2026-10-14T04:39:20Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33736480 2026-09-14T12:26:45Z portal_1197 item_type_3 month_year_09_2026

Data for Mining rainfall spatio-temporal patterns in Twitter : a temporal approach S. C. De Andrade (24882697) Camilo Restrepo-Estrada (24882751) A. C. B. Delbem (24882703) E. M. Mendiondo (24865927) João Porto de Albuquerque (24861934) Rain and rainfall -- Databases -- São Paulo (Brazil) Twitter (Firm) Social networks Migrated from ePrints Social networks are a valuable source of information to support the detection and monitoring of targeted events, such as rainfall episodes. Since the emergence of Web 2.0, several studies have explored the relationship between social network messages and authoritative data in the context of disaster management. However, these studies fail to address the problem of the temporal validity of social network data. This problem is important for establishing the correlation between social network activity and the different phases of rainfall events in real-time, which thus can be useful for detecting and monitoring extreme rainfall events. In light of this, this paper adopts a temporal approach for analyzing the cross-correlation between rainfall gauge data and rainfall-related Twitter messages by means of temporal units and their lag-time. This approach was evaluated by conducting a case study in the city of São Paulo, Brazil, using a dataset of rainfall data provided by the Brazilian National Disaster Monitoring and Early Warning Center. The results provided evidence that the rainfall gauge time-series and the rainfall-related tweets are not synchronized, but they are linked to a lag-time that ranges from −10 to +10 min. Furthermore, our temporal approach is thus able to pave the way for detecting patterns of rainfall in real-time based on social network messages.<br><br>AGILE 2017 dataset The AGILE 2017 is a raw dataset of rainfall gauges and tweets that are openly available in CSV format. This dataset was published at the 20th AGILE Conference The dataset contains: rainfall_gauges.csv: The rainfall data collected from the National Center for Monitoring and Early Warning of Natural Disasters CEMADEN referring to the city of Sao Paulo, Brazil. The rainfall measurements were provided in linear depth (millimeters) and with two sizes of temporal window: 10 minutes when it was raining and 60 minutes otherwise. rainfall_gagues_removed.csv: The rainfall gauges removed due the lack of reliability. tweets.csv: The georeferenced tweets retrieved by Twitter Streaming API for the same period and administrative boundaries of rainfall data. tweets.ts.csv: The frequency of rainfall-related tweets and all tweets. 2026-09-14T12:26:45Z Dataset Dataset 10.82444/warw.33736480.v1 https://figshare.com/articles/dataset/Data_for_Mining_rainfall_spatio-temporal_patterns_in_Twitter_a_temporal_approach/33736480 CC BY-NC-SA 4.0