I want to dissolve a polygon so I get only a lines for the outline of the whole region instead of it being broken up by county.
install.packages (c("tidyverse","mapdata","maps","stringr","viridis"))
library(tidyverse)
library(mapdata)
library(maps)
library(stringr)
library(viridis)
california <- map_data("state", region="california")
california1 <- ggplot() +
geom_polygon(data = california,
aes(x = long, y = lat, group = group),
color="black", fill="NA") +
coord_quickmap()
#california county lines
uscounties <-map_data("county")
ca_county <- uscounties %>% filter(region == "california")
central<- ca_county %>%
filter(subregion %in% c("alpline", "kings", "tulare", "fresno", "inyo", "kern", "madera"))
ca2 <- california1 +
theme_void() +
geom_polygon(data = central,
aes(x = long, y = lat, group = group),
fill = "white", color = "black") +
geom_polygon(color = "black", fill = NA) +
annotate("text", x = -119, y = 46.5, label="Central", colour="black")
ca2
Thanks in advance for the help!
I've answered a similar question before. Reworked it slightly for your use case, with explanations in annotated code below:
library(tidyverse)
library(maps)
# get map (as map object)
county_map <- map("county", regions = "california",
fill = T, plot = FALSE)
# convert to SpatialPolygonsDataFrame object (using maptools & sp packages)
county_map_match <- data.frame(name = county_map$names) %>%
separate(name, c("region", "subregion"), sep = ",", remove = FALSE) %>%
mutate(central = subregion %in% c("alpline", "kings", "tulare",
"fresno", "inyo", "kern", "madera")) %>%
column_to_rownames("name")
county_map <- maptools::map2SpatialPolygons(county_map, ID = county_map$names)
county_map <- sp::SpatialPolygonsDataFrame(county_map, county_map_match)
rm(county_map_match)
# remove any invalidity (using rgeos package) before dissolving
rgeos::gIsValid(county_map) # check
county_map <- rgeos::gBuffer(county_map, byid = TRUE, width = 0)
rgeos::gIsValid(county_map) # check again (invalidities removed)
# dissolve by whether each polygon is part of central area
county_map <- maptools::unionSpatialPolygons(county_map, IDs = county_map$central)
county_map <- fortify(county_map)
county_map <- county_map %>% filter(group == "TRUE.1")
# plot all the central counties as one polygon
ggplot() +
geom_polygon(data = county_map,
aes(x = long, y = lat, group = group),
fill = "white", colour = "black") +
coord_map()
Related
I'm looking to crop the density plot to only land while keeping to sf.
Here's a simple example problem:
library(tidyverse)
library(sf)
library(albersusa)
library(ggthemes)
library(jsonlite)
dat <-
fromJSON(
"https://services1.arcgis.com/Hp6G80Pky0om7QvQ/arcgis/rest/services/Fortune_500_Corporate_Headquarters/FeatureServer/0/query?where=1%3D1&outFields=LATITUDE,LONGITUDE,NAME,PROFIT&outSR=4326&f=json"
)
dat <- as.data.frame(dat$features$attributes)
top_50 <- dat %>%
arrange(desc(PROFIT)) %>%
head(50)
ggplot() +
geom_sf(data = usa_sf()) +
geom_density_2d_filled(aes(x = LONGITUDE, y = LATITUDE),
data = top_50,
alpha = .5) +
xlim(-125,-66.5) +
ylim(20, 50) +
theme_map() +
theme(legend.position = "none")
Not sure if I'm getting close to a solution but here's some of the code I've been trying:
test <- (MASS::kde2d(
top_50$LONGITUDE, top_50$LATITUDE,
lims = c(-125,-66.5, 20, 50)
))
ggpoly2sf <- function(poly, coords = c("long", "lat"),
id = "group", region = "region", crs = 4326) {
sf::st_as_sf(poly, coords = coords, crs = crs) %>%
group_by(!! as.name(id), !! as.name(region)) %>%
summarize(do_union=FALSE) %>%
sf::st_as_sf("POLYGON") %>%
ungroup() %>%
group_by(!! as.name(region)) %>%
summarize(do_union = TRUE) %>%
ungroup()
}
v <- contourLines(test)
vv <- v
for (i in seq_along(v)) vv[[i]]$group <- i
vv <- do.call(rbind, lapply(vv, as.data.frame))
dsi_sf <- ggpoly2sf(vv, coords = c("x", "y"), region = "level") %>% st_as_sf()
usa <- usa_sf()
dsi_i_sf <- st_intersection(usa$geometry, dsi_sf)
ggplot() +
geom_sf(data=usa) +
geom_sf(data=dsi_i_sf,color="red") +
geom_density2d_filled(aes(x = LONGITUDE, y = LATITUDE),
data = top_50,alpha=.4) +
xlim(-125,-66.5) +
ylim(20, 50) +
theme(legend.position = "none")
Create a rectangle of the same plot dimensions:
rec_box <- data.frame(x=c(-125,-125,-66.5,-66.5,-125), y=c(20,50,50,20,20))
Create an outline of the US and extract only the lat/lon points into a dataframe:
outline <- map("usa", plot=FALSE)
outline <- data.frame(x=outline$x,y=outline$y)
Bind the two together to create a polygon with a hole in the middle:
mask <- rbind(rec_box,outline)
Add a geom_polygon() to plot the mask data and color appropriately:
geom_polygon(data=mask,
aes(x=x,y=y),color="white",fill="white")
Everything combined:
library(tidyverse)
library(sf)
library(albersusa)
library(ggthemes)
library(jsonlite)
dat <-
fromJSON(
"https://services1.arcgis.com/Hp6G80Pky0om7QvQ/arcgis/rest/services/Fortune_500_Corporate_Headquarters/FeatureServer/0/query?where=1%3D1&outFields=LATITUDE,LONGITUDE,NAME,PROFIT&outSR=4326&f=json"
)
dat <- as.data.frame(dat$features$attributes)
top_50 <- dat %>%
arrange(desc(PROFIT)) %>%
head(50)
usa <- usa_sf()
outline <- map("usa", plot=FALSE)
outline <- data.frame(x=outline$x,y=outline$y)
rec_box <- data.frame(x=c(-125,-125,-66.5,-66.5,-125), y=c(20,50,50,20,20))
mask <- rbind(rec_box,outline)
ggplot() +
geom_sf(data = usa_sf()) +
geom_density_2d_filled(aes(x = LONGITUDE, y = LATITUDE),
data = top_50,
alpha = .5) +
xlim(-125,-66.5) +
ylim(20, 50) +
geom_polygon(data=mask,
aes(x=x,y=y),color="white",fill="white") +
theme_map() +
theme(legend.position = "none")
Really a thing of beauty.
For a mask layer over the US with AK & HI inset:
library(tidyverse)
library(sf)
library(albersusa)
library(ggthemes)
library(jsonlite)
library(spatstat)
dat <-
fromJSON(
"https://services1.arcgis.com/Hp6G80Pky0om7QvQ/arcgis/rest/services/Fortune_500_Corporate_Headquarters/FeatureServer/0/query?where=1%3D1&outFields=LATITUDE,LONGITUDE,NAME,PROFIT&outSR=4326&f=json"
)
dat <- as.data.frame(dat$features$attributes)
top_50 <- dat %>%
arrange(desc(PROFIT)) %>%
head(50)
usa <- usa_sf()
top50sf <- st_as_sf(top_50, coords = c("LONGITUDE", "LATITUDE")) %>%
st_set_crs(4326) %>%
st_transform(st_crs(usa))
# usa polygons combined
usa_for_mask <- usa_sf() %>%
st_geometry() %>%
st_cast('POLYGON') %>%
st_union()
# bounding box of us & inset AK + HI,
# expand as needed
us_bbox <- st_bbox(usa_for_mask) %>%
st_as_sfc() %>%
st_as_sf()
us_mask <- st_difference(us_bbox, usa_for_mask)
ggplot() +
geom_sf(data = usa) +
geom_density_2d_filled(aes(x = LONGITUDE, y = LATITUDE),
data = top_50,
alpha = .5) +
geom_sf(data = us_mask, fill = 'white') +
xlim(-125,-66.5) +
ylim(20, 50) +
theme_map() +
theme(legend.position = "none")
Created on 2021-04-05 by the reprex package (v1.0.0)
You can expand the bounding box to get rid of the purple border around the plot.
This does what you're asking for, but almost certainly isn't spatially accurate. It can get a point across to a general audience, but don't make any big decisions based on it.
More accurate spatial interpolation methods can be found here:
https://rspatial.org/raster/analysis/4-interpolation.html
https://mgimond.github.io/Spatial/interpolation-in-r.html
I need to create a map of country(Thailand) based on shapes files (preferably colored)and to add the codes of the provinces (from 10 to 96,77 codes) and the corresponding coefficient from h.сsv(also 77 values) file on the map.
I am trying to show my two codes(maybe,one of them will be better for map):
1st:
library(raster)
library(rasterVis)
library(rgdal)
library(rgeos)
library(dismo)
library(sp)
library(maptools)
library(maps)
library(mapdata)
library(XML)
library(foreign)
library(latticeExtra)
library(shapefiles)
library(RColorBrewer)
library(GISTools)
#library(SDMTools)
library(dplyr)
library(tidyr)
library(tidyverse)
library(lubridate)
## preparing shapefiles
thailand_district <- shapefile("C:/usa/archive/TH_Province2012.shp")
thailand_district
crs(thailand_district)
names(thailand_district)
thailand_district_lonlat<- spTransform(thailand_district, CRS("+proj=longlat +datum=WGS84"))
crs(thailand_district_lonlat)
thailand_district_lonlat_s<-gSimplify(thailand_district_lonlat, tol=0.02, topologyPreserve=TRUE)
district_id<-thailand_district_lonlat$A_CODE
province_id<-thailand_district_lonlat$P_CODE
thailand_prov <- shapefile("C:/usa/archive/TH_Province2012.shp")
thailand_prov
crs(thailand_prov)
thailand_prov_lonlat<- spTransform(thailand_prov, CRS("+proj=longlat +datum=WGS84"))
crs(thailand_prov_lonlat)
thailand_prov_lonlat_s<-gSimplify(thailand_prov_lonlat, tol=0.02, topologyPreserve=TRUE)
## preparing centroids
thailand_district_centroids <- getSpPPolygonsLabptSlots(thailand_district_lonlat)
head(thailand_district_centroids)
district_centroids<- data.frame(province_id,district_id, thailand_district_centroids[,1],thailand_district_centroids[,2])
district_centroids<-read.csv("data.scrub.district.csv")
names(district_centroids) <- c("province_id","district_id","longitude", "latitude")
ex<-district_centroids
coordinates(ex)<- cbind("longitude", "latitude")
plot(ex)
thailand_province_centroids <- getSpPPolygonsLabptSlots(thailand_prov_lonlat)
head(thailand_province_centroids)
## read cases
scrub1<-read_csv("C:/usa/archive/scrub_2003-07_180319.csv")
names(scrub1)
scrub1<-dplyr::select(scrub1,Address, The.day.began.to.get.sick..M.D.Y.)
scrub1 <- dplyr::rename(scrub1,date=The.day.began.to.get.sick..M.D.Y.)
scrub1 <-na.omit(scrub1)
scrub2<-read_csv("C:/usa/archive/scrub_2008-11_180319.csv")
scrub2<-dplyr::select(scrub2,Address, The.day.began.to.get.sick..M.D.Y.)
scrub2 <- dplyr::rename(scrub2,date=The.day.began.to.get.sick..M.D.Y.)
scrub3<-read_csv("C:/usa/archive/scrub_2012-18_180319.csv")
scrub3<-dplyr::select(scrub3,Address, The.day.began.to.get.sick..M.D.Y.)
scrub3 <- dplyr::rename(scrub3,date=The.day.began.to.get.sick..M.D.Y.)
scrub<-dplyr::union(scrub1, scrub2)
scrub<-dplyr::union(scrub, scrub3)
scrub$district_id<-(tamboon_id=substr(scrub$Address, 1,4))
scrub <- dplyr::rename(scrub,village_id=Address)
scrub<-tidyr::drop_na(scrub,village_id)
scrub_district <- dplyr::select(scrub,district_id)
# preparation
scrub$date1 <- as.Date(scrub$date,
format = "%d/%m/%Y")
scrub$year<-lubridate::year(scrub$date1)
scrub$YearMonth<-format(scrub$date1, "%Y-%m")
scrubYear<-scrub %>%
drop_na() %>%
group_by(year) %>%
summarise(scrubcases= n())
scrub$district_id<-as.factor(scrub$district_id)
is.factor(scrub$district_id)
scrubDistrict<-scrub %>%
drop_na() %>%
group_by(district_id) %>%
summarise(scrubcases= n())
district_centroids
district_centroids2<-district_centroids %>%
unite("district_id", province_id,district_id2)
district_centroids2$district_id<-gsub("_", "",district_centroids2$district_id )
scrubdistict_longlat<-dplyr::left_join(district_centroids2,scrubDistrict,
by="district_id")
write_csv(scrubdistict_longlat,"data.scrub.district.csv")
scrubClean<-read_csv("data.scrub.district.csv")
mydata<-dplyr::filter(scrubClean, scrubcases > 0)
ex2<-mydata
coordinates(ex2)<-c("longitude","latitude")
bubble(ex2,"scrubcases")
# map
library(tmap)
library(tmaptools)
proj4string(ex2) <- proj4string(thailand_district_lonlat)
tmaptools::palette_explorer()
# thailand
tm1<-tm_shape(thailand_prov_lonlat_s) +
tm_fill(NA) + tm_borders("black")+
tm_borders("black")+
tm_compass(type = "8star", position = c("right", "top"),size = 2)+
tm_scale_bar(breaks = c(0, 100, 100), size = 0.5, position = c("right", "bottom"))+
tm_style( "beaver")
tm1
tm2<-tm_shape(thailand_district_lonlat_s)+
tm_polygons()+
tm_shape(ex2) +
tm_bubbles("scrubcases",col = "lightblue",scale = 2,
border.col = "black", border.alpha = .5,
contrast=1,
title.size="cases / district")
tm2
library(dplyr)
library(tidyr)
library(tmap)
data(World)
names(World)
mygideon<-read_csv("data.gideon.iso.final.csv") %>%
group_by(iso_a3) %>%
summarise(total.outbreaks=n())
world2<-dplyr::left_join(World,mygideon2,by="iso_a3")
tm_shape(world2) +
tm_polygons("total.outbreaks",
style = "fixed",
breaks = c(1,50,100,250,500,750, 1000, 1500, 2500),
palette="Oranges",
title = "Total outbreaks (1940-2018)", contrast = 1.2,
border.col = "gray30", id = "name", n=6,
legend.hist = TRUE,alpha = 1)+
tm_layout(legend.outside = TRUE)
**Error in data.frame(province_id, district_id, thailand_district_centroids[, :
arguments imply differing number of rows : 0, 77.use coordinates method**
2n code.
library(raster)
library(rasterVis)
library(rgdal)
library(rgeos)
library(dismo)
library(sp)
library(maptools)
library(maps)
library(mapdata)
library(XML)
library(foreign)
library(latticeExtra)
library(shapefiles)
library(RColorBrewer)
library(GISTools)
#library(SDMTools)
library(dplyr)
library(tidyr)
library(tidyverse)
library(rgeos) # to fortify without needing gpclib
library(ggplot2)
library(scales) # for formatting ggplot scales with commas
thamap <- readOGR("C:/usa/archive/TH_Province2012.shp")
thamap
crs(thamap)
thamap_lonlat<- spTransform(thamap, CRS("+proj=longlat +datum=WGS84"))
crs(thamap_lonlat)
thamap_lonlat_s<-gSimplify(thamap_lonlat, tol=0.02, topologyPreserve=TRUE)
thamap.fort <- fortify(thamap)
idList <-thamap#data$PROV_CODE
centroids.df <- as.data.frame(coordinates(thamap))
names(centroids.df) <- c("Longitude", "Latitude")
info <- read.csv("h.csv")
pop.df <- data.frame(idList,info,centroids.df)
ggplot(pop.df, aes(map_id = idList)) + #"id" is col in your df, not in the map object
geom_map(aes(fill = info), colour= "grey", map = thamap.fort) +
expand_limits(x = thamap.fort$long, y = thamap.fort$lat) +
scale_fill_gradient(high = "red", low = "white", guide = "colorbar", labels = comma) +
geom_text(aes(label = id, x = Longitude, y = Latitude)) + #add labels at centroids
coord_equal(xlim = c(-90,-30), ylim = c(-60, 20)) +
labs(x = "Longitude", y = "Latitude", title = "map Thailand") +
theme_bw()
Don't know how to automatically pick scale for object of type function. Defaulting to continuous.
Aesthetics must be valid data columns. Problematic aesthetic(s): label = id.
Did you mistype the name of a data column or forget to add after_stat()?
I would really appreciate it if you could help me to fix my codes a little to create the map.
Could you tell me please also,how is it possible to add the data(77 values) from csv file on the map near the codes of provinces?
Thank you very much for your help
Here's a solution using tmap. The shapefile containing the borders of Thailand (country and provinces) is available from https://data.humdata.org/dataset/thailand-administrative-boundaries. The province codes (10-96) are also included in the dataset in character format and can easily be extracted.
library(tmap)
library(sf)
library(tidyverse)
provinces <- st_read(dsn = "tha_adm_rtsd_itos_20190221_SHP_PART_1/tha_admbnda_adm1_rtsd_20190221.shp") %>%
as.tibble() %>%
separate(ADM1_PCODE, into = c("pcode_text", "pcode_num"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
select(geometry, pcode_num) %>%
st_as_sf()
tm_shape(provinces) +
tm_fill(col = "MAP_COLORS") +
tm_text("pcode_num", size = .5) +
tm_borders(lwd = .7, col = "black")
I have seen the answer Pie charts in geom_scatterpie overlapping.
But I do not want to reset the overlapped pie chart coordinate manually.
Is there some function in ggplot2 or something else can avoid overlap in geom_scatterpie() function?
Any help will be highly appreciated!
Reproducible code is at here:
library(scatterpie)
library(tidyverse)
library(geosphere)
library(ggnewscale)
us <- map_data('state') %>% as_tibble()
n = length(unique(us$region))
# creat fake mapping data
temperature_data <- tibble(region = unique(us$region),
temp = rnorm(n = n))
coords <- us %>% select(long, lat, region) %>% distinct(region, .keep_all = T)
category_data <- tibble(region = unique(us$region),
cat_1 = sample(1:100, size = n),
cat_2 = sample(1:100, size = n),
cat_3 = sample(1:100, size = n)) %>% left_join(coords)
us <- left_join(us, temperature_data)
p +
geom_map(map = us, aes(map_id = region, fill = temp), color = 'grey') +
new_scale('fill') +
geom_scatterpie(data = category_data,
aes(long, lat),
cols = c("cat_1", "cat_2", "cat_3"),
alpha = 0.5)
How can I define the boundaries of a country, so rivers outside the country won't appear on the map? The image link below will clarify what I mean:
US Rivers
library(tidyverse) # ggplot2, dplyr, tidyr, readr, purrr, tibble
library(magrittr) # pipes
library(rnaturalearth) # Rivers
library(urbnmapr)
states <- urbnmapr::states
states <- fortify(states)
rivers10 <- ne_download(scale = "medium", type = 'rivers_lake_centerlines', category = 'physical') #, returnclass = "sf"
rivers10 <- fortify(rivers10)
rivers10 <- rivers10 %>%
filter(long >= min(states$long)) %>%
filter(long <= max(states$long)) %>%
filter(lat >= min(states$lat)) %>%
filter(lat <= max(states$lat))
ggplot() +
geom_polygon(data = urbnmapr::states, mapping = aes(x = long, y = lat, group = group),
fill = "#CDCDCD", color = "#25221E") +
coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
geom_path(data = rivers10,
aes(long, lat, group = group), size = 1, color = '#000077') +
theme_minimal()
This is easier if you get the data as spatial objects. Then you can manipulate them to intersect the rivers with the US boundary.
library(tidyverse) # ggplot2, dplyr, tidyr, readr, purrr, tibble
library(rnaturalearth) # Rivers
library(sf)
library(urbnmapr)
states = get_urbn_map('states', sf=TRUE)
rivers10 <- ne_download(scale = "medium", type = 'rivers_lake_centerlines',
category = 'physical', returnclass = "sf")
# Outline of the US
us = st_union(states)
# Transform rivers to the same projection as states and clip to US
rivers10 <- rivers10 %>%
st_transform(st_crs(states)) %>%
st_intersection(us)
ggplot() +
geom_sf(data=states, fill = "#CDCDCD", color = "#25221E") +
geom_sf(data=rivers10, color='#000077') +
theme_minimal()
Created on 2019-12-26 by the reprex package (v0.3.0)
I saw yesterday this beautiful map of McDonalds restaurants in USA. I wanted to replicate it for France (I found some data that can be downloaded here).
I have no problem plotting the dots:
library(readxl)
library(ggplot2)
library(raster)
#open data
mac_do_FR <- read_excel("./mcdo_france.xlsx")
mac_do_FR_df <- as.data.frame(mac_do_FR)
#get a map of France
mapaFR <- getData("GADM", country="France", level=0)
#plot dots on the map
ggplot() +
geom_polygon(data = mapaFR, aes(x = long, y = lat, group = group),
fill = "transparent", size = 0.1, color="black") +
geom_point(data = mac_do_FR_df, aes(x = lon, y = lat),
colour = "orange", size = 1)
I tried several methods (Thiessen polygons, heat maps, buffers), but the results I get are very poor. I can't figure out how the shaded polygons were plotted on the American map. Any pointers?
Here's my result, but it did take some manual data wrangling.
Step 1: Get geospatial data.
library(sp)
# generate a map of France, along with a fortified dataframe version for ease of
# referencing lat / long ranges
mapaFR <- raster::getData("GADM", country="France", level=0)
map.FR <- fortify(mapaFR)
# generate a spatial point version of the same map, defining your own grid size
# (a smaller size yields a higher resolution heatmap in the final product, but will
# take longer to calculate)
grid.size = 0.01
points.FR <- expand.grid(
x = seq(min(map.FR$long), max(map.FR$long), by = grid.size),
y = seq(min(map.FR$lat), max(map.FR$lat), by = grid.size)
)
points.FR <- SpatialPoints(coords = points.FR, proj4string = mapaFR#proj4string)
Step 2: Generate a voronoi diagram based on store locations, & obtain the corresponding polygons as a SpatialPolygonsDataFrame object.
library(deldir)
library(dplyr)
voronoi.tiles <- deldir(mac_do_FR_df$lon, mac_do_FR_df$lat,
rw = c(min(map.FR$long), max(map.FR$long),
min(map.FR$lat), max(map.FR$lat)))
voronoi.tiles <- tile.list(voronoi.tiles)
voronoi.center <- lapply(voronoi.tiles,
function(l) data.frame(x.center = l$pt[1],
y.center = l$pt[2],
ptNum = l$ptNum)) %>%
data.table::rbindlist()
voronoi.polygons <- lapply(voronoi.tiles,
function(l) Polygon(coords = matrix(c(l$x, l$y),
ncol = 2),
hole = FALSE) %>%
list() %>%
Polygons(ID = l$ptNum)) %>%
SpatialPolygons(proj4string = mapaFR#proj4string) %>%
SpatialPolygonsDataFrame(data = voronoi.center,
match.ID = "ptNum")
rm(voronoi.tiles, voronoi.center)
Step 3. Check which voronoi polygon each point on the map overlaps with, & calculate its distance to the corresponding nearest store.
which.voronoi <- over(points.FR, voronoi.polygons)
points.FR <- cbind(as.data.frame(points.FR), which.voronoi)
rm(which.voronoi)
points.FR <- points.FR %>%
rowwise() %>%
mutate(dist = geosphere::distm(x = c(x, y), y = c(x.center, y.center))) %>%
ungroup() %>%
mutate(dist = ifelse(is.na(dist), max(dist, na.rm = TRUE), dist)) %>%
mutate(dist = dist / 1000) # convert from m to km for easier reading
Step 4. Plot, adjusting the fill gradient parameters as needed. I felt the result of a square root transformation looks quite good for emphasizing distances close to a store, while a log transformation is rather too exaggerated, but your mileage may vary.
ggplot() +
geom_raster(data = points.FR %>%
mutate(dist = pmin(dist, 100)),
aes(x = x, y = y, fill = dist)) +
# optional. shows outline of France for reference
geom_polygon(data = map.FR,
aes(x = long, y = lat, group = group),
fill = NA, colour = "white") +
# define colour range, mid point, & transformation (if desired) for fill
scale_fill_gradient2(low = "yellow", mid = "red", high = "black",
midpoint = 4, trans = "sqrt") +
labs(x = "longitude",
y = "latitude",
fill = "Distance in km") +
coord_quickmap()