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.     

A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.     

基于非均质地层参数表征的颗粒流模型构建

非均质地层参数的获取与表征方法是浅层地质体改造的重要依据。基于非均质地层的地质建模,表征具有随机不确定性和模糊不确定性的土体表观参数和物理力学参数。利用 Weibull 分布统计描述局部地层信息,并应用Diamond-Square分形插值方法,结合差分盒子维计算非均质地层分形维数,进一步合理演算整体地层参数。采用PFC3D数值模拟软件,结合马尔科夫链蒙特卡洛方法采样数据,对非均质地层块石颗粒及土颗粒进行建模。通过非均质地层模拟结果,发现其破坏过程与应力应变曲线要比均质土体复杂得多,由于块石大颗粒的含量、分布特性对土样力学性质影响显著。本文建模方法有助于非均质地层参数表征研究。     

九寨沟“8.8”地震背景下火花海钙华坝体无损探测分析

为研究震后九寨沟钙华景区的溶洞、裂隙、暗流等地质特性,丰富钙华景区岩溶洞穴无损探测技术,探讨了多物探技术在多孔疏松钙华地区作业的可靠性。首次运用地质雷达法及高密度电法相结合的无损探测技术,在九寨沟景区火花海特定区域进行了现场勘查和无损探测试验。结合对比两种无损探测成果与测区内出露地质剖面3组勘探数据,结果发现:高密度电法与地质雷达法相结合,两者能优劣互补,相互佐证,在解决钙华区域浅部溶洞、暗流、裂隙等工作中,对探测目标体具有极高的识别度。两种方法探测数据与震后出露剖面地质特征保持极高的一致性,验证了两种无损检测方法在多孔疏松介质条件下数据的真实可靠性,解决了多物探技术在多孔疏松介质地区施工的疑点和难点,丰富了工程物探领域技术。      

基于粒径分维值检验的松散介质渗透系数空间估值——以河北平原为例

为了克服松散含水介质非均质性各向异性对介质渗透系数的求取所带来的困难,以水动力条件为线索,利用互相关法寻找同一时代地层及同一时代地表水的主、次流方向.在主流方向上,建立了石家庄-束鹿-衡水段介质颗粒粒径随距离递变的数学模型,并以此为基础推求无钻孔地段的粒径.同时,运用分形理论,计算了中、上更新统地层主流方向平均粒径空间分布的分维值,并将其作为检验粒径估值正确与否的判据.通过不同岩性的渗透系数-深度模型,实现了用平均粒径预测渗透系数的全过程.     

Sediment transport patterns at Trafalgar offshore windfarm

A simple analytical model of wave propagation has been developed in order to study the potential sediment transport patterns due to the action of currents and waves in the neighborhood of cylindrical structures as well inside a group of these structures. The attention is focused on the study of Trafalgar offshore windfarm, a case in which it has been necessary to analyze the flow trough porous structures in order to model fish growing-cages planned to be installed at each aerogenerator structure. The results are obtained by averaging over one period of wave in order to evaluate the net potential sediment transport. The analysis of the results reveals how the processes of wave diffraction and reflection give rise to periodic patterns of sediment transport around and between the structures.     

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.     

新媒体影响下仿制景观的兴起及其形象塑造：以佛山日本街为例

自2000年以来开始出现的大量仿制景观,从经济、社会、文化和政治方面重塑了中国的城市空间,但却没有引起学者的普遍关注。以新兴出现的微型仿制景观——佛山日本街为例,运用实地调查和话语分析方法,首先理清佛山日本街的仿制要素及其特征,以及佛山日本街的兴起原因,其次利用社交平台的文本数据分析新媒体影响下佛山日本街的形象塑造,进而探讨佛山日本街不同形象的产生根源。研究发现,佛山日本街的仿制要素虽受到日本城市街道形态以及ACGN亚文化的启发,但本质上是一种文化借用式的网红打卡地;资本竞争和新型社交方式是佛山日本街兴起的主要动力要素,而新冠肺炎疫情影响下的旅游环境变化进一步刺激和提升了佛山日本街的打卡热度和知名度;在新媒体的影响下,社交平台用户对佛山日本街的讨论主要聚焦于文化原真性和抄袭侵权两个方面,导致其塑造的形象正面、负面并存。