Urban events mining from spatiotemporal big data

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Urban events mining from spatiotemporal big data"

By

Mr. Mingyang ZHANG


Abstract

Urban events are the sets of real-world occurrences in urban space associated 
with specific topics Discovering, understanding, and forecasting various urban 
events are essential to facilitate smart-city applications and benefit urban 
life. In recent years, mobile devices and sensors widely located in cities have 
collected large amounts of data produced in urban space, which form a 
large-scale, cross-domain, and multi-view data ecosystem. The collected data, 
termed urban big data, provide an unprecedented opportunity to discover and 
analyze urban events. In this thesis, we develop data-driven methodologies for 
urban events mining. We present four of our works on urban anomaly detection, 
urban location representation learning, road network traffic prediction, and 
urban events prediction, respectively.

First, we introduce an urban dynamic decomposition method to detect urban 
anomalies. We propose a neural network model to estimate normal urban dynamics 
using spatiotemporal features and detect abnormal events via residual analysis. 
We validate the effectiveness of the proposed method with both synthetic and 
real-world event datasets. Regions are the primary spatial units for urban 
event analysis. The second work focuses on learning an embedding space from 
urban big data for urban regions. We propose to construct multi-view relations 
among urban regions and learn urban region embeddings through a multi-view 
joint learning model. Experiments on real-world datasets prove the 
effectiveness of the learned embeddings on downstream applications, including 
predicting long-term regional event statistics such as crime rates. Traffic 
prediction is a crucial task for congestion events diagnosis and control. In 
the third work, we propose a traffic prediction model called Adaptive 
Spatiotemporal Convolution Network, which models complicated spatiotemporal 
traffic dependencies adaptively with awareness of the real-time traffic 
conditions. Experiments on real-world traffic datasets from California and 
Beijing demonstrate that the proposed model outperforms the state-of-the-art. 
In the fourth work, we target at location and arrival time prediction of 
individual urban anomalous events. We design a message passing based mechanism 
to model the spatiotemporal impacts of historical events. We make joint 
predictions of event locations and times based on regional states and 
environmental factors. Our method achieves superior performances on two 
real-world urban events datasets compared with existing spatiotemporal 
prediction methods.

In the end, we conclude this thesis with future challenges and research 
directions related to data-driven urban events analysis.


Date:			Friday, 27 May 2022

Time:			4:00pm - 6:00pm

Zoom Meeting: 
https://hkust.zoom.us/j/92141463394?pwd=OWovakJpMXh1Ym5EKzdVUzJtMnpkUT09

Chairperson:		Prof. David LAM (MAE)

Committee Members:	Prof. Pan HUI (Supervisor, EMIA)
 			Prof. Tristan BRAUD (Supervisor)
 			Prof. Dik Lun LEE
 			Prof. Raymond WONG
 			Prof. Hong Kam LO (CIVL)
 			Prof. Jiannong CAO (PolyU)


**** ALL are Welcome ****