Interpolate spatial and temporal data using a Local Climate Zones (LCZ)

Generates an interpolated spatial map using LCZ as background. This function applies geostatistical interpolation techniques (e.g., kriging) to estimate variable values (e.g., air temperature) across the study area, integrating LCZ classifications. The function allows for flexible time period selection, like hour, daytime, nighttime and so on.

Usage

lcz_interp_map(
  x,
  data_frame = "",
  var = "",
  station_id = "",
  ...,
  extract.method = "simple",
  sp.res = 100,
  tp.res = "hour",
  vg.model = "Sph",
  by = NULL,
  impute = NULL,
  isave = FALSE,
  LCZinterp = TRUE
)

Arguments

x

A SpatRaster object representing the LCZ map, obtained via lcz_get_map() functions.

data_frame

A data frame containing air temperature measurements with station metadata. The data must include a date column in hourly or higher resolution.

var

Character. The variable to interpolate (e.g., "airT" for air temperature).

station_id

Character. The column name in data_frame identifying weather stations.

...

Additional arguments passed to selectByDate from the openair package for flexible time selection. Options include:

Year(s): year = 1998:2004 (selects 1998 to 2004) or year = c(1998, 2004).
Month(s): month = 1:6 (January to June) or month = c("January", "December").
Day(s): day = 1:30 (days 1 to 30) or day = 15.
Hour(s): hour = 0:23 (all hours) or hour = 9.
Start/End Date: "YYYY-mm-dd" format (e.g., "1999-02-01").

extract.method

Character. The method used to assign LCZ classes to stations:

"simple": Assigns the LCZ based on the raster cell where the station is located. It often is used in low-density observational network.
"two.step": Filters out stations in heterogeneous LCZ areas (Daniel et al., 2017). It often is used in ultra and high-density observational network, especially in LCZ classes with multiple stations.
"bilinear": Uses bilinear interpolation to assign LCZ values.

sp.res

Numeric. Spatial resolution in meters (default: 100).

tp.res

Character. Temporal resolution for averaging ("hour", "day", etc.).

vg.model

Character. Variogram model for kriging. Default is "Sph" (spherical). Available models:

"Sph": Spherical model.
"Exp": Exponential model.
"Gau": Gaussian model.
"Ste": M. Stein's parameterization.

by

Character. Specifies how to split the time series. Options include "year", "month", "weekday", "weekend", etc.

impute

Character. Method to impute missing values ("mean", "median", "knn", "bag").

isave

Logical. If TRUE, saves the plot to the working directory.

LCZinterp

Logical. If TRUE (default), applies LCZ-based interpolation.

Value

A spatially interpolated map in terra raster GeoTIFF format.

References

Anjos, M., Targino, A. C., Krecl, P., Oukawa, G. Y. & Braga, R. F. Analysis of the urban heat island under different synoptic patterns using local climate zones. Build. Environ. 185, 107268 (2020). Fenner, D., Meier, F., Bechtel, B., Otto, M. & Scherer, D. (2017). Intra and inter ‘local climate zone’ variability of air temperature as observed by crowdsourced citizen weather stations in Berlin, Germany. Meteorol. Z., 26, 525–547. http://www.gstat.org/

Author

Max Anjos (https://github.com/ByMaxAnjos)

Examples

if (FALSE) { # \dontrun{
# Load sample data and LCZ map
data("lcz_data")
lcz_map <- lcz_get_map_generator(ID = "8576bde60bfe774e335190f2e8fdd125dd9f4299")

# Plot the LCZ map
lcz_plot_map(lcz_map)

# Perform interpolation for 2020-01-02 at 04:00h
my_interp <- lcz_interp_map(
  lcz_map, data_frame = lcz_data, var = "airT",
  station_id = "station", year = 2020, month = 1, day = 2, hour = 4,
  sp.res = 100, tp.res = "hour", vg.model = "Sph",
  LCZinterp = TRUE
)

# Perform interpolation for an entire day (returns a raster stack of 24 hours)
my_interp <- lcz_interp_map(
  lcz_map, data_frame = lcz_data, var = "airT",
  station_id = "station", year = 2020, month = 1, day = 2,
  sp.res = 100, tp.res = "hour", vg.model = "Sph",
  LCZinterp = TRUE
)
} # }