scholarly journals PENGELOMPOKAN TITIK GEMPA DI PULAU SULAWESI MENGGUNAKAN ALGORITMA ST-DBSCAN (Spatio Temporal-Density Based Spatial Clustering Application with Noise)

2022 ◽  
Vol 10 (4) ◽  
pp. 554-561
Author(s):  
Denny Jales Manalu ◽  
Rita Rahmawati ◽  
Tatik Widiharih

Earthquake is a natural disaster which is quite serious in Indonesia, especially on Sulawesi Island. Earthquake is fearful because it can’t be predicted when it will come, where it will come, and how strong the vibration, that often causes fatal damage and casualties. In effort to minimize losses caused by earthquake, it is necessary to divide areas which are easily affected by earthquake. One of the methods that can be used in dividing the area is by using the clustering technique. This research by using a clustering method with the ST-DBSCAN (Spatial Temporal-Density Based Spatial Clustering Application with Noise) algorithm on dataset of earthquake points in Sulawesi Island in 2019. This method by using the spatial distance parameters (Eps1 = 0.45), the temporal distance parameters (Eps2 = 7), and minimum number of cluster members (MinPts = 4), resulting in a total of 60 clusters with 8 large clusters and 216 noises 

2019 ◽  
Vol 20 (3) ◽  
pp. 485-494
Author(s):  
M Naveenkumar ◽  
S Domnic

The performance of an efficient and accurate action recognition system heavily depends on distinctive representations for a different class of action sequences. To address this issue, we propose an ensemble network in this paper. We design two multilayer Long Short Term Memory networks to capture spatial and temporal dynamics of the entire sequence, referred to as Spatial-distance Net (SdNet) and Temporal-distance Net (TdNet) respectively. More specifically, SdNet captures the spatial dynamics of joints within a frame and TdNet explores the temporal dynamics of joints between frames along the sequence. Finally, two nets are fused as one Ensemble network, referred to as Spatio -Temporal distance Net (STdNet) to explore both spatial and temporal dynamics. The efficacy of the proposed method is evaluated on two widely used datasets, UTD MHAD and NTU RGB+D, and the proposed STdNet achieved 91.16% and 80.03% accuracies respectively.


2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou

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.


2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.


Author(s):  
J. W. Li ◽  
X. Q. Han ◽  
J. W. Jiang ◽  
Y. Hu ◽  
L. Liu

Abstract. How to establish an effective method of large data analysis of geographic space-time and quickly and accurately find the hidden value behind geographic information has become a current research focus. Researchers have found that clustering analysis methods in data mining field can well mine knowledge and information hidden in complex and massive spatio-temporal data, and density-based clustering is one of the most important clustering methods.However, the traditional DBSCAN clustering algorithm has some drawbacks which are difficult to overcome in parameter selection. For example, the two important parameters of Eps neighborhood and MinPts density need to be set artificially. If the clustering results are reasonable, the more suitable parameters can not be selected according to the guiding principles of parameter setting of traditional DBSCAN clustering algorithm. It can not produce accurate clustering results.To solve the problem of misclassification and density sparsity caused by unreasonable parameter selection in DBSCAN clustering algorithm. In this paper, a DBSCAN-based data efficient density clustering method with improved parameter optimization is proposed. Its evaluation index function (Optimal Distance) is obtained by cycling k-clustering in turn, and the optimal solution is selected. The optimal k-value in k-clustering is used to cluster samples. Through mathematical and physical analysis, we can determine the appropriate parameters of Eps and MinPts. Finally, we can get clustering results by DBSCAN clustering. Experiments show that this method can select parameters reasonably for DBSCAN clustering, which proves the superiority of the method described in this paper.


PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262499
Author(s):  
Negin Alisoltani ◽  
Mostafa Ameli ◽  
Mahdi Zargayouna ◽  
Ludovic Leclercq

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.


2019 ◽  
Author(s):  
Oliver Genschow ◽  
Jochim Hansen ◽  
Michaela Wänke ◽  
Yaacov Trope

In past research on imitation, some findings suggest that imitation is goal based, whereas other findings suggest that imitation can also be based on a direct mapping of a model’s movements without necessarily adopting the model’s goal. We argue that the two forms of imitation are flexibly deployed in accordance with the psychological distance from the model. We specifically hypothesize that individuals are relatively more likely to imitate the model’s goals when s/he is distant but relatively more likely to imitate the model’s specific movements when s/he is proximal. This hypothesis was tested in four experiments using different imitation paradigms and different distance manipulations. Experiment 1 served as a pilot study and demonstrated that temporal distance (vs. proximity) increased imitation of a goal relative to the imitation of a movement. Experiments 2 and 3 measured goal-based and movement-based imitation independently of each other and found that spatial distance (vs. proximity) decreased the rate of goal errors (indicating more goal imitation) compared to movement errors. Experiment 4 demonstrated that psychological distance operates most likely at the input—that is, perceptual—level. The findings are discussed in relation to construal level theory and extant theories of imitation.


2008 ◽  
Vol 137 (6) ◽  
pp. 847-857 ◽  
Author(s):  
S. E. FENTON ◽  
H. E. CLOUGH ◽  
P. J. DIGGLE ◽  
S. J. EVANS ◽  
H. C. DAVISON ◽  
...  

SUMMARYUsing data from a cohort study conducted by the Veterinary Laboratories Agency (VLA), evidence of spatial clustering at distances up to 30 km was found for S. Agama and S. Dublin (P values of 0·001) and borderline evidence was found for spatial clustering of S. Typhimurium (P=0·077). The evolution of infection status of study farms over time was modelled using a Markov Chain model with transition probabilities describing changes in status at each of four visits, allowing for the effect of sampling visit. The degree of geographical clustering of infection, having allowed for temporal effects, was assessed by comparing the residual deviance from a model including a measure of recent neighbourhood infection levels with one excluding this variable. The number of cases arising within a defined distance and time period of an index case was higher than expected. This provides evidence for spatial and spatio-temporal clustering, which suggests either a contagious process (e.g. through direct or indirect farm-to-farm transmission) or geographically localized environmental and/or farm factors which increase the risk of infection. The results emphasize the different epidemiology of the three Salmonella serovars investigated.


2021 ◽  
Author(s):  
Grzegorz Kwiatek ◽  
Maria Leonhardt ◽  
Patricia Martínez-Garzón ◽  
Matti Pentti ◽  
Marco Bohnhoff ◽  
...  

<p>In this study we investigate the statistical spatio-temporal characteristics induced seismicity associated with two stimulation campaigns performed in 2018 and 2020 in a 6.1 km deep geothermal well near Helsinki, Finland as part of the St1 Deep Heat project. We aim to find out whether the seismic activity is passively responding to injection operations, or whether we observe signatures of significant stress transfer and strong interactions between events. The former suggests stable relaxation of seismic energy proportional to hydraulic energy input, while the latter includes stress transfer as an additional source of stress perturbation, hence implying larger seismic hazard.</p><p>The selected catalogs from 2018 and 2020 stimulation contained in total 60,814 and 4,368 seismic events, respectively, recorded during and after stimulation campaigns and above the local magnitude of M -1.5. The analyzed parameters include magnitude-frequency b-value, correlation integral (c-value), fractal dimension (D-value), interevent time statistics, magnitude correlation, interevent time ratio and generalized spatio-temporal distance between earthquakes. The initial observations suggest significant time-invariance of the magnitude-frequency b-value, and increased D and c-values only at high injection rates, the latter also guiding the rate of seismicity. The seismicity covering the stimulation period neither provide signatures of magnitude correlations, nor temporal clustering or anticlustering. The interevent time statistics are generally characterized with Gamma distribution (close to Poissonian distribution), and the generalized spatio-temporal distance suggest very limited triggering (90% of the catalog was classified as background seismicity). The observable parameters suggest the seismicity passively respond to hydraulic energy input rate with little to no time delay, and the total seismic moment is proportional to total hydraulic energy input. The performed study provides the base for implementation of time-dependent probabilistic seismic hazard assessment for the site.</p>


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

Sign in / Sign up

Export Citation Format

Share Document