A Kernel Density Method for Aggregating Boundary Collision Data into Areal Units

2014 ◽  
pp. 317-328
Author(s):  
Ge Cui ◽  
Xin Wang ◽  
Dae-Won Kwon
Keyword(s):  
Kernel Density ◽  
Density Method

scholarly journals Detecting Election Fraud from Irregularities in Vote-Share Distributions

2017 ◽  
Vol 25 (1) ◽  
pp. 41-56 ◽  
Author(s):  
Arturas Rozenas
Keyword(s):  
False Positive ◽  
Software Package ◽  
Simulated Data ◽  
Kernel Density ◽  
Fraud Detection ◽  
Vote Share ◽  
Density Method ◽  
Novel Method ◽  
Election Fraud ◽  
Ruling Party

I develop a novel method to detect election fraud from irregular patterns in the distribution of vote-shares. I build on a widely discussed observation that in some elections where fraud allegations abound, suspiciously many polling stations return coarse vote-shares (e.g., 0.50, 0.60, 0.75) for the ruling party, which seems highly implausible in large electorates. Using analytical results and simulations, I show that sheer frequency of such coarse vote-shares is entirely plausible due to simple numeric laws and does not by itself constitute evidence of fraud. To avoid false positive errors in fraud detection, I propose a resampled kernel density method (RKD) to measure whether the coarse vote-shares occur too frequently to raise a statistically qualified suspicion of fraud. I illustrate the method on election data from Russia and Canada as well as simulated data. A software package is provided for an easy implementation of the method.

scholarly journals Analysis of Fatal and Injury Traffic Accidents in Istanbul Sarıyer District with Spatial Statistics Methods

2021 ◽  
Vol 13 (19) ◽  
pp. 11039
Author(s):  
Mert Ersen ◽  
Ali Hakan Büyüklü ◽  
Semra Taşabat Erpolat
Keyword(s):  
Traffic Accidents ◽  
Kernel Density ◽  
Point Of View ◽  
Density Method ◽  
Spatial Methods ◽  
Advantages And Disadvantages ◽  
Year Effect ◽  
The World ◽  
Day Of The Week ◽  
In The Beginning

Traffic accidents, which continue to increase from year to year in Turkey and in the world, have become a huge problem that can result in serious traumas, injuries, and deaths, as well as their material and moral consequences. Many studies have been carried out in the world and in Turkey to reduce the number of traffic accidents, but these studies have not been very effective in reducing accidents. In this study, 3105 fatal or injured traffic accidents between 2010–2017 in Sarıyer district of Istanbul, Turkey’s largest city in terms of population, were discussed. We analyzed the statistical information on the subject in detail within the framework of geographic information systems. It has been tried to determine the sections where traffic accidents are concentrated in this region with studies based on spatial methods. Thematic accident map was created according to the accident types. In this context, the advantages and disadvantages of these methods were compared using Point Density, Kernel Density, Getis Ord Gi*, and Anselin Local Moran’s I (LISA) Spatial Autocorrelation. In addition, in order to observe the change in accidents, thematic accident and Kernel Density maps were created separately according to accident occurrence types in the beginning and last year. From this point of view, the changes that occurred in the accidents were interpreted. The current study determined that the most accidents were on some streets and these streets divided into regions in a plan. The cases were examined with statistical analyses according to accident types and using the Kernel Density method. In addition, it has been observed that Kernel Density method gives better visual results than other spatial methods. In this study, spatial analysis and statistical analysis methods were used to evaluate traffic accidents more realistically. The day of the week effect and month of the year effect on traffic accidents was investigated for the first time. In addition, it is proposed to bring a new approach to the prevention of traffic accidents by using hotspot, accident type, and day of the week effect.

Estimating wind speed probability distribution using kernel density method

2011 ◽  
Vol 81 (12) ◽  
pp. 2139-2146 ◽  
Author(s):  
Zhilong Qin ◽  
Wenyuan Li ◽  
Xiaofu Xiong
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Kernel Density ◽  
Density Method

scholarly journals DISTRIBUTION OF WATER POLLUTION AREAS BASED ON THE KERNEL DENSITY METHOD FOR LAPINDO MUDFLOW DISASTER OF SIDOARJO

2020 ◽  
Vol 15 (1) ◽  
pp. 40-45
Author(s):  
Moch Shofwan ◽  
Farida Nur Aini
Keyword(s):  
Water Pollution ◽  
High Risk ◽  
Kernel Density ◽  
Secondary Data ◽  
Pollution Risk ◽  
North West ◽  
Density Method ◽  
The North ◽  
Natural Event ◽  
Disaster Area

The Lapindo mudflow disaster in Sidoarjo Regency which occurred on May 29, 2006 is a natural event that is classified as high risk. This disaster has a significant impact, especially for the water pollution caused by various conditions due to the Lapindo mudflow disaster. The purpose of the study is to mapping the location and analyze the distribution of water pollution based on the kernel density method. This research uses spatial approachment through descriptive-quantitative, qualitative and explorative methods. Primary and secondary data are both used in the research. The research results showed that water pollution is found on the north, west, south and east sides of the Lapindo mudflow disaster area in villages in three sub-districts namely Porong, Tanggulangin, and Jabon with the highest radius of pollution risk maximum of 1 Km from the center of the mudflow. The results of the distribution of water pollution areas based on the method of kernel density showed that the distribution of the highest water pollution leads to the north and west sides of the center of the mudflow.

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