Spatiotemporal Big Data Research Articles

Construction of Public Security Rapid Response Communication and Command System Based on Spatiotemporal Big Data

Construction of Public Security Rapid Response Communication and Command System Based on Spatiotemporal Big Data

Data-driven complexity analysis of weighted Shenzhen Metro network based on urban massive mobility in the rush hours

In the context of spatio-temporal big data, the complexity of urban metro network is highlighted. For the safe operation and resilient management of urban rail transit networks, it is advantageous to correctly comprehend the complex topological dynamics characteristics of the weighted metro network based on the massive mobility of passenger flow. In this study, the weighted Shenzhen Metro networks (WSZMNs) in the morning and evening rush hours were modeled based on Space L model and spatio-temporal big data of cross-sectional passenger flow. Combined with six complex indicators, the topological complexity of WSZMNs in two periods was compared based on quantitative and geographical distributions. Based on the multi-attribute decision making method, the weighted comprehensive importance of all nodes in morning and evening rush hours was also quantitatively evaluated and geographically visualized. Results indicate that the WSZMN exhibited some geographical heterogeneity, and the complexity of WSZMN in the morning rush hours was more prominent than in the evening rush hours. Additionally, for the network’s critical stations, essential locations, and significant periods, there was often large-scale and massive mobility of passenger flow. The metro operation management department should strengthen the targeted passenger flow control to improve the safety and resilience of Shenzhen Metro network. The relevant research findings help us get a better understanding of the complexity of metro network system under the massive passenger flow mobility in the rush hours. This study can provide specific theoretical and practical references for the urban smart metro operation department to manage the massive mobility better.

SPsync: Lightweight multi-terminal big spatiotemporal data synchronization solution

With the rapid increase in mobile device usage and the popularity of 5G communication technology, rich Big spatiotemporal Data has become an important research resource. How to quickly synchronize the Big spatiotemporal Data collected by various types of devices to multiple distributed data centers has become the key to further utilizing spatio-temporal data. Therefore, in this paper, a new lightweight Big Spatiotemporal Data synchronization scheme called SPsync, based on a traditional file synchronization algorithm, is proposed. The contributions of SPsync are as follows. (1) SPsync leads to the optimization of the file processing strategy, changing the algorithm from a serial algorithm to a parallel one. (2) Combined with Spark, a distributed Big Data processing framework is advanced to achieve optimization of distributed tasks and further improvement of algorithm performance. Further, we demonstrate the excellent performance of SPsync through rich experiments using multiple simulated and real datasets. Compared to the best incremental synchronization algorithms currently available, the synchronization speedup is 30% faster and CPU usage 20% lower. The algorithm itself is 20% faster and CPU usage reduced by more than 25% in a multi-node scenario.

BS-Join: A novel and efficient mixed batch-stream join method for spatiotemporal data management in Flink

The new computing model, mixed batch-stream data processing, plays a crucial role in big spatiotemporal data managements. As the core of the above computing method, mixed batch-stream data join has high requirements on the throughput and latency due to the coexistence of two types of data sources. Apache Flink is the most suitable distributed system for mixed batch-stream data join, with lower latency than the join calculation model based on Hadoop and Spark, and it simulates remote real-time reading of batch data sources and completes join calculation with the DataStream API. However, as the degree of parallelism increases, frequent remote data reads will cause huge disk and communication pressure, thereby reducing the job efficiency and scalability. To make things trickier, the above effects are further amplified when simulating complex operations such as range joins. Aiming at the above difficulties and the characteristics of mixed batch-stream data join, a cache-based framework supporting mixed batch-stream join computing natively is proposed, which increases the search speed in the process of data join by building indexes in batch data sources. Meanwhile, for equijoin and range join, an optimization mechanism based on hotspot awareness and an optimization mechanism based on skip list are proposed respectively to further improve the job efficiency. In summary, the advantages of our work are highlighted as follows: (1) The proposed framework enables Flink to natively support mixed batch-stream data join, and can improve throughput by 5 times and speedup by 4 times; (2) The optimization mechanism based on hotspot awareness can further improve the efficiency of equijoin; (3) Compared with range queries by traditional Operators in Flink, the throughput can be increased by 6 times while the latency is reduced by 45%.

The Recovery of China's Industrial Parks in the First Wave of COVID-19.

Industrial parks are functional urban areas that carry the capacity to support highly concentrated production activities. The robustness and anti-interference ability of these areas are of great importance to maintaining economic vitality of a country. Focusing on the rate of production recovery (RPR), this paper examines the recovery of 436 major industrial parks in mainland China during the first wave of COVID-19. Leveraging spatio-temporal big data, we measured 14 attributes pertaining to industrial parks, covering four categories, namely spatial location, central city, park development, and public service. We focused on the spatial association and heterogeneity of the recovery patterns and identified the factors that truly affected the recovery of industrial parks with quantitative evaluation of their effects. The results reveal that: (1) RPR of industrial parks are significantly spatially clustered, with an obvious "cold spot" in the early outbreak area of Hubei Province and a prominent "center-periphery" pattern in developed areas, which is highly correlated with the spread of the epidemic. (2) The mechanisms driving the resumption of industrial parks are complex and versatile. All four categories in the variable matrix are related to RPR, including up to eight effective influencing factors. The effect of influencing factors is spatially heterogeneous, and its intensity varies significantly across regions. What is more interesting is that some impact factors show positive effects in some industrial parks while inhibiting the recovery in others. On the basis of the discussion of those findings with practical experiences, the planning and construction strategies of industrial park are suggested to mitigate the impact of similar external shocks.

Detecting the sinks and sources of transportation energy consumption and its forces driving at multiple spatiotemporal scales using trajectory data

Accurate and detailed transportation anthropogenic heat release (TAHR) datasets would benefit atmospheric research and public health. However, detailed temporal and spatial descriptions are yet to be obtained because of the low spatiotemporal resolution of basic data in previous studies. Therefore, we here developed a low-cost and efficient TAHR construction method by introducing trajectory data. Furthermore, we constructed an index to examine TAHR sinks and sources. This enabled the quantification of spatial associations between workplace/residential functional zones and TAHR sinks/sources. The results showed that TAHR varied dramatically at multiple temporal scales. Peak‒trough shapes of TAHR variations were found at hourly levels, while increasing trends were found at daily levels. Central downtown areas experienced significant variation trends at both hourly and daily levels. Sinks of TAHR were more closely associated with workplaces, while TAHR sources were more closely associated with residential functional zones at daily levels. This study provides a reference for the explanation of ecological scientific questions using spatiotemporal big data and provides insights into the mechanisms underlying urban energy consumption, urban planning, and energy conservation.

Impact of particulate matter and urban spatial characteristics on urban vitality using spatiotemporal big data

The rapid development of cities has caused various environmental problems. Recently, in Korea, citizens' interest and concerns regarding particulate matter are on the rise. Particulate matter has an adverse impact on the health and activities of individuals, and this can lead to a decline in urban vitality. The maintenance and management of urban vitality is essential for ensuring a sustainable and livable city; consequently, an understanding of environmental impact is indispensable. Thus, this study aims to demonstrate the impact of particulate matter on urban vitality by using the spatiotemporal big data of particulate matter and floating population that targets Seoul—a world-class high-density city in South Korea. Further, it aims to examine the impact of the spatial and physical characteristics of neighborhoods on the vitality of cities and neighborhoods. The data analyzed the impact of particulate matter on neighborhood vitality by using a spatial panel model that considered the characteristics of time series and local data. The analysis demonstrated that the impact of particulate matter varied during weekdays and weekends. It did not significantly affect the numerous mandatory activities of city dwellers and vitality on weekdays. However, on weekends, when there are relatively more selective activities, particulate matter was found to affect the decline of urban vitality. This means that people are very anxious about particulate matter, and that it leads them to reduce their visits whenever possible on weekends, when selective activities are predominant. In the spatial characteristics of the neighborhood, the degree of development, land use for commercial and business purposes, land-use mix, access to public transportation, and pedestrian environment were found to significantly impact the increase of urban vitality. However, areas with significant land use for residential and industrial purposes, and areas with high altitudes were found to have relatively low urban vitality. Parks and living infrastructure had different effects on urban vitality depending on the type. This study's results can be utilized to establish various policies related to particulate matter and devise measures to enhance urban vitality.

A spatiotemporal data model and an index structure for computational time geography

The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities.

BSMD:A blockchain-based secure storage mechanism for big spatio-temporal data

As more and more mobile devices and IoT terminals are connected to the Internet, a huge amount of spatio-temporal data is generated. In order to cope with the pressure of storing and computing massive amounts of data locally and to share data to fully utilize its value, data owners usually store data in cloud servers. However, the data owner will lose physical control of big spatio-temporal data while the big spatio-temporal data is stored on the cloud, and it will be at the risk of being tampered with and deleted. Therefore, this paper designs a blockchain-based secure storage mechanism, BSMD, which adopts an on-chain and off-chain cooperative storage model to alleviate the shortage of blockchain storage capacity. The updatable subvector commitment designed in this paper is used to construct the on-chain and off-chain secure authentication protocol which ensures the consistency of on-chain and off-chain data and makes the protocol have the capability of batch processing. Finally, the correctness and security of the proposed protocol are proved, and the performance of the protocol is analyzed.

Assessing myocardial infarction severity from the urban environment perspective in Wuhan, China.

Health inequalities are globally widespread due to the regional socioeconomic inequalities. Myocardial infarction (MI) is a leading health problem causing deaths worldwide. Yet medical services for it are often inequitably distributed by region. Moreover, studies concerning MI's potential spatial risk factors generally suffer from difficulties in focusing on too few factors, inappropriate models, and coarse spatial grain of data. To address these issues, this paper integrates registered 1098 MI cases and urban multi-source spatio-temporal big data, and spatially analyses the risk factors for MI severity by applying an advanced interpretable model, the random forest algorithm (RFA)-based SHapley Additive exPlanations (SHAP) model. In addition, a community-scale model between spatio-temporal risk factors and MI cases is constructed to predict the MI severity of all communities in Wuhan, China. The results suggest that those risk factors (i.e., age of patients, medical quality, temperature changes, air pollution and urban habitat) affect the MI severity at the community scale. We found that Wuhan residents in the downtown area are at risk for high MI severity, and the surrounding suburb areas show a donut-shape pattern of risk for medium-to-high MI severity. These patterns draw our attention to the impact of spatial environmental risk factors on MI severity. Thus, this paper provides three recommendations for urban planning to reduce the risk and mortality from severe MI in the aspect of policy implication.

China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned

In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster.

Spatiotemporal data partitioning for distributed random forest algorithm: Air quality prediction using imbalanced big spatiotemporal data on spark distributed framework

Spatiotemporal air quality datasets are typically collected hourly in monitoring stations deployed non-uniformly across a metropolitan city. These datasets are not only big, which poses challenges on the storage and processing capacity of centralized computing systems but also imbalanced and spatially heterogeneous, which may result in biased air quality prediction. To address these challenges, we designed and developed a parallel air quality prediction system equipped with a spatiotemporal data partitioning method, a distributed machine learning algorithm, Hadoop’s distributed data storage platform and its resource scheduler/manager, and Spark’s efficient and in-memory execution environment, which is suitable for running iterative algorithms, e.g., machine learning. Our proposed spatiotemporal partitioning method accounted for imbalance and spatial heterogeneity features of big air quality data in predictive models, which comply with the load-balancing requirement of distributed computing systems. Distributed Random Forest algorithm in the H2O library of the Spark framework was selected as the distributed machine learning algorithm to develop the air quality predictive model. This algorithm is an ensemble forest with algorithm-level adjustments to perform as efficiently as possible for big imbalanced datasets. An application of the parallel quality prediction system for Tehran, Iran showed that the parallel prediction system had considerable speedup gain and improved both the overall accuracy and class precision of air quality prediction when working with imbalanced big spatiotemporal air quality datasets. A future research direction is to add data streaming and visualization functions to the system to provide rapid and reliable air quality prediction for supporting environmental health management. • A parallel air quality prediction system based on Spark and Hadoop frameworks. • A spatiotemporal big data partitioning method based on location and year of data. • Implemented H2O Distributed Random Forest Algorithm using Sparkling Water library. • Achieved overall accuracy of 68% and precision improvements for minority classes. • Observed an increasing trend in speedup gain when adding more parallel processors.

Spatio-Temporal Evolution and Driving Mechanisms of Rural Residentials from the Perspective of the Human-Land Relationship: A Case Study from Luoyang, China

Rural residential area is the core component of rural land, and the process of its transfer exists to guide the change of rural land use. Its evolution is closely related to population migration, so exploring the spatial and temporal evolution of the coupling type of human-land relationship and its driving mechanism is the core scientific basis for the optimal allocation of rural land resources. From the perspective of human-land relationship, this paper introduces spatio-temporal big data on the basis of 3S technology and constructs an elasticity coefficient model. The composite index method is also used to further explore the coupling types of rural human-land relations in the Luoyang region. At the same time, spatial autocorrelation and spatial autoregressive models were used to reveal the driving mechanism of the spatio-temporal evolution of rural human-land relationship types in the Luoyang region. The results showed that: (1) Against the background of the continuous reduction of the rural population in Luoyang, the scale of rural residential area first increases and then decreases, and spatially they continue to gather around the central city (2) At the township scale, the conflict between people and land tends to be moderated. The elasticity coefficient shows a zonal distribution pattern in the southwest of Luoyang region and the townships around the central city. (3) There is a non-negligible spatial correlation between rural human-land relations in Luoyang region. The initial scale of rural residentials, arable land area, rural resident population and industrial output value are positively correlated with the coordination of people-land relationship, and the slope, elevation and distance from the central city have an inverse effect on the harmony of the relationship between people and land.

Individualized passenger travel pattern multi-clustering based on graph regularized tensor latent dirichlet allocation

Individual passenger travel patterns have significant value in understanding passenger’s behavior, such as learning the hidden clusters of locations, time, and passengers. The learned clusters further enable commercially beneficial actions such as customized services, promotions, data-driven urban-use planning, peak hour discovery, and so on. However, the individualized passenger modeling is very challenging for the following reasons: 1) The individual passenger travel data are multi-dimensional spatiotemporal big data, including at least the origin, destination, and time dimensions; 2) Moreover, individualized passenger travel patterns usually depend on the external environment, such as the distances and functions of locations, which are ignored in most current works. This work proposes a multi-clustering model to learn the latent clusters along the multiple dimensions of Origin, Destination, Time, and eventually, Passenger (ODT-P). We develop a graph-regularized tensor Latent Dirichlet Allocation (LDA) model by first extending the traditional LDA model into a tensor version and then applies to individual travel data. Then, the external information of stations is formulated as semantic graphs and incorporated as the Laplacian regularizations; Furthermore, to improve the model scalability when dealing with massive data, an online stochastic learning method based on tensorized variational Expectation-Maximization algorithm is developed. Finally, a case study based on passengers in the Hong Kong metro system is conducted and demonstrates that a better clustering performance is achieved compared to state-of-the-arts with the improvement in point-wise mutual information index and algorithm convergence speed by a factor of two.

APPLICATION AND PLATFORM DESIGN OF SPATIOTEMPORAL DATA OPENING AND SHARING

Abstract. Spatiotemporal data is data that has both temporal attributes and spatial distribution. The core of the opening and sharing of spatiotemporal data is to exchange, share and collaborate with various data, including spatiotemporal data, based on a unified spatiotemporal benchmark. The research methods of domestic and foreign scholars on the opening and sharing of spatiotemporal data mainly focus on spatiotemporal data model, database research, big data mining analysis and visual expression. This paper introduces the connotation and current situation of open sharing of spatiotemporal data. The typical applications of spatiotemporal data open sharing are listed and summarized. This paper proposes to build an open sharing platform for spatiotemporal data, and gives the basic requirements of four parts: spatiotemporal benchmark, spatiotemporal modeling big data, cloud computing-based spatiotemporal big data system and supporting environment. Taking the National Platform for Common Geospatial Information Services (TIANDITU) as an example, the main indicators of the spatiotemporal data open sharing platform are given. Finally, the article concludes and points out the opportunities and challenges faced by the open sharing of spatiotemporal data. In order to play a greater role in social public application services and administrative decision-making.

Discovering optimally representative dynamical locations (ORDL) in big multivariate spatiotemporal data: A case study of precipitation in Australia from space to ground sensors

We develop a method for discovering a set of optimally representative dynamical locations (ORDL), a small subset of observed locations that are the most informative of the dynamics of a real complex system, as embodied in big spatiotemporal data. We achieve this through a two-pronged approach: (a) by reducing the multivariate time series data into a small set of time series with minimal loss of information on the dynamics of the system, (b) by exploiting the best that remote sensing and in-situ observations can offer. In the former, we extend the recently proposed empirical dynamical quantiles for univariate time series to multivariate data using a directional statistical depth measure and principal eigen-decomposition method. In the latter, we perform data fusion to leverage remotely sensed precipitation from multiple satellite platforms in addition to ground-based rain gauges to improve overall accuracy and spatial coverage. We demonstrate our method in the context of precipitation data over 2003–2021 for Australia. Of the six states, the location, ranking and number of ORDL suggest that Queensland has seen the most significant variability in precipitation while that in Victoria has remained relatively stable. Finally, this study has uncovered ungauged locations in data-sparse regions of Australia where the installation of future rain gauges can optimally represent precipitation dynamics in the region under a changing climate.

Risk Assessment of Crowd-Gathering in Urban Open Public Spaces Supported by Spatio-Temporal Big Data

The urban open public spaces are the areas where people tend to gather together, which may lead to great crowd-gathering risk. This paper proposes a new method to assess the rank and spatial distribution of crowd-gathering risk in open public spaces in a large urban area. Firstly, a crowd density estimation method based on Tencent user density (TUD) data is built for different times in open public spaces. Then, a reasonable crowd density threshold is delimited to detect critical crowd situations in open public spaces and find out the key open public spaces that need to have intensive crowd-gathering prevention. For estimating the crowd-gathering risk in key open public spaces, the quantified risk assessment approach is conducted based on the classical risk theory that simultaneously considers the probability of an accident occurring, the severity of the accident consequence, and the risk aversion factor. A case study of the area within the Outer-ring Road of Shanghai was conducted to determine the feasibility of the new method. The thematic maps that describe the ranks and spatial distribution of crowd-gathering risk were generated. According to the risk maps, the government can determine the crowd control measures in different areas to reduce the crowd-gathering risk and prevent dangerous events.

An Information Fusion Model between GeoSOT Grid and Global Hexagonal Equal Area Grid

In order to cope with the rapid growth of spatiotemporal big data, data organization models based on discrete global grid systems have developed rapidly in recent years. Due to the differences in model construction methods, grid level subdivision and coding rules, it is difficult for discrete global grid systems to integrate, share and exchange data between different models. Aiming at the problem of information fusion between a GeoSOT grid and global hexagonal equal area grid system, this paper proposes the GeoSOT equivalent aggregation model (the GEA model). We establish a spatial correlation index method between GeoSOT grids and global hexagonal equal area grids, and based on the spatial correlation index, we propose an interoperable transformation method for grid attributes information. We select the POI (points of interest) data of Beijing bus and subway stations and carry out the transformation experiment of hexagonal grid to GeoSOT grid information so as to verify the effectiveness of the GEA model. The experimental results show that when the 17th-level GeoSOT grid is selected as the particle grid to fit the hexagonal grid, the accuracy and efficiency can be well balanced. The fitting accuracy is 95.51%, and the time consumption is 30.9 ms. We establish the associated index of the GeoSOT grid and the hexagonal grid and finally realized the exchange of information.

GeoMapViz: a framework for distributed management and geospatial data visualization based on massive spatiotemporal data streams

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.

A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

With the widespread use of mobile and sensing devices, and the popularity of online map-based services, such as navigation services, the volume of spatio-temporal data is growing rapidly. Conventional big data technologies in existing distributed systems cannot effectively process spatio-temporal big data with temporal continuity and spatial proximity. How to construct an effective index for the application requirements of spatio-temporal data in a distributed environment has become one of the hotspots of spatio-temporal big data research. Many spatio-temporal indexing methods have been proposed to support efficient query processing of spatio-temporal data. In this article, the various spatio-temporal big data indexing methods proposed by domestic and foreign researchers from 2010 to 2020 are classified and summarized according to the distributed environment and application background, and the hot issues that need to be paid attention to in the future are proposed according to the changes in application requirements