TY - GEN
T1 - Spatio-temporal Event Forecasting and Precursor Identification
AU - Ning, Yue
AU - Zhao, Liang
AU - Chen, Feng
AU - Lu, Chang Tien
AU - Rangwala, Huzefa
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/7/25
Y1 - 2019/7/25
N2 - Spatio-temporal societal event forecasting, which has traditionally been prohibitively challenging, is now becoming possible and experiencing rapid growth thanks to the big data from Open Source Indicators (OSI) such as social media, news sources, blogs, economic indicators, and other meta-data sources. Spatio-temporal societal event forecasting and their precursor discovery benefit the society by providing insight into events such as political crises, humanitarian crises, mass violence, riots, mass migrations, disease outbreaks, economic instability, resource shortages, natural disasters, and others. In contrast to traditional event detection that identifies ongoing events, event forecasting focuses on predicting future events yet to happen. Also different from traditional spatio-temporal predictions on numerical indices, spatio-temporal event forecasting needs to leverage the heterogeneous information from OSI to discover the predictive indicators and mappings to future societal events. While studying large scale societal events, policy makers and practitioners aim to identify precursors to such events to help understand causative attributes and ensure accountability. The resulting problems typically require the predictive modeling techniques that can jointly handle semantic, temporal, and spatial information, and require a design of efficient and interpretable algorithms that scale to high-dimensional large real-world datasets. In this tutorial, we will present a comprehensive review of the state-of-the-art methods for spatio-temporal societal event forecasting. First, we will categorize the inputs OSI and the predicted societal events commonly researched in the literature. Then we will review methods for temporal and spatio-temporal societal event forecasting. Next, we will also discuss the foundations of precursor identification with an introduction to various machine learning approaches that aim to discover precursors while forecasting events. Through the tutorial, we expect to illustrate the basic theoretical and algorithmic ideas and discuss specific applications in all the above settings.
AB - Spatio-temporal societal event forecasting, which has traditionally been prohibitively challenging, is now becoming possible and experiencing rapid growth thanks to the big data from Open Source Indicators (OSI) such as social media, news sources, blogs, economic indicators, and other meta-data sources. Spatio-temporal societal event forecasting and their precursor discovery benefit the society by providing insight into events such as political crises, humanitarian crises, mass violence, riots, mass migrations, disease outbreaks, economic instability, resource shortages, natural disasters, and others. In contrast to traditional event detection that identifies ongoing events, event forecasting focuses on predicting future events yet to happen. Also different from traditional spatio-temporal predictions on numerical indices, spatio-temporal event forecasting needs to leverage the heterogeneous information from OSI to discover the predictive indicators and mappings to future societal events. While studying large scale societal events, policy makers and practitioners aim to identify precursors to such events to help understand causative attributes and ensure accountability. The resulting problems typically require the predictive modeling techniques that can jointly handle semantic, temporal, and spatial information, and require a design of efficient and interpretable algorithms that scale to high-dimensional large real-world datasets. In this tutorial, we will present a comprehensive review of the state-of-the-art methods for spatio-temporal societal event forecasting. First, we will categorize the inputs OSI and the predicted societal events commonly researched in the literature. Then we will review methods for temporal and spatio-temporal societal event forecasting. Next, we will also discuss the foundations of precursor identification with an introduction to various machine learning approaches that aim to discover precursors while forecasting events. Through the tutorial, we expect to illustrate the basic theoretical and algorithmic ideas and discuss specific applications in all the above settings.
KW - Precursor Discovery
KW - Spatio-Temporal Modeling
UR - http://www.scopus.com/inward/record.url?scp=85071181914&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071181914&partnerID=8YFLogxK
U2 - 10.1145/3292500.3332291
DO - 10.1145/3292500.3332291
M3 - Conference contribution
AN - SCOPUS:85071181914
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 3237
EP - 3238
BT - KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
T2 - 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Y2 - 4 August 2019 through 8 August 2019
ER -