Immigration - Where do they live? - Santiago

4.1) Filter Data

Firstly the data is filtered to only have data for the province of Santiago. From now on this area will be referred to as Santiago. It is important to note that this area does not include the comunas of San Bernado or Puente Alto as they are part of different provinces.

visas2019STG <- visas2019 %>% filter(REGIÓN == "Metropolitana de Santiago") 

visas2019STG <- visas2019STG %>% filter(PROVINCIA == 'Santiago')

4.2) Missing Values

Some variables have a class of ‘No Informa’. However, this is not the case for TIT-DEP which has 15,902 without a value. In accordance with the other variables, a class of ‘No Informa’ is added for these missing values. After this all of the observations that have a value of ‘No Informa’ for any of the variables are removed from the data frame as they can reduce the precision of the model.

table(visas2019STG$ACTIVIDAD) 
      
table(visas2019STG$PROFESIÓN) 
            
table(visas2019STG$ESTUDIOS) 

table(visas2019STG$TIT_DEP)

levels(visas2019STG$TIT_DEP)

levels(visas2019STG$TIT_DEP)[1] <- "No Informa"

table(visas2019STG$TIT_DEP)

visas2019STG <- visas2019STG %>% filter(!ACTIVIDAD == "No Informa" ) %>% filter(!PROFESIÓN == "No Informa") %>% filter(!ESTUDIOS == "No Informa") %>% filter(!TIT_DEP == "No Informa")

4.3) Immigrante Age

The date of birth is converted to age.

visas2019STG$NACIMIENTO <- as.Date(visas2019STG$NACIMIENTO)

year <- 2020

Birth_year <- year(visas2019STG$NACIMIENTO)

visas2019STG <- visas2019STG %>% mutate(Age = year - Birth_year)

4.4) Geographic Coordinates

The package chilemapas is used to create a base map for the Province of Santiago.

Chile <- chilemapas::codigos_territoriales
STG <- Chile %>% filter(nombre_provincia == 'Santiago')
Comunas <- chilemapas::mapa_comunas
STGgeo <- left_join(STG, Comunas)

Additionally, accents are added to the names of each of the comunas, so that they can be combined with other data bases that use accents in their spelling of the comunas.

STGgeo[4, 2] = "Conchalí"
STGgeo[6, 2] = "Estación Central"
STGgeo[19, 2] = "Maipú"
STGgeo[20, 2] = "Ñuñoa"
STGgeo[22, 2] = "Peñalolén"
STGgeo[29, 2] = "San Joaquín"
STGgeo[31, 2] = "San Ramón"

The two data bases are combined.

visas2019STG <- visas2019STG %>% rename(nombre_comuna = COMUNA)

visas2019STG$nombre_comuna <- as.factor(visas2019STG$nombre_comuna)

visas2019STG <- left_join(visas2019STG, STGgeo)

5.1) Nationalities

In 2019 156,260 inmigrantes arrived to Santiago with a total of 76 nationalities. However, after following the feature engineering steps outline in Section 4 this number reduces to 98,655 with 76 nationalities. Of this amount Venezuelans are the most prominent representing 58.80% with 58,009 people. An additional point of interest is that of the ten most prominent nationalities eight are from South or Central America, with China and the United States the only exceptions. It is also observed that six of these ten nationalities speak Spanish as a first language.

visas2019STG %>% group_by(PAÍS) %>% count() %>% arrange(-n)

## # A tibble: 75 x 2
## # Groups:   PAÍS [75]
##    PAÍS               n
##    <fct>          <int>
##  1 Venezuela      58009
##  2 Perú           11474
##  3 Colombia        8465
##  4 Haití           7135
##  5 Bolivia         2424
##  6 Ecuador         2119
##  7 Argentina       1988
##  8 Brasil          1335
##  9 China            923
## 10 Estados Unidos   564
## # … with 65 more rows

5.2) Where do they live?

The most popular comuna for immigrants in 2019 was Santiago Centro with 30,207 arrivals. This is not surprising as Santiago Centro is the center of the city where there is more access to services, employment opportunities and housing. However, it must be noted that the data used only refers to the comuna of residence when an immigrant applied for their visa. Therefore, it is possible that they have since moved to a different sector of the city as they have become used to the city and have developed a support network.

comuna_count <- visas2019STG %>% group_by(nombre_comuna) %>% count() %>% arrange(-n)

comuna_count

## # A tibble: 32 x 2
## # Groups:   nombre_comuna [32]
##    nombre_comuna        n
##    <chr>            <int>
##  1 Santiago         30207
##  2 Estación Central  8080
##  3 Independencia     7291
##  4 Quinta Normal     4343
##  5 San Miguel        4124
##  6 Recoleta          3871
##  7 Ñuñoa             3736
##  8 Las Condes        3591
##  9 La Florida        3571
## 10 Maipú             2780
## # … with 22 more rows

The total number of immigrants in each comuna is added to the STGgeo data frame so that it can be mapped below. Each comuna is labelled, with details of which comunas correspond to each number provided in the table below the map.

STGgeo <- left_join(STGgeo, comuna_count, by = "nombre_comuna")

STGgeo <- STGgeo %>% rename(number_inmigrantes = n)

STGgeo <- cbind(STGgeo, st_coordinates(st_centroid(STGgeo$geometry)))

labels <- seq(1,32)

ggplot() + geom_sf(data = STGgeo$geometry, aes(fill = STGgeo$number_inmigrantes)) + 
scale_fill_viridis_c(option = "inferno",trans = 'sqrt') +
geom_text(data = STGgeo, aes(X, Y, label = labels), size = 3, color = "white") +
geom_text(data = STGgeo %>% filter(nombre_comuna == "Santiago"), aes(X, Y, label = "1"), size = 3, color = "black") +
annotation_north_arrow(aes(which_north = "true", location = "br"), pad_y = unit(0.8, "cm")) +
  annotation_scale(aes(location = "br", style = "bar")) +
  theme(panel.grid.major = element_line(color = gray(0.5), linetype = "dashed")) +
  theme (panel.background = element_rect(fill = "light grey")) +
  ggtitle("5.1) Location of immigrants that arrived in 2019") + xlab("Longitude") + ylab("Latitude") +
  labs(fill = "Number of Immigrants")

cbind(STGgeo, labels) %>% select(nombre_comuna, labels)

##          nombre_comuna labels
## 1             Santiago      1
## 2            Cerrillos      2
## 3          Cerro Navia      3
## 4             Conchalí      4
## 5            El Bosque      5
## 6     Estación Central      6
## 7           Huechuraba      7
## 8        Independencia      8
## 9          La Cisterna      9
## 10          La Florida     10
## 11           La Granja     11
## 12          La Pintana     12
## 13            La Reina     13
## 14          Las Condes     14
## 15        Lo Barnechea     15
## 16           Lo Espejo     16
## 17            Lo Prado     17
## 18               Macul     18
## 19               Maipú     19
## 20               Ñuñoa     20
## 21 Pedro Aguirre Cerda     21
## 22           Peñalolén     22
## 23         Providencia     23
## 24            Pudahuel     24
## 25           Quilicura     25
## 26       Quinta Normal     26
## 27            Recoleta     27
## 28               Renca     28
## 29         San Joaquín     29
## 30          San Miguel     30
## 31           San Ramón     31
## 32            Vitacura     32

5.4) The Eastern Sector

This publication and its part two counterpart aims to build a classification model to predict if an immigrant lives in the Eastern Sector. This sector contains the comunas of Providencia, Las Condes, Vitacura, and Lo Barnechea and is located to the north east of the city. These comunas are considered the most wealthy in the city and are identified in the below map.

SectorOriente <- STGgeo %>% filter(nombre_comuna == 'Providencia' | nombre_comuna == 'Las Condes' | nombre_comuna == 'Vitacura' | nombre_comuna == 'Lo Barnechea')

ggplot() + geom_sf(data = STGgeo$geometry, fill = "white") + 
  geom_sf(data = SectorOriente$geometry, fill = "purple") +
geom_text(data = STGgeo, aes(X, Y, label = labels), size = 3, color = "white") +
annotation_north_arrow(aes(which_north = "true", location = "br"), pad_y = unit(0.8, "cm")) +
  annotation_scale(aes(location = "br", style = "bar")) +
  theme(panel.grid.major = element_line(color = gray(0.5), linetype = "dashed")) +
  theme (panel.background = element_rect(fill = "light grey")) +
  ggtitle("5.2 Eastern Sector of Santiago") + xlab("Longitude") + ylab("Latitude")

6.1) Venezuelans

This map shows that Venezuelans were concentrated in Santiago Centro with 21,387 people, corresponding to 36.88% of the Venezuelans that arrived in 2019. Estacion Central and Independencia were the second and third most populated comunas. In the eastern sector there were 1,998 Venezuelans.

venezuela_count <- visas2019STG %>% filter(PAÍS == 'Venezuela') %>% group_by(nombre_comuna) %>% count() %>% arrange(-n)

venezuela_count

## # A tibble: 32 x 2
## # Groups:   nombre_comuna [32]
##    nombre_comuna        n
##    <chr>            <int>
##  1 Santiago         21387
##  2 Estación Central  5628
##  3 Independencia     4710
##  4 San Miguel        3318
##  5 Quinta Normal     2805
##  6 Ñuñoa             2622
##  7 La Florida        2339
##  8 Macul             1575
##  9 Maipú             1537
## 10 La Cisterna       1237
## # … with 22 more rows

visas2019STG %>% filter(PAÍS == 'Venezuela' & nombre_comuna %in% c('Providencia', "Las Condes", 'Vitacura', 'Lo Barnechea')) %>% count()

##      n
## 1 1998

STGgeo <- left_join(STGgeo, venezuela_count, by = "nombre_comuna")

STGgeo <- STGgeo %>% rename(numero_venezuelanos = n)

ggplot() + geom_sf(data = STGgeo$geometry, aes(fill = STGgeo$numero_venezuelanos)) + 
scale_fill_viridis_c(option = "inferno",trans = 'sqrt') +
geom_text(data = STGgeo, aes(X, Y, label = labels), size = 3, color = "white") +
annotation_north_arrow(aes(which_north = "true", location = "br"), pad_y = unit(0.8, "cm")) +
  annotation_scale(aes(location = "br", style = "bar")) +
  theme(panel.grid.major = element_line(color = gray(0.5), linetype = "dashed")) +
  theme (panel.background = element_rect(fill = "light grey")) +
  ggtitle("6.1 Location of Venezuelans that arrived in 2019") + xlab("Longitude") + ylab("Latitude") +
  labs(fill = "Number of immigrants")

haitiano_count <- visas2019STG %>% filter(PAÍS == 'Haití') %>% group_by(nombre_comuna) %>% count() %>% arrange(-n)

visas2019STG %>% filter(PAÍS == 'Haití') %>% count()

##      n
## 1 7135

haitiano_count

## # A tibble: 32 x 2
## # Groups:   nombre_comuna [32]
##    nombre_comuna           n
##    <chr>               <int>
##  1 Quilicura             984
##  2 Estación Central      758
##  3 Santiago              523
##  4 Lo Espejo             426
##  5 Recoleta              375
##  6 Pedro Aguirre Cerda   367
##  7 Cerro Navia           363
##  8 Conchalí              280
##  9 Quinta Normal         252
## 10 El Bosque             248
## # … with 22 more rows

visas2019STG %>% filter(PAÍS == 'Haití' & nombre_comuna %in% c('Providencia', "Las Condes", 'Vitacura', 'Lo Barnechea')) %>% count()

##    n
## 1 25

STGgeo <- left_join(STGgeo, haitiano_count, by = "nombre_comuna")

STGgeo <- STGgeo %>% rename(numero_haitianos = n)

ggplot() + geom_sf(data = STGgeo$geometry, aes(fill = STGgeo$numero_haitianos)) + 
scale_fill_viridis_c(option = "inferno",trans = 'sqrt') +
geom_text(data = STGgeo, aes(X, Y, label = labels), size = 3, color = "white") +
annotation_north_arrow(aes(which_north = "true", location = "br"), pad_y = unit(0.8, "cm")) +
  annotation_scale(aes(location = "br", style = "bar")) +
  theme(panel.grid.major = element_line(color = gray(0.5), linetype = "dashed")) +
  theme (panel.background = element_rect(fill = "light grey")) +
  ggtitle("6.2 Location of Haitians that arrived in 2019") + xlab("Longitude") + ylab("Latitude") +
  labs(fill = "Number of immigrants")

6.3) United States of America

Of the 564 US-Americans that arrived in 2019, 252 (62.41%) lived in the Eastern Sector. As was the case for Venezuelans and Haitians, Santiago Centro again received a high percentage of the arrivals with 80 people (14.18%). It is also interesting that there were various comunas without US-American arrivals in 2019, this was not the case for the other two nationalities analysed so far with Venezuelans and Haitians present in each of Santiago’s comunas.

eeuu_count <- visas2019STG %>% filter(PAÍS == 'Estados Unidos') %>% group_by(nombre_comuna) %>% count() %>% arrange(-n)

eeuu_count

## # A tibble: 23 x 2
## # Groups:   nombre_comuna [23]
##    nombre_comuna        n
##    <chr>            <int>
##  1 Providencia        208
##  2 Las Condes         110
##  3 Santiago            80
##  4 Maipú               57
##  5 Ñuñoa               30
##  6 Vitacura            21
##  7 Lo Barnechea        13
##  8 Estación Central    11
##  9 Macul                5
## 10 Independencia        4
## # … with 13 more rows

visas2019STG %>% filter(PAÍS == 'Estados Unidos') %>% count() 

##     n
## 1 564

visas2019STG %>% filter(PAÍS == 'Estados Unidos' & nombre_comuna %in% c('Providencia', "Las Condes", 'Vitacura', 'Lo Barnechea')) %>% count()

##     n
## 1 352

STGgeo <- left_join(STGgeo, eeuu_count, by = "nombre_comuna")

STGgeo <- STGgeo %>% rename(numero_eeuu = n)

ggplot() + geom_sf(data = STGgeo$geometry, aes(fill = STGgeo$numero_eeuu)) + 
scale_fill_viridis_c(option = "inferno",trans = 'sqrt') +
geom_text(data = STGgeo, aes(X, Y, label = labels), size = 3, color = "white") +
annotation_north_arrow(aes(which_north = "true", location = "br"), pad_y = unit(0.8, "cm")) +
  annotation_scale(aes(location = "br", style = "bar")) +
  theme(panel.grid.major = element_line(color = gray(0.5), linetype = "dashed")) +
  theme (panel.background = element_rect(fill = "light grey")) +
  ggtitle("6.3 Location of US-Americans that arrived in 2019") + xlab("Longitude") + ylab("Latitude") +
  labs(fill = "Number of Immigrants")

6.4) Peruvians

Santiago Centro, Recoleta, and Independencia were the three comunas with the most Peruvian arrivals in 2019 with 2,785 (24.27%), 1,272 (11.09%), and 1,092 (9.51%) respectively. In the Eastern Sector there were 855 (7.45%) Peruvians.

peruano_count <- visas2019STG %>% filter(PAÍS == 'Perú') %>% group_by(nombre_comuna) %>% count() %>% arrange(-n)

visas2019STG %>% filter(PAÍS == 'Perú') %>% count()

##       n
## 1 11474

visas2019STG %>% filter(PAÍS == 'Perú' & nombre_comuna %in% c('Providencia', "Las Condes", 'Vitacura', 'Lo Barnechea')) %>% count()

##     n
## 1 855

peruano_count

## # A tibble: 32 x 2
## # Groups:   nombre_comuna [32]
##    nombre_comuna        n
##    <chr>            <int>
##  1 Santiago          2785
##  2 Recoleta          1272
##  3 Independencia     1092
##  4 Estación Central   634
##  5 Quinta Normal      585
##  6 Conchalí           475
##  7 Peñalolén          415
##  8 Las Condes         373
##  9 La Florida         335
## 10 Lo Prado           320
## # … with 22 more rows

STGgeo <- left_join(STGgeo, peruano_count, by = "nombre_comuna")

STGgeo <- STGgeo %>% rename(numero_peruanos = n)

ggplot() + geom_sf(data = STGgeo$geometry, aes(fill = STGgeo$numero_peruanos)) + 
scale_fill_viridis_c(option = "inferno",trans = 'sqrt') +
geom_text(data = STGgeo, aes(X, Y, label = labels), size = 3, color = "white") +
annotation_north_arrow(aes(which_north = "true", location = "br"), pad_y = unit(0.8, "cm")) +
  annotation_scale(aes(location = "br", style = "bar")) +
  theme(panel.grid.major = element_line(color = gray(0.5), linetype = "dashed")) +
  theme (panel.background = element_rect(fill = "light grey")) +
  ggtitle("6.4 Location of Peruvians tha arrived in 2019") + xlab("Longitude") + ylab("Latitude") +
  labs(fill = "Number of immigrants")

6.5) Analysis Summary

The following conclusions can be taken from the above analysis:

• There were more immigrants from Central America and South America.
• Speaking Spanish is an important factor for determining if there are many immigrants from a certain nationality.
• US-Americans have the highest GDP per capita and were the only analysed nationality with the majority of their population living in the Eastern Sector.
• Haitians have the lowest GDP per capita and also had the lowest percentage of people living in the Eastern Sector.
• Haitians were more widely dispersed with Quilicura, to the north of Santiago having the most Haitians. In comparison Venezuelans, US-Americans, and Peruvians were more concentrated around the center of the city.