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1.
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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A Multiscale Approach for Spatio-Temporal Outlier Detection 总被引：1，自引：0，他引：1
A spatial outlier is a spatially referenced object whose thematic attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents an object that is significantly different from its neighbourhoods even though it may not be significantly different from the entire population. Here we extend this concept to the spatio‐temporal domain and define a spatial‐temporal outlier (ST‐outlier) to be a spatial‐temporal object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial or/and temporal neighbourhoods. Identification of ST‐outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability or deformation. Many methods have been recently proposed to detect spatial outliers, but how to detect the temporal outliers or spatial‐temporal outliers has been seldom discussed. In this paper we propose a multiscale approach to detect ST‐outliers by evaluating the change between consecutive spatial and temporal scales. A four‐step procedure consisting of classification, aggregation, comparison and verification is put forward to address the semantic and dynamic properties of geographic phenomena for ST‐outlier detection. The effectiveness of the approach is illustrated by a practical coastal geomorphic study. 相似文献
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随着手持设备的广泛普及、通信技术的高速发展,社交媒体在突发事件中发挥着重要作用。当突发事件发生时,社交媒体数据不断产生,并携带大量的应急信息,其中包括不同的应急主题、时空分布等信息。本文设计并实现了基于社交媒体的突发事件应急信息系统,重点介绍基于社交媒体的应急信息挖掘技术,以及系统实现的功能。 相似文献
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As tools for collecting data continue to evolve and improve, the information available for research is expanding rapidly. Increasingly, this information is of a spatio‐temporal nature, which enables tracking of phenomena through both space and time. Despite the increasing availability of spatio‐temporal data, however, the methods for processing and analyzing these data are lacking. Existing geocoding techniques are no exception. Geocoding enables the geographic location of people and events to be known and tracked. However, geocoded information is highly generalized and subject to various interpolation errors. In addition, geocoding for spatio‐temporal data is especially challenging because of the inherent dynamism of associated data. This article presents a methodology for geocoding spatio‐temporal data in ArcGIS that utilizes several additional supporting procedures to enhance spatial accuracy, including the use of supplementary land use information, aerial photographs and local knowledge. This hybrid methodology allows for the tracking of phenomenon through space and over time. It is also able to account for reporting inconsistencies, which is a common feature of spatio‐temporal data. The utility of this methodology is demonstrated using an application to spatio‐temporal address records for a highly mobile group of convicted felons in Hamilton County, Ohio. 相似文献
5.
The clustering of spatio‐temporal events has become one of the most important research branches of spatio‐temporal data mining. However, the discovery of clusters of spatio‐temporal events with different shapes and densities remains a challenging problem because of the subjectivity in the choice of two critical parameters: the spatio‐temporal window for estimating the density around each event, and the density threshold for evaluating the significance of clusters. To make the clustering of spatio‐temporal events objective, in this study these two parameters were adaptively generated from statistical information about the dataset. More precisely, the density threshold was statistically modeled as an adjusted significance level controlled by the cardinality and support domain of the dataset, and the appropriate sizes of spatio‐temporal windows for clustering were determined by the spatio‐temporal classification entropy and stability analysis. Experiments on both simulated and earthquake datasets were conducted, and the results show that the proposed method can identify clusters of different shapes and densities. 相似文献
6.
Miriam Baglioni Emiliano Giovannetti Maria Vittoria Masserotti Chiara Renso Laura Spinsanti 《Transactions in GIS》2008,12(Z1):31-44
Querying geographical information systems has been recognized as a difficult task for non‐expert users. Furthermore, user queries are often characterized by semantic aspects not directly managed by traditional spatial databases or GIS. Examples of such semantic geospatial queries are the use of implicit spatial relations between objects, or the reference of domain concepts not explicitly represented in data. To handle such queries, we envisage a system that translates natural language queries into spatial SQL statements on a database, thus improving standard GIS with new semantic capabilities. Within this general objective, the contribution of this article is to introduce a methodology to handle semantic geospatial queries issued over a spatial database. This approach captures semantics from an ontology built upon the spatial database and enriched by domain concepts and properties specifically defined to represent the localization of objects. Some examples of the use of the methodology in the urban domain are presented. 相似文献
7.
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. 相似文献
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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. 相似文献
10.
Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment 总被引：1，自引：0，他引：1
Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection. Our results demonstrate that earthquake footprints can be reliably and accurately identified in our use case. More, a number of relevant semantic topics can be automatically identified without a priori knowledge, revealing clearly differing temporal and spatial signatures. Furthermore, we are able to generate a damage map that indicates where significant losses have occurred. The validation of our results using statistical measures, complemented by the official earthquake footprint by US Geological Survey and the results of the HAZUS loss model, shows that our approach produces valid and reliable outputs. Thus, our approach may improve current disaster management procedures through generating a new and unseen information layer in near real time. 相似文献
11.
Synchronous geocollaboration helps geographically dispersed people to work together in a shared geospatial environment. Its real‐time nature, multiple users' interaction and diversity of work context impose some special social, organizational and technological requirements, making the development of such real‐time geocollaboration systems a challenging task. A conceptual framework is therefore needed to specify and describe what synchronous geocollaboration is, considering its social, spatial and technical aspects. The geo‐social model presented in this article describes a conceptual framework for synchronous geocollaboration systems addressing the above aspects, identifies the core elements of the system and describes how these elements collaborate with each other. This model is presented using application‐level ontology and is then applied to a multi‐agent system based prototype in which multiple users can interact and negotiate in a shared 3D geospatial environment. 相似文献
12.
Chuanrong Zhang Tian Zhao E. Lynn Usery Dalia Varanka Weidong Li 《Transactions in GIS》2020,24(2):508-525
Disaster response operations require fast and coordinated actions based on real‐time disaster situation information. Although crowdsourced geospatial data applications have been demonstrated to be valuable tools for gathering real‐time disaster situation information, they only provide limited utility for disaster response coordination because of the lack of semantic compatibility and interoperability. To help overcome the semantic incompatibility and heterogeneity problems, we use Geospatial Semantic Web (GSW) technologies. We then combine GSW technologies with Web Feature Service requests to access multiple servers. However, a GSW‐based geographic information system often has poor performance due to the complex geometric computations required. The objective of this research is to explore how to use optimization techniques to improve the performance of an interoperable geographic situation‐awareness system (IGSAS) based on GSW technologies for disaster response. We conducted experiments to evaluate various client‐side optimization techniques for improving the performance of an IGSAS prototype for flood disaster response in New Haven, Connecticut. Our experimental results show that the developed prototype can greatly reduce the runtime costs of geospatial semantic queries through on‐the‐fly spatial indexing, tile‐based rendering, efficient algorithms for spatial join, and caching, especially for those spatial‐join geospatial queries that involve a large number of spatial features and heavy geometric computation. 相似文献
13.
Lucas May Petry Carlos Andres Ferrero Luis Otavio Alvares Chiara Renso Vania Bogorny 《Transactions in GIS》2019,23(5):960-975
The large amount of semantically rich mobility data becoming available in the era of big data has led to a need for new trajectory similarity measures. In the context of multiple‐aspect trajectories, where mobility data are enriched with several semantic dimensions, current state‐of‐the‐art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple‐aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple‐aspect trajectories. 相似文献
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Blogs, micro‐blogs and online forums underpin a more interconnected world. People communicate ever more and are increasingly keen to explain and illustrate their lives; showing where they are and what they are doing. Desktop, online and mobile mapping landscapes have never been as rich or diverse yet this challenges cartography to adapt and remain relevant in the modern mapping world. We explore the spatial expression and potential value of micro‐blogging and Twitter as a social networking tool. Examples of “twitter maps” are reviewed that leverage the Twitter API and online map services to locate some component of the “tweet”. Scope, function and design are illustrated through development of two proof‐of‐concept map mashups that support collaborative real‐time mapping and the organisation and display of information for mass user events. Through the experiments in using and organising data in this way we demonstrate the value of “cartoblography”– a framework for mapping the spatial context of micro‐blogging. 相似文献
16.
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
17.
Clodoveu A. Davis Jr. Gisele L. Pappa Diogo Rennó Rocha de Oliveira Filipe de L. Arcanjo 《Transactions in GIS》2011,15(6):735-751
User interaction in social networks, such as Twitter and Facebook, is increasingly becoming a source of useful information on daily events. The online monitoring of short messages posted in such networks often provides insight on the repercussions of events of several different natures, such as (in the recent past) the earthquake and tsunami in Japan, the royal wedding in Britain and the death of Osama bin Laden. Studying the origins and the propagation of messages regarding such topics helps social scientists in their quest for improving the current understanding of human relationships and interactions. However, the actual location associated to a tweet or to a Facebook message can be rather uncertain. Some tweets are posted with an automatically determined location (from an IP address), or with a user‐informed location, both in text form, usually the name of a city. We observe that most Twitter users opt not to publish their location, and many do so in a cryptic way, mentioning non‐existing places or providing less specific place names (such as “Brazil”). In this article, we focus on the problem of enriching the location of tweets using alternative data, particularly the social relationships between Twitter users. Our strategy involves recursively expanding the network of locatable users using following‐follower relationships. Verification is achieved using cross‐validation techniques, in which the location of a fraction of the users with known locations is used to determine the location of the others, thus allowing us to compare the actual location to the inferred one and verify the quality of the estimation. With an estimate of the precision of the method, it can then be applied to locationless tweets. Our intention is to infer the location of as many users as possible, in order to increase the number of tweets that can be used in spatial analyses of social phenomena. The article demonstrates the feasibility of our approach using a dataset comprising tweets that mention keywords related to dengue fever, increasing by 45% the number of locatable tweets. 相似文献
18.
Johannes F. Kruiger Vedran Kasalica Rogier Meerlo Anna‐Lena Lamprecht Enkhbold Nyamsuren Simon Scheider 《Transactions in GIS》2021,25(1):424-449
Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo‐analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo‐analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo‐analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis. 相似文献
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Often, we are faced with questions regarding past events and the answers are hidden in the historical text archives. The growing developments in geographic information retrieval and temporal information retrieval techniques have given new ways to explore digital text archives for spatio‐temporal data. The question is how to retrieve the answers from the text documents. This work contributes to a better understanding of spatio‐temporal information extraction from text documents. Natural language processing techniques were used to develop an information extraction approach using the GATE language processing software. The developed framework uses gazetteer matching, spatio‐temporal relationship extraction and pattern‐based rules to recognize and annotate elements in historical text documents. The extracted spatio‐temporal data is used as input for GIS studies on the time–geography context of the German–Herero resistance war of 1904 in Namibia. Related issues when analyzing the historical data in current GIS are discussed. Particularly problematic are movement data in small scale with poor temporal density and trajectories that are short or connect very distant locations. 相似文献