables which can be used to analyze and explore the relations between energy consumption and building characteristics, technologies, and construction. The data is provided in both CSV format as well as tabular format which can make it helpful for those who prefer to use programs like Excel or other statistical modeling software.

In order to get started with this dataset we've developed a guide outlining how to effectively use it for your research or project needs.

• Understand what's included: Before you start analyzing the data, you should read through the provided documentation so that you fully understand what is included in the datasets. You'll want to be aware of any potential limitations or requirements associated with each type of data point so that your results are valid and reliable when drawing conclusions from them.

• Clean up any outliers: You may need to take some time upfront investigating suspicious outliers within your dataset before using it in any further analyses — otherwise, they can skew results down the road if not dealt with first-hand! Furthermore, they could also make complex statistical modeling more difficult as well since they artificially inflate values depending on their magnitude within each example data point (i.e., one outlier could affect an entire model’s prior distributions). Missing values should also be accounted for too since these may not always appear obvious at first glance when reviewing a table or graphical representation - but accurate statistics must still be obtained either way no matter how messy things seem!

• Exploratory data analysis: After cleaning up your dataset you'll want to do some basic exploring by visualizing different types of summaries like boxplots, histograms and scatter plots etc.. This will give you an initial case into what trends might exist within certain demographic/geographic/etc.. regions & variables which can then help inform future predictive models when needed! Additionally this step will highlight any clear discontinuous changes over time due over-generalization (if applicable), making sure predictors themselves don’t become part noise instead contributing meaningful signals towards overall effect predictions accuracy etc…

• Analyze key metrics & observations: Once exploratory analyses have been carried out on rawsamples post-processing steps are next such as analyzing metrics such ascorrelations amongst explanatory functions; performing significance testing regression models; imputing missing/outlier values and much more depending upon specific project needs at hand… Additionally – interpretation efforts based

Research Ideas

• Creating an energy efficiency rating system for buildings - Using the dataset, an organization can develop a metric to rate the energy efficiency of commercial and residential buildings in a standardized way.
• Developing targeted campaigns to raise awareness about energy conservation - Analyzing data from this dataset can help organizations identify areas of high energy consumption and create targeted campaigns and incentives to encourage people to conserve energy in those areas.
• Estimating costs associated with upgrading building technologies - By evaluating various trends in building technologies and their associated costs, decision-makers can determine the most cost-effective option when it comes time to upgrade their structures' energy efficiency...

This training data was generated using GPT-4o as part of the 'Drawing with LLM' competition (https://www.kaggle.com/competitions/drawing-with-llms). It can be used to fine-tune small language models for the competition or serve as an augmentation dataset alongside other data sources.

The dataset is generated in two steps using the GPT-4o model. - In the first step, topic descriptions relevant to the competition are generated using a specific prompt. By running this prompt multiple times, over 3,000 descriptions were collected.

 
prompt=f""" I am participating in an SVG code generation competition.
  
   The competition involves generating SVG images based on short textual descriptions of everyday objects and scenes, spanning a wide range of categories. The key guidelines are as follows:
  
   - Descriptions are generic and do not contain brand names, trademarks, or personal names.
   - No descriptions include people, even in generic terms.
   - Descriptions are concise—each is no more than 200 characters, with an average length of about 50 characters.
   - Categories cover various domains, with some overlap between public and private test sets.
  
   To train a small LLM model, I am preparing a synthetic dataset. Could you generate 100 unique topics aligned with the competition style?
  
   Requirements:
   - Each topic should range between **20 and 200 characters**, with an **average around 60 characters**.
   - Ensure **diversity and creativity** across topics.
   - **50% of the topics** should come from the categories of **landscapes**, **abstract art**, and **fashion**.
   - Avoid duplication or overly similar phrasing.
  
   Example topics:
                 a purple forest at dusk, gray wool coat with a faux fur collar, a lighthouse overlooking the ocean, burgundy corduroy, pants with patch pockets and silver buttons, orange corduroy overalls, a purple silk scarf with tassel trim, a green lagoon under a cloudy sky, crimson rectangles forming a chaotic grid,  purple pyramids spiraling around a bronze cone, magenta trapezoids layered on a translucent silver sheet,  a snowy plain, black and white checkered pants,  a starlit night over snow-covered peaks, khaki triangles and azure crescents,  a maroon dodecahedron interwoven with teal threads.
  
   Please return the 100 topics in csv format.
   """
 

• In the second step, SVG code is generated by prompting the GPT-4o model. The following prompt is used to query the model to generate svg.

 
  prompt = f"""
      Generate SVG code to visually represent the following text description, while respecting the given constraints.
      
      Allowed Elements: `svg`, `path`, `circle`, `rect`, `ellipse`, `line`, `polyline`, `polygon`, `g`, `linearGradient`, `radialGradient`, `stop`, `defs`
      Allowed Attributes: `viewBox`, `width`, `height`, `fill`, `stroke`, `stroke-width`, `d`, `cx`, `cy`, `r`, `x`, `y`, `rx`, `ry`, `x1`, `y1`, `x2`, `y2`, `points`, `transform`, `opacity`
      

      Please ensure that the generated SVG code is well-formed, valid, and strictly adheres to these constraints. 
      Focus on a clear and concise representation of the input description within the given limitations. 
      Always give the complete SVG code with nothing omitted. Never use an ellipsis.

      The code is scored based on similarity to the description, Visual question anwering and aesthetic components.
      Please generate a detailed svg code accordingly.

      input description: {text}
      """
 

The raw SVG output is then cleaned and sanitized using a competition-specific sanitization class. After that, the cleaned SVG is scored using the SigLIP model to evaluate text-to-SVG similarity. Only SVGs with a score above 0.5 are included in the dataset. On average, out of three SVG generations, only one meets the quality threshold after the cleaning, sanitization, and scoring process.