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Timko, Igor, Michael Böhlen, and Johann Gamper. "Sequenced spatiotemporal aggregation for coarse query granularities." VLDB Journal 20, no. 5 (September 8, 2011): 721–41. http://dx.doi.org/10.1007/s00778-011-0247-5.
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Jiang, Man, Qilong Han, Haitao Zhang, and Hexiang Liu. "Spatiotemporal Data Prediction Model Based on a Multi-Layer Attention Mechanism." International Journal of Data Warehousing and Mining 19, no. 2 (January 16, 2023): 1–15. http://dx.doi.org/10.4018/ijdwm.315822.
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Spatiotemporal data prediction is of great significance in the fields of smart cities and smart manufacturing. Current spatiotemporal data prediction models heavily rely on traditional spatial views or single temporal granularity, which suffer from missing knowledge, including dynamic spatial correlations, periodicity, and mutability. This paper addresses these challenges by proposing a multi-layer attention-based predictive model. The key idea of this paper is to use a multi-layer attention mechanism to model the dynamic spatial correlation of different features. Then, multi-granularity historical features are fused to predict future spatiotemporal data. Experiments on real-world data show that the proposed model outperforms six state-of-the-art benchmark methods.
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Wang, Pengyuan, Xiao Huang, Joseph Mango, Di Zhang, Dong Xu, and Xiang Li. "A Hybrid Population Distribution Prediction Approach Integrating LSTM and CA Models with Micro-Spatiotemporal Granularity: A Case Study of Chongming District, Shanghai." ISPRS International Journal of Geo-Information 10, no. 8 (August 13, 2021): 544. http://dx.doi.org/10.3390/ijgi10080544.
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Studying population prediction under micro-spatiotemporal granularity is of great significance for modern and refined urban traffic management and emergency response to disasters. Existing population studies are mostly based on census and statistical yearbook data due to the limitation of data collecting methods. However, with the advent of techniques in this information age, new emerging data sources with fine granularity and large sample sizes have provided rich materials and unique venues for population research. This article presents a new population prediction model with micro-spatiotemporal granularity based on the long short-term memory (LSTM) and cellular automata (CA) models. We aim at designing a hybrid data-driven model with good adaptability and scalability, which can be used in more refined population prediction. We not only try to integrate these two models, aiming to fully mine the spatiotemporal characteristics, but also propose a method that fuses multi-source geographic data. We tested its functionality using the data from Chongming District, Shanghai, China. The results demonstrated that, among all scenarios, the model trained by three consecutive days (ordinary dates), with the granularity of one hour, incorporated with road networks, achieves the best performance (0.905 as the mean absolute error) and generalization capability.
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Kragh-Furbo, Mette, and Gordon Walker. "Electricity as (Big) Data: Metering, spatiotemporal granularity and value." Big Data & Society 5, no. 1 (January 2018): 205395171875725. http://dx.doi.org/10.1177/2053951718757254.
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Electricity is hidden within wires and networks only revealing its quantity and flow when metered. The making of its properties into data is therefore particularly important to the relations that are formed around electricity as a produced and managed phenomenon. We propose approaching all metering as a situated activity, a form of quantification work in which data is made and becomes mobile in particular spatial and temporal terms, enabling its entry into data infrastructures and schemes of evaluation and value production. We interrogate the transition from the pre-digital into the making of bigger, more spatiotemporally granular electricity data, through focusing on those actors selling and materialising new metering technologies, data infrastructures and services for larger businesses and public sector organisations in the UK. We examine the claims of truth and visibility that accompany these shifts and their enrolment into management techniques that serve to more precisely apportion responsibility for, and evaluate the status of, particular patterns and instances of electricity use. We argue that whilst through becoming Big Data electricity flow is now able to be known and given identity in significantly new terms, enabling new relations to be formed with the many heterogeneous entities implicated in making and managing energy demand, it is necessary to sustain some ambivalence as to the performative consequences that follow for energy governance. We consider the wider application of our conceptualisation of metering, reflecting on comparisons with the introduction of new metering systems in domestic settings and as part of other infrastructural networks.
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Kupfer, John A., Zhenlong Li, Huan Ning, and Xiao Huang. "Using Mobile Device Data to Track the Effects of the COVID-19 Pandemic on Spatiotemporal Patterns of National Park Visitation." Sustainability 13, no. 16 (August 20, 2021): 9366. http://dx.doi.org/10.3390/su13169366.
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Effective quantification of visitation is important for understanding many impacts of the COVID-19 pandemic on national parks and other protected areas. In this study, we mapped and analyzed the spatiotemporal patterns of visitation for six national parks in the western U.S., taking advantage of large mobility records sampled from mobile devices and released by SafeGraph as part of their Social Distancing Metric dataset. Based on comparisons with visitation statistics released by the U.S. National Park Service, our results confirmed that mobility records from digital devices can effectively capture park visitation patterns but with much finer spatiotemporal granularity. In general, triggers of visitation changes corresponded well with the parks’ management responses to COVID-19, with all six parks showing dramatic decreases in the number of visitors (compared to 2019) beginning in March 2020 and continuing through April and May. As restrictions were eased to promote access to the parks and the benefits associated with outdoor recreation, visitation in 2020 approached or even passed that from 2019 by late summer or early autumn at most of the parks. The results also revealed that parks initially saw the greatest increases in visitation after reopening originating from nearby states, with visitorship coming from a broader range of states as time passed. Our study highlights the capability of mobility data for providing spatiotemporally explicit knowledge of place visitation.
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Ma, Jun, Yuexiong Ding, Vincent J. L. Gan, Changqing Lin, and Zhiwei Wan. "Spatiotemporal Prediction of PM2.5 Concentrations at Different Time Granularities Using IDW-BLSTM." IEEE Access 7 (2019): 107897–907. http://dx.doi.org/10.1109/access.2019.2932445.
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Ottaviano, Flavia, Fabing Cui, and Andy H. F. Chow. "Modeling and Data Fusion of Dynamic Highway Traffic." Transportation Research Record: Journal of the Transportation Research Board 2644, no. 1 (January 2017): 92–99. http://dx.doi.org/10.3141/2644-11.
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This paper presents a data fusion framework for processing and integrating data collected from heterogeneous sources on motorways to generate short-term predictions. Considering the heterogeneity in spatiotemporal granularity in data from different sources, an adaptive kernel-based smoothing method was first used to project all data onto a common space–time grid. The data were then integrated through a Kalman filter framework build based on the cell transmission model for generating short-term traffic state prediction. The algorithms were applied and tested with real traffic data collected from the California I-880 corridor in the San Francisco Bay Area from the Mobile Century experiment. Results revealed that the proposed fusion algorithm can work with data sources that are different in their spatiotemporal granularity and improve the accuracy of state estimation through incorporating multiple data sources. The present work contributed to the field of traffic engineering and management with the application of big data analytics.
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Wang, Ruxin, Hongyan Wu, Yongsheng Wu, Jing Zheng, and Ye Li. "Improving influenza surveillance based on multi-granularity deep spatiotemporal neural network." Computers in Biology and Medicine 134 (July 2021): 104482. http://dx.doi.org/10.1016/j.compbiomed.2021.104482.
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Chen, F., C. Jing, H. Zhang, and X. Lv. "WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 493–99. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-493-2022.
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Abstract. Student behavior research can improve learning efficiency, provide decision evidences for infrastructure management. Existing campus-scale behavioral analysis work have not taken into account the students characteristics and spatiotemporal pattern. Moreover, the visualization methods are weak in wholeness, intuitiveness and interactivity perspectives. In this paper, we design a geospatial dashboard-based student behavior analysis and visualization system considering students characteristics and spatiotemporal pattern. This system includes four components: user monitoring, data mining analysis, behavior prediction and spatiotemporal visualization. Furthermore, a deep learning model based on LSTNet to predict student behaviour. Our work takes WiFi log data of a university in Beijing as dataset. The results show that this system can identify student behavior patterns at a finer granularity by visualization method, which is helpful in improving learning and living efficiency.
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Jian, Yang, Jinhong Li, Lu Wei, Lei Gao, and Fuqi Mao. "Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting." Journal of Advanced Transportation 2022 (April 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/4260244.
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As a typical spatiotemporal problem, there are three main challenges in traffic forecasting. First, the road network is a nonregular topology, and it is difficult to extract complex spatial dependence accurately. Second, there are short- and long-term dependencies between traffic dates. Third, there are many other factors besides the influence of spatiotemporal dependence, such as semantic characteristics. To address these issues, we propose a spatiotemporal DeepWalk gated recurrent unit model (ST-DWGRU), a deep learning framework that fuses spatial, temporal, and semantic features for traffic speed forecasting. In the framework, the spatial dependency between nodes of an entire road network is extracted by graph convolutional network (GCN), whereas the temporal dependency between speeds is captured by a gated recurrent unit network (GRU). DeepWalk is used to extract semantic information from road networks. Three publicly available datasets with different time granularities of 15, 30, and 60 min are used to validate the short- and long-time prediction effect of this model. The results show that the ST-DWGRU model significantly outperforms the state-of-the-art baselines.
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Zhou, Kaichun, Zongshun Tian, and Yuanwei Yang. "Periodic Pattern Detection Algorithms for Personal Trajectory Data Based on Spatiotemporal Multi-Granularity." IEEE Access 7 (2019): 99683–93. http://dx.doi.org/10.1109/access.2019.2930619.
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Cardim, Luiz Henrique Anjos, and Nádia Puchalski Kozievitch. "Rastreia Saúde: A Spatiotemporal Disease Tracking System through Open Unstructured Data and GIS." Revista Brasileira de Cartografia 73, no. 4 (October 18, 2021): 999–1016. http://dx.doi.org/10.14393/rbcv73n4-59881.
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Automated disease tracking has become an increasingly important tool today. This article describes the prototype of a disease tracking system for the Brazilian territory, preliminarily tested at the state level, in Paraná, and at the municipal level, in Curitiba. This study aims to extract and present relevant information in the health segment from unstructured data, extracted from news portals. The system generates data that allows analysis at different levels of granularity, from small municipalities to the national level. The results of the study shows the viability of the system and allows the authors to identify some patterns in the processed data.
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Jiang, Feifeng, Jun Ma, and Zheng Li. "Pedestrian volume prediction with high spatiotemporal granularity in urban areas by the enhanced learning model." Sustainable Cities and Society 79 (April 2022): 103653. http://dx.doi.org/10.1016/j.scs.2021.103653.
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Xiao, Chuanliang, Lei Sun, and Ming Ding. "Multiple Spatiotemporal Characteristics-Based Zonal Voltage Control for High Penetrated PVs in Active Distribution Networks." Energies 13, no. 1 (January 3, 2020): 249. http://dx.doi.org/10.3390/en13010249.
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The penetration of photovoltaic (PV) outputs brings great challenges to optimal operation of active distribution networks (ADNs), especially leading to more serious overvoltage problems. This study proposes a zonal voltage control scheme based on multiple spatiotemporal characteristics for highly penetrated PVs in ADNs. In the spatial domain, a community detection algorithm using a reactive/ active power quality function was introduced to partition an ADN into sub-networks. In the time domain, short-term zonal scheduling (SZS) with 1 h granularity was drawn up based on a cluster. The objective was to minimize the supported reactive power and the curtailed active power in reactive and active power sub-networks. Additionally, a real-time zonal voltage control scheme (RZVC) with 1 min granularity was proposed to correct the SZS rapidly by choosing and controlling the key PV inverter to regulate the supported reactive power and the curtailed active power of the inverters to prevent the overvoltage in each sub-network. With the time domain cooperation, the proposed method could achieve economic control and avoid overvoltage caused by errors in the forecast data of the PVs. For the spatial domain, zonal scheduling and zonal voltage control were carried out in each cluster, and the short-term scheduling and voltage controlling problem of the ADN could then be decomposed into several sub-problems. This could simplify the optimization and control which can reduce the computing time. Finally, an actual 10kV, 103-node network in Zhejiang Province of China is employed to verify the effectiveness and feasibility of the proposed approach.
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Jing, Changfeng, Shasha Guo, Hongyang Zhang, Xinxin Lv, and Dongliang Wang. "SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus." ISPRS International Journal of Geo-Information 11, no. 3 (March 12, 2022): 194. http://dx.doi.org/10.3390/ijgi11030194.
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To achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneous users are lacking. Moreover, existing works focuses on mathematic models to identify and predict electricity consumption. Additionally, owing to the complex non-linear interrelationships, interactive visualizations are more effective in detecting patterns. Therefore, by combining geospatial dashboard knowledge and interactive visualization technology, a Smart Electricity dashboard (SmartEle) was designed and developed to interactively visualize big electrical data and interrelated factors. A university campus as the study area. The SmartEle system addressed three challenges. First, it permitted user group-oriented monitoring of electricity consumption patterns, which has seldom been considered in existing studies. Second, a visualization-driven data mining model was proposed, and an interactive visualization dashboard was designed to facilitate the perception of electricity usage patterns at different granularities and from different perspectives. Finally, to deal with the non-linear features of electricity consumption, the ATT-LSTM machine learning model to support multivariate collaborative predicting was proposed to improve the accuracy of short-term electricity consumption predictions. The results demonstrated that the SmartEle system is usable for electricity planning and management.
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Shen, Nuozhou, Haiping Zhang, Haoran Wang, Xuanhong Zhou, Lei Zhou, and Guo’An Tang. "Toward multi-granularity spatiotemporal simulation modeling of crowd movement for dynamic assessment of tourist carrying capacity." GIScience & Remote Sensing 59, no. 1 (November 4, 2022): 1857–81. http://dx.doi.org/10.1080/15481603.2022.2139450.
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Li, Mingxiao, Song Gao, Feng Lu, Huan Tong, and Hengcai Zhang. "Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data." International Journal of Environmental Research and Public Health 16, no. 22 (November 15, 2019): 4522. http://dx.doi.org/10.3390/ijerph16224522.
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The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks.
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Ramadoss, Balakrishnan, and Kannan Rajkumar. "Modelling and Querying the Expressive Semantics of Dance Videos." Journal of Information & Knowledge Management 05, no. 03 (September 2006): 193–210. http://dx.doi.org/10.1142/s0219649206001463.
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Dance videos are interesting and semantics-intensive. At the same time, they are complex type of videos, when compared to all other types such as sports, news and movie videos. In fact, dance video is the one which is less explored by the researchers across the globe. This paper presents a dance video data model to represent the semantics of the dance videos with different granularity levels, identified by the components of the accompanying song. Secondly, the paper proposes a multi-level index structure to efficiently handle containment, temporal, spatial and spatiotemporal query types. Moreover, the index structure has been designed to consider the queries with different levels of constraints. Finally, the paper presents the experimental results depicting the performance of different types of dance video queries.
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Näpflin, Kathrin, Emily A. O’Connor, Lutz Becks, Staffan Bensch, Vincenzo A. Ellis, Nina Hafer-Hahmann, Karin C. Harding, et al. "Genomics of host-pathogen interactions: challenges and opportunities across ecological and spatiotemporal scales." PeerJ 7 (November 5, 2019): e8013. http://dx.doi.org/10.7717/peerj.8013.
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Evolutionary genomics has recently entered a new era in the study of host-pathogen interactions. A variety of novel genomic techniques has transformed the identification, detection and classification of both hosts and pathogens, allowing a greater resolution that helps decipher their underlying dynamics and provides novel insights into their environmental context. Nevertheless, many challenges to a general understanding of host-pathogen interactions remain, in particular in the synthesis and integration of concepts and findings across a variety of systems and different spatiotemporal and ecological scales. In this perspective we aim to highlight some of the commonalities and complexities across diverse studies of host-pathogen interactions, with a focus on ecological, spatiotemporal variation, and the choice of genomic methods used. We performed a quantitative review of recent literature to investigate links, patterns and potential tradeoffs between the complexity of genomic, ecological and spatiotemporal scales undertaken in individual host-pathogen studies. We found that the majority of studies used whole genome resolution to address their research objectives across a broad range of ecological scales, especially when focusing on the pathogen side of the interaction. Nevertheless, genomic studies conducted in a complex spatiotemporal context are currently rare in the literature. Because processes of host-pathogen interactions can be understood at multiple scales, from molecular-, cellular-, and physiological-scales to the levels of populations and ecosystems, we conclude that a major obstacle for synthesis across diverse host-pathogen systems is that data are collected on widely diverging scales with different degrees of resolution. This disparity not only hampers effective infrastructural organization of the data but also data granularity and accessibility. Comprehensive metadata deposited in association with genomic data in easily accessible databases will allow greater inference across systems in the future, especially when combined with open data standards and practices. The standardization and comparability of such data will facilitate early detection of emerging infectious diseases as well as studies of the impact of anthropogenic stressors, such as climate change, on disease dynamics in humans and wildlife.
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Zhao, Jun, Yang Liu, Witold Pedrycz, and Wei Wang. "Spatiotemporal Prediction for Energy System of Steel Industry by Generalized Tensor Granularity Based Evolving Type-2 Fuzzy Neural Network." IEEE Transactions on Industrial Informatics 17, no. 12 (December 2021): 7933–45. http://dx.doi.org/10.1109/tii.2021.3062036.
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Park, Jinwoo, and Daniel W. Goldberg. "A Review of Recent Spatial Accessibility Studies That Benefitted from Advanced Geospatial Information: Multimodal Transportation and Spatiotemporal Disaggregation." ISPRS International Journal of Geo-Information 10, no. 8 (August 9, 2021): 532. http://dx.doi.org/10.3390/ijgi10080532.
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Spatial accessibility provides significant policy implications, describing the spatial disparity of access and supporting the decision-making process for placing additional infrastructure at adequate locations. Several previous reviews have covered spatial accessibility literature, focusing on empirical findings, distance decay functions, and threshold travel times. However, researchers have underexamined how spatial accessibility studies benefitted from the recently enhanced availability of dynamic variables, such as various travel times via different transportation modes and the finer temporal granularity of geospatial data in these studies. Therefore, in our review, we investigated methodological advancements in place-based accessibility measures and scrutinized two recent trends in spatial accessibility studies: multimodal spatial accessibility and temporal changes in spatial accessibility. Based on the critical review, we propose two research agendas: improving the accuracy of measurements with dynamic variable implementation and furnishing policy implications granted from the enhanced accuracy. These agendas particularly call for the action of geographers on the full implementation of dynamic variables and the strong linkage between accessibility and policymaking.
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Uhl, Johannes H., Stefan Leyk, Caitlin M. McShane, Anna E. Braswell, Dylan S. Connor, and Deborah Balk. "Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States." Earth System Science Data 13, no. 1 (January 27, 2021): 119–53. http://dx.doi.org/10.5194/essd-13-119-2021.
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Abstract. The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth's surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250 m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c).
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Jiang, Xuexia, Tadamoto Isogai, Joseph Chi, and Gaudenz Danuser. "Fine-grained, nonlinear registration of live cell movies reveals spatiotemporal organization of diffuse molecular processes." PLOS Computational Biology 18, no. 12 (December 30, 2022): e1009667. http://dx.doi.org/10.1371/journal.pcbi.1009667.
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We present an application of nonlinear image registration to align in microscopy time lapse sequences for every frame the cell outline and interior with the outline and interior of the same cell in a reference frame. The registration relies on a subcellular fiducial marker, a cell motion mask, and a topological regularization that enforces diffeomorphism on the registration without significant loss of granularity. This allows spatiotemporal analysis of extremely noisy and diffuse molecular processes across the entire cell. We validate the registration method for different fiducial markers by measuring the intensity differences between predicted and original time lapse sequences of Actin cytoskeleton images and by uncovering zones of spatially organized GEF- and GTPase signaling dynamics visualized by FRET-based activity biosensors in MDA-MB-231 cells. We then demonstrate applications of the registration method in conjunction with stochastic time-series analysis. We describe distinct zones of locally coherent dynamics of the cytoplasmic protein Profilin in U2OS cells. Further analysis of the Profilin dynamics revealed strong relationships with Actin cytoskeleton reorganization during cell symmetry-breaking and polarization. This study thus provides a framework for extracting information to explore functional interactions between cell morphodynamics, protein distributions, and signaling in cells undergoing continuous shape changes. Matlab code implementing the proposed registration method is available at https://github.com/DanuserLab/Mask-Regularized-Diffeomorphic-Cell-Registration.
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Rusňák, Tomáš, Andrej Halabuk, Ľuboš Halada, Hubert Hilbert, and Katarína Gerhátová. "Detection of Invasive Black Locust (Robinia pseudoacacia) in Small Woody Features Using Spatiotemporal Compositing of Sentinel-2 Data." Remote Sensing 14, no. 4 (February 16, 2022): 971. http://dx.doi.org/10.3390/rs14040971.
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Recognition of invasive species and their distribution is key for managing and protecting native species within both natural and man-made ecosystems. Small woody features (SWF) represent fragmented patches or narrow linear tree features that are of high importance in intensively utilized agricultural landscapes. Simultaneously, they frequently serve as expansion pathways for invasive species such as black locust. In this study, Sentinel-2 products, combined with spatiotemporal compositing approaches, are used to address the challenge of broad area black locust mapping at a high granularity. This is accomplished by conducting a comprehensive analysis of the classification performance of various compositing approaches and multitemporal classification settings throughout four vegetation seasons. The annual, seasonal (bi-monthly), and monthly median values of cloud-masked Sentinel-2 reflectance products are aggregated and stacked into varied time-series datasets per given year. The random forest algorithm is trained and output classification maps validated based on field-based reference datasets across Danubian lowlands (Slovakia). The main results of the study proved the usefulness of spatiotemporal compositing of Sentinel-2 products for mapping black locust in small woody features across wide area. In particular, temporally aggregated monthly composites stacked to seasonal time series datasets yielded consistently high overall accuracies ranging from 89.10% to 91.47% with balanced producer’s and user’s accuracies for each year’s annual series. We presume that a similar approach could be used for a broader scale species distribution mapping, assuming they are spectrally or phenologically distinctive, as is often the case for many invasive species.
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Li, Xiantong, Hua Wang, Pengcheng Sun, and Hongquan Zu. "Spatiotemporal Features—Extracted Travel Time Prediction Leveraging Deep-Learning-Enabled Graph Convolutional Neural Network Model." Sustainability 13, no. 3 (January 25, 2021): 1253. http://dx.doi.org/10.3390/su13031253.
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Travel time prediction is one of the most important parameters to forecast network-wide traffic conditions. Travelers can access traffic roadway networks and arrive in their destinations at the lowest costs guided by accurate travel time estimation on alternative routes. In this study, we propose a long short-term memory (LSTM)-based deep learning model, deep learning on spatiotemporal features with Convolution Neural Network (DLSF-CNN), to extract the spatial–temporal correlation of travel time on different routes to accurately predict route travel time. Specifically, this model utilizes network-wide travel time, considering its topological structure as inputs, and combines convolutional neural network and LSTM techniques to accurately predict travel time. In addition to their spatial dependence, both coarse-grained and fine-grained temporal dependences are fully considered among the road segments along a route as well. The shift problem is formulated in the coarse-grained granularity to predict the route travel time in the next time interval. The experimental tests were conducted using real route travel time obtained by taxi trajectories in Harbin. The test results show that the travel time prediction accuracy of DLSF-CNN is above 90%. Meanwhile, the proposed model outperformed the other machine learning models based on multiple evaluation criteria. The RMSE (Root Mean Squard Error) and R2 (R Squared) increased by 18.6% and 22.46%, respectively. The results indicate the proposed model performs reasonably well under prevailing traffic conditions.
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Chen, Junzhou, Jiancheng Wang, Jiajun Pu, and Ronghui Zhang. "A Three-Stage Anomaly Detection Framework for Traffic Videos." Journal of Advanced Transportation 2022 (July 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/9463559.
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As reported by the United Nations in 2021, road accidents cause 1.3 million deaths and 50 million injuries worldwide each year. Detecting traffic anomalies timely and taking immediate emergency response and rescue measures are essential to reduce casualties, economic losses, and traffic congestion. This paper proposed a three-stage method for video-based traffic anomaly detection. In the first stage, the ViVit network is employed as a feature extractor to capture the spatiotemporal features from the input video. In the second stage, the class and patch tokens are fed separately to the segment-level and video-level traffic anomaly detectors. In the third stage, we finished the construction of the entire composite traffic anomaly detection framework by fusing outputs of two traffic anomaly detectors above with different granularity. Experimental evaluation demonstrates that the proposed method outperforms the SOTA method with 2.07% AUC on the TAD testing overall set and 1.43% AUC on the TAD testing anomaly subset. This work provides a new reference for traffic anomaly detection research.
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Bauer, Cici, Kehe Zhang, Wenjun Li, Dana Bernson, Olaf Dammann, Marc R. LaRochelle, and Thomas J. Stopka. "Small Area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling Approach." JMIR Public Health and Surveillance 9 (February 10, 2023): e41450. http://dx.doi.org/10.2196/41450.
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Background Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. Objective The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts. Methods We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts’ 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas. Results Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019. Conclusions Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.
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Li, Kenan, Sandrah P. Eckel, Erika Garcia, Zhanghua Chen, John P. Wilson, and Frank D. Gilliland. "Geographic Variations in Human Mobility Patterns during the First Six Months of the COVID-19 Pandemic in California." Applied Sciences 13, no. 4 (February 14, 2023): 2440. http://dx.doi.org/10.3390/app13042440.
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Human mobility influenced the spread of the COVID-19 virus, as revealed by the high spatiotemporal granularity location service data gathered from smart devices. We conducted time series clustering analysis to delineate the relationships between human mobility patterns (HMPs) and their social determinants in California (CA) using aggregated smart device tracking data from SafeGraph. We first identified four types of temporal patterns for five human mobility indicator changes by applying dynamic-time-warping self-organizing map clustering methods. We then performed an analysis of variance and linear discriminant analysis on the HMPs with 17 social, economic, and demographic variables. Asians, children under five, adults over 65, and individuals living below the poverty line were found to be among the top contributors to the HMPs, including the HMP with a significant increase in the median home dwelling time and the HMP with emerging weekly patterns in full-time and part-time work devices. Our findings show that the CA shelter-in-place policy had varying impacts on HMPs, with socially disadvantaged places showing less compliance. The HMPs may help practitioners to anticipate the efficacy of non-pharmaceutical interventions on cases and deaths in pandemics.
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Li, Sijia, Chao Wu, Yu Lin, Zhengyang Li, and Qingyun Du. "Urban Morphology Promotes Urban Vibrancy from the Spatiotemporal and Synergetic Perspectives: A Case Study Using Multisource Data in Shenzhen, China." Sustainability 12, no. 12 (June 12, 2020): 4829. http://dx.doi.org/10.3390/su12124829.
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Urban vibrancy is the key and the foundation for monitoring the status of urban spatial development, assisting in data-driven urban development planning and realizing sustainable urban development. Based on a dataset of multisource geographical big data, the understanding and analysis of urban vibrancy can be deepened with fine granularity. The working framework in this study focuses on the comprehensive perspective of urban morphology, which is decomposed into two dimensions (formality and functionality) and four elements (road, block, building, point of interest). The geographically and temporally weighted regression model was first applied to determine the spatiotemporal effect of the morphological metrics on vibrancy, and then, the geographical detector was employed from the perspective of spatially stratified heterogeneity to reveal the synergetic impacts. The following findings were revealed. (1) Dense street networks, small and medium-sized blocks, and the diversification and intensification of building and land use are beneficial to urban vibrancy. (2) Under the premise of adapting to local conditions, urban spaces combine multiple morphological metrics for the accomplishment of a full-region and all-time vibrancy. (3) The mixture of urban functions is worthy of attention for vibrancy growth because of its extraordinary synergy, not its capacity. Morphological metrics serve to foster and prolong urban vibrancy, adapt to urban sustainability, and contend against inefficient, disorderly urban sprawl. These findings provide significant implications for urban planners/designers and policymakers to optimize urban morphology, improve the vibrancy in large cities, and implement high-quality city planning.
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Zuo, Chenyu, Linfang Ding, and Liqiu Meng. "Visual Analytics for Regional Economic Environment Factors Based on a Dashboard Design." Proceedings of the ICA 2 (July 10, 2019): 1–8. http://dx.doi.org/10.5194/ica-proc-2-158-2019.
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<p><strong>Abstract.</strong> Economic environment is vital for commercial investment, city planning and company strategy planning in urban areas. Mastering the economical trend may help the entrepreneurs, government officers and individuals in their decision-making process. In this study, we explore multiple geo-economic datasets using visual analytics methods for understanding the economic environment. More specifically, we user time-series Gross Domestic Product (GDP) data as an economic indicator of economic development and land use data to support the spatial analysis at a refined geographic scale. The spatiotemporal patterns of the regional economic environment are revealed both qualitatively and quantitatively. The work has a three-fold contributions: (1) we apply a grid-based spatial interpolation model to derive GDP values at a file granularity based on land use data; (2) we design a novel interactive dashboard for the GDP data exploration, which serves as a visual analytical tool between data and users; (3) we combine quantitative analysis with visualizations to strengthen the qualitative analysis. The feasibility of visual analytics methods and the dashboard design are tested in one of the most developed regions, Jiangsu Province, China. Both expected and unexpected economical patterns were extracted.</p>
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Victor, Jonathan D., and Mary M. Conte. "Evoked potential and psychophysical analysis of Fourier and non-Fourier motion mechanisms." Visual Neuroscience 9, no. 2 (August 1992): 105–23. http://dx.doi.org/10.1017/s0952523800009573.
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AbstractSome visual stimuli produce a strong percept of motion, even though they fail to excite motion detectors based on Fourier energy or cross correlation. Models which suffice to explain the motion percept in these non-Fourier motion (NFM) stimuli include linear spatiotemporal filtering, followed by rectification, followed by standard motion analysis (Chubb & Sperling 1988). We used the human “motion-onset” evoked potential, which has been assigned to area 17 on the basis of work in the macaque (van Dijk et al., 1986; van Dijk & Spekreijse, 1989), to investigate the neural substrate of the processing stages postulated in the above models. Motion-onset VEPs elicited by FM and NFM matched for spatial and temporal characteristics were indistinguishable in temporal characteristics and scalp topography at a transverse chain of electrodes. Addition of textural cues (granularity and higher-order form) did not influence the response dynamics or scalp topography of NFM responses. However, comparison of responses to NFM stimuli and related stimuli without coherent motion but similar spatial and temporal properties showed that the motion-onset responses were distinct from responses to the onset of fixed flicker-defined contours not undergoing coherent motion. We discuss the implications of these results for computational models of motion analysis.
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Si, Yutian, Liyan Xu, Xiao Peng, and Aihan Liu. "Comparative Diagnosis of the Urban Noise Problem from Infrastructural and Social Sensing Approaches: A Case Study in Ningbo, China." International Journal of Environmental Research and Public Health 19, no. 5 (February 28, 2022): 2809. http://dx.doi.org/10.3390/ijerph19052809.
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Urban noise causes a variety of health problems, and its prevention and control have thus become an important research topic in urban governance. Although existing literature is fairly comprehensive in revealing the physical noise patterns, it lacks the concern of people’s perceived seriousness, especially at the macroscopic, i.e., citywide scale. In this paper, we borrow from the “exposure-perception-behavior” theory in environmental psychology, and propose an analytical framework for diagnosing the urban noise problem that integrates the Infrastructural and Social Sensing perspectives. Utilizing noise monitoring data that fills the spatiotemporal granularity gaps of official noise monitoring, as well as the “12345” urban complaint hotline records which serve as a proxy for residents’ perceived noise levels, we empirically examine the mechanisms for physical magnitude and perceived seriousness of urban noise, respectively, by taking the Jiangbei District of Ningbo City, China as an example. Results show that the existence of perceptual bias and behavioral preference effects did shape people’s perceived noise problem map that is vastly different from that of the physical noise magnitude, in which the semantics of urban places, temporal rhythms of life, and population demographics significantly influenced people’s tolerance of noise. We conclude the paper with suggestions on updating the existing National Standard for urban noise regulation to reflect the perceptual aspect, and also methodological discussions on possible ways to recognize and utilize the perceptual bias in social-sensing big-data to better accommodate urban governance.
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Minelli, Annalisa, Iwan Le Berre, Ingrid Peuziat, and Mathias Rouan. "Reconstruction of Marine Traffic from Sémaphore Data: A Python-GIS Procedure to Build Synthetic Navigation Routes and Analyze Their Temporal Variation." Journal of Marine Science and Engineering 9, no. 3 (March 7, 2021): 294. http://dx.doi.org/10.3390/jmse9030294.
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Originally designed as a mode of telecommunication, the network of French sémaphore is now dedicated to the continuous monitoring and recording of marine traffic along the entire French coast. Although the observation data collected by sémaphores cover 7/7 days and 24/24 h and could provide precious information regarding marine traffic, they remain underexploited. Indeed, these data concern all types of traffic, including leisure boating and smaller craft that are not usually recorded by the most common means of observation, such as AIS, radar and satellite. Based on sémaphore data, traffic pressure and its spatiotemporal distribution can be fully measured to better analyze its interactions with human activities and the environment. One drawback of these data is their initially semantic nature, which requires the development of an original processing method. The protocol developed to analyze the marine traffic of the Iroise Sea and its first results are presented in this article. It is based on a semi-automatic method aimed to clean the original data and quantify the marine traffic along synthetic routes. It includes a procedure that takes into account the temporal evolution of the traffic based on the Allen’s time framework. The results proved interesting as they provide an overview of marine traffic, including all types of vessels, and may be defined for different time periods and granularity. A description of the numerical and geographic instruments created is given; all the written code is released as Open Source software and freely available for download and testing.
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Raveh, Barak, Liping Sun, Kate L. White, Tanmoy Sanyal, Jeremy Tempkin, Dongqing Zheng, Kala Bharath, et al. "Bayesian metamodeling of complex biological systems across varying representations." Proceedings of the National Academy of Sciences 118, no. 35 (August 27, 2021): e2104559118. http://dx.doi.org/10.1073/pnas.2104559118.
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Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.
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Jiang, Haonan, Timo Balz, Francesca Cigna, and Deodato Tapete. "Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data." Remote Sensing 13, no. 7 (March 25, 2021): 1256. http://dx.doi.org/10.3390/rs13071256.
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Wuhan is an important city in central China, with a rapid development that has led to increasingly serious land subsidence over the last decades. Most of the existing Interferometric Synthetic Aperture Radar (InSAR) subsidence monitoring studies in Wuhan are either short-term investigations—and thus can only detect this process within limited time periods—or combinations of different Synthetic Aperture Radar (SAR) datasets with temporal gaps in between. To overcome these constraints, we exploited nearly 300 high-resolution COSMO-SkyMed StripMap HIMAGE scenes acquired between 2012 and 2019 to monitor the long-term subsidence process affecting Wuhan and to reveal its spatiotemporal variations. The results from the Persistent Scatterer Interferometric SAR (PSInSAR) processing highlight several clearly observable subsidence zones. Three of them (i.e., Houhu, Xinrong, and Guanggu) are affected by serious subsidence rates and non-linear temporal behavior, and are investigated in this paper in more detail. The subsidence in Houhu is caused by soft soil consolidation and compression. Soil mechanics are therefore used to estimate when the subsidence is expected to finish and to calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression and is gradually stabilizing. The subsidence curve found for the area around Xinrong shows that the construction of an underground tract of the subway Line 21 caused large-scale settlement in this area. The temporal granularity of the PSInSAR time series also allows precise detection of a rebound phase following a major flooding event in 2016. In the southern industrial park of Guanggu, newly detected subsidence was found. The combination of the subsidence curve with an optical time-series image analysis indicates that urban construction is the main trigger of deformation in this area. While this study unveils previously unknown characters of land subsidence in Wuhan and clarifies the relationship with the urban causative factors, it also proves the benefits of non-linear PSInSAR in the analysis of the temporal evolution of such processes in dynamic and expanding cities.
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Jones, Rodney P. "Excess Winter Mortality (EWM) as a Dynamic Forensic Tool: Where, When, Which Conditions, Gender, Ethnicity and Age." International Journal of Environmental Research and Public Health 18, no. 4 (February 23, 2021): 2161. http://dx.doi.org/10.3390/ijerph18042161.
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To investigate the dynamic issues behind intra- and international variation in EWM (Excess Winter Mortality) using a rolling monthly EWM calculation. This is used to reveal seasonal changes in the EWM calculation and is especially relevant nearer to the equator where EWM does not reach a peak at the same time each year. In addition to latitude country specific factors determine EWM. Females generally show higher EWM. Differences between the genders are highly significant and seem to vary according to the mix of variables active each winter. The EWM for respiratory conditions in England and Wales ranges from 44% to 83%, which is about double the all-cause mortality equivalent. A similar magnitude of respiratory EWM is observed in other temperate countries. Even higher EWM can be seen for specific respiratory conditions. Age has a profound effect on EWM with a peak at puberty and then increases EWM at older ages. The gap between male and female EWM seems to act as a diagnostic tool reflecting the infectious/metrological mix in each winter. Difference due to ethnicity are also observed. An EWM equivalent calculation for sickness absence demonstrates how other health-related variables can be linked to EWM. Midway between the equator and the poles show the highest EWM since such areas tend to neglect the importance of keeping dwellings warm in the winter. Pandemic influenza does not elevate EWM, although seasonal influenza plays a part each winter. Pandemic influenza and changes in influenza strain/variant mix do, however, create structural breaks in the time series and this implies that comparing EWM between studies conducted over different times can be problematic. Cancer is an excellent example of the usefulness of rolling method since cancer EWM drifts each year, in some years increasing winter EWM and in other years diminishing it. In addition, analysis of sub-national EWM in the UK reveals high spatiotemporal granularity indicating roles for infectious outbreaks. The rolling method gives greater insight into the dynamic nature of EWM, which otherwise lies concealed in the current static method.
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Gouripeddi, Ram, Andrew Miller, Karen Eilbeck, Katherine Sward, and Julio C. Facelli. "3399 Systematically Integrating Microbiomes and Exposomes for Translational Research." Journal of Clinical and Translational Science 3, s1 (March 2019): 29–30. http://dx.doi.org/10.1017/cts.2019.71.
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OBJECTIVES/SPECIFIC AIMS: Characterize microbiome metadata describing specimens collected, genomic pipelines and microbiome results, and incorporate them into a data integration platform for enabling harmonization, integration and assimilation of microbial genomics with exposures as spatiotemporal events. METHODS/STUDY POPULATION: We followed similar methods utilized in previous efforts in charactering and developing metadata models for describing microbiome metadata. Due to the heterogeneity in microbiome and exposome data, we aligned them along a conceptual representation of different data used in translational research; microbiomes being biospecimen-derived, and exposomes being a combination of sensor measurements, surveys and computationally modelled data. We performed a review of literature describing microbiome data, metadata, and semantics [4–15], along with existing datasets [16] and developed an initial metadata model. We reviewed the model with microbiome domain experts for its accuracy and completeness, and with translational researchers for its utility in different studies, and iteratively refined it. We then incorporated the logical model into OpenFurther’s metadata repository MDR [17,18] for harmonization of different microbiome datasets, as well as integration and assimilation of microbiome-exposome events utilizing the UPIE. RESULTS/ANTICIPATED RESULTS: Our model for describing the microbiome currently includes three domains (1) the specimen collected for analysis, (2) the microbial genomics pipelines, and (3) details of the microbiome genomics. For (1), we utilized biospecimen data model that harmonizes the data structures of caTissue, OpenSpecimen and other commonly available specimen management platform. (3) includes details about the organisms, isolate, host specifics, sequencing methodology, genomic sequences and annotations, microbiome phenotype, genomic data and storage, genomic copies and associated times stamps. We then incorporated this logical model into the MDR as assets and associations that UPIE utilizes to harmonize different microbiome datasets, followed by integration and assimilation of microbiome-exposome events. Details of (2) are ongoing. DISCUSSION/SIGNIFICANCE OF IMPACT: The role of the microbiome and co-influences from environmental exposures in etio-pathology of various pulmonary conditions isn’t well understood [19–24]. This metadata model for the microbiome provides a systematic approach for integrating microbial genomics with sensor-based environmental and physiological data, and clinical data that are present in varying spatial and temporal granularities and require complex methods for integration, assimilation and analysis. Incorporation of this microbiome model will advance the performance of sensor-based exposure studies of the (UPIE) to support novel research paradigms that will improve our understanding of the role of microbiome in promoting and preventing airway inflammation by performing a range of hypothesis-driven microbiome-exposome pediatric asthma studies across the translational spectrum.
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Ji, Fang, Linfeng Fan, Xingxing Kuang, Xin Li, Bin Cao, Guodong Cheng, Yingying Yao, and Chunmiao Zheng. "How does soil water content influence permafrost evolution on the Qinghai-Tibet plateau under climate warming?" Environmental Research Letters, May 4, 2022. http://dx.doi.org/10.1088/1748-9326/ac6c9a.
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Abstract The active layer thickness (ALT) in permafrost regions regulates hydrological cycles, water sustainability, and ecosystem functions in the cryosphere and is extremely sensitive to climate change. Previous studies often focused on the impacts of rising temperature on the ALT, while the roles of soil water content and soil granularity have rarely been investigated. Here, we incorporate alterations of soil water contents in soil thermal properties across various soil granularities and assess spatiotemporal ALT dynamics on the Qinghai-Tibet Plateau (QTP). The regional average ALT on the QTP is projected to be nearly 4 m by 2100. Our results indicate that soil wetting decelerates the active layer thickening in response to warming, while latent heat exerts stronger control on ALTs than thermal conductivity does. Under similar warming conditions, active layers thicken faster in coarse soils than in fine soils. An important ramification of this study is that neglecting soil wetting may cause overestimations of active layer thickening on the QTP.
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Zha, Cheng, Weidong Min, Qing Han, Xin Xiong, Qi Wang, and Qian Liu. "Multiple Granularity Spatiotemporal Network for Sea Surface Temperature Prediction." IEEE Geoscience and Remote Sensing Letters, 2022, 1. http://dx.doi.org/10.1109/lgrs.2022.3167744.
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Zhou, Zhengyang, Yang Wang, Xike Xie, Lianliang Chen, and Chaochao Zhu. "Foresee Urban Sparse Traffic Accidents: A Spatiotemporal Multi-Granularity Perspective." IEEE Transactions on Knowledge and Data Engineering, 2020, 1. http://dx.doi.org/10.1109/tkde.2020.3034312.
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Zhao, Shuai, Daxing Zhao, Ruiqiang Liu, Zhen Xia, Bo Cheng, and Junliang Chen. "GMAT-DU: Traffic Anomaly Prediction With Fine Spatiotemporal Granularity in Sparse Data." IEEE Transactions on Intelligent Transportation Systems, 2023, 1–15. http://dx.doi.org/10.1109/tits.2023.3249409.
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Cheng, Zhifeng, Jianghao Wang, Kaixin Zhu, Yong Ge, and Chenghu Zhou. "Evaluating spatial statistical and machine learning models in urban dynamic population mapping." Transactions in Urban Data, Science, and Technology, August 5, 2022, 275412312211141. http://dx.doi.org/10.1177/27541231221114169.
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Understanding population dynamics at fine spatiotemporal granularities are valuable to human-centered studies. With the increasing availability of high-frequency human digital footprint data, the past decades have witnessed numerous efforts in mapping populations at fine spatiotemporal scales. However, such research still lacks a unified standard in modeling strategy and auxiliary data selection, especially a systematic comparison between newly developed machine learning techniques and traditional spatial statistical methods under different covariates provisions. Here, we compared two spatial statistical models, the Bayesian space-time model and geographically and temporally weighted regression, with two machine learning techniques, random forest and eXtreme gradient boosting, in a case study of hourly population mapping at 100 m resolution in Beijing. We evaluated the model performance with varied covariates combinations and found that the Bayesian space-time model achieved the best in conjunction with urban function data. Leveraging the optimal model constructed, we mapped dynamic population distribution and concluded human activity patterns on diverse city amenities. This paper emphasizes the importance of spatiotemporal dependency information in fine temporal scale population mapping and the urban function covariates in urban population mapping.
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Liu, Kai, Zhiju Chen, Toshiyuki Yamamoto, and Liheng Tuo. "Exploring the Impact of Spatiotemporal Granularity on the Demand Prediction of Dynamic Ride-Hailing." IEEE Transactions on Intelligent Transportation Systems, 2022, 1–11. http://dx.doi.org/10.1109/tits.2022.3216016.
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Lyu, Fangzheng, Shaohua Wang, Su Yeon Han, Charlie Catlett, and Shaowen Wang. "An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data." Urban Informatics 1, no. 1 (September 9, 2022). http://dx.doi.org/10.1007/s44212-022-00002-4.
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AbstractDue to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM2.5 concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data.
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Xiao, Jiang, Huichuwu Li, Minrui Wu, Hai Jin, M. Jamal Deen, and Jiannong Cao. "A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues." ACM Computing Surveys, April 19, 2022. http://dx.doi.org/10.1145/3530682.
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In the last decade, many studies have significantly pushed the limits of wireless device-free human sensing (WDHS) technology and facilitated various applications, ranging from activity identification to vital sign monitoring. This survey presents a novel taxonomy that classifies the state-of-the-art WDHS systems into eleven categories according to their sensing task type and motion granularity . In particular, existing WDHS systems involve three primary sensing task types. The first type, behavior recognition , is a classification problem of recognizing predefined meaningful behaviors. The second type is movement tracking , monitoring the quantitative values of behavior states integrating with spatiotemporal information. The third type, user identification , leverages the unique features in behaviors to identify who performs the movements. The selected papers in each sensing task type can be further divided into sub-categories according to their motion granularity. Recent advances reveal that WDHS systems within a particular granularity follow similar challenges and design principles. For example, fine-grained hand recognition systems target extracting subtle motion-induced signal changes from the noisy signal responses, and their sensing areas are limited to a relative small range. Coarse-grained activity identification systems need to overcome the interference of other moving objects within the room-level sensing range. A novel research framework is proposed to help to summarize WDHS systems from methodology, evaluation performance, and design goals. Finally, we conclude with several open issues and present the future research directions from the perspectives of data collection , sensing methodology , performance evaluation , and application scenario .
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Hohl, Alexander, Wenwu Tang, Irene Casas, Xun Shi, and Eric Delmelle. "Detecting space–time patterns of disease risk under dynamic background population." Journal of Geographical Systems, April 20, 2022. http://dx.doi.org/10.1007/s10109-022-00377-7.
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AbstractWe are able to collect vast quantities of spatiotemporal data due to recent technological advances. Exploratory space–time data analysis approaches can facilitate the detection of patterns and formation of hypotheses about their driving processes. However, geographic patterns of social phenomena like crime or disease are driven by the underlying population. This research aims for incorporating temporal population dynamics into spatial analysis, a key omission of previous methods. As population data are becoming available at finer spatial and temporal granularity, we are increasingly able to capture the dynamic patterns of human activity. In this paper, we modify the space–time kernel density estimation method by accounting for spatially and temporally dynamic background populations (ST-DB), assess the benefits of considering the temporal dimension and finally, compare ST-DB to its purely spatial counterpart. We delineate clusters and compare them, as well as their significance, across multiple parameter configurations. We apply ST-DB to an outbreak of dengue fever in Cali, Colombia during 2010–2011. Our results show that incorporating the temporal dimension improves our ability to delineate significant clusters. This study addresses an urgent need in the spatiotemporal analysis literature by using population data at high spatial and temporal resolutions.
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Lee, Jeong-Jun, Wenrui Zhang, Yuan Xie, and Peng Li. "SaARSP: An Architecture for Systolic-Array Acceleration of Recurrent Spiking Neural Networks." ACM Journal on Emerging Technologies in Computing Systems, June 27, 2022. http://dx.doi.org/10.1145/3510854.
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Spiking neural networks (SNNs) are brain-inspired event-driven models of computation with promising ultra-low energy dissipation. Rich network dynamics emergent in recurrent spiking neural networks (R-SNNs) can form temporally-based memory, offering great potential in processing complex spatiotemporal data. However, recurrence in network connectivity produces tightly coupled data dependency in both space and time, rendering hardware acceleration of R-SNNs challenging. We present the first work to exploit spatiotemporal parallelisms to accelerate the R-SNN based inference on systolic arrays using an architecture called SaARSP. We decouple the processing of feedforward synaptic connections from that of recurrent connections to allow for the exploitation of parallelisms across multiple time points. We propose a novel time window size optimization (TWSO) technique, to further explore the temporal granularity of the proposed decoupling in terms of optimal time window size and reconfiguration of the systolic array considering layer-dependent connectivity to boost performance. Stationary dataflow and time window size are jointly optimized to trade-off between weight data reuse and movements of partial sums, the two bottlenecks in latency and energy dissipation of the accelerator. The proposed systolic-array architecture offers a unifying solution to an acceleration of both feedforward and recurrent SNNs, and delivers 4,000X EDP improvement on average for different R-SNN benchmarks over a conventional baseline.
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Xiao, Xin, Chaoyang Fang, Hui Lin, Li Liu, Ya Tian, and Qinghua He. "Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China." Computational Urban Science 2, no. 1 (January 4, 2022). http://dx.doi.org/10.1007/s43762-021-00030-x.
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AbstractIn the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo’s overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.
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Dong, Lei, Xiaohui Yuan, Meng Li, Carlo Ratti, and Yu Liu. "A gridded establishment dataset as a proxy for economic activity in China." Scientific Data 8, no. 1 (January 11, 2021). http://dx.doi.org/10.1038/s41597-020-00792-9.
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AbstractMeasuring the geographical distribution of economic activity plays a key role in scientific research and policymaking. However, previous studies and data on economic activity either have a coarse spatial resolution or cover a limited time span, and the high-resolution characteristics of socioeconomic dynamics are largely unknown. Here, we construct a dataset on the economic activity of mainland China, the gridded establishment dataset (GED), which measures the volume of establishments at a 0.01° latitude by 0.01° longitude scale. Specifically, our dataset captures the geographically based opening and closing of approximately 25.5 million firms that registered in mainland China over the period 2005–2015. The characteristics of fine granularity and long-term observability give the GED a high application value. The dataset not only allows us to quantify the spatiotemporal patterns of the establishments, urban vibrancy, and socioeconomic activity, but also helps us uncover the fundamental principles underlying the dynamics of industrial and economic development.
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Dabaghian, Yuri. "From Topological Analyses to Functional Modeling: The Case of Hippocampus." Frontiers in Computational Neuroscience 14 (January 11, 2021). http://dx.doi.org/10.3389/fncom.2020.593166.
Full textAbstract:
Topological data analyses are widely used for describing and conceptualizing large volumes of neurobiological data, e.g., for quantifying spiking outputs of large neuronal ensembles and thus understanding the functions of the corresponding networks. Below we discuss an approach in which convergent topological analyses produce insights into how information may be processed in mammalian hippocampus—a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking data at different timescales and following the course of spatial learning at different levels of spatiotemporal granularity. This approach allows accounting for contributions from various physiological phenomena into spatial cognition—the neuronal spiking statistics, the effects of spiking synchronization by different brain waves, the roles played by synaptic efficacies and so forth. In particular, it is possible to demonstrate that networks with plastic and transient synaptic architectures can encode stable cognitive maps, revealing the characteristic timescales of memory processing.