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Conclusion
Published in Robin Lovelace, Jakub Nowosad, Jannes Muenchow, Geocomputation with R, 2019
Robin Lovelace, Jakub Nowosad, Jannes Muenchow
Package overlap is not necessarily a bad thing. It can increase resilience, performance (partly driven by friendly competition and mutual learning between developers) and choice, a key feature of open source software. In this context the decision to use a particular approach, such as the sf/tidyverse/raster ecosystem advocated in this book should be made with knowledge of alternatives. The sp/rgdal/rgeos ecosystem that sf is designed to supersede, for example, can do many of the things covered in this book and, due to its age, is built on by many other packages.4 Although best known for point pattern analysis, the spatstat package also supports raster and other vector geometries (Baddeley and Turner, 2005). At the time of writing (October 2018) 69 packages depend on it, making it more than a package: spatstat is an alternative R-spatial ecosystem.
A spatial statistical study of the distribution of Sardinian nuraghes
Published in Annals of GIS, 2022
Alfred Stein, Claudio Detotto, Mariana Belgiu
To address the issue of the non-random distribution of the nuraghes, we turn to spatial statistical methods (Baddeley, Rubak, and Turner 2016). At the scale of the Island of Sardinia, the collection of nuraghes can be seen as the realization of a point process, presenting objects that possibly show a distinctive pattern, i.e. densities that vary because of topographic features. Because of their large number, such an analysis is indispensable and can be insightful to quantify relationships with topographic factors. During the past decades, spatial point pattern analysis has developed as a methodology to identify and quantify relationships between observed point data and their determining variables. For spatial clustering, we explored different methods, but concentrate in this paper on the inhomogeneous G- and J-functions.
GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques
Published in International Journal of Injury Control and Safety Promotion, 2021
Amir Mohammadian Amiri, Navid Nadimi, Vahid Khalifeh, Moe Shams
A very basic form of point pattern analysis includes summary statistics, namely the mean centre, standard distance and standard deviational ellipse. These point pattern analysis techniques were commonly utilized before the recent revolutionary progress in the computers power and capacity. More sophisticated forms of spatial analysis methods can be classified into two groups: the density-based and distance-based approaches (Manuel, 2017). As mentioned, KDE, PDE and LDE are the three most well-known spatial analysis techniques. In this study, only KDE and mean centre were employed as the authors have investigated the mentioned techniques in their previous study (Amiri et al., 2018).