Assignment Week 3

Task One

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

2. Generate municipalities centroids and plot them

municipalities_centroids <- municipalities_sf %>% st_centroid()

municipalities_centroids %>% ggplot() + geom_sf()

3. Dissolve SP state polygons to create SP state border

SP_border <- municipalities_sf %>% 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) +
  theme_classic()

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

1. HDI is a variable from municipalities data set

2. Calculate mean HDI by state

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

3. Plot the result

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

## Task Three Produce a polygon/shapefile mapping the area of the municipality ‘Gaucha do Norte’ that is in the indigenous territory “Parque do Xingu”.

1. Import indigenous park shapefile and plot it

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

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

2. Create a shapefile for Gaucha do Norte e Xingu

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

3. Plot the shapefiles to see the overlap

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

4. Create shapefile for intersection and plot it

interaction <- Gaucha %>% st_intersection(Xingu)

## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries

interaction %>% ggplot() + geom_sf(fill="dark green")

5. Plot all together

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

## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown parameters: `aplha`

Calculate the intersection area

st_area(interaction)

## 8109249396 [m^2]

Task Four

In the state of Acre (AC), which two social housing (MCMV) projects are closest to each other? Create a 10km buffer around each housing project.

1. Import housing shapefile

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

2. Select housing in AC state and plot the results

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")

Task Five

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 municipality

mun_Housing_units <- mun_Housing_units %>%
  group_by(COD_MUN,NOME) %>%
  summarise(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"

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 in scale_fill_gradient(low = "#ccece6", high = "dark green", trans =
## "log"): log-2.718282 transformation introduced infinite values.

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