{ "data_id": "43380", "name": "US-Weather-Events-(2016---2020)", "exact_name": "US-Weather-Events-(2016---2020)", "version": 1, "version_label": "v1.0", "description": "Description\nThis is a countrywide weather events dataset that includes 6.3 million events, and covers 49 states of the United States. Examples of weather events are rain, snow, storm, and freezing condition. Some of the events in this dataset are extreme events (e.g. storm) and some could be regarded as regular events (e.g. rain and snow). The data is collected from January 2016 to December 2020, using historical weather reports that were collected from 2,071 airport-based weather stations across the nation. Check here for more details about the dataset. \nAcknowledgements\nPlease cite the following papers if you use this dataset: \n\nMoosavi, Sobhan, Mohammad Hossein Samavatian, Arnab Nandi, Srinivasan Parthasarathy, and Rajiv Ramnath. Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, ACM, 2019.\n\nDescription of Weather Events\nWeather event is a spatiotemporal entity, where such an entity is associated with location and time. Following is the description of available weather event types in this dataset:\n\nSevere-Cold: The case of having extremely low temperature, with temperature below -23.7 degrees of Celsius.\nFog: The case where there is low visibility condition as a result of fog or haze.\nHail: The case of having solid precipitation including ice pellets and hail.\nRain: The case of having rain, ranging from light to heavy.\nSnow: The case of having snow, ranging from light to heavy.\nStorm: The extremely windy condition, where the wind speed is at least 60 km\/h.\nOther Precipitation: Any other type of precipitation which cannot be assigned to previously described event types.\n\nPlease visit our paper to learn how we defined and extracted these events from raw weather observation records. A raw weather observation record shows the original sensor reading data, which includes multiple attributes such as temperature, pressure, humidity, precipitation amount, etc. \nUsage Policy and Legal Disclaimer\nThis dataset is being distributed only for Research purposes, under Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By clicking on the download button below, you are agreeing to use this data only for non-commercial, research, or academic applications. You may need to cite the above paper if you use this dataset.", "format": "arff", "uploader": "Onur Yildirim", "uploader_id": 30126, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-23 12:51:42", "update_comment": null, "last_update": "2022-03-23 12:51:42", "licence": "CC BY-NC-SA 4.0", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102205\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "US-Weather-Events-(2016---2020)", "Description This is a countrywide weather events dataset that includes 6.3 million events, and covers 49 states of the United States. Examples of weather events are rain, snow, storm, and freezing condition. Some of the events in this dataset are extreme events (e.g. storm) and some could be regarded as regular events (e.g. rain and snow). The data is collected from January 2016 to December 2020, using historical weather reports that were collected from 2,071 airport-based weather stations acros " ], "weight": 5 }, "qualities": { "NumberOfInstances": 7479165, "NumberOfFeatures": 14, "NumberOfClasses": null, "NumberOfMissingValues": 73797, "NumberOfInstancesWithMissingValues": 59234, "NumberOfNumericFeatures": 4, "NumberOfSymbolicFeatures": 0, "Dimensionality": 1.8718667123936964e-6, "PercentageOfNumericFeatures": 28.57142857142857, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0.791986806013773, "AutoCorrelation": null, "PercentageOfMissingValues": 0.07047864682373348 }, "tags": [ { "uploader": "38960", "tag": "Machine Learning" }, { "uploader": "38960", "tag": "Manufacturing" } ], "features": [ { "name": "EventId", "index": "0", "type": "string", "distinct": "7479165", "missing": "0" }, { "name": "Type", "index": "1", "type": "string", "distinct": "7", "missing": "0" }, { "name": "Severity", "index": "2", "type": "string", "distinct": "6", "missing": "0" }, { "name": "StartTime(UTC)", "index": "3", "type": "string", "distinct": "1980059", "missing": "0" }, { "name": "EndTime(UTC)", "index": "4", "type": "string", "distinct": "1940056", "missing": "0" }, { "name": "Precipitation(in)", "index": "5", "type": "numeric", "distinct": "1705", "missing": "0", "min": "0", "max": "1104", "mean": "0", "stdev": "1" }, { "name": "TimeZone", "index": "6", "type": "string", "distinct": "4", "missing": "0" }, { "name": "AirportCode", "index": "7", "type": "string", "distinct": "2071", "missing": "0" }, { "name": "LocationLat", "index": "8", "type": "numeric", "distinct": "2056", "missing": "0", "min": "25", "max": "49", "mean": "39", "stdev": "5" }, { "name": "LocationLng", "index": "9", "type": "numeric", "distinct": "2063", "missing": "0", "min": "-125", "max": "0", "mean": "-92", "stdev": "13" }, { "name": "City", "index": "10", "type": "string", "distinct": "1716", "missing": "14563" }, { "name": "County", "index": "11", "type": "string", "distinct": "1100", "missing": "0" }, { "name": "State", "index": "12", "type": "string", "distinct": "48", "missing": "0" }, { "name": "ZipCode", "index": "13", "type": "numeric", "distinct": "2020", "missing": "59234", "min": "1022", "max": "99362", "mean": "52478", "stdev": "25715" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }