HOANG NAM - 1001797 LAB 9
SET WORKING DIRECTORY AND LOAD PACKAGES
setwd("/Volumes/HOANG NAM/Maps Making/Lab 9/02221-Lab9") # Set working directory
library(knitr)
library(ggplot2)
library(data.table)
library(sp)
library(rgdal)
library(ggmap)
library(maptools)
library(plotly)SET UP PART 1
tenders <- fread("tender-bids.csv", header = FALSE, fill =T)
colnames(tenders) <- c("centre", "stall", "area","trade", "bid", "month")SET UP PART 2
tenders <- tenders[centre != "",] #remove empty rows
tenders <- tenders[!1135,] #remove 'lockup' row
tenders <- tenders[!1749,] #remove 'market stalls' row
tenders[1:1134,type:="cooked",] #set pre-lockup rows to type cooked
tenders[1135:1748,type:="lockup",] #set to type lockup
tenders[1749:nrow(tenders),type:="market",] #set to type market
tenders[588:636,month:=bid,] #fixing the NA
tenders[588:636,bid:=trade,]
tenders[588:636,trade:=area,]
tenders[588:636,area:=stall,]
tenders[,area:=as.numeric(gsub(area,pattern=" |\\.",replacement=""))/100]#cleanup
tenders[,bidNum:=as.numeric(gsub(bid,pattern="\\$|,", replacement = "")),] #$price to numeric
tenders[,date:=as.Date(paste0("01-",month), "%d-%b-%Y"),]#date formatingtenders <- tenders[centre != "",] # remove empty rows
tenders <- tenders[!1135,]
tenders[1:1134,type:="cooked",] ## Label Rows 1 to 821 with cooked label in 'type' column
tenders[1135:nrow(tenders),type:="lockup",] ##Label Rows 1135 till end of table with "lockup"
tenders[,bidNum:=as.numeric(gsub(bid, pattern="\\$|,", replacement = "")),] # Remove "$," from bid price and record into new column
tenders[,date:=as.Date(paste0("01-",month), "%d-%b-%Y"),] ## Format date into dd-mm-yy format
tenders[,priceM2:=bidNum/as.numeric(area),] ## Find priceM2 = (bid Price)/(area)DATA MANIPULICATION
tenders[,list(price=mean(priceM2)),by=centre] # return a list of avg price per centre## centre price
## 1: COOKED FOOD STALLS NA
## 2: AMOY STREET FOOD CENTRE 930.57088
## 3: BEO CRESCENT MARKET 30.49304
## 4: BERSEH FOOD CENTRE 250.04824
## 5: BLK 665 BUFFALO ROAD 217.51288
## ---
## 100: BLK 50A MARINE TERRACE 510.25107
## 101: BLK 511 BEDOK NORTH STREET 3 1666.66667
## 102: BLK 630 BEDOK RESERVOIR ROAD 44.63716
## 103: BLK 82 TELOK BLANGAH DRIVE 3005.04202
## 104: BLK 93 TOA PAYOH LORONG 4 100.76923tenders[type=='cooked',list(price=mean(priceM2)),by=centre] # return a list avg price per centre for only cooked food## centre price
## 1: COOKED FOOD STALLS NA
## 2: AMOY STREET FOOD CENTRE 930.57088
## 3: BEO CRESCENT MARKET 149.89435
## 4: BERSEH FOOD CENTRE 250.04824
## 5: BLK 665 BUFFALO ROAD 284.37981
## 6: BLK 1 JALAN KUKOH 41.65605
## 7: BLK 11 TELOK BLANGAH CRESCENT 2354.48029
## 8: BLK 112 JALAN BUKIT MERAH 104.61073
## 9: BLK 115 BUKIT MERAH VIEW 135.12743
## 10: BLK 117 ALJUNIED AVENUE 2 23498.86494
## 11: BLK 120 BUKIT MERAH LANE 1 177.64957
## 12: BLK 127 TOA PAYOH LORONG 1 4742.26203
## 13: BLK 137 TAMPINES STREET 11 222.83814
## 14: BLK 14 HAIG ROAD 410.02825
## 15: BLK 159 MEI CHIN ROAD 78.00092
## 16: BLK 16 BEDOK SOUTH ROAD 135.83423
## 17: BLK 162 ANG MO KIO AVENUE 4 180.99548
## 18: BLK 17 UPPER BOON KENG ROAD 194.90581
## 19: BLK 20 GHIM MOH ROAD 5138.46154
## 20: BLK 216 BEDOK NORTH STREET 1 174.55859
## 21: BLK 22 TOA PAYOH LORONG 7 131.35614
## 22: BLK 226D ANG MO KIO AVENUE 1 57.25534
## 23: BLK 226H ANG MO KIO STREET 22 1832.25806
## 24: BLK 22A HAVELOCK ROAD 430.37634
## 25: BLK 22B HAVELOCK ROAD 47.00941
## 26: BLK 254 JURONG EAST STREET 24 246.08449
## 27: BLK 29 BENDEMEER ROAD 462.96959
## 28: BLK 3 CHANGI VILLAGE ROAD 322.31801
## 29: BLK 32 NEW MARKET ROAD 401.87481
## 30: BLK 320 SHUNFU ROAD 5754.10256
## 31: BLK 335 SMITH STREET 172.79625
## 32: BLK 36 TELOK BLANGAH RISE 81.17021
## 33: BLK 37A TEBAN GARDENS ROAD 232.65864
## 34: BLK 41A CAMBRIDGE ROAD 411.76471
## 35: BLK 44 HOLLAND DRIVE 11175.61076
## 36: BLK 453A ANG MO KIO AVENUE 10 2963.39286
## 37: BLK 49 SIMS PLACE 2116.73525
## 38: BLK 4A JALAN BATU 99.00368
## 39: BLK 503 WEST COAST DRIVE 34578.87719
## 40: BLK 51 OLD AIRPORT ROAD NA
## 41: BLK 527 ANG MO KIO AVENUE 10 NA
## 42: BLK 531A UPPER CROSS STREET NA
## 43: BLK 6 JALAN BUKIT MERAH NA
## 44: BLK 6 TANJONG PAGAR PLAZA 188.89338
## 45: BLK 628 ANG MO KIO AVENUE 4 355.98991
## 46: BLK 7 EMPRESS ROAD 201.58730
## 47: BLK 726 CLEMENTI WEST STREET 2 492.29040
## 48: BLK 75 TOA PAYOH LORONG 5 173.61111
## 49: BLK 79 CIRCUIT ROAD 1613.30986
## 50: BLK 79 TELOK BLANGAH DRIVE 306.36792
## 51: BLK 79A CIRCUIT ROAD 1941.08701
## 52: BLK 80 CIRCUIT ROAD 3241.66667
## 53: BLK 85 REDHILL LANE 409.15114
## 54: BLK 89 CIRCUIT ROAD 72.00328
## 55: BLK 90 WHAMPOA DRIVE 58.52843
## 56: BLK 91 WHAMPOA DRIVE 141.73077
## 57: BLK 93 TOA PAYOH LORONG 4 934.43175
## 58: BUKIT TIMAH MARKET 235.85086
## 59: COMMONWEALTH CRESCENT MARKET 674.18248
## 60: DUNMAN FOOD CENTRE 7200.26923
## 61: EAST COAST LAGOON FOOD VILLAGE 27805.81481
## 62: GEYLANG SERAI MARKET 160.89246
## 63: GOLDEN MILE FOOD CENTRE 1425.31708
## 64: KALLANG ESTATE MARKET 733.33333
## 65: MARKET STREET FOOD CENTRE 297.63294
## 66: MAXWELL FOOD CENTRE 3056.92193
## 67: NEWTON FOOD CENTRE 280.92754
## 68: NORTH BRIDGE ROAD MARKET 75.28231
## 69: SERANGOON GARDEN MARKET 164.66147
## 70: TAMAN JURONG MARKET & FOOD CENTRE 71.19260
## 71: TIONG BAHRU MARKET 200.58236
## 72: ZION RIVERSIDE FOOD CENTRE 203.21152
## centre pricetenders[,list(count=length(bidNum)),by=centre] # return a list of number of bids per centre## centre count
## 1: COOKED FOOD STALLS 1
## 2: AMOY STREET FOOD CENTRE 62
## 3: BEO CRESCENT MARKET 23
## 4: BERSEH FOOD CENTRE 103
## 5: BLK 665 BUFFALO ROAD 50
## ---
## 100: BLK 50A MARINE TERRACE 8
## 101: BLK 511 BEDOK NORTH STREET 3 1
## 102: BLK 630 BEDOK RESERVOIR ROAD 19
## 103: BLK 82 TELOK BLANGAH DRIVE 17
## 104: BLK 93 TOA PAYOH LORONG 4 2ggplot(tenders[,list(count=length(bidNum)),by=date], aes(date, count)) + geom_line()
QUESTIONS
Choose 2-3 questions from the list above or your own additions and include visualizations that answer those questions in your Markdown document. The plots will render within the html document automatically!
Q1: HOW DO THE BIDS CHANGE WITH TIME
g<- ggplot(tenders[,list(AverageBid=mean(bidNum)),by=date],aes(date, AverageBid))+geom_line(color = "red",size = 0.5)+ggtitle("Average Bid vs Time")
ggplotly(g)Q2: WHICH CENTRES HAVE HIGHER PRICE PER SQUARED METRE?
a = aggregate(priceM2 ~ centre, tenders, mean)
p=plot_ly( data = tenders, x = a$centre, y = a$priceM2, type = "bar", barmode = "group", color = ~ a$centre) %>% layout(xaxis= list(showticklabels = FALSE))
hide_legend(p)SPATIAL DATA
h <- readOGR("HAWKERCENTRE.kml", "HAWKERCENTRE")## OGR data source with driver: KML
## Source: "HAWKERCENTRE.kml", layer: "HAWKERCENTRE"
## with 116 features
## It has 2 fieldsplot(h)
h.t=data.frame(toupper(h$Name),h@coords[,1:2]) ##Create simplified data frame with only name and coordinates
colnames(h.t) = c("name","lon","lat") ##Add in names for data frame columns
tenders.sp=merge(tenders,h.t, by.x="centre",by.y="name",all.x=T) ##Merge centres and h by name of hawker centres into new data frame
tenders.sp=tenders.sp[complete.cases(tenders.sp)] #Remove incomplete dataGEOCODING HAWKER CENTRE
##Manually Geo Code each hawker center
centres = tenders[,list(count=.N),by=centre] # Find common entries with the same Hawker Centre names
centres[,location:=paste0(centre,", Singapore"),] #Concatenate values in location column with ", Singapore"
#centres=centres[!88]
#g=geocode(centres[,location,],output="latlon",source="google",sensor=F)
g = read.csv("geocoded.csv")
##Query the lat and lon values for each hawker center by googling
centres = cbind(centres,g) #Combine records by rows
tenders.sp = merge(tenders,centres,by.x="centre",by.y="centre",all.x=T) ##Merge in latlon to tenders data tableDENSITY PLOT
ggplot(tenders.sp, aes(x=lon, y=lat, size=priceM2, color=type)) + geom_point(alpha=0.3) + coord_fixed()
ggplot(tenders.sp, aes(x=lon, y=lat)) + geom_point() + geom_density2d() + coord_fixed()
ggplot(tenders.sp, aes(x=lon, y=lat)) + geom_point() + geom_hex() + coord_fixed()+ggtitle("Plot of the price per m2 of the stalls against type")
QUESTIONS
Do you think there is a specific spatial distribution with regard to the number of bids per trade or type? Or different patterns based on the year? Create a (series of) faceted plot(s) to help you answer that question and include them (and the code leading up to it) in your markdown document.
NUMBER OF BIDS PER TRADE
hide_legend(ggplot(tenders.sp, aes(x=lon, y=lat, color=trade)) + geom_point() + geom_density2d() + coord_fixed()+facet_wrap(~trade)+ggtitle("Number of bids per trade"))All food trades can be found almost everywhere. This might be due to the fact that Singapore has mixes of people in different racial group living in the same areas.
NUMBER OF BIDS PER TYPE
hide_legend(ggplot(tenders.sp, aes(x=lon, y=lat, color=type)) + geom_point() + geom_density2d() + coord_fixed()+facet_wrap(~type)+ggtitle("Number of bids per type"))The faceted plot based on type shows that both cooked and lockup stalls are in the same areas in the spatial distribution. This might be due to an increase in demand for food types. Cooked food stalls are heavily concentrated at a single point while lockup stalls were concentrated by in slightly more locations.
SPATIAL POINT PATERNS
library(spatstat)
centres.sp <- tenders.sp[lat > 0,list(lon=lon[1],lat=lat[1], price=mean(priceM2),count=.N),by=centre]
centres.sp[is.na(price),price:=0,] #NA to 0
coordinates(centres.sp) <- c('lon', 'lat')
centres.ppp <- unmark(as.ppp(centres.sp))
plot(centres.ppp)
sg <- readOGR("sg-all.shp", "sg-all")## OGR data source with driver: ESRI Shapefile
## Source: "sg-all.shp", layer: "sg-all"
## with 1 features
## It has 13 fieldssg.window <- as.owin(sg)
centres.ppp <- centres.ppp[sg.window]
plot(Kest(centres.ppp))
plot(density(centres.ppp, 0.02),main = "Heat map of the density of hawker centres in Singapore")
contour(density(centres.ppp, 0.02),main = "Contour of the density of hawker centres in Singapore")
pop <- as.im(readGDAL("sg-pop.tif"))## sg-pop.tif has GDAL driver GTiff
## and has 37 rows and 58 columnsplot(rhohat(centres.ppp, pop), main = "Centres weighted by population")
plot(rhohat(centres.ppp, pop,weights=centres.sp$price), main ="Centres weighted by average tender price")
plot(pop,main = "Population density and hawker centres locations")
plot(centres.ppp, add=T)
There are 2 areas with high population but low hakwer centre density namely Choa Chu Kang and Punggol.These are the outliers which have then resulted in the dips towards the end of the population spectrum in the rhohat plots aboved. The reasons for a low hawker centre density might be due to a large number of shopping malls which also provide the same service as the hawker centres. As shown in the plot, most of the hawker centres are located at the central areas. This might be due to the fact the these places have alot of working people as well as tourists. Thus Singapore government decides to place these hawker centres there.