A context-aware personalized travel recommendation system based on geotagged social media data mining

The proliferation of digital cameras and the growing practice of online photo sharing using social media sites such as Flickr have resulted in huge volumes of geotagged photos available on the Web. Based on users' traveling preferences elicited from their travel experiences exposed on social media sites by sharing geotagged photos, we propose a new method for recommending tourist locations that are relevant to users (i.e., personalization) in the given context (i.e., context awareness). We obtain user-specific travel preferences from his/her travel history in one city and use these to recommend tourist locations in another city. Our technique is illustrated on a sample of publicly available Flickr dataset containing photos taken in various cities of China. Results show that our context-aware personalized method is able to predict tourists' preferences in a new or unknown city more precisely and generate better recommendations compared to other state-of-the-art landmark recommendation methods.     

Coupling maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists

Modeling the geographic distribution of tourists at a tourist destination is crucial when it comes to enhancing the destination’s resilience to disasters and crises, as it enables the efficient allocation of limited resources to precise geographic locations. Seldom have existing studies explored the geographic distribution of tourists through understanding the mechanisms behind it. This article proposes to couple maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists in order to facilitate disaster and crisis management at tourist destinations. As one of the most popular tourist destinations in the United States, San Diego was chosen as the study area to demonstrate the proposed approach. We modeled the tourist geographic distribution in the study area by quantifying the relationship between the distribution and five environmental factors, including land use, land parcel, elevation, distance to the nearest major road and distance to the nearest transit stop. The geographic distribution’s dependency on and sensitivity to the environmental factors were uncovered. The model was subsequently applied to estimate the potential impacts of one simulated tsunami disaster and one simulated traffic breakdown due to crisis events such as a political protest or a fire hazard. As such, the effectiveness of the approach has been demonstrated with specific disaster and crisis scenarios.     

Location‐based social media behavior and perception: Views of university students

Location‐based social media (LBSM), a specific type of volunteered geographic information (VGI), is increasingly being used as a spatial data source for researchers in geography and related disciplines. Many questions, though, have been raised about VGI data in terms of its quality and its contributors. While a number of studies have explored users’ demographics and motivations for contribution to explicitly geographic forms of VGI, such as OpenStreetMap and Wikimapia, few have focused on these aspects with implicitly geographic forms of VGI, such as LBSM (for example, Twitter and Instagram). This study, through use of an online survey, specifically assesses the LBSM behavior and perceptions of 253 university students, noting differences found in gender, race, and academic standing. We find that the greatest differences are those between males and females, rather than through race or academic standing, and LBSM appears less biased than other forms of VGI.     

碳足迹研究现状与演变：基于社会网络视角分析

温室气体减排已成为世界各国的共识,碳足迹分析作为温室气体管理的工具,在过去二十多年来发展迅速,然而国内外现有研究缺乏对其演化发展的归纳总结。因此,本文选取Web of science收录的期刊数据为研究对象,检索出1996—2017年有关碳足迹的相关文献8840篇,并对此进行了定性和定量分析。结果表明：①美国对碳足迹文献的贡献最大,拥有2275篇出版物以及最高的h指数（83）和被引频次（34803）;②中国和印度等发展中国家近10年发文量增长显著,都位于全球发文量前十的国家中;③荷兰的h指数占总出版物的比重最大;④加利福尼亚大学是该领域最具生产力的机构,拥有222篇出版物,且国际合作水平显著。最后,论文将碳足迹研究分为三个阶段,基于关键词共现分析,厘清碳足迹研究的热点变化,并预测其未来研究趋势。     

Geosocial gauge: a system prototype for knowledge discovery from social media

The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content because of the proliferation of location-aware devices, thus fostering the emergence of geosocial media – a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this article presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our article addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds and a corresponding general system architecture. We present these key components and demonstrate their implementation in our system prototype, GeoSocial Gauge.     

经贸视角下中国周边地缘环境分析——基于社会网络分析方法

周边对一国地区稳定、经济发展等影响重大,中国也一直奉行“邻国是首要”的外交战略,推行“睦邻、富邻、安邻”的邻国政策.主要分析中国及其周边国家贸易网络,在经济依赖度分析等基础上,借助社会网络分析工具进行程度中心性分析、结构同型分析等一系列分析,从而对中国及其周边国家贸易网络进行多方面解剖.结果表明:中国与周边国家存在不平等的经济依赖度;贸易网络存在较为明显的核心—边缘结构;中国在周边贸易网络中占据主导地位,经济影响力不断上升.研究表明,地缘政治研究中引入社会网络分析方法可以实现空间关系的网络化和网络关系的空间化,深化对地缘环境的认识.     

Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns

Understanding urban functions and their relationships with human activities has great implications for smart and sustainable urban development. In this study, we present a novel approach to uncovering urban functions by aggregating human activities inferred from mobile phone positioning and social media data. First, the homes and workplaces (of travelers) are estimated from mobile phone positioning data to annotate the activities conducted at these locations. The remaining activities (such as shopping, schooling, transportation, recreation and entertainment) are labeled using a hidden Markov model with social knowledge learned from social media check-in data over a lengthy period. By aggregating identified human activities, hourly urban functions are inferred, and the diurnal dynamics of those functions are revealed. An empirical analysis was conducted for the case of Shenzhen, China. The results indicate that the proposed approach can capture citywide dynamics of both human activities and urban functions. It also suggests that although many urban areas have been officially labeled with a single land-use type, they may provide different functions over time depending on the types and range of human activities. The study demonstrates that combining different data on human activities could yield an improved understanding of urban functions, which would benefit short-term urban decision-making and long-term urban policy making.     

社交媒体数据在自然灾害应急管理中的应用研究综述

社交媒体是灾害应急管理的一个新兴但尚未充分利用的大数据源。然而,社交媒体数据在灾害应急管理中能发挥什么作用,目前国内的研究较少,而国外的研究亦较为分散,相关应用受限。论文对国内外利用社交媒体数据进行灾害管理相关研究进行了综述,归纳出社交媒体数据在灾害应急管理中应用的潜力、优势和问题。结果发现：① 社交媒体数据含有丰富的灾害信息,能够依靠态势感知和信息共享支持灾害预警、实时监测、损失和救助需求评估、协助快速应急响应以及预测可能的灾害风险等灾害管理工作;② 社交媒体数据的优势在于能保证灾害信息的实时性和连续性,进而提供致灾强度图、损失预测和舆情分析等结果服务于灾害应急管理;③ 地理位置信息有限、专业语料库缺乏、信息噪声处理的复杂性等原因使社交媒体数据分析结果精度受限。如何发挥社交媒体数据社会感知的优势,以弥补传统观测调查手段的不足,更加科学应对灾害应急,仍需要在社交媒体数据处理及多源数据融合分析技术方面取得突破。     

城市旅游流网络结构特征及其影响因素

目前对于旅游流网络形成的影响因素的研究多为区域尺度,因此以上海为例,将国内游客赴上海自由行作为对象,利用网络游记采集2018年上海自由行行程信息,扩充了对旅游节点类型的选取范围,结合社会网络分析法,构建有向旅游流网络,研究国内游客赴上海自由行所形成的旅游流网络结构特征,并通过回归分析研究其影响因素。结果表明,①网络密度较低,网络核心-边缘区结构分层明显,但核心区对边缘区的带动能力有待提高;②目前以核心城区的著名景点以及上海迪士尼乐园为核心旅游节点;③整体而言,网络中旅游节点重要程度的影响因素为核心节点的影响力、旅游节点自身的知名度以及交通便利程度。在此基础上提出了旅游节点应如何提高自身在网络中的重要程度或是融入网络。     

南京城市社会区空间结构——基于第五次人口普查数据的因子生态分析

以人口普查数据为基础,利用城市因子生态分析手段,采用聚类分析方法,对南京城市社会区类型空间结构加以分析。结果表明影响2000年南京城市社会区形成的主因子有外来人口因子、农业人口因子、城市住宅因子、文化程度/职业状况因子和城市失业人口因子五个;将2000年南京城市社会区划分为六个主要类型。南京城市社会区空间分布呈现出明显的"三圈层"结构:城市本地户口居民集中分布的老城区、在老城区渐进蔓延基础上形成的中间圈层、城市远郊区为主的外围圈层;圈层内部的城市社会区空间分布以"圈层＋扇形"的复合结构为主。     

Learning where to offend: Effects of past on future burglary locations

Informed by a growing literature on space-time patterns of repeat and near repeat burglary victimization, a crime location choice model was used to test whether burglars are attracted to areas they previously targeted. Using data in 3337 detected burglaries from one UK police force, and accounting for the distance to the offender's residence, and for other factors that make target areas attractive to burglars, it was demonstrated that burglars were more likely to commit a burglary in an area they had targeted before. This was particularly the case if the prior burglary was (very) recent. Areas near to those in which burglaries had been committed were also more likely to be selected.     

网络社区交流中距离的作用——以新浪微博为例

信息通信技术的快速发展丰富了人们的交流方式,也逐步影响人们的社交网络。本文通过分析新浪微博用户在网络社区中与本地人/外地人、熟人/陌生人的交流差异,探讨空间距离与社会距离在网络社会空间中的作用。在问卷调研中,结合新浪微博用户的网络“痕迹”,较为可靠的获取南京成年人微博用户与本地人/外地人、熟人/陌生人在网络社区中的交流差异。研究表明：①在网络社区中,相较于外地人与陌生人,受访者更倾向与本地人以及熟人交流,网络社区中空间距离与关系距离的作用仍然存在;②受访者在网络社区中与本地人/外地人、熟人/陌生人的交流差异受社会经济属性、居住环境、微博使用频繁程度影响;③虽然理论上网络社区使得用户能够克服距离阻力,延伸其关系网络,但空间距离与关系距离的存在使得实际生活中的社会关系网络仍然扎根于用户在网络社区中的交流。     

基于社会地图的东莞市社会空间研究

既有社会空间研究以社会区研究居多,主要基于社会区理论和因子生态分析方法,较多考虑分析对象的社会属性而非空间特征,对社会空间的空间侧面刻画不足,其分析结果也常常被聚类人口的数据特征所左右。本文尝试基于社会地图方法,以人口普查数据和社会设施机构POI数据首先进行社会空间属性的归纳,制作社会地图,并以此为基础,采用归纳的方法渐进探索中国城市社会空间的特征。具体以东莞市为例,通过社会地图归纳方法将东莞市划分为3大类、11小类社会空间,并具体解析了多种社会空间要素组合类型。和既有基于社会区的分析结果相比,本文对各类社会要素在城市空间上的分布及其组合特征描述更为精细,其结果对认识中国社会空间特质及其后的社会空间政策制定更具参考意义。本文认为,基于社会地图的社会空间研究,有助于自下而上地发现有别于西方的中国城市社会空间特征和结构,也有助于建立基于中国的社会空间理论,值得进一步推广和尝试。     

基于日流量视角的俄罗斯首府城市铁路客运网络空间特征

构建俄罗斯81×81的首府城市的日铁路客运流量矩阵,基于社会网络分析研究其铁路客运网络特征,结果表明:①俄罗斯首府铁路客运网络松散且不均衡,西密东疏,高、中密度联系的城市位于中央联邦区与西伯利亚铁路沿线,俄欧洲北部、欧洲南部、亚洲等3个铁路区系内包含不同的子群与核心.②日铁路客运流量表现出沿西伯利亚铁路的"廊道型"向南...     

基于住房代理数据的城市社会阶层空间异构性研究——以兰州市为例

随着城镇化的深入发展,收入差距造成的社会阶层分化在城市空间上的分异程度亟需从地理学视角进行检视。通过网络爬虫工具获取兰州市商品住房数据,运用Reardon空间分异方法,研究居住—社会阶层的地理分布特征及其空间分异程度。结果表明:居住—社会阶层的地理分布方面,城关区和七里河区呈现出由中心到外围依次为精英阶层、富裕阶层、中产阶层、低产阶层、贫困阶层的圈层式分布,但存在精英区"侵入"低档住宅圈的现象;安宁区和西固区呈现出以中产、低产阶层为主的混合分布模式。居住—社会阶层的空间分异方面,分异趋势较为显著,七里河区、安宁区和西固区的居住—社会阶层分异明显高于城关区;富裕阶层居住区更倾向于融合,低产阶层居住区则有分异趋势;居住—社会阶层的空间分异随研究尺度的降低变得更为显著。     

基于社会网络的全球集装箱海运网络层次体系与派系划分

全球集装箱海运网络层次体系与派系划分对深入了解全球集装箱港口布局和分工及港口投资建设等具有重要的参考价值。基于全球10 大集装箱班轮公司的航运数据,以海运国家为节点构建全球集装箱航运网络,运用社会网络分析方法,采用“核心-边缘”模型,从国家间海运联系广度和国家间海运联系强度两个角度对全球集装箱海运网络进行层次体系划分,并且应用社会网络分析中的C 层次派系法对全球集装箱海运联系网络进行派系划分。结果表明：根据联系广度和联系强度划分出的全球集装箱海运网络“核心-边缘”结构并不相同。但是,中国、荷兰、新加坡、德国、巴西、阿联酋和法国这7 个国家在两种层次划分方法中都处于核心区,可见这7 个国家在全球集装箱海运网络中不仅在连通性上处于核心枢纽位置,在功能性上也处于核心地位。在C层次派系法中,从区域性上看,跨区域派系较少。     

Mapping spatiotemporal patterns of events using social media: a case study of influenza trends

Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US using Twitter data. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data.     

南昌城市社会区研究--基于第五次人口普查数据的分析

中国城市社会区分异的研究起步较晚,现有的研究成果基本都是讨论计划经济体制向市场经济体制转轨初期的城市社会区分异现象。本文以南昌市为例,利用分街道第五次人口普查数据,运用因子生态学的方法,对改革开放20年后社会主义市场经济体制作用下的城市社会区进行研究。研究结果显示,住房状况、文化与职业状况、家庭状况和外来人口状况是南昌城市社会区分异的主要影响因子,其中家庭状况因子的空间分布呈现出同心圆模式和扇形模式的复合特征,其他因子的空间分布则具有扇形模式的特征。根据主因子的分布情况,南昌城市社会区可以划分为七类,社会区的空间分布形成一种同心圆和扇形的复合结构。     

广州城市道路网的社会分异——基于轨迹大数据的出行分布性别差异

社会空间分异是人文地理学研究的重要议题,以往研究主要聚焦基于居住地和活动地的分异现象,缺乏对道路网社会空间分异的关注。本文以广州市中心区域为例,结合日常活动空间理论和空间分析方法,基于手机轨迹大数据,研究不同性别群体日常出行的城市道路网社会空间分异现象及其影响因素。结果显示：基于性别差异的城市道路网社会隔离指数高于居住地、低于就业地,在14:00—15:00时段达到峰值;不同性别群体在路网中的分布总体呈现不均衡的集聚特点,其中女性集聚程度更高;不同性别群体的出行分布和分异受到路网周边建成环境因素的影响;物流运输业就业密度的提高、小学数量的增多或道路等级的提高都会显著提高男性相对出行量,而社会服务业就业密度的提高、菜市场数量或公交站点数量的增多以及到市中心距离的减少都会显著提高女性相对出行量,这些与个体家庭和职业分工的性别差异有一定的关系。本文通过理论假设和实证分析,验证了家庭和职业的分工决策是影响道路网出行分布性别分异的深层次原因,也是解释社会空间分异的重要理论视角。研究结果有助于认识和挖掘基于城市道路网的社会分异现象及其影响机制,并为完善道路网络及周围设施的布局与建设提供借鉴。     

基于三维生态足迹的京津冀城市群自然资本可持续利用分析

生态足迹是区域可持续评估的重要方法,三维生态足迹模型能区分并追踪自然资本存量的消耗与流量的占用。目前三维足迹与自然资本利用研究主要集中在全球和国家尺度。本文以京津冀城市群为例开展区域尺度研究,核算并分析了2010年研究区13个城市的人均区域足迹深度、足迹广度及其组成差异与影响因素,揭示了京津冀城市群自然资本利用格局及生态可持续现状。结果表明：13个城市普遍生态赤字,足迹深度差异受能源消费数量和结构的突出影响,与城市经济发展水平呈“倒N型”库兹涅茨曲线关系。京津冀城市群内4种自然资本利用类型分别为存量资本消耗水平相对于流量资本占用水平严重超前(北京、天津、唐山)、显著超前(石家庄、邯郸)、一般超前(沧州、廊坊、衡水、邢台、保定)及基本一致(秦皇岛、张家口、承德)。上述4种类型地域的区域生态持续性依次从最弱转向较弱、较强、最强。本文在城市群自然资本持续利用的刻画和表达方面取得了较优于传统二维足迹模型的评价结果。