NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages

Social media messages, such as tweets, are frequently used by people during natural disasters to share real‐time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro‐net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models.     

Inferring the Location of Twitter Messages Based on User Relationships

User interaction in social networks, such as Twitter and Facebook, is increasingly becoming a source of useful information on daily events. The online monitoring of short messages posted in such networks often provides insight on the repercussions of events of several different natures, such as (in the recent past) the earthquake and tsunami in Japan, the royal wedding in Britain and the death of Osama bin Laden. Studying the origins and the propagation of messages regarding such topics helps social scientists in their quest for improving the current understanding of human relationships and interactions. However, the actual location associated to a tweet or to a Facebook message can be rather uncertain. Some tweets are posted with an automatically determined location (from an IP address), or with a user‐informed location, both in text form, usually the name of a city. We observe that most Twitter users opt not to publish their location, and many do so in a cryptic way, mentioning non‐existing places or providing less specific place names (such as “Brazil”). In this article, we focus on the problem of enriching the location of tweets using alternative data, particularly the social relationships between Twitter users. Our strategy involves recursively expanding the network of locatable users using following‐follower relationships. Verification is achieved using cross‐validation techniques, in which the location of a fraction of the users with known locations is used to determine the location of the others, thus allowing us to compare the actual location to the inferred one and verify the quality of the estimation. With an estimate of the precision of the method, it can then be applied to locationless tweets. Our intention is to infer the location of as many users as possible, in order to increase the number of tweets that can be used in spatial analyses of social phenomena. The article demonstrates the feasibility of our approach using a dataset comprising tweets that mention keywords related to dengue fever, increasing by 45% the number of locatable tweets.     

基于“天地图”的地震应急服务平台设计

地震应急与社会经济紧密相关,也是防震减灾的重要内容。在总结地震应急相关研究与信息服务的基础上,采用网络技术、GIS技术、数据库技术,设计并开发基于"天地图"的地震应急服务平台,实现震情实时发布、空间查询定位、灾情实时反馈、直接经济损失快速评估、经济人口信息的统计分析等功能。该平台的建立将为震前预测提供完备、现势的数据基础,为震中救援提供可视化的分析平台,为震后恢复重建的资源调配提供数据支持。     

地震诱发山区公路地质灾害信息管理系统研究

分析了基于GIS的高烈度地震山区公路地震信息管理系统应具有的功能及技术路线,研究了如何利用GIS空间分析功能对工程规划进行辅助决策,并以通麦至105道班为例,建立了高烈度地震山区公路地震诱发地质灾害信息管理系统。     

Similarity Measurements on Multi‐Scale Qualitative Locations

Qualitative representation of spatial locations and their similarity measurements are essential for the analysis of linguistic term‐based data. Existing methods have focused on the similarities of spatial relations and spatial scenes but have not considered the variations in geometrical representations and relations over scales. This study developed some new measures to assess the similarities of both single‐ and multi‐scale qualitative locations. Region‐ and cell‐based models were used to formalize qualitative locations of spatial objects with respect to multi‐scale frames of reference. The similarities were assessed by integrating the similarities of frames and qualitative relations. The frame similarity measures how two objects are compared considering the common elements that they occupy in the reference frames. Moreover, the similarity of qualitative relation measures how two relations relate two objects to the corresponding elements in the frames. The location similarities at a single level integrate the similarities of the frames and qualitative relations, whereas the location similarities at multiple scales incorporate the variations in qualitative locations over scales. These methods were used to assess location similarities concerning residential areas, roads, and lakes. The results indicated that the location‐based measurements can disclose the distributions of the similarities and that the cell‐based model is more accurate than the region‐based model.     

Information Rich GIS Dissemination in Disconnected Environments

Information‐rich maps are today rendered from powerful back‐end servers. Unfortunately, there may be circumstances in which access to such Internet‐resident web services is infeasible or unreliable. These disconnected environments can theoretically occur during major power outages, malicious attacks on communications infrastructure, natural disasters, or in remote operating environments. One might also envision applications in which a disconnected service is preferred over Internet‐resident services, such as when the local internet service is overloaded with traffic, making communication though it difficult. To this end, this article presents a software stack (a map stack) that can serve GIS information from low power embedded nodes. The system combines a spatially enabled SQL database, location‐based routing, and multi‐scale map rendering to serve information‐rich GIS data on common hand‐held devices at minimal energy consumption. The maps are rendered on browsers on off‐the‐shelf mobile devices such as smart phones and tablets. The system also provides a crowd sourced capability where end users can annotate maps with up‐to‐date information on the scene of the natural disaster. We have prototyped a fully functional map stack on a battery powered Gumstix Overo air platform. We show that the map stack is a highly extensible platform that provides low latency, and low energy dissemination of maps during a natural disaster.     

利用ArcGIS Mobile的地震灾情速报系统设计与实现

为满足地震灾情速报工作的需要,本文利用ArcGIS Mobile技术,基于PDA设计和开发了移动地震灾情速报系统。结合地震灾情速报工作的特殊性,设计出灾情速报基础空间数据库,实现了灾情信息编辑、灾情区域绘制、灾情速报员GPS定位、灾情同步等功能。     

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.     

基于GIS的地震建筑直接经济损失评估研究

地震发生以后,需要在最短的时间内得到震区总体受灾情况,以便各级政府部门开展地震救援和灾后重建工作。本文探讨了地震建筑直接经济损失评估流程,并依据易损性清单法模型,结合GIS技术,开发了地震灾害损失评估系统。利用系统对2009年云南姚安县地震进行了建筑直接经济损失评估,评估结果表明系统建筑直接经济损失评估结果真实、可靠。     

Gravity Changes Before Large Earthquakes in China: 1998-2005

Although it is well known that coseismic gravity changes take place during an earthquake, previous research has not yielded convincing evidence demonstrating that significant gravity changes occur before large earthquakes. Furthermore, even if we suspect that gravity changes occur before large earthquakes, we have yet to demonstrate how to consistently observe these changes for useful earthquake forecast that would bring benefits to society. We analyzed ground gravity survey data obtained in 1998, 2000, 200...     

Improving geospatial query performance of an interoperable geographic situation‐awareness system for disaster response

Disaster response operations require fast and coordinated actions based on real‐time disaster situation information. Although crowdsourced geospatial data applications have been demonstrated to be valuable tools for gathering real‐time disaster situation information, they only provide limited utility for disaster response coordination because of the lack of semantic compatibility and interoperability. To help overcome the semantic incompatibility and heterogeneity problems, we use Geospatial Semantic Web (GSW) technologies. We then combine GSW technologies with Web Feature Service requests to access multiple servers. However, a GSW‐based geographic information system often has poor performance due to the complex geometric computations required. The objective of this research is to explore how to use optimization techniques to improve the performance of an interoperable geographic situation‐awareness system (IGSAS) based on GSW technologies for disaster response. We conducted experiments to evaluate various client‐side optimization techniques for improving the performance of an IGSAS prototype for flood disaster response in New Haven, Connecticut. Our experimental results show that the developed prototype can greatly reduce the runtime costs of geospatial semantic queries through on‐the‐fly spatial indexing, tile‐based rendering, efficient algorithms for spatial join, and caching, especially for those spatial‐join geospatial queries that involve a large number of spatial features and heavy geometric computation.     

A name‐led approach to profile urban places based on geotagged Twitter data

Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space.     

日本3·11特大地震的GPS震时和震后响应

借助PPP软件,利用位于日本及周边国家的IGS跟踪站和国家海洋局GPS业务观测站数据,提取2011年3月11日日本里氏9.0级特大地震的震时、震后地表震动信息和水汽信息。首先采用动态PPP方法得到GPS站点的震时水平运动轨迹、震时三维坐标时间序列以及站点上空大气可降水量的动态变化;然后采用静态PPP方法得到GPS站点震前和震后的单天解坐标。通过对计算结果进行分析和比较,揭示了GPS站点的震时地表三维形变过程和震后地表永久性形变,验证了日本震后灾区的降雪过程,为GPS技术用于地震监测和灾害预警提供有价值的基础资料。     

面向对象的高分辨率遥感影像建筑物震害信息提取

建筑物的倒损信息是震后灾害评估的一项重要指标。文中应用震后高分辨率遥感影像数据,采用面向对象分类方法,以最优分割参数对影像进行分割,构建多尺度影像对象层次结构。通过影像对象的光谱、形状、纹理等特征及空间拓扑关系建立分类规则库,提取基本完好、受损和完全倒塌三类破坏等级的建筑物震害信息。结果表明,面向对象分类方法能够实现提取三类等级的建筑物震害信息,从而满足地震灾害快速评估要求。     

面向地震应急调查的遥感应用现状及趋势分析

地震是一种会造成人类生命财产重大损失的突发性自然灾害,震后第一时间启动应急响应并开展灾情的快速评估能有效地减轻地震灾害带来的破坏。空间对地观测技术为宏观性的地震应急与调查工作提供了便捷、经济的途径,随着空间对地观测技术与数据处理技术的不断发展,各国学者对遥感应急调查开展了大量深入的研究工作,相关研究成果已广泛地应用于地震应急的实际工作中。但是,遥感数据类型与处理技术的多样化也带来了应急信息的散乱,导致遥感快速应急响应系统性不强,使得应急服务不持续,一定程度上限制了遥感技术的效能;为此,针对现阶段遥感技术在地震应急调查中的应用情况,在总结地震应急调查常用遥感技术手段的基础上,分析了遥感快速应急响应面临的技术挑战,重点梳理了地震应急不同阶段对遥感数据及应急专题产品产出类型与时效性的现实需求,结合震后灾区影像数据的情况,系统地分析了光学、雷达、激光雷达遥感技术在地震应急调查应用中的技术现状与存在的问题。在实际地震应用案例分析的基础,总结剖析遥感应急工作存在的问题,并重点从海量数据快速处理、震害信息智能化提取、多源数据协同分析3个技术层面论述了遥感地震应急面临的核心困难,基于此,结合在轨数据实...     

Enriching the GIScience research agenda: Fusing augmented reality and location‐based social networks

Augmented reality (AR) overlays real‐world views or scenes with virtual, computer‐generated objects that appear to visually coexist in the same space. Location‐based social networks (LBSNs) are platforms for individuals to be connected through the interdependency derived from their physical locations and their location‐tagged social media content. Current research and development in both areas focuses on integrating mobile‐based AR and LBSNs. Several applications (e.g., Sekai Camera and Wallame) have been developed and commercialized successfully. However, little research has been done on the potential impacts and successful evaluation methods of AR‐integrated LBSNs in the GIScience field. To close this gap, the article outlines the impacts and benefits of AR‐integrated LBSNs and highlights the importance of LBSNs in GIScience research. Based on the status quo of AR‐integrated LBSNs, this article discusses—from theoretical and application‐oriented perspectives—how AR‐integrated LBSNs could enrich the GIScience research agenda in three aspects: data conflation, platial GIS, and multimedia storytelling. The article concludes with guidelines on visualization, functionality, and ethics that aim to help users develop and evaluate AR‐integrated LBSNs.     

玉树地震灾情SAR遥感监测与信息服务系统

在玉树地震中,中国首套自主知识产权的机载多波段多极化干涉SAR测图系统,发挥全天时主动遥感的优势,成功服务于抗震救灾中。依据玉树地震灾情综合地理信息监测与评估工作,着重探讨SAR遥感影像用于地震灾情监测与评估中的处理方法和技术路线,研究制定了地震灾区灾情综合地理信息监测指标,通过快速几何处理、快速变化信息提取、快速目标判读和灾害空间危险性评估,实现了玉树地震震区灾情综合地理信息的解译、制图和统计评估。在此基础上,开发了玉树地震灾情综合地理信息服务系统,实现了灾情监测信息的综合管理、可视化查询和统计分析。     

地震及其次生灾害灾情专题图制图模板研究

结合地震灾害应急监测工作对遥感影像地图的需求,根据地震及其次生灾害发生分布特征及其危害情况,研究制定地震及其次生灾害灾情信息专题图制图模板;形成一套地震及其次生灾害灾情信息专题图的制图规范.能够及时准确提供典型灾情信息,进行快速制图工作,为灾害损失评估、调查及监测、灾后重建等提供及时的地理信息服务.     

利用无人机影像制作地震灾区三维景观图

为实时、准确地获取地震灾区的灾情和灾后重建状况,研究如何利用高分辨率无人机影像制作灾区三维景观图。对无人机影像进行几何纠正、影像拼接,利用摄影测量方法生成实验区的DEM,进而将影像制作成正射影像图;将无人机影像纹理映射到DEM上构建灾区的三维地形景观,并以正射影像图为底图对安置区的地物进行三维建模;最后根据规划和管理需要,编制三维景观系统,实现地震灾区三维景观的浏览、查询与分析。实践表明,采用无人机影像制作的三维景观图具有分辨率高、形象逼真等特点;影像的获取和处理以及系统的编制可为灾区重建提供丰富详实的信息。     

Quantitative estimates of collective geo-tagged human activities in response to typhoon Hato using location-aware big data

ABSTRACT

Location-aware big data from social media have been widely used to quantitatively characterize natural disasters and disaster-induced losses. It is not clear how human activities collectively respond to a disaster. In this study, we examined the collective human activities in response to Typhoon Hato at multi spatial scales using aggregated location request data. We proposed a Multilevel Abrupt Changes Detection (MACD) methodological framework to detect and characterize the abrupt changes in location requests in response to Typhoon Hato. Results show that, at the grid level, most anomaly grids were located within a radius of 53?km around the typhoon trajectory. At the city level, there are significant spatial difference in terms of the human activity recovery duration (230?h on average). At the subnational level, the absolute magnitude of abrupt location request changes is strongly correlated with the typhoon-induced economic losses and the population affected.