Map, Area and Paramerters

The LCZ4r general functions are designed to facilitate the retrieval, analysis, and visualization of Local Climate Zones (LCZ) when LCZ related data is not readily available. They are engineered to get the LZ map, compute LCZ classes, and extract specific parameters of interest, including Sky View Factor (SVF), impervious surface fraction, and roughness element height, among others.

Quickstart DEMO video

Introduction to General Functions

(Click to Watch video)

Getting Started

The general functions, each serving a unique purpose:

Function	Description	Data Required	Internet Access Required
`lcz_get_map()`	Download your LCZ map from Global dataset	Not needed	Yes
`lcz_get_map_euro()`	Download your LCZ map from European dataset	Not needed	Yes
`lcz_get_map_usa()`	Download your LCZ map from Continental United States dataset	Not needed	Yes
`lcz_get_map2()`	Download the LCZ map	Not needed	Not needed
`lcz_plot_map()`	Visualize the LCZ map	Not needed	Not needed
`lcz_cal_area()`	Calculate LCZ areas	Not needed	Not needed
`lcz_get_parameters()`	Retrieve LCZ parameters	Not needed	Not needed
`lcz_plot_parameters()`	Visualize LCZ parameters map	Not needed	Not needed

Tips: Utilize the help(lcz_*) function to access comprehensive documentation for each LCZ function. For instance, you can explore details about the lcz_get_map function by typing help(“lcz_get_map”).

Download and visualize the LCZ map

The lcz_get_map() function facilitates obtaining the LCZ map for your chosen city or a specific Region of Interest (ROI). In our demonstration, we utilized a couple of cities around the world.

library(LCZ4r)
# Get the LCZ map for your city
lcz_map <- lcz_get_map(city="São Paulo")

# Visualize the obtained LCZ map
lcz_plot_map(lcz_map)

euro_map <- lcz_get_map_euro(city="Paris")
lcz_plot_map(euro_map)

euro_map <- lcz_get_map_euro(city="London")
lcz_plot_map(euro_map)

lcz_map <- lcz_get_map(city="Sydney")
lcz_plot_map(lcz_map)

lcz_map <- lcz_get_map(city="京")
lcz_plot_map(lcz_map)

usa_map <- lcz_get_map_usa(city = "New York")
lcz_plot_map(usa_map)

my_map <- lcz_get_map(city = "Nairobi")
lcz_plot_map(my_map)

Here are some tips to enhance your experience:

1. As an alternative, users can download their own LCZ map from WUDAPT or LCZ generator application and integrate them into the LCZ4r system using the lcz_get_map2(LCZmap, city = “your city”).
2. Fallback Option: In rare instances where the lcz_get_map() function faces inconsistencies (e.g., due to connectivity issues), you can rely on lcz_get_map2() as an alternative solution.
3. Custom ROI: Utilize the lcz_get_map() function along with a shapefile for your ROI by coding lcz_get_map(roi=“your_shp”). Refer to the R package sf documentation for guidance on loading the shapefile into R. ## Calculate LCZ areas

Calculate LCZ areas

This lcz_cal_area() function calculates the area for each LCZ classes, as follows:

lcz_map <- lcz_get_map(city="Rio de Janeiro")
lcz_cal_area(lcz_map)

Tips: this function provides a table.csv of the calculated LCZ areas, coding like this: lcz_cal_area(lcz_map, iplot = FALSE)

Retrieve and visualize LCZ parameters

This lcz_get_parameters()function gives you to retrieve diverse LCZ parameters, encompassing minimum, maximum, and mean values, drawn from Stewart and Oke (2012). These parameters are convertible into either shapefiles or a raster stack, where multiple rasters are aggregated into a single file.

Here’s a glimpse of the available LCZ parameters:

Code	Parameter	Description	Unit
SVF1	Sky View Factor	Minimum Sky View Factor	[0-1]
SVF2	Sky View Factor	Maximum Sky View Factor	[0-1]
SVF3	Sky View Factor	Mean Sky View Factor	[0-1]
AR1	Aspect ratio	Minimum Aspect Ratio	[0-3]
AR2	Aspect ratio	Maximum Aspect Ratio	[0-3]
AR3	Aspect ratio	Mean Aspect Ratio	[0-3]
BSF1	Building Surface Fraction	Minimum Building Surface Fraction	[%]
BSF2	Building Surface Fraction	Maximum Building Surface Fraction	[%]
BSF3	Building Surface Fraction	Mean Building Surface Fraction	[%]
ISF1	Impervious Surface Fraction	Minimum Impervious Surface Fraction	[%]
ISF2	Impervious Surface Fraction	Maximum Impervious Surface Fraction	[%]
ISF3	Impervious Surface Fraction	Mean Impervious Surface Fraction	[%]
PSF1	Vegetation Surface Fraction	Minimum Vegetation Surface Fraction	[%]
PSF2	Vegetation Surface Fraction	Maximum Vegetation Surface Fraction	[%]
PSF3	Vegetation Surface Fraction	Mean Vegetation Surface Fraction	[%]
TSF1	Tree Surface Fraction	Minimum Tree Surface Fraction	[%]
TSF2	Tree Surface Fraction	Maximum Tree Surface Fraction	[%]
TSF3	Tree Surface Fraction	Mean Tree Surface Fraction	[%]
HRE1	Height Roughness Elements	Minimum Height Roughness Elements	[meters]
HRE2	Height Roughness Elements	Maximum Height Roughness Elements	[meters]
HRE3	Height Roughness Elements	Mean Height Roughness Elements	[meters]
TRC1	Terrain Roughness class	Minimum Terrain Roughness class	[meters]
TRC2	Terrain Roughness class	Maximum Terrain Roughness class	[meters]
TRC3	Terrain Roughness class	Mean Terrain Roughness class	[meters]
SAD1	Surface Admittance	Minimum Surface Admittance	[J m-2 s1/2 K-1]
SAD2	Surface Admittance	Maximum Surface Admittance	[J m-2 s1/2 K-1]
SAD3	Surface Admittance	Mean Surface Admittance	[J m-2 s1/2 K-1]
SAL1	Surface Albedo	Minimum Surface Albedo	[0-0.5]
SAL2	Surface Albedo	Maximum Surface Albedo	[0-0.5]
SAL3	Surface Albedo	Mean Surface Albedo	[0-0.5]
AH1	Anthropogenic Heat Outupt	Minimum Anthropogenic Heat Outupt	[W m-2]
AH2	Anthropogenic Heat Outupt	Maximum Anthropogenic Heat Outupt	[W m-2]
AH3	Anthropogenic Heat Outupt	Mean Anthropogenic Heat Outupt	[W m-2]
z0	Roughness Lenght	Roughness Lenght class	[meters]

Rio de Janeiro LCZ parameters

# Extract the LCZ parameters for raster stack format
lcz_map <- lcz_get_map(city="Rio de Janeiro")
LCZpar <- lcz_get_parameters(lcz_map, istack = TRUE)
LCZpar

Maps

lcz_plot_parameters(LCZpar, iselect = "SVF3")

lcz_plot_parameters(LCZpar, iselect = "BSF2")

lcz_plot_parameters(LCZpar, iselect = "z0")

Have feedback or suggestions?

Do you have an idea for improvement or did you spot a mistake? We’d love to hear from you! Click the button below to create a new issue (Github) and share your feedback or suggestions directly with us.

Max Anjos

November 11, 2024