Mining big spatial data

The clustering of geospatial trajectories finds utility in multiple industries. In this report, two trajectory clustering models are presented, and a web-based front-end application is developed for visualization. The first trajectory clustering model contains two parts: i) trajectory reduction; ii)...

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Main Author: Ye, Ziyuan
Other Authors: Gao Cong
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/166018
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1660182023-04-21T15:38:48Z Mining big spatial data Ye, Ziyuan Gao Cong School of Computer Science and Engineering gaocong@ntu.edu.sg Engineering::Computer science and engineering The clustering of geospatial trajectories finds utility in multiple industries. In this report, two trajectory clustering models are presented, and a web-based front-end application is developed for visualization. The first trajectory clustering model contains two parts: i) trajectory reduction; ii) trajectory clustering. Trajectories are reduced using the Ramer–Douglas–Peucker algorithm and then clustered using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). The second trajectory clustering model adopts a recurrent neural network (RNN) model to learn spatial and temporal invariant representations of trajectories. It consists of three parts: (i) behavior feature extraction, (ii) trajectory representation learning, and (iii) trajectory clustering. For the behavior feature extraction, a sliding window technique is applied. For trajectory representation learning, a sequence-to-sequence autoencoder and decoder RNN network is trained to learn a fixed-length vector for each input trajectory. The vectors learned are then fed into the K-means clustering algorithm. Comparing the two models, we can conclude that trajectory pattern mining with sequence-to-sequence autoencoder and decoder RNN network yields more suitable results. Bachelor of Engineering (Computer Science) 2023-04-18T13:15:23Z 2023-04-18T13:15:23Z 2023 Final Year Project (FYP) Ye, Z. (2023). Mining big spatial data. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166018 https://hdl.handle.net/10356/166018 en SCSE22-0161 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Ye, Ziyuan
Mining big spatial data
description The clustering of geospatial trajectories finds utility in multiple industries. In this report, two trajectory clustering models are presented, and a web-based front-end application is developed for visualization. The first trajectory clustering model contains two parts: i) trajectory reduction; ii) trajectory clustering. Trajectories are reduced using the Ramer–Douglas–Peucker algorithm and then clustered using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). The second trajectory clustering model adopts a recurrent neural network (RNN) model to learn spatial and temporal invariant representations of trajectories. It consists of three parts: (i) behavior feature extraction, (ii) trajectory representation learning, and (iii) trajectory clustering. For the behavior feature extraction, a sliding window technique is applied. For trajectory representation learning, a sequence-to-sequence autoencoder and decoder RNN network is trained to learn a fixed-length vector for each input trajectory. The vectors learned are then fed into the K-means clustering algorithm. Comparing the two models, we can conclude that trajectory pattern mining with sequence-to-sequence autoencoder and decoder RNN network yields more suitable results.
author2 Gao Cong
author_facet Gao Cong
Ye, Ziyuan
format Final Year Project
author Ye, Ziyuan
author_sort Ye, Ziyuan
title Mining big spatial data
title_short Mining big spatial data
title_full Mining big spatial data
title_fullStr Mining big spatial data
title_full_unstemmed Mining big spatial data
title_sort mining big spatial data
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166018
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