Gij.polygon {spnaf}

Calculate spatial autocorrelation with OD data and corresponding polygons.

Description

Calculate spatial autocorrelation with OD data and corresponding polygons.

Usage

Gij.polygon(df, shape, queen = TRUE, snap = 1, method = "t", R = 1000)

Arguments

df

A data.frame that contains your Origin-Destination data. The df must consist of "oid" (origin id), "did" (destination id), "n" (flow weight).

shape

A shapefile (in a polygon type) that matches to your OD dataframe. The shape must have an "id" column to match your ids in df.

queen

A TRUE/FALSE input that is used to calculate spdep's spatial contingency (Please view documents of poly2nb for more information).

snap

A parameter that is also used to calculate spdep's spatial contingency (Please view documents of poly2nb for more information).

method

A string value among "o" (origin based), "d" (destination based), and "t" (both way) which determines the way to generate Spatial Weights. The default value is "t".

R

An integer value to define how many times you want to execute bootstrapping.

Value

The result is in the form of a list which includes a dataframe and a sf object. Both contain Gij statistics and p-value columns merged to your input df. The geometry type of the latter is linestring.

References

Berglund, S., & Karlström, A. (1999). Identifying local spatial association in flow data, Journal of Geographical Systems, 1(3), 219-236. https://doi.org/10.1007/s101090050013

Examples

# Data manipulation
CA <- spnaf::CA
OD <- cbind(CA$FIPS.County.Code.of.Geography.B, CA$FIPS.County.Code.of.Geography.A)
OD <- cbind(OD, CA$Flow.from.Geography.B.to.Geography.A)
OD <- data.frame(OD)
names(OD) <- c("oid", "did", "n")
OD$n <- as.numeric(OD$n)
OD <- OD[order(OD[,1], OD[,2]),]
head(OD) # check the input df's format

# Load sf polygon
CA_polygon <- spnaf::CA_polygon
head(CA_polygon) # it has a geometry column

# Execution of Gij.polygon with data above and given parameters

result <- Gij.polygon(df = OD, shape = CA_polygon, queen = TRUE, snap = 1,
method = 't', R = 1000)


# check the results

head(result[[1]])
head(result[[2]])