scholarly journals The spatial distribution characteristics of the centrality advantage stations in Hangzhou metro planning (2005-2022)

2021 ◽  
Vol 634 (1) ◽  
pp. 012110
Author(s):  
Fang Wu ◽  
Yue Zhu ◽  
Wei Zhu ◽  
Kaiqiang Wang ◽  
Peng Shen ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Distribution Characteristics ◽  
Spatial Distribution Characteristics

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.

scholarly journals Temporal-spatial Distribution Characteristics of Air Pollutants in Chengdu Economic Region, China

2019 ◽  
Vol 118 ◽  
pp. 04027
Author(s):  
Hongjin Tong ◽  
Sha Liu ◽  
Ruixue Liao ◽  
Xiaomei Wei ◽  
Kangli Che ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Temporal Variation ◽  
Air Pollutants ◽  
Interpolation Method ◽  
Central Area ◽  
Average Concentration ◽  
Distribution Characteristics ◽  
Economic Region ◽  
Spatial Distribution Characteristics ◽  
Variation Characteristics

The previous characteristics researches of air pollution were almost based on data from national environmental monitoring stations in 2015. The temporal variation curves of air pollutants and the ArcGIS grid interpolation method were used to analyze the spatial-temporal variation of air pollutants in five cities of Chengdu economic region. In 2015, the monthly change trends of PM2.5, PM10, CO, NO2 and NO of air pollutants in Chengdu economic region were basically the same. The maximum monthly average concentration was in January or December, and the minimum was in May to September. The temporal variation of SO2 was characterized by little fluctuation of monthly concentration. The temporal variation characteristics of O3 were opposite to other pollutants. The spatial distribution of PM10 and PM2.5 was characterized by the largest concentration in Chengdu and the southwest of Meishan, in which they were mainly concentrated in the central area of Chengdu in winter. The average concentration of CO in Chengdu was the largest, followed by Deyang and Mianyang, and Meishan and Ziyang was the smallest. The concentrations of NO2 and NO in Chengdu were the largest, while those in Ziyang were the smallest. The spatial distribution characteristics of O3 were different from other pollutants. The areas with the largest concentration of O3 were Ziyang and a small part of west in Chengdu. The spatial distribution of SO2 was characterized by the largest concentration of SO2 in Ziyang, the lowest concentration in Mianyang and Deyang.

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