Spatial Clustering Method Based on Cloud Model and Data Field

2007 ◽  
pp. 420-427 ◽  
Author(s):  
Haijun Wang ◽  
Yu Deng
Keyword(s):  
Spatial Clustering ◽  
Cloud Model ◽  
Clustering Method ◽  
Data Field

Spatial clustering method based on three-dimensional cloud model

2008 ◽  
Author(s):  
Haijun Wang ◽  
Li Wang ◽  
Yu Deng ◽  
Jia Liu
Keyword(s):  
Spatial Clustering ◽  
Cloud Model ◽  
Three Dimensional ◽  
Clustering Method

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.

A multivariate spatial clustering method for partitioning tree-based orchard data into homogenous zones

2015 ◽  
pp. 233-240
Author(s):  
A. Peeters ◽  
M. Zude ◽  
J. Käthner ◽  
M. Ünlü ◽  
R. Kanber ◽  
...  
Keyword(s):  
Spatial Clustering ◽  
Clustering Method

A dual spatial clustering method in the presence of heterogeneity and noise

2020 ◽  
Vol 24 (6) ◽  
pp. 1799-1826
Author(s):  
Jie Zhu ◽  
Jiazhu Zheng ◽  
Shaoning Di ◽  
Shu Wang ◽  
Jing Yang
Keyword(s):  
Spatial Clustering ◽  
Clustering Method

scholarly journals Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing

2020 ◽  
Vol 12 (22) ◽  
pp. 9662 ◽  
Author(s):  
Disheng Yi ◽  
Yusi Liu ◽  
Jiahui Qin ◽  
Jing Zhang
Keyword(s):  
Traffic Management ◽  
Spatial Clustering ◽  
Spatial Interaction ◽  
The Other ◽  
Temporal Data ◽  
Trajectory Data ◽  
Data Field ◽  
Spatio Temporal ◽  
The Impact

Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.

Classification and Space Cluster for Visualizing GeoInformation

2019 ◽  
Vol 15 (1) ◽  
pp. 19-38
Author(s):  
Toshihiro Osaragi
Keyword(s):  
Spatial Data ◽  
Spatial Clustering ◽  
Effective Means ◽  
Information Criterion ◽  
Original Data ◽  
Spatial Clusters ◽  
Clustering Method ◽  
Numerical Examples ◽  
Loss Of Information ◽  
Tokyo Metropolitan Area

It is necessary to classify numerical values of spatial data when representing them on a map so that, visually, it can be as clearly understood as possible. Inevitably some loss of information from the original data occurs in the process of this classification. A gate loss of information might lead to a misunderstanding of the nature of original data. At the same time, when we understand the spatial distribution of attribute values, forming spatial clusters is regarded as an effective means, in which values can be regarded as statistically equivalent and distribute continuous in the same patches. In this study, a classification method for organizing spatial data is proposed, in which any loss of information is minimized. Also, a spatial clustering method based on Akaike's Information Criterion is proposed. Some numerical examples of their applications are shown using actual spatial data for the Tokyo metropolitan area.

scholarly journals Incremental autoencoders for text streams clustering in social networks

2021 ◽  
Vol 27 (11) ◽  
pp. 1203-1221
Author(s):  
Amal Rekik ◽  
Salma Jamoussi
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Streams ◽  
High Speed ◽  
Clustering Method ◽  
Text Streams ◽  
Data Field ◽  
Learning Technique ◽  
Clustering Data

Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propose, in this paper, a new evolving clustering method which can take into account the incremental nature of the data and meet with its principal requirements. Our method explores a deep learning technique to learn incrementally from unlabelled examples generated at high speed which need to be clustered instantly. To evaluate the performance of our method, we have conducted several experiments using the Sanders, HCR and Terr-Attacks datasets.

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