A prefetching model based on access popularity for geospatial data in a cluster-based caching system

Geospatial tile popularity reflects the general characteristics of user preferences in tile access. However, tile access has both long-term popularity features (characterized as stable) and short-term popularity features (characterized as explosive). Specific features of tile popularity are an important theoretical basis for improving the accuracy of caching and prefetching. This article considers both long-term and short-term popularity features for tile access and presents a Markov prefetching model in a cluster-based caching system based on a Zipf distribution. First, it describes the navigation path and the transition probability path for tile access based on the global features of tile access to find a way to estimate the transition tile access probabilities based on the access pattern, which satisfies Zipf's law. Then, based on temporal and spatial local changes in tile access patterns, the basic Markov model is used to prefetch tiles with the highest probability in the follow-up state for current hot tiles and these tiles are labeled as the set of prefetched objects. Finally, based on the access probability for prefetched tiles, they are evenly distributed in a cluster-based caching system. This method takes into account both global and local space–time changes in tile access patterns. This method not only makes the set of cached objects relatively stable but also adapts to changes in access distribution. Experimental results reveal that this method has a higher prefetch hit rate and a shorter average response time for tile requests and thus can improve the efficiency and stability of cluster-based caching systems.     

A lightweight ensemble spatiotemporal interpolation model for geospatial data

ABSTRACT

Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.     

玉溪市中心城区建设用地集约利用空间差异研究

为科学地分析城市土地集约利用在空间上的分布规律及趋势,以玉溪市中心城区为研究区域,在玉溪市中心城区建设用地集约利用评价的基础上,通过空间相关分析的方法揭示了中心城区建设用地集约度的空间分布特征。结果表明:玉溪市中心城区集约利用度呈现出由城市中心逐渐向外递减的趋势,土地集约利用水平在一定程度上存在集聚效应;从全域空间分析看,各功能区土地利用集约度空间分布在整体上具有较好的正相关性,居住功能区相关性最强,其他功能区最弱;从局部空间的角度来看,集约度的分布既存在空间聚集性又存在空间异质性。     

基于空间点模式分析的城市管理事件空间分布及演化——以武汉市江汉区为例

城市网格化管理系统经过多年运行积累了大量历史事件数据, 这类事件数据在空间上呈现明显集聚分布。确定事件发生的空间分布以及衡量空间分布的集聚程度, 能够为城市管理资源的合理调配、划分提供重要的决策支持。本文应用空间点模式分析方法, 对2011 年1-8 月间武汉市江汉区城市网格化管理系统中的两类主体事件(占道经营和垃圾处理类)进行分析, 研究发现:占道经营事件的“热点”区域1-8 月总体呈减少趋势, 而垃圾处理事件的“热点”区域整体呈递增趋势;两类事件呈现明显的空间集聚, 其特征空间尺度都为1000 m左右。研究表明, 空间点模式分析方法能够为城市管理者提供一种针对城市事件空间集聚模式的直观的可视化分析手段, 以及对空间集聚程度的定量分析方法, 并可为进一步统计建模分析奠定基础。     

A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures

As an important spatiotemporal simulation approach and an effective tool for developing and examining spatial optimization strategies (e.g., land allocation and planning), geospatial cellular automata (CA) models often require multiple data layers and consist of complicated algorithms in order to deal with the complex dynamic processes of interest and the intricate relationships and interactions between the processes and their driving factors. Also, massive amount of data may be used in CA simulations as high-resolution geospatial and non-spatial data are widely available. Thus, geospatial CA models can be both computationally intensive and data intensive, demanding extensive length of computing time and vast memory space. Based on a hybrid parallelism that combines processes with discrete memory and threads with global memory, we developed a parallel geospatial CA model for urban growth simulation over the heterogeneous computer architecture composed of multiple central processing units (CPUs) and graphics processing units (GPUs). Experiments with the datasets of California showed that the overall computing time for a 50-year simulation dropped from 13,647 seconds on a single CPU to 32 seconds using 64 GPU/CPU nodes. We conclude that the hybrid parallelism of geospatial CA over the emerging heterogeneous computer architectures provides scalable solutions to enabling complex simulations and optimizations with massive amount of data that were previously infeasible, sometimes impossible, using individual computing approaches.     

地理空间数据本质特征语义相关度计算模型

关联数据是跨网域整合多源异构地理空间数据的有效方式,语义丰富的关联是准确、快速发现目标数据的关键。根据地理空间数据在空间、时间、内容上的语义关系,提出地理空间数据本质特征语义相关度计算模型。通过构建本质特征的关联指标体系,分层次逐级计算地理空间数据的语义相关度。与传统的语义相关度计算方式不同,以地理元数据为语料库,充分考虑地理空间数据的特点及空间、时间、内容在检索中不同的重要程度,分别采用几何运算、数值运算、词语语义相似度计算和类别层次相关度计算的方式,构建地理空间数据的语义相关度计算模型。该模型具有构建简单、适用于多源异构数据、充分结合了数学运算和专家经验知识等特点。实验表明：模型能够有效地计算地理空间数据本质特征的语义相关度,并具备一定的扩展性。     

Optimizing an index with spatiotemporal patterns to support GEOSS Clearinghouse

A variety of Earth observation systems monitor the Earth and provide petabytes of geospatial data to decision-makers and scientists on a daily basis. However, few studies utilize spatiotemporal patterns to optimize the management of the Big Data. This article reports a new indexing mechanism with spatiotemporal patterns integrated to support Big Earth Observation (EO) metadata indexing for global user access. Specifically, the predefined multiple indices mechanism (PMIM) categorizes heterogeneous user queries based on spatiotemporal patterns, and multiple indices are predefined for various user categories. A new indexing structure, the Access Possibility R-tree (APR-tree), is proposed to build an R-tree-based index using spatiotemporal query patterns. The proposed indexing mechanism was compared with the classic R*-tree index in a number of scenarios. The experimental result shows that the proposed indexing mechanism generally outperforms a regular R*-tree and supports better operation of Global Earth Observation System of Systems (GEOSS) Clearinghouse.     

Spatial Correlation between Traffic and Air Pollution in Beijing

Many studies have attempted to model the sophisticated influence of traffic emissions on air pollution, but most models only calculate the contribution of traffic emissions near monitoring sites. It is difficult to observe the near-surface dynamics such as wind, rain, and human activities and precisely distinguish traffic emissions. These obstacles make model simulation very expensive in practice. The regional distribution patterns that can help adjust policies and actions taken remain unknown. Therefore, this article proposes a grid-oriented geostatistics-based approach to overcome these obstacles. We chose central Beijing as the study area. An experiment implemented the approach on data collected from Global Positioning System navigation software, car rental companies, and meteorology stations. The results suggest that the northwest area of Beijing has high traffic-related air pollution (TRAP) and the southeast area has low TRAP. Unlike modeling-based methods, this work uses geostatistical methods to directly study the spatiotemporal connections between traffic and PM_2.5 (particulate matter with diameter less than 2.5?μm) from the phenomenon. The calculation is conducted under no hypotheses and has little risk of producing results contradictory to facts. This work provides a reference for future TRAP research on directly learning from the phenomenon and assists decision makers with seamless spatiotemporal heat maps of TRAP distribution. Key Words: Beijing, geospatial statistics, PM_2.5, spatial correlation, traffic-related air pollution.     

适用于矢量瓦片缓存替换的视点相关预测区域算法

传统的缓存置换策略未充分考虑数据访问的空间特征,也不适用于基于矢量瓦片的替换。该文根据矢量瓦片的空间数据结构,提出一种适用于矢量瓦片缓存替换的视点相关预测区域算法:首先根据瓦片存储中多分辨率金字塔结构进行空间单元划分,并根据用户操作类型求解矢量瓦片及空间单元热度,从而构建用户视点位置相关的预测区域;然后综合考虑瓦片层级、空间单元热度及距离等因素进行预测区域分析,获得瓦片缓存价值并进行瓦片置换。通过与传统的FIFO、LRU和LFU缓存策略相比较,该算法的瓦片命中率比FIFO和LRU分别提高了近50%和20%,瓦片的请求耗时分别缩短了50%和30%左右,相比LFU也有明显优势。该研究为WebGIS提供了一种更具潜力的瓦片缓存方法。     

Reply to Comment on ‘Marine GIS: Identification of mesoscale oceanic thermal fronts’

Geographic information service (GIService) has become popular in the last decade to develop applications for addressing global challenges. Performance is one of the most important criteria to help users select distributed online GIService for developing geospatial applications including natural hazards and emergency responses. However, performance accuracy is limited by the single-location-based evaluation mechanism while service performance is dynamic in space and time between end-users and services. We propose a spatiotemporal performance evaluation mechanism to improve the accuracy. Specially, a cloud and volunteer computing mechanism is proposed to collect performance information of globally distributed GIServices. A global spatiotemporal performance model is designed to integrate spatiotemporal dynamics for better performance evaluation for users from different regions at different times. This model is tested to support GIService selection in global spatial data infrastructures (SDIs). The experiment confirms that the proposed model provides more accurate evaluations for global users and better supports geospatial resource utilizations in SDIs than previous mechanisms. The methodology can be adopted to improve the services of other regional and global distributed operational systems.     

犯罪热点时空分布研究方法综述

犯罪在地理时空内并不是均匀分布的,而是表现出明显的时空聚集特性,这种聚集性常用“犯罪热点”表述.基于对犯罪热点的理解,从犯罪热点时空分布模式、犯罪热点成因分析以及犯罪热点时空转移及预测等3 个方面总结了当前国内外犯罪热点时空分布相关研究方法的进展.最后,对该领域研究进行了总结与展望.总体上,国内相关研究较少,尚需进一步结合中国国情,提出适用方法.另外,也需要通过相关犯罪理论的深入研究以及其他领域研究方法的借鉴,实现犯罪热点时空分布研究方法的突破与创新.     

基于圈层结构的游客活动空间边界提取新方法

准确刻画游客活动空间边界对于优化景区结构、实施界限管控、提高资源利用效益均有重要意义.由于游客行为的复杂性与边界模糊性,利用传统地理边界提取方法难以有效识别游客活动空间边界.基于层次聚类算法优化后的Delaunay三角网进行核密度估计,解决了多尺度下点核密度对空间边界拟合不精确的问题,同时借鉴圈层结构理论,依据游客空间...     

就近入学空间模型分析——以河南省巩义市初级中学为例

从地理空间的角度理解我国义务教育就近入学政策与择校现象,引入空间模型模拟学校与居民点之间的供需关系,并尝试解释与择校现象密切相关的"热校"和"冷校"问题。使用最近距离模型、引力模型和Huff模型,以河南省巩义市50所初级中学和1 276个居民点为例,在ArcGIS软件中进行模型分析。在模型有效性检验的基础上,引入学校热度指标鉴别"热校"和"冷校",并统计各类学校的规模、生均资源分配和平均入学距离等指标。研究表明:Huff模型能较好地模拟义务教育供需现状;"热校"、"冷校"与其他学校的规模、生均资源和入学距离存在显著差异。该研究对于就近入学政策实施和学校布局调整具有参考价值。     

基于大数据的城市功能区人口时空聚散模式研究

城市功能区和人口流动是行为地理学和城市规划领域的研究热点,不同城市功能区的人口聚散现象更是其重点议题。该文基于POI和腾讯位置服务(LBS)大数据,以武汉市主城区为研究区,利用功能密度指数、功能优势指数识别城市功能区,并通过空间关联判断城市功能区人口流动变化规律,采用聚类分析方法归纳人口时空聚散模式。研究结果表明:1)中心城区功能混合度较高;2)受人群时空需求影响,不同城市功能区的人口流动规律呈现一定差异性;3)根据城市功能区人口流动聚散趋势并综合其构成特征,可分为公共主导-聚散波动、商务主导-持续集聚、居住主导-持续集聚、绿地主导-聚散交替、商业主导-动态平衡和工业主导-先聚后散共6种模式。研究结果对于优化城市空间布局、合理配置城市资源以及提升城市运行效率具有参考意义。     

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.     

An OLS regression model for context-aware tile prefetching in a web map cache

The increasing popularity of web map services has motivated the development of more scalable services in the spatial data infrastructures. Tiled map services have emerged as a scalable alternative to traditional map services. Instead of rendering map images on the fly, a collection of pre-generated image tiles can be served very fast from a server-side cache. However, during the start-up of the service, the cache is initially empty and users experience a poor quality of service. Tile prefetching attempts to improve hit rates by proactively fetching map images without waiting for client requests.

While most popular prefetching policies in traditional web caching consider only the previous access history to make predictions, significant improvements could be achieved in web mapping by taking into account the background geographic information.

This work proposes a regressive model to predict which areas are likely to be requested in the future based on spatial cross-correlation between an unconstrained catalog of geographic features and a record of past cache requests. Tiles that are anticipated to be most frequently requested can be pre-generated and cached for faster retrieval. Trace-driven simulations with several million cache requests from two different nation-wide public web map services in Spain demonstrate that accurate predictions and performance gains can be obtained with the proposed model.     

GeoVisuals: a visual analytics approach to leverage the potential of spatial videos and associated geonarratives

ABSTRACT

Videos embedded with spatial coordinates, especially when combined with additional expert insights, offer the potential to acquire fine-scale multi-time period contextualized data for a variety of different environments. However, while these geospatial multimedia (GSMM) data include abundant spatiotemporal, semantic and visual information, the means to fully leverage their potential using a suite of visual and interactive analysis techniques and tools has thus far been lacking. In this paper, we address this gap by first identifying the types of tasks required of GSMM data, and then presenting a solution platform. This GeoVisuals system utilizes a visual analysis approach built on semantic data points that can be integrated spatially, which in turn enables management in a unified database with combined spatio-temporal and text querying. A set of visualization functions are integrated in two investigation modes: geo-video analysis and geo-location analysis.     

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).     

基于POI大数据的沈阳市住宅与零售业空间关联分析

城市住宅及其价格与区域商服业的空间关联性量化研究是人文-经济地理学的重要研究内容。以辽宁省沈阳市为案例,以住宅和零售业兴趣点（Point of Interest, POI）为数据源,基于空间核密度分析提取住宅和各类零售业的空间聚类形态,量化表达商住空间布局的相关性,并在此基础上运用地统计方法测算房价的空间异质性及其与零售业态空间布局的差异特征。结果表明,零售业的整体空间聚集特征与住宅相似,呈现中心城区块状聚集、外围城区多中心离散的分布格局;零售业与住宅核密度相关系数为0.95,超市、便利店等小规模的零售业与住宅密度相关性较强,商场商厦的聚集效应落后于城市住宅,大型零售业应该在铁西经济技术开发区等住宅密集区规划选址,为居民提供高端购物服务;住宅价格的倒“U”型空间分布模式与零售业空间密度的圈层衰减特征相符。     

塔里木河流域荒漠河岸林胡杨群落的空间格局研究

 以塔里木河流域荒漠河岸林胡杨群落为研究对象,研究优势种胡杨与灰胡杨种群径级结构、空间分布格局与空间关联性。结果表明,胡杨和灰胡杨种群径级结构均呈倒金字塔型,属衰退型种群。胡杨种群在1～25 m尺度内呈聚集分布,灰胡杨种群在2～23 m尺度内呈聚集分布,胡杨种群聚集强度大于灰胡杨。随种群生长发育,胡杨和灰胡杨聚集尺度范围及聚集规模逐渐减弱,大树趋于随机分布。灰胡杨不同生长阶段在0～25 m空间尺度上相互独立,胡杨小树与中树在5～10 m空间尺度上相互依存。胡杨中树与灰胡杨中树在大尺度空间(＞23 m)上相互排斥; 胡杨与灰胡杨种群在小尺度空间(2～3 m)上相互依存,中尺度空间上相互独立。