社交媒体地理大数据的旅游景点热度分析

针对大数据时代,蕴含地理位置信息的社交媒体(Social Media)数据规模正呈爆炸性增长,通过对这类时空数据的挖掘可以整合用户的群体智慧,发现热门景点或线路,为用户提供更加精细的旅行服务。该文基于2005—2016年Flickr图片分享网站中用户分享的带地理标签的图片信息,通过空间聚类以及文本语义挖掘的方法对北京市的热门景点进行了提取。此外,本文还利用北京市的历史天气信息与用户图片信息进行融合,分析在不同时间、不同天气情景下,不同景点的热度分布规律,可以为旅行爱好者提供热门景点在多种视角下的游览规律。     

Integrating social network data into GISystems

Today, online social media outlets provide new and plentiful sources of data on social networks (SNs) and location-based social networks (LBSNs), i.e., geolocated evidence of connections between individuals. While SNs have been used to show how the magnitude of social connectivity decreases with distance, there are few examples of how to include SNs as layers in a GISystem. If SNs, and thus, interpersonal relationships, could be analyzed in a geographic information system (GIS) setting, we could better model how humans socialize, share information, and form social groups within the complex geographic landscape.

Our goal is to facilitate a guide for analyzing SNs (as derived from online social media, telecommunications, surveys, etc.) within geographic space by combining the mature fields of social network analysis (SNA) and GISystems. First, we describe why modeling socialization in geographic space is essential for understanding human behavior. We then outline best practices and techniques for embedding SN nodes and edges in GISystems by introducing terms like ‘social flow’ and ‘anthrospace’, and categorizations for data and spatial aggregation types. Finally, we explore case study vignettes of SNA within GISystems from diverse regions located in Bolivia, China, Côte d’Ivoire, Singapore, the United Kingdom, and the United States, using concepts such as geolocated dyads, ego–alter relationships, node feature roles, modularity, and network transitivity.     

A scientometric analysis of selected GIScience journals

A set of 12,436 papers published in 20 GIScience journals in the period 2000–2014 were analysed to extract publication patterns and trends. This comprehensive scientometric study focuses on multiple aspects: output volume, citations, national output and efficiency (output adjusted with econometric indicators), collaboration, altmetrics (Altmetric score, Twitter mentions, and Mendeley bookmarking), authorship, and length. Examples of notable observations are that 5% countries account for 76% of global GIScience output; a paper published 15 years ago received a median of 12 citations; and the share of international collaborations in GIScience has more than tripled since 2000 (31% papers had authors from multiple countries in 2014, an increase from 10% in 2000).     

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.     

The broken promises of social mix: the case of the Cabrini Green/Near North area in Chicago

Public policies of social mixing have been enacted as the reversal of what segregation and concentrated poverty are presumed to have produced: intensified social problems (i.e., “neighborhood effects”). In addition, the pervasive discourses of diversity have provided more support for the idea of social mixing. Studies on planned and unplanned diverse neighborhoods have shown how certain diverse patterns can emerge and endure over time. Yet these studies have failed to explain how such demographic diversity becomes integration. In this article, I draw on a multidimensional perspective of socio-spatial integration to present a qualitative case study of the Cabrini Green/Near North area in Chicago—a neighborhood with a long history of segregation and recent socially engineered diversity. The case shows how contentious this new coexistence has been, and how segregation has been shifting its mechanisms of enforcement from housing to other spheres of life. I conclude with reflections on four dimensions of socio-spatial integration, and on the troubling policy and theoretical implications of the “social mix” paradigm.     

Metropolitan land-use patterns by economic function: a spatial analysis of firm headquarters and branch office locations in Australian cities

This paper develops a comparative means by which to understand metropolitan spatial structure through the dynamics of economic activities. Clustering and suburbanization have been key processes within the contemporary urban landscape, but few scholarly accounts have systematically merged the two to explain the geographies of economic activity. Using firm location as a variable to discern sector- and industry-based locational requirements, we explore land-use and economic activity in Australia’s five largest metropolitan areas. Drawing upon the respective headquarters and branch office locations of a set of publically traded firms, we seek to establish general spatial patterns across Australian cities using two proxy measures for clustering and suburbanization, being well-established drivers of firm locational choice. Despite the complexity that post-industrial and suburbanizing processes add to metropolitan land-use patterns, we contend that certain patterns exist that can be generalized from one context to another across urban space, and that certain emerging trends such as the development of CBD-fringe precincts merit greater attention.     

基于社交媒体的城市污染信息探测

了解城市污染状况是治理城市污染的首要环节。社交媒体中包含了能够反映人们对周围环境的感受的数据资料,可以帮助人们更直观地了解城市污染现状。本文以新浪微博为例,提出了从社交媒体中获取城市污染相关数据的方案,设计了基于社交媒体的城市污染信息分类法和可视化方法,在实验部分以北京市为例对本方法的结果进行了分析与讨论。     

Modal Choice in the Pittsburgh Metropolitan Statistical Area: An Exploratory Data Analysis

Journey-to-work mode choice is intertwined with ideological and pragmatic issues. This article reexamines such issues using socioeconomic data from the decennial census and American Community Survey (ACS). It investigates the structure of variables with exploratory data analysis (EDA) because this technique advises the formation of hypotheses and the specification of cause and effect. Traditional EDA reveals the nonnormal structure of raw data, mapping illustrates associations between transit and income, and both methods suggest the presence of a transit-by-choice population among affluent metropolitan residents. The results yield three hypotheses concerning propensity to use transit that have previously received little attention.     

Hacked Landscapes: Tensions,Borders, and Positionality in Spatial Literacy

By focusing on critical geographies, landscape, and spatial literacy, this article evaluates a semester-long spatial justice project conducted in a preservice teacher education program. The analysis recognizes the limitations of reading the products literally as a means of comprehending spatial representation. It expands the analysis by hacking the products and producing new landscapes to read against or up against the products as sociospatial texts. It considers the deployment of landscape concepts—borders, the representation of tension, and the gaze and subjectivity of the reader-authors—as central elements of spatial literacy that is of consequence.     

Exploratory Spatiotemporal Analysis in Risk Communication during the MERS Outbreak in South Korea

The 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea gave rise to chaos caused by psychological anxiety, and it has been assumed that people shared rumors about hospital lists through social media. Sharing rumors is a common form of public perception and risk communication among individuals during an outbreak. Social media analysis offers an important window into the spatiotemporal patterns of public perception and risk communication about disease outbreaks. Such processes of socially mediated risk communication are a process of meme diffusion. This article aims to investigate the role of social media meme diffusion and its spatiotemporal patterns in public perception and risk communication. To do so, we applied analytical methods including the daily number of tweets for metropolitan cities and geovisualization with the weighted mean centers. The spatiotemporal patterns shown by Twitter users' interests in specific places, triggered by real space events, demonstrate the spatial interactions among places in public perception and risk communication. Public perception and risk communication about places are relevant to both social networks and spatial proximity to where Twitter users live and are interpreted in reference to both Zipf's law and Tobler's law.