Formative Assessment 3

First task

Produce a map showing the centroids of each municipality in just the state of Säo Paulo, and add the outer boundary of Säo Paulo state.

1. open municipalities and filter for SP state

municipalities <- read_sf("municipalities/municipalities.shp")

municipalities_sp <- municipalities %>% filter(UF=="SP")

2. Generate municipalities centroids and plot them

municipalities_centroids <- municipalities_sp %>% st_centroid()

municipalities_centroids %>% ggplot() + geom_sf()

3. Dissolve SP state polygons to create SP state border

SP_border <- municipalities_sp %>% st_union()

SP_border %>% ggplot() + geom_sf()

4. Plot SP centroids and Sp border together

SP_border %>% ggplot() + geom_sf()+
  geom_sf(data=municipalities_centroids)

Second Task

What is the mean Human Development Index of municipalities in each state of Brazil?

1. Remember that HDI is a variable from municipalities data set. Open table to set it.

2. Calculate mean HDI by state

br_states <- municipalities %>% group_by(UF) %>% summarise(IDHM_mean=mean(IDHM_10))

view(br_states)

3. Plot the result

br_states %>% ggplot() +
  geom_sf(aes(fill=IDHM_mean))+  
  theme_classic() +
  scale_fill_continuous(low="red",high="green")

Third Task

produce a polyline/shapefile mapping the area of the municipality ‘Gaucha do Notre’ that is in the indigenous territory “Parque de xinqgu”

1. Import indogenous park shapefile and plot it

indeginous <- read_sf("BC250_Terra_Indigena_A/BC250_Terra_Indigena_A.shp") %>% st_transform(4326)

br_states %>% ggplot() +
  geom_sf() +
  geom_sf(data = indeginous, fill="red")

2. Create a shapefile for Gaucha do Notre e Xingu

Xingu <- indeginous %>% filter(nome=="Parque do Xingu") %>% st_transform(4326)
Gaucha <- municipalities %>% filter(NOME=="GAUCHA DO NORTE") %>% st_transform(4326)

3 Plot the shape files to see the overlap

Gaucha %>% ggplot() +
  geom_sf(fill="red") +
  geom_sf(data=Xingu, fill="blue", alpha=0.5)

4 create shapefile for intersction and plot it

intersection <-  Gaucha %>% st_intersection(Xingu)
intersection %>% ggplot() + geom_sf(fill="dark green")

5 plot all together

Gaucha %>% ggplot() +
  geom_sf(fill="red", alpha=0.5) +
  geom_sf(data=Xingu, fill="blue", alpha=0.5) +
  geom_sf(data = intersection, fill = "black")

you can calcualte the intersection area

st_area(intersection)

## 8109249396 [m^2]

Fourth Task

In the state of Acre (AC), which two social housing (MCMV) projects are closest to each other? Create 20km buffer around each housinh project.

1. Import housing shapefile

Housing <- read_sf("MCMV_new/MCMV_new.shp")

2. Select housing in AC state and plot that result

Housing_AC <- Housing %>% filter(UF=="AC")
br_states %>% filter(UF=="AC") %>% ggplot() + geom_sf() + geom_sf(data = Housing_AC)

3. calculate distance between housing points and view results

distance <- Housing_AC %>%
  st_transform(29189) %>%
  st_distance() %>%
  as.data.frame()

4. Generate Buffer around housing points

Housing_AC %>% st_transform(29189) %>%
  st_buffer(20000) %>%
  ggplot() + geom_sf(fill="dark green")

5. plot it all together

br_states %>% filter(UF == "AC") %>% ggplot() +
  geom_sf()+
  geom_sf(data = (Housing_AC %>% st_transform(29189) %>% st_buffer(20000)), fill="dark green")+
  geom_sf(data = Housing_AC, color = "red")

Fifth Task

Across Brazil, which municipalities have the lowest and highest number of MCMV housing units (UH) in its territory? Create a map of the distribution of total housing units by municipality.

1. Spatial join between Municipalities and housing

mun_Housing_units <- municipalities %>% st_join(Housing)

2. calculate total of Housing units in each munincipality

mun_Housing_units <- mun_Housing_units %>%
  group_by(COD_MUN, NOME) %>%
  summarize(UH=sum(UH, na.rm = T)) %>%
  ungroup()

## `summarise()` has grouped output by 'COD_MUN'. You can override using the
## `.groups` argument.

3. select municipality with most Housing units

mun_Housing_units %>% arrange(-UH) %>% slice(1) %>%pull(NOME)

## [1] "RIO DE JANEIRO"

mun_Housing_units %>% arrange(UH) %>% slice(1) %>%pull(NOME)

## [1] "CABIXI"

4. plot the distribution of total housing units by municipality.

mun_Housing_units %>% ggplot()+ 
  geom_sf(aes(fill=UH), col=NA) +
  scale_fill_gradient(low="#ccece6", high="dark green", trans="log")+
  theme_classic()

## Warning: Transformation introduced infinite values in discrete y-axis