ABSTRACT
We develop a method for analyzing spatial and spatiotemporal anomalies in geospatial data using topological data analysis (TDA). To do this, we use persistent homology (PH), which allows one to algorithmically detect geometric voids in a data set and quantify the persistence of such voids. We construct an efficient filtered simplicial complex (FSC) such that the voids in our FSC are in one-to-one correspondence with the anomalies. Our approach goes beyond simply identifying anomalies;it also encodes information about the relationships between anomalies. We use vineyards, which one can interpret as time-varying persistence diagrams (which are an approach for visualizing PH), to track how the locations of the anomalies change with time. We conduct two case studies using spatially heterogeneous COVID-19 data. First, we examine vaccination rates in New York City by zip code at a single point in time. Second, we study a year-long data set of COVID-19 case rates in neighborhoods of the city of Los Angeles.
ABSTRACT
Long-term spatiotemporal Land Use and Land Cover (LULC) analysis is an objective tool for assessing patterns of sustainable development (SD). The basic purpose of this research is to define the Driving Mechanisms (DM) and assess the trend of SD in the Burabay district (Kazakhstan), which includes a city, an agro-industrial complex, and a national natural park, based on the integrated use of spatiotemporal data (STD), economic, environmental, and social (EES) indicators. The research was performed on the GEE platform using Landsat and Random Forest. The DM were studied by Multiple Linear Regression and Principal Component Analysis. SD trend was assessed through sequential transformations, aggregations, and integrations of 36 original STD and EES indicators. The overall classification accuracy was 0.85–0.97. Over the past 23 years, pasture area has changed the most (−16.69%), followed by arable land (+14.72%), forest area increased slightly (+1.81%), and built-up land—only +0.16%. The DM of development of the AOI are mainly economic components. There has been a noticeable drop in the development growth of the study area in 2021, which is apparently a consequence of the COVID-19. The upshots of the research can serve as a foundation for evaluating SD and LULC policy.
ABSTRACT
To understand the complex phenomena in social space and monitor the dynamic changes in people's tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. To solve this problem, we proposed a management method of multi-granularity dimensions for spatiotemporal data. This method systematically described dimension granularity and the fuzzy caused by dimension granularity, and used multi-scale integer coding technology to organize and manage multi-granularity dimensions, and realized the integrity of the data query results according to the correlation between the different scale codes. We simulated the time and band data for the experiment. The experimental results showed that: (1) this method effectively solves the problem of incomplete query results of the intersection query method. (2) Compared with traditional string encoding, the query efficiency of multiscale integer encoding is twice as high. (3) The proportion of different dimension granularity has an impact on the query effect of multi-scale integer coding. When the proportion of fine-grained data is high, the advantage of multi-scale integer coding is greater.
ABSTRACT
PurposeCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.Design/methodology/approachThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.FindingsResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.Originality/valueThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.
ABSTRACT
In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject's cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.
ABSTRACT
The furious disease named COVID-19 is an outbreak in the current scenario. To control the spreading of this disease, new models were developed which utilized established methodologies to analyze how different containment strategies will influence the spread of the virus. It presents a novel machine learning approach that can estimate any epidemiological model's parameters based on two types of information: either static or dynamic. It primarily utilizes the Graph model using deep learning approaches and Long-term memories (LSTMs) to obtain mobility data's spatial and temporal properties of SIR and SIRD models. It runs and simulates using data on the Italian COVID dynamics and compares the model predictions to previously observed epidemics. © 2022 Scrivener Publishing LLC.
ABSTRACT
Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.
ABSTRACT
COVID-19 is not the last virus;there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims an HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. The proposed STRENUOUS system discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject's cross-path can be infected and suggest quarantine followed by testing. The proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus.
ABSTRACT
The recent waves of COVID-19 highlighted the importance of understanding and quantifying spatiotemporal interactions to infer, model, and predict disease spread in real time. In this demonstration paper, we present a robust infrastructure for interactive exploration of interregional and international spatiotemporal interactions via time-lagged correlations of increases in COVID-19 incidence. This infrastructure consists of: (i) an operational data store (ODS) coupled with automated scripts for downloading, cleaning, and processing data from heterogeneous sources;(ii) a server application handling on-demand analyses of the database data through a RESTful API;and (iii) a web application providing the interactive dashboard to explore various correlation and geostatistical metrics of the integrated data in spacetime. The environment allows users to study focal spatiotemporal trends and the potential of regions to export and import the virus. Moreover, the application has the potential to reveal the effect of the national border to mitigate the interaction, particularly the spread of the virus. The infrastructure serves COVID-19 data from Germany, Poland, and Czechia, with the possibility of extension to other regions and topics. The dashboard is under active development and accessible on www.where2test.de/correlation. © 2022 Owner/Author.
ABSTRACT
Data visualization is a powerful tool for understating information through visual cues. However, allowing novices to create visualization artifacts for what they want to see is not easy, just as not everyone can write SQL queries. Arguably, the most natural way to specify what to visualize is through natural language or speech, similar to our daily search on Google or Apple Siri, leaving to the system the task of reasoning about what to visualize and how. In this demo, we present Sevi an end-to-end data visualization system that acts as a virtual assistant to allow novices to create visualizations through either natural language or speech. Sevi is powered by two main components: Speech2Text which is based on Google Cloud Speech-to-Text Rest API, and Text2VIS, which uses an end-to-end neural machine translation model called ncNet trained using a cross-domain benchmark called nvBench. Both ncNet and nvBench have been developed by us. We will walk the audience through two general domain datasets, one related to COVID-19 and the other on NBA player statistics, to highlight how Sevi enables novices to easily create data visualizations. Because nvBench contains Text2VIS training samples from 105 domains (e.g., sport, college, hospital, etc.), the audience can play with speech or text input with any of these domains.
ABSTRACT
Since the onset of the COVID-19 pandemic, mil-lions of coronavirus sequences have been rapidly deposited in publicly available repositories. The sequences have been used primarily to monitor the evolution and transmission of the virus. In addition, the data can be combined with spatiotemporal information and mapped over space and time to understand transmission dynamics further. For example, the first COVID-19 cases in Australia were genetically related to the dominant strain in Wuhan, China, and spread via international travel. These data are currently available through the Global Initiative on Sharing Avian Influenza Data (GISAID) yet generally remains an untapped resource for data scientists to analyze such multi-dimensional data. Therefore, in this study, we demonstrate a system named Phyloview, a highly interactive visual environment that can be used to examine the spatiotemporal evolution of COVID-19 (from-to) over time using the case study of Louisiana, USA. PhyloView (powered by ArcGIsInsights) facilitates the visualization and exploration of the different dimensions of the phylogenetic data and can be layered with other types of spatiotemporal data for further investigation. Our system has the potential to be shared as a model to be used by health officials that can access relevant data through GISAID, visualize, and analyze it. Such data is essential for a better understanding, predicting, and responding to infectious diseases. © 2022 IEEE.
ABSTRACT
Aiming at the problem of COVID-19 epidemic data visualization, this paper proposes a spatiotemporal visualization analysis method based on the technology of scraping crawler and visualization, and carries on the visualization analysis and research, intuitively shows the development and change of the epidemic situation in different countries and regions, and excavates its spatiotemporal variation rules. Firstly, we use scrapy crawler framework to collect COVID-19 epidemic data;then, the collected data were cleaned and processed to construct a spatiotemporal data set of COVID-19 epidemic;finally, pyecharts is used to analyze the dataset data visually. The results showed the changes and trends of epidemic situation in different countries and regions, and provided reference for epidemic prevention and control. © 2021 IEEE.
ABSTRACT
Together with rapid development of location-based services and big-data platforms especially in urban areas, huge amount of spatiotemporal data are collected without properly used;on the other hand, state-of-the-art quantitative policy effect assessment techniques usually require panel data as input. To solve both issues, this paper follows the following approach: obtaining panel data by aggregating spatiotemporal data and feeding them to the effect assessment module. With the help of high-performance computing techniques which are able to deal with huge amount of data, we build framework Aggr-analysis which applies clustering algorithms to shrink the raw data set and find associations between different data sets via co-location analysis. Finally, we prove the effectiveness by an example: analysis of resident activities during the COVID-19 Pandemic. We apply Aggr-analysis to process the share-bike usage data and POI (Point Of Interest) data in Beijing, then obtain the panel data required by DID (Difference-in-Differences) method. Supplemented with environmental data, we conclude the net effect of the COVID-19 breakout on society and economy - the pandemic has reduced the overall resident mobility by 64.8% within two months.
ABSTRACT
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.
ABSTRACT
COVID-19 is not the last virus;there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims a HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. Proposed STRENUOUS system discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject’s cross-path can be infected and suggest quarantine followed by testing. The Proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus. IEEE
ABSTRACT
The spread of infectious diseases is usually a highly nonlinear space-time diffusion process. Epidemiological models can not only be used to predict the epidemic trend, but also be used to systematically and scientifically study the transmission mechanism of the complex processes under different hypothetical intervention scenarios, which provide crucial analytical and planning tools for public health studies and policy-making. Since host behavior is one of the critical driven factors for the dynamics of infectious diseases, it is important to effectively integrate human spatiotemporal behavior into the epidemiological models for human-hosted infectious diseases. Due to the rapid development of human mobility research and applications aided by big trajectory data, many of the epidemiological models for Coronavirus Disease 2019 (COVID-19) have already coupled human mobility. By incorporating real trajectory data such as mobile phone location data at an individual or aggregated level, researchers are working towards the direction of accurately depicting the real world, so as to improve the effectiveness of the model in guiding actual epidemic prevention and control. The epidemic trend prediction, Non-pharmaceutical Interventions (NPIs) evaluation, vaccination strategy design, and transmission driven factors have been studied by the epidemiological models coupled with human mobility, which provides scientific decision-making aid for controlling epidemic in different countries and regions. In order to systematically understand this important progress of epidemiological models, this study collected and summarized relevant literatures. First, the interactions between the COVID-19 epidemic and human mobility were analyzed, which demonstrated the necessity of integrating the complex spatiotemporal behavior, such as population-based or individual-based mobility, activity, and contact interaction, into the epidemiological models. Then, according to the modeling purpose and mechanism, the models integrated with human mobility were discussed by two types: short-term epidemic prediction models and process simulation models. Among them, based on the coupling methods of human mobility, short-term epidemic prediction models can further be divided into models coupled with first-order and second-order human mobility, while process simulation models can be divided into models coupled with population-based mobility and individual-based mobility. Finally, we concluded that epidemiological models integrating human mobility should be developed towards more complex human spatiotemporal behaviors with a fine spatial granularity. Besides, it is in urgent need to improve the model capability to better understand the disease spread processes over space and time, break through the bottleneck of the huge computational cost of fine-grained models, cooperate cutting-edge artificial intelligence approaches, and develop more universal and accessible modeling data sets and tools for general users. 2021, Science Press. All right reserved.
ABSTRACT
Many cities in China have adopted a series of Non-Pharmaceutical Interventions (NPIs) and rapidly suppressed the 1 st wave of COVID-19 epidemic in 2020. It is critical to evaluate the effectiveness of these NPIs for future epidemic control. However, as a variety of NPIs were applied together in practice, it is difficult to evaluate the effectiveness of a single type of intervention by epidemiological observation. Taking Shenzhen city as an example, this study used a spatially explicit agent-based model by integrating mobile phone location data, travel survey data, building survey data and other multi-source spatiotemporal big data to evaluate the effectiveness of different types of NPIs in the suppression of the 1 st wave of COVID-19 epidemic in Shenzhen. The simulation results show that the peak of the epidemic would have appeared on the 127 th day since Jan 1st of 2020, resulting in an average of 72.26% of the population to be infected without any interventions. In the 1 st wave of Shenzhen epidemic, except for the hospitalization of confirmed cases and intercity traffic restrictions, the stay-at-home order was the most effective one, followed by comprehensive isolation and quarantine measures (for close contacts, imported population and suspected cases), mask wearing, and orderly resumption of work. The stay-at-home order and comprehensive isolation and quarantine measures can effectively control the large-scale outbreak of the COVID-19, which are identified as the core measures;Mask wearing and orderly resumption of work can only reduce the overall infection size and delay the epidemic peak, which are identified as secondary measures. Considering the socioeconomic costs and the receding compliance to interventions in the post-epidemic period, this study suggests that the core measures and secondary measures should be combined to control the sporadic cases. Specifically, the local government can give the highest priority to isolation and quarantine measures for confirmed cases and high-risk individuals, complemented by mask wearing. In addition, our model can reveal the high-risk infection areas at a community level, which can help deploy control measures within an urban environment. In summary, this study demonstrated the advantages of integrating spatiotemporal big data and agent-based models to simulate the spread processes of infectious diseases in an urban environment: it can not only simulate the evolving processes of an epidemic at a fine-grained scale, but also evaluate the effectiveness of the NPIs at an individual level and for activity-travel behaviors, which can be useful for precise intervention. 2021, Science Press. All right reserved.
ABSTRACT
In 2019, COVID-19 quickly spread across the world, infecting billions of people and disrupting the normal lives of citizens in every country. Governments, organizations, and research institutions all over the world are dedicating vast resources to research effective strategies to fight this rapidly propagating virus. With virus testing, most countries publish the number of confirmed cases, dead cases, recovered cases, and locations routinely through various channels and forms. This important data source has enabled researchers worldwide to perform different COVID-19 scientific studies, such as modeling this virus's spreading patterns, developing prevention strategies, and studying the impact of COVID-19 on other aspects of society. However, one major challenge is that there is no standardized, updated, and high-quality data product that covers COVID-19 cases data internationally. This is because different countries may publish their data in unique channels, formats, and time intervals, which hinders researchers from fetching necessary COVID-19 datasets effectively, especially for fine-scale studies. Although existing solutions such as John's Hopkins COVID-19 Dashboard and 1point3acres COVID-19 tracker are widely used, it is difficult for users to access their original dataset and customize those data to meet specific requirements in categories, data structure, and data source selection. To address this challenge, we developed a toolset using cloud-based web scraping to extract, refine, unify, and store COVID-19 cases data at multiple scales for all available countries around the world automatically. The toolset then publishes the data for public access in an effective manner, which could offer users a real time COVID-19 dynamic dataset with a global view. Two case studies are presented about how to utilize the datasets. This toolset can also be easily extended to fulfill other purposes with its open-source nature.
ABSTRACT
Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread. To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients' trajectory data. By using innovative space-time analytics, this framework reveals the spatiotemporal patterns of patients' mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.
ABSTRACT
The current pandemic of the coronavirus disease (COVID-19) has highlighted the importance of rapid control of the transmission of infectious diseases. This is particularly important for COVID-19, where many individuals are asymptomatic or have only mild symptoms but can still spread the disease. Current systems for controlling transmission rely on patients to report their symptoms to medical professionals and be able to recall and trace all their contacts from the previous few days. This is unrealistic in the modern world. However, existing smartphone-based GPS and social media technology may provide a suitable alternative. We, therefore, developed a mini-program within the app WeChat. This analyzes data from all users and traces close contacts of all patients. This permits early tracing and quarantine of potential sources of infection. Data from the mini-program can also be merged with other data to predict epidemic trends, calculate individual and population risks, and provide recommendations for individual and population protection action. It may also improve our understanding of how the disease spreads. However, there are a number of unresolved questions about the use of smartphone data for health surveillance, including how to protect individual privacy and provide safeguards against data breaches.