Big Spatiotemporal Data Analytics: a research and innovation frontier

Yang, Chaowei; Clarke, Keith; Shekhar, Shashi; Tao, Chao

doi:10.1080/13658816.2019.1698743

Cited by 69 publications

(51 citation statements)

References 79 publications

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“…In the future, the ArcCI system will be enhanced [40] by (1) including more scalable computing resources for the dynamic on-demand Web service, which would enable users to process and analyze HSR images using pixel-based or object-based methods; (2) integrating data fusion analysis by combining low spatial resolution satellite images to extract geophysical properties at different scales;…”

Section: Resultsmentioning

confidence: 99%

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

Sha

Miao

et al. 2020

Data

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Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented through an open data portal. The ArcCI module offers an architecture based on cloud computing and big data components for HSR sea ice images, including functionalities of (1) data acquisition through File Transfer Protocol (FTP) transfer, front-end uploading, and physical transfer; (2) data storage based on Hadoop distributed file system and matured operational relational database; (3) distributed image processing including object-based image classification and parameter extraction of sea ice features; (4) 3D visualization of dynamic spatiotemporal distribution of extracted parameters with flexible statistical charts. Arctic researchers can search and find arctic sea ice HSR image and relevant metadata in the open data portal, obtain extracted ice parameters, and conduct visual analytics interactively. Users with large number of images can leverage the service to process their image in high performance manner on cloud, and manage, analyze results in one place. The ArcCI module will assist domain scientists on investigating polar sea ice, and can be easily transferred to other HSR image processing research projects.

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Section: Resultsmentioning

confidence: 99%

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

Sha

Miao

et al. 2020

Data

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“…Landsat, Sentinel, and MODIS). However, functionalities of handling the big spatiotemporal data are not readily available regarding query optimization, preprocessing, and streaming information extraction from the massive spatiotemporal data (Yang et al 2017;Li et al 2017aLi et al , 2017bYang et al 2019a).…”

Section: Applicationmentioning

confidence: 99%

Spatiotemporal event detection: a review

Bambacus

Cervone

et al. 2020

International Journal of Digital Earth

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The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.

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“…Liu et al, 2015;Xu et al, 2016). Open data sources provide fine-scale and dynamic spatiotemporal big data for accessibility research (Yang, Clarke, Shekhar, & Tao, 2019). Web mapping services provide a more accurate approach for obtaining travel impedance data between origin and destination for MTM (N. Xia et al, 2018).…”

Section: Residential Transport Mode Choices With Multimodal Transportmentioning

confidence: 99%

Measuring spatiotemporal accessibility for pediatric clinical services with multimodal transport modes: an exploratory analysis in Nanjing, China

Zhou¹,

Li²,

Chen³

et al. 2020

Preprint

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Background: Healthcare accessibility research is developing towards a focus on multimodal transport modes (MTM) and spatiotemporal variation. Dynamic traffic conditions lead residents to make distinct traveling decisions in different timepoints, which has an impact on the spatiotemporal accessibility of healthcare. Pediatric clinic services (PCS) are one of the typical healthcare services that require a diagnosis through a professional physician clinic.Results: This paper aims to examine a methodological framework for the spatiotemporal accessibility of PCS (STA-PCS) and obtains its spatiotemporal variation characteristics. We design a spatial time impedance of multimodal transport modes (STI-MTM) model, which considers residential transport mode choices and adopt a gravity model based on web mapping data and population spatial distribution data to measure STA-PCS. We selected Nanjing, China, as the study area to estimate the STA-PCS value at four timepoints. The results indicate that the spatial aggregate of PCS is evident, and dynamic traffic factors influence the volatility of STA-PCS.Conclusions: This work holds pragmatic implications for policymakers on the STA-PCS considered travel characteristics based on georeferenced social media data.

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Big Spatiotemporal Data Analytics: a research and innovation frontier

Cited by 69 publications

References 79 publications

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

Spatiotemporal event detection: a review

Measuring spatiotemporal accessibility for pediatric clinical services with multimodal transport modes: an exploratory analysis in Nanjing, China

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