MovementVis with R

We will learn how to visualise movements by using appropriate R packages in this blog.

Author

Affiliation

Alicia Zhang

School of Computing and Information Systems, Singapore Management University

Published

July 3, 2021

DOI

Installing and loading the required libraries

Use the below code to install and load packages.

packages = c('raster','sf','clock',
             'tmap','tidyverse','rgdal')
for (p in packages){
  if(!require(p,character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

Importing Raster file

ap <- raster("data/Geospatial/MC2-tourist.tif")
ap

class      : RasterLayer 
band       : 1  (of  3  bands)
dimensions : 1595, 2706, 4316070  (nrow, ncol, ncell)
resolution : 3.16216e-05, 3.16216e-05  (x, y)
extent     : 24.82419, 24.90976, 36.04499, 36.09543  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
source     : MC2-tourist.tif 
names      : MC2.tourist 
values     : 0, 255  (min, max)

bgmap <- raster("data/Geospatial/MC2-tourist.tif")
bgmap

class      : RasterLayer 
band       : 1  (of  3  bands)
dimensions : 1595, 2706, 4316070  (nrow, ncol, ncell)
resolution : 3.16216e-05, 3.16216e-05  (x, y)
extent     : 24.82419, 24.90976, 36.04499, 36.09543  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
source     : MC2-tourist.tif 
names      : MC2.tourist 
values     : 0, 255  (min, max)

Plotting Raster Layer

tmap_mode("plot") #plot: static version
tm_shape(bgmap) +
  tm_raster(bgmap,
            legend.show = FALSE)

tm_shape(bgmap) + #data
tm_rgb(bgmap,r=1,g=2,b=3,
        alpha = NA,
        saturation = 1,
        interpolate = TRUE,
         max.value = 255)

Abila_st <- st_read(dsn="data/Geospatial",
                    layer="Abila")

Reading layer `Abila' from data source `/Users/zhangying/Alicia-zy/DataViz_Blog/_posts/2021-07-03-movementvis-with-r/data/Geospatial' using driver `ESRI Shapefile'
Simple feature collection with 3290 features and 9 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: 24.82401 ymin: 36.04502 xmax: 24.90997 ymax: 36.09492
CRS:           4326

Importing Aspatial Data

gps <- read_csv("data/aspatial/gps.csv")
glimpse(gps)

Rows: 685,169
Columns: 4
$ Timestamp <chr> "01/06/2014 06:28:01", "01/06/2014 06:28:01", "01/…
$ id        <dbl> 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35…
$ lat       <dbl> 36.07623, 36.07622, 36.07621, 36.07622, 36.07621, …
$ long      <dbl> 24.87469, 24.87460, 24.87444, 24.87425, 24.87417, …

converting Data-Time Field

gps$Timestamp <- date_time_parse(gps$Timestamp, 
                                 zone = "",
                                 format="%m/%d/%Y %H:%M:%S") #function from clock package
gps$id <- as_factor(gps$id) #from tidyverse package

converting Aspatial Data into a Simple Feature Data Frame

gps_sf <- st_as_sf(gps,
                   coords=c("long","lat"),
                   crs=4326) # wgs84 geographic coordinate system

converting movement path from GPS Points

gps_path <- gps_sf %>%
  group_by(id) %>%
  summarize(m=mean(Timestamp),
            do_union=FALSE)%>%
  st_cast("LINESTRING")

Plotting the gps Paths

gps_path_selected <- gps_path %>%
  filter(id==1) 

tmap_mode("view")
tm_shape(bgmap) +
tm_rgb(bgmap,r=1,g=2,b=3,
        alpha = NA,
        saturation = 1,
        interpolate = TRUE,
         max.value = 255)+
  tm_shape(gps_path_selected) +
  tm_lines()