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Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

J.L.P. Barker, C.J.A. Macleod
2019 Environmental Modelling & Software  
Abstract Social media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/emergency management situations, including floods: by communicating potential risks  ...  open-source libraries (SciKit Learn/Gensim) Keywords Flood management; Twitter; volunteered geographic information; natural language processing; word embeddings; social geodata.  ...  Acknowledgements Funding Sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  ... 
doi:10.1016/j.envsoft.2018.11.013 fatcat:gletgkjr3ze5jdtstuspc3q46u

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems [article]

Ali Hamdi, Khaled Shaban, Abdelkarim Erradi, Amr Mohamed, Shakila Khan Rumi, Flora Salim
2021 arXiv   pre-print
Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time.  ...  We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet  ...  Spatiotemporal Prediction Data mining predictive models aim to predict target variables based on learning from annotated features of observations.  ... 
arXiv:2103.17128v1 fatcat:ci5pt5bytndr5inolznjsaizpi

Spatiotemporal event detection: a review

Manzhu Yu, Myra Bambacus, Guido Cervone, Keith Clarke, Daniel Duffy, Qunying Huang, Jing Li, Wenwen Li, Zhenlong Li, Qian Liu, Bernd Resch, Jingchao Yang (+1 others)
2020 International Journal of Digital Earth  
Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena.  ...  The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social  ...  The research is supported by NSF (CNS 1841520 and ACI 1835507), NASA (80NSSC19P2033), and the NSF Spatiotemporal I/UCRC IAB members.  ... 
doi:10.1080/17538947.2020.1738569 fatcat:urbuc2zii5bajjmmkzu6idyrg4

An overview of GeoAI applications in health and healthcare

Maged N. Kamel Boulos, Guochao Peng, Trang VoPham
2019 International Journal of Health Geographics  
A mobile phone app was developed using machine learning to predict patient mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on over 30 phone sensors  ...  In particular, machine learning includes AI methods and algorithms for computers to obtain knowledge by iteratively extracting and learning from patterns hidden in raw data [12] .  ... 
doi:10.1186/s12942-019-0171-2 pmid:31043176 pmcid:PMC6495523 fatcat:sfrleigt6rhnfatwmvhuchjtbi

A Survey on Societal Event Forecasting with Deep Learning [article]

Songgaojun Deng, Yue Ning
2021 arXiv   pre-print
data such as social media, news sources, blogs, economic indicators, and other meta-data sources.  ...  We first introduce how event forecasting problems are formulated as a machine learning prediction task.  ...  GNNs learn embeddings/hidden features for each node in a graph by using topological information.  ... 
arXiv:2112.06345v1 fatcat:jtdlo67bbbazhj6xea55h6bbqa

How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review

Umar Ali Bukar, Fatimah Sidi, Marzanah A. Jabar, Rozi Nor Haizan Nor, Salfarina Abdullah, Iskandar Ishak, Mustafa Alabadla, Ali Alkhalifah
2022 Sustainability  
Advanced technological approaches such as social media, machine learning (ML), social network analysis (SNA), and big data are vital to a sustainable crisis management decisions and communication.  ...  sustainable crisis management to support decision making, information management, communication, collaboration and cooperation, location-based services, community resilience, situational awareness, and social  ...  [35] focuses on social media usage for information transmission and prediction amid environmental problems, whereas [36] highlights social media usage advancements in data gathering, evaluation, and  ... 
doi:10.3390/su14105854 fatcat:njo6yy6tevebbkzqzwz4vieptm

Urban Anomaly Analytics: Description, Detection, and Prediction [article]

Mingyang Zhang, Tong Li, Yue Yu, Yong Li, Pan Hui, Yu Zheng
2020 arXiv   pre-print
Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras  ...  Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically.  ...  Social media Social media data from Twitter and Weibo are widely employed for event discovering as well [120] .  ... 
arXiv:2004.12094v1 fatcat:ixtrfb546nblbjub7jvzqgumrq

Survey of Analytical Methods for Big Data of Quality Inspection

Yingcheng Xu, Wei Jiang, Xiuli Ning, Bisong Liu, Ya Li
2018 IOP Conference Series: Materials Science and Engineering  
depth neural network and visualization analysis from the characteristics of quality inspection data.  ...  It brings great challenge for data analysis as the features of huge volume, various types, high timeliness and low value density.  ...  , product quality arbitration, lab product detection, product injury and accident, but also includes emerging social media data with extensive user interaction such as Blog, WiKi, Microblog, forum, social  ... 
doi:10.1088/1757-899x/466/1/012030 fatcat:f2oiyrbzkvdw7ia5wdsfzegxju

2021 Index IEEE Transactions on Computational Social Systems Vol. 8

2021 IEEE Transactions on Computational Social Systems  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Zhu, H., +, TCSS Feb. 2021 179-190 Aug. 2021 856-869 Mental health ReMEMBeR: Ranking Metric Embedding-Based Multicontextual Behavior Depression Intensity Estimation via Social Media: A Deep Learning Profiling  ... 
doi:10.1109/tcss.2021.3137918 fatcat:kdc6nwrfbncixnxc5xkjwqvuqq

Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models

Hyesook Son, Seokyeon Kim, Hanbyul Yeon, Yejin Kim, Yun Jang, Seung-Eock Kim
2021 Applied Sciences  
The input characteristics include space-time and data features, and we apply temporal prediction networks, including gated recurrent units (GRU), long short term memory (LSTM), and spatiotemporal prediction  ...  When predicting data that are measured with sensors in multiple locations, it is necessary to train a deep learning model with spatiotemporal characteristics of the data.  ...  Spatiotemporal prediction models are applied in various fields, such as traffic, weather, social media, flights, and human migration.  ... 
doi:10.3390/app11135853 fatcat:pvupjsy4cbaqfat6uelfieopya

Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources

S. Salcedo-Sanz, P. Ghamisi, M. Piles, M. Werner, L. Cuadra, A. Moreno-Martínez, E. Izquierdo-Verdiguier, J. Muñoz-Marí, A. Mosavi, G. Camps-Valls
2020 Information Fusion  
Earth observation is well equipped with remote sensing systems, mounted on satellites and airborne platforms, but it also involves in-situ observations, numerical models and social media data streams,  ...  This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation.  ...  It has been shown that simple features extracted from social media metadata alone can increase the convergence speed and final quality of a deep learning model predicting urban land use modeled after local  ... 
doi:10.1016/j.inffus.2020.07.004 fatcat:m57jbkxnhjfqvgt5ol6iei35ta

Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities

Dorota Kamrowska-Załuska
2021 Land  
smaller than social media data); Mobile phone data; gps data from high spatiotemporal precision; for GPS from float cars: for gps from floating car data: does not show all trips, smaller Urban flows analyses  ...  Using sensors or social media, and other socially generated information resulting from their participation in social, economic, or civic activities, citizens are turning from being passive subjects of  ... 
doi:10.3390/land10111209 fatcat:ncelgv6pqnbrpjbgusubv7jh64

Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
predictive learning, representation learning, anomaly detection and classification.  ...  health care and environmental management.  ...  weathers and social media texts to predict the future demand.  ... 
arXiv:1906.04928v2 fatcat:4zrdtgkvirfuniq3rb2gl7ohpy

Sensing and Modeling Human Behavior Using Social Media and Mobile Data [article]

Abhinav Mehrotra, Mirco Musolesi
2017 arXiv   pre-print
In this article we discuss how the availability of new technologies such as online social media and mobile smartphones has allowed researchers to passively collect human behavioral data at a scale and  ...  In the past years we have witnessed the emergence of the new discipline of computational social science, which promotes a new data-driven and computation-based approach to social sciences.  ...  They show that machine learning models can successfully be used to predict changes in depressive states of users by using location data.  ... 
arXiv:1702.01181v2 fatcat:pxgjufslorgttbvvj6lpdjgc5a

Transport-domain applications of widely used data sources in the smart transportation: A survey [article]

Sina Dabiri, Kevin Heaslip
2018 arXiv   pre-print
Secondly, as the most salient feature of this study, the transport-domain applications of each data source that have been conducted by the previous studies are reviewed and classified into the main groups  ...  from a set of heterogeneous but complementary data sources.  ...  Other than deploying advanced learning frameworks (e.g., recurrent neural networks and word embedding models) to extract semantic and syntactic information on social media contents, enriching social media  ... 
arXiv:1803.10902v3 fatcat:tc67qy4x4vbtjb76qi6mbwrqy4
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