A conceptual framework for studying collective reactions to events in location-based social media     

Alexander Dunkel  Gennady Andrienko  Natalia Andrienko  Dirk Burghardt  Eva Hauthal  Ross Purves 《International journal of geographical information science》2019,33(4):780-804

Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events.  相似文献   

Deep spatio-temporal residual neural networks for road-network-based data modeling     

Yibin Ren  Yang Zhang 《International journal of geographical information science》2019,33(9):1894-1912

Recently, researchers have introduced deep learning methods such as convolutional neural networks (CNN) to model spatio-temporal data and achieved better results than those with conventional methods. However, these CNN-based models employ a grid map to represent spatial data, which is unsuitable for road-network-based data. To address this problem, we propose a deep spatio-temporal residual neural network for road-network-based data modeling (DSTR-RNet). The proposed model constructs locally-connected neural network layers (LCNR) to model road network topology and integrates residual learning to model the spatio-temporal dependency. We test the DSTR-RNet by predicting the traffic flow of Didi cab service, in an 8-km² region with 2,616 road segments in Chengdu, China. The results demonstrate that the DSTR-RNet maintains the spatial precision and topology of the road network as well as improves the prediction accuracy. We discuss the prediction errors and compare the prediction results to those of grid-based CNN models. We also explore the sensitivity of the model to its parameters; this will aid the application of this model to network-based data modeling.  相似文献   

基于深度学习的极光亚暴时-空自动检测     

杨秋菊  任杰  向晗 《地球物理学报》2022,65(3):898-907

准确快速地检测极光亚暴具有重要的意义.现有利用机器学习技术自动检测亚暴起始时刻的方法无法同时兼顾检测精度和效率.本文基于深度学习技术提出了一个端到端的亚暴起始检测模型,该模型利用双流卷积网络提取亚暴的时-空特征,并用三个一维时序卷积层获得亚暴起始的概率序列.该模型在Polar卫星1996-1998年极光观测上获得了87...  相似文献   

Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model     

《ISPRS Journal of Photogrammetry and Remote Sensing》2014

Cloud cover is generally present in remotely sensed images, which limits the potential of the images for ground information extraction. Therefore, removing the clouds and recovering the ground information for the cloud-contaminated images is often necessary in many applications. In this paper, an effective method based on similar pixel replacement is developed to solve this task. A missing pixel is filled using an appropriate similar pixel within the remaining region of the target image. A multitemporal image is used as the guidance to locate the similar pixels. A pixel-offset based spatio-temporal Markov random fields (MRF) global function is built to find the most suitable similar pixel. The proposed method was tested on MODIS and Landsat images and their land surface temperature products, and the experiments verify that the proposed method can achieve highly accurate results and is effective at dealing with the obvious atmospheric and seasonal differences between multitemporal images.  相似文献   

Assessing the regional spatio-temporal pattern of water stress: A case study in Zhangye City of China     

《Physics and Chemistry of the Earth》2015

Water scarcity and stress have attracted increasing attention as water has become increasingly regarded as one of the most critical resources in the world’s sustainable development. The Water Poverty Index (WPI), an interdisciplinary but straightforward measure that considers water availability from both the bio-geophysical perspective and the socio-economic perspective of people’s capacity to access water, has been successfully applied at national, regional, and local levels around the world. However, the general assessment of water stress at a macro level over only a snapshot limits the understanding of the geographic differences in and dynamics of water stress; this will, in turn, mislead decision-makers and may result in improper water strategies being implemented. In addition, to date, the typologies and trajectories of water stress have been underexplored. To fill this knowledge gap, we examine the spatio-temporal patterns, trajectories, and typologies of water stress using an adapted WPI for six counties in Zhangye City, which lies within an arid region of China, in order to provide policy priorities for each county. The results of our assessment indicate that water stress has become more severe over time (2005–2011) in most of the counties in Zhangye City. The results also show a distinct spatial variation in water scarcity and stress. Specifically, the results for Shandan county reflect its progressive policies on water access and management, and this county is regarded as engaging in good water governance. In contrast, Ganzhou district has faced more severe water pressure and is regarded as practicing poor water governance. Typology results show that each county faces its own particular challenges and opportunities in the context of water scarcity and stress. In addition, the trajectory map reveals that none of the counties has shown substantial improvement in both water access and management, a finding that should draw decision-makers’ close attention.  相似文献   

昆明市县区收入差距的时空演进及其成因分析     

李正  武友德  舒劲  吴映梅 《地域研究与开发》2011,30(1):74-78

以昆明市各县区为研究单位,对收入差距问题作了实证分析。采用统计法和建立绝对β-收敛模型测度了县区收入差距,描述其时序过程和空间演替特征;利用Theil指标法揭示区域差距的构成与来源,并运用回归法探讨其影响因素。结论证实,昆明市区域差异演进并没有偏离收敛性假说:县区收入差距以2003年为拐点呈阶段性律动,总体差距趋小而梯度间差距和城乡间差距深化,社会消费、储蓄、财政支出是其影响因素,而普遍认为有促进作用的市场因素在概念模型分析中却不显著。为此,发挥市场力量势在必行。  相似文献   

Spatio-temporal evaluation matrices for geospatial data     

Joc Triglav  Dušan Petrovič  Bojan Stopar 《International Journal of Applied Earth Observation and Geoinformation》2011

The global geospatial community is investing substantial effort in providing tools for geospatial data-quality information analysis and systematizing the criteria for geospatial data quality. The importance of these activities is increasing, especially in the last decade, which has witnessed an enormous expansion of geospatial data use in general and especially among mass users. Although geospatial data producers are striving to define and present data-quality standards to users and users increasingly need to assess the fitness for use of the data, the success of these activities is still far from what is expected or required. As a consequence, neglect or misunderstanding of data quality among users results in misuse or risks. This paper presents an aid in spatio-temporal quality evaluation through the use of spatio-temporal evaluation matrices (STEM) and the index of spatio-temporal anticipations (INSTANT) matrices. With the help of these two simple tools, geospatial data producers can systematically categorize and visualize the granularity of their spatio-temporal data, and users can present their requirements in the same way using business intelligence principles and a Web 2.0 approach. The basic principles and some examples are presented in the paper, and potential further applied research activities are briefly described.  相似文献   

Estimating the prediction performance of spatial models via spatial k-fold cross validation     

Jonne Pohjankukka  Tapio Pahikkala  Paavo Nevalainen  Jukka Heikkonen 《International journal of geographical information science》2017,31(10):2001-2019

In machine learning, one often assumes the data are independent when evaluating model performance. However, this rarely holds in practice. Geographic information datasets are an example where the data points have stronger dependencies among each other the closer they are geographically. This phenomenon known as spatial autocorrelation (SAC) causes the standard cross validation (CV) methods to produce optimistically biased prediction performance estimates for spatial models, which can result in increased costs and accidents in practical applications. To overcome this problem, we propose a modified version of the CV method called spatial k-fold cross validation (SKCV), which provides a useful estimate for model prediction performance without optimistic bias due to SAC. We test SKCV with three real-world cases involving open natural data showing that the estimates produced by the ordinary CV are up to 40% more optimistic than those of SKCV. Both regression and classification cases are considered in our experiments. In addition, we will show how the SKCV method can be applied as a criterion for selecting data sampling density for new research area.  相似文献   

Kinetic prisms: incorporating acceleration limits into space–time prisms     

Bart Kuijpers  Harvey J. Miller  Walied Othman 《International journal of geographical information science》2017,31(11):2164-2194

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Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining     

《International Journal of Digital Earth》2013,6(3):238-256

ABSTRACT

Holistic understanding of wind behaviour over space, time and height is essential for harvesting wind energy application. This study presents a novel approach for mapping frequent wind profile patterns using multi-dimensional sequential pattern mining (MDSPM). This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded (0.125°?×?0.125°) wind data for the Netherlands every 6?h and at six height levels. The wind data were first transformed into two spatio-temporal sequence databases (for speed and direction, respectively). Then, the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multi-dimensional sequential patterns, which were then visualized using a 3D wind rose, a circular histogram and a geographical map. These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines. Our analysis identified four frequent wind profile patterns. One of them highly suitable to harvest wind energy at a height of 128?m and 68.97% of the geographical area covered by this pattern already contains wind turbines. This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.  相似文献