K-function Method Research Articles

Exploring spatial associations between near-miss and police-reported crashes: The Heinrich’s law in traffic safety

This research explores the spatial association between police-reported crashes and near-miss crashes experienced by community members using the network cross K-function method and Geographic Information Systems (GIS). The near-miss crashes were collected from a field survey conducted in Seoul, South Korea, and police-reported crashes in the study area were also collected for comparison. The results show that minor and severe police-reported crashes have statistically significant spatial associations, and near-miss and police-reported crashes also have significant associations. Thus, the Heinrich’s law, which postulates that police-reported minor and severe crashes and near-miss crashes may share underlying locational factors, has merit to apply in traffic safety. These findings indicate that road users’ near-miss crash experiences reflecting local knowledge can supplement police-reported traffic crashes to identifying crash hotspots in road networks and developing safety improvement measures and strategies.

Geographically Weighted Flow Cross K-Function for Network-Constrained Flow Data

Network-constrained spatial flows are usually used to describe movements between two spatial places on a road network. The analysis of the spatial associations between different types of network-constrained spatial flows plays a key role in understanding the spatial relationships among different movements. However, existing studies usually do not consider the effect of distance decay, which may reduce the effectiveness of the detected bivariate spatial flow patterns. Moreover, most existing studies are based on the planar space assumption, which is not suitable for network-constrained spatial flows. To overcome these problems, this study proposed a new statistical method, the Geographically Weighted Network Flow Cross K-function, which improves the Flow Cross K-Function method by taking the distance decay effect and the constraints of road networks into account. Both global and local versions are extended in this study: the global version measures the overall spatial association and the local version identifies the exact locations where a spatial association occurs. The experiments on simulated datasets show that the proposed method can identify predefined bivariate flow patterns. In a case study, the proposed method is also applied to flow data comprising Xiamen taxi and ride-hailing datasets. The results demonstrate that the proposed method effectively identifies the competitive relationships between taxi and ride-hailing services.

Spatial Distribution Pattern Analysis of Unsafe Setting of Electric Wires in Urban Area Using K-function Methods: A Case Study of Guangming District

Hidden fire risk are important disaster-inducing factors for potential fire accidents, which provides a breakthrough point for early intervention and prevention of potential fire accidents. Unsafe setting of electric wires is a very dangerous type of hidden fire risk that could cause fire accidents. It is of great significance to analyze the distribution pattern of hidden fire risk and related factors of this type of hidden fire hazard for early intervention. However, few researches have been done in the field of hidden fire risk. In this study, two network K-function methods, the univariate network K-function method and the bivariate network K-function method, were applied to analyze spatial patterns and its related factors of unsafe setting of electric wires as a hidden fire risk. The research data came from daily inspection in urban grid management of Guangming district, Shenzhen. The results indicated that the spatial distribution of unsafe setting of electric wires is highly related to the transportation network in the study area. The distribution pattern is influenced by the distribution of residential quarters and office buildings. The methods in this study can also be used to analyze other types of hidden fire hazards.

Spatial autocorrelation of Neogene-Quaternary lava along the Snake River Plain, Idaho, USA

The sequence of eruption, spatial pattern, and spatio-temporal relationships among the Neogene-Quaternary rhyolitic and basaltic lava along the Snake River Plain (SRP) in Idaho are analyzed applying the spatial methods of global and local Moran’s I, standard deviational ellipse, and Ripley’s K-function. The results of the analyses by the Moran’s I and K-function methods indicate a higher spatial autocorrelation, hence clustering, of rhyolitic lava compared to the more dispersed basaltic lava in each center of eruption along the SRP. The clustered nature of rhyolitic lava around each caldera either reflects the original spread and large thickness of the rhyolitic lava, or the absence of younger cover strata or lava like the distribution of rhyolite in the present caldera at the Yellowstone National Park. The standard deviational ellipses (SDEs) of the lavas indicate that younger basaltic lava that erupted from newer calderas overlapped older rhyolitic and basaltic lava as the position of the Yellowstone hotspot progressively migrated to the northeast along the SRP. The less eccentric SDEs of rhyolitic lava in each caldera probably reflect the original caldera-scale spread of viscous felsic lava, compared to the more eccentric and larger SDEs of basaltic lava which represent basalt’s wider and more directed spread due to its higher fluidity and ability to flow longer distances along the trend of the SRP. The alignment of the long axes of the lava SDEs with the trend of the Eastern SRP and the trend of systematic spatial overlap of older lava by successively younger basaltic lava corroborate the previously reported migration of the centers of eruption along the ESRP as the Yellowstone hotspot migrated to the northeast.

A space-time analysis of Mycoplasma bovis: bulk tank milk antibody screening results from all Danish dairy herds in 2013-2014.

Background Mycoplasma bovis is an important pathogen causing severe disease outbreaks in cattle farms. Since 2011, there has been an apparent increase in M. bovis outbreaks among Danish dairy cattle herds. The dairy cattle industry performed cross-sectional antibody screening for M. bovis on four occasions, using the indirect BIO K 302 M. bovis enzyme-linked immunosorbent assay (ELISA) (Bio-X, Belgium) in bulk tank milk from all dairy herds between June 2013 and July 2014. The objective of this study was to investigate the evolution of the spatial distribution of M. bovis in the Danish dairy herd population throughout the study period. Repeated bulk tank milk samples were used as a proxy for the herd-level diagnosis. Descriptive and spatial analyses were performed for the four screening rounds. Based on a previous diagnostic test evaluation study, the M. bovis status for each herd was determined as test-positive or test-negative using a cut-off of 50 optical density coefficient %. The spatial global clustering was evaluated through a modified K-function method, and local clusters were identified by scan statistics.ResultsThe results showed that M. bovis test-positive herds had a dynamic pattern in space. The global clustering analysis showed that M. bovis test-positive herds were spatially correlated in rounds one, three and four. These findings were supported to some extent by the local clustering analysis, which found significant high- and low-risk spatial clusters in rounds one and three in the north and south of the mainland.ConclusionThe clusters with a high risk of observing test-positive herds did not remain between sampling rounds, indicating that M. bovis did not tend to persist upon emergence in dairy herds. In contrast, the clusters with a low risk of observing test-positive herds persisted in the same area throughout the study period.

Space–time interaction of residential burglaries in Wuhan, China

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.

基于加权Ripley's K-function的多尺度景观格局分析——以江苏盐城滨海湿地为例

PDF HTML阅读 XML下载 导出引用 引用提醒 基于加权Ripley's K-function的多尺度景观格局分析——以江苏盐城滨海湿地为例 DOI: 10.5846/stxb201307191915 作者: 作者单位: 南京大学地理与海洋科学学院,南京大学地理与海洋科学学院,南京大学 作者简介: 通讯作者: 中图分类号: 基金项目: 科技部重点支撑项目(2013CB956503);国家海洋局2010年海洋公益性行业科研专项(201005006)资助 Multi-scale analysis of landscape characteristics in coastal wetlands in Yancheng, Jiangsu, based on the weighted Ripley's K-function method Author: Affiliation: The School of Geographic and Oceanographic Sciences,Nanjing University,The School of Geographic and Oceanographic Sciences,Nanjing University,The School of Geographic and Oceanographic Sciences,Nanjing University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以1987,1992,1997,2002,2007年的遥感影像为例,首次尝试使用加权Ripley's K-function的多尺度格局分析方法,计算了20年来景观异质性在江苏盐城滨海湿地的时间变化和空间分布趋势。通过对研究区的样带划分以及景观类型的点状化处理,建立滨海湿地样带图层和1987-2007年间各类型景观的点格局数据库,从而分析滨海湿地不同类型景观的空间聚集特征变化。基于加权Ripley's K-function的计算表明,在各级空间尺度和时间变化上,各类型湿地的斑块都呈现出空间聚集分布状态,且1987年以来,不同湿地类型的聚集空间特征尺度和空间分布强度均出现了大幅的增减变化,除互花米草滩之外的自然湿地的聚集空间特征尺度和强度都有明显下降甚至少到无法被检测到,而人工湿地却呈现聚集特征尺度和强度的双增长,且该聚集程度有逐渐增强的趋势。分析表明,既考虑样点的空间位置信息又考虑样点分布范围的加权Ripley's K-function方法能很好地表征湿地景观在多尺度上的变异,且与传统空间景观指数等分析方法的结论在一定程度上保持一致。 Abstract:Based on remote sensing images from 1987, 1992, 1997, 2002, and 2007, the present study used the weighted Ripley's K-function multi-scale analysis method to calculate temporal changes and trends in the spatial distribution (i.e., heterogeneity) of coastal wetland landscape patterns over 20 years in Yancheng, Jiangsu. To analyze changes in the spatial clustering characteristics of different wetland landscape types, we divided the area into small belt transects and created a point pattern database for changing wetland landscapes from 1987 to 2007. The results obtained based on weighted Ripley's K-function analyses demonstrate that, over different spatial and temporal scales, all wetland landscapes presented an aggregated distribution. Moreover, for different wetland types, most patch radius and crowding indices indicate an obvious increase or decrease since 1987. Except for Spartina alterniflora, the aggregated patch radius and crowding of all other natural wetlands dropped sharply or even became undetectable, whereas both the aggregated patch radius and crowding indices in the constructed wetland increased rapidly, at higher rates over time. Our analyses revealed that the weighted Ripley's K-function method, which considers both the location and attributes of point samples, can clearly reveal spatial variations of landscapes at multiple scales. These novel results also agree with those obtained using other conventional analysis methods, such as landscape metrics. Additionally, in future, a deeper analysis of the mechanisms controlling such landscapes at various spatial scales will be conducted to provide quantitative implications for wetland management planning. Specifically, after the wetland polygon data were processed in ArcGIS, we transferred and calculated the spatial indices (patch radius and crowding) in the Matlab R2011a. It was found that, to some extent, the weighted Ripley's K-function could mitigate the occurrence of two false results that were caused by the traditional Ripley's K-function. First, in conventional analysis, large wetland areas are more likely to produce limited numbers of point samples when they are exported directly into a dimensionless point from a polygon; this has resulted in the spatial distribution of some wetlands being interpreted as random or dispersed, even when they exhibit obvious aggregations in the GIS layers. Conversely, the weighted Ripley's K-function takes the dimensions of such points into consideration; accordingly, it is able to reflect changes in the area of study objects in continuous space, thus allowing characterization of changes in the spatial clustering of wetlands at multiple scales. In this study, the extraction and spatial distribution of wetlands distributed in a large area proved the validity of the improved method. Second, traditional methods have tended to produce "false" conclusions or overestimate aggregation when applied to the most seriously fragmented wetlands, which typically generate numerous point samples. In contrast, sample areas in the improved method are weighted, allowing spatial distributions to be represented more accurately. For example, in the present study, we were able to avoid the "over-aggregation" of the spatial distribution of Aeluropus littoralis that may have been produced by traditional methods. Thus, we were able to reveal actual changes in the distribution of Aeluropus littoralis, demonstrating that it shrunk gradually (and almost disappeared) at various scales owing to crowding. 参考文献 相似文献 引证文献

Clustering of place of birth for women with breast cancer: differences by tumor characteristics

There is increasing evidence that exposures in early life affect breast cancer risk, and that breast cancer etiology differs by tumor subtype. If environmental exposures in early life contribute to risk, it is expected that there would be clustering of women with breast cancer by their place of birth, and that clustering might differ by subtype. We examined spatial associations between place of birth and breast cancer by subtype, using hormone receptor status and molecular profiles of breast tumors. Data were drawn from the Western New York Exposures and Breast Cancer study, a population-based case-control study of incident, pathologically confirmed breast cancer (1996-2001) in Erie and Niagara Counties. Included were women born in the study area (579 cases and 931 controls). Clustering of breast cancer subgroups relative to controls was examined by the k-function method in groups stratified by estrogen receptor (ER), progesterone receptor (PR), and HER2 status, and by DNA methylation status and p53 mutation status, and the k-function difference was used to compare relative spatial aggregation and spatial range of the difference between case subgroups and controls. We found a tendency to cluster among ER positive, PR positive, and HER2 negative cases (i.e., luminal A subtype), especially among premenopausal women, but not among the other groups defined by hormonal receptor status, or by either methylation or p53 mutation status. While our findings cannot rule out clustering of cases by birth place because of shared behaviors related to residence location, they also suggest that early life environmental exposures may affect subsequent breast cancer risk, and that premenopausal breast tumors of the luminal A subtype may be more affected by these early life exposures than other subtypes.

Space-time clustering of childhood central nervous system tumours in Yorkshire, UK.

BackgroundWe specifically tested the aetiological hypothesis that a factor influencing geographical or temporal heterogeneity of childhood central nervous system (CNS) tumour incidence was related to exposure to a transient environmental agent.MethodsInformation was extracted on individuals aged 0-14 years, diagnosed with a CNS tumour between the 1st January 1974 and 31st December 2006 from the Yorkshire Specialist Register of Cancer in Children and Young People. Ordnance Survey eight-digit grid references were allocated to each case with respect to addresses at the time of birth and the time of diagnosis, locating each address to within 0.1 km. The following diagnostic groups were specified a priori for analysis: ependymoma; astrocytoma; primitive neuroectodermal tumours (PNETs); other gliomas; total CNS tumours. We applied the K-function method for testing global space-time clustering using fixed geographical distance thresholds. Tests were repeated using variable nearest neighbour (NN) thresholds.ResultsThere was statistically significant global space-time clustering for PNETs only, based on time and place of diagnosis (P = 0.03 and 0.01 using the fixed geographical distance and the variable NN threshold versions of the K-function method respectively).ConclusionsThere was some evidence for a transient environmental component to the aetiology of PNETs. However, a possible role for chance cannot be excluded.

Space-Time Clustering Analyses of Occurrence of Cerebral Palsy in Northern England for Births 1991 to 2003

The etiology of congenital cerebral palsy is unclear. Recent studies have suggested that maternal infection is involved. Indirect, but supportive, evidence for an infectious hypothesis would be provided by the finding of space-time clustering. We analyzed all 906 cases born during the period 1991-2003 using a population-based registry from Northern England. We applied the K-function method for testing global space-time clustering and used residential addresses at the time of birth. The Knox method was used to determine the spatio-temporal range over which global clustering occurred. Tests were repeated using nearest neighbor thresholds to allow for variable population density. Additionally, Kulldorff's scan statistic identified specific space-time clusters. There was statistically significant evidence of global space-time clustering (p <or= 0.05). Clustering occurred over a range of close distances, but was most pronounced for cases born within 1-4 months of one another. A specific space-time cluster was identified during 1994. The application of spatial statistics for the study of cerebral palsy is novel. There is tentative evidence for the involvement of spatially and temporally varying environmental etiological factors. These findings may support a role for infections or similar agents. However, the role of chance cannot be excluded.

The SANET Toolbox: New Methods for Network Spatial Analysis

This paper describes new methods, called network spatial methods, for analyzing spatial phenomena that occur on a network or alongside a network (referred to as network spatial phenomena). First, the paper reviews network spatial phenomena discussed in the related literature. Second, the paper shows the uniform network transformation, which is used in the study of non-uniform distributions on a network, such as the densities of traffic and population. Third, the paper outlines a class of network spatial methods, including nearest neighbor distance methods, K-function methods, cell count methods, clumping methods, the Voronoi diagrams and spatial interpolation methods. Fourth, the paper shows three commonly used computational methods to facilitate network spatial analysis. Fifth, the paper describes the functions of a GIS-based software package, called SANET, that perform network spatial methods. Sixth, the paper compares network spatial methods with the corresponding planar spatial methods by applying both methods to the same data set. This comparison clearly demonstrates how different conclusions can result. The conclusion summarizes the major findings.

Uniform network transformation for points pattern analysis on a non-uniform network

In the real world, there are many kinds of phenomena that are represented by points on a network, such as traffic accidents on a street network. To analyse these phenomena, the basic point pattern methods (i.e. the nearest neighbour distance method, the quadrat method, the K-function method and the clumping method) defined on a plane (referred to as the planar basic point pattern methods) are extended to the basic point pattern methods on a network (referred to as the network basic point pattern methods). However, like the planar basic point pattern methods, the network basic point pattern methods assume a uniform network and this assumption is hard to accept when analysing actual phenomena. To overcome this limitation, this paper formulates a transformation, called the uniform network transformation, that transforms a non-uniform network into a uniform network. This transformation provides a simple procedure for analysing point patterns on non-uniform networks: first, a given non-uniform network is transformed into a uniform network; second, the network basic point pattern methods (which assume a uniform network) are applied to this transformed uniform network. No modification to the network basic point pattern methods is necessary. The paper also shows an actual application of this transformation to traffic accidents in Chosei, Japan.

Spatial analysis of roadside Acacia populations on a road network using the network K-function

Spatial patterning of plant distributions has long been recognised as being important in understanding underlying ecological processes. Ripley’s K-function is a frequently used method for studying the spatial pattern of mapped point data in ecology. However, application of this method to point patterns on road networks is inappropriate, as the K-function assumes an infinite homogenous environment in calculating Euclidean distances. A new technique for analysing the distribution of points on a network has been developed, called the network K-function (for univariate analysis) and network cross K-function (for bivariate analysis). To investigate its applicability for ecological data-sets, this method was applied to point location data for roadside populations of three Acacia species in a fragmented agricultural landscape of south-eastern Australia. Kernel estimations of the observed density of spatial point patterns for each species showed strong spatial heterogeneity. Combined univariate and bivariate network K-function analyses confirmed significant clustering of populations at various scales, and spatial patterns of Acacia decora suggests that roadworks activities may have a stronger controlling influence than environmental determinants on population dynamics. The network K-function method will become a useful statistical tool for the analyses of ecological data along roads, field margins, streams and other networks.

SAINF: A toolbox for analyzing the effect of point-like, line-like and polygon-like infrastructural features on the distribution of point-like non-infrastructural features

This paper introduces a GIS-based toolbox, called SAINF, for analyzing the effects of infrastructural features on the distribution of non-infrastructural features. One of the distinct characteristics of SAINF is that it can deal not only with point-like infrastructural features but also line-like (e.g. railways) and polygon-like (e.g. big parks) features. SAINF analyzes these effects with the conditional nearest neighbor distance method and the cross K-function method. First, the paper briefly refers to these methods. Second, the paper illustrates how to use SAINF with an actual example. SAINF can be downloaded via the Internet without charge for non-profit uses.

Comparison of planar and network K-functions in traffic accident analysis

The network and planar K-function methods are applied to traffic accident data to illustrate the risk of false positive detection associated with the use of a statistic designed for a planar space to analyze a network-constrained phenomenon. We also demonstrate the benefits of using a method specifically designed for a network space. The results clearly indicate that the planar K-function analysis is problematic since it entails a significant chance of over-detecting clustered patterns. Analyses are implemented based on Monte Carlo simulation and applied to 1997 traffic accident data in the Buffalo, NY area.

Space-time clustering patterns in childhood solid tumours other than central nervous system tumours.

The aetiology of most childhood solid tumours (other than central nervous system [CNS] tumours) is unclear. To investigate whether certain environmental exposures may be involved, we have analysed for space-time clustering using population-based data from North West England for the period 1954-98. Knox tests for space-time interactions between cases were applied with fixed thresholds of close in space, <5 km, and close in time, <1 year apart. Addresses at birth and at diagnosis were used. Tests were repeated replacing geographical distance with distance to the Nth nearest neighbour. N was chosen such that the mean distance was 5 km. Data were also examined by a second order procedure based on K-functions. There was significant evidence of space-time clustering for Wilms' tumours (p = 0.03 and 0.04, using the geographical distance and nearest neighbour versions of the Knox test; and p = 0.07 and 0.03, using the geographical distance and nearest neighbour versions of the K-function method), and soft tissue sarcomas (p = 0.01, using both the geographical distance and nearest neighbour versions of the Knox test; and p = 0.001 and 0.002, using the geographical distance and nearest neighbour versions of the K-function method) based on time and location at birth, but not time and location at diagnosis. There was little or no evidence of space-time clustering amongst other diagnostic groups. These are the first results to demonstrate space-time clustering for childhood Wilms' tumours and soft tissue sarcomas. The results are consistent with environmental exposure hypotheses, relating to locations pre-natally or peri-natally.

The K‐Function Method on a Network and Its Computational Implementation

This paper proposes two statistical methods, called the network K‐function method and the network cross K‐function method, for analyzing the distribution of points on a network. First, by extending the ordinary K‐function method defined on a homogeneous infinite plane with the Euclidean distance, the paper formulates the K‐function method and the cross K‐function method on a finite irregular network with the shortest‐path distance. Second, the paper shows advantages of the network K‐function methods, such as that the network K‐function methods can deal with spatial point processes on a street network in a small district, and that they can exactly take the boundary effect into account. Third, the paper develops the computational implementation of the network K‐functions, and shows that the computational order of the K‐function method is O(n2Q log nQ) and that of the network cross K‐function is O(nQ log U3Q), where nQ is the number of nodes of a network.

An exploratory method for detecting multi-level clumps in the distribution of points – a computational tool, VCM (variable clumping method)

This paper shows an explanatory tool (a FORTRAN program linked to GIS software), called VCM (Variable Clumping Method), for detecting statistically significant `multi-level clumps' in the distribution of points. The multi-level clumps imply a set of clumps whose clumping radius varies from a small radius to a large radius (in spirits, VCM is similar to the K-function method). Compared with the ordinary (fixed radius) clumping method, VCM can detect overall significant clumps in the distribution of points.

Clustering and abundance estimation for Neyman-Scott models and line transect surveys

This paper considers the estimation of clustering parameters and mean species intensity based on likelihood theory for the simplified Neyman-Scott Poisson model, with observations taken from line transect surveys with a Gaussian detection function. The estimators and accompanying standard error expressions are tractable and easy to calculate, and, coming from likelihood methods, often will have high efficiency. Such properties compare favourably with those of existing K-function methods which however are semiparametric in nature and less reliant on specific parametric assumptions. The likelihood analysis reveals auxiliary information which could be used to check the form of the detection function. Clustering; Cramer-Rao bound; Detection function; K-function; Line transect survey; Maximum likelihood; Neyman-Scott line transect distribution; Neyman-Scott Poisson process.