Modeling Use of Space from Social Media Data Using a Biased Random Walker

Individuals and other entities move through space as a function of local characteristics of place, their internal behavioral models, and the topological structure of the underlying space. When a collection of locations (i.e. geotagged photos or other geotagged social media information) from a large number of individuals is assembled, it becomes possible to understand the interrelationship between the individuals and the space they occupy. This research systematically considers this interrelationship through an examination of the effect of the intersection of behavioral and spatial characteristics on individuals moving on street networks. The research illustrates how social media data, in combination with a biased random walker, can be used to understand and model the interaction of spatial structure and social‐environmental factors on influencing individuals' use of their environment. The biased walker offers a flexible approach to incorporate consideration of both social‐environmental and structural factors into a model and we demonstrate this through a case study wherein we are able to use the random walker to model the characteristics of Flickr users in New York City.     

一种个人地理标记数据的可视化方法

个人地理标记数据是个人通过笔记本、平板电脑或者手机等设备发布的包含地理位置且与个人相关的文本、照片和视频等信息。本文针对个人地理标记数据的特点,提出了一种适用于地理标签数据的可视化方法——个人地理标记数据拓扑图,并设计和实现了核心算法。为了比较和评估该方法的效能,对"文本列表""普通地图"和"标记拓扑图"3种用户界面进行了可用性测试,并对测试数据进行了方差分析。测试结论表明,3种不同用户界面在查找个人地理标记数据的效率方面存在显著性差异,其中文本列表和标记拓扑图均优于普通地图,文本列表和标记拓扑图在查找时间上的差异并不显著,标记拓扑图在查找时间均值上略优于文本列表。     

关注点推荐算法的霍克斯过程法

关注点（point-of-interest,POI）推荐是基于位置的社交网络（location-based social network,LBSN）中重要的个性化位置服务。针对LBSN中用户签到数据的复杂性和高度稀疏性问题,本文提出了一种基于霍克斯过程的上下文感知协同过滤关注点推荐算法（HWCF）。首先,根据用户签到关注点的地理空间聚集现象分析用户行为特征,筛选用户候选关注点;然后,利用霍克斯过程对候选关注点建模,通过融合空间距离信息、空间序列变换信息、时间信息、用户偏好、关注点流行度等多种上下文信息计算用户访问候选关注点的概率,对访问概率排序得到top-k推荐列表;最后,对算法参数的取值及调整过程进行讨论。试验结果表明,HWCF算法比其他的关注点推荐算法具有更好的推荐效果。     

Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference

As mapping is costly and labor‐intensive work, government mapping agencies are less and less willing to absorb these costs. In order to reduce the updating cycle and cost, researchers have started to use user generated content (UGC) for updating road maps; however, the existing methods either rely heavily on manual labor or cannot extract enough information for road maps. In view of the above problems, this article proposes a UGC‐based automatic road map inference method. In this method, data mining techniques and natural language processing tools are applied to trajectory data and geotagged data in social media to extract not only spatial information – the location of the road network – but also attribute information – road class and road name – in an effort to create a complete road map. A case study using floating car data, collected by the National Commercial Vehicle Monitoring Platform of China, and geotagged text data from Flickr and Google Maps/Earth, validates the effectiveness of this method in inferring road maps.     

Using Reverse Viewshed Analysis to Assess the Location Correctness of Visually Generated VGI

With the increased availability of user generated data, assessing the quality and credibility of such data becomes important. In this article, we propose to assess the location correctness of visually generated Volunteered Geographic Information (VGI) as a quality reference measure. The location correctness is determined by checking the visibility of the point of interest from the position of the visually generated VGI (observer point); as an example we utilize Flickr photographs. Therefore we first collect all Flickr photographs that conform to a certain point of interest through their textual labelling. Then we conduct a reverse viewshed analysis for the point of interest to determine if it lies within the area of visibility of the observer points. If the point of interest lies outside the visibility of a given observer point, the respective geotagged image is considered to be incorrectly geotagged. In this way, we analyze sample datasets of photographs and make observations regarding the dependency of certain user/photo metadata and (in)correct geotags and labels. In future the dependency relationship between the location correctness and user/photo metadata can be used to automatically infer user credibility. In other words, attributes such as profile completeness, together with location correctness, can serve as a weighted score to assess credibility.     

Mapping floods due to Hurricane Sandy using NPP VIIRS and ATMS data and geotagged Flickr imagery

In this study, we present an approach to estimate the extent of large-scale coastal floods caused by Hurricane Sandy using passive optical and microwave remote sensing data. The approach estimates the water fraction from coarse-resolution VIIRS and ATMS data through mixed-pixel linear decomposition. Based on the water fraction difference, using the physical characteristics of water inundation in a basin, the flood map derived from the coarse-resolution VIIRS and ATMS measurements was extrapolated to a higher spatial resolution of 30 m using topographic information. It is found that flood map derived from VIIRS shows less inundated area than the Federal Emergency Management Agency (FEMA) flood map and the ground observations. The bias was mainly caused by the time difference in observations. This is because VIIRS can only detect flood under clear conditions, while we can only find some clear-sky data around the New York area on 4 November 2012, when most flooding water already receded. Meanwhile, microwave measurements can penetrate through clouds and sense surface water bodies under clear-or-cloudy conditions. We therefore developed a new method to derive flood maps from passive microwave ATMS observations. To evaluate the flood mapping method, the corresponding ground observations and the FEMA storm surge flooding (SSF) products are used. The results show there was good agreement between our ATMS and the FEMA SSF flood areas, with a correlation of 0.95. Furthermore, we compared our results to geotagged Flickr contributions reporting flooding, and found that 95% of these Flickr reports were distributed within the ATMS-derived flood area, supporting the argument that such crowd-generated content can be valuable for remote sensing operations. Overall, the methodology presented in this paper was able to produce high-quality and high-resolution flood maps over large-scale coastal areas.     

Discovering Landmark Preferences and Movement Patterns from Photo Postings

This article presents a geovisual analytics approach to discovering people's preferences for landmarks and movement patterns from photos posted on the Flickr website. The approach combines an exploratory spatio‐temporal analysis of geographic coordinates and dates representing locations and time of taking photos with basic thematic information available through the Google Maps Web mapping service, and interpretation of the analyzed area. The article describes data aggregation and filtering techniques to reduce the size of the dataset and focuses on information addressing research questions. The results of analysis for the Seattle metropolitan area help to distinguish between sites that are occasionally popular among the photographers and can be considered as potential attractions from sites that are regularly visited and already known as city landmarks. The analysis of photographers' movements across the metropolitan area shows that most photographers' itineraries are short and highly localized.     

一种基于Heat Map的地理标签数据可视化表达的研究

地理标签数据是指蕴含在网页、照片、微博等信息媒介中的地理空间信息,其表现形式通常是经纬度坐标。通过分析地理标签数据的研究现状,对地理标签数据进行分类,并归纳地理标签数据具有属性数据非结构化、海量信息分布不均、强调位置相对关系等特点。针对其中一个特点,通过对K-means算法进行改进,结合计算机图形学相关知识,利用热力图表达地理标签数据的分布特征。最后,通过与ArcGIS核密度图、散点图进行比较,得出该热力图算法具有表达效果明显、用户体验好等优点。     

Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter

The analysis of social media content for the extraction of geospatial information and event‐related knowledge has recently received substantial attention. In this article we present an approach that leverages the complementary nature of social multimedia content by utilizing heterogeneous sources of social media feeds to assess the impact area of a natural disaster. More specifically, we introduce a novel social multimedia triangulation process that uses both Twitter and Flickr content in an integrated two‐step process: Twitter content is used to identify toponym references associated with a disaster; this information is then used to provide approximate orientation for the associated Flickr imagery, allowing us to delineate the impact area as the overlap of multiple view footprints. In this approach, we practically crowdsource approximate orientations from Twitter content and use this information to orient Flickr imagery accordingly and identify the impact area through viewshed analysis and viewpoint integration. This approach enables us to avoid computationally intensive image analysis tasks associated with traditional image orientation, while allowing us to triangulate numerous images by having them pointed towards the crowdsourced toponym location. The article presents our approach and demonstrates its performance using a real‐world wildfire event as a representative application case study.     

利用自由形状线特征的遥感影像分级匹配方法

为较好地解决自由形状线特征匹配研究中线特征丰富信息的充分利用与有效描述这对矛盾,提出一种利用分级匹配策略的遥感影像匹配方法。为保证方法具有较高的匹配精度,从影像中检测亚像素边缘并进行有效的跟踪以得到连续性较好的自由形状线特征;再从中提取闭合线特征、线特征交点和角点等较稳定的特征作为共轭实体进行粗匹配,在确定各类特征的待选同名特征后,根据面积、角度等几何信息以及模型参数的分布特点逐步剔除误匹配,进而利用同名特征确定精匹配的初始参数;精匹配时对线特征中的亚像素边缘点加以利用,采用多层次二维迭代最邻近点(ICP)法依次利用由低到高采样率的边缘点进行匹配。试验结果表明,粗匹配选取的特征性能稳定,具有较高的匹配正确率和精度,能为精匹配提供较准确的初始参数,精匹配能达到与最小二乘影像匹配相当的亚像素级匹配精度,并且对具有较小仿射变形的影像也能实现稳定、准确的匹配。     

Spatiotemporal analysis of photo contribution patterns to Panoramio and Flickr

ABSTRACT

Photo-sharing services provide a rich resource of crowdsourced spatial data consisting of georeferenced imagery and metadata. Shared photos can provide valuable information for a variety of applications and geospatial analysis tasks, such as identifying tourist hot spots or traveled routes. Understanding the spatiotemporal patterns of photo contributions will allow analysts to assess the suitability of these data for related analysis tasks. Using California as a study area, this paper analyzes various aspects of photo contribution patterns of Panoramio and Flickr. It identifies areas where annual photo contributions are still growing and areas that undergo a decline in annual contributions. Multiple regression is used to identify which environmental correlates are associated with an increase in photo-sharing activities. Furthermore, panel data of annual contributions between 2006 and 2013 for California subcounties will be used in a regression model to demonstrate that there is a positive feedback effect between Panoramio and Flickr photo contributions, but no neighborhood effect. The results of this paper provide insight into the data quality of crowdsourced image collections. These collections are commonly used for geospatial applications, including tourist information services and the computation of scenic routes.     

Visualisation and personalisation of multi-representations city models

ABSTRACT

City models have a wide variety of uses that require different kind of data representation or data models. Having a dynamic model that enables picking the right representations (meshes, volumetric data, point cloud, etc.) can prove useful to adapt an application to each user's needs. In this paper, we present an original method to create personalised visualisations of 3D city models on the fly. By organising the server data in a hierarchy of tiles, we are able to generate personalised models based on the user's preferences. These preferences take the shape of a set of rules that apply to each tile or city object and allow the user to choose which representation of the object to use depending on its position or semantic information (classification, height, etc.). Our method is designed around existing standards, guaranteeing the interoperability of the produced models.     

基于邻近模式的多比例尺居民地松弛迭代匹配

空间目标匹配是实现多源空间信息融合、空间对象变化检测与动态更新的重要前提。针对多比例尺居民地匹配问题,提出了一种基于邻近模式的松弛迭代匹配方法。该方法首先利用缓冲区分析与空间邻近关系检测候选匹配目标与邻近模式,同时计算候选匹配目标或邻近模式间的几何相似性得到初始匹配概率矩阵;然后对邻近候选匹配对进行上下文兼容性建模,利用松弛迭代方法求解多比例尺居民地的最优匹配模型,选取匹配概率最大并满足上下文一致的候选匹配目标或邻近模式为最终匹配结果。实验结果表明,所提出的多比例尺居民地匹配方法具有较高的匹配精度,能有效克服形状轮廓同质化与非均匀性偏差问题,并准确识别1:M、M:N的复杂匹配关系。     

基于语义规则的高分辨率影像道路交叉口提取方法

为了准确提取遥感影像上道路交叉口目标,提出一种利用特征语义规则从高分辨率影像上提取道路交叉口的方法。该算法构建交叉口模型时将其视为由同质区域像素集合及区域轮廓边界构成的面对象,提取过程分为两步:1)利用辐射、纹理特征语义匹配提取交叉口候选区域;2)通过几何特征语义匹配筛选候选区域、识别交叉口属性。利用多源遥感影像对算法正确性及合理性进行验证,结果表明:算法能准确、完整地提取道路交叉口,可为影像道路网构建提供辅助信息。     

Natural Conversational Interfaces to Geospatial Databases

Natural (spoken) language, combined with gestures and other human modalities, provides a promising alternative for interacting with computers, but such benefit has not been explored for interactions with geographical information systems. This paper presents a conceptual framework for enabling conversational human‐GIS interactions. Conversations with a GIS are modeled as human‐computer collaborative activities within a task domain. We adopt a mental state view of collaboration and discourse and propose a plan‐based computational model for conversational grounding and dialogue generation. At the implementation level, our approach is to introduce a dialogue agent, GeoDialogue, between a user and a geographical information server. GeoDialogue actively recognizes user's information needs, reasons about detailed cartographic and database procedures, and acts cooperatively to assist user's problem solving. GeoDialogue serves as a semantic ‘bridge’ between the human language and the formal language that a GIS understands. The behavior of such dialogue‐assisted human‐GIS interfaces is illustrated through a scenario simulating a session of emergency response during a hurricane event.     

综合多要素的地理空间数据众包任务推荐方法

针对境外欠发达国家（或地区）地理空间数据和志愿者不足问题,为提高有限志愿者的贡献积极性和有效性,本文提出了一种综合多要素的地理空间数据众包任务推荐方法。首先采用网格将研究区域划分为若干任务;然后引入三角核函数计算用户空间偏好,结合时间遗忘率综合计算用户的时空偏好,借鉴TF-IDF和余弦相似度计算语义偏好,并融合时空、语义偏好获取初始兴趣推荐列表;最后利用隐语义模型预测用户标注每个任务的信誉（能力）,并根据用户信誉对初始推荐列表重排序。为验证本文方法有效性,以有一定数据基础的巴基斯坦首都伊斯兰堡为试验区,采用OpenStreetMap平台收集的用户及众包数据开展任务区推荐试验,试验数据按照8：2的比例随机划分为训练集和测试集。试验结果表明,该方法不仅能提高推荐任务接受率,还能提高用户完成任务的有效性。     

An integrated heterogeneous web service retrieval via combination of instance- and metadata-based schema matching method

Schema matching is a critical step in the integration of heterogeneous web service, which contains various types of web services and multi-version services of the same type. Mapping loss or mismatch usually occurs due to schema differences in structure and content and the variety in concept definition and organization. Current instance schema matching methods are not mature enough for heterogeneous web service because they cannot deal with the instance data in web service domain and capture all the semantics, especially metadata semantics. The metadata-based and the instance-based matching methods, in the case of being employed individually, are not efficient to determine the concept relationships, which are crucial for finding high-quality matches between schema attributes. In this paper, we propose an improved schema matching method, based on the combination of instance and metadata (CIM) matcher. The main method of our approach is to utilize schema structure, element labels, and the corresponding instance data information. The matching process is divided into two phases. In the first phase, the metadata-based matchers are used to compute the element label similarity of multi-version open geospatial consortium web service schema, and the generated matching results are raw mappings, which will be reused in the next instance matching phase. In the second phase, the designed instance matching algorithms are employed to the instance data of the raw mappings and fine mappings are generated. Finally, the raw mappings and the fine mappings are combined, and the final mappings are obtained. Our experiments are executed on different versions of web coverage service and web feature service instance data deployed in Geoserver. The results indicate that, the CIM method can obtain more accurate matching results and is flexible enough to handle the web service instance data.     

多特征约束的高分辨率光学遥感影像道路提取

针对复杂场景中的遥感影像道路提取问题,论文提出了一种多特征约束的影像道路提取方法,并开展了论文方法可行性论证。该方法首先,根据道路宽度的先验知识以及道路的几何特征,提出一种改进的线段二次提取模型,利用线段长度和道路宽度确定候选道路种子点集;其次,输入道路结构信息,基于道路辐射特征对候选道路种子点进行整体匹配评价;再次,当候选道路种子无法符合辐射特征要求时,提出一种浅色机动车检测模型,以此将浅色机动车结果作为道路上下文特征,利用道路上下文特征对候选道路种子点进行分析;然后,构建道路拓扑分析模型,依据道路拓扑特征对候选道路种子点进行最终验证;最后,对提取道路种子点进行优化处理,并提出道路跟踪及拟合方法。通过不同复杂场景、不同分辨率高分辨率遥感影像下开展的不同方法实验结果对比分析表明,相对于其他商用软件ECognition和Erdas的方法,本方法自动化程度更高,运行效率高,适用于解决道路类型多样化、几何光谱噪声大的复杂场景道路提取问题。     

一种基于标签云的位置关联文本信息可视化方法

互联网的广泛应用产生了越来越多与地理空间位置关联的文本信息。现有地理信息系统一般通过外部链接来浏览这些数据,需要频繁的缩放、漫游和点击操作,而其他方法又难以有效表达出空间位置关系。提出了一种基于标签云的位置关联文本信息可视化方法———标签云地图,给出了标签云地图的设计思路和实现流程,并以腾讯微博的真实数据集为例建立了原型,重点研究了点状和面状地理要素的Cartogram生成算法,关键字和词频的提取算法,面向不同尺度和不同时间的标签云显示规则的标签位置生成算法。实验表明,该方法能够帮助用户从大量的位置关联文本信息中快速感知并把握信息的总体特征和发展趋势。     

How well do we understand Earth observation electro-optical sensor parameters?

Since the launch of LANDSAT-1, the Earth observation systems' capability and utilisation has increased substantially. The past decade has seen a number of remote sensing satellites with improved capabilities. Understanding the sensor parameters from a user's standpoint is not that easy. In the context of increasing relevance and dependence on fusion of data from various sensors, there is an urgent need to standardise the sensor parameters specified. The present paper is an attempt to raise some of the concerns regarding specifying spatial, radiometric, spectral and temporal resolution. I solicit inputs from the readers on the points raised to formulate a consolidated recommendation for the ISPRS Council to propose to the international community.