首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 93 毫秒
1.
Borrowing methods from epidemiology, studies of spatiotemporal regularities of crime have been booming in various industrialized countries. However, few such attempts are empirical studies using crime data in developing countries due to a lack of data availability. Utilizing a recent burglary dataset in Wuhan, the fourth largest city in China, current research applied the sequential kernel density estimation and the space–time K-function methods to analyze the spatiotemporal changes of hotspots of residential burglaries. The results show that, both spatial and spatiotemporal clustering exists. The hotspots were relatively stable over time. The space–time clustering, however, shows significant concentrations both in space and over time. In addition, analytic results show significant effects of distance decay in terms of occurrences of burglary incidents along the spatial and temporal dimensions. Moreover, findings from the research provide critical information on the space–time rhythm of crime, and therefore can be utilized in crime prevention practice. Finally, the implications of the findings and limitations are discussed.  相似文献   

2.
Spatial sciences are confronted with increasing amounts of high-dimensional data. These data commonly exhibit spatial and temporal dimensions. To explore, extract, and generalize inherent patterns in large spatiotemporal data sets, clustering algorithms are indispensable. These clustering algorithms must account for the distinct special properties of space and time to outline meaningful clusters in such data sets. Therefore, this research develops a hierarchical method based on self-organizing maps. The hierarchical architecture permits independent modeling of spatial and temporal dependence. To exemplify the utility of the method, this research uses an artificial data set and a socio-economic data set of the Ostregion, Austria, from the years 1961 to 2001. The results for the artificial data set demonstrate that the proposed method produces meaningful clusters that cannot be achieved when disregarding differences in spatial and temporal dependence. The results for the socio-economic data set show that the proposed method is an effective and powerful tool for analyzing spatiotemporal patterns in a regional context.  相似文献   

3.
Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US using Twitter data. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data.  相似文献   

4.
This article reports on the results from a spatiotemporal analysis of disaggregate fire incident data. The innovative analysis presented here focuses on the exploration of spatial and temporal patterns for four principal fire incident categories: property, vehicle, secondary fires, and malicious false alarms. This research extends previous work on spatial exploration of spatiotemporal patterns by demonstrating the benefits of comaps and kernel density estimation in examining temporal and spatiotemporal dynamics in calls for services. Results indicate that fire incidents are not static in either time or space and that spatiotemporal variation is related to incident type. The application of these techniques has the potential to inform policy decisions both from a reactive, resource‐allocation perspective and from a more proactive perspective, such as through spatial targeting of preventive measures.  相似文献   

5.
Dasymetric Spatiotemporal Interpolation   总被引：2，自引：0，他引：2  
This research applies the principles of dasymetric mapping to spatiotemporal interpolation by extending the spatial concepts of zone and area to their temporal analogs of interval and duration, respectively. An example application of dasymetric spatiotemporal interpolation using crime event data is presented. Results indicate that dasymetric spatiotemporal interpolation significantly improves the accuracy of estimates over areal or duration weighting. In addition, even when dasymetric interpolation in either the spatial or temporal dimension is relatively weak, combining dasymetric estimation in both space and time dimensions simultaneously has the potential to amplify the accuracy of the overall dasymetric estimation.  相似文献   

6.
Managing geophysical data generated by emerging spatiotemporal data sources (e.g. geosensor networks) presents a growing challenge to Geographic Information System science. The presence of correlation poses difficulties with respect to traditional spatial data analysis. This paper describes a novel spatiotemporal analytical scheme that allows us to yield a characterization of correlation in geophysical data along the spatial and temporal dimensions. We resort to a multivariate statistical model, namely CoKriging, in order to derive accurate spatiotemporal interpolation models. These predict unknown data by utilizing not only their own geosensor values at the same time, but also information from near past data. We use a window-based computation methodology that leverages the power of temporal correlation in a spatial modeling phase. This is done by also fitting the computed interpolation model to data which may change over time. In an assessment, using various geophysical data sets, we show that the presented algorithm is often able to deal with both spatial and temporal correlations. This helps to gain accuracy during the interpolation phase, compared to spatial and spatiotemporal competitors. Specifically, we evaluate the efficacy of the interpolation phase by using established machine-learning metrics (i.e. root mean squared error, Akaike information criterion and computation time).  相似文献   

7.
ABSTRACT

Kernel Density Estimation (KDE) is an important approach to analyse spatial distribution of point features and linear features over 2-D planar space. Some network-based KDE methods have been developed in recent years, which focus on estimating density distribution of point events over 1-D network space. However, the existing KDE methods are not appropriate for analysing the distribution characteristics of certain kind of features or events, such as traffic jams, queue at intersections and taxi carrying passenger events. These events occur and distribute in 1-D road network space, and present a continuous linear distribution along network. This paper presents a novel Network Kernel Density Estimation method for Linear features (NKDE-L) to analyse the space–time distribution characteristics of linear features over 1-D network space. We first analyse the density distribution of each linear feature along networks, then estimate the density distribution for the whole network space in terms of the network distance and network topology. In the case study, we apply the NKDE-L to analyse the space–time dynamics of taxis’ pick-up events, with real road network and taxi trace data in Wuhan. Taxis’ pick-up events are defined and extracted as linear events (LE) in this paper. We first conduct a space–time statistics of pick-up LE in different temporal granularities. Then we analyse the space–time density distribution of the pick-up events in the road network using the NKDE-L, and uncover some dynamic patterns of people’s activities and traffic condition. In addition, we compare the NKDE-L with quadrat method and planar KDE. The comparison results prove the advantages of the NKDE-L in analysing spatial distribution patterns of linear features in network space.  相似文献   

8.
地理学时空数据分析方法   总被引：13，自引：4，他引：9  
随着地理空间观测数据的多年积累,地球环境、社会和健康数据监测能力的增强,地理信息系统和计算机网络的发展,时空数据集大量生成,时空数据分析实践呈现快速增长。本文对此进行了分析和归纳,总结了时空数据分析的7类主要方法,包括：时空数据可视化,目的是通过视觉启发假设和选择分析模型;空间统计指标的时序分析,反映空间格局随时间变化;时空变化指标,体现时空变化的综合统计量;时空格局和异常探测,揭示时空过程的不变和变化部分;时空插值,以获得未抽样点的数值;时空回归,建立因变量和解释变量之间的统计关系;时空过程建模,建立时空过程的机理数学模型;时空演化树,利用空间数据重建时空演化路径。通过简述这些方法的基本原理、输入输出、适用条件以及软件实现,为时空数据分析提供工具和方法手段。  相似文献   

9.
This article aims to develop a new type of temporal pattern analysis—temporal point pattern analysis (TPPA)—by treating the distribution of activities as a point pattern on a two-dimensional plane using the start time and end time of activities as axes. Geographic information systems (GIS) methods, which are originally used in spatial point pattern analysis in GIS, are introduced to support TPPA. This article presents a case study to understand the temporal patterns of the library visiting activities of university students using a four-week smart card data set in Chengdu City, China. Several methods from GIS are applied, including the measurement of mean centers, kernel density, nearest neighbor distances, and optimized hot spot analysis. Results show that the GIS methods can reveal a lot of information on the temporal pattern of activities, thereby proving the reasonability of the proposed TPPA of activities. Key Words: GIS, human activities, library visiting, smart card data, visualization.  相似文献   

10.
Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data.  相似文献   

11.
广州市多类型商业中心识别与空间模式   总被引：3，自引：5，他引：3  
不同职能类型商业中心识别对研究城市商业空间结构有重要意义。与传统识别方法相比,大数据的分析更为精确和便捷。本文以广州市核心区59125条城市热点(POI)数据为基础,利用核密度分析、统计分析、最邻近距离分析等方法识别广州市多类型商业中心的边界,探索其商业空间结构与模式。结果表明：①广州市商业结构呈现明显双核集聚式分布,传统的越秀分区与现代的天河分区构成当前广州市商业空间的双中心;②不同类型的商业中心在空间上呈现显著分异,其中城市生活与公共服务中心在越秀区,商务与金融中心在天河区,休闲娱乐中心呈现分散集聚式特征;③广州市商业结构的空间模式是“圈层+组团”式分布,其中,生活、公共服务、商务职能集中分布于内圈层,娱乐休闲职能呈组团状镶嵌于各圈层中。  相似文献   

12.
时间地理核密度估计是经典核密度估计(KDE)基于时间地理的一种扩展,主要是将标准核函数的定义域扩展至时间地理的时空可达域,以通过增强定义域在时空方面的物理意义来避免非零密度被分配到可达域之外的问题。时空可达域包括时空碟和由时空碟复合而成的潜在路径区域(PPA)。这2类可达域用作核函数的定义域能解决上述问题,但也带来了新的问题。基于时空碟构建的核函数能叠加成PPA上的概率密度,但敏感于时空碟的时间点。而基于PPA构建的核函数相较于理想布朗桥模型缺乏双峰特性,且也不能生成时空碟上的概率密度函数。因此,时间地理与KDE相结合的研究还处于应用前的理论探索阶段,论文的目标就是对这一进程进行梳理并引出未来的发展趋势。论文围绕时空轨迹不确定性量化这一目标,首先回顾了时间地理与核密度估计的不同功用,然后对两者相融合的意义、框架和模式进行了阐述。最后,认为时间地理的可达域代替核密度估计的定义域是改进时空轨迹不确定性测度的重要手段,但距离目标的落地还有一定的距离。  相似文献   

13.
Spatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches.  相似文献   

14.
In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic–temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process.  相似文献   

15.
Road density (i.e., km/km2) is a useful broad index of the road network in a landscape and has been linked to several ecological effects of roads. However, previous studies have shown that road density, estimated by grid computing, has weak correlation with landscape fragmentation. In this article, we propose a new measure of road density, namely, kernel density estimation function (KDE) and quantify the relation between road density and landscape fragmentation. The results show that road density estimated by KDE (km/km2) elucidates the spatial pattern of the road network in the region. Areas with higher road density are dominated by a larger proportion of built-up landscape and less possession of forest and vice versa. Road networks segregated the landscape into smaller pieces and a greater number of patches. Furthermore, Spearman rank correlation model indicates that road density (km/km2) is positively related to landscape fragmentation. Our results suggest that road density, estimated by KDE, may be a better correlate with effects of the road on landscape fragmentation. Through KDE, the regional spatial pattern of road density and the prediction of the impact of the road on landscape fragmentation could be effectively acquired.  相似文献   

16.
Pattern analysis techniques currently common within geography tend to focus either on characterizing patterns of spatial and/or temporal recurrence of a single event type (e.g., incidence of flu cases) or on comparing sequences of a limited number of event types where relationships between events are already represented in the data (e.g., movement patterns). The availability of large amounts of multivariate spatiotemporal data, however, requires new methods for pattern analysis. Here, we present a technique for finding associations among many different event types where the associations among these varying event types are not explicitly represented in the data or known in advance. This pattern discovery method, known as T-pattern analysis, was first developed within the field of psychology for the purpose of finding patterns in personal interactions. We have adapted and extended the T-pattern method to take the unique characteristics of geographic data into account and implemented it within a geovisualization toolkit for an integrated computational-geovisual environment we call STempo. To demonstrate how T-pattern analysis can be employed in geographic research for discovering patterns in complex spatiotemporal data, we describe a case study featuring events from news reports about Yemen during the Arab Spring of 2011–2012. Using supplementary data from the Global Database of Events, Language, and Tone, we briefly summarize and reference a separate validation study, then evaluate the scalability of the T-pattern approach. We conclude with ideas for further extensions of the T-pattern technique to increase its utility for spatiotemporal analysis.  相似文献   

17.
范淑斌  申悦 《地理科学进展》2022,41(11):2086-2098
可达性是人文地理学和相关学科研究的核心议题之一。在“以人为本”理念的影响下,以时间地理学理论为基础、基于人的研究范式的时空可达性测度方法受到学者关注,成为生活质量、社会公平等议题的重要切入点。论文通过刻画整日潜在活动空间对个体的时空可达性进行测度,并以上海市郊区为案例地区,基于2017年居民活动日志一手调查数据开展实证研究。首先利用路网分析、二次开发等方法,对个体工作日和休息日的潜在活动空间进行测度;其次以弹性时间、整日潜在活动空间面积和可达设施密度为测度指标,利用GIS三维可视化、方差分析等方法分析时空可达性的特征及其在空间和时间维度的分异;最后,利用多元回归分析方法,探讨区位因素、时间因素和社会经济属性对居民时空可达性的影响。研究结果表明,上海市郊区居民的时空可达性在空间和时间维度上均存在着明显的分异,其中远郊居民面临着更强的时空制约和更大的空间困境;区位因素和时间因素是影响居民时空可达性的重要因素。该研究是时空可达性的测度方法在郊区中的实证检验,揭示了时空可达性的动态特征和个体间差异,为设施的时空优化配置和郊区新城建设中居民生活质量的提高提供了实证依据。  相似文献   

18.
吴朝宁  李仁杰  郭风华 《地理学报》2021,76(6):1537-1552
准确刻画游客活动空间边界对于优化景区结构、实施界限管控、提高资源利用效益均有重要意义。由于游客行为的复杂性与边界模糊性,利用传统地理边界提取方法难以有效识别游客活动空间边界。基于层次聚类算法优化后的Delaunay三角网进行核密度估计,解决了多尺度下点核密度对空间边界拟合不精确的问题,同时借鉴圈层结构理论,依据游客空间集聚特征建立景区层次结构,利用大量游客长时间签到蕴含的时空信息,分析游客空间分布扩张规律,挖掘地理要素关系,建立“Hie-Density”模型,提出基于圈层结构理论的游客活动空间边界定量提取新方法。本文通过微观视角下圈层子系统的协同作用探究主体系统的宏观演化,证明了“Hie-Density”模型支持对多种游客分布模式进行描述,同时能够依据模型变化曲线定量识别游客活动最佳边界、空间集散状态、中心分裂特征及边界演化方向。多案例实证表明,本方法适用于各类景区的多尺度复杂游客活动空间边界提取,为地理时空数据挖掘提供了新视角和新方法。  相似文献   

19.
Temporal limitations of GIS databases are never more apparent than when the time of a change to any spatial object is unknown. This paper examines an unusual type of spatiotemporal imprecision where an event occurs at a known location but at an unknown time. Aoristic analysis can provide a temporal weight and give an indication of the probability that the event occurred within a defined period. Visualisation of temporal weights can be enhanced by modifications to existing surface generation algorithms and a temporal intensity surface can be created. An example from burglaries in Central Nottingham (UK) shows that aoristic analysis can smooth irregularities arising from poor database interrogation, and provide an alternative conceptualisation of space and time that is both comprehensible and meaningful.  相似文献   

20.
ABSTRACT

Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号