Spatiotemporal Big Data Research Articles

GLMI: An Efficient Spatiotemporal Index Leveraging Geohash and Piecewise Linear Models for Optimized Query Performance

Spatiotemporal big data contain information in multiple dimensions such as space and time. Spatiotemporal data have the characteristics of large volume, intricate spatiotemporal relationship, and uneven spatiotemporal distribution. Index structure is one of the most important technologies used to improve system data analysis and workload. However, it is difficult to dynamically adjust with data density, resulting in increased maintenance costs and retrieval complexity. At the same time, maintaining the proximity of spatiotemporal data in spatial or temporal dimensions is crucial for efficient spatiotemporal analysis. To address these challenges, this paper proposes a learned index method, GLMI (Geohash and piecewise linear model-based index for spatiotemporal data). GLMI uses dynamic space partitioning based on the Hilbert curve to reduce the impact of data skew on index performance. In the time dimension, a piecewise linear model was constructed using the ShrinkingCone algorithm, and a buffer was designed to support the fast writing of spatiotemporal data. Compared with the current mainstream traditional high-dimensional indexes and the ZM index, GLMI has a smaller space consumption and shorter construction time compared to high-dimensional learned indexes on real traffic itinerary and trajectory record datasets. Meanwhile, GLMI also has an advantage in query efficiency.

Open Geospatial Engine: A Cloud-based Spatiotemporal Computing Platform

Abstract. The quantity and diversity of Earth spatiotemporal big data have significantly increased in recent years, providing the potential to comprehensively analyse complex spatiotemporal problems from new perspectives. However, integrating, managing, and applying multi-source heterogeneous Earth spatiotemporal big data remains a challenge. To address this issue, this study proposes a cloud-based spatiotemporal computing platform, the Open Geospatial Engine (OGE), for the unified organization and joint analysis of Earth spatiotemporal big data in multiple dimensions and scales. The framework of this platform comprises three modules: data management, computing engine, and service interface. The data management module adopts the GeoCube model to effectively integrate and manage multi-source heterogeneous spatiotemporal data through multi-dimensional aligned tiles. The computing engine module seamlessly maps the GeoCube model to the cloud environment by extending Spark RDD, making the GeoCube model capable of high-performance distributed computing. Following OGC specifications, the service interface module integrates and shares data, operators, and spatiotemporal analysis models through extensible APIs in an interactive development environment. Combined with a series of high-performance optimization techniques, OGE simplifies data queries and the construction of complex analytical applications by integrating these three modules. The applicability of OGE is demonstrated by case studies involving multi-dimensional queries and joint analysis of long-time-series and heterogeneous spatiotemporal data.

Emotions, behaviors and places: Mapping sentiments with behaviors in Japanese tweets

Emotions are the basis of human behavior and an important part of spatial constructions. Despite numerous efforts to leverage spatio-temporal big data from social media to capture public sentiments, there is a lack of understanding of how individuals' behaviors play a role in people's sentiments at places they visit. Thus, this study aims to advance our understanding of the complex relationships between sentiments, behaviors, and places. Through a case study in Japan in 2021, we analyzed one year of countrywide geotagged social media by fine-tuning a pre-trained Japanese BERT model (accuracy: 74 %). The results emphasize the importance of understanding user emotions by considering people's behaviors, even within similar types of locations. Our research framework would be beneficial for urban planners and policymakers to enhance urban spaces.

A backfitting maximum likelihood estimator for hierarchical and geographically weighted regression modelling, with a case study of house prices in Beijing

Geographically weighted regression (GWR) and its extensions are important local modelling techniques for exploring spatial heterogeneity in regression relationships. However, when dealing with spatial data of overlapping samples – for example, when precise locational information is aggregated to a shared neighbourhood to avoid revealing the addresses of individual survey respondents – GWR-based models can encounter several problems, including obtaining reliable bandwidths. Because data with this characteristic exhibit spatial hierarchical structures, we propose combining hierarchical linear modelling (HLM) with GWR to give a hierarchical and geographically weighted regression (HGWR) model that divides coefficients into sample-level fixed effects, group-level fixed effects, sample-level random effects, and group-level spatially weighted effects. This paper presents a back-fitting likelihood estimator to fit the model, a simulation experiment that suggests that HGWR is better able to capture these effects and the spatial heterogeneity within them than are traditional HLM or GWR models, and a case study looking at predictors of housing price in Beijing, China. The ability of HGWR to tackle both spatial and group-level heterogeneity simultaneously suggests its potential as a promising data modelling tool for handling spatio-temporal big data with spatially hierarchical structures.

The whole process route planning algorithm based on AIS spatio-temporal big data analysis

ABSTRACT This paper proposes a route planning algorithm that can comprehensively integrate ship safety, economic benefits and algorithm universality. Firstly, the Polynomial Approximation with the Exponential Kernel method was used to smooth the repaired trajectory and the similarity of the smoothed trajectory was evaluated using Hausdorff distance. The results showed that the similarity of ship trajectories was effectively improved by 36.53%. Using the Douglas Pucker algorithm to compress the smoothed trajectory, the compression rate exceeded 92%. Then, the HDBSCAN self-adjusting clustering analysis method was used to cluster the ship trajectory and the silhouette coefficient index was used to quantitatively evaluate the clustering effect, the calculation result was 0.9032. Finally, using the average centre method to extract the centres of each cluster, 299 key turning points were obtained and connected to construct an undirected route network. Based on this, the Dijkstra algorithm was used to achieve optimal route 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.

Leveraging AI techniques for predicting spatial distribution and determinants of carbon emission in China's Yangtze River Delta

This study focuses on the prediction and management of carbon emissions (CE) under the backdrop of global warming, with a particular emphasis on developing spatial planning strategies for urban clusters. In this context, we integrate artificial intelligence technologies to devise an optimized spatial analysis method based on the attributes of multi-source, urban-level spatio-temporal big data on CE. This method enhances both the accuracy and interpretability of CE data processing. Our objectives are to accurately analyze the current status of CE, predict the future spatial distribution of urban CE in the Yangtze River Delta (YRD), and identify key driving factors. We aim to provide pragmatic recommendations for sustainable urban carbon management planning. The findings indicate that: (1) the algorithm designed by us demonstrates excellent fitting capabilities in the analysis of CE data in the YRD, achieving a fitting accuracy of 0.93; (2) it is predicted that from 2025 to 2030, areas with higher CE in the YRD will be primarily concentrated in the 'Provincial Capital Belt' and the 'Heavy Industry Belt'; (3) the economic foundation has been identified as the most significant factor influencing CE in the YRD; (4) projections suggest that CE in the YRD are likely to peak by 2030.

A general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids

AbstractOne of the basic scientific problems concerning geographic information science is how to rapidly organize, query, and compute spatiotemporal big data. The spatiotemporal discrete global grid system (DGGS) provides a homogenized discrete structure for processing multiscale and multitype spatiotemporal data. To date, most research in spatiotemporal DGGS has focused on spatial discretization while neglecting temporal discretization. Here, we propose a general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids. We subdivide continuous time into multiscale temporal grids, which are then encoded as integers. Moreover, we designed integer code operations, including hierarchical traversal, neighborhood finding, and temporal relationship calculations. Compared to the multiscale time segment integer coding (MTSIC) approach, the proposed method resulted in 22% higher encoding efficiency, 10.92 times faster decoding, 2.81 times better parent code finding efficiency, 41% improved efficiency, 100% accuracy in finding children codes (compared to less than 100% with MTSIC), and a 62% enhancement in temporal relationship calculation efficiency. The application of querying spatiotemporal trajectory data validates the feasibility and practicality of substituting conventional string‐based time and floating‐point location coordinates with spatiotemporal integer codes to query data. The time encoding and operation methods proposed here indicate high efficiency, superior accuracy, and broad application prospects.

Spatiotemporal Big Data Empower Community Modeling, Monitoring, Evaluation, and Optimization for Sustainable Community Development: A review of challenges and opportunities

Spatiotemporal Big Data Empower Community Modeling, Monitoring, Evaluation, and Optimization for Sustainable Community Development: A review of challenges and opportunities

Heinrich’s Law for Traffic Incidents – Using the Digital Tachograph Data to Identify Traffic Accident Hotspots

Heinrich’s Law indicates an empirical ratio between serious accidents, minor accidents and near misses in industrial sites, but has not been discussed in the context of road traffic accidents. Digital tachographs (DTG), a type of IoT device collecting spatiotemporal big data of vehicle trajectories, allow0 for examining a linkage between abnormal driving behaviours and the prevalence of road traffic incidents. According to the Traffic Safety Act implemented in 2011, DTG has been mandatorily pre-installed on most commercial vehicles in South Korea. The data have been analysed to evaluate the data processing method or promote eco-driving or safe driving, but only a few studies have examined an association between driving behaviours and actual traffic accidents using the limited data. We obtained 7,785,124 DTG sensing records from 1,523 commercial taxis driving within the city limits of Seoul at least once in 2013 and integrated them with 57,139 traffic accident cases during the same period. Using the integrated GIS database, we performed a grid-based spatiotemporal mapping and analysis to calculate a ratio among abnormal driving events, minor and major traffic accidents by road type. The findings suggest a potential for enhancing road safety by monitoring and controlling abnormal driving patterns as a precursor for accidents.

A COMPREHENSIVE MEASUREMENT MODEL FOR JOB-HOUSING BALANCE CONSIDERING SPATIAL INTERACTIONS: A CASE STUDY IN SHANGHAI

Abstract. Assessing jobs-housing balance (JHB) is crucial for the optimization of urban spatial pattern and transportation planning. However, due to the limitations of traditional data and the inability to integrate evaluation indicators, the evaluation results may be bias, conflicting with common sense. Considering interaction of multiple factors, a Job-housing balance measurement model (JHBM-SEM) was proposed to accurately evaluate the degree of JHB in a certain area. To address the problem of multi-indicator fusion, the structural equation model is used to incorporate the advantages, limitations and complementary relationships of each evaluation indicator into the evaluation process. A function relationship is defined between factor loadings, path coefficients, and variable scores to construct the measurement model and obtain comprehensive evaluation results. In addition, by integrating multi-source spatiotemporal big data mainly based on mobile signaling data, the paper can effectively mine the current status of job-housing balance in cities, and solve problems such as low precision and granularity of traditional data. The model was validated using Shanghai as an example, and the results show that compared with classical methods, the model's results are more consistent with the real situation of regional jobhousing patterns. It can identify the pseudo-balance phenomenon in underdeveloped areas of the suburbs and make reasonable evaluations, and the intermediate process can explain the direction of the imbalance factors in the region.

RESEARCH ON EFFICIENT INDEXING OF LARGE-SCALE GEOSPATIAL DATA BASED ON MULTI-LEVEL GEOGRAPHIC GRID

Abstract. With the implementation of unified natural resource management in China, national geographic conditions monitoring data have been identified as fundamental data for natural resource survey and monitoring. The efficiency of information extraction from massive spatio-temporal data to support natural resource management has emerged as a critical indicator for maximizing the value of geographic conditions monitoring data and enhancing data-driven decision management. Traditional spatial indices are computationally intensive, and when confronted with immense data volume or uneven data scale, issues such as extensive index computations and poor scale adaptability arise, impeding the efficient retrieval of complex geospatial data. In response to the need for efficient indexing of massive geospatial monitoring data at a scale of 100 million, a multi-level geographic spatial index framework based on geographic grids is proposed. Within the geographic conditions spatio-temporal database, a three-level spatial index of "zone-grid-space" is constructed, utilizing massive land cover data for analysis and testing. The results demonstrate that the multi-level spatial index method exhibits excellent scale adaptability, and grid coding dimensionality reduction and numerical operations effectively reduce the computational load of spatial retrievals of complex vector patches. This method significantly improves the retrieval efficiency of large-scale national geographic conditions data, providing an efficient technique for lightweight information extraction of large-scale monitoring geospatial data within spatial computing systems. The method holds reference value for on-demand retrieval, analysis, and decision-making of natural resource spatio-temporal big data.

Dynamics of pesticides in surface water bodies by applying data mining to spatiotemporal big data. A case study for the Puglia Region

Surface water pollution by pesticides is a primary concern in many parts of the world. Therefore, an effective monitoring program is essential to assess the environmental state of aquatic ecosystems and evaluate mitigation strategies.In the Puglia Region (southern Italy), a complex seasonal monitoring program for detecting pesticide residues in water was initiated in 2018 and is still underway (over four years). The program was based on site-specific assessments and identified 170 Plant Protection Products (PPPs) to identify residues in the surface water bodies of Puglia.In this context, the present work aims to analyse pesticide data obtained from the regional monitoring of rivers over four years using a multidisciplinary approach, which includes statistical methods, data mining, and mapping tools to extract as much information as possible.To this end, data mining was applied to identify the prevalent mixtures of pesticide residues found at the monitoring stations and correlate these results with possible causative factors. The results showed that surface water bodies are subject to different pressures derived from the massive use of PPPs, and several seasonal and territorial-related factors were identified to be strictly correlated with pesticide concentration results. Nine main PPPs mixtures have been identified in the Puglia River. Glyphosate, AMPA, imidacloprid, and azoxystrobin represent the main residues detected in the surface aquatic environments regarding the amount and frequency revealed.The methodological approach proposed in the present work can represent a good “model study” to be used by researchers to interpret water quality trends and data variability from long-term monitoring studies. Moreover, our results can significantly support the decision-making process and implementation of environmental mitigation measures by optimising the results of complex monitoring programs.

A Big Data Grided Organization and Management Method for Cropland Quality Evaluation

A new gridded spatio-temporal big data fusion method is proposed for the organization and management of cropland big data, which could serve the analysis application of cropland quality evaluation and other analyses of geographic big data. Compared with traditional big data fusion methods, this method maps the spatio-temporal and attribute features of multi-source data to grid cells in order to achieve the structural unity and orderly organization of spatio-temporal big data with format differences, semantic ambiguities, and different coordinate projections. Firstly, this paper constructs a dissected cropland big data fusion model and completes the design of a conceptual model and logic model, constructs a cropland data organization model based on DGGS (discrete global grid system) and Hash coding, and realizes the unified management of vector data, raster data and text data by using multilevel grids. Secondly, this paper researches the evaluation methods of grid-scale adaptability, and generates distributed multilevel grid datasets to meet the needs of cropland area quality evaluation. Finally, typical data such as soil organic matter data, road network data, cropland area data, and statistic data in Da’an County, China, were selected to carry out the experiment. The experiment verifies that the method could not only realize the unified organization and efficient management of cultivated land big data with multimodal characteristics, but also support the evaluation of cropland quality.

Research on the construction and reform path of online and offline hybrid English teaching model based on time series

Abstract With the widespread use of blended English teaching, the teaching model has become increasingly rigid and needs to be reformed. This paper proposes an analytical model of student learning behavior under blended English teaching based on big spatiotemporal data and constructs a structure of offline and online student learning behavior. The DWT-FCM clustering method for learning behaviors is constructed by using a similar distance based on time series instead of Euclidean distance in the traditional fuzzy C-mean algorithm. The maximum information coefficient was used to analyze the correlation between learning behaviors and grades based on MI. the DWT-FCM algorithm divided learning behaviors into five clusters, where the discussion number of cluster 0 reached an average of 45.2, which was 3.2 times higher than that of cluster 1. Cluster 4 has an average number of task completions of only 1.88, which will skip almost the entire course taken. The correlations of the number of task completion, course video progress, and assignment evaluation scores are all highly correlated, with correlation coefficients reaching 0.884 and 0.825, respectively. This study suggests the influence of learning characteristics on English performance in a blended teaching model, which can effectively guide the reform of the blended English teaching model.

Population Spatialization Based on Convolutional Neural Network and Multi-Source Geospatial Big Data

This article adopts a more refined method to create a population spatial distribution model for Guizhou Province in 2021. Firstly, we divide the population data of Guizhou Province into different characteristic regions. Then, we use these regions as independent variables and population as dependent variables for analysis using convolutional neural network (CNN). In addition, we also introduced multi-source spatiotemporal data such as Points of Interest (POI), nighttime lighting, and Digital Elevation Model (DEM) for modeling to further enhance the accuracy and accuracy of the model. Through this method, we successfully generated the population spatial data of Guizhou Province in 2021 with a resolution of 1km. These data intuitively display the spatial distribution of the population in Guizhou Province, and have important reference value for policy makers, researchers, and planners.In addition, in order to verify the accuracy of our model, we evaluated and compared the accuracy of the model results. The results show that our model has high accuracy in expressing the spatial distribution of population, andcan accurately reflect the spatial distribution of population at different scales.In summary, this article successfully created a population spatial distribution model for Guizhou Province in 2021 using multiple spatiotemporal big data and multiple linear statistical regression methods, and evaluated and compared the accuracy of the model results. These results have important practical significance for understanding the population distribution, formulating land use planning and public policies in Guizhou Province.

Associations between greenspace characteristics and population emotion perceptions in three dimensions

Introduction: Mental disorders are considered to be the most significant threat to public health. Mitigation effects of urban green spaces have been widely documented. However, the conclusions are inconsistent because of the representativeness of the original data.Method: We measured the mental perception of urban green spaces using geospatial big data instead of field observation or questionnaire data. Street view data were applied to calculate urban green space characteristics in three dimensions.Results: The positive mental perception percentage around the chosen urban parks increased as the buffer diameter decreased. The temporal variations of positive mental perceptions around the selected urban parks exhibited an obvious peak-trough shape. The spatial associations between the positive mental perception percentage and urban green space characteristics varied geographically. The spatial associations became less similar as the spatial buffer diameter decreased. At the same spatial scale level, the green view played a dominant role in the spatial distribution of positive mental perceptions.Discussion: Shrinking the deviations of urban green space characteristics and increasing the mean and maximum values of urban green space characteristics will favor the improvement of public mental health. This study provides a reference for explaining ecological scientific questions using spatiotemporal big data. It also provides insights into the mechanisms underlying the relationship between ecological processes and public health.

Multidimensional Spatial Driving Factors of Urban Vitality Evolution at the Subdistrict Scale of Changsha City, China, Based on the Time Series of Human Activities

Urban vitality is an important reflection of a city’s development potential and urban quality. This study used exploratory spatio-temporal big data such as social media check-ins to portray the spatio-temporal evolution of urban vitality at the subdistrict scale in Changsha, a city in central China, from 2013 to 2021, finding that urban vitality in Changsha exhibited central agglomeration and outward circling expansion over time, and then we used Geodetector and spatial regression analyses to explain the interactive effects and spatio-temporal heterogeneity of the spatial elements of subdistrict form, subdistrict function, and subdistrict economy on urban vitality. The results show the following: (1) The subdistrict form and subdistrict function dimensions had a significant effect on urban vitality, and the effect of the economic dimension of the subdistrict was not significant. (2) The interaction effect of the density of entertainment and leisure facilities and the density of business office facilities in subdistrict function was the dominant factor in the change of urban vitality. (3) Under the spatio-temporal effect, land use diversity and park facility density had the strongest positive effect on urban vitality; road density and shopping facility density had the weakest effect. The study aimed to provide a reference for the optimization and allocation of spatial elements of subdistricts in sustainable urban development and urban renewal, in order to achieve the purpose of urban vitality creation and enhancement.

Spatio-Temporal Relevance Classification from Geographic Texts Using Deep Learning

The growing proliferation of geographic information presents a substantial challenge to the traditional framework of a geographic information analysis and service. The dynamic integration and representation of geographic knowledge, such as triples, with spatio-temporal information play a crucial role in constructing a comprehensive spatio-temporal knowledge graph and facilitating the effective utilization of spatio-temporal big data for knowledge-driven service applications. The existing knowledge graph (or geographic knowledge graph) takes spatio-temporal as the attribute of entity, ignoring the role of spatio-temporal information for accurate retrieval of entity objects and adaptive expression of entity objects. This study approaches the correlation between geographic knowledge and spatio-temporal information as a text classification problem, with the aim of addressing the challenge of establishing meaningful connections among spatio-temporal data using advanced deep learning techniques. Specifically, we leverage Wikipedia as a valuable data source for collecting and filtering geographic texts. The Open Information Extraction (OpenIE) tool is employed to extract triples from each sentence, followed by manual annotation of the sentences’ spatio-temporal relevance. This process leads to the formation of quadruples (time relevance/space relevance) or quintuples (spatio-temporal relevance). Subsequently, a comprehensive spatio-temporal classification dataset is constructed for experiment verification. Ten prominent deep learning text classification models are then utilized to conduct experiments covering various aspects of time, space, and spatio-temporal relationships. The experimental results demonstrate that the Bidirectional Encoder Representations from Transformer-Region-based Convolutional Neural Network (BERT-RCNN) model exhibits the highest performance among the evaluated models. Overall, this study establishes a foundation for future knowledge extraction endeavors.