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A tidy interface for spatial dependence

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JosiahParry/sfdep

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CRAN statusR-CMD-checkLifecycle: stable

sfdep builds on the great shoulders of spdep package for spatialdependence. sfdep creates an sf and tidyverse friendly interface to thepackage as well as introduces new functionality that is not present inspdep. sfdep utilizes list columns extensively to make this interfacepossible.

Installation

Install the released version from CRAN

install.packages("sfdep")

You can install the development version of sfdep like so:

remotes::install_github("josiahparry/sfdep")

Usage

There are three main categories of functionality relating to geometryneighbors, weights, and local indicators of spatial association (LISAs).

Neighbors

The most fundamental usage is to find contiguous neighbors from apolygon. This is done withst_contiguity() which, by default createsqueen weights. If rook weights are desired, setqueen = FALSE.Additional arguments can be passed to the underlyingspdep::poly2nb()via....st_contiguity() creates an object of classnb as used byspdep.

library(sf)library(sfdep)library(dplyr)# grab geometrygeo<- st_geometry(guerry)nb<- st_contiguity(geo)nb#> Neighbour list object:#> Number of regions: 85#> Number of nonzero links: 420#> Percentage nonzero weights: 5.813149#> Average number of links: 4.941176

We can identify higher order neighbors withst_nb_lag() and thecumulative higher order neighbors withst_nb_lag_cumul().

st_nb_lag(nb,2)#> Neighbour list object:#> Number of regions: 85#> Number of nonzero links: 756#> Percentage nonzero weights: 10.46367#> Average number of links: 8.894118st_nb_lag_cumul(nb,2)#> Neighbour list object:#> Number of regions: 85#> Number of nonzero links: 1176#> Percentage nonzero weights: 16.27682#> Average number of links: 13.83529

Other point geometry neighbor functions arest_knn(),st_dist_band(),st_nb_dists().

Weights

Polygon weights are created withst_weights() (which callsspdep::nb2listw). By default they are row standardized weights.

wt<- st_weights(nb)wt[1:2]#> [[1]]#> [1] 0.25 0.25 0.25 0.25#>#> [[2]]#> [1] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667

Other point based weights can be created withst_nb_dists(),st_kernel_weights() andst_inverse_weights().

Local Indicators of Spatial Association (LISAs)

LISAs are created from a combination of neighbors and weights and areintended to be used inside of a dplyr pipeline. The below is a workedexample of calculating the spatial lag and the local moran.

g<-guerry %>%   mutate(nb= st_contiguity(geometry),wt= st_weights(nb)    )

Then calculate the spatial lag withst_lag(). Given that we’ve onlymodified an sf object, we can visualize this with ggplot2.

library(ggplot2)# create spatial lagg %>%   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). Utilizelocal_moran() to do this.local_moran() will createa 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 certainvariables hoisted.

Create the Local Moran data frame column.

lisa<-g %>%   mutate(moran= local_moran(crime_pers,nb,wt))pull(lisa,moran) %>%   glimpse()#> Rows: 85#> Columns: 12#> $ ii           <dbl> 0.52226452, 0.82801651, 0.80353997, 0.74188966, 0.2311871…#> $ eii          <dbl> -0.0436664933, 0.0169987175, -0.0106696690, -0.0015410148…#> $ var_ii       <dbl> 0.3648295427, 0.1244317578, 0.1409560743, 0.2311181704, 0…#> $ z_ii         <dbl> 0.9369545, 2.2991365, 2.1686743, 1.5464057, 1.1463544, 1.…#> $ p_ii         <dbl> 0.348781971, 0.021497187, 0.030107416, 0.122006629, 0.251…#> $ p_ii_sim     <dbl> 0.376, 0.016, 0.036, 0.092, 0.284, 0.124, 0.560, 0.108, 0…#> $ p_folded_sim <dbl> 0.188, 0.008, 0.018, 0.046, 0.142, 0.062, 0.280, 0.054, 0…#> $ skewness     <dbl> 0.186247324, -0.166050386, -0.065842084, -0.148874532, 0.…#> $ kurtosis     <dbl> -0.256988635, -0.083615702, -0.115769407, -0.105166850, 0…#> $ mean         <fct> High-High, High-High, High-High, Low-Low, Low-Low, Low-Lo…#> $ median       <fct> High-High, High-High, High-High, Low-Low, Low-Low, Low-Lo…#> $ pysal        <fct> High-High, High-High, High-High, Low-Low, Low-Low, Low-Lo…

Visualize this by converting insignificant values to NA. This uses acutoff of 0.1.

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"))

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