scholarly journals Identification of Vehicle-Pedestrian Collision Hotspots at the Micro-Level Using Network Kernel Density Estimation and Random Forests: A Case Study in Shanghai, China

2018 ◽  
Vol 10 (12) ◽  
pp. 4762 ◽  
Author(s):  
Shenjun Yao ◽  
Jinzi Wang ◽  
Lei Fang ◽  
Jianping Wu
Keyword(s):  
Density Estimation ◽  
Random Forests ◽  
Kernel Density Estimation ◽  
Hot Spots ◽  
Kernel Density ◽  
Pedestrian Safety ◽  
Threshold Value ◽  
The Difference ◽  
Pedestrian Crashes ◽  
Crash Reduction

The improvement of pedestrian safety plays a crucial role in developing a safe and friendly walking environments, which can contribute to urban sustainability. A preliminary step in improving pedestrian safety is to identify hazardous road locations for pedestrians. This study proposes a framework for the identification of vehicle-pedestrian collision hot spots by integrating the information about both the likelihood of the occurrence of vehicle-pedestrian collisions and the potential for the reduction in vehicle-pedestrian crashes. First, a vehicle-pedestrian collision density surface was produced via network kernel density estimation. By assigning a threshold value, possible vehicle-pedestrian hot spots were identified. To obtain the potential for vehicle-pedestrian collision reduction, random forests was employed to model the density with a set of variables describing vehicle and pedestrian flows. The potential for crash reduction was then measured as the difference between the observed vehicle-pedestrian crash density and the prediction produced by the random forests models. The final hotspots were determined by excluding those with a crash reduction value of no more than zero. The method was applied to the identification of hazardous road locations for pedestrians in a district in Shanghai, China. The result indicates that the method is useful for decision-making support.

Generation and Implementation of Grasping Rectangle Based on Hierarchical Shape Context and Kernel Density Estimation towards Robotic Grasping Unknown Objects

2013 ◽  
Vol 433-435 ◽  
pp. 537-544
Author(s):  
Guo Liang Kang ◽  
Shi Yin Qin
Keyword(s):  
Density Estimation ◽  
Random Forests ◽  
Kernel Density Estimation ◽  
Stereo Matching ◽  
Vision System ◽  
Kernel Density ◽  
Robotic Grasping ◽  
Shape Context ◽  
3D Space ◽  
Unknown Objects

This paper focuses on the perception step of robotic grasping unknown objects in order to get a stable grasping hypothesis. At first, hierarchical shape context feature is proposed to depict the local and global shape character of a sample point along the edges of the object. Moreover a kind of random forests classifier is adopted to recognize the grasping candidates in the image from vision system so that a 2D grasping rectangle can be generated through kernel density estimation. Finally, by means of stereo matching, the grasping rectangle can be mapped into the 3D space. Thus, the center of the grasping rectangle can be applied as the center of the gripper. The approaching vector and the grasping rectangle direction can be employed to determine the pose of the gripper. Simulated experiments showed that a reasonable and stable grasping rectangle can be generated for various unknown objects.

scholarly journals Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne

2020 ◽  
Vol 31 (4) ◽  
pp. 36-58
Author(s):  
Elizabeth Hovenden ◽  
Gang-Jun Liu
Keyword(s):  
Land Use ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Hot Spots ◽  
Hot Spot ◽  
Kernel Density ◽  
Spatial Dependency ◽  
Moran's I ◽  
Analysis Techniques ◽  
Global Moran’S I

Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression.

scholarly journals Identifying the Birds Diversity Hot Spots in Teeb Protected Area South-eastern Iraq by Using Kernel Density Estimation

2021 ◽  
Vol 790 (1) ◽  
pp. 012084
Author(s):  
Thaer Kareem Hassan ◽  
Widad Fadhullah ◽  
Salwan Ali Abed ◽  
Mudhafar A. Salim ◽  
Kudhur A. Al-Kenani ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Hot Spots ◽  
Protected Area ◽  
Kernel Density ◽  
South Eastern

scholarly journals Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation

2022 ◽  
Vol 11 (1) ◽  
pp. 55
Author(s):  
Guiming Zhang
Keyword(s):  
Big Data ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Hot Spots ◽  
Graphics Processing Units ◽  
Spatial Scales ◽  
Kernel Density ◽  
Point Pattern ◽  
Computational Tool ◽  
Geographic Data

Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics.

scholarly journals Author Correction: An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenzhong Shi ◽  
Chengzhuo Tong ◽  
Anshu Zhang ◽  
Bin Wang ◽  
Zhicheng Shi ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Spatiotemporal Variations ◽  
Estimation Model

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6

scholarly journals Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun
Keyword(s):  
Spatial Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Function Method ◽  
Kernel Density ◽  
Fire Occurrence ◽  
Distribution Characteristics ◽  
Fire Point ◽  
Spatial Distribution Characteristics ◽  
Grassland Fire

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.

Sign in / Sign up

Export Citation Format

Share Document