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 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 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 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.

scholarly journals Robust Background Modeling with Kernel Density Estimation

2015 ◽  
Vol 11 (8) ◽  
pp. 13
Author(s):  
Man Hua ◽  
Yanling Li ◽  
Yinhui Luo
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Video Processing ◽  
Moving Objects ◽  
Kernel Density ◽  
Local Threshold ◽  
Global Threshold ◽  
Different Types ◽  
Temporal Persistence ◽  
And Performance

Modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. In this paper, we proposed a novel adaptive approach modeling background and segmenting moving object with non-parametric kernel density estimation. Unlike previous approaches to object detection which detect objects by global threshold, we use a local threshold to reflect temporal persistence. With combined of global threshold and local thresholds, the proposed approach can handle scenes containing gradual illumination variations and noise and has no bootstrapping limitations. Experimental results on different types of videos demonstrate the utility and performance of the proposed approach.

scholarly journals Ther varom mid j hia: Tracing linguistic diffusion in the history of Norwegian using kernel density estimation

2019 ◽  
Vol 27 (1) ◽  
pp. 5-34
Author(s):  
Tam Blaxter
Keyword(s):  
Density Estimation ◽  
Middle Ages ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Gaussian Kernel ◽  
Historical Sources ◽  
Nearest Neighbours ◽  
Different Types ◽  
Language Data ◽  
History Of

Abstract Tracing the diffusion of linguistic innovations in space from historical sources is challenging. The complexity of the datasets needed in combination with the noisy reality of historical language data mean that it has not been practical until recently. However, bigger historical corpora with richer spatial and temporal information allow us to attempt it. This paper presents an investigation into changes affecting first person non-singular pronouns in the history of Norwegian: first, individual changes affecting the dual (vit > mit) and plural (vér > mér), followed by loss of the dual-plural distinction by merger into either form or replacement of both by Danish-Swedish vi. To create dynamic spatial visualisations of these changes, the use of kernel density estimation is proposed. This term covers a range of statistical tools depending on the kernel function. The paper argues for a Gaussian kernel in time and an adaptive uniform (k-nearest neighbours) kernel in space, allowing uncertainty or multiple localisation to be incorporated into calculations. The results for this dataset allow us to make a link between Modern Norwegian dialectological patterns and language use in the Middle Ages; they also exemplify different types of diffusion process in the spread of linguistic innovations.

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

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