Graph-Based Spatial-Temporal Cluster Evolution: Representation, Analysis, and Implementation
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Date
2023-08-28
Authors
da Silva Portugal, Ivens
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Spatial-temporal data are information about real-world entities that exist in a location, the spatial dimension, and during a period of time, the temporal dimension. These real-world entities, such as vehicles, people, or parcels and called spatial-temporal objects, may move, group, and continue the movement together, forming clusters. Although there have been significant research efforts to understand clusters, there is a lack of research that provides methods and software tools to support the representation, analysis, and implementation of graph-based spatial-temporal cluster evolution. Understanding this evolution is critical for dealing with spatial-temporal problems encountered in domains, such as service supply and demand, supply chain management, traffic and travel flows, human mobility, and city planning. This thesis presents an approach to graph-based cluster evolution and its representation, analysis, and implementation. The proposed solution introduces a representation of the structure of a spatial-temporal cluster with the identification of the cluster at several timestamps and linkages, and a representation of 14 spatial-temporal relationships clusters have during their existence. The proposed solution also introduces a graph representation of cluster evolution with nodes acting as clusters and edges as relationships. This solution provides analysis methods for the structure of spatial-temporal clusters that monitor the cluster changes in both location and size over time, and analysis methods for the spatial-temporal cluster relationships the clusters have during existence that calculate the frequency or density of such relationships in specific locations. The solution also provides analysis methods for a graph-based representation of spatial-temporal cluster evolution including integrated results that examine spatial-temporal clusters and their connections, and can provide, for example, aggregated results at a location or time of the day, identify ever-increasing or ever-decreasing regions, growth or decay rates, and measure the similarity between the evolution of two clusters. The approach also provides an implementation of the proposed representation and analysis methods. The effectiveness of the approach is evaluated through four case studies using different spatial-temporal datasets to show the results that can be produced, which include, exploratory analyses and specific analyses on ever-increasing and ever-decreasing regions, similarity values, and the movements the clusters represent. Overall, the proposed approach advances research in the spatial-temporal domain by providing novel representation and analysis methods as well as implementation tools that can improve the understanding about how clusters evolve in space and time. Such results can lead to many advantages such as higher income, reduced costs, and better transportation services, as well as the discovery of trends in cluster movement and improved decision-making processes in city planning.
Description
Keywords
graph, spatial-temporal, data analysis, cluster evolution, clustering, machine learning