Neighbor Distance Method Research Articles

Network space analysis–based identification of road traffic accident hotspots: a case study

This study proposed a method for identifying traffic accident (TA) hotspots. The method combines kernel density estimation (KDE) with network kernel density estimation (NKDE). The hotspots identified through NKDE can be overlaid on the high-risk areas (hot zones) identified using planar KDE, resulting in accurate hotspot identification. The research site was Zhenjiang City in Jiangsu Province, China; data on 410 fatal traffic accidents from 2017 to 2020 were collected. An average nearest neighbour (ANN) ratio (0.563) was obtained using the global auto nearest neighbour distance method; thus, the fatal TAs had a cluster-type distribution in the city. Subsequently, the maximum clustering distance (7812.842591 m) in the research site was analysed using Ripley’s K-function, which yielded a reasonable KDE bandwidth and enabled identifying the distribution of hot zones for fatal TAs. Precise hotspot identification was achieved by overlaying the results of NKDE-based analysis in these hot zones. The proposed method was verified using data on 131 fatal TAs during January to July 2021. The results revealed that, in Zhenjiang City, the identification rate for hot zones and hotspots in hot zones was 71.75% and 38.29%, respectively, and the overall hotspot identification rate was 27.48%, demonstrating the method’s ability to relatively accurately identify hotspot locations, information that can help traffic authorities implement preventive measures.

Comparison of outlier detection techniques in non-stationary time series data

This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques used in this study were Mahalanobis distance, depth-based, robust kernel-based outlier factor (RKOF), generalized dispersion, Kth nearest neighbors distance (KNND), and principal component (PC) methods. When detecting extreme maxima, the Mahalanobis and the principal component methods performed better in correctly detecting outliers in the dataset. Also, the Mahalanobis method could identify more outliers than the others, making it the "best" method for the extreme minima category. The kth nearest neighbor distance method was the "best" method for not over-detecting the number of outliers for extreme minima. However, the Mahalanobis distance and the principal component methods were the "best" performed methods for not over-detecting the number of outliers for the extreme maxima category. Therefore, the Mahalanobis outlier detection technique is recommended for detecting outlier in nonstationary time series data.

Smart Fuzzy Logic-Based Density and Distribution Adaptive Scheme for Efficient Data Dissemination in Vehicular Ad Hoc Networks

In vehicular Ad Hoc Networks (VANETs), smart data dissemination is crucial for efficient exchange of traffic and road information. Given the dynamic nature of VANET, the challenge is to design an adaptive multi-hop broadcast scheme that achieves high reachability while efficiently utilizing the bandwidth by reducing the number of redundant transmissions. In this paper, we propose a novel intelligent fuzzy logic based density and distribution adaptive broadcast protocol for VANETs. The proposed protocol estimates the spatial distribution of vehicles in the network employing the Nearest Neighbor Distance method, and uses it to adapt the transmission range to enhance reachability. To reduce packet collisions, the protocol intelligently adapts the contention window size to the network density and spatial distribution. Bloom filter technique is used to reduce the overhead resulting from the inclusion of the neighbor IDs in the header of the broadcast message, which is needed in identifying the set of potential rebroadcasting vehicles. Our simulation results confirmed the effectiveness of the proposed scheme in enhancing reachability while efficiently utilizing bandwidth.

Mega-towns in China: Their spatial distribution features and growth mechanisms

As a special outcome of urbanization, mega-towns not only play an important role in the process of socio-economic development, but also are important contributors to urbanization. Based on a spatial database of mega-towns in China, this paper explores the spatial distribution features and growth mechanisms of China’s 238 mega-towns using the nearest neighbour distance method, kernel density estimation, regression analysis, global autocorrelation, local autocorrelation and other spatial analysis methods. Results of spatial distribution features show that: (1) on the national scale, the existing 238 mega-towns mainly gathered in the southeast coastal areas of China; they formed two spatial core agglomerations, several secondary ones and a southeast coastal agglomeration belt; (2) on the regional scale, each economic region’s index was less than 1, indicating that mega-towns in each region tended to be spatially agglomerated due to the close relationship with regional development level and their number; (3) on the provincial scale, 68% of provincial-level units in China tended to be a spatial agglomeration of mega-towns; only one province had a random distribution; the number of mega-towns in those evenly-distributed provinces was generally small. The growth of mega-towns was determined by a combination of various natural and humanistic factors, including topography, location, economy, population, traffic, and national policy. This paper chose digital elevation model (DEM), location advantage, economic density, population density, and highway density distribution as corresponding indicators as quantitative factors. By combining their local autocorrelation analysis, these factors all showed certain influence on the spatial growth of mega-towns and together scheduled it. In the future, provinces and cities should make full use of the mega-town functions to promote their socioeconomic development, especially the central and western regions in China.

甘肃草地4种毒杂草潜在入侵区预测研究

PDF HTML阅读 XML下载 导出引用 引用提醒 甘肃草地4种毒杂草潜在入侵区预测研究 DOI: 10.5846/stxb201809111946 作者: 作者单位: 西北民族大学,西北民族大学,西北民族大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31560127）；西北民族大学中央高校基本科研业务费资金资助项目（31920180116）；西北民族大学"双一流"和特色发展引导专项资金资助项目 Predictive studies of potential invasive areas for four poisonous weeds in Gansu Grassland Author: Affiliation: School of Mathematics and Computer Science,Northwest Minzu University,School of Mathematics and Computer Science,Northwest Minzu University,School of Mathematics and Computer Science,Northwest Minzu 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:针对4种著名的草原毒杂草：醉马草，黄花棘豆，狼毒和露蕊乌头，应用生态位模型分别研究其在甘肃的潜在扩散区域。首先，通过最近邻体距离法和相关性分析分别选取样本数据和环境变量，接着应用最大熵方法（Maxent）建立生态位模型，预测了4种毒杂草的潜在分布区。最后通过Matlab和ENMTools计算了地理分布重心、平均海拔、等级分布区比例、生态位宽度、生态位重合度和地理分布重合度。研究结果表明：4种毒杂草中醉马草和狼毒的环境适应能力较强，但醉马草的分布范围更为广泛，从祁连山脉一直延伸到甘南草原，扩散重心基本在祁连山西侧，而狼毒分布范围主要在甘肃南部，地理分布重心大致位于兰州地区。黄花棘豆的分布范围主要集中在祁连山脉，而露蕊乌头更偏向甘南草原地区。 Abstract:Here, the potential distribution of four famous poisonous weeds, Achnatherum inebrians, Oxytropis ochrocephala, Stellera chamaejasme, and Aconitum gymnandrum, was studied using ecological niche models. Firstly, occurrence records and environment variables were selected using the nearest neighbor distance method and correlation analysis. Next, the ecological niche models were established using maximum entropy (Maxent) to predict the potential distribution areas of the four poisonous weeds. Finally, Matlab and ENMTools were applied to compute the geographical distribution centroid, the average elevation, the percentage of hierarchical distribution areas, the niche breadth, the niche overlap, and the range overlap. The results show that, among the four poisonous weeds, A. inebrians and S. chamaejasme have strongly environmental adaptability. However, the distribution range of A. inebrians is relatively wider, extending from Qilian Mountains to Gannan Grassland, and the geographical distribution centroid is basically on the western side of Qilian Mountains. The distribution range of S. chamaejasme is mainly in southern Gansu, and its geographical distribution centroid mainly locates in the Lanzhou area. O. ochrocephala is mainly distributed in Qilian Mountains, whereas A. gymnandrum is mainly found in Gannan Grassland. 参考文献 相似文献 引证文献

The XXL Survey

Context. This work is part of a series of studies focusing on the environment and the properties of the X-ray selected active galactic nuclei (AGN) population from the XXL survey. The present survey, given its large area, continuity, extensive multiwavelength coverage, and large-scale structure information, is ideal for this kind of study. Here, we focus on the XXL-South (XXL-S) field. Aims. Our main aim is to study the environment of the various types of X-ray selected AGN and investigate its possible role in AGN triggering and evolution. Methods. We studied the large-scale (>1 Mpc) environment up to redshift z = 1 using the nearest neighbour distance method to compare various pairs of AGN types. We also investigated the small-scale environment (<0.4 Mpc) by calculating the local overdensities of optical galaxies. In addition, we built a catalogue of AGN concentrations with two or more members using the hierarchical clustering method and we correlated them with the X-ray galaxy clusters detected in the XXL survey. Results. It is found that radio detected X-ray sources are more obscured than non-radio ones, though not all radio sources are obscured AGN. We did not find any significant differences in the large-scale clustering between luminous and faint X-ray AGN, or between obscured and unobscured ones, or between radio and non-radio sources. At local scales (<0.4 Mpc), AGN typically reside in overdense regions, compared to non-AGN; however, no differences were found between the various types of AGN. A majority of AGN concentrations with two or more members are found in the neighbourhood of X-ray galaxy clusters within <25–45 Mpc. Our results suggest that X-ray AGN are typically located in supercluster filaments, but they are also found in over- and underdense regions.

A novel mixing index and its application in particle mixing behavior study in multiple-spouted bed

Particle mixing is a very important process in many industrial reactors. It is important to consider how to quantify the particle mixing behavior accurately under different conditions. In this paper, the drawbacks of traditional mixing indexes, such as Lacey index are discussed, and then a novel particle mixing index, without grid dependency and particle color dependency was proposed based on the neighbor distance method and coordination number concept. Its characteristics such as parameters selection, calculation method and multi-direction were discussed in details. The proposed mixing index was used for investigating particle mixing behavior in the multiple-spouted bed under conditions of different gas velocity ratios and different particle density ratios. The experimental results have been used to validate the simulation results at first. It is found that the improved mixing index is more superior and effective compared with the traditional mixing index methods, and suitable for the investigation of particle mixing behavior. It was indicated that the density ratio is a key factor to influence particle mixing process. The novel particle mixing index without grid dependency and particle color dependency is recommended to study particle mixing behavior in other particulate processes in the future.

Realizing privacy preserving genome-wide association studies.

Motivation: As genomics moves into the clinic, there has been much interest in using this medical data for research. At the same time the use of such data raises many privacy concerns. These circumstances have led to the development of various methods to perform genome-wide association studies (GWAS) on patient records while ensuring privacy. In particular, there has been growing interest in applying differentially private techniques to this challenge. Unfortunately, up until now all methods for finding high scoring SNPs in a differentially private manner have had major drawbacks in terms of either accuracy or computational efficiency.Results: Here we overcome these limitations with a substantially modified version of the neighbor distance method for performing differentially private GWAS, and thus are able to produce a more viable mechanism. Specifically, we use input perturbation and an adaptive boundary method to overcome accuracy issues. We also design and implement a convex analysis based algorithm to calculate the neighbor distance for each SNP in constant time, overcoming the major computational bottleneck in the neighbor distance method. It is our hope that methods such as ours will pave the way for more widespread use of patient data in biomedical research.Availability and implementation: A python implementation is available at http://groups.csail.mit.edu/cb/DiffPriv/.Contact: bab@csail.mit.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway

Analyzing the pattern of traffic accidents on road segments can highlight the hazardous locations where the accidents occur frequently and help to determine problematic parts of the roads. The objective of this paper is to utilize accident hotspots to analyze the effect of different measures on the behavioral factors in driving. Every change in the road and its environment affects the choices of the driver and therefore the safety of the road itself. A spatio-temporal analysis of hotspots therefore can highlight the road segments where measures had positive or negative effects on the behavioral factors in driving. In this paper 2175 accidents resulted in injury or death on the South Anatolian Motorway in Turkey for the years between 2006 and 2009 are considered. The network-based kernel density estimation is used as the hotspot detection method and the K-function and the nearest neighbor distance methods are taken into account to check the significance of the hotspots. A chi-square test is performed to find out whether temporal changes on hotspots are significant or not. A comparison of characteristics related driver attributes like age, experience, etc. for accidents in hotspots vs. accidents outside of hotspots is performed to see if the temporal change of hotspots is caused by structural changes on the road. For a better understanding of the effects on the driver characteristics, the accidents are analyzed in five groups based on three different grouping schemes. In the first grouping approach, all accident data are considered. Then the accident data is grouped according to direction of the traffic flow. Lastly, the accident data is classified in terms of the vehicle type. The resultant spatial and temporal changes in the accident patterns are evaluated and changes on the road structure related to behavioral factors in driving are suggested.

Comparative Study on the Characterization Method of Particle Mixing Index Using DEM Method

Different methods of particle mixing index are summarized in detail, such as average height method, nearest neighbour method, neighbour distance method, Lacey method, mixing entropy method, coordination number method, particle-scale index method and Siiria method. All these particle mixing indexes are calculated in two classical particle related process, that are the rotating drum and the spouted bed based on simulation results using DEM method. Then the comparative analysis of different kinds of particle mixing index is given in different aspects, such as calculation complexity, parameter dependency, grid dependency, color dependency and direction dependency. The performances of different particle mixing indexes are evaluated, the advantages and disadvantages of different indexes are pointed out, and the recommendation adaptation ranges are given finally. General speaking, the neighbour distance method seems as the best choice for studying the particle mixing process because of its superior performance.

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.

ネットワーク空間における線または面に対する点分布を解析するための最近隣距離法とそのツールの開発

ネットワーク空間における線または面に対する点分布を解析するための最近隣距離法とそのツールの開発

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.

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.

A Statistical Analysis of Compound Effect of Urban Infrastructure on the Spatial Distribution of Apartment Buildings with Multi Nearest Neighbor Distance Method

A Statistical Analysis of Compound Effect of Urban Infrastructure on the Spatial Distribution of Apartment Buildings with Multi Nearest Neighbor Distance Method

The Multi Nearest Neighbor Distance Method for Analyzing the Compound Effect of Infrastructural Elements on the Distribution of Activity Points

We propose a statistical method to analyze the compound effect of infrastructural elements (such as stations, arterial streets, and lakes) on the distribution of activity points (such as retail stores) over a region. First, we formulate a function that explicitly shows the compound effect of infrastructural elements. Second, we show an efficient computational method for estimating this compound function from data. Third, we develop multivariate statistical methods for testing several hypotheses about these compound effects. Last, we examine the compound effect of arterial streets and subway stations on the distribution of “high‐class” apartment buildings in Sumida‐Kohto, Tokyo.