Large-scale Spatiotemporal Data Research Articles

Improving community resilience to disrupted food access: Empirical spatio-temporal analysis of volunteer-based crowdsourced food delivery

Unplanned disaster events can greatly disrupt access to essential resources, with calamitous outcomes for already vulnerable households. This is particularly challenging when concurrent extreme events affect both the ability of households to travel and the functioning of traditional transportation networks that supply resources. This paper examines the use of volunteer-based crowdsourced food delivery as a community resilience tactic to improve food accessibility during overlapping disruptions with lasting effects, such as the COVID-19 pandemic and climate disasters. The study uses large-scale spatio-temporal data (n = 28,512) on crowdsourced food deliveries in Houston, TX, spanning from 2020 through 2022, merged with data on community demographics and significant disruptive events occurring in the two-year timespan. Three research lenses are applied to understand the effectiveness of crowdsourced food delivery programs for food access recovery: 1) geographic analysis illustrates hot spots of demand and impacts of disasters on requests for food assistance within the study area; 2) linear spatio-temporal modeling identifies a distinction between shelter-in-place emergencies and evacuation emergencies regarding demand for food assistance; 3) structural equation modeling identifies socially vulnerable identity clusters that impact requests for food assistance. The findings from the study suggest that volunteer-based crowdsourced food delivery adds to the resilience of food insecure communities, supporting its effectiveness in serving its intended populations. The paper contributes to the literature by illustrating how resilience is a function of time and space, and that similarly, there is value in a dynamic representation of community vulnerability. The results point to a new approach to resource recovery following disaster events by shifting the burden of transportation from resource-seekers and traditional transportation systems to home delivery by a crowdsourced volunteer network.

TrafPS: A shapley-based visual analytics approach to interpret traffic

Recent achievements in deep learning (DL) have demonstrated its potential in predicting traffic flows. Such predictions are beneficial for understanding the situation and making traffic control decisions. However, most state-of-the-art DL models are considered “black boxes” with little to no transparency of the underlying mechanisms for end users. Some previous studies attempted to “open the black box” and increase the interpretability of generated predictions. However, handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain challenging. To overcome these challenges, we present TrafPS, a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning. The measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different levels. Based on the task requirements from domain experts, we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow patterns. Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

Spatio-Temporal Big Data Collaborative Storage Mechanism Based on Incremental Aggregation Subvector Commitment in On-Chain and Off-Chain Systems

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system.

CUPID: An efficient spatio-temporal data engine

In the IoT era, abundant spatio-temporal data is generated from various devices thanks to the prevalence of positioning techniques. Due to a lack of effective systems to manipulate the data, advanced scalable and efficient data management systems are necessary to support more and more urban applications.We propose Cupid, which is powered by scholars from Chongqing University and Guangzhou Urban Planning and Design Survey Research Institute, an efficient spatio-temporal Data engine. It extends JUST by introducing many functionalities to make it more applicable to deployment, and can efficiently manage large-scale spatio-temporal data. In Cupid, Apache HBase is utilized as the storage, GeoMesa serves as the spatio-temporal data indexing tool, and Apache Spark acts as the execution engine. We introduce many optimizations to ensure usability and reduce computational overhead. To make Cupid easy to use, we design and implement a SQL-like query language. Furthermore, to deploy the system as a PaaS while speeding up the execution, we leverage Apache Livy, a service that enables easy interaction with a Spark cluster over REST interfaces, together with gRPC to effectively collect results. Extensive experiments illustrate that Cupid outperforms other state-of-the-art spatio-temporal big data management systems in terms of efficiency and scalability.

Australian property market explorer – a front-end native tool for visualizing property sales data

Online interactive 3D visualizations can enhance understanding of the built environment, including non-physical aspects such as property value and price. With access to large-scale open datasets, a key challenge in building such interactive 3D mapping interfaces is the efficiency of visualization methods. Modern GPU-accelerated front-end mapping libraries present a solution to this problem. This research proposes a ‘front-end’ native approach to create a web-based visual analytics framework for large scale spatiotemporal data. The Australian Property Market Explorer exemplifies this framework by visualizing a large dataset of property sales in New South Wales, Australia. This tool allows users to explore and analyse property market patterns at various geographic scales. The proposed framework offers advantages over traditional methods such as scalability and reliability while promising use-cases such as aiding planners in infrastructure and transportation decisions and assisting property valuation professionals in analysing property price trends.

SERT: A transformer based model for multivariate temporal sensor data with missing values for environmental monitoring

Environmental monitoring is crucial to our understanding of climate change, biodiversity loss and pollution. The availability of large-scale spatio-temporal data from sources such as sensors and satellites allows us to develop sophisticated models for forecasting and understanding key drivers. However, the data collected from sensors often contain missing values due to faulty equipment or maintenance issues. The missing values rarely occur simultaneously leading to data that are multivariate misaligned sparse time series. We propose two models that are capable of performing multivariate spatio-temporal forecasting while handling missing data naturally without the need for imputation. The first model is a transformer-based model, which we name SERT (Spatio-temporal Encoder Representations from Transformers). The second is a simpler model named SST-ANN (Sparse Spatio-Temporal Artificial Neural Network) which is capable of providing interpretable results. We conduct extensive experiments on two different datasets for multivariate spatio-temporal forecasting and show that our models have competitive or superior performance to those at the state-of-the-art.

High-Resolution Image Processing and Spatiotemporal Data Transmission System Based on GPU Acceleration

With the development of information technology and the increasing demand for data processing, the serial mode of the central processing unit (CPU) is difficult to efficiently transmit large-scale spatiotemporal data, and the processing effect for high-resolution images is not good. This paper designed a high-resolution image processing and spatiotemporal data transmission system based on graphics processing unit (GPU) acceleration to improve the processing efficiency of large-scale spatiotemporal data. In this paper, traffic spatiotemporal data was taken as an example for analysis. Large-scale traffic image data was collected by road monitoring equipment, and image compression was performed on the collected image. Fourier transform was used to eliminate image data redundancy, and GPU-accelerated parallel processing was used to achieve fast image defogging and data transmission. This paper selected 2TB of traffic spatiotemporal data with image resolutions of 540P, 720P, 1080P, 1440P, and 2160P. GPU acceleration was performed using the Compute Unified Device Architecture (CUDA). In images with a resolution of 2160P, the processing time for CPU and GPU acceleration was 2900ms and 28ms, respectively, with an acceleration ratio of 103.6. A high-resolution image processing and spatiotemporal data transmission system based on GPU acceleration can improve the efficiency of traffic spatiotemporal data processing and have excellent concurrent processing capabilities.

Gather or Scatter: Stackelberg-Game-Based Task Decision for Blockchain-Assisted Socially Aware Crowdsensing Framework

Mobile Crowdsensing (MC), an excellent solution to large-scale spatio-temporal data sensing problems, has recently received lots of attention from both industry and academia. In the MC system, any requester can acquire the sensing data for his points of interest (PoIs) by offering some payments to attract a group of mobile users capable of completing these PoI-related sensing tasks. However, the current MC work neglected three vital factors, more or less. First, they assume that these distributed users are mutually independent in MC, ignoring the social effects. Actually, the sensing data collected by one user may be corroborated by others’ sensing data, so-called “information corroboration”. Second, all rational and selfish users are inclined to gather to perform these tasks due to information corroboration. Meanwhile, they may be strategic about their participation levels to maximize profits. However, more similar sensing data will undoubtedly lower the information value, so any user has a tradeoff between gather and scatter. Third, although mobile users can obtain some payments, privacy issues may still prevent them from participating in MC. In this paper, we propose a secure blockchain-assisted socially-aware MC framework by adopting the smart contract technique of Ethereum. For this framework, we further devise a two-stage Stackelberg game model to assist the requester (i.e., the leader in the game) in properly pricing each PoI-related sensing task, so that mobile users (i.e., the followers in the game) can exactly select their tasks and determine their participation levels. To analyze the game equilibrium, we extend the traditional Hessian matrix method to a multi-dimension case involving the multi-user multi-task hyperspace setting. We conduct extensive experiments to prove the equilibrium and effectiveness of the proposed solution. We also implement a prototype and deploy the smart contract to an official Ethereum test network to demonstrate the practicability of the proposed framework.

Influence of residential built environment on human mobility in Xining: A mobile phone data perspective

The emergence of large-scale spatiotemporal trajectory data offers an excellent opportunity to characterize collective human mobility patterns and their relationship with the urban built environment. Such an approach can generate a complementary understanding of traditional individual travel behavior based on travel survey data. Using mobile phone data, this study aims to investigate how residential built environments affect residents’ mobility at an aggregated level. Specifically, three indicators (movement distance, activity space, and the number of stops) were derived from raw mobile phone data to characterize human mobility. The decay coefficients and average values were then employed to reveal the aggregated characteristics of movement distance, activity space, and the number of stops. Furthermore, linear regression was applied to examine the relationship between human mobility indicators and residential built environments. The results indicate that the living-built environment could better explain the activity space than the movement distance and number of stops. In addition, some differences in the relationship between human mobility and residential built environments are identified between weekdays and weekends. These findings could provide new insights into human mobility and its interaction with the built environment, thus advancing our understanding of human travel behavior from both individual and collective perspectives.

WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic

ABSTRACT The outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people’s daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of multi-source heterogeneous data fusion, deep mining, and comprehensive applications. The Spatio-Temporal Artificial Intelligence (STAI) technology, which focuses on integrating spatial related time-series data, artificial intelligence models, and digital tools to provide intelligent computing platforms and applications, opens up new opportunities for scientific epidemic control. To this end, we leverage STAI and long-term experience in location-based intelligent services in the work. Specifically, we devise and develop a STAI-driven digital infrastructure, namely, WAYZ Disease Control Intelligent Platform (WDCIP), which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection, processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios. According to the platform implementation logic, our work can be performed and summarized from three aspects: (1) a STAI-driven integrated system; (2) a hybrid GNN-based approach for hierarchical risk assessment (as the core algorithm of WDCIP); and (3) comprehensive applications for social epidemic containment. This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources, where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic. So far, WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic.

Development of Big Data-Analysis Pipeline for Mobile Phone Data with Mobipack and Spatial Enhancement

Frequent and granular population data are essential for decision making. Further-more, for progress monitoring towards achieving the sustainable development goals (SDGs), data availability at global scales as well as at different disaggregated levels is required. The high population coverage of mobile cellular signals has been accelerating the generation of large-scale spatiotemporal data such as call detail record (CDR) data. This has enabled resource-scarce countries to collect digital footprints at scales and resolutions that would otherwise be impossible to achieve solely through traditional surveys. However, using such data requires multiple processes, algorithms, and considerable effort. This paper proposes a big data-analysis pipeline built exclusively on an open-source framework with our spatial enhancement library and a proposed open-source mobility analysis package called Mobipack. Mobipack consists of useful modules for mobility analysis, including data anonymization, origin–destination extraction, trip extraction, zone analysis, route interpolation, and a set of mobility indicators. Several implemented use cases are presented to demonstrate the advantages and usefulness of the proposed system. In addition, we explain how a large-scale data platform that requires efficient resource allocation can be con-structed for managing data as well as how it can be used and maintained in a sustainable manner. The platform can further help to enhance the capacity of CDR data analysis, which usually requires a specific skill set and is time-consuming to implement from scratch. The proposed system is suited for baseline processing and the effective handling of CDR data; thus, it allows for improved support and on-time preparation.

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.

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.

Applying Bayesian spatio-temporal models to demand analysis of shared bicycle

Shared bicycle provides a cheap and healthy mobility alternative to travelers especially for the “first–last mile” trips. Although the temporal and spatial correlation of regional shared bicycle needs has been confirmed in the literature in recent years, the interdependencies between them are not yet fully understood. In this paper, a spatio-temporal Bayesian modeling method is proposed to quantify regional shared bicycle demand and identify the impact of various factors on the cycling trips. By combining the Integrated Nested Laplace Approximation (INLA) and Stochastic Partial Differential Equation (SPDE), it guarantees the establishment of the feasibility of algorithms on large-scale spatiotemporal data structures. In particular, the massive rental records of Mobike in Shanghai in August 2016 are used as the study observation. We establish a series of Bayesian models with different temporal and spatial structures, and uses the Deviation Information Criteria to verify the relevance of the models in the temporal and spatial dimensions. Moreover, the Kolmogorov–Smirnov test is proposed to fit different distributions to obtain the optimal distribution family of travel demand data. Our research efforts have further been made to evaluate the impact of meteorological factors, population density and per capita GDP on travel demand. The result shows that the model of temporal and spatial correlation structure can better assess the regional distribution of future shared bicycle riding demands, and the influence of temperature and precipitation on cycling demand is more significant. The study’s findings will help guide the development of future shared bicycle regional scheduling work, and improve economic benefits on the basis of meeting traveler’ needs.

A spatio-temporal model for detecting the effect of cocaine use disorder on functional connectivity

Drug addiction can lead to many health-related problems and social concerns. Researchers are interested in the association between long-term drug usage and abnormal functional connectivity. Functional connectivity obtained from functional magnetic resonance imaging data promotes a variety of fundamental understandings in such association. Due to the complex correlation structure and large dimensionality, the modeling and analysis of the functional connectivity from neuroimage are challenging. By proposing a spatio-temporal model for multi-subject neuroimage data, we incorporate voxel-level spatio-temporal dependencies of whole-brain measurements to improve the accuracy of statistical inference. To tackle large-scale spatio-temporal neuroimage data, we develop a computational efficient algorithm to estimate the parameters. Our method is used to first identify functional connectivity, and then detect the effect of cocaine use disorder (CUD) on functional connectivity between different brain regions. The functional connectivity identified by our spatio-temporal model matches existing studies on brain networks, and further indicates that CUD may alter the functional connectivity in the medial orbitofrontal cortex subregions and the supplementary motor areas.

Identification of factors impacting on the transmission and mortality of COVID-19

This paper proposes a dynamic infectious disease model for COVID-19 daily counts data and estimate the model using the Langevinized EnKF algorithm, which is scalable for large-scale spatio-temporal data, converges to the right filtering distribution, and is thus suitable for performing statistical inference and quantifying uncertainty for the underlying dynamic system. Under the framework of the proposed dynamic infectious disease model, we tested the impact of temperature, precipitation, state emergency order and stay home order on the spread of COVID-19 based on the United States county-wise daily counts data. Our numerical results show that warm and humid weather can significantly slow the spread of COVID-19, and the state emergency and stay home orders also help to slow it. This finding provides guidance and support to future policies or acts for mitigating the community transmission and lowering the mortality rate of COVID-19.

Scalable low-rank tensor learning for spatiotemporal traffic data imputation

Missing value problem in spatiotemporal traffic data has long been a challenging topic, in particular for large-scale and high-dimensional data with complex missing mechanisms and diverse degrees of missingness. Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data. However, despite the promising results, these approaches do not scale well to large data tensors. In this paper, we focus on addressing the missing data imputation problem for large-scale spatiotemporal traffic data. To achieve both high accuracy and efficiency, we develop a scalable tensor learning model—Low-Tubal-Rank Smoothing Tensor Completion (LSTC-Tubal)—based on the existing framework of Low-Rank Tensor Completion, which is well-suited for spatiotemporal traffic data that is characterized by multidimensional structure of location × time of day × day. In particular, the proposed LSTC-Tubal model involves a scalable tensor nuclear norm minimization scheme by integrating linear unitary transformation. Therefore, tensor nuclear norm minimization can be solved by singular value thresholding on the transformed matrix of each day while the day-to-day correlation can be effectively preserved by the unitary transform matrix. Before setting up the experiment, we consider some real-world data sets, including two large-scale 5-min traffic speed data sets collected by the California PeMS system with 11160 sensors: 1) PeMS-4W covers the data over 4 weeks (i.e., 288×28 time points), and 2) PeMS-8W covers the data over 8 weeks (i.e., 288×56 time points). We compare LSTC-Tubal with some state-of-the-art baseline models, and find that LSTC-Tubal can achieve competitively accuracy with a significantly lower computational cost. In addition, the LSTC-Tubal will also benefit other tasks in modeling large-scale spatiotemporal traffic data, such as network-level traffic forecasting.

Spatio-Temporal Multi-Task Learning via Tensor Decomposition

Predictive modeling of large-scale spatio-temporal data is an important but challenging problem as it requires training models that can simultaneously predict the target variables of interest at multiple locations while preserving the spatial and temporal dependencies of the data. In this paper, we investigate the effectiveness of applying a multi-task learning approach based on supervised tensor decomposition to the spatio-temporal prediction problem. Our proposed framework, known as SMART, encodes the data as a third-order tensor and extracts a set of interpretable, spatial and temporal latent factors from the data. An ensemble of spatial and temporal prediction models are trained using the latent factors as their predictor variables. Outputs from the ensemble model are aggregated to make predictions on test instances. The framework also allows known patterns from the domain to be incorporated as constraints to guide the tensor decomposition and ensemble learning processes. As the data may grow over space and time, an incremental learning version of the framework is given to efficiently update the models. We perform extensive experiments using a global-scale climate dataset to evaluate the accuracy and efficiency of the models as well as interpretability of the latent factors.

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.

Parallel computing for Fast Spatiotemporal Weighted Regression

The Spatiotemporal Weighted Regression (STWR) model is an extension of the Geographically Weighted Regression (GWR) model for exploring the heterogeneity of spatiotemporal processes. A key feature of STWR is that it utilizes the data points observed at previous time stages to make better fit and prediction at the latest time stage. Because the temporal bandwidths and a few other parameters need to be optimized in STWR, the model calibration is computationally intensive. In particular, when the data amount is large, the calibration of STWR becomes heavily time-consuming. For example, with 10,000 points in 10 time stages, it takes about 2307 s for a single-core PC to process the calibration of STWR. Both the distance and the weighted matrix in STWR are memory intensive, which may easily cause memory insufficiency as data amount increases. To improve the efficiency of computing, we developed a parallel computing method for STWR by employing the Message Passing Interface (MPI). A cache in the MPI processing approach was proposed for the calibration routine. Also, a matrix splitting strategy was designed to address the problem of memory insufficiency. We named the overall design as Fast STWR (F-STWR). In the experiment, we tested F-STWR in a High-Performance Computing (HPC) environment with a total number of 204,611 observations in 19 years. The results show that F-STWR can significantly improve STWR's capability of processing large-scale spatiotemporal data.