Los Angeles as a digital place: The geographies of user‐generated content

Online representations of places are becoming pivotal in informing our understanding of urban life. Content production on online platforms is grounded in the geography of their users and their digital infrastructure. These constraints shape place representation, that is, the amount, quality, and type of digital information available in a geographic area. In this article we study the place representation of user‐generated content (UGC) in Los Angeles County, relating the spatial distribution of the data to its geo‐demographic context. Adopting a comparative and multi‐platform approach, this quantitative analysis investigates the spatial relationship between four diverse UGC datasets and their context at the census tract level (about 685,000 geo‐located tweets, 9,700 Wikipedia pages, 4 million OpenStreetMap objects, and 180,000 Foursquare venues). The context includes the ethnicity, age, income, education, and deprivation of residents, as well as public infrastructure. An exploratory spatial analysis and regression‐based models indicate that the four UGC platforms possess distinct geographies of place representation. To a moderate extent, the presence of Twitter, OpenStreetMap, and Foursquare data is influenced by population density, ethnicity, education, and income. However, each platform responds to different socio‐economic factors and clusters emerge in disparate hotspots. Unexpectedly, Twitter data tend to be located in denser, more deprived areas, and the geography of Wikipedia appears peculiar and harder to explain. These trends are compared with previous findings for the area of Greater London.     

A name‐led approach to profile urban places based on geotagged Twitter data

Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space.     

Analyzing relationship between user‐generated content and local visual information with augmented reality‐based location‐based social networks

Location‐based social networks (LBSNs) have become an important source of spatial data for geographers and GIScientists to acquire knowledge of human–place interactions. A number of studies have used geotagged data from LBSNs to investigate how user‐generated content (UGC) can be affected by or correlated with the external environment. However, local visual information at the micro‐level, such as brightness, colorfulness, or particular objects/events in the surrounding environment, is usually not captured and thus becomes a missing component in LBSN analysis. To provide a solution to this issue, we argue in this study that the integration of augmented reality (AR) and LBSNs proves to be a promising avenue. In this first empirical study on AR‐based LBSNs, we propose a methodological framework to extract and analyze data from AR‐based LBSNs and demonstrate the framework via a case study with WallaMe. Our findings bolster existing psychological findings on the color–mood relationship and display intriguing geographic patterns of the influence of local visual information on UGC in social media.     

Reference data enhancement for geographic information retrieval using linked data

Gazetteers are instrumental in recognizing place names in documents such as Web pages, news, and social media messages. However, creating and maintaining gazetteers is still a complex task. Even though some online gazetteers provide rich sets of geographic names in planetary scale (e.g. GeoNames), other sources must be used to recognize references to urban locations, such as street names, neighborhood names or landmarks. We propose integrating Linked Data sources to create a gazetteer that combines a broad coverage of places with urban detail, including content on geographic and semantic relationships involving places, their multiple names and related non‐geographic entities. Our final goal is to expand the possibilities for recognizing, disambiguating and filtering references to places in texts for geographic information retrieval (GIR) and related applications. The resulting ontological gazetteer, named LoG (Linked OntoGazetteer), is accessible through Web services by applications and research initiatives on GIR, text processing, named entity recognition and others. The gazetteer currently contains over 13 million places, 140 million attributes and relationships, and 4.5 million non‐geographic entities. Data sources include GeoNames, Freebase, DBPedia and LinkedGeoData, which is based on OpenStreetMap data. An analysis on how these datasets overlap and complement one another is also presented.     

Modeling Use of Space from Social Media Data Using a Biased Random Walker

Individuals and other entities move through space as a function of local characteristics of place, their internal behavioral models, and the topological structure of the underlying space. When a collection of locations (i.e. geotagged photos or other geotagged social media information) from a large number of individuals is assembled, it becomes possible to understand the interrelationship between the individuals and the space they occupy. This research systematically considers this interrelationship through an examination of the effect of the intersection of behavioral and spatial characteristics on individuals moving on street networks. The research illustrates how social media data, in combination with a biased random walker, can be used to understand and model the interaction of spatial structure and social‐environmental factors on influencing individuals' use of their environment. The biased walker offers a flexible approach to incorporate consideration of both social‐environmental and structural factors into a model and we demonstrate this through a case study wherein we are able to use the random walker to model the characteristics of Flickr users in New York City.     

Understanding the interplay between bus,metro, and cab ridership dynamics in Shenzhen,China

The most common mass transit modes in metropolitan cities include buses, subways, and taxicabs, each of which contribute to an interconnected complex network that delivers urban dwellers to their destinations. Understanding the intertwined usages of these three transit modes at different places and time allows for better sensing of urban mobility and the built environment. In this article, we leverage a comprehensive data collection of bus, metro, and taxicab ridership from Shenzhen, China to unveil the spatio‐temporal interplay between different mass transit modes. To achieve this goal, we develop a novel spectral clustering framework that imposes spatio‐temporal similarities between mass transit mode usage in urban space and differentiates urban spaces associated with distinct ridership patterns of mass transit modes. Five resulting categories of urban spaces are identified and interpreted with auxiliary knowledge of the city's metro network and land‐use functionality. In general, different categorized urban spaces are associated with different accessibility levels (such as high‐, medium‐, and low‐ranked) and different urban functionalities (such as residential, commercial, leisure‐dominant, and home–work balanced). The results indicate that different mass transit modes cooperate or compete based on demographic and socioeconomic attributes of the underlying urban environments. Our proposed analytical framework provides a novel and effective way to explore the mass transit system and the functional heterogeneity in cities. It demonstrates great potential for assisting policymakers and municipal managers in optimizing public transportation facility allocation and city‐wide daily commuting distribution.     

Extracting central places from the link structure in Wikipedia

Explicit information about places is captured in an increasing number of geospatial datasets. This article presents evidence that relationships between places can also be captured implicitly. It demonstrates that the hierarchy of central places in Germany is reflected in the link structure of the German language edition of Wikipedia. The official upper and middle centers declared, based on German spatial laws, are used as a reference dataset. The characteristics of the link structure around their Wikipedia pages, which link to each other or mention each other, and how often, are used to develop a bottom‐up method for extracting central places from Wikipedia. The method relies solely on the structure and number of links and mentions between the corresponding Wikipedia pages; no spatial information is used in the extraction process. The output of this method shows significant overlap with the official central place structure, especially for the upper centers. The results indicate that real‐world relationships are in fact reflected in the link structure on the web in the case of Wikipedia.     

顾及通名语义的汉语地名相似度匹配算法

地名匹配是地理信息检索、多源地理空间数据集成及更新中的关键技术问题。本文根据规范汉语地名构词特点,依据地名通名与地名类型的关系,建立规范地名通名语义知识库,并将由其提供的地名语义作为地名相似度匹配的重要指标。针对基于字面和空间数据的地名匹配方法存在的不足,面向规范地名提出一种综合了地名专名字面相似度和地名通名语义相似度两种因素的复合相似度匹配算法模型。该模型模拟人的认知习惯,根据通名语义相似度程度,通过单调函数关系动态设置专名和通名相似度各自的权重值,利用动态加权方法求得复合地名相似度指标。在上述模型基础上,本文提出了汉语地名匹配策略和流程,利用通名蕴含的语义增强汉语地名匹配算法的理论基础和完备性,提高了地名匹配算法准确率。实验结果表明该模型符合认知习惯,验证了该方法的合理性和有效性。     

Places of the Holocaust: Towards a model of GIS of place

Progress toward developing a GIS of place can only follow from an understanding of what place is, and this understanding draws on geographical theory. Here—following Agnew, Tuan, and others—we consider place as being made up of three components—location, locale, and sense of place—which are recognizable at multiple scales and vary historically as a product of social and political processes. Using the testimonies of two survivors of the Holocaust, we sketch the components of a model for a GIS of place that allows for this theory of place to be visualized and analyzed. The model is, crucially, both multi‐scalar and sensitive to uncertainty, as a GIS of place needs to be able to zoom in and out of the different scales at which place is experienced, as well as capture both uncertain data and uncertainty as data. We see potential in the representations proposed for scaling up from the anecdotal to the general in the sense that any narrative can be grouped and classified according to places and scales as shown here. The challenge in developing a GIS of place along the lines we propose here is to design a new set of functionalities that can do so.     

Detecting Non‐personal and Spam Users on Geo‐tagged Twitter Network

With the rapid growth and popularity of mobile devices and location‐aware technologies, online social networks such as Twitter have become an important data source for scientists to conduct geo‐social network research. Non‐personal accounts, spam users and junk tweets, however, pose severe problems to the extraction of meaningful information and the validation of any research findings on tweets or twitter users. Therefore, the detection of such users is a critical and fundamental step for twitter‐related geographic research. In this study, we develop a methodological framework to: (1) extract user characteristics based on geographic, graph‐based and content‐based features of tweets; (2) construct a training dataset by manually inspecting and labeling a large sample of twitter users; and (3) derive reliable rules and knowledge for detecting non‐personal users with supervised classification methods. The extracted geographic characteristics of a user include maximum speed, mean speed, the number of different counties that the user has been to, and others. Content‐based characteristics for a user include the number of tweets per month, the percentage of tweets with URLs or Hashtags, and the percentage of tweets with emotions, detected with sentiment analysis. The extracted rules are theoretically interesting and practically useful. Specifically, the results show that geographic features, such as the average speed and frequency of county changes, can serve as important indicators of non‐personal users. For non‐spatial characteristics, the percentage of tweets with a high human factor index, the percentage of tweets with URLs, and the percentage of tweets with mentioned/replied users are the top three features in detecting non‐personal users.     

Geospatial preparedness: Empirical study of the joint effort to provide geospatial support to disaster response

In a disaster aftermath in places lacking geospatial preparedness, the United Nations Office for the Coordination of Humanitarian Affairs creates a framework for cooperation with the Coordinated Data Scramble initiative, where Information Management Officers (IMOs) from different organizations work together in supporting the coordination of humanitarian aid. The perspective of these IMOs has been considered to identify the factors influencing the use of GIS in this context. The results show the requirement for a geodata management strategy, including geodata gathering, maintenance, and decision‐making processes based on those geodata. Geodata should be reliable and up‐to‐date. It requires consistent and useful metadata and the possibility of contacting the geodata source. Security and political issues limit information sharing. In this context, OpenStreetMap is often used as a source of information. Therefore, improving OpenStreetMap improves geospatial preparedness. Nevertheless, the use of this open platform highlights issues related to information privacy.     

Extracting invariant characteristics of sketch maps: Towards place query‐by‐sketch

In geography, invariant aspects of sketches are essential to study because they reflect the human perception of real‐world places. A person's perception of a place can be expressed in sketches. In this article, we quantitatively and qualitatively analyzed the characteristics of single objects and characteristics among objects in sketches and the real world to find reliable invariants that can be used to establish references/correspondences between sketch and world in a matching process. These characteristics include category, shape, name, and relative size of each object. Moreover, quantity and spatial relationships—such as topological, ordering, and location relationships—among all objects are also analyzed to assess consistency between sketched and actual places. The approach presented in this study extracts the reliable invariants for query‐by‐sketch and prioritizes their relevance for a sketch‐map matching process.     

A generic space definition framework to support seamless indoor/outdoor navigation systems

Contemporary public buildings are becoming conglomerates of open, semi‐open and closed spaces, with indoor, outdoor and underground sections. For humans and robots to navigate seamlessly through such environments, new flexible approaches need to be developed. Navigation systems generally rely on a network (nodes and edges) as an abstraction of underlying space availability. However, indoor and outdoor networks have different origins. While indoor systems rely on indoor space subdivision approaches, current outdoor systems utilize road‐based network approaches. Linking such networks via particular nodes is possible but restrictive. Many spaces in the built environment are not strictly indoor or outdoor spaces and are thus often omitted from navigation networks, further limiting navigation options. To overcome these shortcomings, we introduce a new space definition framework in which the entire built environment is categorized into indoor, outdoor, semi‐indoor and semi‐outdoor spaces. We provide strict definitions for the four space categories. Our framework allows the same navigation network extraction approaches to be used and therefore enables seamless indoor/outdoor path computation for single or combinations of locomotion modes. The notions of semi‐indoor and semi‐outdoor spaces offer new options for further tailoring of the navigation path with respect to environmental factors, which we demonstrate with two use cases.     

大众点评数据下的城市场所范围感知方法

“场所（place）”是具有特定语义和人文体验的地理位置,是虚拟地理环境中地理知识表达的核心要素,能够为深入理解与表达人们的地理空间认知,以及基于虚拟地理环境的分析模拟提供支持,因而对城市内部现有场所的识别与模糊建模是城市空间结构研究的基础之一。众源数据为提取和表达模糊场所提供了新的途径,然而目前研究多针对单个或少数几个场所进行建模。本研究针对城市多场所空间范围的多尺度建模,提出基于自适应核密度估计的模糊集方法,为进一步理解城市场所的模糊认知范围提供了可视化解决方案。并以北京市五环内场所为实例,采用大众点评网中商户自行上传的兴趣点（POI）数据,对其进行提取与表达。通过对比百度地图所展示的场所相应范围,发现该方法在大众点评数据集下的应用能更好地揭示商圈语义下的场所认知。     

客家聚落地名时空分布与演变方法研究

针对目前地名文化中客家聚落地名研究的不足,该文从客家迁徙历史的角度,提出了客家地名定性和定量的研究方法,以客家摇篮石城地名为研究对象,采用核密度分析法和多元logit回归模型方法,深入分析石城客家地名的时空分布特征和历史演变因素。结果显示:在时间上,客家地名数量整体呈现上升趋势,增长时间分为5个阶段,隋唐萌芽时期、宋朝大量增长期、元朝缓慢增长期、明朝平稳增长期和清朝快速增长期;在空间上,客家地名分布显现出聚集特征,但聚集程度各异,聚集点和聚集区域随着时间的推移而变化。根据logit模型结果显示,地形、河流、人口和经济等多种因素交融在一起,对客家地名的演化产生了重大而深远的影响。     

Geospatial Human‐environment Simulation through Integration of Massive Multiplayer Online Games and Geographic Information Systems

This article reports on the initial development of a generic framework for integrating Geographic Information Systems (GIS) with Massive Multi‐player Online Gaming (MMOG) technology to support the integrated modeling of human‐environment resource management and decision‐making. We review Web 2.0 concepts, online maps, and games as key technologies to realize a participatory construction of spatial simulation and decision making practices. Through a design‐based research approach we develop a prototype framework, “GeoGame”, that allows users to play board‐game‐style simulations on top of an online map. Through several iterations we demonstrate the implementation of a range of design artifacts including: real‐time, multi‐user editing of online maps, web services, game lobby, user‐modifiable rules and scenarios building, chat, discussion, and market transactions. Based on observational, analytical, experimental and functional evaluations of design artifacts as well as a literature review, we argue that a MMO GeoGame‐framework offers a viable approach to address the complex dynamics of human‐environmental systems that require a simultaneous reconciliation of both top‐down and bottom‐up decision making where stakeholders are an integral part of a modeling environment. Further research will offer additional insight into the development of social‐environmental models using stakeholder input and the use of such models to explore properties of complex dynamic systems.     

Analytical queries on semantic trajectories using graph databases

This article studies the analysis of moving object data collected by location‐aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so‐called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non‐spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real‐world public data case study, the article shows that trajectory queries are expressed more naturally on the graph‐based representation than over the relational alternative, and perform better in many typical cases.     

How friends share urban space: An exploratory spatiotemporal analysis using mobile phone data

This study proposes a framework to investigate the roles of urban spaces in connecting social contacts (i.e., “friends”). The framework is applied to a Call Detail Record (CDR) dataset collected in Singapore. First, a comparative analysis is performed to understand how friends share urban space differently from random people. Then, we derive two metrics to quantify the “bonding” and “bridging” capabilities of places in the city. The two metrics reflect the potential of a place in connecting friends and random people (e.g., chance encounters), respectively. Finally, we examine the temporal signature of the places’ bonding capabilities, and associate the results with various types of Points of Interest (POIs). We find that: (1) friends are more likely to share urban space than random people, and they also share more locations; (2) a place could play different roles in connecting friends vs. random people, and the relationship (between bonding and bridging) varies depending on the time and type of a day (weekdays vs. weekends); (3) the temporal signature of bonding capability is strongly related to the semantics of a place; (4) certain POI types (e.g., shopping malls) tend to have a much higher impact on bonding capability than others (e.g., sports centers).