基于社交媒体共词网络的灾情发展态势探测方法

对灾害发生过程中产生的社交媒体数据进行主题演化探测和分析可以反映灾情的发展态势。提出了一种基于共词网络社区演化进行灾情发展态势感知的方法,首先依据词频-逆文档频率方法筛选出与主题相关的关键词汇,基于关键词的共现关系,构建以关键词为节点的社交媒体共词网络,结合模块度最优化思想,对社交媒体共词网络进行主题社区探测;然后在验证主题探测的基础上,基于时间窗口划分,对相邻时间窗口的主题社区进行演化类型判别,进而得到主题社区演化的结果;最后以2012年"7.21北京特大暴雨"灾害事件为例,利用该方法对收集到的含关键词的微博数据进行主题演化分析。实验结果表明,该方法能够很好地反映主题的演化过程,并能进一步揭示灾情的发展态势,帮助应急管理者了解灾害的发展过程,从而辅助管理者在合适的时间采取相应的应急响应措施。     

社交媒体数据的获取与处理分析研究

带有地理空间信息的社交媒体数据是由众多专业和非专业用户主观发布并通过社交媒体向公众或组织提供的一种开放地理空间数据。为了高效地获取签到数据以及保证签到数据的正确性、可靠性、完整性,满足数据挖掘算法的需要,本文以微博签到数据为例,提出了获取数据的关键技术,包括调用微博API的方法、研究区域格网化的获取方法,提高了数据的获取效率。并且针对获取的原始数据提出了对其处理的方案并对获取的数据结果进行了相关的统计描述。     

社交媒体的情感挖掘在服务减灾中的应用

社交媒体因其广泛的公众参与性和多源信息的快速传播性已成为灾情信息获取的重要途径,在近年来的灾害应急救援中发挥着重要的作用。我国是一个风灾频发的国家,有效的管理和利用社交媒体数据辅助减灾救援有着重要的理论和现实意义。然而目前,国内面向微博文本理解和情感分析在减轻灾害方面的研究还十分稀缺。针对目前研究的不足,本文以中文社交媒体为研究对象,通过机器学习的方法挖掘风灾期间的公众情感变化,并结合GIS空间分析技术对灾情的发展与影响进行刻画,最后以2017年台风"天鸽"登陆珠海市为案例证明方法的可行性。     

基于社交媒体的突发事件应急信息挖掘与分析

社交媒体越来越多地被看作是随人们移动的传感器,感知周围发生的事件。当突发事件发生时,大量含有位置信息的文字迅速地充斥整个社交网络。本文探讨突发事件应急信息挖掘与分析的一种新思路。基于社交媒体,建立实时应急主题分类模型,从大量、实时的文本流中快速提取、定位应急信息;针对不同主题,利用统计分析和空间分析方法,探寻突发事件的时间趋势和空间分布,为应急响应提供决策支持。     

挖掘社交媒体数据探究自然灾害时公众注意力的变化

随着互联网行业的发展,在灾难发生期间,社交媒体已经成为公众重要的交流手段,通过对受灾地区公众的社交媒体数据进行合理的抽取与内容分析,可以为应急管理人员提供有效的决策支持。本研究选取了2021年10月山西暴雨期间的微博数据作为研究数据,通过运用词频-逆文档频率算法（TFIDF）、中文词法分析（LAC）和百度AI(Artificial Intelligence)情感分析等方法对社交媒体数据进行综合分析以获取该灾害下公众情感以及公众注意力焦点变化趋势,为新媒体时代救援减灾工作提供支撑。     

基于社交媒体的应急信息系统设计与实现

随着手持设备的广泛普及、通信技术的高速发展,社交媒体在突发事件中发挥着重要作用。当突发事件发生时,社交媒体数据不断产生,并携带大量的应急信息,其中包括不同的应急主题、时空分布等信息。本文设计并实现了基于社交媒体的突发事件应急信息系统,重点介绍基于社交媒体的应急信息挖掘技术,以及系统实现的功能。     

面向新浪微博签到数据的时空热点事件检测方法

传统面向文本数据的事件检测方法在处理以微博为代表的社交媒体数据时面临着效率和准确性的挑战。同时,社交媒体数据中富含的位置信息常常不能被有效地识别和利用,这无疑会影响到事件检测的效果。本文基于对已有研究的总结归纳,定义了一类面向微博签到数据的时空热点事件,并提出了一种新的微博时空热点事件检测方法对其进行识别。通过两组实际数据的实验,证明该方法能够有效地从海量的微博数据中挖掘出具有时空特征的热点事件。     

基于改进Markov算法的人类活动信息挖掘

近年来,基于位置的社交媒体飞速发展,为人类移动规律的挖掘与研究带来新的数据源。基于扩展Markov模型,加入时间维度,提出一种利用社交媒体时空数据挖掘人类活动规律的方法,探索用户的活动位置和活动位置的变化规律。应用该方法对北京市新浪微博用户的个体和群体活动规律进行探索,可有效挖掘人类在以小时为单位细粒度时段的移动规律并由此反映区位人口的动态变化。     

一种基于共词网络的社交媒体数据主题挖掘方法

对社交媒体所包含文本数据的深入挖掘,有利于有效地进行后续的时空分析。提出了一种新的基于共词网络的社交媒体数据主题挖掘方法,依据词频-逆文档频率分析,自动筛选出与主题相关的关键词汇,基于微博间是否包含相同的关键词汇,提出构建以微博为节点的共词网络,并结合Louvain社区探测算法进行文本主题挖掘。所提出的方法是一种无监督方法,且具有不需要指定聚类数目的优点。实验表明,该方法在主题挖掘表现上,准确率和召回率均优于常用的文档主题生成模型。以收集的2012年北京暴雨期间包含关键词的微博为例,利用提出的方法对微博数据集进行挖掘和时空分析,结果表明所提方法在实际应用中的有效性。     

移动轨迹数据可视化方法总结

作为移动社交网络的主体,人们移动带来的位置轨迹不仅记录了人的行为历史,也记录了人与社会的交互活动信息。移动社交网络中位置轨迹数据的分析与利用为解决城市问题提供了一种新的思路。本文概述了轨迹数据可视分析中的几种方法,总结了轨迹数据可视分析研究中存在的问题和面临的挑战。     

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.     

A spatial analysis of non‐English Twitter activity in Houston,TX

The use of social media data in geographic studies has become common, yet the question of social media's validity in such contexts is often overlooked. Social media data suffers from a variety of biases and limitations; nevertheless, with a proper understanding of the drawbacks, these data can be powerful. As cities seek to become “smarter,” they can potentially use social media data to creatively address the needs of their most vulnerable groups, such as ethnic minorities. However, questions remain unanswered regarding who uses these social networking platforms, how people use these platforms, and how representative social media data is of users' everyday lives. Using several forms of regression, I explore the relationships between a conventional data source (the U.S. Census) and a subset of Twitter data potentially representative of minority groups: tweets created by users with an account language other than English. A considerable amount of non‐stationarity is uncovered, which should serve as a warning against sweeping statements regarding the demographics of users and where people prefer to post. Further, I find that precisely located Twitter data informs us more about the digital status of places and less about users' day‐to‐day travel patterns.     

A spatial‐temporal‐semantic approach for detecting local events using geo‐social media data

Social media networks allow users to post what they are involved in with location information in a real‐time manner. It is therefore possible to collect large amounts of information related to local events from existing social networks. Mining this abundant information can feed users and organizations with situational awareness to make responsive plans for ongoing events. Despite the fact that a number of studies have been conducted to detect local events using social media data, the event content is not efficiently summarized and/or the correlation between abnormal neighboring regions is not investigated. This article presents a spatial‐temporal‐semantic approach to local event detection using geo‐social media data. Geographical regularities are first measured to extract spatio‐temporal outliers, of which the corresponding tweet content is automatically summarized using the topic modeling method. The correlation between outliers is subsequently examined by investigating their spatial adjacency and semantic similarity. A case study on the 2014 Toronto International Film Festival (TIFF) is conducted using Twitter data to evaluate our approach. This reveals that up to 87% of the events detected are correctly identified compared with the official TIFF schedule. This work is beneficial for authorities to keep track of urban dynamics and helps build smart cities by providing new ways of detecting what is happening in them.     

Geo-questionnaires in urban planning: recruitment methods,participant engagement,and data quality

ABSTRACT

Recent focus on sustainable urban development and livability has increased the demand for new data sourcing techniques to capture experiences and preferences of urban dwellers. At the same time, developments of geospatial technologies and social media have enabled new types of user-generated geographic information and spatially explicit online communication. As a result, new public participation GIS methods for engaging large groups of individuals have emerged. One such method is geo-questionnaire, an online questionnaire with mapping capabilities, which has been used to elicit geographic data in variety of topics and geographical contexts. This article presents two recent cases, in which geo-questionnaires have been used in Polish cities to obtain public input on quality of life and development preferences in local land use planning. The article evaluates participant recruitment methods focusing on sample representativeness, participant engagement, and data quality. Recruitment via social media was found to increase bias towards younger population. Paper questionnaires used along the online version provided for better representation of target population’s age structure, but did not reduce bias related to educational attainment. We discuss how these issues relate to data usability and generalizability in the context of digital divide, and suggest directions for future research.     

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.     

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.     

Integrated GIS and Remote Sensing Approach to Map Pollution in Upper Lake,Bhopal, India

Abstract

Upper Lake is the lifeline of Bhopal City, India for drinking and other water needs. In recent years, environmentalists have expressed their serious concern on deteriorating water quality of this lake. Conventional field sampling methods for monitoring lake water quality lack spatial information about the pollution in the lake. It is desirable to have spatial information about the lake for better management and control. In the present paper the remote sensing data from IRS-1C LISS III have been integrated into a GIS environment to analyse and create a pollution zone map of the Upper Lake.

Spectral reflectance analysis was carried out to find the suitability of wavelengths for determining chlorophyll‐a concentration (chl‐a), suspended solid concentration (SSC) and secchi depth (SD). Empirical models relating spectral reflectance and chl‐a, SSC and SD were developed using least square regression analysis. These models were found valid on unused samples. Chl‐a, SSC and SD distribution maps were generated using proposed models and were incorporated as datalayers in the GIS for further analysis of pollution zones. The spatial information of pollution offered by the pollution zone map could delineate regions of lake having high pollution load. The methodology employed in this work can be used for regular monitoring of the pollution in surface water bodies and serve the data needs for better management of the water quality.     

Constructing a Near Real‐time Space‐time Cube to Depict Urban Ambient Air Pollution Scenario

This study adopts a near real‐time space‐time cube approach to portray a dynamic urban air pollution scenario across space and time. Originating from time geography, space‐time cubes provide an approach to integrate spatial and temporal air pollution information into a 3D space. The base of the cube represents the variation of air pollution in a 2D geographical space while the height represents time. This way, the changes of pollution over time can be described by the different component layers of the cube from the base up. The diurnal ambient ozone (O₃) pollution in Houston, Texas is modeled in this study using the space‐time air pollution cube. Two methods, land use regression (LUR) modeling and spatial interpolation, were applied to build the hourly component layers for the air pollution cube. It was found that the LUR modeling performed better than the spatial interpolation in predicting air pollution level. With the availability of real‐time air pollution data, this approach can be extended to produce real‐time air pollution cube is for more accurate air pollution measurement across space and time, which can provide important support to studies in epidemiology, health geography, and environmental regulation.