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1.
In the age of Big Data, the widespread use of location‐awareness technologies has made it possible to collect spatio‐temporal interaction data for analyzing flow patterns in both physical space and cyberspace. This research attempts to explore and interpret patterns embedded in the network of phone‐call interaction and the network of phone‐users’ movements, by considering the geographical context of mobile phone cells. We adopt an agglomerative clustering algorithm based on a Newman‐Girvan modularity metric and propose an alternative modularity function incorporating a gravity model to discover the clustering structures of spatial‐interaction communities using a mobile phone dataset from one week in a city in China. The results verify the distance decay effect and spatial continuity that control the process of partitioning phone‐call interaction, which indicates that people tend to communicate within a spatial‐proximity community. Furthermore, we discover that a high correlation exists between phone‐users’ movements in physical space and phone‐call interaction in cyberspace. Our approach presents a combined qualitative‐quantitative framework to identify clusters and interaction patterns, and explains how geographical context influences communities of callers and receivers. The findings of this empirical study are valuable for urban structure studies as well as for the detection of communities in spatial networks. 相似文献
2.
Gengchen Mai Krzysztof Janowicz Ling Cai Rui Zhu Blake Regalia Bo Yan Meilin Shi Ni Lao 《Transactions in GIS》2020,24(3):623-655
Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. Most models that do consider space primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location‐aware KG embedding model called SE‐KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query–answer pairs called DBGeo to evaluate the performance of SE‐KGE in comparison to multiple baselines. Evaluation results show that SE‐KGE outperforms these baselines on the DBGeo data set for the geographic logic query answering task. This demonstrates the effectiveness of our spatially‐explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin. 相似文献
3.
多层次空间同位模式自适应挖掘方法 总被引：1，自引：1，他引：0
空间同位模式挖掘旨在从空间数据中发现频繁发生在邻近位置的事件集合,对于揭示地理现象间的共生规律具有重要价值。由于地理现象的空间异质特质,空间同位模式也存在区域性分异的特点,在不同空间层次上的分析结果各异。然而,现有方法仅从全局视角挖掘空间同位模式,发现局部空间同位模式依然是一个亟待解决的难题。为此,本文基于由整体到局部的思想,提出了一种多层次空间同位模式自适应挖掘方法。首先,从全局视角提取频繁的空间同位模式,将全局不频繁的空间同位模式作为候选的局部空间同位模式;然后,通过对候选局部同位模式进行自适应聚类自动识别其局部分布区域,并在这些局部区域内度量候选模式的频繁程度;进而,提出了一种叠置推绎的方法,从频繁子模式的局部区域中进一步推绎获得超模式的局部分布区域,最终生成所有频繁的局部空间同位模式集合。通过试验分析与比较发现,本文方法不仅可以发现全局的空间同位模式,还能有效提取具有区域性分布特征的局部空间同位模式,可以从多个空间层次上反映地理事件间的共生规则。 相似文献
4.
空间思维是地理科学认知学习与研究的基本思维模式。为满足新媒体融合背景下地理信息科学专业的素质人才培养需求，本文设计了包括地理空间模拟体验、空间信息地图绘读、隐喻信息语义认知和地理系统综合分析等针对空间思维能力训练的教学内容；明晰了地理空间思维在信息感知与整理、隐含信息挖掘、信息表达视觉化、行业服务支撑、空间治理决策等新媒体信息深度挖掘方向上的应用。本文研究有利于本科学生充分利用新媒体学习工具、系统化专业知识体系和提高自身地理空间思维素养，可以为地理信息科学本科专业教学提供有益的探索。 相似文献
5.
India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion. 相似文献
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Geo‐SOM is a useful geovisualization technique for revealing patterns in spatial data, but is ineffective in supporting interactive exploration of patterns hidden in different Geo‐SOM sizes. Based on the divide and group principle in geovisualization, the article proposes a new methodology that combines Geo‐SOM and hierarchical clustering to tackle this problem. Geo‐SOM was used to “divide” the dataset into several homogeneous subsets; hierarchical clustering was then used to “group” neighboring homogeneous subsets for pattern exploration in different levels of granularity, thus permitting exploration of patterns at multiple scales. An artificial dataset was used for validating the method's effectiveness. As a case study, the rush hour motorcycle flow data in Taipei City, Taiwan were analyzed. Compared with the best result generated solely by Geo‐SOM, the proposed method performed better in capturing the homogeneous zones in the artificial dataset. For the case study, the proposed method discovered six clusters with unique data and spatial patterns at different levels of granularity, while the original Geo‐SOM only identified two. Among the four hierarchical clustering methods, Ward's clustering performed the best in pattern discovery. The results demonstrated the effectiveness of the approach in visually and interactively exploring data and spatial patterns in geospatial data. 相似文献
7.
多源地理大数据为地理现象的分布格局、相互作用及动态演化提供了前所未有的社会感知手段。城市是人类活动最为集中的区域,产生了多种地理大数据,并支持对于城市空间的理解。城市内部的分异格局是城市研究和规划所要面对的重要议题,社会感知数据提供了从"人-地-静-动"4个维度刻画城市分异格局的途径。梳理了不同类型大数据对于表达这4个维度特征的支持,并借鉴"生态位"模型,通过一个实例研究展示了集成多源数据量化城市空间分异特征的应用,最后讨论了相关的理论问题。 相似文献
8.
Extracting urban functional regions from points of interest and human activities on location‐based social networks 
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下载免费PDF全文 Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behavior. However, it is difficult to quantify the correct mix or the spatial relations among different POI types indicative of specific urban functions. In this research, we develop a statistical framework to help discover semantically meaningful topics and functional regions based on the co‐occurrence patterns of POI types. The framework applies the latent Dirichlet allocation (LDA) topic modeling technique and incorporates user check‐in activities on location‐based social networks. Using a large corpus of about 100,000 Foursquare venues and user check‐in behavior in the 10 most populated urban areas of the US, we demonstrate the effectiveness of our proposed methodology by identifying distinctive types of latent topics and, further, by extracting urban functional regions using K‐means clustering and Delaunay triangulation spatial constraints clustering. We show that a region can support multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non‐adjacent locations. Since each region can be modeled as a vector consisting of multinomial topic distributions, similar regions with regard to their thematic topic signatures can be identified. Compared with remote sensing images which mainly uncover the physical landscape of urban environments, our popularity‐based POI topic modeling approach can be seen as a complementary social sensing view on urban space based on human activities. 相似文献
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For an effective interpretation of spatio‐temporal patterns of crime clusters/hotspots, we explore the possibility of three‐dimensional mapping of crime events in a space‐time cube with the aid of space‐time variants of kernel density estimation and scan statistics. Using the crime occurrence dataset of snatch‐and‐run offences in Kyoto City from 2003 to 2004, we confirm that the proposed methodology enables simultaneous visualisation of the geographical extent and duration of crime clusters, by which stable and transient space‐time crime clusters can be intuitively differentiated. Also, the combined use of the two statistical techniques revealed temporal inter‐cluster associations showing that transient clusters alternatively appeared in a pair of hotspot regions, suggesting a new type of “displacement” phenomenon of crime. Highlighting the complementary aspects of the two space‐time statistical approaches, we conclude that combining these approaches in a space‐time cube display is particularly valuable for a spatio‐temporal exploratory data analysis of clusters to extract new knowledge of crime epidemiology from a data set of space‐time crime events. 相似文献
10.
A methodology for analyzing geographic data using the techniques of: (1) qualitative geometric abstraction; and (2) ontological analysis of geographic features is described. The first technique is a bottom‐up approach to extract qualitative spatial relations from geographic representations (raster or vector) while the second technique is a top‐down approach to determine which qualitative relations can possibly hold between the parts of the geographic features. The process of analyzing geographic data includes the extraction of both the features and the qualitative relations among features. These qualitative relations are then used to classify the geographic features within the “space” of ontological possibilities. In this article bays in Wisconsin and their cartographic representation are used as a running example and the subject of a case study. 相似文献
11.
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
12.
When studying spatial patterns, GIScientists often employ distance‐based methods and techniques, such as network analysis. When studying human behavior, however, spatial patterns often emerge that cannot be adequately examined assuming a physical conceptualization of distance. Such patterns emerged during our study of the process of ghettoization of Jews as implemented in Budapest during the course of 1944. As part of an NSF‐sponsored research project on the geography of the Holocaust, we built a Historical GIS of the Budapest Ghetto with the objective of discovering patterns of Jewish concentration and dispersion as well as simulating potential daily spatial interactions between the Jewish and the non‐Jewish population. Spatial analytical techniques allowed us to discover distinct spatial patterns of isolation, interrelation and concentration, but a whole set of patterns appeared that were the opposite of what we expected, and that could only be explained by thinking of distance not in spatial terms but in social ones. In this article we employ social network analysis to examine the geography of oppression in the Budapest ghetto. What jumped out from our study is the interweaving of space and place – intended as a community bounded by social relations and living in a specific time and location. 相似文献
13.
Anyone involved in teaching the principles and applications of geographic information science and technology (GIS&T) understands the challenges of effective instruction within an environment subject to nearly constant change. Indeed, the dynamic nature of GIS&T introduces both new paradigms and increasingly powerful tools for exploring spatial relationships. However, while past efforts among educators and practitioners have identified knowledge and competencies important to GIS&T learning, less attention has been directed at methods used to teach GIS&T. For example, while some instructors employ traditional approaches such as lectures and structured laboratory exercises, others have shifted to active learning strategies such as “flipped classrooms” and collaborative, project‐oriented assignments. In this article, we assess the pedagogical approaches used to teach GIS&T courses through an Internet‐based survey of 318 college and university faculty. Our findings demonstrate that active learning pedagogies are becoming more firmly established, supplementing or replacing traditional teaching approaches. Contrary to our assumptions, age and teaching experience are not factors that influence the adoption of active learning strategies. Along with assessing instructional approaches, our survey identifies the challenges associated with teaching GIS&T, as identified by survey respondents. 相似文献
14.
Identifying Environmental and Human Factors Associated With Tick Bites using Volunteered Reports and Frequent Pattern Mining 总被引：1，自引：0，他引：1 下载免费PDF全文
下载免费PDF全文 Irene Garcia‐Martí Raul Zurita‐Milla Arno Swart Kees C. van den Wijngaard Arnold J.H. van Vliet Sita Bennema Margriet Harms 《Transactions in GIS》2017,21(2):277-299
Tick populations and tick‐borne diseases like Lyme borreliosis have been steadily increasing since the mid‐1990s. Realizing the threat that ticks pose to public health, two Dutch citizen science projects have collected tick bite reports since 2006. This unique volunteered geographical dataset, which currently has nearly 35,000 reports, was used to identify environmental and other circumstantial factors associated with tick bites. For this, we first enriched the tick bite reports with temperature, precipitation, vegetation and volunteered data associated with the location of the tick bite. Using this enriched dataset, we then derived a series of features to characterize the environmental and volunteer‐related conditions in which each tick bite occurred. Next, we discretized these features using the Jenks Natural Breaks algorithm and, after that, we mined frequent environmental patterns associated with tick bites using the AprioriClose algorithm. Finally, we checked that these patterns are specifically associated with the tick bites by comparing them with the frequent patterns mined from pseudo‐random locations. The frequent patterns were visualized using heat maps and ring maps and two representative patterns associated with tick bites were projected into geographic space to study their spatio‐temporal distribution. Our results show that factors linked to human activity are more relevant to model tick bites than seasonal accumulations of temperature, vegetation or precipitation. In particular, the number of warm and dry days per season are present in a significant number of patterns and the majority of tick bites are produced within a distance of half a kilometer of a forest, recreational or built‐up area. The study of patterns in the time‐series revealed that there are several persistent patterns consistently occurring each year and the validation process showed that the volunteer tick bites collection is capturing environmental conditions associated with tick bites, suggesting that these reports have a high scientific value. These results support the creation of a Dutch tick bite risk map that, in turn, will open the door to the design of public health interventions to reduce the incidence of Lyme disease. 相似文献
15.
Min Deng Wentao Yang Qiliang Liu Rui Jin Feng Xu Yunfei Zhang 《Transactions in GIS》2018,22(1):183-201
Space–time series prediction plays a key role in the domain of geographic data mining and knowledge discovery. In general, the existing methods of space–time series prediction can be divided into two main categories: statistical machine learning methods. Comparatively, machine leaning methods have obvious advantages with respect to handling nonlinear problems. However, space–time dependence and the heterogeneity of space–time data are not well addressed by the existing machine learning methods. Because of this limitation, an accurate prediction of a space–time series is still a challenging problem. Therefore, in this study, both space–time dependence and heterogeneity are incorporated into the feedback artificial neural network, and heterogeneous space–time artificial neural networks (HSTANNs) are developed for space–time series prediction. First, to handle spatial heterogeneity, space–time series clustering is used to divide the study area into a set of homogeneous sub‐areas. Then, a space–time autocorrelation analysis is employed to explore the space–time dependence structure of the dataset. Finally, a HSTANN is established for each sub‐area. Further, HSTANNs are applied to predict the concentrations of fine particulate matter (PM2.5) in Beijing–Tianjin–Hebei. The experimental results show that when compared with other methods, the accuracy of the forecasting results is considerably improved by using HSTANNs. 相似文献
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A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales 下载免费PDF全文
下载免费PDF全文 The availability of geospatial data has increased significantly over recent decades. As a result, the question of how to update spatial data across different scales has become an attractive topic. One promising strategy is to use an updated larger‐scale dataset as a reference for detecting and updating changed objects represented in a to‐be‐updated smaller‐scale dataset. For such an update method, an understanding of the different types of changes that can occur is crucial. Using polygonal building data as an example, this study examines the various possible changes from different perspectives, such as the reasons for their occurrence, the forms in which they manifest, and their effects on output. Then, we apply map algebra theory to establish a cartographic model for updating polygonal building data. Supported by concepts of map algebra, an update procedure involving change detection, filtering, and fusion is implemented through a series of set operations. In addition to traditional polygon overlay functions, the constrained Delaunay triangulation model and knowledge of map generalization procedures are employed to construct set operations. The proposed method has been validated through tests using real‐world data. The experimental results show that our method is effective for updating 1:10k map data using 1:2k map data. 相似文献
18.
Much is done nowadays to provide cyclists with safe and sustainable road infrastructure. Its development requires the investigation of road usage and interactions between traffic commuters. This article is focused on exploiting crowdsourced user‐generated data, namely GPS trajectories collected by cyclists and road network infrastructure generated by citizens, to extract and analyze spatial patterns and road‐type use of cyclists in urban environments. Since user‐generated data shows data‐deficiencies, we introduce tailored spatial data‐handling processes for which several algorithms are developed and implemented. These include data filtering and segmentation, map‐matching and spatial arrangement of GPS trajectories with the road network. A spatial analysis and a characterization of road‐type use are then carried out to investigate and identify specific spatial patterns of cycle routes. The proposed analysis was applied to the cities of Amsterdam (The Netherlands) and Osnabrück (Germany), proving its feasibility and reliability in mining road‐type use and extracting pattern information and preferences. This information can help users who wish to explore friendlier and more interesting cycle patterns, based on collective usage, as well as city planners and transportation experts wishing to pinpoint areas most in need of further development and planning. 相似文献
19.
Tiago M Delboni Karla A V Borges Alberto H F Laender Clodoveu A Davis Jr 《Transactions in GIS》2007,11(3):377-397
The need for better Web search tools is getting increasing attention nowadays. About 20% of the queries currently submitted to search engines include geographic references. Thus, it is particularly important to work with the semantics of such queries, both by understanding the terminology and by recognizing geographic references in natural language text. In this paper, we explore the use of natural language expressions, which we call positioning expressions, to perform geographic searches on the Web, without resorting to geocoded data or gazetteers. Such positioning expressions denote the location of a subject of interest with respect to a landmark. Our approach leads to a query expansion technique that can be explored by virtually any keyword‐based search engine. Results obtained in our experiments show an expressive improvement over the traditional keyword‐based search and a potential path for tackling many kinds of common geographic queries. 相似文献
20.
Mapping Large Spatial Flow Data with Hierarchical Clustering 总被引：6，自引：0，他引：6
It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high‐level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi‐resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area. 相似文献