Part 3 out of 4
3. Can you infer the owners of each credit card and loyalty card? What is your evidence? Where are there uncertainties in your method? Where are there uncertainties in the data?
In order to tag the owners of each credit card and loyalty card to the car id, we would need to combine several factors together to triangulate the results. The two conditions that will be used to triangulate the data between the three datasets are:
The locations coordinates would be assigned by referencing the tourist map of Abila. However, from the earlier section, we discovered that the tourist map provided might not be accurate in locating the location coordinates as the icons on the tourist map might not represent the exact coordinates of the location.
Furthermore, the tourist map do not have all the locations marked by its logo, which will not allow a full join with the locations in the cc transaction data. Table 1 shows the locations from the cc dataset whose logo could not be located visually on the tourist map of Abila. Ranking the number of transaction at each location in descending order, there are high volumes of transactions at those locations and the coordinates are necessary to be mapped.
## Transactions on 13/01/2014 at "Frydos Autosupply n' More"
locations <- data.frame(location = cc$location) %>%
group_by(location) %>% summarize(number_transactions=n())
knitr::kable(locations %>%
dplyr::filter(location == "Abila Zacharo" |
location == "Brewed Awakenings" |
location == "Daily Dealz" |
location == "Hippokampos" |
location == "Kalami Kafenion" |
location == "Kronos Pipe and Irrigation" |
location == "Octavio's Office Supplies" |
location == "Shoppers' Delight" |
location == "Stewart and Sons Fabrication") %>%
arrange(desc(number_transactions)), "simple",
caption="Table of location with no traceable coordinates")
| location | number_transactions |
|---|---|
| Hippokampos | 171 |
| Abila Zacharo | 72 |
| Kalami Kafenion | 64 |
| Brewed Awakenings | 30 |
| Shoppers’ Delight | 20 |
| Stewart and Sons Fabrication | 18 |
| Kronos Pipe and Irrigation | 6 |
| Octavio’s Office Supplies | 4 |
| Daily Dealz | 1 |
Figure 1 shows the map marked with blue dots representing the stationary GPS coordinate of all the cars except for each employee house. The popular locations can be determined by the frequency of the blue dots at a particular location on the map.
Cross referencing with the transactions table, the locations coordinates were tag with their corresponding coordinates by cross-referencing to the car GPS data and geo-referenced data.
## Getting coordinates of car stop positions
first_gps <- gps_stop %>%
group_by(id) %>%
filter(row_number()==1) %>%
ungroup(id)
gps_pts <- gps_stop %>% ungroup(id) %>%
add_row(first_gps) %>% group_by(id) %>% arrange(timestamp) %>%
filter(!(start_vec==1 & stop_vec==1)) %>%
group_by(id) %>% arrange(timestamp) %>%
mutate( start.time = ifelse(start_vec== 0 & stop_vec==0, timestamp, NA),
start.time = ifelse(start_vec==1, timestamp,NA),
end.time=ifelse(stop_vec==1, timestamp, NA),
start.gps = ifelse(start_vec==0 & stop_vec==0, geometry,NA),
start.gps = ifelse(start_vec==1, geometry,NA),
end.gps=ifelse(stop_vec==1, geometry,NA),
end.time = ifelse(start_vec==1, lead(end.time), end.time),
end.gps = ifelse(start_vec==1, lead(end.gps), end.gps)) %>%
filter(!is.na(start.time))%>%
mutate(end.gps = ifelse(end.gps=='NULL',start.gps,end.gps),
end.time = ifelse(is.na(end.time),start.time, end.time),
start.time= as_datetime(start.time),
end.time=as_datetime(end.time),
next.start.time=lead(start.time),
driving.time=round(difftime(end.time,start.time,units='mins'),2)) %>%
dplyr::select(id, date, start.time, end.time, start.gps, end.gps,
next.start.time, driving.time) %>%
mutate(start.gps=purrr::map(start.gps, st_point) %>% st_as_sfc(crs=4326))%>%
mutate(end.gps=purrr::map(end.gps, st_point) %>% st_as_sfc(crs=4326))
car$CarID <- as_factor(car$CarID)
gps_pts <- left_join(gps_pts, car, by=c("id"="CarID"))
gps_stop_points1 <- gps_pts %>%
mutate(time.stop = difftime(next.start.time, end.time, units=c("mins")),
time.stop = as.numeric(time.stop))%>%
filter(time.stop < 300) %>%
dplyr::select(id, start.time, start.gps)
## Generate map with the stop positions in blue dots
tmap_mode("view")
map_POI<-tm_shape(bgmap) +
tm_rgb(bgmap, r=1, g=2, b=3, alpha=NA, saturation=1,
interpolate=TRUE, max.value=255) +
tm_shape(gps_stop_points1)+
tm_dots(col="blue", shape=30,id="id",
popup.vars=c("Car ID"="id",
"Stationary timestamp" = "start.time",
"GPS:"="start.gps"))
tmap_leaflet(map_POI)
Figure 1: GPS stationary locations
The car id were triangulated by tabulating the centroid coordinates of the GPS data from the stationary GPS stop locations from the map. However, there are few limitations by using the methodology mentioned earlier for tagging the owners.
The interactive heatmap in Figure 2 shows the percentage that were successfully match with the car GPS and cc transaction data by the conditions mentioned earlier. The histogram was also plotted to visualise the distribution of the result. From the two visualisation, we observed that the methodology yield some high percentage match for the car id owner with the cc owner.
# Tagging location coordinates
location_tag <- data.frame(location = c(locations$location,"GAStech"),
long =c(centroid(rbind(c(24.82590612, 36.05102229),c(24.82591819, 36.05092013),c(24.82598413, 36.05097547)))[1],
centroid(rbind(c(24.84592966, 36.07443715),c(24.84598782, 36.07434876),c(24.84595026, 36.07437836)))[1],
centroid(rbind(c(24.85097804, 36.06349268),c(24.85099445, 36.06342076),c(24.85103178, 36.06348173)))[1],
centroid(rbind(c(24.87617634, 36.07713037),c(24.87621582, 36.07713598),c(24.87619872, 36.07715385)))[1],
centroid(rbind(c(24.85626503, 36.07529323),c(24.85631411, 36.07523202),c(24.85634841, 36.07528136)))[1],
centroid(rbind(c(24.85089145, 36.08172086),c(24.85096025, 36.08176242),c(24.85087799, 36.08180554)))[1],
centroid(rbind(c(24.90119998, 36.05402165),c(24.90128202, 36.05408823),c(24.90116585, 36.05411015)))[1],
NA,
centroid(rbind(c(24.88089399, 36.05851786),c(24.88092086, 36.05858619),c(24.8808655, 36.05856303)))[1],
centroid(rbind(c(24.8951996, 36.07073983),c(24.89517891, 36.07062423),c(24.89526281, 36.07069274)))[1],
centroid(rbind(c(24.88983886, 36.05469486),c(24.88978433, 36.05463184),c(24.88977321, 36.05467589)))[1],
centroid(rbind(c(24.86416839, 36.07332041),c(24.86417651, 36.07336116),c(24.86419582, 36.07332868)))[1],
NA,
centroid(rbind(c(24.86068835, 36.08962196),c(24.86068191, 36.08954231),c(24.8607611, 36.08960361)))[1],
centroid(rbind(c(24.84132949, 36.07213193),c(24.84134818, 36.07212045),c(24.4134819, 36.07212044)))[1],
centroid(rbind(c(24.905573, 36.06044638),c(24.90561679, 36.06033304),c(24.90568587, 36.06040053)))[1],
centroid(rbind(c(24.85804364, 36.05970763),c(24.8580772, 36.05975308),c(24.8579808, 36.05976284)))[1],
centroid(rbind(c(24.85804364, 36.05970763),c(24.8580772, 36.05975308),c(24.8579808, 36.05976284)))[1],
centroid(rbind(c(24.90096913, 36.05842562),c(24.90107066, 36.05844726),c(24.90097455, 36.05850897)))[1],
centroid(rbind(c(24.88586605, 36.063639),c(24.88595361, 36.06364584),c(24.88586737, 36.06371539)))[1],
centroid(rbind(c(24.85756422, 36.07660977),c(24.85763811, 36.07664766),c(24.857573, 36.07669909)))[1],
centroid(rbind(c(24.87330651, 36.06751231),c(24.87335583, 36.06750587),c(24.87333867, 36.06755141)))[1],
centroid(rbind(c(24.85237319, 36.06582037),c(24.85241027, 36.06582475),c(24.85237372, 36.06584816)))[1],
centroid(rbind(c(24.89986767, 36.05442391),c(24.89996154, 36.05448329),c(24.89987365, 36.05453273)))[1],
centroid(rbind(c(24.84983351, 36.06587998),c(24.84983936, 36.06582196),c(24.8497762, 36.06583535)))[1],
NA,
centroid(rbind(c(24.88551872, 36.05840982),c(24.88542068, 36.0584603), c(24.88553455, 36.05844325)))[1],
centroid(rbind(c(24.83307421, 36.0653098),c(24.83314028, 36.06523446), c(24.84143955, 36.06403449),c(24.84141463, 36.06410072)))[1],
NA,
centroid(rbind(c(24.87077341, 36.05196196),c(24.87081903, 36.05192066),c(24.87083665, 36.05197804)))[1],
centroid(rbind(c(24.85227441, 36.06324941),c(24.85226894, 36.06330479),c(24.8523291, 36.0632684)))[1],
NA,NA,
centroid(rbind(c(24.87148791, 36.06774029),c(24.8714995, 36.06774623),c(24.87149104, 36.06776587)))[1],
centroid(rbind(c(24.87956897, 36.04802112),c(24.8795714, 36.04804908), c(24.8795745, 36.0480309)))[1]),
lat = c(centroid(rbind(c(24.82590612, 36.05102229),c(24.82591819, 36.05092013),c(24.82598413, 36.05097547)))[2],
centroid(rbind(c(24.84592966, 36.07443715),c(24.84598782, 36.07434876),c(24.84595026, 36.07437836)))[2],
centroid(rbind(c(24.85097804, 36.06349268),c(24.85099445, 36.06342076),c(24.85103178, 36.06348173)))[2],
centroid(rbind(c(24.87617634, 36.07713037),c(24.87621582, 36.07713598),c(24.87619872, 36.07715385)))[2],
centroid(rbind(c(24.85626503, 36.07529323),c(24.85631411, 36.07523202),c(24.85634841, 36.07528136)))[2],
centroid(rbind(c(24.85089145, 36.08172086),c(24.85096025, 36.08176242),c(24.85087799, 36.08180554)))[2],
centroid(rbind(c(24.90119998, 36.05402165),c(24.90128202, 36.05408823),c(24.90116585, 36.05411015)))[2],
NA,
centroid(rbind(c(24.88089399, 36.05851786),c(24.88092086, 36.05858619),c(24.8808655, 36.05856303)))[2],
centroid(rbind(c(24.8951996, 36.07073983),c(24.89517891, 36.07062423),c(24.89526281, 36.07069274)))[2],
centroid(rbind(c(24.88983886, 36.05469486),c(24.88978433, 36.05463184),c(24.88977321, 36.05467589)))[2],
centroid(rbind(c(24.86416839, 36.07332041),c(24.86417651, 36.07336116),c(24.86419582, 36.07332868)))[2],
NA,
centroid(rbind(c(24.86068835, 36.08962196),c(24.86068191, 36.08954231),c(24.8607611, 36.08960361)))[2],
centroid(rbind(c(24.84132949, 36.07213193),c(24.84134818, 36.07212045),c(24.4134819, 36.07212044)))[2],
centroid(rbind(c(24.905573, 36.06044638),c(24.90561679, 36.06033304),c(24.90568587, 36.06040053)))[2],
centroid(rbind(c(24.85804364, 36.05970763),c(24.8580772, 36.05975308),c(24.8579808, 36.05976284)))[2],
centroid(rbind(c(24.85804364, 36.05970763),c(24.8580772, 36.05975308),c(24.8579808, 36.05976284)))[2],
centroid(rbind(c(24.90096913, 36.05842562),c(24.90107066, 36.05844726),c(24.90097455, 36.05850897)))[2],
centroid(rbind(c(24.88586605, 36.063639),c(24.88595361, 36.06364584),c(24.88586737, 36.06371539)))[2],
centroid(rbind(c(24.85756422, 36.07660977),c(24.85763811, 36.07664766),c(24.857573, 36.07669909)))[2],
centroid(rbind(c(24.87330651, 36.06751231),c(24.87335583, 36.06750587),c(24.87333867, 36.06755141)))[2],
centroid(rbind(c(24.85237319, 36.06582037), c(24.85241027, 36.06582475),c(24.85237372, 36.06584816)))[2],
centroid(rbind(c(24.89986767, 36.05442391),c(24.89996154, 36.05448329), c(24.89987365, 36.05453273)))[2],
centroid(rbind(c(24.84983351, 36.06587998),c(24.84983936, 36.06582196),c(24.8497762, 36.06583535)))[2],
NA,
centroid(rbind(c(24.83307421, 36.0653098),c(24.83314028, 36.06523446), c(24.84143955, 36.06403449),c(24.84141463, 36.06410072)))[1],
centroid(rbind(c(24.88551872, 36.05840982),c(24.88542068, 36.0584603), c(24.88553455, 36.05844325)))[2],
NA,
centroid(rbind(c(24.87077341, 36.05196196),c(24.87081903, 36.05192066),c(24.87083665, 36.05197804)))[2],
centroid(rbind(c(24.85227441, 36.06324941),c(24.85226894, 36.06330479),c(24.8523291, 36.0632684)))[2],
NA,NA,
centroid(rbind(c(24.87148791, 36.06774029),c(24.8714995, 36.06774623),c(24.87149104, 36.06776587)))[2],
centroid(rbind(c(24.87956897, 36.04802112),c(24.8795714, 36.04804908), c(24.8795745, 36.0480309)))[2]))
location_tag <- location_tag %>% na.omit()
location_tag <- st_as_sf(location_tag, coords=c("long","lat"), crs=4326)
## join GPS data with transaction data with location coordinates
final_trans_gps <- inner_join(final_trans_1, location_tag, by=c("location")) %>%
rename(loc.coord=geometry)
## Join with car GPS and tag the location to car gps
gps_match <- final_trans_gps %>%
left_join(gps_pts , by=c("date"))%>%
group_by(last4ccnum) %>% arrange(datetime) %>%
filter(datetime > end.time & datetime <= next.start.time + minutes(30)) %>%
mutate(diff.dist = st_distance(loc.coord, end.gps, by_element=TRUE),
diff.dist = as.numeric(diff.dist)) %>%
filter(diff.dist <500)
tagging <-gps_match %>%group_by(last4ccnum, id)%>%
summarize(tag=n()) %>% arrange(desc(tag))
## Get total count of transactions minus the 4 locations per cc num
trans_collapse <- cc %>% mutate(last4ccnum=as_factor(last4ccnum)) %>%
filter(!(location %in% c("Bean There Done That",
"Brewed Awakenings",
"Coffee Shack",
"Jack's Magical Beans"))) %>%
group_by(last4ccnum) %>% summarize(total=n())
## Limit to top 3 match only by percentage
tagging_cc_gps <- left_join(tagging, trans_collapse, by=c("last4ccnum")) %>%
mutate(percent=round(tag/total*100,2))
tag_plot<-ggplot(tagging_cc_gps, aes(x=id, y=last4ccnum,fill=percent))+
geom_tile() + scale_fill_gradient(low="sienna1", high="navyblue") +
xlab("Car ID") +ylab("CC last 4 number")+
labs(fill="% match")
histogram<-ggplot(tagging_cc_gps,aes(percent))+geom_histogram(binwidth=5)+
stat_function(fun=dnorm,aes(color="red"),
args=list(mean=mean(tagging_cc_gps$percent),
sd=sd(tagging_cc_gps$percent)))
ggplotly(tag_plot) %>% layout(hoverlabel=list(bgcolor="white"))
Figure 2: Car GPS tagging to CC number
Figure 2: Car GPS tagging to CC number
Hence, we can confidently infer that matches over 75% will be accurate. However, as there are more cc owners (55 unique owners) than car owners (35 unique car id) and the truck drivers share vehicles (5 unique truck id), we will drop the truck drivers with car id of 100 and above. Observation of the heatmap in figure 2 reveals that car id 23, car id 29 and car id 30 has matches of more than one cc number and car id 28 does not have a match with more than 75%.
From Table 2, we observe that car id 23 matches to three unique cc number with matches over 75%. The highest percentage match to cc 3484 at 91.43% shows high probability for inference, hence the observation that matches to cc 8202 and 8411 will be dropped.
For car id 29 and 30, the matches to cc number percentage are relatively high and defers less than 10%. Further investigation on the GPS map location will be performed to verify which match to retain.
## Get the match of car id to cc last4ccnum
tagging <- tagging_cc_gps %>% mutate(id=as.character(id), id=as.numeric(id)) %>%
filter(percent>=75 & id<100)
knitr::kable(tagging %>% filter(id==23 | id==29 | id==30) %>%
arrange(id), "simple",
caption="Table of employees record and their cc and loyalty number")
| last4ccnum | id | tag | total | percent |
|---|---|---|---|---|
| 3484 | 23 | 31 | 35 | 88.57 |
| 8202 | 23 | 25 | 33 | 75.76 |
| 8411 | 23 | 25 | 32 | 78.12 |
| 3547 | 29 | 18 | 20 | 90.00 |
| 5921 | 29 | 13 | 14 | 92.86 |
| 6901 | 30 | 31 | 37 | 83.78 |
| 8202 | 30 | 25 | 33 | 75.76 |
final_tagging <- tagging %>%
filter(!(last4ccnum==8202 & id==23), !(last4ccnum==8411 & id ==23))
Investigation of car id 28 low cc transactions matches was visualised in Figure 3 and it revealed that the GPS coordinates of car id 28 has lots of noise. The noise in the GPS line caused a wider spread of GPS line in the visualisation on the map and also zig-zag incoherent GPS path. This most probably signifies a faulty GPS signal on the car.
Secondly, we observe that the stop position was not accurate. For example, the frequency of GPS stop coordinates at the extreme south of the map should be at GAStech. Hence, the GPS stop coordinates seems to deviate in the North-West direction. The most probable explanation will be a faulty GPS system since the GPS points were noisy and not correctly geo-referenced on the map.
## Map geometry for original car id 28 data
gps_path5 <- gps_sf %>%
filter(id==28) %>%
group_by(id) %>%
summarize(m = mean(timestamp), do_union=FALSE) %>% st_cast("LINESTRING")
gps_28_points <- gps_stop %>% filter(id ==28)
## Plot interactive map
tmap_mode("view")
map5<-tm_shape(bgmap) +
tm_rgb(bgmap, r=1, g=2, b=3, alpha=NA, saturation=1,
interpolate=TRUE, max.value=255) +
tm_shape(gps_path5)+
tm_lines() +
tm_shape(gps_28_points)+
tm_dots(col="blue")
tmap_leaflet(map5)
Figure 3: Original GPS for car id 28
After re-calibrating the GPS coordinates for car id 28, Figure 4 shows the GPS movement data for car id 28. With the re-calibrated GPS data, we would match it with the cc transaction data to infer which cc belongs to car id 28.
From the map in Figure 4, the unqiue observation was that car id 28 visited Ahaggo Museum on the 18th and 19th of Jan and frequently patronise Jack’s Magical Beans and Ouzeri Elian over the two weeks.
From the cc transaction table, a search of Ahaggo Museum revealed that cc 1286, 7384 and 9241 made transactions on the 18th and 19th of Jan. Next, a search of Jack’s Magical Beans shows that only cc 9241 out of the three cc made transactions at the location. Lastly, a search of Ouzeri Elian on the datatable reveals that cc 9241 made 6 transactions at the location. Hence, we are confident to infer that car id 28 is the owner of cc 9241.
## Map geometry for re-calibrated Car id 28
gps28 <- gps %>% filter(id==28) %>%
mutate(long = long +0.005,
lat=lat-0.002)
gps_sf28 <- st_as_sf(gps28, coords=c("long","lat"), crs=4326)
gps_path28 <- gps_sf28 %>% group_by(id) %>%
summarize(m = mean(timestamp), do_union=FALSE) %>% st_cast("LINESTRING")
gps28_pt <- gps_sf28 %>%
group_by(id) %>% arrange(timestamp) %>%
mutate(start_diff= as.numeric(timestamp - lag(timestamp,default=first(timestamp)))/60,
stop_diff= as.numeric(lead(timestamp)-timestamp)/60,
date = as.Date(timestamp)) %>%
rename(gps.coord=geometry) %>%
filter(start_diff>5 | stop_diff >5) %>%
mutate(start_vec=ifelse(start_diff>5,1,0), stop_vec=ifelse(stop_diff>5,1,0))
## Plot interactive map
tmap_mode("view")
map6<-tm_shape(bgmap) +
tm_rgb(bgmap, r=1, g=2, b=3, alpha=NA, saturation=1,
interpolate=TRUE, max.value=255) +
tm_shape(gps_path28)+
tm_lines() +
tm_shape(gps28_pt)+
tm_dots(col="blue")
tmap_leaflet(map6)
Figure 4: Re-calibrated GPS for car id 28
final_tagging <- final_tagging %>%
dplyr::select(last4ccnum, id) %>%
mutate(last4ccnum = as.character(last4ccnum),
id = as.character(id)) %>%
bind_rows(c(last4ccnum="9241", id="28"))
Next, we will focus on car id 29 where it matches 90% of cc 3547 transactions and 100% of cc 5921. The high proportion of matches on both credit card warrants some investigation into the data.
Looking at table 3 for both cc number, we observe that cc 3547 has transactions between 12/01/2014 to 19/01/2014 and cc 5921 has transactions between 06/01/2014 to 10/01/2014. Cross-referencing the GPS data for car id 29 in Figure 5, we can observe that the cc transactions matches the GPS data of car id 29. A possible deduction is that the owner of car id 29 used both cc card as there was no overlap in the transaction dates for both cc. Possible scenario could be that the owner switch the CC from 5921 to 3547 after 10/01/2014. However, there might be missing data on 11/01/2014 where it was not captured on both cc. Hence, we will tag car id 29 to both cc 5921 and 3547.
gps_path29 <- gps_sf %>%
filter(id==29) %>%
group_by(id) %>%
summarize(m = mean(timestamp), do_union=FALSE) %>% st_cast("LINESTRING")
gps29_pt <- gps_stop_points1 %>% filter(id==29)
tmap_mode("view")
map7<-tm_shape(bgmap) +
tm_rgb(bgmap, r=1, g=2, b=3, alpha=NA, saturation=1,
interpolate=TRUE, max.value=255) +
tm_shape(gps_path29)+
tm_lines() +
tm_shape(gps29_pt)+
tm_dots(col="blue")
cc3547 <- cc %>% filter(last4ccnum==3547) %>% dplyr::select(-datetime, -date)
cc5921 <- cc %>% filter(last4ccnum==5921) %>% dplyr::select(-datetime, -date)
knitr::kable(list(cc3547,cc5921),caption="Transactions for CC 3547 & 5921")
|
|
tmap_leaflet(map7)
Figure 5: GPS for car id 29
Lastly, we will look at car id 30 with cc 6901 and 8202. The GPS data for car id 30 was visualise in Figure 6 and the transaction from cc 6901 and 8202 in table 4.
Comparing the GPS data map and cc translation data, we focused on locations with a lower frequency of visit and locations in a less congested area for easier verification. From the 3 locations and transaction details below, we can deduce that cc 6901 matches car id 30.
gps_path_30 <- gps_sf %>%
filter(id==30) %>%
group_by(id) %>%
summarize(m = mean(timestamp), do_union=FALSE) %>% st_cast("LINESTRING")
gps_stop_points30 <- gps_pts %>%
mutate(time.stop = difftime(next.start.time, end.time, units=c("mins")),
time.stop = as.numeric(time.stop))%>%
filter(time.stop < 300 & id==30) %>%
dplyr::select(id, start.time, start.gps)
## Plot interactive map
tmap_mode("view")
map8<-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_30) +
tm_lines(col ="red") +
tm_shape(gps_stop_points30)+
tm_dots(col="blue", shape=30)
tmap_leaflet(map8)
Figure 6: GPS for car id 30
cc6901 <- cc %>% filter(last4ccnum==6901) %>% dplyr::select(-datetime, -date)
cc8202 <- cc %>% filter(last4ccnum==8202) %>% dplyr::select(-datetime, -date)
knitr::kable(list(cc6901,cc8202),caption="Transactions for CC 6901 & 8202")
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The tagging of all 35 car owners (excluding truck drivers) have been completed and verified.
For attribution, please cite this work as
Lim (2021, July 23). Yong Kai: Assignment: VAST Mini-Challenge 2. Retrieved from https://limyongkai.netlify.app/posts/2021-07-23-vastmc2part3/
BibTeX citation
@misc{lim2021assignment:,
author = {Lim, Yong Kai},
title = {Yong Kai: Assignment: VAST Mini-Challenge 2},
url = {https://limyongkai.netlify.app/posts/2021-07-23-vastmc2part3/},
year = {2021}
}