nbstat | R Documentation |
Calculating descriptive neighbourhood statistics based on regions (polygons) with contiguous boundaries and resulting a data frame
nbstat(
polygon_sf,
ID_col,
link_data,
data_ID_col,
data_col,
func = "sum",
row.names = NULL
)
polygon_sf |
|
ID_col |
Column of |
link_data |
|
data_ID_col |
Column with unique ID of each polygon in |
data_col |
Column with regarded numeric values in |
func |
Descriptive statistic ( |
row.names |
row.names for the |
The function is based on spdep::poly2nb
, which creates neighbours lists. The input is a sf
object (spatial data frame) and the results are 1) a nb
list (poly2nb
result) and 2) a data.frame
.
list
with three entries:
nbmat : |
Object of class |
nbmat_data : |
Object of class |
nbmat_data_aggreagte : |
Object of class |
Thomas Wieland
Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.18335/region.v7i2.324")}
Wieland T (2022) Spatial patterns of excess mortality in the first year of the COVID-19 pandemic in Germany. European Journal of Geography 13(4), 18-33. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.48088/ejg.t.wie.13.4.018.033")}
nbmatrix
data(RKI_Corona_counties)
# German counties (Source: Robert Koch Institute)
Corona_nbstat <-
nbstat (
RKI_Corona_counties,
ID_col="AGS",
link_data = RKI_Corona_counties,
data_ID_col = "AGS",
data_col = "EWZ",
func = "sum"
)
Corona_nbstat$nbmat_data_aggregate
# Sum of population (EWZ) of neighbouring counties
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