Inclass_Ex02
Po Theodora
4/28/2020
Overview
In this exercise, we will learn how to handle geospatial data in R by using appropriate R packages
Learning Outcome
By the end of this hands-on exercise, you should aquire the following competencies: - import geospatial data by using appropriate functions of sf packages, - import geospatial data as R spatial object type by using appropriate functions of sp packages, - georeference geospatial data using either apporpriate rgeo understand the concepts of Spatial Data Frame and to apply these understanding in handling geospatial data. - understand the concepts of simple features and to apply these understanding in handling geospatial data. - perform geospatial data handling tasks by using appropriate functions of sp, rgdal, rgeos and/or sf packages.
Data Acquisition
Extract necessary data sets from the appropriate source 1. Master Plan 2014 Subzone Boundary (Web) from data.gov.sg (www.data.gov.sg) 2. Pre-Schools Location from data.gov.sg (www.data.gov.sg) 3. Cycle path from LTADataMall (https://www.mytransport.sg/content/mytransport/home/dataMall.html)
Setting Up my Environment (install R packages to read geometric data model)
Check if sp, rgdal, rgeos have been installed. If not, then install the uninstalled packages After installing, launch sp, rgdal, rgeos
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)
}## Loading required package: sp## Loading required package: rgdal## rgdal: version: 1.4-8, (SVN revision 845)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.4.2, released 2019/06/28
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/gdal
## GDAL binary built with GEOS: FALSE
## Loaded PROJ.4 runtime: Rel. 5.2.0, September 15th, 2018, [PJ_VERSION: 520]
## Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/proj
## Linking to sp version: 1.3-2## Loading required package: rgeos## rgeos version: 0.5-2, (SVN revision 621)
## GEOS runtime version: 3.7.2-CAPI-1.11.2
## Linking to sp version: 1.3-1
## Polygon checking: TRUE## Loading required package: sf## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0## Loading required package: tidyverse## ── Attaching packages ────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.0 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ───────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Working with sf packages
Import the follwing geospatial data into R: 1. MP14_SUBZONE_WEB_PL - a polygon feature layer in ESRI shapefile format, 2. CyclingPath - a line feature layer in ESRI shapefile format, and 3. PreSchool - a point feature layer in kml file format.
Importing Geospatial Data using st_red
Importing a polygon feature layer in shapefile format
The code chunk below uses st_read() function of sf package to import MP14_SUBZONE_WEB_PL data into R as simple feature dataframes
sf_mpsz = st_read(dsn="data/geospatial",
layer="MP14_SUBZONE_WEB_PL")## Reading layer `MP14_SUBZONE_WEB_PL' from data source `/Users/theodora/Desktop/IS415/Lesson 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
## proj4string: +proj=tmerc +lat_0=1.366666666666667 +lon_0=103.8333333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defsImporting polyline feature data in shapefile formate
The code chunk below uses st_read() function of sf package to import the CyclingPath layer into R as simple feature dataframes.
sf_cyclingpath <- st_read(dsn="data/geospatial", layer="CyclingPath")## Reading layer `CyclingPath' from data source `/Users/theodora/Desktop/IS415/Lesson 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
## proj4string: +proj=tmerc +lat_0=1.366666666666667 +lon_0=103.8333333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defsImporting Geospatial Data in kml format
sf_preschool = st_read("data/geospatial/pre-schools-location-kml.kml")## Reading layer `PRESCHOOLS_LOCATION' from data source `/Users/theodora/Desktop/IS415/Lesson 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
## CRS: 4326Checking the contents of a SpatialDataFrame
Check the contents of sf_mpsz data object by using summary()
summary(sf_mpsz)## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND
## Min. : 1.0 Min. : 1.000 ADMIRALTY : 1 AMSZ01 : 1 N:274
## 1st Qu.: 81.5 1st Qu.: 2.000 AIRPORT ROAD : 1 AMSZ02 : 1 Y: 49
## Median :162.0 Median : 4.000 ALEXANDRA HILL : 1 AMSZ03 : 1
## Mean :162.0 Mean : 4.625 ALEXANDRA NORTH: 1 AMSZ04 : 1
## 3rd Qu.:242.5 3rd Qu.: 6.500 ALJUNIED : 1 AMSZ05 : 1
## Max. :323.0 Max. :17.000 ANAK BUKIT : 1 AMSZ06 : 1
## (Other) :317 (Other):317
## PLN_AREA_N PLN_AREA_C REGION_N REGION_C
## BUKIT MERAH : 17 BM : 17 CENTRAL REGION :134 CR :134
## QUEENSTOWN : 15 QT : 15 EAST REGION : 30 ER : 30
## ANG MO KIO : 12 AM : 12 NORTH REGION : 41 NER: 48
## DOWNTOWN CORE: 12 DT : 12 NORTH-EAST REGION: 48 NR : 41
## TOA PAYOH : 12 TP : 12 WEST REGION : 70 WR : 70
## HOUGANG : 10 HG : 10
## (Other) :245 (Other):245
## INC_CRC FMEL_UPD_D X_ADDR Y_ADDR
## 00F5E30B5C9B7AD8: 1 Min. :2014-12-05 Min. : 5093 Min. :19579
## 013B509B8EDF15BE: 1 1st Qu.:2014-12-05 1st Qu.:21864 1st Qu.:31776
## 01A4287FB060A0A6: 1 Median :2014-12-05 Median :28465 Median :35113
## 029BD940F4455194: 1 Mean :2014-12-05 Mean :27257 Mean :36106
## 0524461C92F35D94: 1 3rd Qu.:2014-12-05 3rd Qu.:31674 3rd Qu.:39869
## 05FD555397CBEE7A: 1 Max. :2014-12-05 Max. :50425 Max. :49553
## (Other) :317
## SHAPE_Leng SHAPE_Area geometry
## Min. : 871.5 Min. : 39438 MULTIPOLYGON :323
## 1st Qu.: 3709.6 1st Qu.: 628261 epsg:NA : 0
## Median : 5211.9 Median : 1229894 +proj=tmer...: 0
## Mean : 6524.4 Mean : 2420882
## 3rd Qu.: 6942.6 3rd Qu.: 2106483
## Max. :68083.9 Max. :69748299
## To view the first few records in the spatial data object
head(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
## proj4string: +proj=tmerc +lat_0=1.366666666666667 +lon_0=103.8333333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defs
## 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...Plotting the spatial data
To view the spatial data, plot function can be used
plot(sf_mpsz)## Warning: plotting the first 9 out of 15 attributes; use max.plot = 15 to plot
## all
plot(sf_cyclingpath)
plot(sf_preschool)
Working with projection
Assigning EPSG code to sf_mpsz simple feature dataframe ### First, check projection of sf_mpsz
st_crs(sf_mpsz)## Coordinate Reference System:
## No user input
## wkt:
## PROJCS["SVY21",
## GEOGCS["SVY21[WGS84]",
## DATUM["WGS_1984",
## SPHEROID["WGS_84",6378137.0,298.257223563]],
## PRIMEM["Greenwich",0.0],
## UNIT["Degree",0.0174532925199433]],
## PROJECTION["Transverse_Mercator"],
## PARAMETER["False_Easting",28001.642],
## PARAMETER["False_Northing",38744.572],
## PARAMETER["Central_Meridian",103.8333333333333],
## PARAMETER["Scale_Factor",1.0],
## PARAMETER["Latitude_Of_Origin",1.366666666666667],
## UNIT["Meter",1.0]]Next, assigning EPSG3414 to sf_mpsz simple feature dataframe
st_mpsz3414 <- st_set_crs(sf_mpsz, 3414)## Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
## thatTransforming the projection of sf_preschool from wgs84 to svy21
First, check the projection of sf_preschool
st_crs(sf_preschool)## Coordinate Reference System:
## User input: 4326
## wkt:
## GEOGCS["WGS 84",
## DATUM["WGS_1984",
## SPHEROID["WGS 84",6378137,298.257223563,
## AUTHORITY["EPSG","7030"]],
## AUTHORITY["EPSG","6326"]],
## PRIMEM["Greenwich",0,
## AUTHORITY["EPSG","8901"]],
## UNIT["degree",0.0174532925199433,
## AUTHORITY["EPSG","9122"]],
## AUTHORITY["EPSG","4326"]]Next, transforming of sf_preschool simple feature dataframe onto svy21 projected coordinate system (EPSG3414)
sf_preschool3414 <- st_transform(sf_preschool,3414)
st_crs(sf_preschool3414)## Coordinate Reference System:
## User input: EPSG:3414
## wkt:
## PROJCS["SVY21 / Singapore TM",
## GEOGCS["SVY21",
## DATUM["SVY21",
## SPHEROID["WGS 84",6378137,298.257223563,
## AUTHORITY["EPSG","7030"]],
## AUTHORITY["EPSG","6757"]],
## PRIMEM["Greenwich",0,
## AUTHORITY["EPSG","8901"]],
## UNIT["degree",0.0174532925199433,
## AUTHORITY["EPSG","9122"]],
## AUTHORITY["EPSG","4757"]],
## PROJECTION["Transverse_Mercator"],
## PARAMETER["latitude_of_origin",1.366666666666667],
## PARAMETER["central_meridian",103.8333333333333],
## PARAMETER["scale_factor",1],
## PARAMETER["false_easting",28001.642],
## PARAMETER["false_northing",38744.572],
## UNIT["metre",1,
## AUTHORITY["EPSG","9001"]],
## AUTHORITY["EPSG","3414"]]Geoprocessing with sf package
The scenario: The authority is planning to upgrade the exiting cycling path. To do so, they need to acquire 5 metres reserve land on the both sides of the current cycling path. You are tasked to determine the extend of the land needed to be acquired and their total areas.
The solution: Creating a 5-meter buffer around cycling path and calculate the total area of the buffers
sf_buffer_cycling <- st_buffer(sf_cyclingpath, dist=5, nQuadSegs = 30)
sf_buffer_cycling$AREA <- st_area(sf_buffer_cycling)
sum(sf_buffer_cycling$AREA)## 773143.9 [m^2]plot(sf_buffer_cycling)
Working with sp, gdal, rgeos Packages
Importing a shapefile
mpsz_sp <- readOGR(dsn = "data/geospatial",
layer = "MP14_SUBZONE_WEB_PL") ## OGR data source with driver: ESRI Shapefile
## Source: "/Users/theodora/Desktop/IS415/Lesson 2/data/geospatial", layer: "MP14_SUBZONE_WEB_PL"
## with 323 features
## It has 15 fieldsChecking 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=1.366666666666667 +lon_0=103.8333333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defs
## +ellps=WGS84 +towgs84=0,0,0]
## Data attributes:
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND
## Min. : 1.0 Min. : 1.000 ADMIRALTY : 1 AMSZ01 : 1 N:274
## 1st Qu.: 81.5 1st Qu.: 2.000 AIRPORT ROAD : 1 AMSZ02 : 1 Y: 49
## Median :162.0 Median : 4.000 ALEXANDRA HILL : 1 AMSZ03 : 1
## Mean :162.0 Mean : 4.625 ALEXANDRA NORTH: 1 AMSZ04 : 1
## 3rd Qu.:242.5 3rd Qu.: 6.500 ALJUNIED : 1 AMSZ05 : 1
## Max. :323.0 Max. :17.000 ANAK BUKIT : 1 AMSZ06 : 1
## (Other) :317 (Other):317
## PLN_AREA_N PLN_AREA_C REGION_N REGION_C
## BUKIT MERAH : 17 BM : 17 CENTRAL REGION :134 CR :134
## QUEENSTOWN : 15 QT : 15 EAST REGION : 30 ER : 30
## ANG MO KIO : 12 AM : 12 NORTH REGION : 41 NER: 48
## DOWNTOWN CORE: 12 DT : 12 NORTH-EAST REGION: 48 NR : 41
## TOA PAYOH : 12 TP : 12 WEST REGION : 70 WR : 70
## HOUGANG : 10 HG : 10
## (Other) :245 (Other):245
## INC_CRC FMEL_UPD_D X_ADDR Y_ADDR
## 00F5E30B5C9B7AD8: 1 2014/12/05:323 Min. : 5093 Min. :19579
## 013B509B8EDF15BE: 1 1st Qu.:21864 1st Qu.:31776
## 01A4287FB060A0A6: 1 Median :28465 Median :35113
## 029BD940F4455194: 1 Mean :27257 Mean :36106
## 0524461C92F35D94: 1 3rd Qu.:31674 3rd Qu.:39869
## 05FD555397CBEE7A: 1 Max. :50425 Max. :49553
## (Other) :317
## SHAPE_Leng SHAPE_Area
## Min. : 871.5 Min. : 39438
## 1st Qu.: 3709.6 1st Qu.: 628261
## Median : 5211.9 Median : 1229894
## Mean : 6524.4 Mean : 2420882
## 3rd Qu.: 6942.6 3rd Qu.: 2106483
## Max. :68083.9 Max. :69748299
## head(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":
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## An object of class "Polygons"
## Slot "Polygons":
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## An object of class "Polygon"
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##
## Slot "area":
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## Slot "hole":
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## +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defs
## +ellps=WGS84 +towgs84=0,0,0Plotting the spatial data
plot(mpsz_sp)
Using the previous functions, import the Pre-School and Cycling Path GIS data files into R spatial objects. The solution: The pre-schools GIS data is in kml format. Before we can import the data file into R, 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] 1Importing kml GIS data
Notice that the file called pre-schools-location-kml is just the folder (refer to the list above). In order to important the layer, we need to use PRESCHOOL_LOCATION layer instead.
preschool <- readOGR("data/geospatial/pre-schools-location-kml.kml",
"PRESCHOOLS_LOCATION")## OGR data source with driver: KML
## Source: "/Users/theodora/Desktop/IS415/Lesson 2/data/geospatial/pre-schools-location-kml.kml", layer: "PRESCHOOLS_LOCATION"
## with 1359 features
## It has 2 fieldsImporting GIS data layer from LTADataMall
In this section, you will learn how to import a line geospatial data into R. The geospatial data is the CyclingPath shapefile from LTA DataMall (https://www.mytransport.sg/content/mytransport/home/dataMall.html)
cyclingpath <- readOGR (dsn = "data/geospatial",
layer = "CyclingPath")## OGR data source with driver: ESRI Shapefile
## Source: "/Users/theodora/Desktop/IS415/Lesson 2/data/geospatial", layer: "CyclingPath"
## with 1625 features
## It has 2 fieldsShow the codes you used to check the contents of preschool and cyclingpath spatial objects. Describe their spatial data models, boundary coordinates, projection, and attribute variables.
Assigning a coordinate system
Use CRS and spTransform functions of rgdal
mpsz_svy21 <- spTransform(mpsz_sp,
CRS("+init=epsg:3414"))Reprojecting a geospatial data
Change the projection system of the preschool data set from wgs84 to svy21
preschool_svy21 <- spTransform(preschool,
CRS("+init=epsg:3414"))Geoprocessing with rgeos
The scenario
The authority is planning to upgrade the exiting cycling path. To do so, they need to acquire 5 metres reserve land on the both sides of the current cycling path. You are tasked to determine the extend of the land need to be acquired and their total areas.
The solution:
buf_cyclingpath <- gBuffer(cyclingpath, width = 5)The solution:
buf_cyclingpath <- gBuffer(cyclingpath, byid = TRUE,
width = 5)The solution:
buf_cyclingpath@data$Area <- gArea(buf_cyclingpath,
byid = TRUE)
sum(buf_cyclingpath@data$Area)## [1] 771024.9