SOVAS: a scalable online visual analytic system for big climate data analysis

ABSTRACT

Big climate data offers great opportunities for scientific discovery but demands efficient and effective analytics to investigate unknown and complex patterns. Most existing online processing and analytics systems for climate studies only support fixed user interface with predefined functions. These systems are often not scalable to handle massive climate data that could easily accumulate terabytes daily. To address the major limitations of existing online systems for climate studies, this paper presents a scalable online visual analytic system, known as SOVAS, to balance both usability and flexibility. SOVAS, enabled by a set of key techniques, supports large-scale climate data analytics and knowledge discovery in a scalable and sharable environment. This research not only contributes to the community an efficient tool for analyzing big climate data but also contributes to the literature by providing valuable technical references for tackling spatiotemporal big data challenges.     

Spatiotemporal data model for network time geographic analysis in the era of big data

There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.     

A spatiotemporal calculus for reasoning about land-use trajectories

Earth observation images are a powerful source of data about changes in our planet. Given the magnitude of global environmental changes taking place, it is important that Earth Science researchers have access to spatiotemporal reasoning tools. One area of particular interest is land-use change. Using data obtained from images, researchers would like to express abstractions such as ‘land abandonment’, ‘forest regrowth’, and ‘agricultural intensification’. These abstractions are specific types of land-use trajectories, defined as multi-year paths from one land cover into another. Given this need, this paper introduces a spatiotemporal calculus for reasoning about land-use trajectories. Using Allen’s interval logic as a basis, we introduce new predicates that express cases of recurrence, conversion and evolution in land-use change. The proposed predicates are sufficient and necessary to express different kinds of land-use trajectories. Users can build expressions that describe how humans modify Earth’s terrestrial surface. In this way, scientists can better understand the environmental and economic effects of land-use change.     

A spatiotemporal regression-kriging model for space-time interpolation: a case study of chlorophyll-a prediction in the coastal areas of Zhejiang,China

Spatiotemporal kriging (STK) is recognized as a fundamental space-time prediction method in geo-statistics. Spatiotemporal regression kriging (STRK), which combines space-time regression with STK of the regression residuals, is widely used in various fields, due to its ability to take into account both the external covariate information and spatiotemporal autocorrelation in the sample data. To handle the spatiotemporal non-stationary relationship in the trend component of STRK, this paper extends conventional STRK to incorporate it with an improved geographically and temporally weighted regression (I-GTWR) model. A new geo-statistical model, named geographically and temporally weighted regression spatiotemporal kriging (GTWR-STK), is proposed based on the decomposition of deterministic trend and stochastic residual components. To assess the efficacy of our method, a case study of chlorophyll-a (Chl-a) prediction in the coastal areas of Zhejiang, China, for the years 2002 to 2015 was carried out. The results show that the presented method generated reliable results that outperform the GTWR, geographically and temporally weighted regression kriging (GTWR-K) and spatiotemporal ordinary kriging (STOK) models. In addition, employing the optimal spatiotemporal distance obtained by I-GTWR calibration to fit the spatiotemporal variograms of residual mapping is confirmed to be feasible, and it considerably simplifies the residual estimation of STK interpolation.     

地球系统空间格网及其应用模式

全球变化与地球系统科学研究涉及跨圈层、跨投影带的中-大-超大尺度问题,对全球地学信息系统(GGIS)、全球空间格网(GSG)及数字地球提出了新挑战。在剖析GSG研究现状的基础上,指出需从地球系统整体上设计一个多领域普适的全球三维空间格网———地球系统空间格网(ESSG),以支撑全球变化及地球系统科学研究。结合领域特点,提出了构建ESSG的8项基本要求,并基于球体退化八叉树格网(SDOG)设计并实现了一种满足该要求的SDOG-ESSG模型;介绍了SDOG-ESSG模型在地球系统空间数据集成、三维建模、多尺度表达、对象变化表达、数据检索与云服务、过程模拟及空间环境安全规划与可视化决策等方面的7种典型应用模式。     

“双碳”目标下中国服务业碳强度时空交互特征及跃迁机制

为实现国家自主贡献承诺,如期达到“碳达峰、碳中和”目标,中国服务业的低碳发展是必然趋势。基于多种空间分析方法,从时空交互视角研究了中国服务业碳强度差异格局、空间关联、动态演化及跃迁机制。结果表明：(1) 2005—2019年中国服务业碳强度的总体差异存在动态收敛趋势,在空间上也呈现显著的聚类现象,且空间集聚水平逐渐趋于稳定。(2)在服务业碳强度局部空间结构与依赖方向上,西北与东北地区波动性较强,东部沿海地区相对稳定;在碳强度时空跃迁的过程中整体表现出一定的转移惰性,具有较强的空间依赖或路径锁定特征,其中中部、西部的多数地区始终保持高碳强度属性,是制约中国服务业协同减排的关键区域。(3)服务业碳强度的时空网络格局主要以正向关联为主,表现出较强的空间整合性,但少数邻接省域仍存在一定程度的时空竞争。(4)各地区服务业碳强度时空跃迁的驱动模式存在差异,其中,东部沿海省份主要受人口—城镇化制约模式的影响,西北、西南和东北的多数地区主要受技术—规制驱动模式的影响。自东南至西北,中国服务业碳强度的跃迁模式逐渐呈现出“同向制约—反向发展—同向发展”的阶梯递变格局。因此,政府减排政策的制定不仅应统筹考虑...     

Spatiotemporal correlation in WebGIS group-user intensive access patterns

Group-user intensive access to WebGIS exhibits spatiotemporal behaviour patterns with aggregation features and regularity distributions when geospatial data are accessed repeatedly over time and aggregated in certain spatial areas. We argue that these observable group-user access patterns provide a foundation for improved optimization of WebGIS so that it can respond to volume intensive requests with a higher quality of service and improve performance. Subsequently, a measure of access popularity distribution must precisely reflect the access aggregation and regularity features found in group-user intensive access. In our research, we considered both the temporal distribution characteristics and spatial correlation in the access popularity of tiled geospatial data (tiles). Based on the observation that group-user access follows a Zipf-like law, we built a tile-access popularity distribution based on time-sequence, to express the access aggregation of group-users with heavy-tailed characteristics. Considering the spatial locality of user-browsed tiles, we built a quantitative expression for the correlation between tile-access popularities and the distances to hotspot tiles, reflecting the attenuation of tile-access popularity to distance. Moreover, given the geographical spatial dependency and scale attribute of tiles, and the time-sequence of tile-access popularity, we built a Poisson regression model to express the degree of correlation among the accesses to adjacent tiles at different scales, reflecting the spatiotemporal correlation in tile access patterns. Experiments verify the accuracy of our Poisson regression model, which we then applied to a cluster-based cache-prefetching scenario. The results show that our model successfully reflects the spatiotemporal aggregation features of group-user intensive access and group-user behaviour patterns in WebGIS. The refined mathematical method in our model represents a time-sequence distribution of intensive access to tiles and the spatial aggregation and correlation in access to tiles at different scales, quantitatively expressing group-user spatiotemporal behaviour patterns with aggregation features and a regular distribution. Our proposed model provides a precise and empirical basis for performance-optimization strategies in WebGIS services, such as planning computing resource allocation and utilization, distributed storage of geospatial data, and providing distributed services so as to respond rapidly to geospatial data requests, thus addressing the challenges of volume-intensive user access.     

Mining spatiotemporal association patterns from complex geographic phenomena

ABSTRACT

Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.     

A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduce

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (~10× speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328%). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.     

时空大数据背景下并行数据处理分析挖掘的进展及趋势

随着互联网、物联网和云计算的高速发展,与时间、空间相关的数据呈现出“爆炸式”增长的趋势,时空大数据时代已经来临。时空大数据除具备大数据典型的“4V”特性外,还具备丰富的语义特征和时空动态关联特性,已经成为地理学者分析自然地理环境、感知人类社会活动规律的重要资源。然而在具体研究应用中,传统数据处理和分析方法已无法满足时空大数据高效存取、实时处理、智能挖掘的性能需求。因此,时空大数据与高性能计算/云计算融合是必然的发展趋势。在此背景下,本文首先从大数据的起源出发,回顾了大数据概念的发展历程,以及时空大数据的特有特征;然后分析了时空大数据研究应用产生的性能需求,总结了底层平台软硬件的发展现状;进而重点从时空大数据的存储管理、时空分析和领域挖掘3个角度对并行化现状进行了总结,阐述了其中存在的问题;最后指出了时空大数据研究发展趋势。     

Optimization of simulation and visualization analysis of dam-failure flood disaster for diverse computing systems

Simulation and subsequent visualization in a network environment are important to glean insights into spatiotemporal processes. As computing systems become increasingly diverse in hardware architectures, operating systems, screen sizes, human–computer interactions and network capabilities, effective simulation and visualization must become adaptive to a wide range of diverse devices. This paper focuses on the optimization of simulation and visualization analysis of the dam-failure flood spatiotemporal process for diverse computing systems. First, an adaptive browser/server architecture of the dam-failure simulation application was designed to fill the hardware performance and visualization context gap that exists within diverse computing systems. Second, a data flow and an optimization method for multilevel time-series flood data were given to provide more support to network simulation, visualization and analysis on diversified terminals. Finally, a user interaction friendly and plugin-free prototype system was developed. The experiment results demonstrate that the methods addressed in this paper can cope with the challenge in simulation, visualization and interaction of a dam-failure simulation application on diversified terminals.     

Information-associated join indices for spatial range search

Abstract

Spatial join indices are join indices constructed for spatial objects. Similar to join indices in relational database systems, spatial join indices improve efficiency of spatial join operations. In this paper, a spatial-information-associated join indexing mechanism is developed to speed up spatial queries, especially, spatial range queries. Three distance-associated join index structures: basic, ring-structured and hierarchical, are developed and studied. Such join indexing structures can be further extended to include orientation information for flexible applications, which leads to zone-structured and other spatial-information-associated join indices. Our performance study and analysis show that spatial-information-associated join indices substantially improve the performance of spatial queries and that different structures are best suited for different applications.     

地理学时空数据分析方法

随着地理空间观测数据的多年积累,地球环境、社会和健康数据监测能力的增强,地理信息系统和计算机网络的发展,时空数据集大量生成,时空数据分析实践呈现快速增长。本文对此进行了分析和归纳,总结了时空数据分析的7类主要方法,包括：时空数据可视化,目的是通过视觉启发假设和选择分析模型;空间统计指标的时序分析,反映空间格局随时间变化;时空变化指标,体现时空变化的综合统计量;时空格局和异常探测,揭示时空过程的不变和变化部分;时空插值,以获得未抽样点的数值;时空回归,建立因变量和解释变量之间的统计关系;时空过程建模,建立时空过程的机理数学模型;时空演化树,利用空间数据重建时空演化路径。通过简述这些方法的基本原理、输入输出、适用条件以及软件实现,为时空数据分析提供工具和方法手段。     

Exploring spatiotemporal heterogeneity in online travel searches: a local spatial model approach

ABSTRACT

Online travel searches are important forms of travel virtual spaces. Previous studies have neglected to analyze the spatial features of the travel searches themselves, and the spatial heterogeneity of their influencing factors. In this study, a travel search index based on the Baidu index was established for analyzing travel searches. Meanwhile, a local spatial model was created for the linear features in order to discuss the spatiotemporal heterogeneity of the influencing factors. The results of this study indicated that travel searches have obvious spatial inequality, and economically developed regions had displayed advantages in the travel search network. The fitting results of the local model were found to be superior to global model. The number of attractions and the GDP of the origin were found to have promoting effects on the travel searches, whereas distances had shown inhibiting effects. These effects presented significant spatiotemporal heterogeneity. It was also found that within the travel search virtual space, the distance effects still existed, but the intensity was weaker than in the real space. The local spatial model for the linear features provided a new spatial analysis method for understanding the travel search network, as well as other types of networks (flow patterns).     

A land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology

ABSTRACT

Big data have shifted spatial optimization from a purely computational-intensive problem to a data-intensive challenge. This is especially the case for spatiotemporal (ST) land use/land cover change (LUCC) research. In addition to greater variety, for example, from sensing platforms, big data offer datasets at higher spatial and temporal resolutions; these new offerings require new methods to optimize data handling and analysis. We propose a LUCC-based geospatial cyberinfrastructure (GCI) that optimizes big data handling and analysis, in this case with raster data. The GCI provides three levels of optimization. First, we employ spatial optimization with graph-based image segmentation. Second, we propose ST Atom Model to temporally optimize the image segments for LUCC. At last, the first two domain ST optimizations are supported by the computational optimization for big data analysis. The evaluation is conducted using DMTI (DMTI Spatial Inc.) Satellite StreetView imagery datasets acquired for the Greater Montreal area, Canada in 2006, 2009, and 2012 (534 GB, 60 cm spatial resolution, RGB image). Our LUCC-based GCI builds an optimization bridge among LUCC, ST modelling, and big data.     

Hierarchical self-organizing maps for clustering spatiotemporal data

Spatial sciences are confronted with increasing amounts of high-dimensional data. These data commonly exhibit spatial and temporal dimensions. To explore, extract, and generalize inherent patterns in large spatiotemporal data sets, clustering algorithms are indispensable. These clustering algorithms must account for the distinct special properties of space and time to outline meaningful clusters in such data sets. Therefore, this research develops a hierarchical method based on self-organizing maps. The hierarchical architecture permits independent modeling of spatial and temporal dependence. To exemplify the utility of the method, this research uses an artificial data set and a socio-economic data set of the Ostregion, Austria, from the years 1961 to 2001. The results for the artificial data set demonstrate that the proposed method produces meaningful clusters that cannot be achieved when disregarding differences in spatial and temporal dependence. The results for the socio-economic data set show that the proposed method is an effective and powerful tool for analyzing spatiotemporal patterns in a regional context.     

地理环境与时空行为对主观幸福感的影响研究进展

主观幸福感是目前国内外研究热点,与提升居民生活质量和建设宜居城市密切相关。已有大部分文献侧重单一空间尺度的研究,分析社会经济属性和地理环境要素(包括建成环境、社会环境、环境污染)对主观幸福感的影响;也有部分研究关注居民日常出行属性和活动特征对主观幸福感的作用机制,探讨长期幸福感与短期幸福感的内在关系。论文对上述研究进行较为系统的梳理与评价,综合考虑地理环境、时空行为与主观幸福感的复杂关系,构建主观幸福感的理论研究框架,总结时空行为视角下多尺度、多维度地理环境要素对主观幸福感的影响机制以及作用路径,并探讨主观幸福感的时空动态规律以及微观行为机制,为改善城市人居环境、优化居民行为模式提供科学依据和政策建议。     

Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study

Travel activities are embodied as people’s needs to be physically present at certain locations. The development of Information and Communication Technologies (ICTs, such as mobile phones) has introduced new data sources for modeling human activities. Based on the scattered spatiotemporal points provided in mobile phone datasets, it is feasible to study the patterns (e.g., the scale, shape, and regularity) of human activities. In this paper, we propose methods for analyzing the distribution of human activity space from both individual and urban perspectives based on mobile phone data. The Weibull distribution is utilized to model three predefined measurements of activity space (radius, shape index, and entropy). The correlation between demographic factors (age and gender) and the usage of urban space is also tested to reveal underlying patterns. The results of this research will enhance the understanding of human activities in different urban systems and demographic groups, as well as providing novel methods to expand the important and widely applicable area of geographic knowledge discovery in the age of instant access.     

Using adaptively coupled models and high-performance computing for enabling the computability of dust storm forecasting

Forecasting dust storms for large geographical areas with high resolution poses great challenges for scientific and computational research. Limitations of computing power and the scalability of parallel systems preclude an immediate solution to such challenges. This article reports our research on using adaptively coupled models to resolve the computational challenges and enable the computability of dust storm forecasting by dividing the large geographical domain into multiple subdomains based on spatiotemporal distributions of the dust storm. A dust storm model (Eta-8bin) performs a quick forecasting with low resolution (22 km) to identify potential hotspots with high dust concentration. A finer model, non-hydrostatic mesoscale model (NMM-dust) performs high-resolution (3 km) forecasting over the much smaller hotspots in parallel to reduce computational requirements and computing time. We also adopted spatiotemporal principles among computing resources and subdomains to optimize parallel systems and improve the performance of high-resolution NMM-dust model. This research enabled the computability of high-resolution, large-area dust storm forecasting using the adaptively coupled execution of the two models Eta-8bin and NMM-dust.