RecNet: a deep neural network for personalized POI recommendation in location-based social networks

How to exploit various features of users and points of interest (POIs) for accurate POI recommendation is important in location-based social networks (LBSNs). In this paper, a novel POI recommendation framework, named RecNet, is proposed, which is developed based on a deep neural network (DNN) to incorporate various features in LBSNs and learn their joint influence on user behavior. More specifically, co-visiting, geographical and categorical influences in LBSNs are exploited to alleviate the data sparsity issue in POI recommendation and are converted to feature vector representations of POIs and users via feature embedding. Moreover, the embedded POIs and users are fed into a DNN pairwise to adaptively learn high-order interactions between features. Our method is evaluated on two publicly available LBSNs datasets and experimental results show that RecNet outperforms state-of-the-art algorithms for POI recommendation.     

Travel impedance agreement among online road network data providers

ABSTRACT

Online mapping providers offer unprecedented access to spatial data and analytical tools; however, the number of analytical queries that can be requested is usually limited. As such, Volunteered Geographic Information (VGI) services offer a viable alternative, provided that the quality of the underlying spatialtheir data is adequate. In this paper, we evaluate the agreement in travel impedance between estimates from MapQuest Open, which embraces OpenStreetMap (OSM) data–a is based on VGI datasetfrom OpenStreetMap (OSM), and estimates from two other popular commercial providers, namely Google Maps? and ArcGIS? Online. Our framework is articulated around three components, which simulates potentialcalculates shortest routes, estimates their travel impedance using a routing service Application Program Interface (API), and extracts the average number of contributors for each route. We develop an experimental setup with a simulated dataset for the state of North Carolina. Our results suggest a strong correlation of travel impedance among all three road network providers. and that travel impedanceThe agreement is the greatest in areas with a denser road network and the smallest for routes of shorter distances. Most importantly, tTravel estimates from MapQuest Open are nearly identical to both commercial providers when the average number of OSM contributors along the route is larger. The latter finding contributes to a growing body of literature on Linus’s law, recognizing that a larger group of contributors holds the potential to validate and correct inherent errors to the source dataset.     

日常生活数字化转向的时间地理学应对

信息和通信技术(ICTs)对日常生活产生重要影响,而时间地理学及时应对了日常生活的数字化转向。论文梳理了日常生活数字化转向的基本特征,包括破碎化、多任务与时空替代等方面。相关实证研究试图回答ICTs是否以及如何改变日常生活,但是忽略了对其内在机制的阐释。时间地理学应对日常生活数字化转向的最新研究包括理论扩展与可视化及分析工具发展等方面,这为理解与刻画日常生活的数字化转向提供了新的理论框架与有效工具。研究发现,首先,经典时间地理学的理论和概念可扩展应用于虚拟空间中的行为。时空间去耦合是日常生活数字化转向的内在机制。其次,基于GIS开发了物质—虚拟混合空间行为的可视化和测度方法。最后,新时间地理学对日常活动复杂情境的分析直观揭示了日常生活数字化转向的时空去耦合机制与破碎化、多任务、时空替代等基本特征,并提出前后台活动、ICT介导的活动束、ICT介导的秩序口袋等理解移动ICTs影响下日常活动转型的创新概念。最后,以时间地理学为基础,提出以实虚活动系统为核心的日常生活数字化转向研究框架,并从数字生活、智慧社会和安全城市等方面讨论未来研究议题。     

基于智慧交通的可达性与交通出行碳排放——理论与实证

信息与通信技术的发展推动了智慧交通建设,并将进一步提高城市交通可达性和影响居民出行。在全球应对气候变化、减少碳排放的共识背景下,由于"智慧交通—可达性—出行行为(碳排放)"间复杂的影响作用机制,已有的研究难以判断智慧化背景下的城市交通可达性提高将增加抑或减少出行碳排放。如何通过提高城市交通可达性来缓解交通拥堵,保障城市交通运输系统的有效运行,并提高居民出行效率,减少出行碳排放是当前中国智慧交通发展面临的关键问题之一。针对以上科学问题,本文尝试提出基于智慧交通的可达性与交通出行碳排放的理论框架,并以广州为研究案例地,研究了社区居民通勤碳排放特征及其影响机理,社区出行低碳指数格局及其影响因素的空间异质性,以及基于碳排放—位置分配模型的公共中心规划支持系统设计与应用,可为今后的相关研究提供借鉴。     

Integrating general principles into mixed-integer programming to optimize schematic network maps

ABSTRACT

Schematic maps are popular for the representation of transport networks. Many automated methods have been developed to generate such maps. In these methods, optimization techniques work with various sets of constraints. Most of these constraints govern geometric properties of individual features. A few constraints address relationships among features, but none explicitly deal with the main structure of an entire network. We believe that preservation of main structure is the most important and preservation of relative relations is helpful. This is because human perception follows a global-to-local process. These constraints have recently been formed into four general principles, with two for global structure and two for relativity of features. This study develops an automated method by integrating these principles into the mixed-integer programming (MIP) framework. Experimental evaluations have been conducted with two sets of real-world transit networks. In comparison to the existing method, the proposed method has smaller fractal dimensions, better computational performance and higher scores in terms of clarity, recognition of major lines, visual simplicity and satisfaction. Therefore, it is concluded that the proposed method can generate schematic maps with improved clarity and aesthetics. The idea in this study is also helpful for the design of other visual representations.     

Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks

ABSTRACT

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data.     

A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management

In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.     

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 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.     

Environmental governance through information: China and Vietnam

In the field of environmental governance information is starting to become increasingly important, not least because of globalization and the information and communication technologies revolution. The notion of informational governance is a recent coinage that acknowledges the (partial) switch from regulatory‐based forms of governance to information‐based modes. In the information‐rich centres and nodes of the network society, where information is widely produced, disseminated and accessible, this might prove analytically useful. But what are the contours of informational governance in information‐poor environments? This paper looks into the (limited) emergence of informational governance arrangements in environmental protection in two cases characterizing the informational periphery: China and Vietnam.     

基于报纸媒体内容分析和信息熵的广交会综合影响力时空分布

从报纸媒体的角度研究广交会作为重大事件的综合影响力的时空分布特点。以中国知网(http://www.cnki.net/) 所属“中国重要报纸全文数据库”(CCND) 2000-05-05 到2011-05-30 时间段内“广交会”为主题的4333 条新闻报道为研究文本, 通过传播学中的内容分析法建构分析类目, 统计类目包括新闻的时间、所属的届数与期数、报社所在城市、新闻版面、新闻字数等, 共由3 个编码员进行统计, 并对类目编码进行信度检测。在对新闻内容进行计量统计的基础上, 引进信息熵度量信息量, 利用信息熵和报道篇数构建广交会综合影响力指标, 进一步构建广交会空间综合影响力指数方程, 分析广交会综合影响力在全国的分布情况, 并检验综合影响力指标和经济指标的相关性。结果发现:① 从时间分布来看, 以新闻报道数量来衡量, 在事件(广交会) 进行时其综合影响力存在时间衰减现象, 各展期综合影响力的年际变化则呈现为周期性的年度重复现象。② 从空间分布来看, 广交会综合影响力空间分布与城市参展商数量、经济情况和距离有着密切的联系:受影响大的城市主要集中在沿海经济发达的省份, 受影响小的城市主要分布在西部和北部, 整体上综合影响力大小和城市分布的密度由东到西递减;综合影响力总体上存在显著的空间距离衰减, 但对于北京和上海这两个特殊城市而言, 空间距离已经不是广交会综合影响力的主要因素, 而在广交会举办地的“当地”(广东省内) 较短的距离内, 距离衰减并不显著, 当空间距离超出一定范围, 则存在明显的距离衰减现象。     

信息通讯技术对老年人的社区满意度影响路径——以南京市锁金社区为例

在中国老龄化进程加快以及智慧养老快速发展的背景下,如何运用信息通讯技术(ICT)提高老年人社区生活质量成为地理学与规划学等领域的关注热点。本文选取南京市首个智慧平台落户社区——锁金社区为实证调研对象,通过构建结构方程模型结合问卷调查,探究信息通讯技术对老年人的社区满意度影响路径,其中问卷内容主要包括个人特征属性、个人ICT使用情况、社会支持情况、社区满意度等。结果表明：老年人信息设备的拥有及使用越趋向多元化,其网络活动越频繁;受教育使用水平越高、使用互联网意愿更强烈的老年人其网络活动越频繁;信息通讯技术主要通过社交类网络活动促进老年人的家庭、邻里交往,提高老年人的社会支持,从而提升社区满意度;与社区建成环境相比,社会支持对于老年人的社区满意度影响更大。因此,合理运用信息通讯技术加强老年人与家人、邻里等的联系强度,是提高老年人社区生活质量的一条可行路径。研究结果对于智慧社区、智慧养老建设具有一定的借鉴意义。