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1.
“All the World's a Stage”: A GIS Framework for Recreating Personal Time‐Space from Qualitative and Quantitative Sources 
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下载免费PDF全文 This article presents a methodological model for the study of the space‐time patterns of everyday life. The framework utilizes a wide range of qualitative and quantitative sources to create two environmental stages, social and built, which place and contextualize the daily mobilities of individuals as they traverse urban environments. Additionally, this study outlines a procedure to fully integrate narrative sources in a GIS. By placing qualitative sources, such as narratives, within a stage‐based GIS, researchers can begin to tell rich spatial stories about the lived experiences of segregation, social interaction, and environmental exposure. The article concludes with a case study utilizing the diary of a postal clerk to outline the wide applicability of this model for space‐time GIS research. 相似文献
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May Yuan 《Transactions in GIS》2018,22(4):900-912
This article highlights the key intellectual development in human dynamics research, examines the modeling emphases in publications, and argues for research directions in need. Human dynamics research is discussed in two broad directions: spacing time and timing space, to model human activities and interactions. Time is essential to human dynamics research. Space, while often being overlooked, in complement with time is critical to understanding human dynamics because knowing where activities take place is essential to knowing how and why people act and interact. Some interactions allow remote or asynchronized participations, and others require movement to collocate individuals for participating in synchronized activities. A spacing time approach examines the temporal gaps between interactions. A timing space approach investigates the spatial pulses between interactions. Primary research in the spacing time of human dynamics established queueing theories to explain the bursts and heavy‐tailed distribution of human interactions. Although research on the timing space of human dynamics enjoys growing popularity with data from geo‐tagged social media and location‐aware social internet of things (SIoT), its publications remain mostly exploratory. This article suggests a hierarchical framework to systematically study human dynamics and relate findings to build the body of knowledge about human dynamics. 相似文献
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Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference 总被引：1，自引：0，他引：1 下载免费PDF全文
下载免费PDF全文 As mapping is costly and labor‐intensive work, government mapping agencies are less and less willing to absorb these costs. In order to reduce the updating cycle and cost, researchers have started to use user generated content (UGC) for updating road maps; however, the existing methods either rely heavily on manual labor or cannot extract enough information for road maps. In view of the above problems, this article proposes a UGC‐based automatic road map inference method. In this method, data mining techniques and natural language processing tools are applied to trajectory data and geotagged data in social media to extract not only spatial information – the location of the road network – but also attribute information – road class and road name – in an effort to create a complete road map. A case study using floating car data, collected by the National Commercial Vehicle Monitoring Platform of China, and geotagged text data from Flickr and Google Maps/Earth, validates the effectiveness of this method in inferring road maps. 相似文献
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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. 相似文献
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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. 相似文献
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The spatial representation of a city is typically formed by top‐down jurisdictional boundaries. A parallel approach would be to consider representing a city based on platial characteristics, that is, a bottom‐up landscape created through individual and collectively derived representations. This study contributes to this discourse through the exploratory examination of the ecology notions of home range and habitat applied to humans in an urban context. Using spatial data collected through a WebGIS platform, we employ a spatial definition of sense of place and social capital to understand the platial nature of the city and, simultaneously, defining home range and habitat as platial notions. We found spatial variability among individual home range and habitat and the difficulty of traditional administrative boundaries to represent these areas. This research defines and presents home range and habitat to partially describe the emergent nature of platial theory and explores their operationalization at the urban level. 相似文献
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ABSTRACT In recent years, social media platforms have played a critical role in mitigation for a wide range of disasters. The highly up-to-date social responses and vast spatial coverage from millions of citizen sensors enable a timely and comprehensive disaster investigation. However, automatic retrieval of on-topic social media posts, especially considering both of their visual and textual information, remains a challenge. This paper presents an automatic approach to labeling on-topic social media posts using visual-textual fused features. Two convolutional neural networks (CNNs), Inception-V3 CNN and word embedded CNN, are applied to extract visual and textual features respectively from social media posts. Well-trained on our training sets, the extracted visual and textual features are further concatenated to form a fused feature to feed the final classification process. The results suggest that both CNNs perform remarkably well in learning visual and textual features. The fused feature proves that additional visual feature leads to more robustness compared with the situation where only textual feature is used. The on-topic posts, classified by their texts and pictures automatically, represent timely disaster documentation during an event. Coupling with rich spatial contexts when geotagged, social media could greatly aid in a variety of disaster mitigation approaches. 相似文献
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When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data. 相似文献
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Crowd sensing and spatiotemporal analysis in urban open space using multi-viewpoint geotagged videos
Feng Liu Zhigang Han Hongquan Song Jiayao Wang Chun Liu Gaohan Ban 《Transactions in GIS》2023,27(2):494-515
Increasing concern for urban public safety has motivated the deployment of a large number of surveillance cameras in open spaces such as city squares, stations, and shopping malls. The efficient detection of crowd dynamics in urban open spaces using multi-viewpoint surveillance videos continues to be a fundamental problem in the field of urban security. The use of existing methods for extracting features from video images has resulted in significant progress in single-camera image space. However, surveillance videos are geotagged videos with location information, and few studies have fully exploited the spatial semantics of these videos. In this study, multi-viewpoint videos in geographic space are used to fuse object trajectories for crowd sensing and spatiotemporal analysis. The YOLOv3-DeepSORT model is used to detect a pedestrian and extract the corresponding image coordinates, combine spatial semantics (such as the positions of the pedestrian in the field of view of the camera) to build a projection transformation matrix and map the object recorded by a single camera to geographic space. Trajectories from multi-viewpoint videos are fused based on the features of location, time, and directions to generate a complete pedestrian trajectory. Then, crowd spatial pattern analysis, density estimation, and motion trend analysis are performed. Experimental results demonstrate that the proposed method can be used to identify crowd dynamics and analyze the corresponding spatiotemporal pattern in an urban open space from a global perspective, providing a means of intelligent spatiotemporal analysis of geotagged videos. 相似文献
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Abstract Much of the human dimensions of environmental change research emphasize the mapping and modeling of land use and land cover patterns over space and time, and the linkages between people, place, and environment as proximate and distal forces of landscape dynamics. Spatial digital technologies, framed within a GIScience (GISc) context, figure prominently in the characterization of land use and land cover through remote sensing technologies, and in the assessment of social and demographic factors and local and regional site and situation considerations achieved through global positioning systems, data visualizations, and spatial and statistical analyses. Here, we describe some fundamental approaches for linking data across thematic domains, essential for the study of human‐environment interactions. The goal is to generate compatible data sets that extend across social, biophysical, and geographical domains so that the causes and consequences of land use and land cover dynamics might be explored within a spatially‐explicit context. 相似文献
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Enriching the GIScience research agenda: Fusing augmented reality and location‐based social networks 下载免费PDF全文
下载免费PDF全文 Augmented reality (AR) overlays real‐world views or scenes with virtual, computer‐generated objects that appear to visually coexist in the same space. Location‐based social networks (LBSNs) are platforms for individuals to be connected through the interdependency derived from their physical locations and their location‐tagged social media content. Current research and development in both areas focuses on integrating mobile‐based AR and LBSNs. Several applications (e.g., Sekai Camera and Wallame) have been developed and commercialized successfully. However, little research has been done on the potential impacts and successful evaluation methods of AR‐integrated LBSNs in the GIScience field. To close this gap, the article outlines the impacts and benefits of AR‐integrated LBSNs and highlights the importance of LBSNs in GIScience research. Based on the status quo of AR‐integrated LBSNs, this article discusses—from theoretical and application‐oriented perspectives—how AR‐integrated LBSNs could enrich the GIScience research agenda in three aspects: data conflation, platial GIS, and multimedia storytelling. The article concludes with guidelines on visualization, functionality, and ethics that aim to help users develop and evaluate AR‐integrated LBSNs. 相似文献
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ABSTRACTMassive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic biases. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the United States, we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties. 相似文献
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Vaccination is a primary means to control infectious diseases. Few studies on vaccination strategies have explicitly considered the mobility of individuals. This article aims to evaluate the efficacy of three vaccination strategies in a dynamic social network, in which individuals are mobile between and within communities. The three vaccination strategies are applied to this social network for evaluation, including a travel‐based, a contact‐based, and a random vaccination strategy. Simulation results show that the contact‐based strategy, commonly seen in previous studies, is not always the most effective strategy in dynamic networks. This strategy is preferable for a population with a large number of intercommunity travelers, for instance in urban areas. On the other hand, the travel‐based strategy, although directly accounting for individual mobility, is not necessarily the most effective in dynamic networks either. This strategy is recommended for a population with a small number of intercommunity travelers, such as rural areas. In addition, one advantage of the travel‐based strategy over the other two is its efficacy in confining the spatial extent of affected areas. Results suggest that the intercommunity travel of individuals should be a major consideration for choosing proper vaccination strategies. By adding the spatial context into vaccination strategies, this research provides new insights into community‐based planning for infectious disease control. 相似文献
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Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data 总被引：2，自引：0，他引：2
Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people’s social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people’s interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships. 相似文献
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ABSTRACTUrban functions are closely related to people’s spatiotemporal activity patterns, transportation needs, and a city’s business distribution and development trends. Studies investigating urban functions have used different data sources, such as remotely sensed imageries, observation, photography, and cognitive maps. However, these data sources usually suffer from low spatial, temporal, and thematic resolution. This article attempts to investigate human activities to understand urban functions through crowdsourcing social media data. In this study, we mined Twitter and Foursquare data to extract and analyze six types of human activities. The spatiotemporal analysis revealed hotspots for different activity intensities at different temporal resolution. We also applied the classified model in a real-time system to extract information of various urban functions. This study demonstrates the significance and usefulness of social sensing in analyzing urban functions. By combining different platforms of social media data and analyzing people’s geo-tagged city experience, this article contributes to leverage voluntary local knowledge to better depict human dynamics, discover spatiotemporal city characteristics, and convey information about cities. 相似文献
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高度活跃的城市是社会稳定发展的基础。基于地理标签感知的城市活力能够量化城市发展现状,探索城市活力的影响机制,为精细化城市治理提供技术支撑。传统城市活力研究依赖于街区的活力调查,时间长,费用高。本文研究利用兴趣点和社交媒体签到等地理标签数据,提出了城市活力度量指标,探索性分析城市活力的分布模式。基于土地利用、道路和建筑物等数据计算建成环境指标,构建城市活力和建成环境之间的普通线性回归与空间自回归模型,揭示了影响城市活力的建成环境因素。基于深圳市的试验结果表明:兴趣点和社交媒体签到数据能够较好地指示城市活力。深圳市的城市活力主要受商业用地、工业用地、土地混合利用以及路网密度、地铁站点密度的影响。住宅用地和建筑物占地密度对基于POI的城市活力具有显著影响。 相似文献
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《制图学和地理信息科学》2013,40(4):175-186
Understanding the spatial dimension of fear of crime in the urban environment is important to understanding behaviors in response to this concern. Making this connection between perception and action has long been a goal of scholars in the social and health sciences, though this complex relationship has yet to be fully elucidated. Specifically, in studies on fear of crime and its influence on behavior, a variety of definitions and methods have been employed. This situation has yielded insights, as well as inconsistencies. In the past decade, Geographic Information Systems (GIS) has been added to this methodological mix, though it too has contributed limited understanding of the environmental perception-behavior nexus. During this time, some scholars have integrated a traditional technique for accessing environmental perception, the sketch map, with this newer technology. This article provides a review and critical assessment of the way GIS has been used to understand fear of crime, specifically through the integration of sketch maps. This focus is framed by an overview of substantive and methodological concerns and concludes with a discussion of continued research needs. As behavioral responses to fear of crime are acknowledged to impact physical and mental health and overall well-being, in addition to the viability of neighborhoods, research in this area will continue apace. However, for integration of sketch maps in GIS to be a valuable methodological contributor to this line of inquiry, users of the approach must understand its complexities. This article outlines these issues so that they may be considered in future research and may improve the ability for this approach to yield new understanding of fear of crime. 相似文献
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互联网的广泛应用产生了越来越多与地理空间位置关联的文本信息。现有地理信息系统一般通过外部链接来浏览这些数据,需要频繁的缩放、漫游和点击操作,而其他方法又难以有效表达出空间位置关系。提出了一种基于标签云的位置关联文本信息可视化方法———标签云地图,给出了标签云地图的设计思路和实现流程,并以腾讯微博的真实数据集为例建立了原型,重点研究了点状和面状地理要素的Cartogram生成算法,关键字和词频的提取算法,面向不同尺度和不同时间的标签云显示规则的标签位置生成算法。实验表明,该方法能够帮助用户从大量的位置关联文本信息中快速感知并把握信息的总体特征和发展趋势。 相似文献
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Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data 下载免费PDF全文
下载免费PDF全文 The accurate mapping of urban housing prices at a fine scale is essential to policymaking and urban studies, such as adjusting economic factors and determining reasonable levels of residential subsidies. Previous studies focus mainly on housing price analysis at a macro scale, without fine‐scale study due to a lack of available data and effective models. By integrating a convolutional neural network for united mining (UMCNN) and random forest (RF), this study proposes an effective deep‐learning‐based framework for fusing multi‐source geospatial data, including high spatial resolution (HSR) remotely sensed imagery and several types of social media data, and maps urban housing prices at a very fine scale. With the collected housing price data from China's biggest online real estate market, we produced the spatial distribution of housing prices at a spatial resolution of 5 m in Shenzhen, China. By comparing with eight other multi‐source data mining techniques, the UMCNN obtained the highest housing price simulation accuracy (Pearson R = 0.922, OA = 85.82%). The results also demonstrated a complex spatial heterogeneity inside Shenzhen's housing price distribution. In future studies, we will work continuously on housing price policymaking and residential issues by including additional sources of spatial data. 相似文献