spNetwork - Spatial Analysis on Network

Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).

Last updated 6 months ago

kernelkernel-density-estimationnetworknetwork-analysisspatialspatial-analysisspatial-data-analysis

7.38 score 35 stars 46 scripts 228 downloads

geocmeans - Implementing Methods for Spatial Fuzzy Unsupervised Classification

Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed. The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).

Last updated 7 days ago

clusteringcmeansfuzzy-classification-algorithmsspatial-analysisspatial-fuzzy-cmeansunsupervised-learning

6.04 score 27 stars 82 scripts 298 downloads