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1.
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
2.
Xiaojing Wu Raul Zurita-Milla Menno-Jan Kraak 《International journal of geographical information science》2013,27(4):624-642
Clustering allows considering groups of similar data elements at a higher level of abstraction. This facilitates the extraction of patterns and useful information from large amounts of spatio-temporal data. Till now, most studies have focused on the extraction of patterns from a spatial or a temporal aspect. Here we use the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) to enable the simultaneous analysis of spatial and temporal patterns in geo-referenced time series (time evolving values of a property observed at fixed geographical locations). In addition, we present three geovisualization techniques to fully explore the co-clustering results: heatmaps offer a straightforward overview of the results; small multiples display the spatial and temporal patterns in geographic maps; ringmaps illustrate the temporal patterns associated to cyclic timestamps. To illustrate this study, we used Dutch daily average temperature data collected at 28 weather stations from 1992 to 2011. The co-clustering algorithm was applied hierarchically to understand the spatio-temporal patterns found in the data at the yearly, monthly and daily resolutions. Results pointed out that there is a transition in temperature patterns from northeast to southwest and from ‘cold’ to ‘hot’ years/months/days with only 3 years belonging to ‘cool’ or ‘cold’ years. Because of its characteristics, this newly introduced algorithm can concurrently analyse spatial and temporal patterns by identifying location-timestamp co-clusters that contain values that are similar along both the spatial and the temporal dimensions. 相似文献
3.
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
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Karl Rehrl Simon Gröchenig Stefan Kranzinger 《International journal of geographical information science》2020,34(10):1953-1979
ABSTRACT Trajectory data mining is a lively research field in the domain of spatio-temporal data mining. Trajectory pattern mining comprises a set of specific pattern mining methods, which are applied as consecutive steps on a trajectory with the goal to extract and classify re-occurring spatio-temporal patterns. Despite the common nature and frequent usage of such methods by the GIScience community, a methodological approach is missing so far, especially when it comes to the use of machine learning-based classification methods. The current work closes this gap by proposing and evaluating a machine learning-based 3-steps trajectory data mining methodology using the detection and classification of stop points in vehicle trajectories as example. The work describes in detail the applied methodologies with respect to the three mining steps ‘stop detection’, ‘feature extraction’ and ‘classification in traffic-relevant and non-traffic-relevant stops’ and evaluates six machine learning-based classification algorithms using a real-world dataset of 15,498 vehicle trajectories with 5,899 detected stops (thereof 2,032 manually classified). Due to its exemplary nature, the presented methodology is suited to act as blueprint for similar trajectory data mining problems. 相似文献
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Shuhui Gong John Cartlidge Ruibin Bai Yang Yue Qingquan Li Guoping Qiu 《International journal of geographical information science》2020,34(6):1210-1234
ABSTRACTGlobal positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips. 相似文献
6.
Discovery of local topics by using latent spatio-temporal relationships in geo-social media 总被引：1，自引：0，他引：1
Kyoung-Sook Kim Isao Kojima Hirotaka Ogawa 《International journal of geographical information science》2016,30(9):1899-1922
ABSTRACTSocial networks have played a crucial role as information channels for people to understanding their daily lives beyond merely being communication tools. In particular, coupling social networks with geographic location has boosted the worth of social media to not only enable comprehension of the effects of natural phenomena such as global warming and disasters, but also the social patterns of human societies. However, the high rate of social data generation and the large amounts of noisy data makes it difficult to directly apply social media to decision-making processes. This article proposes a new system of analyzing the spatio-temporal patterns of social phenomena in real time and the discovery of local topics based on their latent spatio-temporal relationships. We will first describe a model that represents the local patterns of populations of geo-tagged social media. We will then define a local topic whose keywords share a region in space and time and present a system implementation based on existing open source technologies. We evaluated the model of local topics with several ways of visualization in experiments and demonstrated a certain social pattern from a dataset of daily Twitter streams. The results obtained from experiments revealed certain keywords had a strong spatio-temporal proximity even though they did not occur in the same message. 相似文献
7.
Abstract According to recent research, one of the most promising strategies for intraurban job growth lies promoting localized clusters that produce goods and services which are primarily sold within a single city, metropolitan area, or urban region. However, in order to design urban policies to create or reinforce local clusters, the first challenge is to measure in a reliable way the clustering tendencies of different kinds of economic units in intraurban space. The aim is to compare the similarities and differences in results obtained from two methods designed to measure global clustering tendencies (the planar and network K-functions) in terms of characterization, scale, and intensity of intraurban localization patterns for tertiary economic units in a Latin American metropolis. It is concluded that the network K-function is a more appropriate method for measuring agglomeration patterns, scale, and intensity at the intra-urban level. 相似文献
8.
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
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Tong Zhang Yicong Li Hui Yang Chenrong Cui Jing Li Qinghua Qiao 《International journal of geographical information science》2020,34(6):1137-1161
ABSTRACTEffective public transit planning needs to address realistic travel demands, which can be illustrated by corridors across major residential areas and activity centers. It is vital to identify public transit corridors that contain the most significant transit travel demand patterns. We propose a two-stage approach to discover primary public transit corridors at high spatio-temporal resolutions using massive real-world smart card and bus trajectory data, which manifest rich transit demand patterns over space and time. The first stage was to reconstruct chained trips for individual passengers using multi-source massive public transit data. In the second stage, a shared-flow clustering algorithm was developed to identify public transit corridors based on reconstructed individual transit trips. The proposed approach was evaluated using transit data collected in Shenzhen, China. Experimental results demonstrated that the proposed approach is a practical tool for extracting time-varying corridors for many potential applications, such as transit planning and management. 相似文献
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Julian Hagenauer Marco Helbich 《International journal of geographical information science》2013,27(2):251-266
This study aims to introduce contextual Neural Gas (CNG), a variant of the Neural Gas algorithm, which explicitly accounts for spatial dependencies within spatial data. The main idea of the CNG is to map spatially close observations to neurons, which are close with respect to their rank distance. Thus, spatial dependency is incorporated independently from the attribute values of the data. To discuss and compare the performance of the CNG and GeoSOM, this study draws from a series of experiments, which are based on two artificial and one real-world dataset. The experimental results of the artificial datasets show that the CNG produces more homogenous clusters, a better ratio of positional accuracy, and a lower quantization error than the GeoSOM. The results of the real-world dataset illustrate that the resulting patterns of the CNG are theoretically more sound and coherent than that of the GeoSOM, which emphasizes its applicability for geographic analysis tasks. 相似文献
12.
ABSTRACTThis study uses a novel spatial approach to compare population density change across cities and over time. It examines spatio-temporal change in Australia’s five most populated capital cities from 1981 to 2011, and documents the established and emerging patterns of population distribution. The settlement patterns of Australian cities have changed substantially in the last 30 years. From the doughnut cities of the 1980s, programs of consolidation, renewal and densification have changed and concentrated population in our cities. Australian cities in the 1980s were characterised by sparsely populated, low density centres with growth concentrated to the suburban fringes. ‘Smart Growth’ and the ‘New Urbanism’ movements in the 1990s advocated higher dwelling density living and the inner cities re-emerged, inner areas were redeveloped, and the population distribution shifted towards increased inner city population densities. Policies aimed at re-populating the inner city dominated and the resultant changes are now visible in Australia’s five most populated capital cities. While this pattern has been reported in a number of studies, questions remain regarding the extent of these changes and how to analyse and visualise them across urban space. This paper reports on a spatial method which addresses the limitations of changing statistical boundaries to identify the changing patterns in Australian cities over time and space. 相似文献
13.
基于产业空间聚集分布情况探寻城市结构特征,是当前大都市区实证研究中的聚焦点所在,但由于方法论的限制而无法真正揭示产业地理集聚之间的内在关联性。基于已有研究基础,试图通过完善潜力模型、设置距离参数、结合主成分分析法实现对产业地理集聚测度方法论的完善与发展,并选取极具代表性大都市区核心城市——沈阳市为样本单元,以2008年的经济普查部门企业数据开展实证检验。结果表明：沈阳市部门企业之间除了交通运输、仓储和邮政中心产业属于地方化经济外,其余的均为企业关联;水利、环境和公共设施管理业产业依附于制造业呈临街抑或隔街集聚,而与公共管理和组织产业之间同街道集聚;支配主角之间,存在中心CBD主宰制造业的布局,而制造业又在很大程度上影响着交通运输、仓储和邮政中心的布局;企业地理集聚形成的城市结构依然是一个明显的“单中心圈层”结构,没有表现出“去中心化”抑或多极化或分散化演变趋势。研究成果与现实情况基本吻合,侧面说明该模式对揭示城市产业地理集聚模式以及由此形成的城市结构特征具有一定的解释力。 相似文献
14.
Qiliang Liu Zhihui Wu Wenkai Liu Yaolin Liu 《International journal of geographical information science》2020,34(10):1903-1929
ABSTRACT Urban black holes and volcanoes are typical traffic anomalies that are useful for optimizing urban planning and maintaining public safety. It is still challenging to detect arbitrarily shaped urban black holes and volcanoes considering the network constraints with less prior knowledge. This study models urban black holes and volcanoes as bivariate spatial clusters and develops a network-constrained bivariate clustering method for detecting statistically significant urban black holes and volcanoes with irregular shapes. First, an edge-expansion strategy is proposed to construct the network-constrained neighborhoods without the time-consuming calculation of the network distance between each pair of objects. Then, a network-constrained spatial scan statistic is constructed to detect urban black holes and volcanoes, and a multidirectional optimization method is developed to identify arbitrarily shaped urban black holes and volcanoes. Finally, the statistical significance of multiscale urban black holes and volcanoes is evaluated using Monte Carlo simulation. The proposed method is compared with three state-of-the-art methods using both simulated data and Beijing taxicab spatial trajectory data. The comparison shows that the proposed method can detect urban black holes and volcanoes more accurately and completely and is useful for detecting spatiotemporal variations of traffic anomalies. 相似文献
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Traditional spatial clustering methods have the disadvantage of “hardware division”, and can not describe the physical characteristics of spatial entity effectively. In view of the above, this paper sets forth a general multi-dimensional cloud model, which describes the characteristics of spatial objects more reasonably according to the idea of non-homogeneous and non-symmetry. Based on infrastructures’ classification and demarcation in Zhanjiang, a detailed interpretation of clustering results is made from the spatial distribution of membership degree of clustering, the comparative study of Fuzzy C-means and a coupled analysis of residential land prices. General multi-dimensional cloud model reflects the integrated characteristics of spatial objects better, reveals the spatial distribution of potential information, and realizes spatial division more accurately in complex circumstances. However, due to the complexity of spatial interactions between geographical entities, the generation of cloud model is a specific and challenging task. 相似文献
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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
19.
Traditional spatial clustering methods have the disadvantage of “hardware division“, and can not describe the physical characteristics
of spatial entity effectively. In view of the above, this paper sets forth a general multi-dimensional cloud model, which
describes the characteristics of spatial objects more reasonably according to the idea of non-homogeneous and non-symmetry.
Based on infrastructures’ classification and demarcation in Zhanjiang, a detailed interpretation of clustering results is
made from the spatial distribution of membership degree of clustering, the comparative study of Fuzzy C-means and a coupled
analysis of residential land prices. General multi-dimensional cloud model reflects the integrated characteristics of spatial
objects better, reveals the spatial distribution of potential information, and realizes spatial division more accurately in
complex circumstances. However, due to the complexity of spatial interactions between geographical entities, the generation
of cloud model is a specific and challenging task. 相似文献
20.
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