多源异构POI融合方法及应用

针对不同来源的POI在数据格式、坐标参考、属性结构等方面存在差异的问题,提出了一种多源异构POI的融合方法。该方法首先将经过实测并质检的公开版地图数据做成一个母库;然后将其他来源的POI数据经过坐标统一后与母库进行空间位置和门址等属性信息的比对,将母库中没有的POI数据融合进去,最终形成内容规整、信息量丰富的POI融合库;最后进行基于空间约束关系的门址挂接,即使每一个POI点与对应的门牌建立关联关系,提升POI库的应用途径。试验结果表明,该方法能够实现多源POI数据的高效融合,与门址进行挂接后,可满足日益增加的LBS和基于地图的"点式"服务（即Location Point）的需求。     

基于多特征相似性的多源POI匹配方法

本文针对多源POI的特征差异性导致同名对象识别难的问题,提出了一种多特征相似性的多源POI匹配方法。兼顾空间与非空间属性,选取名称、位置、地址、分类4个特征进行相似度计算;采用层次分析法对各特征指标进行重要性分析,得到特征权值;根据总相似度对候选匹配对象进行筛选,以确定最终匹配对象。试验结果显示,该方法具有较高的匹配精度,更适用于多源异构POI数据的匹配,可满足多源POI数据的高效匹配需求。     

A Snake-based Approach for TIGER Road Data Conflation

The TIGER (Topologically Integrated Geographic Encoding and Referencing) system has served the U.S. Census Bureau and other agencies' geographic needs successfully for two decades. Poor positional accuracy has however made it extremely difficult to integrate TIGER with advanced technologies and data sources such as GPS, high resolution imagery, and state/local GIS data. In this paper, a potential solution for conflation of TIGER road centerline data with other geospatial data is presented. The first two steps of the approach (feature matching and map alignment) remain the same as in traditional conflation. Following these steps, a third is added in which active contour models (snakes) are used to automatically move the vertices of TIGER roads to high-accuracy roads, rather than transferring attributes between the two datasets. This approach has benefits over traditional conflation methodology. It overcomes the problem of splitting vector road line segments, and it can be extended for vector imagery conflation as well. Thus, a variety of data sources (GIS, GPS, and Remote Sensing) could be used to improve TIGER data. Preliminary test results indicate that the three-step approach proposed in this paper performs very well. The positional accuracy of TIGER road centerline can be improved from an original 100 plus meters' RMS error to only 3 meters. Such an improvement can make TIGER data more useful for much broader application.     

基于空间相似性的面实体匹配算法研究

同一地物在不同来源的地图上通常存在着差异,其识别或匹配对于不同数据源的地图编制来说很关键。面状地物要素在很多地图表示中都占有很大的比例。基于人眼综合已有信息来识别同名实体的思想,本文提出了基于空间相似性的面实体匹配算法。该算法将面实体作为一个整体看待,采用加权平均法来综合面实体的位置、形状、大小等特征的相似度,进而根据获得的总相似度大小确定匹配实体。算法在确定位置相似度时选择形状中心点对面实体进行惟一标识;采用形状描述函数来计算形状相似度,不容易受各种干扰而影响精度,避免了形状信息的丢失;面实体的大小通过其覆盖面积来度量。实验结果表明该方法具有良好的稳定性和可靠性。     

基于LBSN和多图融合的兴趣点推荐EI北大核心CSCD

兴趣点推荐作为推荐领域的一个重要分支一直备受研究者青睐。本文提出一种基于位置的社交网络(LBSN)和多图融合的兴趣点推荐方法GraphPOI。综合分析用户和兴趣点的内在因素和外部表征,首先,对用户-兴趣点的评分矩阵进行学习得到用户和兴趣点的内部潜在向量;其次,根据评分矩阵构造用户-兴趣点交互图,得到兴趣点在用户空间的表征向量以及用户在兴趣点空间的表征向量;然后,对兴趣点按其地理位置进行聚类,得到兴趣点在位置空间的表征向量,结合兴趣点在用户空间的表征向量进而得到兴趣点的外部表征向量;对用户社交图中的信息扩散现象进行建模,捕获用户的朋友关系,得到用户在社交空间的表征向量,结合用户在兴趣点空间的表征向量进而得到用户的外部表征向量;最后,结合用户和兴趣点的内部潜在向量与外部表征向量,得到用户和兴趣点的最终向量表示,并将其输入到多层神经网络模型中进行评分预测。在Yelp数据集上对所提模型进行验证,结果表明本文方法能够有效提升兴趣点推荐的准确性。     

Extracting urban functional regions from points of interest and human activities on location‐based social networks

Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behavior. However, it is difficult to quantify the correct mix or the spatial relations among different POI types indicative of specific urban functions. In this research, we develop a statistical framework to help discover semantically meaningful topics and functional regions based on the co‐occurrence patterns of POI types. The framework applies the latent Dirichlet allocation (LDA) topic modeling technique and incorporates user check‐in activities on location‐based social networks. Using a large corpus of about 100,000 Foursquare venues and user check‐in behavior in the 10 most populated urban areas of the US, we demonstrate the effectiveness of our proposed methodology by identifying distinctive types of latent topics and, further, by extracting urban functional regions using K‐means clustering and Delaunay triangulation spatial constraints clustering. We show that a region can support multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non‐adjacent locations. Since each region can be modeled as a vector consisting of multinomial topic distributions, similar regions with regard to their thematic topic signatures can be identified. Compared with remote sensing images which mainly uncover the physical landscape of urban environments, our popularity‐based POI topic modeling approach can be seen as a complementary social sensing view on urban space based on human activities.     

Optimal spatial data matching for conflation: A network flow‐based approach

Spatial data conflation involves the matching and merging of counterpart features in multiple datasets. It has applications in practical spatial analysis in a variety of fields. Conceptually, the feature‐matching problem can be viewed as an optimization problem of seeking a match plan that minimizes the total discrepancy between datasets. In this article, we propose a powerful yet efficient optimization model for feature matching based on the classic network flow problem in operations research. We begin with a review of the existing optimization‐based methods and point out limitations of current models. We then demonstrate how to utilize the structure of the network‐flow model to approach the feature‐matching problem, as well as the important factors for designing optimization‐based conflation models. The proposed model can be solved by general linear programming solvers or network flow solvers. Due to the network flow formulation we adopt, the proposed model can be solved in polynomial time. Computational experiments show that the proposed model significantly outperforms existing optimization‐based conflation models. We conclude with a summary of findings and point out directions of future research.     

基于概率的地图实体匹配方法

数字地图合并是通过同名实体匹配和合并变换技术,调整相关地物实体的几何、属性等差异,实现同一地区不同来源地图数据的集成和融合。其中同名实体匹配是极为重要的第一步,也是一个存在大量不确定性的过程,匹配阈值的选取、实体非一对一的匹配关系是匹配中的关键难题,匹配效果不佳或出现错误匹配直接影响着后续合并结果的正确性。本文提出一种基于概率理论的匹配模型,该模型融合多种匹配指标,通过计算实体匹配概率大小来确定匹配实体。该方法避免了匹配指标精确阈值的选取,并且能够有效地解决匹配中非一对一的情况。     

基于网络爬虫的导航深度服务信息自动采集

根据近来对地理信息Web服务搜索引擎的研究,设计一种基于网络爬虫自动采集POI(Point Of Interest)深度服务信息的方法。使用网络爬虫与DOM(Document Object Model)技术从发布相关POI深度服务信息的网站抓取实时信息,并通过编辑距离与针对地址匹配的改进最大公共子序列分析页面内容与POI主题相关度,进而将相关度最高的深度服务信息与POI点匹配,生成深度服务信息点特征。实验证明了方法的有效性。     

Relaxation‐Based Point Feature Matching for Vector Map Conflation

With the rapid advance of geospatial technologies, the availability of geospatial data from a wide variety of sources has increased dramatically. It is beneficial to integrate / conflate these multi‐source geospatial datasets, since the integration of multi‐source geospatial data can provide insights and capabilities not possible with individual datasets. However, multi‐source datasets over the same geographical area are often disparate. Accurately integrating geospatial data from different sources is a challenging task. Among the subtasks of integration/conflation, the most crucial one is feature matching, which identifies the features from different datasets as presentations of the same real‐world geographic entity. In this article we present a new relaxation‐based point feature matching approach to match the road intersections from two GIS vector road datasets. The relaxation labeling algorithm utilizes iterated local context updates to achieve a globally consistent result. The contextual constraints (relative distances between points) are incorporated into the compatibility function employed in each iteration's updates. The point‐to‐point matching confidence matrix is initialized using the road connectivity information at each point. Both the traditional proximity‐based approach and our relaxation‐based point matching approach are implemented and experiments are conducted over 18 test sites in rural and suburban areas of Columbia, MO. The test results show that our relaxation labeling approach has much better performance than the proximity matching approach in both simple and complex situations.     

顾及位置关系的网络POI地址信息标准化处理方法

针对互联网POI(兴趣点)地址信息中广泛存在的地址要素不完整、文字表达不一致等不规范现象,提出一种顾及位置关系的网络POI地址信息标准化处理方法,首先对POI信息进行切分提取并逐层匹配地址树模型;然后基于4种位置关系从标准POI库中选出相应集合,作为丰富和修正非标准POI地址要素的候选;最后通过最小粒度地址要素的回溯,实现POI地址信息的快速标准化处理。试验表明该方法可以获得较高的准确率,尤其适用于在互联网数据环境中的POI地址信息标准化。     

Linear feature conflation: An optimization-based matching model with connectivity constraints

Geospatial data conflation is the process of combining multiple datasets about a geographic phenomenon to produce a single, richer dataset. It has received increased research attention due to its many applications in map making, transportation, planning, and temporal geospatial analyses, among many others. One approach to conflation, attempted from the outset in the literature, is the use of optimization-based conflation methods. Conflation is treated as a natural optimization problem of minimizing the total number of discrepancies while finding corresponding features from two datasets. Optimization-based conflation has several advantages over traditional methods including conciseness, being able to find an optimal solution, and ease of implementation. However, current optimization-based conflation methods are also limited. A main shortcoming with current optimized conflation models (and other traditional methods as well) is that they are often too weak and cannot utilize the spatial context in each dataset while matching corresponding features. In particular, current optimal conflation models match a feature to targets independently from other features and therefore treat each GIS dataset as a collection of unrelated elements, reminiscent of the spaghetti GIS data model. Important contextual information such as the connectivity between adjacent elements (such as roads) is neglected during the matching. Consequently, such models may produce topologically inconsistent results. In this article, we address this issue by introducing new optimization-based conflation models with structural constraints to preserve the connectivity and contiguity relation among features. The model is implemented using integer linear programming and compared with traditional spaghetti-style models on multiple test datasets. Experimental results show that the new element connectivity (ec-bimatching) model reduces false matches and consistently outperforms traditional models.     

遥感影像与POI数据相结合的城市建成区提取适用性研究——以沈阳市为例

城市建成区的发展状况是地理国情监测的重要内容,本文基于遥感影像数据和POI数据对城市建成区进行提取,针对二者的适用性问题进行了研究。试验以沈阳市为研究区域,在研究区域内选择2016年遥感影像数据和POI数据作为数据源进行对比分析。首先,对遥感影像数据和POI数据进行预处理;其次,通过监督分类的方法对遥感影像进行建成区的提取;然后,采用核密度估计法分析POI数据并提取出建成区;最后,利用叠加分析法对比分析这两种数据的适用性。试验结果表明：使用遥感影像数据作为数据源可以较为全面客观地反映城市建成区的发展现状;利用POI数据提取出的城市建成区具有较强的经济属性,能够很好地反映出城市中的经济活跃区。     

Reference data enhancement for geographic information retrieval using linked data

Gazetteers are instrumental in recognizing place names in documents such as Web pages, news, and social media messages. However, creating and maintaining gazetteers is still a complex task. Even though some online gazetteers provide rich sets of geographic names in planetary scale (e.g. GeoNames), other sources must be used to recognize references to urban locations, such as street names, neighborhood names or landmarks. We propose integrating Linked Data sources to create a gazetteer that combines a broad coverage of places with urban detail, including content on geographic and semantic relationships involving places, their multiple names and related non‐geographic entities. Our final goal is to expand the possibilities for recognizing, disambiguating and filtering references to places in texts for geographic information retrieval (GIR) and related applications. The resulting ontological gazetteer, named LoG (Linked OntoGazetteer), is accessible through Web services by applications and research initiatives on GIR, text processing, named entity recognition and others. The gazetteer currently contains over 13 million places, 140 million attributes and relationships, and 4.5 million non‐geographic entities. Data sources include GeoNames, Freebase, DBPedia and LinkedGeoData, which is based on OpenStreetMap data. An analysis on how these datasets overlap and complement one another is also presented.     

地理空间数据结合手机信令等多源数据刻画城市居民出行特征

本文提出了以地理空间数据为支撑,结合手机信令、POI等多源数据刻画城市居民出行特征的方法。首先将信令数据与地理信息区块绑定,根据时间特征和地理区块的社会属性,识别居民的基本职住娱信息;然后综合民生POI点、出行特征拓展关键词、图谱等多源数据,运用工作日通勤分析模型和节假日出行特征提取模型,识别用户的通勤距离、通勤方式、日均通勤频次、周均工作时长、节假日出行场景、出行频次、驻留时长等内容,并形成涵盖职住娱信息的出行特征类标签集。以成都市为例,采集连续1个月的手机信令数据和同时期的POI等数据,验证了该方法的可行性,该成果作为反映城市实际人口规模数量和空间分布特征的城市人口地图大数据产品的重要内容,为政府部门、商企用户开展相关分析业务提供数据支撑。     

利用B/S架构的POI信息组织方法及可视化策略研究

分析了网络上构建兴趣点管理体系的现状,顾及语义要素对于不同尺度特征数据符号化的积极作用及结合超媒体模型信息链的组织特点,探讨了POI信息组织方案,并在充分考虑瓦片数据组织方式的特点及当前成熟Web技术综合表现能力的基础上,提出了一种基于纯B/S架构的POI可视化策略。以湖北省福利彩票网点管理系统为例进行了分析,证明该策略不仅能借助可视化的统计分析结果直观地提供辅助决策参考,且较强的平台移植性可服务于各类专题POI应用。     

城市地图数字合并变换算法的比较研究

地图数字合并是实现不同来源数据融合和集成的方法和技术。匹配与调整变换是数字合并的两个主要过程,匹配过程存在大量的不确定性,使得一些实体无法找到匹配实体。因而在调整变换过程中,选用合适的方法以达到最优效果尤其重要。从定性与定量分析的角度,确定了定性与定量的检验标准,并采取一定实验手段,对城市地图数字合并采用的调整变换方法进行比较分析,依据分析结果,得出适用于城市地图数字合并的调整变换方法。     

城市地图数据库面实体匹配技术

同一地区不同来源地图数据库同名面实体的识别或匹配对空间数据库的集成与信息共享非常有意义。该文研究了城市地图数据库同名面实体匹配的有关问题 ,提出了基于模糊拓扑关系分类的面实体匹配方法 ,该方法充分考虑了源地图数据库的不确定性 ,能处理非一对一的匹配情况。匹配结果不仅可以作为城市地图数据库集成和信息共享的依据 ,还可以用来分析两个地图数据库之间的差异 ,进行变化检测与自动更新     

点状地名信息的加权泰森多边形检索法

鉴于地名库中多数地名仅以中心点坐标形式记录其空间位置信息,缺乏其空间范围的具体描述,造成地名检索中的片面性和局限性,本文在深入分析地名及其空间属性、空间关系的基础上,考虑同类型地名的不同性质在检索中的重要作用,利用泰森多边形在地名边界近似中的优势,提出了针对点状地名信息的加权泰森多边形检索法,依据同类型地名的面积属性作为权重指标构建泰森多边形,以近似表达地名的空间范围,进而描述地名间的各类空间关系;并从拓扑关系、方位关系与距离关系3个方面给出了具体公式以计算地名间的空间相似性;最后以行政区划边界近似与检索为例验证该方法。试验结果表明,该方法可较好地近似表达地名空间范围及空间关系,增强了检索词与地理信息资源在空间位置上的相似性度量,检索结果优于传统方法。     

城市POI的空间数据分析与可视化表达

随着科技的迅速发展,地理空间数据已经成为人们日常生活中的一部分,城市POI数据则是涵盖城市公共基础设施属性信息与位置信息的大型地理空间数据。针对现阶段对城市POI数据利用程度不充分的问题,本文运用密度分析、热点分析以及服务范围分析等方法研究大连金普新区城市POI数据的空间分布特征。结果表明:大连金普新区城市POI数据呈现"二区八带多点"的分布格局,金石滩街道、中长街道、先进街道以及大李家街道医疗、教育、娱乐设施较为完善,且远离工厂,比较宜居。本研究可为市民生活、工作、学习、娱乐提供参考,也可为城市发展、管理维护提供服务。