Mining spatiotemporal association patterns from complex geographic phenomena

He, Zhanjun; Deng, Min; Cai, Jiannan; Xie, Zhen; Guan, Qingfeng; Yang, Chao

doi:10.1080/13658816.2019.1566549

Cited by 56 publications

(32 citation statements)

References 34 publications

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“…These techniques, although based on statistical foundations, integrate data mining including Machine Learning (ML) and Artificial Intelligence (AI) based methods [50][51][52]. However, when there are other factors affecting the disease incidence, it becomes a complex phenomenon [53]. The candidate driving factors can be incorporated into the model as a third feature or a set of features.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan

Khalique

Khan

Butt

et al. 2020

IJERPH

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Public health management can generate actionable results when diseases are studied in context with other candidate factors contributing to disease dynamics. In order to fully understand the interdependent relationships of multiple geospatial features involved in disease dynamics, it is important to construct an effective representation model that is able to reveal the relationship patterns and trends. The purpose of this work is to combine disease incidence spatio-temporal data with other features of interest in a mutlivariate spatio-temporal model for investigating characteristic disease and feature patterns over identified hotspots. We present an integrated approach in the form of a disease management model for analyzing spatio-temporal dynamics of disease in connection with other determinants. Our approach aligns spatio-temporal profiles of disease with other driving factors in public health context to identify hotspots and patterns of disease and features of interest in the identified locations. We evaluate our model against cholera disease outbreaks from 2015–2019 in Punjab province of Pakistan. The experimental results showed that the presented model effectively address the complex dynamics of disease incidences in the presence of other features of interest over a geographic area representing populations and sub populations during a given time. The presented methodology provides an effective mechanism for identifying disease hotspots in multiple dimensions and relation between the hotspots for cost-effective and optimal resource allocation as well as a sound reference for further predictive and forecasting analysis.

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Section: Related Workmentioning

confidence: 99%

An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan

Khalique

Khan

Butt

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…In this way, the method was applied to a real case study for air pollution, where the objective is to find correspondences of air pollution with other parameters that affect this phenomenon. Recently, He [5] proposed an adaptive spatiotemporal episode pattern mining algorithm, which can discover the candidate driving factors for the occurrence of complex geographic events. The proposed approach was applied to analyze the air pollution in the region of Beijing-Tianjin-Hebei.…”

Section: A Analysis and Visualization Of Air Pollution Propagationmentioning

confidence: 99%

“…The previous analysis works are mainly carried out in two aspects [5]- [10]. One is to explore the co-occurrence relationships of air pollution in different regions [5]- [7], to identify the hidden laws of propagation. However, this kind of method's accuracy is heavily dependent on the cooccurrence interval and is not accepted by scientists.…”

Section: Introductionmentioning

confidence: 99%

Visual Analytics of Air Pollution Propagation Through Dynamic Network Analysis

Ren

Zhang

et al. 2020

IEEE Access

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The propagation of pollutants between regions has become a noticeable factor affecting air pollution. Given the complicated propagation relationship, most of the existing works lack an effective perception mechanism of geographic correlations and time-varying features, which is crucial in exploring and understanding the propagation mechanism by integrating empirical knowledge and data inherent characteristics. In this paper, we abstract the complicated propagation relationship between regions as a dynamic network, and introduce visual analytics techniques to explore the spatiotemporal multivariate patterns of air pollution propagation. A particle tracking based model is first proposed to construct pollution propagation networks under multi-source factors. It combines numerical simulation and data characteristics simultaneously, and detects active pollution source areas based on long-term transport relationships and temporal correlations. Based on it, we extract propagation patterns and analyze the temporal evolution of diachronic propagation networks. Moreover, we design an interactive system to achieve an in-depth analysis of air pollution issues. Through elaborate multi-level glyphs and linkage views, the system facilitates users to perceive and explore propagation mechanism in spatiotemporal multivariate information, and compare propagation patterns from global and local perspectives. We present several case studies to demonstrate the usefulness of our work in air pollution propagation analysis.

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“…The framework integrate spatiotemporal clustering, Bayesian probability and Monte Carlo simulations. • He et al (2019b) proposed a complex pattern mining algorithm to discover eventbased spatiotemporal association patterns, representative of geographic dynamics. The algorithm adopts a hierarchical framework to support mining beyond point data representation, reveal dynamic characteristics of complex geographic phenomena and discover their associated factors.…”

Section: The Special Sectionmentioning

confidence: 99%