1: Urban Mobility Analysis

Author

Magdalene Chan

Published

November 18, 2023

Modified

November 18, 2023

Urban Mobility analysis by using passenger volume by origin-destination bus stops.

Getting started

The code chunk below uses p_load() of pacman package to check if the required packages have been installed on the computer. If they are, the packages will be launched. The packages used are:

tmap: for thematic mapping
sf: for geospatial data wrangling
tidyverse: for non-spatial data wrangling

pacman::p_load(tmap, sf, tidyverse)

The data sets used are:

Master Plan 2019 Subzone Boundary (No Sea) (last updated Dec 2019) from data.gov.sg - reformatted into shapefile format
Bus Stop Location (Last updated Jul 2023) from LTADataMall retrieved on 18 Nov 2023
Passenger Volume by Origin Destination Bus Stops for Aug-Oct 2023 from LTADataMall retrieved on 18 Nov 2023

Import Passenger Volume by Origin-Destination Bus Stops

The code chunk below uses the read_csv() function of readr package to import the csv file into R and save it as a R dataframe called odbus.

odbus <- read_csv("data/aspatial/origin_destination_bus_202308.csv")

ORIGIN_PT_CODE and DESTINATION_PT_CODE are numeric variables that are categorical in nature. As such, they should be transformed to factor so that R treats them as a grouping variable.

odbus$ORIGIN_PT_CODE <- as.factor(odbus$ORIGIN_PT_CODE)
odbus$DESTINATION_PT_CODE <- as.factor(odbus$DESTINATION_PT_CODE)

Extract Commuting Flow data

The code chunk below extracts commuting flows on weekday during the rush hour (7am, 8am, 9am).

origtrip_7_9 <- odbus %>%
  filter(DAY_TYPE == "WEEKDAY") %>%
  filter(TIME_PER_HOUR >= 7 & TIME_PER_HOUR <= 9) %>%
  group_by(ORIGIN_PT_CODE) %>%
  summarise(TRIPS = sum(TOTAL_TRIPS))

Import Geospatial Data

Two geospatial data are used in this exercise.

Import Bus Stop Locations

The code chunk below uses the st_read() function of sf package to import BusStop shapefile into R as a simple feature data frame called BusStop. As BusStop uses svy21 projected coordinate system, the crs is set to 3414.

BusStop <- st_read(dsn = "data/geospatial", 
                layer = "BusStop") %>%
  st_transform(crs=3414)

Reading layer `BusStop' from data source 
  `C:\magdalenecjw\ISSS624 Geospatial\In_Class_Exercise\Ex1\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 5161 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21

Import Planning Subzone data

The code chunk below uses the st_read() function of sf package to import MPSZ-2019 shapefile into R as a simple feature data frame called mpsz. To ensure we can use mpsz together with BusStop, mpsz is reprojected to the svy21 projected coordinate system (crs=3413).

mpsz <- st_read(dsn = "data/geospatial", 
                layer = "MPSZ-2019") %>%
  st_transform(crs=3414)

Reading layer `MPSZ-2019' from data source 
  `C:\magdalenecjw\ISSS624 Geospatial\In_Class_Exercise\Ex1\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84