基于社交媒体数据挖掘的旅游者情绪感知

随着智能移动终端和社交媒体的普及,带有地理标签的社交媒体数据大量涌现,其“文本—位置—时间”的多维特征使得精细时空尺度上的旅游者情绪感知成为可能。该文基于2017-2019年旅游者发布的新浪微博数据,采用BERT模型对微博数据进行文本分析,探讨旅游者情绪的时空分布规律及不同主题下旅游者的情绪特征,并分析导致旅游者产生负面情绪的相关因素。研究发现,微博中旅游者情绪呈现昼夜、周和季节性节律变化,不同性别旅游者在情绪反应强度及情绪节律上存在差异,对“天气”和“餐饮”主题易产生强烈情绪。该文提出的旅游者情绪挖掘方法可从多维度、多层次挖掘旅游者情绪特征,为旅游目的地舆情监测和预警系统提供借鉴。     

边界效应下居民跨珠海-澳门边界活动特征与机制分析

作为跨行政等级主体的澳门,与大湾区其他城市之间存在制度与地理双重边界,跨珠海-澳门（珠澳）边界活动更具复杂性。以珠海拱北口岸的跨边界大湾区居民作为研究对象,根据身份属性将其分为内地访澳旅客、内地通勤劳工、来陆娱购澳人以及在陆居住澳人四大类。通过问卷调查与深度访谈,采用时空路径和2-模社会网络分析的方法,探究在边界效应作用下居民跨边界活动的时空特征及社会交往特征,并对跨边界活动的影响机制进行分析。研究发现：边界效应下大湾区居民跨珠澳边界活动空间的交互性不足,个人活动主要集中在局部空间。在局部空间下,内地访澳旅客与来陆娱购澳人跨边界活动内容和交往对象单一,活动交往以工具性联系为主,缺乏信息与感情的传递;内地通勤劳工跨边界交往对象多样化,社会网络密度较大,但社会网络往往是由“强关系”创造的人情网,社会文化边界仍有待消融;在陆居住澳人跨边界社会网络密度以及联系多样性较大,具有良好的社会交互预期。居民的跨珠澳边界活动受到一国两制与通行制度制约、异质文化与梯度经济吸引以及社会心理与差异性政策调节的共同作用。     

URBAN SPACES OF AMERICAN INDIANS IN THE EXILES

Released in 1961, The Exiles is a low‐budget docudrama that chronicles the lives of three American Indians over a period of twelve hours in a downtown Los Angeles neighborhood in the late 1950s. Contemporary neorealist filmmaking appears to have influenced the film, whereas itsg narrative is ethnographic in form. An examination of the film and its dialogue reveals the ways in which American Indians who recently migrated from their reservation to the city have socially constructed the urban spaces within the framework of the physical setting provided to them. The nature of the engagements of the male and female protagonists with the sites and with other American Indians in this small urban sphere provides substantive clues to the nature and level of their respective transitions into the realm of the white‐dominated society of Los Angeles and beyond.     

European capital cities as political frontiers

The capital is the centre of the established order but it always had to accept a relatively uncontrollable world of individuals redefining urban space in their own way or pioneering new means of support. As their existence is both a political nuisance and a foreshadowing of institutional changes (sometimes revolutionary) of nation-wide importance, I call such ways of life political frontiers. One may wonder what will become of political frontiers in the information society (or postmodern society) that, apart from borderless, has been described as society without a centre as well. Actually information society is the prototype of a frontier society with self-responsible groups and individuals. However, capitals – even stripped of their governmental functions – may offer a symbolical environment (a condensation of space and time) that precisely satisfies the wants of those who wish to politicise a way of life. Niches for pioneering behaviour may get dispersed over a wider territory but new types of political activity will gravitate to the capital even if the capital's official political status has dwindled.     

唐代治乱分期与气候变化的关系*

将唐代定量划分为治世与乱世5个时期,探讨了唐代治乱分期与气候变化的关系。治世、乱世的划分标准参考一套系统的战争数据集,并用单因素方差分析的方法验证各项数据指标在治世与乱世间的差异性,从而说明唐代治世、乱世分期的合理性。治世、乱世分期结果如下: 公元618—626年为乱世,公元627—742年为治世,公元743—784年为乱世,公元785—859年为治世,公元860—907年为乱世。方差分析的结果显示,除了总边境战争外,气候变化、总战争、反叛战争、进攻型边境战争、防御型边境战争、农业丰歉等级和人口增长率在治世与乱世之间均表现出显著差异。用战争定量体现唐代社会治世、乱世变化,探讨气候变化与社会治、乱的关系即是探讨气候变化与战争的关系,乱世的主要战争类型是反叛战争和防御型边境战争,治世的主要战争类型是进攻型边境战争。相关分析结果表明,唐代温度降低、降水减少的时期,农业收成减少导致资源供给不足,更容易发生反叛战争;温度暖期、降水充沛的时期,农业产量提高,刺激了快速增长的人口,在技术改善能力有限、土地资源一定的情况下,更易发生以对外扩张为目的的边境战争,因此气候变化是影响唐代社会治乱变化的重要原因之一。     

The Econo-techno-social Design of Invasive Animal Management: costs and benefits or beneficiaries and benefactors?

This paper examines invasive animal management institutions, using theories at the interstices of anthropology and geography, to question current approaches to management based on neoclassical and neoliberal economic rationales. I present an analysis of two feral pig management regimes in Far North Queensland, Australia: (i) bounty systems of payment for feral pig control; and (ii) a community-based feral pig trapping program. I show how these management methods reshape important social and cultural processes through their overlapping technological and economic elements. On the basis of this analysis, I propose a conceptual framework for invasive animal management planning that incorporates a beneficiary–benefactor analysis alongside cost–benefit analyses. I argue that ecological-economic theories of pest management may be usefully enhanced by addressing the links between economic and social behaviours.     

快速城镇化背景下居住空间分异与失地农民社会融合的作用机制研究——以长三角地区为例

伴随着快速城镇化进程,中国城镇化已从速度型向质量型转变,这一转变的核心是实现人的城镇化,即农业转移人口的市民化。失地农民的居住形态由原来的宅基地建房为主转变成征地补偿房、租房和自购商品房等多种形式并存,即出现了居住空间分异现象。居住空间分异对失地农民的社会融合会产生重要影响,但其作用机制的有关研究尚少。论文基于长三角地区的调研数据,以“社会—空间”为主要视角,从住房类型(含区位)和住房人均面积2个维度衡量居住空间,通过建立回归模型,分析居住空间对城郊失地农民社会融合的影响。研究发现,其作用机制为：一是不同居住空间的城郊失地农民的社会融合程度有显著差别;二是住房类型对城郊失地农民的社会融合影响最大,住房面积则影响不大;三是性别、年龄、职业、收入、获得非农户口的时间等因素也与住房类型共同产生作用。为推动失地农民的社会融合、提升新型城镇化的质量,可采取的措施有推行混合居住模式、增强就业能力以及政府增加投入提升城郊失地农民社会保障水平等。     

Classifying urban land use by integrating remote sensing and social media data

Urban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However, the accurate classification of urban functional zones is challenging due to the complexity of urban systems. Many studies have focused on urban land use classification by considering features that are extracted from either high spatial resolution (HSR) remote sensing images or social media data, but few studies consider both features due to the lack of available models. In our study, we propose a novel scene classification framework to identify dominant urban land use type at the level of traffic analysis zone by integrating probabilistic topic models and support vector machine. A land use word dictionary inside the framework was built by fusing natural–physical features from HSR images and socioeconomic semantic features from multisource social media data. In addition to comparing with manual interpretation data, we designed several experiments to test the land use classification accuracy of our proposed model with different combinations of previously acquired semantic features. The classification results (overall accuracy = 0.865, Kappa = 0.828) demonstrate the effectiveness of our strategy that blends features extracted from multisource geospatial data as semantic features to train the classification model. This method can be applied to help urban planners analyze fine urban structures and monitor urban land use changes, and additional data from multiple sources will be blended into this proposed framework in the future.     

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.