library(sf) library(ggplot2) #Step1: Reading Spain borders esp0 <- st_read(dsn="./data/gadm41_ESP_shp/gadm41_ESP_0.shp", quiet = TRUE) #Step2: Reading wildfires fires <- read.csv('./data/fires_2021/viirs-snpp_2021_Spain.csv') #Step3: Turning the acq_date variable into a date variable fires$acq_date<-as.Date(fires$acq_date) #Step4: Make sf object from points fires<-st_as_sf(fires, coords = c("longitude", "latitude")) #Step5: Adding a CRS to the shapefile fires <- st_set_crs(fires, st_crs(esp0)) # 4326 is the EPSG code for WGS84 #Step6: Extract month and year information fires$month <- format(fires$acq_date, "%m") fires$year <- format(fires$acq_date, "%Y") #Step7: Simplifying our data fires<-subset(fires, type==0) #Step8: Reading only August fires_aug<-subset(fires, acq_date>"2021-07-31" & acq_date<"2021-09-01")library(sf) library(ggplot2) #Step1: Reading Spain borders esp0 <- st_read(dsn="./data/gadm41_ESP_shp/gadm41_ESP_0.shp", quiet = TRUE) #Step2: Reading wildfires fires <- read.csv('./data/fires_2021/viirs-snpp_2021_Spain.csv') #Step3: Turning the acq_date variable into a date variable fires$acq_date<-as.Date(fires$acq_date) #Step4: Make sf object from points fires<-st_as_sf(fires, coords = c("longitude", "latitude")) #Step5: Adding a CRS to the shapefile fires <- st_set_crs(fires, st_crs(esp0)) # 4326 is the EPSG code for WGS84 #Step6: Extract month and year information fires$month <- format(fires$acq_date, "%m") fires$year <- format(fires$acq_date, "%Y") #Step7: Simplifying our data fires<-subset(fires, type==0) #Step8: Reading only August fires_aug<-subset(fires, acq_date>"2021-07-31" & acq_date<"2021-09-01")library(sf) library(ggplot2) #Step1: Reading Spain borders esp0 <- st_read(dsn="./data/gadm41_ESP_shp/gadm41_ESP_0.shp", quiet = TRUE) #Step2: Reading wildfires fires <- read.csv('./data/fires_2021/viirs-snpp_2021_Spain.csv') #Step3: Turning the acq_date variable into a date variable fires$acq_date<-as.Date(fires$acq_date) #Step4: Make sf object from points fires<-st_as_sf(fires, coords = c("longitude", "latitude")) #Step5: Adding a CRS to the shapefile fires <- st_set_crs(fires, st_crs(esp0)) # 4326 is the EPSG code for WGS84 #Step6: Extract month and year information fires$month <- format(fires$acq_date, "%m") fires$year <- format(fires$acq_date, "%Y") #Step7: Simplifying our data fires<-subset(fires, type==0) #Step8: Reading only August fires_aug<-subset(fires, acq_date>"2021-07-31" & acq_date<"2021-09-01")library(sf) library(ggplot2) #Step1: Reading Spain borders esp0 <- st_read(dsn="./data/gadm41_ESP_shp/gadm41_ESP_0.shp", quiet = TRUE) #Step2: Reading wildfires fires <- read.csv('./data/fires_2021/viirs-snpp_2021_Spain.csv') #Step3: Turning the acq_date variable into a date variable fires$acq_date<-as.Date(fires$acq_date) #Step4: Make sf object from points fires<-st_as_sf(fires, coords = c("longitude", "latitude")) #Step5: Adding a CRS to the shapefile fires <- st_set_crs(fires, st_crs(esp0)) # 4326 is the EPSG code for WGS84 #Step6: Extract month and year information fires$month <- format(fires$acq_date, "%m") fires$year <- format(fires$acq_date, "%Y") #Step7: Simplifying our data fires<-subset(fires, type==0) #Step8: Reading only August fires_aug<-subset(fires, acq_date>"2021-07-31" & acq_date<"2021-09-01")library(sf) library(ggplot2) #Step1: Reading Spain borders esp0 <- st_read(dsn="./data/gadm41_ESP_shp/gadm41_ESP_0.shp", quiet = TRUE) #Step2: Reading wildfires fires <- read.csv('./data/fires_2021/viirs-snpp_2021_Spain.csv') #Step3: Turning the acq_date variable into a date variable fires$acq_date<-as.Date(fires$acq_date) #Step4: Make sf object from points fires<-st_as_sf(fires, coords = c("longitude", "latitude")) #Step5: Adding a CRS to the shapefile fires <- st_set_crs(fires, st_crs(esp0)) # 4326 is the EPSG code for WGS84 #Step6: Extract month and year information fires$month <- format(fires$acq_date, "%m") fires$year <- format(fires$acq_date, "%Y") #Step7: Simplifying our data fires<-subset(fires, type==0) #Step8: Reading only August fires_aug<-subset(fires, acq_date>"2021-07-31" & acq_date<"2021-09-01")library(sf) library(ggplot2) #Step1: Reading Spain borders esp0 <- st_read(dsn="./data/gadm41_ESP_shp/gadm41_ESP_0.shp", quiet = TRUE) #Step2: Reading wildfires fires <- read.csv('./data/fires_2021/viirs-snpp_2021_Spain.csv') #Step3: Turning the acq_date variable into a date variable fires$acq_date<-as.Date(fires$acq_date) #Step4: Make sf object from points fires<-st_as_sf(fires, coords = c("longitude", "latitude")) #Step5: Adding a CRS to the shapefile fires <- st_set_crs(fires, st_crs(esp0)) # 4326 is the EPSG code for WGS84 #Step6: Extract month and year information fires$month <- format(fires$acq_date, "%m") fires$year <- format(fires$acq_date, "%Y") #Step7: Simplifying our data fires<-subset(fires, type==0) #Step8: Reading only August fires_aug<-subset(fires, acq_date>"2021-07-31" & acq_date<"2021-09-01")library(sf) library(ggplot2) #Step1: Reading Spain borders esp0 <- st_read(dsn="./data/gadm41_ESP_shp/gadm41_ESP_0.shp", quiet = TRUE) #Step2: Reading wildfires fires <- read.csv('./data/fires_2021/viirs-snpp_2021_Spain.csv') #Step3: Turning the acq_date variable into a date variable fires$acq_date<-as.Date(fires$acq_date) #Step4: Make sf object from points fires<-st_as_sf(fires, coords = c("longitude", "latitude")) #Step5: Adding a CRS to the shapefile fires <- st_set_crs(fires, st_crs(esp0)) # 4326 is the EPSG code for WGS84 #Step6: Extract month and year information fires$month <- format(fires$acq_date, "%m") fires$year <- format(fires$acq_date, "%Y") #Step7: Simplifying our data fires<-subset(fires, type==0) #Step8: Reading only August fires_aug<-subset(fires, acq_date>"2021-07-31" & acq_date<"2021-09-01")library(sf) library(ggplot2) #Step1: Reading Spain borders esp0 <- st_read(dsn="./data/gadm41_ESP_shp/gadm41_ESP_0.shp", quiet = TRUE) #Step2: Reading wildfires fires <- read.csv('./data/fires_2021/viirs-snpp_2021_Spain.csv') #Step3: Turning the acq_date variable into a date variable fires$acq_date<-as.Date(fires$acq_date) #Step4: Make sf object from points fires<-st_as_sf(fires, coords = c("longitude", "latitude")) #Step5: Adding a CRS to the shapefile fires <- st_set_crs(fires, st_crs(esp0)) # 4326 is the EPSG code for WGS84 #Step6: Extract month and year information fires$month <- format(fires$acq_date, "%m") fires$year <- format(fires$acq_date, "%Y") #Step7: Simplifying our data fires<-subset(fires, type==0) #Step8: Reading only August fires_aug<-subset(fires, acq_date>"2021-07-31" & acq_date<"2021-09-01")
L18: Geometric Operations with Rasters 1
Grids, counting points per grid
Intro
This short tutorial focuses on:
- creating grids
- cropping them out to a shape
- counting points by grid
Rasterizing point counts
We might be interested in counting points by grid.
Let us say that we want to count wildfires in Spain: download the wildfires in Spain in 2021.
You can also download the shapefiles for Spain.
Rasterizing point counts
We might be interested in counting points by grid.
Let us say that we want to count wildfires in Spain: download the wildfires in Spain in 2021.
You can also download the shapefiles for Spain.
#Step9: Simplify shapefiles esp0b<-st_simplify(esp0, dTolerance=0.1) #Step10: Simplifying country polygon esp0 = st_cast(esp0b,"POLYGON") #Step11: Calculating polygon area esp0$area<-st_area(esp0) #Step12: Turning the units into square km esp0$area2<-units::set_units(esp0$area, km^2) #Step13: Only choosing the largest polygon esp0c<-subset(esp0, area2==max(esp0$area2)) #Step14: Making a box object around the polygon bbox <- st_bbox(esp0c) #Step15: Extracting min and max latitudes and longitudes min_lat_y <- bbox["ymin"] max_lat_y <- bbox["ymax"] min_lon_x <- bbox["xmin"] max_lon_x <- bbox["xmax"]#Step9: Simplify shapefiles esp0b<-st_simplify(esp0, dTolerance=0.1) #Step10: Simplifying country polygon esp0 = st_cast(esp0b,"POLYGON") #Step11: Calculating polygon area esp0$area<-st_area(esp0) #Step12: Turning the units into square km esp0$area2<-units::set_units(esp0$area, km^2) #Step13: Only choosing the largest polygon esp0c<-subset(esp0, area2==max(esp0$area2)) #Step14: Making a box object around the polygon bbox <- st_bbox(esp0c) #Step15: Extracting min and max latitudes and longitudes min_lat_y <- bbox["ymin"] max_lat_y <- bbox["ymax"] min_lon_x <- bbox["xmin"] max_lon_x <- bbox["xmax"]#Step9: Simplify shapefiles esp0b<-st_simplify(esp0, dTolerance=0.1) #Step10: Simplifying country polygon esp0 = st_cast(esp0b,"POLYGON") #Step11: Calculating polygon area esp0$area<-st_area(esp0) #Step12: Turning the units into square km esp0$area2<-units::set_units(esp0$area, km^2) #Step13: Only choosing the largest polygon esp0c<-subset(esp0, area2==max(esp0$area2)) #Step14: Making a box object around the polygon bbox <- st_bbox(esp0c) #Step15: Extracting min and max latitudes and longitudes min_lat_y <- bbox["ymin"] max_lat_y <- bbox["ymax"] min_lon_x <- bbox["xmin"] max_lon_x <- bbox["xmax"]#Step9: Simplify shapefiles esp0b<-st_simplify(esp0, dTolerance=0.1) #Step10: Simplifying country polygon esp0 = st_cast(esp0b,"POLYGON") #Step11: Calculating polygon area esp0$area<-st_area(esp0) #Step12: Turning the units into square km esp0$area2<-units::set_units(esp0$area, km^2) #Step13: Only choosing the largest polygon esp0c<-subset(esp0, area2==max(esp0$area2)) #Step14: Making a box object around the polygon bbox <- st_bbox(esp0c) #Step15: Extracting min and max latitudes and longitudes min_lat_y <- bbox["ymin"] max_lat_y <- bbox["ymax"] min_lon_x <- bbox["xmin"] max_lon_x <- bbox["xmax"]#Step9: Simplify shapefiles esp0b<-st_simplify(esp0, dTolerance=0.1) #Step10: Simplifying country polygon esp0 = st_cast(esp0b,"POLYGON") #Step11: Calculating polygon area esp0$area<-st_area(esp0) #Step12: Turning the units into square km esp0$area2<-units::set_units(esp0$area, km^2) #Step13: Only choosing the largest polygon esp0c<-subset(esp0, area2==max(esp0$area2)) #Step14: Making a box object around the polygon bbox <- st_bbox(esp0c) #Step15: Extracting min and max latitudes and longitudes min_lat_y <- bbox["ymin"] max_lat_y <- bbox["ymax"] min_lon_x <- bbox["xmin"] max_lon_x <- bbox["xmax"]#Step9: Simplify shapefiles esp0b<-st_simplify(esp0, dTolerance=0.1) #Step10: Simplifying country polygon esp0 = st_cast(esp0b,"POLYGON") #Step11: Calculating polygon area esp0$area<-st_area(esp0) #Step12: Turning the units into square km esp0$area2<-units::set_units(esp0$area, km^2) #Step13: Only choosing the largest polygon esp0c<-subset(esp0, area2==max(esp0$area2)) #Step14: Making a box object around the polygon bbox <- st_bbox(esp0c) #Step15: Extracting min and max latitudes and longitudes min_lat_y <- bbox["ymin"] max_lat_y <- bbox["ymax"] min_lon_x <- bbox["xmin"] max_lon_x <- bbox["xmax"]#Step9: Simplify shapefiles esp0b<-st_simplify(esp0, dTolerance=0.1) #Step10: Simplifying country polygon esp0 = st_cast(esp0b,"POLYGON") #Step11: Calculating polygon area esp0$area<-st_area(esp0) #Step12: Turning the units into square km esp0$area2<-units::set_units(esp0$area, km^2) #Step13: Only choosing the largest polygon esp0c<-subset(esp0, area2==max(esp0$area2)) #Step14: Making a box object around the polygon bbox <- st_bbox(esp0c) #Step15: Extracting min and max latitudes and longitudes min_lat_y <- bbox["ymin"] max_lat_y <- bbox["ymax"] min_lon_x <- bbox["xmin"] max_lon_x <- bbox["xmax"]
Rasterizing point counts
The following is a spatial join that is a bit more convoluted.
It is however more efficient than st_intersection.
library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)library(dplyr) #Step16: Creating a grid around the country shapefile r <- terra::rast(esp0, res=(0.5 * 0.5)) v <- terra::as.polygons(r, extent=FALSE) #Step17: Turning the spatraster into an sf object grid_sf<- sf::st_as_sf(v) #Step18: Adding id grid_sf$id<-rownames(grid_sf) #Step19: Turning grids to points grid_centr<-st_centroid(grid_sf) #Step20: Performing the spatial join grid_centr_joined<-st_join(grid_centr, esp0c) #Step21: Dropping geometry grid_centr_df1<-st_drop_geometry(grid_centr_joined) #Step22: Dropping grids outside of the Spain polygons based on a variable grid_centr_df2<-subset(grid_centr_df1, !is.na(COUNTRY)) #Step23: Selecting the id variable grid_centr_df3<-subset(grid_centr_df2, select = c(id, COUNTRY)) #Step25: Joining grids with centroids merged<-left_join(grid_sf, grid_centr_df3, by=c("id"="id")) #Step26: Eliminating grids outside of the polygon xsubset<-subset(merged, id %in% grid_centr_df3$id)
Rasterizing point counts
#Step27: Intersect points with grid x <- st_intersects(fires_aug, xsubset, sparse = FALSE) #Step28: Adding the points by grid x <- colSums(x) #Step29: Adding the sum the grid xsubset$no_fires<-x #Step30: Mapping the grid fig2<-ggplot() + geom_sf(data=xsubset, aes(fill=no_fires)) + theme_void() + scale_fill_viridis_b()+ #geom_sf(data=esp0, fill=NA)+ coord_sf(xlim = c(min_lon_x, max_lon_x), ylim = c(min_lat_y, max_lat_y))#Step27: Intersect points with grid x <- st_intersects(fires_aug, xsubset, sparse = FALSE) #Step28: Adding the points by grid x <- colSums(x) #Step29: Adding the sum the grid xsubset$no_fires<-x #Step30: Mapping the grid fig2<-ggplot() + geom_sf(data=xsubset, aes(fill=no_fires)) + theme_void() + scale_fill_viridis_b()+ #geom_sf(data=esp0, fill=NA)+ coord_sf(xlim = c(min_lon_x, max_lon_x), ylim = c(min_lat_y, max_lat_y))#Step27: Intersect points with grid x <- st_intersects(fires_aug, xsubset, sparse = FALSE) #Step28: Adding the points by grid x <- colSums(x) #Step29: Adding the sum the grid xsubset$no_fires<-x #Step30: Mapping the grid fig2<-ggplot() + geom_sf(data=xsubset, aes(fill=no_fires)) + theme_void() + scale_fill_viridis_b()+ #geom_sf(data=esp0, fill=NA)+ coord_sf(xlim = c(min_lon_x, max_lon_x), ylim = c(min_lat_y, max_lat_y))#Step27: Intersect points with grid x <- st_intersects(fires_aug, xsubset, sparse = FALSE) #Step28: Adding the points by grid x <- colSums(x) #Step29: Adding the sum the grid xsubset$no_fires<-x #Step30: Mapping the grid fig2<-ggplot() + geom_sf(data=xsubset, aes(fill=no_fires)) + theme_void() + scale_fill_viridis_b()+ #geom_sf(data=esp0, fill=NA)+ coord_sf(xlim = c(min_lon_x, max_lon_x), ylim = c(min_lat_y, max_lat_y))
Rasterizing point counts
Using logs
We may also use logs for more visible differences
Conclusion
In this short presentation, we learned about:
- creating grids and cropping them
- counting number of points per polygon
Popescu (JCU): Lecture 18
L18: Geometric Operations with Rasters 1 Grids, counting points per grid Bogdan G. Popescu bogdan.popescu@johncabot.edu John Cabot University