Real-time Spatio-temporal Data Research Articles

Geostatistics on Real-Time Geodata Streams—High-Frequent Dynamic Autocorrelation with an Extended Spatiotemporal Moran’s I Index

The availability of spatial and spatiotemporal big data is increasing rapidly. Spatially and temporally high resolved data are especially gathered via the Internet of Things. This data can often be accessed as data streams that push new data tuples continuously and make the data available in real time. Such real-time spatiotemporal data have great potential for new analysis approaches based on modern data processing technologies. The ability to retrieve spatial big data in real time, as well as process it in real time, demands new analysis methodologies that catch up with the instantaneous and continuous character of today’s spatiotemporal data. In this work, we present an evaluation of a high-frequent dynamic spatiotemporal autocorrelation approach. This approach allows for geostatistical analysis of streaming spatiotemporal data in real time and can provide insights into spatiotemporal processes while they are still ongoing. To evaluate this new approach, it was applied to mobility data from New York City. The results show that a high-frequent dynamic spatiotemporal autocorrelation approach provides comparable and meaningful results. In this way, high-frequent geostatistical analyses in real time can become an addition to retrospective analyses based on historical data.

Sustainable Destination Management Using Visitors’ Movements: Applying Mobile Positioning Data

Understanding visitors’ movements is crucial to achieving the goals of sustainable destination management while dealing with the environmental, social, and cultural impact of tourism. This study examines the movement patterns of visitors within Taeanhaean National Park (TNP) by adopting a destination measurement approach from a longitudinal perspective. The spatial distribution of the visitors’ activities and movement patterns was obtained by using mobile positioning data (MPD) and we applied the theoretical concept of tourism destinations, which considers geographical, temporal, and compositional dimensions. Given the destination attributes and conservation values, the MPD analysis proved suitable as an aid for park managers to allocate resources efficiently and define the characteristics of park and recreation facilities. This analysis has extended our knowledge of visitors’ patterns at a large marine national park by increasing consistency and high resolution of real-time spatio-temporal data for longer periods and better representing the study population. The study results will allow park managers to implement destination management planning focused on influencing spatial distributions of visitors’ movements in a specific environment.

Real-time road safety optimization through network-level data management

With the increasing connectedness of vehicles, real-time spatio-temporal data can be collected from citywide road networks. Innovative data management solutions can process the collected data for the purpose of reducing travel time. However, a majority of the existing solutions have missed the opportunity to better manage the collected data for improving road safety at the network level. We propose an efficient data management framework that uses network-level data to improve road safety for citywide applications. Our framework uses a graph-based data structure to maintain real-time network-level traffic data. Based on the graph, the framework uses a novel technique to generate driving instructions for individual vehicles. By following the instructions, inter-vehicular spacing can be increased, leading to an improvement of road safety. Experimental results show that our framework improves road safety, measured based on the time to collision between vehicles, from the state-of-the-art traffic data management solutions by a large margin while achieving lower travel times compared with the solutions. The framework is also readily deployable for large-scale real-time applications due to its low computation costs.

Hilbert Curve and Cassandra Based Indexing and Storing Approach for Large-Scale Spatiotemporal Data

Objectives Because of the fast growing acquisition of real-time spatiotemporal data for various applications such as smart city or real-time air-quality monitoring, the traditional database technologies can-not satisfy the higher standards for large-scale data indexing, querying, and storing operations. As the via-ble alternative, NoSQL databases that are scalable and possess fast input/output capabilities offer potential solutions to accommodate the needs. Methods We propose a Hilbert curve and Cassandra technologies based approach for efficient indexing and storing of large-scale spatiotemporal datasets aiming to provide an effective framework for processing, querying, and analyzing large amount of data with spatial and temporal features. For example, the dataset of vehicle trajectories contains valuable spatial and temporal features those are being employed in the real world. The collected spatiotemporal datasets are preprocessed in order to fit the proposed structures for different applications. Specifically, two types of query applications com -monly used in the real world are the spatiotemporal range query and query upon vehicle IDs respectively. Two corresponding indexing structures are designed and implemented in order to accommodate the requests. S2 Geometry Library open sourced by Google is utilized to divide the earth surface into grids, and data points fall in grids are assigned with the specific IDs as the keys. The keys and columns are so designed by applying the Hilbert curve and Cassandra techniques that the resultant structures will physically store the spatially neighboring data points close to each other, and they are more suitable for large-scale spatiotempo-ral data querying and analyzing applications. Results The datasets acquired from the real applications are used to conduct the computational experiments to validate the efficiency of the proposed approach. The que-ry efficiency and the time consumed to store large amount of spatiotemporal data are investigated and bench-marked against some existing database technologies. Conclusions The computational experiments reveal the superiority of the proposed approach comparing to the existing methodologies, the required time to store (insert) data in the database is reduced by 6 times while the time needed to query data is decreased by at least 10 times. The efficiency of the proposed methodology is validate further by applying it to query the vehicle trajectories gathering the real-time air quality data.

Real-time and private spatio-temporal data aggregation with local differential privacy

Technology and usage advances in wireless communication and smart mobile devices with localization capabilities enable a large number of emerging applications of location-based services, e.g. mobile crowdsourcing applications, which are facilitating our daily life. However, collecting and sharing location data to service providers of applications will give rise to mobile users’ concerns on their privacy, especially location privacy. In this paper, we investigate the problem of real-time spatio-temporal data aggregation with privacy preservation in the local setting. In response to this, we propose a systematic solution based on stringent local differential privacy preservation technique to deal with the problem. Firstly, we introduce a novel definition of (ε, δ)-local differential privacy with the ability to capture the temporal correlation in spatio-temporal data and provide differential privacy preservation. Secondly, we develop an efficient framework of generalized randomized response (GRR) based real-time and private spatio-temporal data aggregation with an untrusted server. Finally, we conduct experiments on two real-world datasets to evaluate our framework. The results show that our GRR based framework significantly outperforms that based on PIM and LRM in data utility and demonstrate its superiority to achieve better trade-off of privacy and utility for real-time spatio-temporal data aggregation with stringent privacy preservation.

A multi-agent system for distributed smartphone sensing cycling in smart cities

PurposeThe purpose of this paper is to propose a distributed smartphone sensing-enabled system, which assumes an intelligent transport signaling (ITS) infrastructure that operates traffic lights in a smart city (SC). The system is able to handle priorities between groups of cyclists (crowd-cycling) and traffic when approaching traffic lights at road junctions.Design/methodology/approachThe system takes into consideration normal probability density function (PDF) and analytics computed for a certain group of cyclists (i.e. crowd-cycling). An inference model is built based on real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the problem is treated under the umbrella of multi-agent systems (MAS) modeling. The proposed model is experimentally evaluated by incorporating a real GPS trace data set from the SC of Melbourne, Australia. The MAS model is applied to the data set according to the quantitative and qualitative criteria adopted. Cyclists’ satisfaction (CS) is defined as a function, which measures the satisfaction of the cyclists. This is the case where the cyclists wait the least amount of time at traffic lights and move as fast as they can toward their destination. ITS system satisfaction (SS) is defined as a function that measures the satisfaction of the ITS system. This is the case where the system serves the maximum number of cyclists with the fewest transitions between the lights. Smart city satisfaction (SCS) is defined as a function that measures the overall satisfaction of the cyclists and the ITS system in the SC based on CS and SS. SCS defines three SC policies (SCP), namely, CS is maximum and SS is minimum then the SC is cyclist-friendly (SCP1), CS is average and SS is average then the SC is equally cyclist and ITS system friendly (SCP2) and CS is minimum and SS is maximum then the SC is ITS system friendly (SCP3).FindingsResults are promising toward the integration of the proposed system with contemporary SCs, as the stakeholders are able to choose between the proposed SCPs according to the SC infrastructure. More specifically, cyclist-friendly SCs can adopt SCP1, SCs that treat cyclists and ITS equally can adopt SCP2 and ITS friendly SCs can adopt SCP3.Originality/valueThe proposed approach uses internet connectivity available in modern smartphones, which provide users control over the data they provide to us, to obviate the installation of additional sensing infrastructure. It extends related study by assuming an ITS system, which turns traffic lights green by considering the normal PDF and the analytics computed for a certain group of cyclists. The inference model is built based on the real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the system is treated under the umbrella of MAS. MAS has been used in the literature to model complex systems by incorporating intelligent agents. In this study, the authors treat agents as proxy threads running in the cloud, as they require high computation power not available to smartphones.

A NEW PROCESS-ORIENTED AND SPATIOTEMPORAL DATA MODEL FOR GIS DATA

Abstract. With the rapid development of wireless sensor and information technology, there is a trend of transition from "digital monitoring" to "intelligence monitoring" advancing process. The traditional model cannot completely match the dynamic data to accurately describe changes of geographical and environmental changes. In this paper, we try to build a process-oriented and real-time spatiotemporal data model to meet the demands. With various types of monitoring devices, detection methods and the utilization of new technologies, the model can simulate the possible waterlog area in a specific year by analyzing the given data. By testing and modifying the spatiotemporal model, we can come to a rational conclusion that our model can forecast the actual situation in certain extent.

Spatio-temporal quality control: implications and applications for data consumers and aggregators

Quality control for real-time spatio-temporal data is often presented from the perspective of the original owner and provider of the data, and focuses on general techniques for outlier detection or uses domain-specific knowledge and rules to assess quality. The impact of quality control on the data aggregator and redistributor is neglected. As sensor networks proliferate, multiple providers can distribute and redistribute the same original sensor data. Relationships between providers become complex, with data acquired from original and third-party sources. One provider may acquire data from another, and so forth, resulting in larger data sets with value-added components such as quality indicators, but with costs such as increased lag between original observation times and (re)distribution times. The focus of this paper is to define and demonstrate quality control measures for real-time, spatio-temporal data from the perspective of an aggregator to provide tools for evaluation and comparison of overlapping, real-time, spatio-temporal data providers and for assessment and optimization of data acquisition, system operation and data redistribution. We define simple measures that account for temporal completeness and spatial coverage. The measures and methods developed are tested on real-world data and applications. Our results show that these simple measures combine to form methods that are useful in comparing providers and identifying patterns in data which can then be exploited to optimize system performance relative to bandwidth, and to assess the impact of provider quality control mechanisms. The simple measures presented demonstrate the utility of quantifying data quality from the perspective of the data aggregator and redistributor.

Time-resolved local strain tracking microscopy for cell mechanics.

A uniaxial cell stretching technique to measure time-resolved local substrate strain while simultaneously imaging adherent cells is presented. The experimental setup comprises a uniaxial stretcher platform compatible with inverted microscopy and transparent elastomer samples with embedded fluorescent beads. This integration enables the acquisition of real-time spatiotemporal data, which is then processed using a single-particle tracking algorithm to track the positions of fluorescent beads for the subsequent computation of local strain. The present local strain tracking method is demonstrated using polydimethylsiloxane (PDMS) samples of rectangular and dogbone geometries. The comparison of experimental results and finite element simulations for the two sample geometries illustrates the capability of the present system to accurately quantify local deformation even when the strain distribution is non-uniform over the sample. For a regular dogbone sample, the experimentally obtained value of local strain at the center of the sample is 77%, while the average strain calculated using the applied cross-head displacement is 48%. This observation indicates that considerable errors may arise when cross-head measurement is utilized to estimate strain in the case of non-uniform sample geometry. Finally, the compatibility of the proposed platform with biological samples is tested using a unibody PDMS sample with a well to contain cells and culture media. HeLa S3 cells are plated on collagen-coated samples and cell adhesion and proliferation are observed. Samples with adherent cells are then stretched to demonstrate simultaneous cell imaging and tracking of embedded fluorescent beads.

Real-time and Spatio-temporal Crowd-sourced Social Network Data Publishing with Differential Privacy

Nowadays gigantic crowd-sourced data from mobile devices have become widely available in social networks, enabling the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of crowd-sourced social network data to the public will pose considerable threats to mobile users’ privacy. In this paper, we investigate the problem of real-time spatio-temporal data publishing in social networks with privacy preservation. Specifically, we consider continuous publication of population statistics and design RescueDP—an online aggregate monitoring framework over infinite streams with $w$ -event privacy guarantee. Its key components including adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. Moreover, we further propose an enhanced RescueDP with neural networks to accurately predict the values of statistics and improve the utility of released data. Both RescueDP and the enhanced RescueDP are proved satisfying $w$ -event privacy. We evaluate the proposed schemes with real-world as well as synthetic datasets and compare them with two $w$ -event privacy-assured representative methods. Experimental results show that the proposed schemes outperform the existing methods and improve the utility of real-time data sharing with strong privacy guarantee.

Two-Dimensional Code Based Spatiotemporal Modeling and Its Application in Object Tracing

Space-time consistency is of great importance for mapping things from physical world to cyber world, which could be one of the fundamental issues in constructing cyber-physical system (CPS). A two-dimensional code spatiotemporal modeling method is proposed in this paper. We apply the two-dimensional code as an information carrier to store object's characteristics (object's ID, attributes, instructions, spatiotemporal data, etc.), the smartphone as a mobile device to obtain the object's real-time spatiotemporal data with its system time and GPS module; then the object's spatiotemporal information can be transmitted and stored in a backend server with a database. Meanwhile, the object's spatiotemporal information can also be inquired by smartphone users and other web users. A use case that applied this proposed method to trace object's smartphone information is described later, and the result shows that tracing objects with two-dimensional code has many advantages, such as fast reading and transmitting, low cost, and robust, confidential, and strong readability; furthermore, the proposed modeling method keeps the consistency of object's time and space in CPS.

Real-Time Space–Time Integration in GIScience and Geography

Space–time integration has long been the topic of study and speculation in geography. However, in recent years an entirely new form of space–time integration has become possible in geographic information systems (GIS) and GIScience: real-time space–time integration and interaction. Although real-time spatiotemporal data are now being generated almost ubiquitously, and their applications in research and commerce are widespread and rapidly accelerating, the ability to continuously create and interact with fused space–time data in geography and GIScience is a recent phenomenon, made possible by the invention and development of real-time interactive (RTI) Global Positioning System (GPS)/GIS technology and functionality in the late 1980s and early 1990s. This innovation has since functioned as a core change agent in geography, cartography, GIScience, and many related fields, profoundly realigning traditional relationships and structures, expanding research horizons, and transforming the ways in which geographic data are now collected, mapped, modeled, and used, both in geography and in science and society more broadly. Real-time space–time interactive functionality remains today the underlying process generating the current explosion of fused spatiotemporal data, new geographic research initiatives, and myriad geospatial applications in governments, businesses, and society. This article addresses briefly the development of these real-time space–time functions and capabilities; their impact on geography, cartography, and GIScience; and some implications for how discovery and change can occur in geography and GIScience and how we might foster continued innovation in these fields.