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.     

旅华外国游客旅游消费偏好的实证研究

为了探索旅华外国游客的旅游消费偏好规律,利用国家基金项目组2004年7-8月在全国6个旅游热点城市的旅游市场调查资料,通过定性、定量分析得出旅华外国游客的旅游决策可控 因素和旅游消费偏好行为;提出旅游偏好既和外国游客本身特质有关,又与目的地国的国度 形象、旅游宣传促销密切相关;形象导向模式在一定程度上决定了游客的选择偏好. 研究表明旅华外国游客具有明显旅游消费偏好行为.     

Personalized sightseeing tours: a model for visits in art museums

This article describes a method to provide adapted visit tours in art museums according to the preferences expressed by the visitor and exhibits prestige. It is based on a dual approach with, on the one hand an automatic textual analysis of the official information available online (labels of exhibits) that allows to rank the exhibit attractiveness for a standard museum visitor. On the other hand, individual preferences are also taken into account to adapt the visit according to the personal cultural awareness of the visitor. We use operations research to solve a routing optimization problem, aiming at finding a visit tour with time constraints and maximization of the visitor satisfaction. Depending on the instance size and the problem scale, an integer linear programming (ILP) model and a greedy algorithm are proposed to recommend personalized visit tours and applied on two museums: ‘Musée de l’Orangerie’ in Paris and ‘National Gallery’ in London. The obtained results show that it is possible to recommend a good tour to visitors of an art museum by taking into account the common prestige of the exhibits and the individual interests, joining automatic text summarization and routing optimization in a limited geographical space.     

The Geography of Personal Name Forms

The geographic distribution of formal and informal names is considered. Given names are variable and bearers may identify themselves with one name form or another (e.g., William, Will, Billy, Bill, W. T., “Skip”). The form of name chosen by state legislators was classified as formal or informal and the proportions of each were plotted by state. Three regions are identified by their relative use of name formality: the Northeast, where formal names are the rule, the South, centering on Arkansas, where informal names are common, and the West, also characterized by informal names. The West is divided between the Mountain and Pacific states, with large numbers of informal names and the states which they surround, which tend toward formal names. Some states, especially those of the Midwest, do not participate in this process. Indiana and South Carolina are exceptions to the general patterns. Informal names presume to create solidarity between legislators and voters. The South and West are apparently less formal than the Northeast in general, also in name usage.     

The Geography of Personal Name Forms

The geographic distribution of formal and informal names is considered. Given names are variable and bearers may identify themselves with one name form or another (e.g., William, Will, Billy, Bill, W. T., “Skip”). The form of name chosen by state legislators was classified as formal or informal and the proportions of each were plotted by state. Three regions are identified by their relative use of name formality: the Northeast, where formal names are the rule, the South, centering on Arkansas, where informal names are common, and the West, also characterized by informal names. The West is divided between the Mountain and Pacific states, with large numbers of informal names and the states which they surround, which tend toward formal names. Some states, especially those of the Midwest, do not participate in this process. Indiana and South Carolina are exceptions to the general patterns. Informal names presume to create solidarity between legislators and voters. The South and West are apparently less formal than the Northeast in general, also in name usage.     

Characterizing geographical preferences of international tourists and the local influential factors in China using geo-tagged photos on social media

Understanding the geographical preferences of international tourists is critical for the tourism planning and marketing. However, it is not an easy endeavor to gather the corresponding information, given the absence of city-level tourism statistical data and high costs of participant survey. This paper characterizes the geographical preferences of international tourists using geo-tagged photos on social media (the Flickr in particular). Data are harvested for 333 prefecture-level cities in China from 2008 to 2013, and the intensity of photo sharing (IPS: the ratio between total number of uploaded photos and total area of the city/region) is used an indicator of tourist geographical preferences. IPS visualization shows the geography that tourism hotpots generally concentrate in regional capital cities and economically developed megaregions. More specifically, the East Asia and Oceania tourists exhibit more preferences towards the eastern coastal cities, while the Europe and North America visitors show increasing interest in exploring the western and northern places. Spatial regression is employed to quantify the local influential factors of tourists’ geographical preferences. It is found that international tourists usually consider the local economy, accessibility, infrastructure and cultural attractions when they choose their destinations in China. East Asia and Oceania visitors particularly appreciate the local economy and cultural attractions. Europe and North America tourists especially value the cultural attractions and local openness. The demonstrated methodological framework is not restricted to Flickr data, and it can be applicable to social media offer geo-tagging service. This paper is therefore believed to advance the applications of social media into geographical research.     

国家级媒体平台全球网络建构的地理学空间分析——以中国长城平台为例

以中国长城平台为例,探索中国媒体全球网络建构格局、过程与机制,以城市为网络建构的地点、国际电视媒体为纽带、19个中国城市及30个GaWC（Globalization and World Cities,全球化与世界城市）为研究对象,集成联系强度、城市媒体影响力指数、媒体传播方向异质性研究方法,对中国国际电视媒体输出至世界城市、世界城市电视媒体输入至中国港澳台地区的双向互动过程进行地理学空间可视化分析,探讨长城平台全球网络建构机制。结果表明：① 长城平台中国境内媒体来源整体呈一核三区多节点格局;② 长城平台海外输出网络反映了中国城市与世界城市间的媒体网络联系,以北京、香港与GaWC中Alpha级（世界一线城市）纽约、吉隆坡、曼谷及Beta级（世界二线城市）河内、内罗毕等世界城市形成逐级扩散网络;③ 中国港澳台地区作为境外媒体输入的重要节点,主要与北美、东南亚、西欧形成空间关联。     

Spatial subdivision of complex indoor environments for 3D indoor navigation

As we realize that we spend most of our time in increasingly complex indoor environments, applications to assist indoor activities (e.g. guidance) have gained a lot of attention in the recent years. The advances in ubiquitous computing made possible the development of several spatial models intending to support context-aware and fine-grained indoor navigation systems. However, the available models often rely on simplified representations (e.g. 2D plans) and ignore the indoor features (e.g. furniture), thereby missing to reflect the complexity of the indoor environment. In this paper, we introduce the Flexible Space Subdivision framework (FSS) that allows to automatically identify the spaces that can be used for indoor navigation purpose. We propose a classification of indoor objects based on their ability to autonomously change location and we define a spatial subdivision of the indoor environment based on the classified objects and their functions. The framework can consider any 3D indoor configuration, the static and dynamic activities it hosts and it enables the possibility to consider all types of locomotion (e.g. walking, flying, etc.). It relies on input 3D models with geometric, semantic and topological information and identifies a set of subspaces with dedicated properties. We assess the framework against criteria defined in previous researches and we provide an example.     

基于链接测度旅游景区吸引力

目前用于测度旅游吸引力的数据多是非公开的第二手资料或采集费时费力的第一手资料。针对以上问题,该文提出了一个基于公开的链接数据度量景区旅游吸引力的新方法。通过北京景区的实证研究,发现景区链接和游客在数量上存在显著的正相关。这种关联表明景区链接数对景区吸引力具有指示作用,链接可以作为测度旅游吸引力的新数据源。研究结果显示,景区客源市场的地理距离影响了景区链接与游客之间的关联程度:景区的客源市场越远,关联度越高;反之,客源市场越近,关联度越低,甚至没有。     

城市系统微观模拟中的个体数据获取新方法

目前自上而下的城市系统宏观模拟并不能解决城市这一复杂系统中出现的部分问题,城市系统微观模拟(如多主体系统MAS) 已经成为城市系统模拟的新思路,其主要是基于个体数据(如个人、家庭、公司或建筑物) 开展的。国际国内这方面的应用都受到个体样本稀缺的限制。微观模拟所需要的个体样本数据是原有的统计制度所不能适应的,尤其是在中国,个体样本在统计公报或年鉴中不公开,仅可通过典型调查来补充。本文旨在探索稀疏数据环境下构建城市系统微观模拟的个体样本数据的新方法。该方法基于已有的多源统计数据、典型调查数据以及个体的通用规则,反演出个体样本的属性信息和空间分布,进而可以以GIS图层的形式直接作为微观模拟的数据基础。通过本方法获取的样本,能够符合已有的统计资料,并遵照了样本的基本特征,可以作为现有数据条件下的微观模拟模型的数据输入。同时该方法的应用简单,统计意义上的准确度高,适合我国统计制度下的微观模拟模型的构建。     

产业地理集聚与外商直接投资产业分布——以北京市制造业为例

外商直接投资理论强调跨国公司的垄断优势和东道国的区位优势决定外商直接投资的产业分布。本文认为外商直接投资的产业分布还可能取决于因产业地理集聚和地方化贸易所形成的竞争优势。本文采用1999~2004年北京外商直接投资的产业流资料建立面板数据回归模型,引入产业地理集聚系数、产业联系等变量来研究影响外资产业分布的因素。统计分析表明,在空间上比较集聚、产业内联系较强的产业能够吸引更多的外商直接投资;前期外商投资对于跟进投资具有显著的示范效应、信息溢出效应以及产业联系效应,从而直接导致外资的产业累积效应。外商也强烈偏好资本技术密集型产业以及在国际市场上具有显著比较优势、盈利率高的产业。外商在充分利用其垄断优势、产业比较优势和竞争优势的同时,也尽量避开实际劳动成本较高和进入壁垒较高的垄断性产业。本文的结果显示,发挥本地优势,完善产业配套,促成产业集群是提升城市吸引外商直接投资的有效途径。     

国外河口水文研究的动向

近十多年来,随着河口水文要素监测技术的现代化,物理和数学模型的不断完善,有力地推进了河口水文学的发展。本文从研究内容和研究方法两个方面对国外河口水文研究的动向进行了分析,着重介绍河口环流、河口锋、河口最大浑浊带研究的进展,并对现场观测、物理模型和数学模型等三种研究方法进行评价,可为发展具有我国特色的河口水文学提供借鉴。     

Ecology of stone-encrusting organisms in the Greenland Sea—a review

The Arctic is one of the most rapidly warming regions on Earth. This area is therefore very suitable for conducting studies focused on the influence of climate change on the biota. Marine communities of coastal waters are particularly sensitive to the current environmental changes (e.g., ice-scour intensity); understanding how community structure changes in response to local perturbations is thus important for providing an insight into how future communities may respond to climate change. This review focuses on the fauna colonizing stones of the Greenland Sea. It summarizes the current state of knowledge about the ecology of organisms inhabiting these substrata across a range of depths, from the intertidal to deeper parts of the continental shelf. In the intertidal zone, no stable or developed assemblage on the rocks is visible. The intertidal zone seems to be fully controlled by physical forces. In contrast, below the intertidal zone a rich and abundant fauna starts to appear on these substrata. Both biotic (e.g., competitive interactions) and abiotic (e.g., ice scour, size of the rock) processes seem to shape stone assemblages in the subtidal zone, yet their influence varies with depth. For example, the abundance of encrusting organisms decreases with depth, as does the intensity of competitive interactions. However, species richness on rocks seems to be in general higher in the deeper parts of the shelf. Possible scenarios of climate change influence on the encrusting biota, gaps in our knowledge about the ecology of stone-dwelling faunal assemblages, as well as possible directions of future research, are discussed.     

Patterns and Controls of Hurricane-Caused Forest Damage: A Landscape-Scale Analysis of Treefall Direction Following Hurricane Katrina∗

To clarify broad-scale patterns and controls of treefall directionality from Hurricane Katrina, we examined fall directions across a 4,500 km² landscape mosaic in southern Mississippi using georeferenced, planar-rectified aerial photographs. Analyses using directional statistics, measures of local spatial autocorrelation, and general linear modeling indicated that treefall was significantly directional for nearly all of our locations and constrained primarily by mesoscale surface wind directions and landscape setting. None of our plots exhibited fall angles consistent with damage caused by wind reversals following the passage of the storm or by microbursts or tornadoes spawned by the hurricane. When coupled with results from other studies focused on hurricane-caused damage, these results suggest that it is possible to develop empirical, landscape-scale models of wind impacts or stand vulnerability using basic site information (e.g., topography, soils), biotic conditions (e.g., land cover, forest attributes), and generalized, but readily available, estimates of surface wind flow patterns.     

Design of a view-based DSS for location planning

Abstract.

The traditional approach in DSS falls short of providing a highly interactive problem solving environment for planning. Often, cumbersome procedures are required to implement optional plans and obtain feedback information. In dynamic graphic systems, the user is able to view different linked graphic representations (e.g., spatial or statistical graphs) of statistical data and interact (e.g., selecting items) with these graphics. In this paper we describe the design of a DSS for planning facility locations, which uses principles of dynamic graphics to achieve a highly interactive problem solving environment. As in dynamic graphic systems, the user interacts with the DSS through active and linked views. However, where views in dynamic graphics are different representations of a given dataset, the views in the DSS are active data structures describing the facility system to be planned from different perspectives. The declarative and procedural forms of knowledge involved are identified by a logical analysis of planning problems. A frame-based formalism is proposed to represent the knowledge contained in the views. The main advantage of this view-based approach is that it offers the user a highly flexible and interactive environment for performing ‘what-if’ analyses.     

Discovery of local topics by using latent spatio-temporal relationships in geo-social media

ABSTRACT

Social networks have played a crucial role as information channels for people to understanding their daily lives beyond merely being communication tools. In particular, coupling social networks with geographic location has boosted the worth of social media to not only enable comprehension of the effects of natural phenomena such as global warming and disasters, but also the social patterns of human societies. However, the high rate of social data generation and the large amounts of noisy data makes it difficult to directly apply social media to decision-making processes. This article proposes a new system of analyzing the spatio-temporal patterns of social phenomena in real time and the discovery of local topics based on their latent spatio-temporal relationships. We will first describe a model that represents the local patterns of populations of geo-tagged social media. We will then define a local topic whose keywords share a region in space and time and present a system implementation based on existing open source technologies. We evaluated the model of local topics with several ways of visualization in experiments and demonstrated a certain social pattern from a dataset of daily Twitter streams. The results obtained from experiments revealed certain keywords had a strong spatio-temporal proximity even though they did not occur in the same message.     

A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution.     

Perceived deficiencies in the provision of climate and weather information for tourism: A New Zealand media analysis

Climate and weather are important for tourism and leisure as they impact on destination attractiveness and subsequent visitation. Through an archive analysis of media articles and a content analysis of Regional Tourism Organisation (RTO) websites, this paper explores perceived deficiencies in the currently available climate and weather information for tourists in New Zealand. While media reports focused on concern by many RTOs over the poor regional images generated by inaccurate and poorly presented climate and weather data, the RTO websites were found to contain limited climate and weather information. There is considerable potential for RTOs to improve in this respect.