7002 - Week 3 Assessment

Task 1. 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("C:/Users/accou/Downloads/Spatial Week 3/municipalities/municipalities.shp")

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

2. Generate Municipalities Centroids and Plot

municipalities_centroids <- municipalities_sp %>% st_centroid()
municipalities_centroids %>% ggplot()+geom_sf()

3. Dissolve SP State Polygons to creat 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)+
    theme_classic() +
  labs(title = "Sao Paulo and Their Centroids")

### Task 2: What is the mean human development index of municipalities in each state of Brazil?

1.HDI mean by state

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

2. Plotting results

br_states %>% ggplot()+
  geom_sf(aes(fill=IDHM_mean))+
  theme_classic()+scale_fill_continuous(low = "red",high="green")+
labs(title = "Brazilian Sates by Mean Human Development Index")

Task 3: Produce a polygon/shape file mapping the area of the municipality ‘Gaucha do Norte’ that is in the indegenous territory “Parque do Xingu”

indegenous <- read_sf("C:/Users/accou/Downloads/Spatial Week 3/BC250_Terra_Indigena_A/BC250_Terra_Indigena_A.shp")

## Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet, : GDAL
## Message 1: C:\Users\accou\Downloads\Spatial Week
## 3\BC250_Terra_Indigena_A\BC250_Terra_Indigena_A.shp contains polygon(s) with
## rings with invalid winding order. Autocorrecting them, but that shapefile
## should be corrected using ogr2ogr for example.

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

2. Shapefile for Gaucha do Norte e Xingu

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

3. Plotting shapefile to see overlap

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

4. Create shapefile for interactions and plot it

intersection <- Gaucha %>% st_intersection(Xingu)

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

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

5. Plotting 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")

Intersection area

st_area(intersection)

## 8109249396 [m^2]

Task4 : Inther state of Acre (AC), which two social housing (MCMV) projects are closest to each other? Create a 20km buffer around each housing project.

1. Load housing shapefile

Housing <- read_sf("C:/Users/accou/Downloads/Spatial Week 3/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. Calculating 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(10000) %>% 
  ggplot()+geom_sf(fill = "dark green")

5. Plotting it all together

br_states %>% filter(UF=="AC") %>% ggplot() + geom_sf()+
  geom_sf(data=(Housing_AC %>% st_transform(29189) %>% st_buffer(10000)),fill = "dark green")+
  geom_sf(data=Housing_AC,color ="red")

Task5 : 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) %>% 
  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"

3. 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()+
labs(title = "Total Housing Units by Municipality")

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