https://github.com/josiahparry/sfdep
A tidy interface for spatial dependence
https://github.com/josiahparry/sfdep
r-spatial rstats spatial
Last synced: about 1 month ago
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A tidy interface for spatial dependence
- Host: GitHub
- URL: https://github.com/josiahparry/sfdep
- Owner: JosiahParry
- License: gpl-3.0
- Created: 2022-01-08T15:05:49.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-09-05T16:03:56.000Z (9 months ago)
- Last Synced: 2025-04-06T01:27:03.849Z (about 1 month ago)
- Topics: r-spatial, rstats, spatial
- Language: R
- Homepage: https://sfdep.josiahparry.com/
- Size: 20.9 MB
- Stars: 131
- Watchers: 5
- Forks: 5
- Open Issues: 15
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# sfdep
[](https://CRAN.R-project.org/package=sfdep)
[](https://github.com/JosiahParry/sfdep/actions/workflows/R-CMD-check.yaml)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
sfdep builds on the great shoulders of spdep package for spatial dependence. sfdep creates an sf and tidyverse friendly interface to the package as well as introduces new functionality that is not present in spdep. sfdep utilizes list columns extensively to make this interface possible.
## Installation
Install the released version from CRAN
```r
install.packages("sfdep")
```
You can install the development version of sfdep like so:
``` r
remotes::install_github("josiahparry/sfdep")
```
## Usage
There are three main categories of functionality relating to geometry neighbors, weights, and local indicators of spatial association (LISAs).
### Neighbors
The most fundamental usage is to find contiguous neighbors from a polygon. This is done with `st_contiguity()` which, by default creates queen weights. If rook weights are desired, set `queen = FALSE`. Additional arguments can be passed to the underlying `spdep::poly2nb()` via `...`. `st_contiguity()` creates an object of class `nb` as used by `spdep`.
```{r message=FALSE}
library(sf)
library(sfdep)
library(dplyr)
# grab geometry
geo <- st_geometry(guerry)
nb <- st_contiguity(geo)
nb
```
We can identify higher order neighbors with `st_nb_lag()` and the cumulative higher order neighbors with `st_nb_lag_cumul()`.
```{r}
st_nb_lag(nb, 2)
st_nb_lag_cumul(nb, 2)
```
Other point geometry neighbor functions are `st_knn()`, `st_dist_band()`, `st_nb_dists()`.
### Weights
Polygon weights are created with `st_weights()` (which calls `spdep::nb2listw`). By default they are row standardized weights.
```{r}
wt <- st_weights(nb)
wt[1:2]
```
Other point based weights can be created with `st_nb_dists()`, `st_kernel_weights()` and `st_inverse_weights()`.
### Local Indicators of Spatial Association (LISAs)
LISAs are created from a combination of neighbors and weights and are intended to be used inside of a dplyr pipeline. The below is a worked example of calculating the spatial lag and the local moran.
```{r, message = FALSE, warn = FALSE}
g <- guerry %>%
mutate(
nb = st_contiguity(geometry),
wt = st_weights(nb)
)
```
Then calculate the spatial lag with `st_lag()`. Given that we've only modified an sf object, we can visualize this with ggplot2.
```{r}
library(ggplot2)
# create spatial lag
g %>%
mutate(crime_pers_lag = st_lag(crime_pers, nb, wt)) %>%
ggplot(aes(fill = crime_pers_lag)) +
geom_sf(lwd = 0.2, color = "black") +
theme_void()
```
Most users will be interested in local indicators of spatial association (LISA). Utilize `local_moran()` to do this. `local_moran()` will create a data frame column which contains a number of informative variables. For example the cluster that a polygon falls into based on mean, median, or pysal calculations. This will need to be unnested or certain variables hoisted.
Create the Local Moran data frame column.
```{r}
lisa <- g %>%
mutate(moran = local_moran(crime_pers, nb, wt))
pull(lisa, moran) %>%
glimpse()
```
Visualize this by converting insignificant values to NA. This uses a cutoff of 0.1.
```{r}
lisa %>%
tidyr::unnest(moran) %>%
mutate(pysal = ifelse(p_folded_sim <= 0.1, as.character(pysal), NA)) |>
ggplot(aes(fill = pysal)) +
geom_sf() +
geom_sf(lwd = 0.2, color = "black") +
theme_void() +
scale_fill_manual(values = c("#1C4769", "#24975E", "#EACA97", "#B20016"))
```
## Other topics:
* [Emerging Hot Spot Analysis](https://sfdep.josiahparry.com/articles/understanding-emerging-hotspots.html#using-emerging_hotspot_analysis)
* [Spatio-Temporal Data](https://sfdep.josiahparry.com/articles/spacetime-s3.html)
* [`{sfnetworks}` integration](https://sfdep.josiahparry.com/reference/index.html#networks)