Spatio-temporal Clustering Algorithm Research Articles

DBSTC: an effective method for discovering cluster features with different spatiotemporal densities

ABSTRACTSpatiotemporal clustering is one of the most advanced research topics in geospatial data mining. It has been challenging to discover cluster features with different spatiotemporal densities in geographic information data set. This paper presents an effective density-based spatiotemporal clustering algorithm (DBSTC). First, we propose a method to measure the degree of similarity of a core point to the geometric center of its spatiotemporal reachable neighborhood, which can effectively solve the isolated noise point misclassification problem that exists in the shared nearest neighbor methods. Second, we propose an ordered reachable time window distribution algorithm to calculate the reachable time window for each spatiotemporal point in the data set to solve the problem of different clusters with different temporal densities. The effectiveness and advantages of the DBSTC algorithm are demonstrated in several simulated data sets. In addition, practical applications to seismic data sets demonstrate the capability of the DBSTC algorithm to uncover clusters of foreshocks and aftershocks and help to improve the understanding of the underlying mechanisms of dynamic spatiotemporal processes in digital earth.

Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty

Multi-Airport Systems (MAS), or Metroplexes, serve air traffic demand in cities with two or more airports. Due to the spatial proximity and operational interdependency of the airports, Metroplex airspaces are characterized by high complexity, and current system structures fail to provide satisfactory utilization of the available airspace resources. In order to support system-level design and management towards increased operational efficiency in such systems, an accurate depiction of major demand patterns is a prerequisite. This paper proposes a framework for the robust identification of significant air traffic flow patterns in Metroplex systems, which is aligned with the dynamic route service policy for the effective management of Metroplex operations. We first characterize deterministic demand through a spatio-temporal clustering algorithm that takes into account changes in the traffic flows over the planning horizon. Then, in order to handle uncertainties in the demand, a Distributionally Robust Optimization (DRO) approach is proposed, which takes into account demand variations and prediction errors in a robust way to ensure the reliability of the demand identification. The DRO-based approach is applied on pre-tactical (i.e. one-day planning) as well as operational levels (i.e. 2-h rolling horizon). The framework is applied to Time Based Flow Management (TBFM) data from the New York Metroplex. The framework and results are validated by Subject Matter Experts (SMEs).

A Two-Step Clustering Approach to Extract Locations from Individual GPS Trajectory Data

High-accuracy location identification is the basis of location awareness and location services. However, because of the influence of GPS signal loss, data drift and repeated access in the individual trajectory data, the efficiency and accuracy of existing algorithms have some deficiencies. Therefore, we propose a two-step clustering approach to extract individuals’ locations according to their GPS trajectory data. Firstly, we defined three different types of stop points; secondly, we extracted these points from the trajectory data by using the spatio-temporal clustering algorithm based on time and distance. The experimental results show that the spatio-temporal clustering algorithm outperformed traditional extraction algorithms. It can avoid the problems caused by repeated access and can substantially reduce the effects of GPS signal loss and data drift. Finally, an improved clustering algorithm based on a fast search and identification of density peaks was applied to discover the trajectory locations. Compared to the existing algorithms, our method shows better performance and accuracy.

Spatio-temporal graph clustering algorithm based on attribute and structural similarity

Spatio-temporal graph clustering algorithm based on attribute and structural similarity

A Spatio-Temporal malware and country clustering algorithm: 2012 IIJ MITF case study

A huge number of botnet malware variants can be downloaded by zombie personal computers as secondary injections and upgrades according to their botmasters to perform different distributed/coordinated cyber attacks such as phishing, spam e-mail, malicious Web sites, ransomware, DDoS. In order to generate a faster response to new threats and better understanding of botnet activities, grouping them based on their malicious behaviors has become extremely important. This paper presents a Spatio-Temporal malware clustering algorithm based on its (weekly-hourly-country) features. The dataset contains more than 32 million of malware download logs from 100 honeypots set up by Malware Investigation Task Force (MITF) of Internet Initiative Japan Inc. (IIJ) from 2011 to 2012. The Top-20 malware clustering results coincidentally correspond to Conficker.B and Conficker.C with relatively high precision and recall rates up to 100.0, 88.9 % and 91.7, 100.0 %, respectively. On the other hand, the resulting two clusters of Top-20 countries are comparable to those with high and low growth rates recently reported in 2015 by Asghari et al. Therefore, our approach can be validated and evaluated to yield precision and recall of up to 75.0 and 86.7 %, respectively.

Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters

The identification of distinct seismic regions and features’ extraction of theirs could provide valuable information towards the better understanding of the underlying physics, the generation mechanism and the behavior of the seismic phenomenon. This research paper works towards that direction and unveils the potential presence of a distinct seismic region located in between the Ionean sea and the Cretan see, extending south-east of Peloponnesus, Greece. This observation has emerged as a result of the development and application of a spatio temporal clustering algorithm based on expert knowledge upon seismic data provided by the Geodynamics Institute of Athens, and is further supported by geological observations, which have unveiled the presence of two parallel groups of underground faults beneath the newly discovered potential distinct seismic region. The overall spatio temporal clustering results throughout the Greek vicinity are also in accordance with empirical observations reported in the literature and coincide with cartographic groups of underground faults of Greece.

Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory

This paper attempts to mine the hidden individual behavior pattern from the raw users’ trajectory data. Based on DBSCAN, a novel spatio-temporal data clustering algorithm named Speed-based Clustering Algorithm was put forward to find slow-speed subtrajectories (i.e., stops) of the single trajectory that the user stopped for a longer time. The algorithm used maximal speed and minimal stopping time to compute the stops and introduced the quantile function to estimate the value of the parameter, which showed more effectively and accurately than DBSCAN and certain improved DBSCAN algorithms in the experimental results. In addition, after the stops are connected with POIs that have the characteristic of an information presentation, the paper designed a POI-Behavior Mapping Table to analyze the user’s activities according to the stopping time and visiting frequency, on the basis of which the user’s daily regular behavior pattern can be mined from the history trajectories. In the end, LBS operators are able to provide intelligent and personalized services so as to achieve precise marketing in terms of the characteristics of the individual behavior.

Discrete nonhomogeneous and nonstationary logistic and Markov regression models for spatiotemporal data with unresolved external influences

Dynamical systems with different characteristic behavior at multiple scales can be modeled with hybrid methods combining a discrete model (e.g., corresponding to the microscale) triggered by a continuous mechanism and vice versa. A data-driven black-box-type framework is proposed, where the discrete model is parametrized with adaptive regression techniques and the output of the continuous counterpart (e.g., output of partial differential equations) is coupled to the discrete system of interest in the form of a fixed exogenous time series of external factors. Data availability represents a significant issue for this type of coupled discrete-continuous model, and it is shown that missing information/observations can be incorporated in the model via a nonstationary and nonhomogeneous formulation. An unbiased estimator for the discrete model dynamics in presence of unobserved external impacts is derived and used to construct a data-based nonstationary and nonhomogeneous parameter estimator based on an appropriately regularized spatiotemporal clustering algorithm. One-step and long-term predictions are considered, and a new Bayesian approach to discrete data assimilation of hidden information is proposed. To illustrate our method, we apply it to synthetic data sets and compare it with standard techniques of the machine-learning community (such as maximum-likelihood estimation, artificial neural networks and support vector machines).

Spatio-temporal polygonal clustering with space and time as first-class citizens

Detecting spatio-temporal clusters, i.e. clusters of objects similar to each other occurring together across space and time, has important real-world applications such as climate change, drought analysis, detection of outbreak of epidemics (e.g. bird flu), bioterrorist attacks (e.g. anthrax release), and detection of increased military activity. Research in spatio-temporal clustering has focused on grouping individual objects with similar trajectories, detecting moving clusters, or discovering convoys of objects. However, most of these solutions are based on using a piece-meal approach where snapshot clusters are formed at each time stamp and then the series of snapshot clusters are analyzed to discover moving clusters. This approach has two fundamental limitations. First, it is point-based and is not readily applicable to polygonal datasets. Second, its static analysis approach at each time slice is susceptible to inaccurate tracking of dynamic cluster especially when clusters change over both time and space. In this paper we present a spatio-temporal polygonal clustering algorithm known as the Spatio-Temporal Polygonal Clustering (STPC) algorithm. STPC clusters spatial polygons taking into account their spatial and topological properties, treating time as a first-class citizen, and integrating density-based clustering with moving cluster analysis. Our experiments on the drought analysis application, flu spread analysis and crime cluster detection show the validity and robustness of our algorithm in an important geospatial application.

A novel method for discovering spatio-temporal clusters of different sizes, shapes, and densities in the presence of noise

The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery. In this paper, a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors (STSNN) is proposed to detect spatio-temporal clusters of different sizes, shapes, and densities in spatio-temporal databases with a large amount of noise. The concepts of windowed distance and shared nearest neighbor are utilized to define a novel spatio-temporal density for a spatio-temporal entity with definite mathematical meanings. Then, the density-based clustering strategy is employed to uncover spatio-temporal clusters. The spatio-temporal clustering algorithm developed in this paper is easily implemented and less sensitive to density variation among spatio-temporal entities. Experiments are undertaken on several simulated data-sets to demonstrate the effectiveness and advantage of the STSNN algorithm. Also, the real-world applications on two seismic databases show that the STSNN algorithm has the ability to uncover foreshocks and aftershocks effectively.

A general method of spatio-temporal clustering analysis

Spatio-temporal clustering has been a hot topic in the field of spatio-temporal data mining and knowledge discovery. It can be employed to uncover and interpret developmental trends of geographic phenomenon in the real world. However, existing spatio-temporal clustering methods seldom consider both spatiotemporal autocorrelations and heterogeneities among spatio-temporal entities, and the coupling in space and time has not been well highlighted. In this paper, a unified framework for the clustering analysis of spatio-temporal data is proposed, and a novel spatio-temporal clustering algorithm is developed by means of a spatio-temporal statistics methodology and intelligence computation technology. Our method is applied successfully to finding spatio-temporal cluster in China’s annual temperature database for the period 1951–1992.

Dynamic Multiscale Spatiotemporal Models for Gaussian Areal Data

SummaryWe introduce a new class of dynamic multiscale models for spatiotemporal processes arising from Gaussian areal data. Specifically, we use nested geographical structures to decompose the original process into multiscale coefficients which evolve through time following state space equations. Our approach naturally accommodates data that are observed on irregular grids as well as heteroscedasticity. Moreover, we propose a multiscale spatiotemporal clustering algorithm that facilitates estimation of the nested geographical multiscale structure. In addition, we present a singular forward filter backward sampler for efficient Bayesian estimation. Our multiscale spatiotemporal methodology decomposes large data analysis problems into many smaller components and thus leads to scalable and highly efficient computational procedures. Finally, we illustrate the utility and flexibility of our dynamic multiscale framework through two spatiotemporal applications. The first example considers mortality ratios in the state of Missouri whereas the second example examines agricultural production in Espírito Santo State, Brazil.

Parallel k-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets

Identification of geographic ecoregions has long been of interest to environmental scientists and ecologists for identifying regions of similar ecological and environmental conditions. Such classifications are important for predicting suitable species ranges, for stratification of ecological samples, and to help prioritize habitat preservation and remediation efforts. Hargrove and Hoffman [1, 2] have developed geographical spatio-temporal clustering algorithms and codes and have successfully applied them to a variety of environmental science domains, including ecological regionalization; environmental monitoring network design; analysis of satellite-, airborne-, and ground-based remote sensing, and climate model-model and model-measurement intercomparison. With the advances in state-of-the-art satellite remote sensing and climate models, observations and model outputs are available at increasingly high spatial and temporal resolutions. Long time series of these high resolution datasets are extremely large in size and growing. Analysis and knowledge extraction from these large datasets are not just algorithmic and ecological problems, but also pose a complex computational problem. This paper focuses on the development of a massively parallel multivariate geographical spatio-temporal clustering code for analysis of very large datasets using tens of thousands processors on one of the fastest supercomputers in the world.

Use of the Köppen–Trewartha climate classification to evaluate climatic refugia in statistically derived ecoregions for the People’s Republic of China

Changes in climate as projected by state-of-the-art climate models are likely to result in novel combinations of climate and topo-edaphic factors that will have substantial impacts on the distribution and persistence of natural vegetation and animal species. We have used multivariate techniques to quantify some of these changes; the method employed was the Multivariate Spatio-Temporal Clustering (MSTC) algorithm. We used the MSTC to quantitatively define ecoregions for the People's Republic of China for historical and projected future climates. Using the Koppen-Trewartha classification system we were able to quantify some of the temperature and precipitation relationships of the ecoregions. We then tested the hypothesis that impacts to environments will be lower for ecoregions that retain their approximate geographic locations. Our results showed that climate in 2050, as