GIS als Hilfsmittel zur Analyse räumlicher Strukturen im östlichen Sachsen und Thüringen des 10. und frühen 11. Jahrhunderts: Eine Königslandschaft neu betrachtet

2019 ◽  
Vol 24 (1) ◽  
pp. 91-111
Author(s):  
Pierre Fütterer
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Historical Data ◽  
Kernel Density ◽  
Spatial Structures ◽  
Harz Mountains ◽  
Medieval Studies ◽  
Settlement Density ◽  
Investigation Area ◽  
Very High

Abstract For some time, the use of GIS in the context of medieval studies has been increasing. Aside from providing opportunities to visualise historical data in an uncomplicated way, GIS offers numerous tools such as viewshed, kernel density estimation or georeferencing, allowing new insights into historical contexts, which at the same time can reveal new avenues for research. This paper illustrates both the potential and difficulties of working with GIS on the analysis of spatial structures in early medieval Eastern Saxony and Thuringia. The main outcomes are a very high settlement density in the entire investigation area, with special concentrations in the eastern Harz foreland, the Hassegau, as well as a rapid and early expansion of the Harz Mountains.

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

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.

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