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 Analysis of spatial distribution of touristic accommodation in Poland with the kernel density estimation of POIs

2021 ◽  
Vol 20 (3) ◽  
pp. 159-171
Author(s):  
Mirosław Bełej
Keyword(s):  
Spatial Distribution ◽  
Spatial Heterogeneity ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Tourist Attraction ◽  
Points Of Interest ◽  
Different Types ◽  
Close Relationship ◽  
Gis Software

Motives: Using Points-of-Interest (POIs) data and GIS software, the spatial heterogeneity of different types of accommodation could cheap, easily and quick be analyzed. Aim: The use of kernel density estimation (KDE) of Points-of-Interest data to shown spatial distribution of different types of accommodation in Poland. Results: There is a close relationship between the type of accommodation and the type of tourist attraction.

scholarly journals Density Estimation of Mobile Users’ Address Queries before and during the COVID-19 Pandemic

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 5
Author(s):  
M. Fevzi Esen ◽  
Tutku Tuncalı Yaman
Keyword(s):  
Spatial Distribution ◽  
Density Estimation ◽  
Social Mobility ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Mobile Users ◽  
Heat Map

The aim of this study was to monitor social mobility using mobile users’ address searches before and during the outbreak of COVID-19. Mobile Google users’ address inquiries between the dates of 15 February 2020 and 27 July 2020 in the historical peninsula of Istanbul were gathered. The spatial distribution of the searches was examined and a heat map was produced based on kernel density estimation (KDE). The density of the inquiries started to decline in March, which is the month in which the first cases were reported in Turkey. An increase was reported in address queries in June and July.

scholarly journals Fire occurrence zones: kernel density estimation of historical wildfire ignitions at the national level, Greece

2014 ◽  
Vol 10 (4) ◽  
pp. 630-639 ◽  
Author(s):  
Nikos Koutsias ◽  
Panagiotis Balatsos ◽  
Kostas Kalabokidis
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
National Level ◽  
Kernel Density ◽  
Fire Occurrence

scholarly journals Analysis On Spatial Distribution And Influencing Factors Of China National Forest Villages Based on GIS

2021 ◽  
Author(s):  
Zhen Wei ◽  
Fengtai Zhang ◽  
Yuzhen Li ◽  
Youzhi An ◽  
Changcheng Sun ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Influencing Factors ◽  
Hot Spots ◽  
Kernel Density ◽  
National Forest ◽  
Distribution Characteristics ◽  
The North ◽  
Spatial Distribution Characteristics ◽  
Cold Spots

Abstract As a carrier to promote rural greening and beautification and further implement the rural revitalization strategy, it is of great significance to study the spatial distribution characteristics of China national forest villages and its influencing factors. Taking 7586 China national forest villages as examples, the spatial distribution characteristics and influencing factors of China national forest villages were studied by using such methods as nearest neighbor index, Tyson polygon, cold hot spot analysis, standard deviation ellipse and kernel density index. The results showed that: (1) the overall clustering distribution characteristics of China national forest villages were significant, and the distribution type was agglomeration. There was no breakthrough in the Hu Huanyong Line, and the southeast of the line was the main concentration area. (2) From the perspective of spatial clustering, it shows the distribution characteristics of "hot spots in the south and cold spots in the north". Hot spots are mostly located in the south, represented by Sichuan, Guangdong, Hunan, etc., while cold spots are mostly in the north, mainly in Xinjiang, Xizang, etc.(3) From the perspective of spatial distribution direction, the standard deviation ellipse coincidence degree of the two batches is relatively high. The two batches are distributed in a dense direction from northeast to southwest with Suizhou city, Hubei as the geometric center. The concentration degree of the second batch increases on the basis of the first batch, showing a trend of migration and distribution in the southwest.(4) From the perspective of the distribution characteristics of kernel density, the distribution of kernel density has a strong correlation with two factors, one is the forest vegetation coverage, the other is the distribution location of urban agglomeration;(5) Elevation, slope direction, river basin, traffic are important factors affecting the distribution of China national forest villages, which show the spatial distribution characteristics of "low altitude, positive, near water and convenient transportation". Based on the spatial distribution characteristics and influencing factors, the paper puts forward policy Suggestions for the evaluation and construction of China national forest villages in the future.

scholarly journals Polycentric intra-urban development of hotels: Evidence from Budapest

2019 ◽  
Vol 92 (4) ◽  
pp. 429-442
Author(s):  
Tomasz Napierała
Keyword(s):  
Spatial Distribution ◽  
Theoretical Model ◽  
Urban Development ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Economic Transition ◽  
Kernel Density ◽  
Theoretical Concept ◽  
Evolutionary Changes ◽  
Location Policy

The aim of the paper is to understand evolutionary changes of hotel intra-urban location policy during the period of the economic transition. Thus, the theoretical model of polycentric intra-urban development of hotel facilities is introduced in this research. Polycentric development is defined as the result of two ongoing and contrary tendencies: (1) spatial sprawl of hotel facilities resulting from new hotel investments, and (2) concentration of hotel enterprises, which is the effect of demand-based and production-based agglomeration processes of hotel facilities in particular locations. To examine this theoretical concept, the changes of spatial distribution of hotel entities in Budapest since 1982 were investigated. Kernel density estimation was applied to identify the number, location, and area of clusters of hotel services. Empirical evidence confirms the proposed theoretical model of polycentric intra-urban development of hotels, although significant hotel clusters are only formed in the central districts of Budapest.

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