TRABAJO PRACTICO 5
juan vargas
23/5/2020
TRABAJO PRACTICO N°5
CIENCIA DE DATOS PARA CIUDADES II: Analizando recorridos y distancias en la Ciudad de BOSTON, MASSACHUSETTS
Snyders, Federico / Vargas, Juan
library(tidyverse)## -- Attaching packages ----------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.0 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.5
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts -------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(sf)## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3library(ggmap)## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.## Please cite ggmap if you use it! See citation("ggmap") for details.library(osrm)## Data: (c) OpenStreetMap contributors, ODbL 1.0 - http://www.openstreetmap.org/copyright## Routing: OSRM - http://project-osrm.org/library(leaflet)#Descargamos el shp de la ciudad de Boston con sus barrios, al igual que el archivo que contiene las comisarias. Fuente: https://data.boston.gov
BOSTON <- st_read("Boston_Neighborhoods.shp")%>%
st_transform(crs=4326)## Reading layer `Boston_Neighborhoods' from data source `C:\Users\usuario\Documents\JUAN\POSGRADOS\MAESTRIA EN ECONOMIA URBANA\2020\CIENCIA DE DATOS PARA CIUDADES II\CLASE 5\TP5\TP_5\Boston_Neighborhoods.shp' using driver `ESRI Shapefile'
## Simple feature collection with 26 features and 7 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 739715.3 ymin: 2908294 xmax: 812255.1 ymax: 2970086
## proj4string: +proj=lcc +lat_1=42.68333333333333 +lat_2=41.71666666666667 +lat_0=41 +lon_0=-71.5 +x_0=200000.0001016002 +y_0=750000.0000000001 +datum=NAD83 +units=us-ft +no_defsnames(BOSTON)## [1] "OBJECTID" "Name" "Acres" "Neighborho" "SqMiles"
## [6] "ShapeSTAre" "ShapeSTLen" "geometry"BOSTON_POLICIAS <- st_read("Boston_Police_Stations.shp")%>%
st_transform(crs=4326)## Reading layer `Boston_Police_Stations' from data source `C:\Users\usuario\Documents\JUAN\POSGRADOS\MAESTRIA EN ECONOMIA URBANA\2020\CIENCIA DE DATOS PARA CIUDADES II\CLASE 5\TP5\TP_5\Boston_Police_Stations.shp' using driver `ESRI Shapefile'
## Simple feature collection with 13 features and 13 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 750621.8 ymin: 2918710 xmax: 780828 ymax: 2962523
## proj4string: +proj=lcc +lat_1=42.68333333333333 +lat_2=41.71666666666667 +lat_0=41 +lon_0=-71.5 +x_0=200000.0001016002 +y_0=750000.0000000001 +datum=NAD83 +units=us-ft +no_defsnames(BOSTON_POLICIAS)## [1] "OBJECTID" "BLDG_ID" "BID" "ADDRESS" "POINT_X"
## [6] "POINT_Y" "NAME" "NEIGHBORHO" "CITY" "ZIP"
## [11] "FT_SQFT" "STORY_HT" "PARCEL_ID" "geometry"#Buscamos el centroide de cada barrio de la ciudad y lo mostramos en un mapa
BOSTON_centroides <- BOSTON %>%
st_centroid()## Warning in st_centroid.sf(.): st_centroid assumes attributes are constant over
## geometries of x## Warning in st_centroid.sfc(st_geometry(x), of_largest_polygon =
## of_largest_polygon): st_centroid does not give correct centroids for longitude/
## latitude dataggplot()+
geom_sf(data=BOSTON, fill="light gray", alpha= .5 )+
geom_sf(data=BOSTON_centroides , size=2, color = "red")+
geom_sf_text(data=BOSTON, aes(label = Name), size=2, colour = "black")+
labs(title = "BARRIOS DE BOSTON",
subtitle = "Centro geometrico de cada barrio",
fill="Barrios",
caption= "Fuente: https://data.boston.gov/dataset")+
theme_void()## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
#Seleccionamos como barrio centrico South Boston y el barrio Hyde Park como barrio periferico.
BOSTON_centroides <- BOSTON_centroides %>%
filter(Name=="Hyde Park" | Name =="South Boston")%>%
mutate(ubicacion=if_else(Name=="South Boston", "CENTRO", "PERIFERIA"))BOSTON_centroides <- cbind(BOSTON_centroides, st_coordinates(BOSTON_centroides)) %>%
st_set_geometry(NULL)BOSTON_POLICIAS <- cbind(BOSTON_POLICIAS, st_coordinates(BOSTON_POLICIAS)) %>%
st_set_geometry(NULL)#En el siguiente mapa se muestran los centroides del barrio periferico y central, al igual que la ubicacion de cada comisaria.
ggplot()+
geom_sf(data=BOSTON, fill="light blue", alpha= .5 )+
geom_point(data= BOSTON_POLICIAS, aes(x=X, y=Y), color= "blue", size=3)+
geom_point(data=BOSTON_centroides, aes(x=X, y=Y, color=ubicacion), size=5)+
labs(title = "BARRIOS y SERVICOS DE BOSTON",
subtitle = "Centro geometrico de cada barrio",
color="Ubicacion",
caption= "Fuente:https://data.boston.gov/dataset")+
theme_void()
RUTEO
#Realizamos el ruteo para las comisarias desde el barrio central South Boston
ruteo_a_COMISARIAS <- function(o_nombre, o_x, o_y, d_nombre, d_x, d_y) {
ruta <- osrmRoute(src = c(o_nombre, o_x, o_y),
dst = c(d_nombre, d_x, d_y),
returnclass = "sf",
overview = "full")
cbind(ORIGEN = o_nombre, DESTINO = d_nombre, ruta)
}COMISARIAS_SouthBoston <- BOSTON_POLICIAS %>%
mutate(ORIGEN="South Boston") %>%
left_join(BOSTON_centroides, by=c("ORIGEN"="Name")) %>%
rename(NOMBRE_ORIGEN=ORIGEN,
LON_ORIGEN= X.y,
LAT_ORIGEN= Y.y,
NOMBRE_DESTINO= NAME,
LON_DESTINO= X.x,
LAT_DESTINO= Y.x)%>%
select(NOMBRE_ORIGEN, LON_ORIGEN, LAT_ORIGEN, NOMBRE_DESTINO, LON_DESTINO, LAT_DESTINO)## Warning: Column `ORIGEN`/`Name` joining character vector and factor, coercing
## into character vectorruteo_POLICIAS_SouthBoston <- list(COMISARIAS_SouthBoston$NOMBRE_ORIGEN, COMISARIAS_SouthBoston$LON_ORIGEN,COMISARIAS_SouthBoston$LAT_ORIGEN,
COMISARIAS_SouthBoston$NOMBRE_DESTINO, COMISARIAS_SouthBoston$LON_DESTINO,COMISARIAS_SouthBoston$LAT_DESTINO)
ruteo_centrico <- pmap(ruteo_POLICIAS_SouthBoston, ruteo_a_COMISARIAS) %>%
reduce(rbind)ruteo_centrico <-mutate(ruteo_centrico, RUTA = paste(ORIGEN,"a", DESTINO))arrange(ruteo_centrico,distance)## Simple feature collection with 13 features and 7 fields
## geometry type: LINESTRING
## dimension: XY
## bbox: xmin: -71.151 ymin: 42.2526 xmax: -71.03647 ymax: 42.38412
## CRS: EPSG:4326
## First 10 features:
## ORIGEN DESTINO src dst duration
## 1 South Boston District C-6 Police Station South Boston 8 3.433333
## 2 South Boston District D-4 Police Station South Boston 10 7.188333
## 3 South Boston Boston Police Headquarters South Boston 1 10.508333
## 4 South Boston District B-2 Police Station South Boston 5 11.580000
## 5 South Boston District A-1 Police Station South Boston 2 11.918333
## 6 South Boston District C-11 Police Station South Boston 7 12.918333
## 7 South Boston District E-13 Police Station South Boston 11 16.841667
## 8 South Boston District B-3 Police Station South Boston 6 16.443333
## 9 South Boston District A-7 Police Station South Boston 4 14.738333
## 10 South Boston District A-15 Police Station South Boston 3 15.550000
## distance RUTA
## 1 1.2157 South Boston a District C-6 Police Station
## 2 2.6794 South Boston a District D-4 Police Station
## 3 4.3258 South Boston a Boston Police Headquarters
## 4 4.9952 South Boston a District B-2 Police Station
## 5 5.1802 South Boston a District A-1 Police Station
## 6 6.3539 South Boston a District C-11 Police Station
## 7 7.1249 South Boston a District E-13 Police Station
## 8 7.3018 South Boston a District B-3 Police Station
## 9 7.6408 South Boston a District A-7 Police Station
## 10 9.2201 South Boston a District A-15 Police Station
## geometry
## 1 LINESTRING (-71.04465 42.33...
## 2 LINESTRING (-71.04465 42.33...
## 3 LINESTRING (-71.04465 42.33...
## 4 LINESTRING (-71.04465 42.33...
## 5 LINESTRING (-71.04465 42.33...
## 6 LINESTRING (-71.04465 42.33...
## 7 LINESTRING (-71.04465 42.33...
## 8 LINESTRING (-71.04465 42.33...
## 9 LINESTRING (-71.04465 42.33...
## 10 LINESTRING (-71.04465 42.33...#Observamos que el la comisaria mas cercana se encuentra a 1.21 km con un tiempo de viajeen condiciones normales es de 3.43 minutos y la mas alejada esta a 9.22 km con un tiempo en condiciones normales es de viaje de 15.55.
#El siguiente mapa muestra las rutas desde el barrio centrio con destino a las comisarias.
leaflet(ruteo_centrico) %>%
addTiles() %>%
addProviderTiles(providers$CartoDB.DarkMatter) %>%
addPolylines(color = ~colorNumeric("plasma", ruteo_centrico$distance)(distance),
label = ruteo_centrico$RUTA %>%
lapply(htmltools::HTML)) %>%
addLegend("bottomright", pal = colorNumeric("plasma", ruteo_centrico$distance), values = ~distance,
title = "Distancia",
opacity = 0.6)COMISARIAS_HydePark <- BOSTON_POLICIAS %>%
mutate(ORIGEN="Hyde Park") %>%
left_join(BOSTON_centroides, by=c("ORIGEN"="Name"))%>%
rename(NOMBRE_ORIGEN=ORIGEN,
LON_ORIGEN=X.y,
LAT_ORIGEN=Y.y,
NOMBRE_DESTINO=NAME,
LON_DESTINO=X.x,
LAT_DESTINO=Y.x) %>%
select(NOMBRE_ORIGEN, LON_ORIGEN, LAT_ORIGEN, NOMBRE_DESTINO, LON_DESTINO, LAT_DESTINO)## Warning: Column `ORIGEN`/`Name` joining character vector and factor, coercing
## into character vector#Realizamos el ruteo para las comisarias desde el barrio periferico Hyde Park
ruteo_POLICIAS_HydePark <- list(COMISARIAS_HydePark$NOMBRE_ORIGEN, COMISARIAS_HydePark$LON_ORIGEN,COMISARIAS_HydePark$LAT_ORIGEN,
COMISARIAS_HydePark$NOMBRE_DESTINO, COMISARIAS_HydePark$LON_DESTINO,COMISARIAS_HydePark$LAT_DESTINO)
ruteo_periferico <- pmap(ruteo_POLICIAS_HydePark, ruteo_a_COMISARIAS) %>%
reduce(rbind)ruteo_periferico <-mutate(ruteo_periferico, RUTA = paste(ORIGEN,"a", DESTINO))arrange(ruteo_periferico, distance)## Simple feature collection with 13 features and 7 fields
## geometry type: LINESTRING
## dimension: XY
## bbox: xmin: -71.15529 ymin: 42.25254 xmax: -71.03647 ymax: 42.38412
## CRS: EPSG:4326
## First 10 features:
## ORIGEN DESTINO src dst duration distance
## 1 Hyde Park District E-18 Police Station Hyde Park 12 1.641667 0.3707
## 2 Hyde Park District E-5 Police Station Hyde Park 13 8.095000 5.4754
## 3 Hyde Park District B-3 Police Station Hyde Park 6 11.998333 5.9259
## 4 Hyde Park District E-13 Police Station Hyde Park 11 13.725000 6.6145
## 5 Hyde Park District C-11 Police Station Hyde Park 7 18.586667 8.8677
## 6 Hyde Park District B-2 Police Station Hyde Park 5 19.743333 9.2533
## 7 Hyde Park Boston Police Headquarters Hyde Park 1 21.893333 9.9374
## 8 Hyde Park District D-14 Police Station Hyde Park 9 22.693333 13.8882
## 9 Hyde Park District C-6 Police Station Hyde Park 8 24.763333 18.5258
## 10 Hyde Park District D-4 Police Station Hyde Park 10 24.498333 18.6604
## RUTA geometry
## 1 Hyde Park a District E-18 Police Station LINESTRING (-71.12672 42.25...
## 2 Hyde Park a District E-5 Police Station LINESTRING (-71.12672 42.25...
## 3 Hyde Park a District B-3 Police Station LINESTRING (-71.12672 42.25...
## 4 Hyde Park a District E-13 Police Station LINESTRING (-71.12672 42.25...
## 5 Hyde Park a District C-11 Police Station LINESTRING (-71.12672 42.25...
## 6 Hyde Park a District B-2 Police Station LINESTRING (-71.12672 42.25...
## 7 Hyde Park a Boston Police Headquarters LINESTRING (-71.12672 42.25...
## 8 Hyde Park a District D-14 Police Station LINESTRING (-71.12672 42.25...
## 9 Hyde Park a District C-6 Police Station LINESTRING (-71.12672 42.25...
## 10 Hyde Park a District D-4 Police Station LINESTRING (-71.12672 42.25...#Apreciamos que la distancia minima a una comisaria es de 370 mts y el tiempo de viaje en condiciones normales es de 1.64 minutos, mientras que la mas distante esta a 18.66 km con un tiempo de viaje en condiciones normales de 24.49 minutos
#El siguiente mapa muestra las rutas a las comisarias desde el barrio periferico.
leaflet(ruteo_periferico) %>%
addTiles() %>%
addProviderTiles(providers$CartoDB.DarkMatter) %>%
addPolylines(color = ~colorNumeric("plasma", ruteo_periferico$distance)(distance),
label = ruteo_periferico$RUTA %>%
lapply(htmltools::HTML)) %>%
addLegend("bottomright", pal = colorNumeric("plasma", ruteo_periferico$distance), values = ~distance,
title = "Distancia",
opacity = 0.6)