Modeling big spatio-temporal geo-hazards data for forecasting by error-correction cointegration and dimension-reduction

Spatio-temporal data is one of the most important assets in the context of smart cities. Spatio-temporal big data comes from a variety of sensor devices, implies the state of urban operation, insight into the development trend. Due to the multidimensional characteristics and diverse analysis needs of spatial-temporal data, data analysis based on spatial-temporal data must take into account the large capacity, diversity and frequent changes of data. This makes spatial and temporal data analysis more difficult. In order to simplify the analysis of spatio-temporal data, a service-oriented intelligent framework is proposed. Firstly, the concept of spatio-temporal data service is introduced into the framework, and several common spatio-temporal data service models are defined. Then, a configurable scripting language was proposed to define the analytic application. We also developed a prototype tool to implement spatio-temporal data services on Hadoop. In order to prove the applicability of our method, we demonstrate the effectiveness of our work through a practical application-based study.

Spatiotemporal big data have multisource, heterogeneous, high-dimensional and spatiotemporal associations. Due to the limited computing and network resources, while the spatiotemporal data to be rendered are large and dynamic, efficient visual analysis has always been a popular topic and has had difficulty in the research of spatiotemporal big data. As one of the important means of big data visualization, thermal maps play an important role in expressing data flow, information flow, and trajectory flow. At the same time, the development of a distributed computing framework also provides technical support for the online calculation and visualization of spatiotemporal data streams. In response to the above problems, this paper designs and implements GeoMapViz, a distributed management based on massive spatiotemporal data streams and a multiscale geographic spatial visualization framework, which is oriented by the expression of thermal maps of massive point datasets. First, based on the concept of the tile pyramid model and spatiotemporal cube, we propose a thermal map sequential tile pyramid (TS_Tile) model, which realizes scalable storage and efficient retrieval of data flow. GeoMapViz adopts a high-performance Flink stream computing cluster to implement the large-scale parallel construction of hierarchical tile pyramids, implements distributed storage and index construction of data based on HBase and Geomesa, and uses Geoserver to manage the map service to provide a spatiotemporal range query interface. Finally, through using an open dataset as a system simulation test, the results show that the TS_Tile model can effectively organize large-scale, time-space and multidimensional thermal map data, and the query and visualization of the heatmap can reach a subsecond response. Furthermore, GeoMapViz supports the integration of the thermal map and original flow and provides a feasible solution for the visual analysis of large-scale spatiotemporal data.

Spatiotemporal data mining is the core research topic of geographic information science. In the era of big data, the explosive growth of geographic spatiotemporal data puts forward an urgent demand for spatiotemporal knowledge discovery, which promotes the continuous development of spatiotemporal data mining technology. However, the universal heterogeneity and sparse distribution characteristics of spatiotemporal big data restrict the realization of spatiotemporal data mining algorithms, and significantly affect the description and analysis capabilities of natural and social complex systems. Thus, this paper focuses on the series of bottlenecks faced in the expression and application of heterogeneous and sparsely distributed spatiotemporal data. We systematically summarized the research status and existing problems of several key spatiotemporal mining tasks including missing spatiotemporal data interpolation, sparse spatiotemporal data reconstruction, and spatiotemporal state prediction, condensed four key scientific problems, and gave four corresponding solutions. The proposed methods are expected to enrich the method system in the field of spatiotemporal data mining and improve the quality and application value of spatiotemporal data modeling.

Spatio-temporal data are continuously generated and grow exponentially with the rapid proliferation of mobile devices. Efficient storage and indexing of spatio-temporal Big Data are crucial for supporting fast queries. Due to their high write throughput and scalability, distributed NoSQL data stores such as HBase are widely adopted as storage engines in spatio-temporal Big Data systems. These systems require a key-value-based storage model to convert one or a group of spatio-temporal data points into a single key-value pair. However, existing techniques cannot govern an inherent dilemma caused by adding a time dimension within spatial information, that is, we can either optimize for space-preferred or time-preferred queries but not both. In this paper, we propose Cymo, a flexible storage model with query-aware indexing that dynamically adapts to varying query patterns across different spatial regions and evolving query behaviors over time. Our key idea is to partition the spatio-temporal space into multiple subspaces, allowing each subspace to optimize its storage model based on its specific query characteristics. Cymo features a learning model that predicts query patterns for each subspace using historical workloads. Additionally, it introduces a virtual layer that abstracts the heterogeneity of different storage models, providing a unified query interface. This design ensures that storage model variations remain transparent to applications while enabling effective query optimization based on diverse query patterns. We have implemented Cymo on HBase and integrated it into GeoMesa, a representative spatio-temporal Big Data system. Experimental results on real taxi datasets demonstrate that Cymo significantly improves query performance, achieving speedups of 1.53× to 10× compared to GeoMesa. The open-source implementation of Cymo has been released for public access.

In environmental sciences, it is common to collect and analyze spatio-temporal multivariate data concerning several variables which are measured in time over a spatial domain. The spatio-temporal data are usually sparce in space, due to the high cost of the equipment, and temporal dense since the required variables are regularly sampled in time.In the literature different methods have been proposed for the analysis of such spatio-temporal data which exhibit a correlation in space and time as well as in-between variables. Among them it is worth recalling the generalization of Blind Source Separation technique for multivariate space-time random field (stBSS) and the space-time linear coregionalization model (ST-LCM). These methods are useful to simplify the spatio-temporal multivariate analysis since by a linear transformation of the original observations only the independent components which exhibit a spatio-temporal correlation are retained (lower than the number of observed variables) and modelled.In this paper a multivariate study regarding seven environmental variables (evapotranspiration level, minimum and maximum temperature, minimum and maximum humidity, wind speed and precipitation) measured between 2000 and 2022 in Veneto region (Italy) will be proposed.  Both the stBSS and the joint diagonalization of the empirical covariance matrix approach will be used to identify the hidden components, and properly chosen spatio-temporal models will be fitted to the latent components. Note that for the first approach a BSS model for the multivariate random field will be assumed, whereas for second one a space-time linear coregionalization model (ST-LCM) for the independent components will be fitted to the matrix-valued covariance function estimated for seven relevant environmental variables.Finally, the fitted models have been used to predict evapotranspiration levels and a comparison of the values obtained by using the two different techniques will be provided.

With the rapid development and popularization of vehicle networking technology, the spatio-temporal big data generated by vehicles has brought new opportunities to the field of marketing. This paper aims to explore the marketing path analysis based on the spatiotemporal big data of the Internet of vehicles, and provide accurate marketing strategies for enterprises by digging deeply into vehicle driving data, driving habits and location information, etc. First, this paper introduces the basic concepts and characteristics of spatiotemporal big data of the Internet of vehicles, then introduces the collection and processing of the data of the Internet of vehicles, and then builds the feature engineering and the text based on CatBoost model to predict the future of consumers. Finally, this paper analyzes the application strategy path of spatiotemporal big data in marketing, including precision marketing, personalized recommendation and precision advertising based on user portraits. The method proposed in this paper can not only improve user experience, but also create higher business value for enterprises.

This study investigated how to manage and use the spatio-temporal data during land use. To realize spatio-temporal data mining, the key issue to be solved in technology is how to organize spatio-temporal data. The spatio-temporal data model is just generated to solve this problem. According to analysis of spatio-temporal data changing character, two factors are very important in spatio-temporal data model. One is object, and the other is event. The object is an essential element that comprises spatio-temporal data information. The event is a fundamental factor that result in changing spatio-temporal data. A new spatio-temporal data model based on both object and event (OESTDM) was put forward to meet the demand. How to organize data, build relations between objects and storage data in spatio-temporal database are also emphatically discussed. At last, a dynamic land use information system based on OESTDM is presented. The system realized the function of managing spatio-temporal data, the retrospect of history information, tracing land parcel change, Area-Data-Change-Statistic and Analysis, and so on efficiently, by taking the land parcel data of some regions in Zhejiang province for study.

Smart cities cannot be separated from spatiotemporal location big data. This paper summarizes the needs of spatiotemporal location big data for public epidemic events at home and abroad, investigates the specific services that current spatiotemporal location big data can provide in public epidemic events, and discusses the current stage of spatiotemporal location big data services that cannot meet the requirements of public epidemic events. This paper also focuses on how to improve the spatiotemporal location big data services in the next stage to better serve the public epidemic prevention and control, and gives advice on spatio-temporal location service construction at ordinary times and data sharing mechanism in wartime. This paper aims to take scientific measures to improve the spatiotemporal big data services, strives to establish a technical support system for public epidemic prevention and control at ordinary times, and introduces a wartime spatiotemporal location big data sharing mechanism for epidemic prevention and control.

Recent rapid development of wireless communication, mobile computing, global navigational satellite systems (GNSS), and spatially enabled sensors is leading to an exponential growth of available spatio-temporal data produced continuously at hight speed. Spatio-temporal data streams, i.e. real-time, transient, time-varying sequences of spatiotemporal data items, demonstrates at least two Big Data core features: volume and velocity. To handle the volumes of data and computation they involve, these applications need to be distributed over clusters. However, despite substantial work on cluster programming models for batch computation, there are few similarly high-level tools for stream processing. Obviously, there is a clear need for highly scalable spatio-temporal stream computing framework that can operate at high data rates and process massive amounts of big spatio-temporal data streams. In this chapter we present our approach and framework for an integrated big spatio-temporal data stream processing. The key concept here is that streaming data and persistent data are not intrinsically different - the persistent spatio-temporal data is simply streaming data that has been entered into the persistent structures.

Availability of location-based services and mobile computing enabled the collection of spatial and temporal data. Spatiotemporal database integrates spatial and temporal aspects of data. The objective of this chapter is to offer conceptual clarity and state of the art technologies and tools for spatiotemporal data engineering. This chapter discusses spatiotemporal data representation, modeling, and database design, indexing, analysis, and mining for developing the applications. It also covers spatiotemporal big data processing systems.

HKUST Electronic Theses Urban events mining from spatiotemporal big data by Mingyang Zhang thesis 2022 1 online resource (xiii, 138 pages) : illustrations (some color) Urban events are the sets of real-world occurrences in urban space associated with specific topics.…Read more ›

Abstract. Advanced analysis of spatio-temporal data is improving our knowledge about natural phenomena such as landslides and volcanic activity. Furthermore, it can contribute to a deeper understanding of the built environment by predicting city development. However, the data produced by the modelling of these observations can easily rise to terra bytes of 4D (3D space plus time) data. Also, the repeatability of such analysis is essential. Thus, repeatable and reliable access to big spatio-temporal data, models and simulation results should be guaranteed. However, today´s Geographic Information Systems (GIS) are not prepared to meet these requirements. Therefore, we present a comprehensive spatio-temporal data model for GIS and geodatabase management systems that can be used as the “heart” of 4D GIS. The comprehensive model enables a flexible management of time-series-based geometric and topological data. It cares of storage reduction and computational efficiency when storing, retrieving, and processing surface- and volume-based data in 3D space plus time. Furthermore, the model can handle geometry and topology changes of 4D data, i.e., mesh changes of triangulated surfaces and tetrahedral volumes over time. The model also allows the attachment of detailed semantic information to geometric and topological entities. The various concepts, implementation and the benefits of our comprehensive model are presented. Finally, conclusions are drawn from the approach and an outlook is given on future research including demanding applications based on multiscale digital elevation models.

Due to the increasing volume of spatio-temporal data generated from remote sensing, sensor networks and computational simulation, there is a need for a generic, domain-independent framework for spatio-temporal data analysis. This research presents a generic set of data processing and manipulation tools for spatio-temporal raster data called multidimensional map algebra (MMA). MMA is an extension of conventional map algebra that operates not only on data that are two-dimensional in space but also on data that are: (1) one-dimensional in time; (2) both two-dimensional in space and one-dimensional in time; (3) three-dimensional in space; and (4) both three-dimensional in space and one-dimensional in time. MMA data types, neighborhoods, lags, and functions are presented, including rules for combining data types of different dimensionality within local, focal, and zonal functions. A prototype implementation in JAVA is provided as a demonstration and syntax specification for the functions. Challenges to continued development of MMA include performance and efficiency issues for processing very large multidimensional data sets.

With the expansion of the Internet of Things, spatio-temporal data has been widely used and generated. The rise of 'big data' in space and time has led to the flood of new applications with statistical analysis characteristics. In addition, applications based on statistical analysis of these data must deal with the large capacity, diversity and frequent changes of data, as well as the query, integration and visualisation of data. Developing such applications is essentially a challenging and time-consuming task. In order to simplify the statistical analysis of spatio-temporal data, a service-oriented method is proposed in this paper. This method defines the model of spatio-temporal data service and functional service. Define a process-based application of spatio-temporal big data statistics to invoke basic data services and functional services. This paper proposes an implementation method of spatio-temporal data service and functional service based on Hadoop environment. Taking the highway big data analysis as an example, the validity and applicability of this method are verified. The effectiveness of this method is verified by an example. The validity and applicability of the method are verified by the case study of expressway large data analysis. An example is given to verify the validity of the method.

Spatiotemporal data is multi-dimensional, including time dimension, space dimension, and attribute dimension. Because the spatial characteristics, attribute characteristics, associations, etc. of information objects change with time dimension, various topological relationships are intertwined. The existing spatio-temporal data structure model is difficult to express the temporal and spatial characteristics of information objects and the dynamic evolution of spatiotemporal relationships. Not conducive to the query of time and space nodes. On the other hand, the space-time model mostly stays in the theoretical stage and lacks practical application. The main work of this research is as follows:1. For the spurious coupling of spatiotemporal relationship of existing spatiotemporal data, reconstruct spatiotemporal data, design spatiotemporal data structure, and sort out spatiotemporal relationship. 2.A multi-timeline ground state correction model based on region contour is proposed, which reduces the storage redundancy of spatial information, improves the efficiency of information query, and improves the scalability of the system. 3.Design and implement a time-space-based hierarchical spatiotemporal correlation information system, which flexibly switches the spatiotemporal background of information objects at different time nodes, realizes the dynamic linkage of spatiotemporal data, and facilitates data analysis and data mining of spatiotemporal objects.