The SANET toolbox: new methods for network spatial analysis. This paper describes new methods, called network spatial methods, for analyzing spatial phenomena that occur on a network or alongside a network (referred to as network spatial phenomena). First, the paper reviews network spatial phenomena discussed in the related literature. Second, the paper shows the uniform network transformation, which is used in the study of non-uniform distributions on a network, such as the densities of traffic and population. Third, the paper outlines a class of network spatial methods, including nearest neighbor distance methods, K-function methods, cell count methods, clumping methods, the Voronoi diagrams and spatial interpolation methods. Fourth, the paper shows three commonly used computational methods to facilitate network spatial analysis. Fifth, the paper describes the functions of a GIS-based software package, called SANET, that perform network spatial methods. Sixth, the paper compares network spatial methods with the corresponding planar spatial methods by applying both methods to the same data set. This comparison clearly demonstrates how different conclusions can result. The conclusion summarizes the major findings.

References in zbMATH (referenced in 25 articles , 1 standard article )

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  1. Anderes, Ethan; Møller, Jesper; Rasmussen, Jakob G.: Isotropic covariance functions on graphs and their edges (2020)
  2. Bonnet, Édouard; Cabello, Sergio; Mohar, Bojan; Pérez-Rosés, Hebert: The inverse Voronoi problem in graphs. I: Hardness (2020)
  3. Cronie, Ottmar; Moradi, Mehdi; Mateu, Jorge: Inhomogeneous higher-order summary statistics for point processes on linear networks (2020)
  4. McSwiggan, Greg; Baddeley, Adrian; Nair, Gopalan: Estimation of relative risk for events on a linear network (2020)
  5. Uppala, Medha; Handcock, Mark S.: Modeling wildfire ignition origins in southern California using linear network point processes (2020)
  6. Moradi, M. Mehdi; Cronie, Ottmar; Rubak, Ege; Lachieze-Rey, Raphael; Mateu, Jorge; Baddeley, Adrian: Resample-smoothing of Voronoi intensity estimators (2019)
  7. Suman Rakshit; Adrian Baddeley; Gopalan Nair: Efficient Code for Second Order Analysis of Events on a Linear Network (2019) not zbMATH
  8. Bielik, M.; König, R.; Schneider, S.; Varoudis, T.: Measuring the impact of street network configuration on the accessibility to people and walking attractors (2018)
  9. Jentsch, Peter C.; Anand, Madhur; Bauch, Chris T.: Spatial correlation as an early warning signal of regime shifts in a multiplex disease-behaviour network (2018)
  10. van Lieshout, M. N. M.: Nearest-neighbour Markov point processes on graphs with Euclidean edges (2018)
  11. Bai, Hexiang; Li, Deyu; Ge, Yong; Wang, Jinfeng: Detecting nominal variables’ spatial associations using conditional probabilities of neighboring surface objects’ categories (2016)
  12. Bose, Prosenjit; De Carufel, Jean-Lou; Grimm, Carsten; Maheshwari, Anil; Smid, Michiel: Optimal data structures for farthest-point queries in cactus networks (2015)
  13. Blanquero, Rafael; Carrizosa, Emilio: Solving the median problem with continuous demand on a network (2013)
  14. Ang, Qi Wei; Baddeley, Adrian; Nair, Gopalan: Geometrically corrected second order analysis of events on a linear network, with applications to ecology and criminology (2012)
  15. Benigni, Matthew; Furrer, Reinhard: Spatio-temporal improvised explosive device monitoring: improving detection to minimise attacks (2012)
  16. Okabe, Atsuyuki; Sugihara, Kokichi: Spatial analysis along networks. Statistical and computational methods. (2012)
  17. Hiyoshi, Hisamoto: Spline interpolation on networks (2010)
  18. Griffith, Daniel A.: Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows (2009) ioport
  19. Okabe, Atsuyuki: Preface (2009) ioport
  20. Yamada, Ikuho; Rogerson, Peter A.; Lee, Gyoungju: \textitgeosurveillance: a GIS-based system for the detection and monitoring of spatial clusters (2009) ioport

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