Edirne

Mon, 01 Jan 0001 00:00:00 +0000

1. libraries

library(DBI)
library(sf)
#test123

## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1

library(leaflet)
library(ggplot2)
library(tmap)
library(dplyr)

##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union

library(nngeo)

2. Connect to PostgreSQL Database

library(DBI)
db <- 'urbanoccupations_db' #provide the name of your db
host_db <- 'aws-eu-central-1-portal.1.dblayer.com' #i.e. # i.e. 'ec2-54-83-201-96.compute-1.amazonaws.com'
db_port <- '18368' # or any other port specified by the DBA
db_u <- 'online_user'
db_p <- 'Peculiar-Crazy9-Trailing'
con <- dbConnect(RPostgres::Postgres(), dbname = db, host=host_db, port=db_port, user=db_u, password=db_p)

3. Access PostgreSQL layer and query dataset for Motorized Transport

file_motor = dbGetQuery(con, "select * from piet_phd_data.DH_1940_Edirne_Transport WHERE network_ty IN ('Ausgebaute Allwetterstrasse', 'Fahrstrasse')")
newGeom = st_as_sfc(structure(as.character(file_motor$geom), class = "WKB"),EWKB=TRUE)
file_motor_geom = st_set_geometry(file_motor, newGeom)
# plot(file_motor_geom$geometry)
map_motor = ggplot(file_motor_geom) + geom_sf()
map_motor

4. Access PostgreSQL layer and query dataset for cart Transport

file_cart = dbGetQuery(con, "select * from piet_phd_data.DH_1940_Edirne_Transport WHERE network_ty IN ('Karawanenweg', 'Saumweg')")
newGeom = st_as_sfc(structure(as.character(file_cart$geom), class = "WKB"),EWKB=TRUE)
file_cart_geom = st_set_geometry(file_cart, newGeom)
map_cart = ggplot(file_cart_geom) + geom_sf() + aes(colour = "red")
map_cart

5. Access PostgreSQL layer and query dataset for Walking paths

file_walk = dbGetQuery(con, "select * from piet_phd_data.DH_1940_Edirne_Transport WHERE network_ty IN ('Fußweg')")
newGeom = st_as_sfc(structure(as.character(file_walk$geom), class = "WKB"),EWKB=TRUE)
file_walk_geom = st_set_geometry(file_walk, newGeom)
map_cart = ggplot(file_walk_geom) + geom_sf() + aes(col = "red")
map_cart

6a. Access PostgreSQL layer and query dataset for Settlement layer

file_settlements = dbGetQuery(con, "select * from piet_phd_data.nfs_settlements_1940_pop_xy where not geom in ('0101000020E610000000000000000000000000000000000000')")
newGeom = st_as_sfc(structure(as.character(file_settlements$geom), class = "WKB"),EWKB=TRUE)
file_settlements_geom = st_set_geometry(file_settlements, newGeom)
plot(file_settlements_geom$geometry)

6b. Visualize first few rows of data as a sample

library (knitr)
kable(head(file_settlements, 10), caption = "Sample of Dataset")

id	place_id	il_original	ilce_original	bucak_commune_original	belediye_koy_original	male	female	both	comments	geom	newpoint_x	newpoint_y
1	1	Edirne	MerkezNahiyesi	Merkez	Azatli	385	406	791	NA	0101000020E610000024287E8CB9B33A40B2D7BB3FDEBF4440	26.70205	41.49897
2	2	Edirne	MerkezNahiyesi	Merkez	Bosna	610	578	1188	NA	0101000020E61000003F52448655903A40EE08A7052FD04440	26.56380	41.62644
3	3	Edirne	MerkezNahiyesi	Merkez	Büyük döllük	316	362	678	NA	0101000020E6100000029EB47059993A4056B8E52329E14440	26.59902	41.75907
4	4	Edirne	MerkezNahiyesi	Merkez	Dayakadin	247	250	497	NA	0101000020E61000001AD578E926A93A40D9CEF753E3C94440	26.66075	41.57725
5	5	Edirne	MerkezNahiyesi	Merkez	Dayran	380	372	752	NA	0101000020E61000006C19FF3EE39A3A40FCCEDA6D17BE4440	26.60503	41.48509
6	6	Edirne	MerkezNahiyesi	Merkez	Demirbanli	243	262	505	NA	0101000020E6100000F0F96184F0BC3A4025404D2D5BD94440	26.73804	41.69810
7	7	Edirne	MerkezNahiyesi	Merkez	Degirmenyeni	237	259	496	NA	0101000020E6100000ADBD4F55A18D3A40A587A1D5C9E14440	26.55324	41.76397
8	8	Edirne	MerkezNahiyesi	Merkez	Hasanaga	2516	98	2614	NA	0101000020E610000067D13B15709F3A408A75AA7CCFDC4440	26.62280	41.72508
9	9	Edirne	MerkezNahiyesi	Merkez	Hatip	94	89	183	NA	0101000020E61000002E3883BF5F903A4084F23E8EE6E84440	26.56396	41.81954
10	10	Edirne	MerkezNahiyesi	Merkez	Hadira ga	233	254	487	NA	0101000020E6100000C5FEB27BF2A83A4083DDB06D51DE4440	26.65995	41.73686

Table 1: Sample of Dataset

8. example of a leaflet map in Rmarkdown

8a.Calculate symbology column

#Classification of population figures and addition of column "colorpal" for symbolisation
file_settlements_geom$colorpal <- cut(file_settlements_geom$both, c(0,250,500,1000,2500,20000), include.lowest = F, labels = c('250', '500', '1000', '2500', '2500+'))
# Colour coding of the legend and symbols in leaflet map
beatCol <- colorFactor(palette = 'BuPu', file_settlements_geom$colorpal)

8b. Map creation

map <- leaflet() %>%
addTiles() %>%
addMiniMap( position = "topleft", toggleDisplay = TRUE, autoToggleDisplay = TRUE, collapsedWidth = 19, collapsedHeight = 19, minimized=TRUE) %>%
addProviderTiles(providers$Stamen.Toner, group = 'Toner') %>%
addProviderTiles(provider = providers$Esri.WorldImagery, group = 'ESRI') %>%
addCircleMarkers(data=file_settlements_geom, file_settlements_geom$newpoint_x, file_settlements_geom$newpoint_y, color = ~beatCol(colorpal), radius = ~sqrt(both/1000), group = 'Settlements', opacity = 1, weight = 10, popup = ~paste0("<b>", belediye_koy_original, "</b>","<br/>", format(both, big.mark=","))) %>%
addPolylines(data=file_motor_geom$geometry, color = "Green", smoothFactor = 1, opacity = 0.5, weight =5, group = 'Motorways') %>%
addPolylines(data=file_cart_geom$geometry, color = "Pink", smoothFactor = 1, opacity = 0.4, weight =2, group = 'Cart roads') %>%
addPolylines(data=file_walk_geom$geometry, color = "Red", smoothFactor = 1, opacity = 0.5, weight =4, group = 'Walking paths') %>%
addLegend('bottomright', pal = beatCol, values = file_settlements_geom$colorpal, title = '<b>Settlement size</b>', opacity = 1) %>%
addLayersControl(
baseGroups = c("Toner (default)", "ESRI"),
overlayGroups = c("Settlements", "Motorways", "Cart roads", "Walking paths"),
options = layersControlOptions(collapsed = FALSE)
)
map

Edirne | PJG PhD Data

Edirne

1. libraries

2. Connect to PostgreSQL Database

3. Access PostgreSQL layer and query dataset for Motorized Transport

4. Access PostgreSQL layer and query dataset for cart Transport

5. Access PostgreSQL layer and query dataset for Walking paths

6a. Access PostgreSQL layer and query dataset for Settlement layer

6b. Visualize first few rows of data as a sample

8. example of a leaflet map in Rmarkdown

8a.Calculate symbology column

8b. Map creation

9 Exploratory Data Analysis (EDA)

9a Calculate distance from settlements to the nearest roads per type

9b From the top Nearest cities in the regiion

9c Correlation between Male/Female ratio distribution and Settlement size