scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

scholarly journals Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points

2019 ◽  
Vol 11 (23) ◽  
pp. 6870 ◽  
Author(s):  
Tong Zhou ◽  
Xintao Liu ◽  
Zhen Qian ◽  
Haoxuan Chen ◽  
Fei Tao
Keyword(s):  
Travel Behavior ◽  
Spatial Clustering ◽  
Route Planning ◽  
Sustainable Urban Development ◽  
Heat Index ◽  
Time Interval ◽  
Dynamic Visualization ◽  
Trajectory Data ◽  
Dynamic Update ◽  
Taxi Trajectory

This paper proposes a novel method for dynamically extracting and monitoring the entrances of areas of interest (AOIs). Most AOIs in China, such as buildings and communities, are enclosed by walls and are only accessible via one or more entrances. The entrances are not marked on most maps for route planning and navigation in an accurate way. In this work, the extraction scheme of the entrances is based on taxi trajectory data with a 30 s sampling time interval. After fine-grained data cleaning, the position accuracy of the drop-off points extracted from taxi trajectory data is guaranteed. Next, the location of the entrances is extracted, combining the density-based spatial clustering of applications with noise (DBSCAN) with the boundary of the AOI under the constraint of the road network. Based on the above processing, the dynamic update scheme of the entrance is designed. First, a time series analysis is conducted using the clusters of drop-off points within the adjacent AOI, and then, a relative heat index ( R H I ) is applied to detect the recent access status (closed or open) of the entrances. The results show the average accuracy of the current extraction algorithm is improved by 24.3% over the K-means algorithm, and the R H I can reduce the limitation of map symbols in describing the access status. The proposed scheme can, therefore, help optimize the dynamic visualization of the entry symbols in mobile navigation maps, and facilitate human travel behavior and way-finding, which is of great help to sustainable urban development.

scholarly journals Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

2021 ◽  
Vol 9 (6) ◽  
pp. 566
Author(s):  
Lianhui Wang ◽  
Pengfei Chen ◽  
Linying Chen ◽  
Junmin Mou
Keyword(s):  
Traffic Management ◽  
Performance Metrics ◽  
Spatial Clustering ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Clustering Methods ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Shape Characteristics

The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.

scholarly journals Spatiotemporal Data Clustering: A Survey of Methods

2019 ◽  
Vol 8 (3) ◽  
pp. 112 ◽  
Author(s):  
Zhicheng Shi ◽  
Lilian Pun-Cheng
Keyword(s):  
Spatial Clustering ◽  
Human Mobility ◽  
Media Analysis ◽  
Spatiotemporal Data ◽  
Clustering Methods ◽  
Social Media Analysis ◽  
Partitional Clustering ◽  
Significant Events ◽  
Analysis Methods ◽  
Spatial Attributes

Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as transportation, social media analysis, crime analysis, and human mobility analysis. The development of ST data analysis methods can uncover potentially interesting and useful information. Due to the complexity of ST data and the diversity of objectives, a number of ST analysis methods exist, including but not limited to clustering, prediction, and change detection. As one of the most important methods, clustering has been widely used in many applications. It is a process of grouping data with similar spatial attributes, temporal attributes, or both, from which many significant events and regular phenomena can be discovered. In this paper, some representative ST clustering methods are reviewed, most of which are extended from spatial clustering. These methods are broadly divided into hypothesis testing-based methods and partitional clustering methods that have been applied differently in previous research. Research trends and the challenges of ST clustering are also discussed.

scholarly journals Classification of Countries According to their Tourism Statistics via Different Cluster Analysis Methods and the Place of Turkey in this Structure

2013 ◽  
Author(s):  
Selay Giray
Keyword(s):  
Cluster Analysis ◽  
Hong Kong ◽  
Russian Federation ◽  
Fuzzy Clustering ◽  
Clustering Methods ◽  
Clustering Method ◽  
Analysis Methods ◽  
Fuzzy Clustering Method ◽  
The World

The aim of this study is to classify the countries according to their tourism indicators via different cluster analysis methods and compare the findings. Using classical cluster analysis and fuzzy clustering together will be more appropriate to determine the World tourism structure. In this way the findings can be interpreted more detailed and comparatively. Data obtained from website of Worldbank (3 basic international tourism statistics of 159 countries for the year 2010) and findings are gained using NCSS (statistical software) 2007. According to the findings of fuzzy clustering method, Turkey belogs to a cluster which contains ABD, United Kingdom, China, Austria, France, Germany, Italy, Malaysia, Spain, Hong Kong, Russian Federation, and Ukraine. According to the findings of classical clustering method (k means), Turkey is in the same cluster with same countries except Hong Kong. Also the findings of two techniques are similar about Turkey. Such a result can be expected correspondingly grading the countries about international their tourism data in 2011. Different clustering methods findings are steady about Euroasian countries too. Except Russian Federation and Ukraine all of the other Euroasian countries are located together in same cluster depending upon two different clustering methods. In conclusion two different clustering methods provide consistent (similar) results about the classification of countries according their internatianol tourism statistics.

scholarly journals Comparative Study on Spatial Clustering Methods for Identifying Traffic Accident Hotspots

2021 ◽  
Author(s):  
Mohd. Minhajuddin Aquil ◽  
Mir Iqbal Faheem
Keyword(s):  
Traffic Accident ◽  
Road Network ◽  
Traffic Accidents ◽  
Spatial Clustering ◽  
Clustering Methods ◽  
Loss Of Life ◽  
Road Users ◽  
Analysis Methods ◽  
Accident Data ◽  
Nearest Neighborhood

Traffic accidents in an urban road network are inevitable as a result claims and disputes arise among different road users. It is imperative to estimate the likelihood of traffic accidents resulting from different factors that contribute to loss of life, property and health of road users. There is a pressing need to reduce traffic accidents by identifying the location of accident hotspots using suitable analysis methods and examining them which is essential for the safety of road users. In this research traffic accident hotspots are identified using two spatial clustering analysis methods namely Getis-Ord Gi* and Nearest Neighborhood Hierarchy (NNH). These methods are compared and evaluated using the Prediction Accuracy Index (PAI) for their degree of accuracy. In this study, a cumulative traffic accident data of Hyderabad city of Telangana state over four years is researched upon and considered. Getis-Ord Gi* analysis measures the concentration ratio based on Z score identified as high (positive Z-values) and low values (negative Z-values). NNH analysis is another spatial clustering method which displays hotspot regions in the form of Convex hulls and Ellipses. The choice of the above two clustering methods represents the significance of the precision required. The findings of the study reveal that NNH method performed better compared to Getis-Ord Gi* method in its ability to detect hotspots. The above research methodology can be performed to any size of road network area globally having relevant accident data for the identification of hotspots for reducing the traffic accidents.

scholarly journals An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fang Liu ◽  
Wei Bi ◽  
Wei Hao ◽  
Fan Gao ◽  
Jinjun Tang
Keyword(s):  
Large Scale ◽  
Spatial Clustering ◽  
Urban Traffic ◽  
Clustering Methods ◽  
Travel Patterns ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Clustering Method ◽  
Urban Travel ◽  
Fuzzy Clustering Method

Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method.

scholarly journals Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Qi Diao ◽  
Yaping Dai ◽  
Qichao An ◽  
Weixing Li ◽  
Xiaoxue Feng ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Local Density ◽  
Search Algorithm ◽  
Real Data ◽  
Nearest Neighbors ◽  
Adjusted Rand Index ◽  
Clustering Methods ◽  
K Nearest Neighbors ◽  
Density Peaks

This paper presents an improved clustering algorithm for categorizing data with arbitrary shapes. Most of the conventional clustering approaches work only with round-shaped clusters. This task can be accomplished by quickly searching and finding clustering methods for density peaks (DPC), but in some cases, it is limited by density peaks and allocation strategy. To overcome these limitations, two improvements are proposed in this paper. To describe the clustering center more comprehensively, the definitions of local density and relative distance are fused with multiple distances, including K-nearest neighbors (KNN) and shared-nearest neighbors (SNN). A similarity-first search algorithm is designed to search the most matching cluster centers for noncenter points in a weighted KNN graph. Extensive comparison with several existing DPC methods, e.g., traditional DPC algorithm, density-based spatial clustering of applications with noise (DBSCAN), affinity propagation (AP), FKNN-DPC, and K-means methods, has been carried out. Experiments based on synthetic data and real data show that the proposed clustering algorithm can outperform DPC, DBSCAN, AP, and K-means in terms of the clustering accuracy (ACC), the adjusted mutual information (AMI), and the adjusted Rand index (ARI).

scholarly journals Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming

2018 ◽  
Vol 7 (3.15) ◽  
pp. 63
Author(s):  
Marina Yusoff ◽  
Muhammad Najib Bin Fathi ◽  
. .
Keyword(s):  
Learning Style ◽  
Teaching And Learning ◽  
Spatial Clustering ◽  
Mathematical Science ◽  
Clustering Methods ◽  
Clustering Method ◽  
Neurolinguistic Programming ◽  
Job Interview ◽  
First Impression ◽  
Student Learning Style

Students’ performance is a key point to get a better first impression during a job interview with an employer. However, there are several factors, which affect students’ performances during their study. One of them is their learning style, which is under Neurolinguistic Programming (NLP) approach. Learning style is divided into a few behavioral categories, Visual, Auditory and Kinesthetics (VAK). This paper addresses the evaluation of clustering methods for the identification of learning style based on system preferences. It starts with the distribution of questionnaires to acquire the information on the VAK for each student. About 167 respondents in the Faculty of Computer and Mathematical Science are collected. It is then pre- processed to prepare the data for clustering method evaluations. Three clustering methods; Simple K-Mean, Hierarchical and Density-Based Spatial Clustering of Applications with Noise are evaluated. The findings show that Simple K-Mean offers the most accurate prediction. Upon completion, by using the dataset, Simple K-Means technique estimated four clusters that yield the highest accuracy of 74.85 % compared to Hierarchical Clustering, which estimated four clusters and Density- Based Spatial Clustering of Applications with Noise which estimated three clusters with 52.69% and 61.68 % respectively. The clustering method demonstrates the capability of categorizing the learning style of students based on three categories; visual, auditory and kinesthetic. This outcome would be beneficial to lecturers or teachers in university and school with an automatically clustering the students’ learning style and would assist them in teaching and learning, respectively.  

scholarly journals Evidence for rapid avoidance of rockfish habitat under reduced quota and comprehensive at-sea monitoring in the British Columbia Pacific halibut fishery

2020 ◽  
Vol 77 (8) ◽  
pp. 1409-1420
Author(s):  
Robyn E. Forrest ◽  
Ian J. Stewart ◽  
Cole C. Monnahan ◽  
Katherine H. Bannar-Martin ◽  
Lisa C. Lacko
Keyword(s):  
British Columbia ◽  
Spatial Clustering ◽  
Clustering Methods ◽  
Quota System ◽  
Encounter Rates ◽  
System A ◽  
Pacific Halibut ◽  
Hippoglossus Stenolepis ◽  
Footprint Area ◽  
Management Changes

The British Columbia longline fishery for Pacific halibut (Hippoglossus stenolepis) has experienced important recent management changes, including the introduction of comprehensive electronic catch monitoring on all vessels; an integrated transferable quota system; a reduction in Pacific halibut quotas; and, beginning in 2016, sharp decreases in quota for yelloweye rockfish (Sebastes ruberrimus, an incidentally caught species). We describe this fishery before integration, after integration, and after the yelloweye rockfish quota reduction using spatial clustering methods to define discrete fishing opportunities. We calculate the relative utilization of these fishing opportunities and their overlap with areas with high encounter rates of yelloweye rockfish during each of the three periods. The spatial footprint (area fished) increased before integration, then decreased after integration. Each period showed shifts in utilization among four large fishing areas. Immediately after the reductions in yelloweye rockfish quota, fishing opportunities with high encounter rates of yelloweye rockfish had significantly lower utilization than areas with low encounter rates, implying rapid avoidance behaviour.

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