Multidimensional Spatio-temporal Data Research Articles

Assessing the Impacts of Climate and Land Use Change on Water Conservation in the Three-River Headstreams Region of China Based on the Integration of the InVEST Model and Machine Learning

The Three-River Headstreams Region (TRHR) serves as the cradle of China’s three major rivers—the Yangtze, Yellow, and Lancang—rendering its water conservation (WC) capacity quintessentially significant for Asian water resource security. This study integrated the InVEST model and random forest model to holistically elucidate the spatiotemporal characteristics and factors influencing WC in the TRHR from 1980 to 2018. The results revealed that the WC growth rate was 5.65 mm/10a in the TRHR during the study period, yet pronounced regional disparities were observed among different basins, especially with the Lancang River Basin (LRB), which exhibited a decrease at a rate of 5.08 mm per decade despite having the highest WC. Through Theil–Sen trend analysis, the Mann–Kendall abrupt change test, and the cumulative deviation method, meteorological, vegetative, and land use abrupt changes in approximately 2000 were identified as the primary drivers for the abrupt surge in WC within the TRHR. Furthermore, precipitation and the aridity index were the core feature variables affecting WC. However, a positive transition in land use patterns post-2000 was also revealed, and its favorable effect on WC was not as significant as the abrupt climatic changes. This study offers new perspectives on managing multidimensional spatiotemporal data and contributes to laying the groundwork for machine learning applications in water conservation. Additionally, it potentially provides useful references for decision-making processes related to ecosystem security.

Back temporal autoregressive matrix factorization for high-dimensional time series prediction

While prediction for large-scale multidimensional spatio-temporal data is widely used in applications such as business analytics and open source metrics, service data collected from these applications often have values that are missing from the data. The accuracy of prediction results requires that the prediction model used is highly robust and scalable, something that is typically lacking in traditional prediction models. To this end, we propose a back temporal autoregressive matrix factorization (BTAMF) framework, which can construct an efficient prediction model for time series data with missing values by mining the trend and periodic characteristics of the prediction results. It first uses the target data and its left neighbors to construct trend autoregressive and then uses the target data and its associated periodic data to construct periodic autoregressive to mine the trend and periodicity properties of time series data. We then integrate the tensor/matrix factorization and both autoregressive models into a single optimization model for missing value completion and prediction through optimization of the objective functions. This scheme, which exploits global and local information of the target data, can directly predict the data without pre-completing the missing values, which provides a strong guarantee of the temporal integrity of the time series data. To validate the efficient completion and prediction effects of BTAMF in real-world scenarios, we select ten real-world behavioral datasets from GitHub and seven comparison models for experiments. Our experimental results show that BTAMF has a stronger advantage over current state-of-the-art methods in terms of scalability and prediction accuracy.

A novel grey prediction model based on tensor higher-order singular value decomposition and its application in short-term traffic flow

Short-term traffic flow prediction is the key to traffic guidance and control and can directly affect the performance of intelligent transportation systems. Traffic flow data has the characteristics of volatility, chaos, and randomness, which affect the accuracy of the prediction model. Based on the multi-dimensional spatio-temporal data characteristics of traffic flow data, this paper combines the tensor higher-order singular value decomposition theory and the modeling mechanism of the classical grey model GM(1,1) model and establishes the grey GM(1,1) model with tensor higher-order singular values. The tensor higher-order singular value decomposition reflects the periodic, multi-modal, and holistic nature of traffic flow data, which can mitigate the volatility and randomness of traffic flow data and improve the accuracy of the model. Then, the new model is applied to highway short-time traffic flow prediction, analyzing the spatio-temporal nature of traffic flow data, giving the detailed steps of model modeling, and analyzing the correlation between the original traffic flow data and tensor approximation data using grey correlation degrees. There are three cases to illustrate the effectiveness of the model. Case 1 shows that the results of MAPE from nine modeling objects are stable at about 5%, which indicates that the new model has some stability; Case 2 shows that the new model is more adaptable to short-time traffic flow prediction based on the results of three different modeling and prediction objects; Case 3 uses the new model to compare it with two traditional grey forecasting models and two optimization models, and the results indicate that the new model has a total MAPE value of 5.172%, which is better than the other four grey forecasting models. Finally, the new model is applied to short-time traffic flow prediction, and its prediction results are consistent with the trend of the original traffic flow data, indicating that it can reveal the real-time characteristics of the traffic system to a certain extent and provide a reliable theoretical basis for traffic planning, control, and optimization.

MDST-DBSCAN: A Density-Based Clustering Method for Multidimensional Spatiotemporal Data

The increasing use of mobile devices and the growing popularity of location-based ser-vices have generated massive spatiotemporal data over the last several years. While it provides new opportunities to enhance our understanding of various urban dynamics, it poses challenges at the same time due to the complex structure and large-volume characteristic of the spatiotemporal data. To facilitate the process and analysis of such spatiotemporal data, various data mining and clustering methods have been proposed, but there still needs to develop a more flexible and computationally efficient method. The purpose of this paper is to present a clustering method that can work with large-scale, multidimensional spatiotemporal data in a reliable and efficient manner. The proposed method, called MDST-DBSCAN, is applied to idealized patterns and a real data set, and the results from both examples demonstrate that it can identify clusters accurately within a reasonable amount of time. MDST-DBSCAN performs well on both spatial and spatiotemporal data, and it can be particularly useful for exploring massive spatiotemporal data, such as detailed real estate transactions data in Seoul, Korea.

Automated Zone Identification for Variable-Rate Services in Precision Agriculture

Varying the rate of application of agronomic inputs generates many positive economic and environmental impacts. Increasingly, technologies that enable variable rate application are becoming a distinctive feature of precision agriculture. Nonetheless, a prerequisite, and crucial challenge, remains the optimal and operational designation of distinct application zones for differing agronomic operations. Core to this challenge is the conflation and fusion of diverse data sources ranging from satellite imagery to real-time in-situ data from farms. At present, zones for variable rate application are often defined manually by agronomists and farmers. This paper proposes a novel methodology for the automatic definition of zones for variable rate application. This approach comprises multi-dimensional spatio-temporal data integration methods, clustering-based data classification and a zone creation and representation procedure. In this way, the harmonization of heterogeneous data sources, augmented with different clustering algorithms, enable the delineation of management zones and subsequent construction of maps for potential variable rate applications. Experimental results confirm the effectiveness and efficiency of the proposed approach.

Bayesian Temporal Factorization for Multidimensional Time Series Prediction

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in many real-world applications such as monitoring urban traffic and air quality. Making predictions on these time series has become a critical challenge due to not only the large-scale and high-dimensional nature but also the considerable amount of missing data. In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series-in particular spatiotemporal data-in the presence of missing values. By integrating low-rank matrix/tensor factorization and vector autoregressive (VAR) process into a single probabilistic graphical model, this framework can characterize both global and local consistencies in large-scale time series data. The graphical model allows us to effectively perform probabilistic predictions and produce uncertainty estimates without imputing those missing values. We develop efficient Gibbs sampling algorithms for model inference and model updating for real-time prediction and test the proposed BTF framework on several real-world spatiotemporal data sets for both missing data imputation and multi-step rolling prediction tasks. The numerical experiments demonstrate the superiority of the proposed BTF approaches over existing state-of-the-art methods.

Creating and evaluating web-based visualizations of multi-dimensional spatiotemporal data

Abstract. In this article, we present a method for visualizing multi-dimensional spatio-temporal data in an interactive web-based geovisualization. Our case study focuses on publicly available weather data in Germany. After processing the data with Python and desktop GIS, we integrated the data as web services in a browser-based application. This application displays several weather parameters with different types of visualisations, such as static maps, animated maps and charts. The usability of the web-based geovisualization was evaluated with a free-examination and a goal-directed task, using eye-tracking analysis. The evaluation focused on the question how people use static maps, animated maps and charts, dependent on different tasks. The results suggest that visualization elements such as animated maps, static maps and charts are particularly useful for certain types of tasks, and that more answering time correlates with less accurate answers.

Exploring Multidimensional Spatiotemporal Point Patterns Based on an Improved Affinity Propagation Algorithm.

Affinity propagation (AP) is a clustering algorithm for point data used in image recognition that can be used to solve various problems, such as initial class representative point selection, large-scale sparse matrix calculations, and large-scale data with fewer parameter settings. However, the AP clustering algorithm does not consider spatiotemporal information and multiple thematic attributes simultaneously, which leads to poor performance in discovering patterns from massive spatiotemporal points (e.g., trajectory points). To resolve this issue, a multidimensional spatiotemporal affinity propagation (MDST-AP) algorithm is proposed in this study. First, the similarity of spatial and nonspatial attributes is measured in Gaussian kernel space instead of Euclidean space, which helps address the multidimensional linear inseparability problem. Then, the Davies-Bouldin (DB) index is applied to optimize the parameter value of the MDST-AP algorithm, which is applied to analyze road congestion in Beijing via taxi trajectories. Experiments on different datasets and algorithms indicated that the MDST-AP algorithm can process multidimensional spatiotemporal data points faster and more effectively.

Spatial-feature data cube for spatiotemporal remote sensing data processing and analysis

With the rapid development in Earth observation technology, a variety of satellite sensors have provided large and open sets of remote sensing data. However, traditional methods of analysis are no longer available for time-serial remote sensing data analysis that typically handles multidimensional spatio-temporal data models. Moreover, researchers have found it trivial and tedious to obtain ready-to-analyze data for Earth science models from regular Earth observation data. For an easy and efficient time-serial remote sensing data analysis, a spatial-featured data cube analysis tool based on multidimensional data model is proposed for time-serial remote sensing data processing and analysis. For the performance consideration, a distributed execution engine was also used for efficient implementation of large-scale tasks in parallel. Finally, through experiments on both normalized difference vegetation index product and water detection within a 20-year period, we confirmed that our approach is efficient and scalable for a long time-series analysis.

Multidimensional Tensor Scan for Drug Overdose Surveillance

ObjectiveWe present the multidimensional tensor scan (MDTS), a newmethod for identifying emerging patterns in multidimensionalspatio-temporal data, and demonstrate the utility of this approachfor discovering emerging geographic, demographic, and behavioraltrends in fatal drug overdoses.IntroductionDrug overdoses are an increasingly serious problem in the UnitedStates and worldwide. The CDC estimates that 47,055 drug overdosedeaths occurred in the United States in 2014, 61% of which involvedopioids (including heroin, pain relievers such as oxycodone, andsynthetics).1Overdose deaths involving opioids increased 3-foldfrom 2000 to 2014.1These statistics motivate public health to identifyemerging trends in overdoses, including geographic, demographic,and behavioral patterns (e.g., which combinations of drugs areinvolved). Early detection can inform prevention and response efforts,as well as quantifying the effects of drug legislation and other policychanges.The fast subset scan2detects significant spatial patterns of diseaseby efficiently maximizing a log-likelihood ratio statistic over subsetsof data points, and has recently been extended to multidimensionaldata (MD-Scan).3While MD-Scan is a potentially useful tool for drugoverdose surveillance, the high dimensionality and sparsity of the datarequires a new approach to estimate and represent baselines (expectedcounts), maintaining both accuracy and efficient computation whensearching over subsets.MethodsThe multidimensional tensor scan (MDTS) is a new approach tosubset scanning in multidimensional data. In addition to detectingthe spatial area (subset of locations) and time window affected byan emerging outbreak, MDTS can also identify the affected subsetof values for each observed attribute. For example, given the drugoverdose surveillance data described below, MDTS can identify theaffected genders, races, age ranges, and which drugs were involved.MDTS finds subsets of the attribute space with higher than expectedcase counts, first using a novel tensor decomposition approachto estimate the expected counts. MDTS then iteratively applies aconditional optimization step, optimizing over all subsets of valuesfor each attribute conditional on the current subsets of values for allother attributes3, and using the linear-time subset scanning property2to make each conditional optimization step computationally efficient.The resulting approach has high power to detect and characterizeemerging trends which may only affect a subset of the monitoredpopulation (e.g., specific ages, genders, neighborhoods, or users ofparticular combinations of drugs).ResultsWe used MDTS to analyze publicly available data from theAllegheny County, PA medical examiner’s office and to detectemerging overdose patterns and trends. The dataset consists of~2000 fatal accidental drug overdoses between 2008 and 2015.For each overdose victim, we have date, location (zip code), agedecile, gender, race, and the presence/absence of 27 commonlyabused drugs in their system. The highest-scoring clusters discoveredby MDTS were shared with Allegheny County’s Dept. of HumanServices and their feedback obtained.One set of potentially relevant findings from our analysisinvolved fentanyl, a dangerous and potent opioid which has been aserious problem in western PA. In addition to identifying two well-known, large clusters of overdoses—14 deaths in January 2014 and26 deaths in March-April 2015—MDTS was able to provide additionalinformation about each cluster. For example, the first cluster waslikely due to fentanyl-laced heroin, while the second was more likelydue to fentanyl disguised as heroin (only 11 victims had heroin intheir system). Moreover, the second cluster was initially confinedto the Pittsburgh suburb of McKeesport and a typical demographic(white males ages 20-49), before spreading across the county. Ouranalysis demonstrated that prospective surveillance using MDTSwould have identified the cluster as early as March 29th, enablingtargeted prevention efforts. MDTS also discovered a previouslyunidentified, highly localized cluster of fentanyl-related overdosesaffecting an unusual and underserved demographic (elderly blackmales near downtown Pittsburgh). This cluster occurred in January-February 2015, and may have been related to the larger cluster offentanyl-related overdoses that occurred two months later. Finally,we identified multiple overdose clusters involving combinationsof methadone and Xanax between 2008 and 2012, and observeddramatic reductions in these clusters corresponding to the passageof the Methadone Death and Incident Review Act (October 2012),which increased state oversight of methadone clinics and prescribingphysicians.ConclusionsRetrospective analysis of Allegheny County overdose datasuggests high potential utility for a prospective overdose surveillancesystem, which would enable public health users to identify emergingpatterns of overdoses in their early stages and facilitate targeted andeffective health interventions. The MDTS approach can also be usedfor other multidimensional public health surveillance tasks, such asSTI surveillance, where the patterns or outbreaks of interest may havedemographic, geographic, and behavioral components.

Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquakes in Indonesia

Spatio temporal data clustering is challenge task. The result of clustering data are utilized to investigate the seismic parameters. Seismic parameters are used to describe the characteristics of earthquake behavior. One of the effective technique to study multidimensional spatio temporal data is visualization. But, visualization of multidimensional data is complicated problem. Because, this analysis consists of observed data cluster and seismic parameters. In this paper, we propose a visualization system, called as IES (Indonesia Earthquake System), for cluster analysis, spatio temporal analysis, and visualize the multidimensional data of seismic parameters. We analyze the cluster analysis by using automatic clustering, that consists of get optimal number of cluster and Hierarchical K-means clustering. We explore the visual cluster and multidimensional data in low dimensional space visualization. We made experiment with observed data, that consists of seismic data around Indonesian archipelago during 2004 to 2014.Keywords: Clustering, visualization, multidimensional data, seismic parameters.

Disentangling multidimensional spatio-temporal data into their common and aberrant responses.

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA), which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data.

Parallel indexing technique for spatio-temporal data

The requirements for efficient access and management of massive multi-dimensional spatio-temporal data in geographical information system and its applications are well recognized and researched. The most popular spatio-temporal access method is the R-Tree and its variants. However, it is difficult to use them for parallel access to multi-dimensional spatio-temporal data because R-Trees, and variants thereof, are in hierarchical structures which have severe overlapping problems in high dimensional space. We extended a two-dimensional interval space representation of intervals to a multi-dimensional parallel space, and present a set of formulae to transform spatio-temporal queries into parallel interval set operations. This transformation reduces problems of multi-dimensional object relationships to simpler two-dimensional spatial intersection problems. Experimental results show that the new parallel approach presented in this paper has superior range query performance than R*-trees for handling multi-dimensional spatio-temporal data and multi-dimensional interval data. When the number of CPU cores is larger than that of the space dimensions, the insertion performance of this new approach is also superior to R*-trees. The proposed approach provides a potential parallel indexing solution for fast data retrieval of massive four-dimensional or higher dimensional spatio-temporal data.

Optimized management of large-scale data sets stored on tertiary storage systems

We focus on extending RasDaMan's multidimensional query language using a new concept called object framing. With this extension, users no longer are restricted to performing range queries in the shape of multidimensional hypercubes. It can be able to formulate range queries by indicating complex frames. Thus, object framing represents a generalization of geometric operations. A new system, optimized toward high-performance computing, extends the RasDaMan (Raster Data Management) database management system to allow flexible management of multidimensional spatiotemporal data and to reduce tertiary storage access time.