Black-Spot Analysis in Hungary Based on Kernel Density Estimation

Baranyai, Dávid; Sipos, Tibor

doi:10.3390/su14148335

Cited by 8 publications

(5 citation statements)

References 37 publications

(27 reference statements)

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“…2 ). To analyze the rationality of the layout of existing station sites, these data were processed as follows: Firstly, we calculated and normalize the kernel density of every category 23 , 24 with Eqs. ( 1 ) and ( 2 ).…”

Section: Data Sources and Methodsmentioning

confidence: 99%

Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China

Niu,

Wang,

Liu

et al. 2023

Sci Rep

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Urban economic development, congestion relief, and traffic efficiency are all greatly impacted by the thoughtful planning of urban metro station layout. with the urban area of Lanzhou as an example, the suitability of the station locations of the built metro stations of the rail transit lines 1 and 2 in the study area have been evaluated using multi-source heterogeneous spatial data through data collection, feature matrix construction, the use of random forest and K-fold cross-validation, among other methods. The average Gini reduction value was used to examine the contribution rate of each feature indicator based on the examination of model truthfulness. According to the study's findings: (1) K-fold cross-validation was applied to test the random forest model that was built using the built metro stations and particular factors. The average accuracy of the tests and out-of-bag data (OOB) of tenfold cross-validation were 89.62% and 91.285%, respectively. Additionally, the AUC area under the ROC curve was 0.9823, indicating that this time, from the perspective of the natural environment, traffic location, and social factors The 19 elements selected from the views of the urban function structure, social economics, and natural environment are closely associated to the locations of the metro station in the research region, and the prediction the findings are more reliable; (2) It becomes apparent that more than half of the built station sites display excellent agreement with the predicted sites in terms of geographical location by superimposing the built metro station sites with the prediction results and tally up their cumulative prediction probability values within the 300 m buffering zone; (3) Based on the contribution rate of each indicator to the model, transport facilities, companies, population density, night lighting, science, education and culture, residential communities, and road network density are identified as the primary influential factors, each accounting for over 6.6%. Subsequently, land use, elevation, and slope are found to have relatively lower contributions. The results of the research provided important information for the local metro's best location selection and planning.

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Section: Data Sources and Methodsmentioning

confidence: 99%

Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China

Niu,

Wang,

Liu

et al. 2023

Sci Rep

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show abstract

“…The estimation formula of kernel density is applied in this research. The estimation formula of kernel density is [6] :…”

Section: Kernel Density Analysismentioning

confidence: 99%

Spatial Differences of Commercial Vitality in Main Urban Area of Kunming City, China

2024

AJHSS

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The data used in this study includes 100,305 Point of Interest (POI) collected in 2022. The spatial pattern of commercial vitality in the main urban area of Kunming City and its differentiation are analyzed by average nearest neighbor distance analysis and kernel density estimation. The results show that: (1) The overall spatial distribution is unbalanced, indicating that there may be discrepancies in the development of commercial activities throughout the city. ( 2) The spatial distribution of commercial vitality in the main urban area of Kunming City is not balanced, and the spatial distribution of commercial vitality in the core area of the urban area differs greatly from that outside the first ring road.(3) The hotspots of commercial vitality are mainly located in the first ring of the core area, as well as the southeast area of the new urban district and the second and third rings.

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“…It involves overlaying the density of each feature point within a region to obtain the distribution hotspots of the points in the area. In this study, kernel density estimation is used to analyze the spatial differentiation characteristics of the main medical institutions in the urban area of Kunming.The estimation formula for kernel density is [7] :…”

Section: Kernel Density Analysismentioning

confidence: 99%

Spatial Distribution Pattern and Characteristics of Medical Institution in Kunming's Main Urban District Based on GIS

2024

phpm

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Taking the data of 3029 medical institution points in the main urban area of Kunming in 2022 as the basic data, including community clinics, general hospitals, and specialized hospitals, this study uses methods such as average nearest neighbor distance analysis, kernel density analysis, and spatial autocorrelation analysis to map the spatial distribution of medical institutions and explore their characteristics. The results show that: ① The medical points in the main urban area of Kunming exhibit strong spatial clustering characteristics. Among them, community clinic medical points have the strongest clustering, while the clustering of general hospital medical points is relatively poor. ② The spatial distribution of medical institutions in the main urban area of Kunming is not balanced. Medical institutions in the main urban area of Kunming are significantly clustered in the core area (within the first ring road) and dispersed in the outer area (outside the first ring road). ③ Different medical institutions in the main urban area of Kunming have different distributions. Specialized hospitals and general hospitals are mainly concentrated in the Cuihu area, while clinics have a high distribution density in areas such as Beichen, Baiyun, and Century City, in addition to the Cuihu area. Among the three types of medical institutions, clinics have the highest accessibility, while the accessibility of general hospitals is relatively low. This paper aims to conduct GIS spatial analysis of the existing medical institutions in the main urban area of Kunming, with the hope of providing important reference for the layout and planning of medical facilities.

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Black-Spot Analysis in Hungary Based on Kernel Density Estimation

Cited by 8 publications

References 37 publications

Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China

Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China

Spatial Differences of Commercial Vitality in Main Urban Area of Kunming City, China

Spatial Distribution Pattern and Characteristics of Medical Institution in Kunming's Main Urban District Based on GIS

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