网络文本数据时空感知计算概念、方法及应用场景

网络文本数据作为网络传感数据的一种,因其包含大量的时空信息,以及丰富的语义信息,不仅能够用于研究社会问题的时空特征,也能够对社会现象的时空规律提供语义上的可解释性,因此存在一定的应用价值,目前已逐渐得到研究者的关注。本文提出了网络文本数据的一般概念表示,分析了网络文本数据的类型和特征,重点梳理了网络文本数据时空感知计算的计算方法,总结了文本优先+地理背景和地理优先+文本增强两种计算模式,并从静态到动态、场景到区域的视角总结了4个相关应用场景。     

Visualizing spatiotemporal trajectories of mobile social media users using space–time cube

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators.     

面向对象的时空概念建模方法

在面向对象技术的基础上，把地理信息的时空特征与对象模型融合到一起，产生了语义丰富的时空扩展对象模型，以支持包含时空信息的系统需求分析和设计。     

社交网络时空大数据聚类挖掘有效选择分析

来自社交网络的时空大数据具有海量和高动态的特性,有效选择时空数据进行聚焦挖掘分析至关重要。以微博位置签到数据为例,首先,对时空大数据空间聚类挖掘的有效选择问题进行了研究,针对社交网络时空数据不确定性问题,提出了时空大数据针对聚类挖掘的有效选择方法。聚类挖掘有效选择方法提出从空间、时间或属性等维度对时空大数据进行分割。然后,对分割得到的数据集进行空间探索分析(exploratory spatial data analysis,ESDA),得到具有聚类挖掘潜力的数据集。最后,以武汉市微博位置签到数据进行商圈热点探测为例,对提出的社交网络时空大数据聚类挖掘有效选择方法进行验证。结果表明,有效选择方法可以得到挖掘效率和精准性更高的时空数据集。     

Crowdsourcing functions of the living city from Twitter and Foursquare data

ABSTRACT

Urban functions are closely related to people’s spatiotemporal activity patterns, transportation needs, and a city’s business distribution and development trends. Studies investigating urban functions have used different data sources, such as remotely sensed imageries, observation, photography, and cognitive maps. However, these data sources usually suffer from low spatial, temporal, and thematic resolution. This article attempts to investigate human activities to understand urban functions through crowdsourcing social media data. In this study, we mined Twitter and Foursquare data to extract and analyze six types of human activities. The spatiotemporal analysis revealed hotspots for different activity intensities at different temporal resolution. We also applied the classified model in a real-time system to extract information of various urban functions. This study demonstrates the significance and usefulness of social sensing in analyzing urban functions. By combining different platforms of social media data and analyzing people’s geo-tagged city experience, this article contributes to leverage voluntary local knowledge to better depict human dynamics, discover spatiotemporal city characteristics, and convey information about cities.     

Modeling spatiotemporal topological relationships between moving object trajectories along road networks based on region connection calculus

Considering the attempts to model spatiotemporal topological relationships between moving object trajectories, the conceptual and computational framework for moving objects along a road network has not received much attention. This paper aims to draw an improved model based on Region Connection Calculus (RCC) theory to represent the spatiotemporal topological relationships between moving object trajectories along road networks. This paper first uses a dimension reduction method based on a linear-reference transformation to model the moving object trajectories segments, and then defines new time–connection and space–connection relations between two trajectory segments. On this basis, the paper proposes an extension to the RCC-based spatiotemporal binary relationship set so that the combined semantics of the spatiotemporal predicates can be described completely. A case study was carried out using Floating Car Data in Guangzhou city. The computational results show that in a real application, the occurrence frequencies of the RCC-based binary relationships are distributed nonuniformly and the semantics of some binary relationships with the highest occurrence are coarse. Therefore, the partition of the spatiotemporal connection relations and the finer aspects of the spatiotemporal relationship model may require further research work.     

多源卫星遥感影像时空融合研究的现状及展望

高空间分辨率的地表或者大气环境动态监测需要高时间-空间分辨率的卫星遥感影像作为数据支撑,但由于卫星传感器硬件技术及卫星发射成本等客观因素的限制,使得获取高时空分辨率遥感影像的较为便捷高效、低成本的可行手段就是将分别具有高时间和高空间分辨率的多源遥感影像进行时空融合,从而生成不同研究和应用所需的高时空分辨率卫星影像。现阶段,虽然国内外的学者进行了大量的时空融合算法研究,但是这些研究都局限于特定的数据类型、算法原理、应用目的等客观限制,而且其发展呈现出多样性。本文对现有主流的时空融合算法研究进行了归纳总结,将其分为4种:(1)基于地物组分的时空融合;(2)基于地表空间信息的时空融合;(3)基于地物时相变化的时空融合;(4)组合性的时空融合。同时,本文还对时空融合算法中存在的问题和面临的挑战进行了分析,并对其未来的发展方向进行了前瞻性的展望。     

SensePlace3: a geovisual framework to analyze place–time–attribute information in social media

SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources.     

融合图像和时空信息的社交媒体用户活动分类方法

社交媒体签到数据中蕴含着大量的用户活动信息。理解社交媒体用户的活动和行为类型,对探索人类的移动性和行为模式等有着重要意义。提出了一种针对新浪微博（简称为微博）的用户活动分类方法,结合图像表达和时空数据分类技术,识别微博签到数据所代表的用户活动类型。首先,根据兴趣点属性信息将微博签到数据所代表的用户活动分为餐饮、生活服务、校园、户外、娱乐、出行6大类;然后,基于卷积神经网络和K近邻分类方法,融合签到数据中的图像场景信息与时空信息,对微博用户的活动行为进行分类。实验结果表明,所提方法能够显著提高微博用户活动类型识别的准确性,为精确探索人类行为活动提供更加有效的数据支持。     

关于地理信息系统未来发展的思考

在Geographic Information System(GIS)近60年的发展演进中,理论和技术都取得了长足的进步;应用领域扩大到社会的方方面面,社会影响力越来越大;体系结构、开发模式和服务模式等都发生了深刻的变化。为了推动GISystem进一步向前发展,在综述GIS中之“S”的三种含义即System（系统）、Science（科学）和Service（服务）以及GIS近60年来取得的丰硕成果的基础上,重点就以下三个问题进行了讨论：（1）如何认识地理信息系统（GISystem）。首先解析地理信息系统的三个关键词（系统、信息、地理信息）,据此论述了地理信息系统的内涵,分析了地理信息系统同地图、计算机地图制图、地图数据库的关系,认为地理信息系统源于又超越了地图、计算机地图制图和地图数据库,并认为地理信息系统具有装备的特性。（2）地理信息系统是怎样发展演进的。重点分析了由“地理信息系统”到“地理信息服务”的发展演进的社会需求背景、技术背景、学科背景,从应用领域、数据资源和功能的扩展及体系结构、开发模式和服务模式等方面,分析了地理信息系统发展演进的主要表现。（3）地理信息系统的未来发展将走向...     

Big Data and cloud computing: innovation opportunities and challenges

ABSTRACT

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.     

面向地理环境主体GIS初探

依据人地关系理论，提出发展面向地理环境主体的GIS理念。面向地理环境主体的GIS，主要研究在相关地理生态与社会经济环境中关于主体时空分布、社会经济活动行为时空特征与规律的表达、计算、模拟与分析。探讨了面向地理环境主体GIS的主要特征，以及智能体建模、时空行为数据模型与可视化表现方法。     

基于时空数据挖掘的城市体检评估方法与实践——以无锡市为例

时空数据挖掘作为当前研究的热点之一,为城市研究提供了新的思路和技术手段。本文以无锡市为例,利用手机信令数据、车辆轨迹实时数据、基础地理信息等多源异构时空数据,构建相应的数据挖掘模型,进行了职住平衡、区域联系度、交通拥堵等城市特征的挖掘与分析,构建多源数据挖掘的城市评估体系,探索了时空数据挖掘在城市体检评估中的应用,为城市规划和运行管理提供了科学评估与决策。     

Thinking about Space‐Time Connections: Spatiotemporal Scheduling of Individual Activities

This article presents a spatiotemporal model for scheduling applications that is driven by the events and activities individuals plan and manage every day. The framework is presented using an ontological approach where ontologies at different levels of generalization, e.g. domain, application, and task ontologies, are linked together through participation and inheritance relationships. S_Events are entered into a schedule as a new S_Entry, or modifications can be made to existing entries including reschedule, postpone, change location, and delete as schedules vary over time. These schedule updates are formalized through changes to planned start and end times and the planned locations of S_Entries are expressed using SWRL, a semantic web rule language. SWRL is also used for reasoning about schedule changes and the space‐time conflicts that can occur. The sequence of entries in a schedule gives rise to S_trajectories representing the locations that individuals plan to visit in order to carry out their schedule, adding an additional spatial element to the framework. A prototype Geoscheduler application maps S_Entries against a timeline, offering a spatiotemporal visualization of scheduled activities showing the evolution of a schedule over space‐time and affecting spatiotemporal accessibility for individuals.     

Estimating spatiotemporal aggregation scales by revisiting the spatiotemporal L-function

As an important statistical tool for spatial data, the L-function is widely used in the estimation of the spatial aggregation scale. However, for heterogeneous spatiotemporal points, the formulation of the existing spatiotemporal L-function makes it liable to produce poor estimates of spatiotemporal aggregation scales, which is essential in revealing process evolutions, such as spatiotemporal scopes of seismic sequence and spatiotemporal spread extents of an epidemic. This technical note analyzes the mechanism whereby the existing L-function incorrectly estimates the spatiotemporal aggregation scales, and presents a modified spatiotemporal L-function that overcomes these issues. Moreover, a local version of the improved L-function is developed to identify aggregations for validation. The results of simulation experiments and a seismic case study demonstrate that our method estimates the spatiotemporal aggregation scales accurately, which verify the veracity of our corrections to the existing spatiotemporal L-function.     

从新型智慧城市建设看城市测绘发展

地理信息或时空信息是新型智慧城市建设不可或缺的信息资源,城市测绘是保障和服务于城市规划、建设与管理的测绘地理信息活动。本文在阐述新型智慧城市建设模式、建设过程、技术参考模型和重点建设任务的基础上,从智慧城市规划设计评价与标准编制、智慧城市时空信息平台建设和专题时空信息应用等方面探讨城市测绘在智慧城市建设中的角色和任务,以期为思考和实践新型智慧城市背景下的城市测绘发展提供参考。     

Research challenges and opportunities in mapping social media and Big Data

Social media and Big Data have transformed our world into interconnected cyberspace and realspace. Cartographers can now trace, monitor, and map the spread of social movements, disease outbreaks, nature hazards, and popular events by digitally collecting social media and Big Data with locational contents, such as global positioning system tags and user location profiles. The dynamic characteristics of social media and Big Data provide a great research opportunity for cartographers to map and analyze human behaviors, communications, and movements. However, there are many challenges and pitfalls in cartographic research associated with spatiotemporal analysis of social media contents and Big Data. This short paper will address important research challenges and major opportunities for cartographers to process and visualize Big Data and social media.     

基于Web新闻的案(事)件抽取与时空分析系统

新闻,自古以来便是人们了解社会动态的重要途径,大数据时代,由于Web新闻自身所具有的客观性和真实性,其蕴含的数据价值凸显。针对新闻网站中案(事)件信息丰富、易采集等优点,研究开发一套基于Web新闻的案(事)件抽取与时空分析系统,抓取各个新闻网站对发生于福州的案(事)件相关信息的报道,对新闻信息进行判别清洗与解析,采用支持向量机进行案(事)件类别分类,多类别分类精度达75%,抽取经分类处理之后的案(事)件文本中的案(事)件时空信息并进行时空分析,以毒品案(事)件为例,将解析结果与公安毒品案(事)件分别做核密度估计,结果表明,福州毒品事件集中发生于茶园派出所和象园派出所等辖区。该系统有利于分析福州社会动态,也为公安部门提供了信息辅助。     

动态多基态变粒度时空修正模型在矿区地质环境中的应用

针对矿区地质环境污染面积动态变迁和实时监测问题,首先根据TGIS中的时间属性表达特性,分别将时间粒度表达和变粒度时空存储因子设计与现有的动态多级基态修正模型进行综合集成表示,提出了一种动态多基态变粒度的修正模型,实现了时间粒度动态变化过程中多基态自动确立问题;其次结合某矿区地质环境面积变迁的时空数据特征,分别设计了时空数据库的五库结构和数据库组织结构,实现了矿区地质环境面积变迁时空数据库的逻辑和物理结构设计;然后通过结合TGIS和视频播放器原理,实现了矿区地质环境面积变化场景的回放,并采用空间数据库查询语句实现了地质环境面积变化的超前预演;最后以某矿区地质环境数据为例,实践表明：动态多基态变粒度的修正模型可实时监测矿区地质环境类型面积的变迁过程,增加了复垦区域的面积,实现了矿区地质环境面积变迁数据的存储、再现和未来变迁的预演。     

顾及邻近关系的NPP/VIIRS序列影像时空插值方法

针对可见红外成像辐射仪(visible infrared imaging radiometer suite,VIIRS)月度夜光遥感影像的数据缺失问题,提出一种利用地物邻近关系相关性的像元时空插值方法,以时、空关系互相作为约束条件,将时序变化一致性较好的像元数据作为空间插值的参考,将空间关系一致性较好的月度数据作为时序插值的参考,通过构建不同的卷积核, 在时序和空间维度分别对初步插值结果进行卷积运算,求得待插值像元的时空插值。以2015年江苏省月度夜光遥感影像修复为例,对不同维度时空插值方法进行对比分析,结果表明, 空间维度插值虽然顾及到像元的空间关联性,仍无法满足数据大范围缺失的插值要求,插值结果整体偏低;时间维度插值考虑到像元的时间趋势性,插值精度较空间维度插值有一定提高,但部分月份插值结果有较大偏差;相对于三次Hermit插值,时空插值方法获得的月度影像灯光亮度总和的最大相对误差、年度影像灯光亮度总和相对误差以及逐像元差值均显著降低。总的来看,所提时空插值方法在插值过程中同时顾及到VIIRS数据的时间趋势平稳性和空间结构稳定性,影像插值精度提高明显,且对待插值月份前后时序数据没有严格要求,更具有广泛性。