Hands-on_Ex02
Erika Aldisa
8/31/2020
Geospatial Data Wrangling with R
IS415 Geospatial Analytics and Application
Instructor: Dr. Kam Tin Seong.
Assoc. Professor of Information Systems (Practice)
1. Getting started
packages = c('sp', 'rgdal', 'rgeos', 'sf', 'tidyverse')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}2.1 Importing GIS data using st_read()
Datasets:
MP14_SUBZONE_WEB_PL - a polygon feature layer in ESRI shapefile format
CyclingPath - a line feature layer in ESRI shapefile format
PreSchool - a point feature layer in kml file format
sf_mpsz = st_read(dsn = "data/geospatial", layer = "MP14_SUBZONE_WEB_PL")## Reading layer `MP14_SUBZONE_WEB_PL' from data source `D:\SMU Year 3 Sem 1\IS415 Geospatial Analytics and Application\Week 2\Hands-on Ex 2\data\geospatial' using driver `ESRI Shapefile'
## Simple feature collection with 323 features and 15 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
## projected CRS: SVY21sf_cyclingpath = st_read(dsn = "data/geospatial", layer = "CyclingPath")## Reading layer `CyclingPath' from data source `D:\SMU Year 3 Sem 1\IS415 Geospatial Analytics and Application\Week 2\Hands-on Ex 2\data\geospatial' using driver `ESRI Shapefile'
## Simple feature collection with 1625 features and 2 fields
## geometry type: LINESTRING
## dimension: XY
## bbox: xmin: 12711.19 ymin: 28711.33 xmax: 42626.09 ymax: 48948.15
## projected CRS: SVY21sf_preschool = st_read("data/geospatial/pre-schools-location-kml.kml")## Reading layer `PRESCHOOLS_LOCATION' from data source `D:\SMU Year 3 Sem 1\IS415 Geospatial Analytics and Application\Week 2\Hands-on Ex 2\data\geospatial\pre-schools-location-kml.kml' using driver `KML'
## Simple feature collection with 1359 features and 2 fields
## geometry type: POINT
## dimension: XYZ
## bbox: xmin: 103.6824 ymin: 1.248403 xmax: 103.9897 ymax: 1.462134
## z_range: zmin: 0 zmax: 0
## geographic CRS: WGS 842.2 Checking the contents of a SpatialDataFrame
summary(sf_mpsz)## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C
## Min. : 1.0 Min. : 1.000 Length:323 Length:323
## 1st Qu.: 81.5 1st Qu.: 2.000 Class :character Class :character
## Median :162.0 Median : 4.000 Mode :character Mode :character
## Mean :162.0 Mean : 4.625
## 3rd Qu.:242.5 3rd Qu.: 6.500
## Max. :323.0 Max. :17.000
## CA_IND PLN_AREA_N PLN_AREA_C REGION_N
## Length:323 Length:323 Length:323 Length:323
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## REGION_C INC_CRC FMEL_UPD_D X_ADDR
## Length:323 Length:323 Min. :2014-12-05 Min. : 5093
## Class :character Class :character 1st Qu.:2014-12-05 1st Qu.:21864
## Mode :character Mode :character Median :2014-12-05 Median :28465
## Mean :2014-12-05 Mean :27257
## 3rd Qu.:2014-12-05 3rd Qu.:31674
## Max. :2014-12-05 Max. :50425
## Y_ADDR SHAPE_Leng SHAPE_Area geometry
## Min. :19579 Min. : 871.5 Min. : 39438 MULTIPOLYGON :323
## 1st Qu.:31776 1st Qu.: 3709.6 1st Qu.: 628261 epsg:NA : 0
## Median :35113 Median : 5211.9 Median : 1229894 +proj=tmer...: 0
## Mean :36106 Mean : 6524.4 Mean : 2420882
## 3rd Qu.:39869 3rd Qu.: 6942.6 3rd Qu.: 2106483
## Max. :49553 Max. :68083.9 Max. :69748299head(sf_mpsz, n=4)## Simple feature collection with 4 features and 15 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 26403.48 ymin: 28369.47 xmax: 32362.39 ymax: 30396.46
## projected CRS: SVY21
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
## 1 1 1 MARINA SOUTH MSSZ01 Y MARINA SOUTH
## 2 2 1 PEARL'S HILL OTSZ01 Y OUTRAM
## 3 3 3 BOAT QUAY SRSZ03 Y SINGAPORE RIVER
## 4 4 8 HENDERSON HILL BMSZ08 N BUKIT MERAH
## PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
## 1 MS CENTRAL REGION CR 5ED7EB253F99252E 2014-12-05 31595.84
## 2 OT CENTRAL REGION CR 8C7149B9EB32EEFC 2014-12-05 28679.06
## 3 SR CENTRAL REGION CR C35FEFF02B13E0E5 2014-12-05 29654.96
## 4 BM CENTRAL REGION CR 3775D82C5DDBEFBD 2014-12-05 26782.83
## Y_ADDR SHAPE_Leng SHAPE_Area geometry
## 1 29220.19 5267.381 1630379.3 MULTIPOLYGON (((31495.56 30...
## 2 29782.05 3506.107 559816.2 MULTIPOLYGON (((29092.28 30...
## 3 29974.66 1740.926 160807.5 MULTIPOLYGON (((29932.33 29...
## 4 29933.77 3313.625 595428.9 MULTIPOLYGON (((27131.28 30...2.2.1 Plotting the spatial data
plot(sf_mpsz)
2.3 Working with Projection
Assigning EPSG code to sf_mpsz simple feature dataframe
st_crs(sf_mpsz)## Coordinate Reference System:
## User input: SVY21
## wkt:
## PROJCRS["SVY21",
## BASEGEOGCRS["SVY21[WGS84]",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]],
## ID["EPSG",6326]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["Degree",0.0174532925199433]]],
## CONVERSION["unnamed",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",1.36666666666667,
## ANGLEUNIT["Degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",103.833333333333,
## ANGLEUNIT["Degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",1,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",28001.642,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",38744.572,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["(E)",east,
## ORDER[1],
## LENGTHUNIT["metre",1,
## ID["EPSG",9001]]],
## AXIS["(N)",north,
## ORDER[2],
## LENGTHUNIT["metre",1,
## ID["EPSG",9001]]]]sf_mpsz3414 <- st_set_crs(sf_mpsz, 3414)st_set_crs changes the EPSG and not the projection system, st_transform changes the projection system (SVY21/ WGS84)
check the CSR again
st_crs(sf_mpsz3414)## Coordinate Reference System:
## User input: EPSG:3414
## wkt:
## PROJCRS["SVY21 / Singapore TM",
## BASEGEOGCRS["SVY21",
## DATUM["SVY21",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4757]],
## CONVERSION["Singapore Transverse Mercator",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",1.36666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",103.833333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",1,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",28001.642,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",38744.572,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["northing (N)",north,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["easting (E)",east,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["unknown"],
## AREA["Singapore"],
## BBOX[1.13,103.59,1.47,104.07]],
## ID["EPSG",3414]]transforming projection of sf_preschool from wgs84 to svy21
st_crs(sf_preschool)## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]sf_preschool3414 <- st_transform(sf_preschool, 3414)
st_crs(sf_preschool3414)## Coordinate Reference System:
## User input: EPSG:3414
## wkt:
## PROJCRS["SVY21 / Singapore TM",
## BASEGEOGCRS["SVY21",
## DATUM["SVY21",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4757]],
## CONVERSION["Singapore Transverse Mercator",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",1.36666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",103.833333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",1,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",28001.642,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",38744.572,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["northing (N)",north,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["easting (E)",east,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["unknown"],
## AREA["Singapore"],
## BBOX[1.13,103.59,1.47,104.07]],
## ID["EPSG",3414]]2.4 Conversion to sp class
There are many geospatial analysis packages that require the input geospatial data in sp’s Spatial classes. Hence, we need to convert simple feature data frame to sp’s Spatial classes
sp_preschool <- as_Spatial(sf_preschool3414)summary(sp_preschool)## Object of class SpatialPointsDataFrame
## Coordinates:
## min max
## coords.x1 11203.01 45404.24
## coords.x2 25667.60 49300.88
## coords.x3 0.00 0.00
## Is projected: TRUE
## proj4string :
## [+proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0
## +units=m +no_defs]
## Number of points: 1359
## Data attributes:
## Name Description
## Length:1359 Length:1359
## Class :character Class :character
## Mode :character Mode :character2.5 Geoprocessing with sf package
2.5.1 Buffering
sf_buffer_cycling <- st_buffer(sf_cyclingpath,dist=5, nQuadSegs = 30)head(sf_buffer_cycling)## Simple feature collection with 6 features and 2 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 26555.24 ymin: 47269.75 xmax: 26880.62 ymax: 47653.8
## projected CRS: SVY21
## CYL_PATH_C CYL_PATH_1 geometry
## 1 SBWG Sembawang POLYGON ((26829 47350.2, 26...
## 2 SBWG Sembawang POLYGON ((26762.25 47301.32...
## 3 SBWG Sembawang POLYGON ((26877.06 47486.52...
## 4 SBWG Sembawang POLYGON ((26851.7 47370.81,...
## 5 SBWG Sembawang POLYGON ((26800.4 47652.23,...
## 6 SBWG Sembawang POLYGON ((26560.81 47623.83...sf_buffer_cycling$AREA <- st_area(sf_buffer_cycling)
sum(sf_buffer_cycling$AREA)## 773143.9 [m^2]plot(st_geometry(sf_buffer_cycling))
hist(sf_buffer_cycling$AREA)
2.5.2 Point-in-polygon count
How do we find out no. of pre-schools in each planning subzone?
Firstly, we have to identify the pre-schools in each Planning Subzone by using st_intersects().
sf_mpsz3414$`PreSch Count` <- lengths(st_intersects(sf_mpsz3414, sf_preschool3414))what’s the difference between st_interacts() and st_intersection()?
st_intersects() returns for every geometry pair whether they intersect (dense matrix) or which elements intersect st_intersection() returns a geometry for the intersection instead of the logicals
summary(sf_mpsz3414$`PreSch Count`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 2.000 4.207 6.000 37.000To list the planning subzone with most number of pre-school, we can use top_n() function from the dplyr package
top_n(sf_mpsz3414,1, `PreSch Count`)## Simple feature collection with 1 feature and 16 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 23449.05 ymin: 46001.23 xmax: 25594.22 ymax: 47996.47
## projected CRS: SVY21 / Singapore TM
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N PLN_AREA_C
## 1 290 3 WOODLANDS EAST WDSZ03 N WOODLANDS WD
## REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR Y_ADDR
## 1 NORTH REGION NR C90769E43EE6B0F2 2014-12-05 24506.64 46991.63
## SHAPE_Leng SHAPE_Area geometry PreSch Count
## 1 6603.608 2553464 MULTIPOLYGON (((24786.75 46... 37Calculate the density of pre-school by planning subzone
sf_mpsz3414$Area <- sf_mpsz3414 %>%
st_area()sf_mpsz3414 <- sf_mpsz3414 %>%
mutate(`PreSch Density` = `PreSch Count`/ Area * 1000000)ggplot(data = sf_mpsz3414, aes(x= as.numeric(`PreSch Density`))) +
geom_histogram(bins = 20, color= 'black', fill = 'light blue')
ggplot(sf_mpsz3414, aes(y=`PreSch Count`, x = as.numeric(`PreSch Density`) )) +
geom_point(color='black', fill='light blue')
Working with sp, gdal and rgeos (OPTIONAL)
3.1 Importing a shapefile
To import the GIS data layer into R, readOGR() function from rgdal package will be used.
mpsz_sp <- readOGR(dsn = "data/geospatial", layer = "MP14_SUBZONE_WEB_PL") ## OGR data source with driver: ESRI Shapefile
## Source: "D:\SMU Year 3 Sem 1\IS415 Geospatial Analytics and Application\Week 2\Hands-on Ex 2\data\geospatial", layer: "MP14_SUBZONE_WEB_PL"
## with 323 features
## It has 15 fields3.1.1 Checking the contents of a SpatialPolygonDataFrame
summary(mpsz_sp)## Object of class SpatialPolygonsDataFrame
## Coordinates:
## min max
## x 2667.538 56396.44
## y 15748.721 50256.33
## Is projected: TRUE
## proj4string :
## [+proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defs]
## Data attributes:
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C
## Min. : 1.0 Min. : 1.000 Length:323 Length:323
## 1st Qu.: 81.5 1st Qu.: 2.000 Class :character Class :character
## Median :162.0 Median : 4.000 Mode :character Mode :character
## Mean :162.0 Mean : 4.625
## 3rd Qu.:242.5 3rd Qu.: 6.500
## Max. :323.0 Max. :17.000
## CA_IND PLN_AREA_N PLN_AREA_C REGION_N
## Length:323 Length:323 Length:323 Length:323
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## REGION_C INC_CRC FMEL_UPD_D X_ADDR
## Length:323 Length:323 Length:323 Min. : 5093
## Class :character Class :character Class :character 1st Qu.:21864
## Mode :character Mode :character Mode :character Median :28465
## Mean :27257
## 3rd Qu.:31674
## Max. :50425
## Y_ADDR SHAPE_Leng SHAPE_Area
## Min. :19579 Min. : 871.5 Min. : 39438
## 1st Qu.:31776 1st Qu.: 3709.6 1st Qu.: 628261
## Median :35113 Median : 5211.9 Median : 1229894
## Mean :36106 Mean : 6524.4 Mean : 2420882
## 3rd Qu.:39869 3rd Qu.: 6942.6 3rd Qu.: 2106483
## Max. :49553 Max. :68083.9 Max. :69748299head(mpsz_sp, n=4) ## An object of class "SpatialPolygonsDataFrame"
## Slot "data":
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
## 0 1 1 MARINA SOUTH MSSZ01 Y MARINA SOUTH
## 1 2 1 PEARL'S HILL OTSZ01 Y OUTRAM
## 2 3 3 BOAT QUAY SRSZ03 Y SINGAPORE RIVER
## 3 4 8 HENDERSON HILL BMSZ08 N BUKIT MERAH
## PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
## 0 MS CENTRAL REGION CR 5ED7EB253F99252E 2014/12/05 31595.84
## 1 OT CENTRAL REGION CR 8C7149B9EB32EEFC 2014/12/05 28679.06
## 2 SR CENTRAL REGION CR C35FEFF02B13E0E5 2014/12/05 29654.96
## 3 BM CENTRAL REGION CR 3775D82C5DDBEFBD 2014/12/05 26782.83
## Y_ADDR SHAPE_Leng SHAPE_Area
## 0 29220.19 5267.381 1630379.3
## 1 29782.05 3506.107 559816.2
## 2 29974.66 1740.926 160807.5
## 3 29933.77 3313.625 595428.9
##
## Slot "polygons":
## [[1]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] 31595.84 29220.19
##
## Slot "area":
## [1] 1630379
##
## Slot "hole":
## [1] FALSE
##
## Slot "ringDir":
## [1] 1
##
## Slot "coords":
## [,1] [,2]
## [1,] 31495.56 30140.01
## [2,] 31980.96 29671.60
## [3,] 32332.90 29331.98
## [4,] 32362.39 29303.52
## [5,] 32362.32 29303.47
## [6,] 32277.93 29241.61
## [7,] 32277.63 29241.39
## [8,] 32242.98 29215.99
## [9,] 32243.85 29212.25
## [10,] 32244.71 29204.11
## [11,] 32244.40 29197.33
## [12,] 32243.11 29190.23
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## [27,] 32276.14 29177.44
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## [52,] 32286.96 29175.64
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## [62,] 32278.15 29174.29
## [63,] 32256.92 29158.73
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## [70,] 32303.86 29052.94
## [71,] 32312.76 29024.82
## [72,] 32317.12 28996.06
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## [74,] 32310.75 28915.59
## [75,] 32306.43 28897.24
## [76,] 32306.09 28896.26
## [77,] 32297.72 28872.16
## [78,] 32290.71 28856.11
## [79,] 32286.03 28845.38
## [80,] 32280.67 28835.20
## [81,] 32270.45 28818.77
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## [84,] 32237.60 28780.92
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## [110,] 30844.87 29021.96
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## +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defs3.1.2 Plotting the spatial data
plot(mpsz_sp)
3.2 Now It’s Your Turn
The pre-schools GIS data is in kml format, we will use ogrListLayers function of rgdal package to check the actual data structure of the kml data file.
ogrListLayers("data/geospatial/pre-schools-location-kml.kml")## [1] "PRESCHOOLS_LOCATION"
## attr(,"driver")
## [1] "KML"
## attr(,"nlayers")
## [1] 13.2.1 Importing kml GIS data
preschool <- readOGR("data/geospatial/pre-schools-location-kml.kml","PRESCHOOLS_LOCATION")## OGR data source with driver: KML
## Source: "D:\SMU Year 3 Sem 1\IS415 Geospatial Analytics and Application\Week 2\Hands-on Ex 2\data\geospatial\pre-schools-location-kml.kml", layer: "PRESCHOOLS_LOCATION"
## with 1359 features
## It has 2 fields3.2.2 Importing GIS data layer from LTADataMall
cyclingpath <- readOGR (dsn = "data/geospatial", layer = "CyclingPath")## OGR data source with driver: ESRI Shapefile
## Source: "D:\SMU Year 3 Sem 1\IS415 Geospatial Analytics and Application\Week 2\Hands-on Ex 2\data\geospatial", layer: "CyclingPath"
## with 1625 features
## It has 2 fields3.2.3 Assigning a coordinate system
mpsz_svy21 <- spTransform(mpsz_sp, CRS("+init=epsg:3414"))3.3 Reprojecting a geospatial data
preschool_svy21 <- spTransform(preschool,CRS("+init=epsg:3414"))3.4 Geoprocessing with rgeos
buf_cyclingpath <- gBuffer(cyclingpath, byid = TRUE, width = 5)buf_cyclingpath@data$Area <- gArea(buf_cyclingpath, byid = TRUE)
sum(buf_cyclingpath@data$Area)## [1] 771024.9