A spatiotemporal data collection of viral cases for COVID-19 rapid response

Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic.Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures.To address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data standards.Within the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city levels.This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step.This rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal analysis.The data is available in Harvard Dataverse platform(https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://github.com/stccenter/COVID-19-Data).

A vocabulary recommendation method for spatiotemporal data discovery based on Bayesian network and ontologies

In the research field of spatiotemporal data discovery,how to utilize the semantic characteristics of spatiotemporal datasets is an important topic.This paper presented a content-based recommendation method,and applied Bayesian networks and ontologies into the vocabulary recommendation process for spatiotemporal data discovery.The source data of this research was from the MUDROD(Mining and Utilizing Dataset Relevancy from Oceanographic Datasets)search platform.From the historical search log,major keywords were extracted and organized according to ontologies in a hierarchical structure.Using the search history,the posterior probability between each subclass and their super class in the ontologies was calculated,indicating a recommendation likelihood.We created a Bayesian network model for inference based on ontologies.This model can address the following two objectives:(1)Given one class in the ontology,the model can judge which class has the biggest likelihood to be selected for recommendation.(2)Based on the search history of a user,the Bayesian network model can judge which class has the biggest probability to be recommended.Comparison experimentation with existing system and evaluation experimentation with expert knowledge show that this method is specifically helpful for spatiotemporal data discovery.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.

Spatial Distribution Characteristics and Influencing Factors of Traditional Villages in Northern Guangxi Based on Spatiotemporal Big Data and Spatial Syntax

Public space as an extension of private living spaces carries the different social life and customs of human settlement.To analyze the spatial distribution characteristics of traditional villages in northern Guangxi based on spatial syntax and its influencing factors,this paper analyzed and compared the spatial structure and morphology of traditional villages in northern Guangxi by using the theory of spatial syntax and linguistics as the quantitative analysis method of spatial syntax,and verified the feasibility of expanding the application of spatial syntax,finally,the generality and characteristics of the spatial structure and form of traditional villages in northern Guangxi were put forward.Protection has been implemented.According to the comprehensibility data in this paper,the comprehensibility of the village 1 in northern Guangxi is 0.52,the village 2 is 0.40,the village 3 is 0.35,the village 4 is 0.48,the village 5 is 0.55 and the village 6 is 0.50.It showed that in the selected 6 village samples,except for the 3 ones in northern Guangxi,the local space of the other 3 villages could better perceive the overall space,which also reflected the overall space permeability of most traditional villages in northern Guangxi was good.

Stream Segmentation-A Data Fusion Approach for Sensor Networks

Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitoring of environment, building infrastructures and human health. Many researchers view the sensor networks as databases, and the monitoring tasks are performed as subscriptions, queries, and alert. However, this point is not precise. First, databases can only deal with well-formed data types, with well-defined schema for their interpretation, while the raw data collected by the sensor networks, in most cases, do not fit to this requirement. Second, sensor networks have to deal with very dynamic targets, environment and resources, while databases are more static. In order to fill this gap between sensor networks and databases, we propose a novel approach, referred to as 'spatiotemporal data stream segmentation', or 'stream segmentation' for short, to address the dynamic nature and deal with 'raw' data of sensor networks. Stream segmentation is defined using Bayesian Networks in the context of sensor networks, and two application examples are given to demonstrate the usefulness of the approach.

Research on three-dimension ocean observation data integration and service technology

Currently,ocean data portals are being developed around the world based on Geographic Information Systems(GIS) as a source of ocean data and information.However,given the relatively high temporal frequency and the intrinsic spatial nature of ocean data and information,no current GIS software is adequate to deal effectively and efficiently with spatiotemporal data.Furthermore,while existing ocean data portals are generally designed to meet the basic needs of a broad range of users,they are sometimes very complicated for general audiences,especially for those without training in GIS.In this paper,a new technical architecture for an ocean data integration and service system is put forward that consists of four layers:the operation layer,the extract,transform,and load(ETL) layer,the data warehouse layer,and the presentation layer.The integration technology based on the XML,ontology,and spatiotemporal data organization scheme for the data warehouse layer is then discussed.In addition,the ocean observing data service technology realized in the presentation layer is also discussed in detail,including the development of the web portal and ocean data sharing platform.The application on the Taiwan Strait shows that the technology studied in this paper can facilitate sharing,access,and use of ocean observation data.The paper is based on an ongoing research project for the development of an ocean observing information system for the Taiwan Strait that will facilitate the prevention of ocean disasters.

Updating Strategy of Campus Space Based on Multi-source Data:A Case Study of West Campus of Yangtze University

Under the macro background of rapid urbanization and social transformation in China,campus space renewal has become an important practice and carrier for the sustainable development of schools.The study on campus space by big data and quantitative reflection of spatial distribution of applicable people in different areas of the campus can provide a certain scientific basis for campus space updating.West campus of Yangtze University is taken as research object.Based on cognitive map method,questionnaire survey method,behavior trajectory and correlation analysis method,the types and characteristics of campus space composition,campus satisfaction,usage and its relevance are analyzed.Research results show that ①the overall imageability of campus space is higher,which has a significantly positive correlation with the satisfaction of campus environment,and has no correlation with users’ behavior activities.The use frequency of non teaching areas varies greatly in different periods of time.②The correlation between the surrounding green vegetation and the image degree of campus landmarks is the most significant,and the coefficient is 0.886.③The correlation between spatial size suitability and regional image degree is the most significant,and the coefficient is 0.937.④The correlation between public activity facilities in the region and node image degree is the most significant,and the coefficient is 0.995.According to the research results,the corresponding solutions are put forward to provide scientific and quantitative reference suggestions for the renewal and transformation of the campus.

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Spatio-temporal heterogeneous data is the database for decisionmaking in many fields,and checking its accuracy can provide data support for making decisions.Due to the randomness,complexity,global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions,traditional detection methods can not guarantee both detection speed and accuracy.Therefore,this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks.Firstly,the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology.Secondly,design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy.Finally,the proposed method is compared with three methods,ARIMA,T-GCN,and STGCN,in real scenarios to verify its effectiveness in terms of detection speed,detection accuracy and stability.The experimental results show that the RMSE,MAE,and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree,which are 13.82/12.08,2.77/2.41,and 16.70/14.73,respectively.Also,it detects the shortest time of 672.31/887.36,respectively.In addition,the evaluation results are the same under different time periods of processing and complex topology environment,which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.

Sparse representation-based correlation analysis of non-stationary spatiotemporal big data

As the basic data of digital city and smart city research,Spatiotemporal series data contain rich geographic information.Alongside the accumulation of spatial time-series data,we are also encountering new challenges related to analyzing and mining the correlations among the data.Because the traditional methods of analysis also have their own suitable condition restrictions for the new features,we propose a new analytical framework based on sparse representation to describe the time,space,and spatial-time correlation.First,before analyzing the correlation,we discuss sparse representation based on the K-singular value decomposition(K-SVD)algorithm to ensure that the sparse coefficients are in the same sparse domain.We then present new computing methods to calculate the time,spatial,and spatial-time correlation coefficients in the sparse domain;we then discuss the functions’properties.Finally,we discuss change regulations for the gross domestic product(GDP),population,and Normalized Difference Vegetation Index(NDVI)spatial time-series data in China’s Jing-Jin-Ji region to confirm the effectiveness and adaptability of the new methods.

Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different cities.To effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration prediction.Specifically,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data series.The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features.Considering that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance.The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data.In order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other baselines.Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.

Taxi origin and destination demand prediction based on deep learning:a review

Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.

A Spatiotemporal Causality Based Governance Framework for Noisy Urban Sensory Data

Urban sensing is one of the fundamental building blocks of urban computing.It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural environment in urban areas.Nevertheless,issues such as uneven distribution,low sampling rate and high failure ratio of sensors often make their readings less reliable.This paper provides an innovative framework to detect the noise data as well as to repair them from a spatial-temporal causality perspective rather than to deal with them inclividually.This can be achieved by connecting data through monitored objects,using the Skip-gram model to estimate spatial correlation and long shortterm memory to estimate temporal correlation.The framework consists of three major modules:1)a space embedded Bidirectional Long Short-Term Memory(BiLSTM)-based sequence labeling module to detect the noise data and the latent missing data;2)a space embedded BiLSTM-based sequence predicting module calculating the value of the missing data;3)an object characteristics fusion repairing module to correct the spatial and temporal dislocation sensory data.The approach is evaluated with real-world data collected by over 3000 electronic traffic bayonet devices in a citywide scale of a medium-sized city in China,and the result is superior to those of several referenced approaches.With a 12.9%improvement,in data accuracy over the raw data,the proposed framework plays a significant,role in various real-world use cases in urban governance,such as criminal investigation,traffic violation monitoring,and equipment maintenance.

Big Earth data analytics:a survey

Big Earth data are produced from satellite observations,Internet-ofThings,model simulations,and other sources.The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding,responding,and addressing challenges of Earth sciences and applications.In the past years,new technologies(such as cloud computing,big data and artificial intelligence)have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically intractable.This paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing domain.We first introduce the concepts of big Earth data.The architecture,various functionalities,and supporting modules are then reviewed from a generic methodology aspect.Analytical methods supporting the functionalities are surveyed and analyzed in the context of different tools.The driven questions are exemplified through cutting-edge Earth science researches and applications.A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future.

WMO:an ontology for the semantic enrichment of wetland monitoring data

Rich observation data generated by ubiquitous sensors are vital for wetland monitoring,spanning from the prediction of natural disasters to emergency response.Such sensors use different data acquisition and description methods and,if combined,could provide a comprehensive description of the wetland.Unfortunately,these data remain hidden in isolated silos,and their variety makes integration and interoperability a significant challenge.In this work,we develop a semantic model for wetland monitoring data using an agile and modular approach,namely,wetland monitoring ontology(WMO),which containsfive modules:wetland ecosystem,monitoring indicator,monitoring context,geospatial context,and temporal context.The proposed ontology supports the semantic interoperability and integration of wetland monitoring data from multiple sources,domains,modes,and spatiotemporal scales.We also provide two real-world use cases to validate the WMO and demonstrate the WMO’s usability and reusability.

Spatio-temporal evolution and influencing factors of geopolitical relations among Arctic countries based on news big data

Global warming has caused the Arctic Ocean ice cover to shrink.This endangers the environment but has made traversing the Arctic channel possible.Therefore,the strategic position of the Arctic has been significantly improved.As a near-Arctic country,China has formulated relevant policies that will be directly impacted by changes in the international relations between the eight Arctic countries(regions).A comprehensive and real-time analysis of the various characteristics of the Arctic geographical relationship is required in China,which helps formulate political,economic,and diplomatic countermeasures.Massive global real-time open databases provide news data from major media in various countries.This makes it possible to monitor geographical relationships in real-time.This paper explores key elements of the social development of eight Arctic countries(regions)over 2013-2019 based on the GDELT database and the method of labeled latent Dirichlet allocation.This paper also constructs the national interaction network and identifies the evolution pattern for the relationships between Arctic countries(regions).The following conclusions are drawn.(1)Arctic news hotspot is now focusing on climate change/ice cap melting which is becoming the main driving factor for changes in geographical relationships in the Arctic.(2)There is a strong correlation between the number of news pieces about ice cap melting and the sea ice area.(3)With the melting of the ice caps,the social,economic,and military activities in the Arctic have been booming,and the competition for dominance is becoming increasingly fierce.In general,there is a pattern of domination by Russia and Canada.

Advances in spatiotemporal graph neural network prediction research

Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.

A survey of urban visual analytics:Advances and future directions

Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models.Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities.To promote further academic research and assist the development of industrial urban analytics systems,we comprehensively review urban visual analytics studies from four perspectives.In particular,we identify 8 urban domains and 22 types of popular visualization,analyze 7 types of computational method,and categorize existing systems into 4 types based on their integration of visualization techniques and computational models.We conclude with potential research directions and opportunities.

Assessing relevance of tweets for risk communication

Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event.Primarily,tweets containing information about the flooding events and its impacts were analysed.Examination of the relationships between tweet volume and its content with precipitation amount,damage extent,and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages.However,only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event.By providing insight into the quality of social media data and its usefulness to emergency management activities,this study contributes to the literature on quality of big data.Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.

Recognizing mixed urban functions from human activities using representation learning methods

When various urban functions are integrated into one location,they form a mixture of functions.The emerging big data promote an alternative way to identify mixed functions.However,current methods are largely unable to extract deep features in these data,resulting in low accuracy.In this study,we focused on recognizing mixed urban functions from the perspective of human activities,which are essential indicators of functional areas in a city.We proposed a framework to comprehensively extract deep features of human activities in big data,including activity dynamics,mobility interactions,and activity semantics,through representation learning methods.Then,integrating these features,we employed fuzzy clustering to identify the mixture of urban functions.We conducted a case study using taxiflow and social media data in Beijing,China,in whichfive urban functions and their correlations with land use were recognized.The mixture degree of urban functions in each location was revealed,which had a negative correlation with taxi trip distance.The results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human activities.This study has important implications for urban planners in understanding urban systems and developing better strategies.