Mining Rainfall Spatio-Temporal Patterns in Twitter: A Temporal Approach

Andrade, Sidgley Camargo de; Restrepo-Estrada, Camilo; Delbem, Alexandre Cláudio Botazzo; Mendiondo, Eduardo Mário; Albuquerque, João Porto de

doi:10.1007/978-3-319-56759-4_2

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…In this case study, we define a spatial application to support spatial queries that analyze the geographic location of tweets in relation to different sources, such as single locations or regions of a city. We consider a dataset of tweets posted on three different cities in the period of November 7, 2016 to February 28, 2017 81 . The amount of geotagged data for each city are: (i) 891 367 tweets from São Paulo, Brazil; (ii) 849 026 tweets from Rio de Janeiro, Brazil; and (iii) 68 884 tweets from Medellín, Colombia.…”

Section: Case Studiesmentioning

confidence: 99%

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

2020

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Summary Spatial analytics systems (SASs) represent a technology capable of managing huge volumes of spatial data using frameworks such as Apache Hadoop and Apache Spark. An increasing number of SASs have been proposed, requiring a comparison among them. However, existing comparisons in the literature provide a system‐centric view based on performance evaluations. Thus, there is a lack of comparisons based on the user‐centric view, that is, comparisons that help users to understand how the characteristics of SASs are useful to meet the specific requirements of their spatial applications. In this article, we provide a user‐centric comparison of the following SASs based on Hadoop and Spark: Hadoop‐GIS, SpatialHadoop, SpatialSpark, GeoSpark, GeoMesa Spark, SIMBA, LocationSpark, STARK, Magellan, SparkGIS, and Elcano. This comparison employs an extensive set of criteria related to the general characteristics of these systems, to the aspects of spatial data handling, and to the aspects inherent to distributed systems. Based on this comparison, we introduce guidelines to help users to choose an appropriate SAS. We also describe two case studies based on real‐world applications to illustrate the use of these guidelines. Finally, we discuss chronological tendencies related to SASs and identify limitations that SASs should address to improve user experience.

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Section: Case Studiesmentioning

confidence: 99%

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

2020

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“…Assim, além da busca em acervos históricos, as evidências documentais também estão disponíveis em meio eletrônico, tais como mídias sociais (e.g. Kryvasheyeu et al, 2016;De Andrade et al, 2017). Um exemplo de aplicação desse tipo de fonte de dados se encontra em Collischonn & Kobiyama (2019).…”

Section: Evidência Documentalunclassified

Panorama geral sobre dados hidrológicos com ênfase em eventos hidrológicos extremos

Vanelli

Fan

Kobiyama

2020

REGA

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“…Social media also can be used to monitor rainfall in real time. De Andrade et al (), for example, collected Twitter messages combined with official rainfall data to detect rainfall patters in real time. Other promising crowdsourcing approaches for assessing areal rainfall pattern use optical sensors or wipers of moving cars (Rabiei, Haberlandt, Sester, & Fitzner, ; Rabiei, Haberlandt, Sester, Fitzner, & Wallner, ) or the attenuation of radio signals by rain between transmitters and receivers of cellular communication networks (Overeem, Leijnse, & Uijlenhoet, ).…”

Section: Citizen Science and Flood Hazard Assessmentmentioning

confidence: 99%

Flood hazard assessment and the role of citizen science

Frischknecht

Dao

et al. 2019

J Flood Risk Management

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Flood hazard assessment is at the core of flood risk management. In order to develop an efficient flood hazard assessment, it is of primary importance to have a well‐defined flood scenario encompassing all processes that could occur during an event. Understanding and assessing these processes requires meteorological, topographical and land‐use data as well as historical observations. Nowadays, flood delineation is based upon hydrological and hydraulic modelling, ground data collection, and remote sensing. Despite the advantages of these tools, they also present some specific limitations, either intrinsic to the approaches or linked with constraints of the local context. With the rapid advancement of web 2.0 technologies (e.g., Flickr and Wikimapia) and the increase of the use of participatory research, citizen science has the potential to provide valuable and complementary information at all levels of flood risk management and in particular for flood hazard assessment. After reviewing the capabilities and limitations of the current tools used in flood hazard assessment, this paper demonstrates the role that citizen science can play in providing key information on factors leading to flooding and on flood hazard parameters.

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Mining Rainfall Spatio-Temporal Patterns in Twitter: A Temporal Approach

Cited by 8 publications

References 14 publications

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

Panorama geral sobre dados hidrológicos com ênfase em eventos hidrológicos extremos

Flood hazard assessment and the role of citizen science

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