Explore the vast range of titles available.

兴趣点，又称POI（points of interest）是网络地图、导航地图中重要的表达要素，包括餐饮、娱乐、金融机构、旅游景点、地标建筑、加油站、停车场等人们日常生活中最为经常使用的信息。其数据的准确性、属性的丰富程度、表达的清晰度及其实时显示效率都将影响地图的服务质量。当前POI表达存在许多问题，特别是在用户搜索特定信息时，由于查询结果数据量较大，造成POI的叠置、压盖等，这一问题严重影响了用户对POI信息的查询与检索。地图综合提供了大量的算子算法以实现点或点群要素的选取，但是它们在综合效率方面亟待提高。面向矢量数据处理的并行计算，其数据划分不仅需要满足负载均衡、划分算法高效等要求，而且对于划分后各部分数据在计算前后拓扑关系的保持也显得尤为重要。兴趣点与路划网络是密切相关的要素，两者之间存在着相互依存的空间关系。本文提出基于路划网眼划分兴趣点的方法，既能保证兴趣点的划分效率，又能保证不同分区内POI选取计算的正确性。选择点选取算法中的“圆”增长算法，采用典型试验区域的路划网眼数据，基于不同节点数划分兴趣点数据，实现兴趣点选取并行计算。试验证明，该方法不仅保证了兴趣点划分的均衡性，而且可以提高兴趣点选取计算效率。通过这一研究，对面向矢量数据的地理信息分析、地图制图综合等复杂算法的并行计算具有重要意义。

兴趣点（point of interest,POI）是地理信息服务的重要形式。互联网上的POI信息大多位于深网网络（deep web）中,其数据量极其庞大。随着互联网技术与应用的快速普及和地理信息服务的深入发展,POI信息资源规模不断增长、更新更为频繁,充分挖掘深网网络中蕴含的POI数据,对于丰富地理信息资源、提升空间信息服务与内容管理能力具有重要意义。当前,通用搜索引擎和普通深网爬行方法难以有效获取深网POI数据,

移动及Web环境下,集成各种自发地理信息POI数据与地理框架背景数据的混搭式地图应用,越来越多地出现在主流地理信息平台及LBS 服务中.由于缺乏适宜的在线多尺度可视化机制,这种POI数据表达上通常出现拥挤、压盖等冲突现象.针对该问题,本研究将传统的尺度变换方法与在线环境相结合,提出一种面向城市设施POI数据的多尺度可视化策略.即由服务器端通过预处理方式对POI数据进行多层次结构化组织;在此基础上,客户端依据显示比例尺导出对应层次的POI目标,并通过移位操作解决局部存在的符号表达冲突现象.试验表明,该方法符合数字化网络应用的在线实时需求,同时也能获得较高质量的多尺度表达效果.

As a vital indicator for measuring urban development, urban areas are expected to be identified explicitly and conveniently with widely available data sets, thereby benefiting planning decisions and relevant urban studies. Existing approaches to identifying urban areas are normally based on midresolution sensing data sets, low-resolution socioeconomic information (e.g., population density) in space (e.g., cells with several square kilometers or even larger towns or wards). Yet, few of these approaches pay attention to defining urban areas with high-resolution microdata for large areas by incorporating morphological and functional characteristics. This article investigates an automated framework to delineate urban areas at the block level, using increasingly available ordnance surveys for generating all blocks (or geounits) and ubiquitous points of interest (POIs) for inferring density of each block. A vector cellular automata model was adopted for identifying urban blocks from all generated blocks, taking into account density, neighborhood condition, and other spatial variables of each block. We applied this approach for mapping urban areas of all 654 Chinese cities and compared them with those interpreted from midresolution remote sensing images and inferred by population density and road intersections. Our proposed framework is proven to be more straightforward, time-saving, and fine-scaled compared with other existing ones. It asserts the need for consistency, efficiency, and availability in defining urban areas with consideration of omnipresent spatial and functional factors across cities.

The logistics nodes and logistics enterprises are the core carriers and organiza- tional subjects of the logistics space, and their location characteristics and differentiation strategies are of key importance to optimizing urban logistics spatial patterns and ensuring reasonable resource allocation. Based on Tencent Online Maps Platform from December 2014, 4396 logistics points of interest （POI） were collected in Beijing, China. By the methods of industrial concentration evaluation and kernel density analysis, the spatial distribution pattern of logistics in Beijing are explored, the interaction mechanism among the type differ- ence, supply-demand side factors and location choice behavior are clarified, and the internal mechanism of spatial differentiation under the combined influence of transportation, land rent and assets are revealed. The following conclusions are drawn in the paper. （1） Logistics en- terprises and logistics nodes exhibit the characteristic of both co-agglomeration and spatial separation in location, and logistics activities display the spatial pattern of ＂marginal area of downtown area, suburbs and exurban area＂, which have a weak coupling degree with logis- tics employment space. （2） The public logistics space, namely, logistics parks and logistics centers, is produced under the guidance of the government, and the terminal logistics space consisting of logistics distribution centers serving for the specific industries and terminal users is dominated by enterprises. The Iocational differentiation between the two modes of logistics space is significant. （3） In the formation of the logistics spatial location, the government can change the traffic condition by re-planning the transport routes and freight station locations, and control the land rent and availability of different areas by increasing or decreasing the land use of logistics, to impact the enterprise behavior and form different types of logistics space and function differentiation. In comparison, logistics enterprises meet the diverse de- mands of service objects through differentiation of asset allocation to promote the specializa- tion of division and form the object differentiation of logistics space.

Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.