共查询到20条相似文献，搜索用时 239 毫秒
1.
Pengfei Chen Xiaolin Zhou Zhewei Liu Xuandi Fu 《International journal of geographical information science》2013,27(12):2337-2362
ABSTRACTAn increasing number of social media users are becoming used to disseminate activities through geotagged posts. The massive available geotagged posts enable collections of users’ footprints over time and offer effective opportunities for mobility prediction. Using geotagged posts for spatio-temporal prediction of future location, however, is challenging. Previous studies either focus on next-place prediction or rely on dense data sources such as GPS data. Introduced in this article is a novel method for future location prediction of individuals based on geotagged social media data. This method employs the hierarchical density-based clustering algorithm with adaptive parameter selection to identify the regions frequently visited by a social media user. A multi-feature weighted Bayesian model is then developed to forecast users’ spatio-temporal locations by combining multiple factors affecting human mobility patterns. Further, an updating strategy is designed to efficiently adjust, over time, the proposed model to the dynamics in users’ mobility patterns. Based on two real-life datasets, the proposed approach outperforms a state-of-the-art method in prediction accuracy by up to 5.34% and 3.30%. Tests show prediction reliability is high with quality predictions, but low in the identification of erroneous locations. 相似文献
2.
Caglar Koylu 《International journal of geographical information science》2019,33(4):805-832
Interpersonal communication on online social networks has a significant impact on the society by not only diffusing information, but also forming social ties, norms, and behaviors. Knowing how the conversational discourse semantically and geographically vary over time can help uncover the changing dynamics of interpersonal ties and the digital traces of social events. This article introduces a framework for modeling and visualizing the semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. The framework consists of (1) a topic modeling workflow for modeling topics and extracting the evolution of conversational discourse; (2) a geo-social network modeling and smoothing approach to projecting connection characteristics and semantics of communication onto geographic space and time; (3) a web-based geovisual analytics environment for exploring semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. To demonstrate, geo-located and reciprocal user mention and reply tweets over the course of the 2016 primary and presidential elections in the United States from 1 August 2015 to 15 November 2016 were analyzed. The large portion of the topics extracted from mention tweets were related to daily life routines, human activities, and interests such as school, work, sports, dating, wearing, birthday celebration, music, food, and live-tweeting. Specific focus on the analysis of political conversations revealed that the content of conversational discourse was split between civil rights and election-related discussions of the political campaigns and candidates. These political topics exhibited major shifts in terms of content and the popularity in reaction to primaries, debates, and events throughout the study period. While civil rights discussions were more dominant and in higher intensity across the nation and throughout the whole time period, election-specific conversations resulted in temporally varying local hotspots that correlated with locations of primaries and events. 相似文献
3.
基于社交媒体签到数据的城市居民暴雨洪涝响应时空分析 总被引：3，自引：2，他引：1
暴雨洪涝等小型地域性气候灾害给城市韧性带来挑战。以南京暴雨洪涝为例,通过挖掘新浪微博签到数据,构建公众感知指数和公众情绪指数,分析居民对暴雨洪涝响应的时空格局。在时间维度上,居民对暴雨洪涝的响应主要集中在暴雨洪涝期,并随灾害的严重程度而变化;在暴雨洪涝期内,居民在社交媒体上对暴雨洪涝的响应集中在早、晚高峰。在空间维度上,居民对暴雨洪涝的响应集中在主城区和3个新市区;重要交通基础枢纽地区和低海拔、经历快速城市化的新市区的居民对暴雨洪涝担忧程度更高。时空分析表明,暴雨洪涝对居民的交通出行影响最明显。基于时空间分析,最后从硬件和软件设施上为提升暴雨洪涝的城市韧性提供相关政策建议。 相似文献
4.
Yibin Ren Huanfa Chen Tao Cheng Yang Zhang Ge Chen 《International journal of geographical information science》2020,34(4):802-823
ABSTRACTThe spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns. 相似文献
5.
The concept of social capital has been used by numerous authors to investigate various topics. As yet, however, little attention has been paid to its relationship with mobility and social exclusion. Those findings which have been published suggest that the maintenance of social capital and associated networks within and between communities largely depends on mobility, but that local social networks are being undermined as a result of growing car ownership and use. This paper draws on the results of recent rural transport research to suggest that, at the same time, strong local social capital appears important in conferring mobility on certain social groups, especially those without access to a car. In the context of community transport, our analysis uses a geographic framework to attempt to explore these positions and reviews arising policy and research implications. 相似文献
6.
Qunying Huang 《International journal of geographical information science》2017,31(3):523-541
Social media applications are widely deployed in mobile platforms equipped with built-in GPS tracking devices, and these devices have led to an unprecedented collection of geolocated data (geo-tags). Geo-tags, along with place names, offer new opportunities to explore the trajectory and mobility patterns of social media users. However, trajectory data captured by social media are sparsely and irregularly spaced and therefore have varying degrees of resolution in both space and time. Previous studies on next location prediction are mostly applicable for detecting the upcoming location of a moving object using dense GPS trajectories where locations are recorded at regular time intervals (e.g., 1 minute). Additionally, point features are commonly used to represent the locations of visits, but using point features cannot capture the variability of human mobility. This article introduces a new methodology to predict an individual’s next location based on sparse footprints accumulated over a long time period using social networks, and uses polygons to represent the location corresponding to the physical activity area of individuals. First, the density-based spatial clustering algorithm is employed to discover the most representative activity zones that an individual frequently visits on a daily basis, and a polygon-based region is then derived for each representative activity zone. A sparse mobility Markov chain model considering both the movements and online behaviors of the social media user is trained and used to predict the user’s next location. Initial experiments with a group of Washington DC Twitter users demonstrate that the proposed methodology successfully discovers the activity regions and predicts the user’s next location with accuracy approaching 78.94%. 相似文献
7.
地理时空三向聚类分析方法的构建与实践 总被引：1，自引：0，他引：1
随着地理数据获取能力的不断提升,地理数据体量呈指数增长,数据种类、数据性质更加多元化。对数据的有效甄别和归类成为理解地理现象时空特征、演化过程和行为机制的关键。传统聚类方法面临数据体量大、维数高、质量差的挑战,加之对地理空间与时间关联分析的需求,对聚类方法改进和提升研究的要求越来越迫切。本文介绍了从单向到三向聚类构建思路的变革。单向聚类是仅在样本或属性方向上进行聚类,易忽视非常相似的局部特征、易犯“横看成岭侧成峰”的错误。双向聚类是基于数据矩阵内元素值的相似性,形成一个子矩阵分割方案,使子矩阵内元素相似度尽可能高,子矩阵间元素相似度尽可能低,从而实现行列两方向的同时聚类,避免了单向聚类的不足。鉴于双向聚类难以满足地理研究超出双向的解译需求,本文提出并研发了一个全新的三向聚类方法,给出了运用该方法开展地理时空格局过程探测的流程,总结了如何根据研究涉及的“空间—时间—尺度—属性”构建三维数据体;最后,展示了三向聚类的地理实践案例。结果表明：① 三向聚类是一种大数据时代探测地理数据时空分异规律的有效方法,可以解决数据维度高、质量低等问题;② 面对不同的地理问题,三向聚类在算法层面上是通用的,不同之处仅在于：根据不同问题涉及的空间、时间、尺度、属性的不同,构建不同的数据体;不同数据体聚类得到的不同结果回答不同的地理问题;③ 三向聚类可以实现地理数据的时空分异规律多方向、多尺度、多层次的联合解译,揭示地理特征时空尺度叠加效应。最后,论文强调根据地理问题组织数据的重要性,期待未来能够提升三向聚类在多空间尺度、多属性方面的地理研究实践。 相似文献
8.
高被引华人科学家知识网络的空间结构及影响因素 总被引：1，自引：1，他引：0
知识网络的空间结构特征与影响因素是经济地理学探讨的热点议题之一,以往研究侧重于产业案例的分析,主要关注国家和城市层面的知识网络,而对科学家等个人层面的网络研究较为缺乏。以2014—2015年全球高被引科学家为原始数据,筛选出高被引华人科学家,并基于Web of Science数据库,检索高被引华人科学家之间合著论文的数据构建知识网络,借助社会网络分析方法对高被引华人科学家知识网络的空间结构进行分析;并运用负二项回归模型,从地理邻近性、社会邻近性、制度邻近性3个维度,探讨高被引华人科学家知识网络的影响机制。研究发现：① 高被引华人科学家知识网络存在核心-边缘结构特征,且具有小世界网络的网络特征;② 此知识网络呈现“小集聚大分散”的空间结构特征,地理邻近性作用明显;③ 高被引华人科学家知识网络形成过程中会受到科学家自身科研能力等因素的影响,地理距离和科学家之间的联系呈现负相关关系,地理邻近性的影响仍然存在,社会邻近性和制度邻近性均对知识网络的形成有促进作用。 相似文献
9.
Ick-Hoi Kim Chen-Chieh Feng Yi-Chen Wang Brian H. Spitzberg Ming-Hsiang Tsou 《The Professional geographer》2017,69(4):629-643
The 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea gave rise to chaos caused by psychological anxiety, and it has been assumed that people shared rumors about hospital lists through social media. Sharing rumors is a common form of public perception and risk communication among individuals during an outbreak. Social media analysis offers an important window into the spatiotemporal patterns of public perception and risk communication about disease outbreaks. Such processes of socially mediated risk communication are a process of meme diffusion. This article aims to investigate the role of social media meme diffusion and its spatiotemporal patterns in public perception and risk communication. To do so, we applied analytical methods including the daily number of tweets for metropolitan cities and geovisualization with the weighted mean centers. The spatiotemporal patterns shown by Twitter users' interests in specific places, triggered by real space events, demonstrate the spatial interactions among places in public perception and risk communication. Public perception and risk communication about places are relevant to both social networks and spatial proximity to where Twitter users live and are interpreted in reference to both Zipf's law and Tobler's law. 相似文献
10.
复杂网络视角下时空行为轨迹模式挖掘研究 总被引：3，自引：0，他引：3
针对时空行为轨迹大数据的序列性、时空交互性、多维度性等复杂特性,构建结合时间地理学与复杂网络的分析框架,建立时空行为路径与时空行为网络之间的转换关系,利用复杂网络社群发现算法对时空行为轨迹进行社群聚类、模式挖掘与可视化。基于北京郊区居民一周内活动出行GPS轨迹数据的案例分析发现：① 复杂网络分析方法可以有效挖掘具有相似行为的群体特征和识别出典型的行为模式。② 可以灵活处理多元异构与多维度的行为轨迹大数据以及满足不同叙事、不同空间相互作用、不同时序的应用需求。③ 北京郊区被调查居民的行为模式存在日间差异与空间分异。 相似文献
11.
广州市零售商业中心的居民消费时空行为及其机制 总被引：6，自引：0，他引：6
居民消费行为的空间选择及其影响因素是地理学的重点关注领域,已有研究对行为中时间选择、时空关系及内在机制的研究相对缺乏。为揭示居民消费时空行为形成的影响因素以及各因素内在相互作用机制,基于2016年广州城市居民消费行为调查数据,分析消费者到大型商业中心内消费的时空特征,构建结构方程模型,探讨消费时空行为影响因素和作用机制。研究表明：居民在商业中心的消费行为存在明显的时空差异。在影响路径方面,消费者社会经济属性通过影响消费偏好导致消费时空行为的差异,商业空间属性既可以直接影响消费时空行为,也可以通过影响消费偏好而间接对其施加影响,消费偏好既可以直接影响消费时空行为,又是消费者社会经济属性和商业空间属性影响消费时空行为的中间变量。在显著性因素方面,年龄、家庭结构、在广州居住时间、就业状况、家庭月收入等消费者社会经济属性变量会显著影响消费时空行为,各个商业空间属性变量都在不同程度上影响消费行为在时间和空间上的差异,仅有出行交通方式、出行花费时间2个消费偏好变量会显著影响消费者消费时空间行为。本文结论可以加深对商业中心内消费时空行为影响因素及其作用机制的理解,并为商业网点规划调整、商业中心转型升级提供建议。 相似文献
12.
According to the highway data and some socioeconomic data of 1990, 1994, 2000, 2005 and 2009 of county units in the Pearl River Delta, this paper measured urban integrated power of different counties in different years by factor analysis, and estimated each county’s potential in each year by means of expanded potential model. Based on that, the spatio-temporal association patterns and evolution of county potential were analyzed using spatio-temporal autocorrelation methods, and the validity of spatio-temporal association patterns was verified by comparing with spatial association patterns and cross-correlation function. The main results are shown as follows: (1) The global spatio-temporal association of county potential showed a positive effect during the study period. But this positive effect was not strong, and it had been slowly strengthened during 1994-2005 and decayed during 2005-2009. The local spatio-temporal association characteristics of most counties’ potential kept relatively stable and focused on a positive autocorrelation, however, there were obvious transformations in some counties among four types of local spatio-temporal association (i.e., HH, LL, HL and LH). (2) The distribution difference and its change of local spatio-temporal association types of county potential were obvious. Spatio-temporal HH type units were located in the central zone and Shenzhen-Dongguan region of the eastern zone, but the central spatio-temporal HH area shrunk to the Guangzhou-Foshan core metropolitan region only after 2000; the spatio-temporal LL area in the western zone kept relatively stable with a surface-shaped continuous distribution pattern, new LL type units emerged in the south-central zone since 2005, the eastern LL area expanded during 1994-2000, but then gradually shrunk and scattered at the eastern edge in 2009; the spatio-temporal HL and LH areas varied significantly. (3) The local spatio-temporal association patterns of county potential among the three zones presented significant disparity, and obvious difference between the eastern and central zones tended to decrease, whereas that between the western zone and the central and eastern zones further expanded. (4) Spatio-temporal autocorrelation methods can efficiently mine the spatio-temporal association patterns of county potential, and can better reveal the complicated spatio-temporal interaction between counties than ESDA methods. 相似文献
13.
《The Journal of geography》2012,111(1):23-31
Abstract Atmospheric circulation and resultant surface pressure patterns are important discussion topics in many introductory geography, meteorology, and earth science courses. In the past, summary materials developed to aid in teaching these concepts have used a north-south oriented meridional cross-section of the troposphere (e.g., the three-cell model). We suggest that while the Hadley Cell model works well for the tropics, an alternative depiction incorporating two map views should be used to present a more meaningful view of the mean extratrop-ical circulation. The combination of a tropical cross-section map and an extra-tropical map will assist students in learning about first-order motions associated with the Earth's atmospheric circulation. 相似文献
14.
Yibin Ren Yang Zhang 《International journal of geographical information science》2019,33(9):1894-1912
Recently, researchers have introduced deep learning methods such as convolutional neural networks (CNN) to model spatio-temporal data and achieved better results than those with conventional methods. However, these CNN-based models employ a grid map to represent spatial data, which is unsuitable for road-network-based data. To address this problem, we propose a deep spatio-temporal residual neural network for road-network-based data modeling (DSTR-RNet). The proposed model constructs locally-connected neural network layers (LCNR) to model road network topology and integrates residual learning to model the spatio-temporal dependency. We test the DSTR-RNet by predicting the traffic flow of Didi cab service, in an 8-km2 region with 2,616 road segments in Chengdu, China. The results demonstrate that the DSTR-RNet maintains the spatial precision and topology of the road network as well as improves the prediction accuracy. We discuss the prediction errors and compare the prediction results to those of grid-based CNN models. We also explore the sensitivity of the model to its parameters; this will aid the application of this model to network-based data modeling. 相似文献
15.
以“Web of ScienceTM核心合集”和CNKI核心文献库为数据源,运用CiteSpace软件进行文献计量分析,从发文时间、地区分布、学科分布、研究机构、关键词共现与高被引文献等方面,总结比较了2000年以来中外犯罪地理研究进展,并展望了未来的研究趋势。结果发现：1）国内外犯罪地理发文量整体呈现持续增长态势,美国发文量首位度明显。学科分布国外较为广泛,国内相对集中,且存在较大发展空间。研究机构之间的合作网络国外较强,国内机构联系较弱,后续研究力量正处于培育发展阶段。2）不同时期国内外研究关注的热点不同：国外侧重于暴力犯罪、恐怖主义犯罪、因种族歧视和性别歧视等引发的多类型犯罪研究,从微观到宏观,涉及地区、国家甚至全球层面;国内聚焦于城市社区“两抢一盗”犯罪、省域拐卖儿童犯罪和毒品犯罪等类型,微观和宏观并举,实证案例研究逐渐增多。3）随着多学科的交叉融合发展,国内外犯罪地理发展势头良好。犯罪分布模式、空间防控对策与犯罪风险模拟仍是当下较为活跃的研究议题,“3S”技术开发和大数据应用将成为犯罪地理研究的两条并行趋势线。未来需要以综合性思维审视犯罪地理环境,持续关注犯罪地理研究的潜在领域。同时,信息技术发展与计量模型应用为犯罪地理带来新契机,必须立足于当下国际社会环境,加强个人、组织和团体机构之间的研究合作,交流和分享经验成果,探索多样化的犯罪防控模式,并采取全球合作的方式应对区域所面临的犯罪挑战。 相似文献
16.
17.
Enrico Steiger Bernd Resch Alexander Zipf 《International journal of geographical information science》2016,30(9):1694-1716
ABSTRACTThe investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data. 相似文献
18.
Di Zhu Ximeng Cheng Fan Zhang Xin Yao Yong Gao 《International journal of geographical information science》2020,34(4):735-758
ABSTRACTSpatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks (CGANs), provide us with a perspective for formalizing spatial interpolation as a conditional generative task. In this article, we design a novel deep learning architecture named conditional encoder-decoder generative adversarial neural networks (CEDGANs) for spatial interpolation, therein combining the encoder-decoder structure with adversarial learning to capture deep representations of sampled spatial data and their interactions with local structural patterns. A case study on elevations in China demonstrates the ability of our model to achieve outstanding interpolation results compared to benchmark methods. Further experiments uncover the learned spatial knowledge in the model’s hidden layers and test the potential to generalize our adversarial interpolation idea across domains. This work is an endeavor to investigate deep spatial knowledge using artificial intelligence. The proposed model can benefit practical scenarios and enlighten future research in various geographical applications related to spatial prediction. 相似文献
19.
基于T-GIS的广州市居民日常活动时空关系 总被引：18，自引：3，他引：15
随着人文主义思潮的兴起和居民生活水平的提高,关于生活质量的改善问题日益受到重视,以研究个体活动时空关系为重点的时间地理学研究也得到发展。T-GIS 能较好地反映和记录个体活动动态过程,为时间地理学的研究提供了新的技术手段。近年来在工业化、城市化、信息化、住房与就业市场化等多重因素的共同作用下,中国城市的内部空间结构发生了急剧的演化,城市居民的日常活动也发生变化,居民微观行为和日常活动组织及社会空间的研究正成为解释城市空间重构及其机制的重要研究视角。本研究结合T-GIS 和时间地理学基本理论,以广州市为案例,利用居民出行日志的问卷调查数据,开发基于ArcGIS 的居民行为链时空分析工具,揭示了典型时间断面居民的空间分布特征和居民日常活动社会分异的的时空关系。结果表明,居民出行行为具有很强的时空关联性。城市中心区在一天不同时间都保持较强的吸引力,成为居民日常活动中各类活动的主要空间载体;城市空间的拓展,改变了部分居民特别是居住在外围街区居民的日常生活习惯;居民的日常活动时空关系存在一定的阶层分化,低阶层日常总体上离开居住地活动的时间最长,但日常活动的活动空间最小,人均月交通费用最低,主要活动空间位于城市中心区和部分传统单位生活区周边;高阶层日常总体上离开居住地活动的时间最短,其活动范围却最大,主要活动空间位于新城市中心区及其周边地区,人均月交通费用最高;中阶层的活动空间相对均衡,交通费用适中。这种时空关联性的分析,有助于揭示居民的日常活动与城市内部空间结构的关系,拓展基于日常活动过程的城市社会空间研究及交通需求评估,为城市规划和管理提供可靠的依据。 相似文献
20.
《Geografisk tidskrift / udgivet af Bestyrelsen for Det Kongelige danske geografiske selskab》2012,112(2):146-162
ABSTRACTOnline travel searches are important forms of travel virtual spaces. Previous studies have neglected to analyze the spatial features of the travel searches themselves, and the spatial heterogeneity of their influencing factors. In this study, a travel search index based on the Baidu index was established for analyzing travel searches. Meanwhile, a local spatial model was created for the linear features in order to discuss the spatiotemporal heterogeneity of the influencing factors. The results of this study indicated that travel searches have obvious spatial inequality, and economically developed regions had displayed advantages in the travel search network. The fitting results of the local model were found to be superior to global model. The number of attractions and the GDP of the origin were found to have promoting effects on the travel searches, whereas distances had shown inhibiting effects. These effects presented significant spatiotemporal heterogeneity. It was also found that within the travel search virtual space, the distance effects still existed, but the intensity was weaker than in the real space. The local spatial model for the linear features provided a new spatial analysis method for understanding the travel search network, as well as other types of networks (flow patterns). 相似文献