Data: CA | Data: CA_polygon | Function: Gij.flow | How to execute | Interpretation of the result | Visualization of all flows and Significant Flows(<0.05) only | Reference
Package: spnaf 1.1.0
Youngbin Lee
spnaf: Spatial Network Autocorrelation for Flow Data
Identify statistically significant flow clusters using the local spatial network autocorrelation statistic G_ij* proposed by 'Berglund' and 'Karlström' (1999) <doi:10.1007/s101090050013>. The metric, an extended statistic of 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>, detects a group of flows having similar traits in terms of directionality. You provide OD data and the associated polygon to get results with several parameters, some of which are defined by spdep package.
Authors:
spnaf_1.1.0.tar.gz
spnaf_1.1.0.tar.gz(r-4.7-any)spnaf_1.1.0.tar.gz(r-4.6-any)
spnaf_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
spnaf/json (API)
| # Install 'spnaf' in R: |
| install.packages('spnaf', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- CA - Sample migration data by counties in California.
- CA_polygon - Sample polygon data of California counties.
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:5aa246f65d. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 161 | ||
| source / vignettes | OK | 235 | ||
| linux-release-x86_64 | OK | 162 | ||
| wasm-release | OK | 182 |
Dependencies:bootclassclassIntclicpp11DBIdeldirdplyre1071genericsglueKernSmoothlatticelifecyclemagrittrMASSpillarpkgconfigproxypurrrR6Rcpprlangs2sfspspDataspdepstringistringrtibbletidyrtidyselectunitsutf8vctrswithrwk
