Date: 2026-01-07

# Mapping land use data

#### 2026-01-07

Here are a few quick examples to illustrate how you can use the {aopdata} package to map the spatial distribution of activities and urban services in Brazilian cities.

    # load libraries
    library(aopdata)
    library(data.table)
    library(ggplot2)
    library(sf)
    library(scales)

## Download land use data

    df <- aopdata::read_landuse(
      city = 'Fortaleza',
      year = 2019,
      geometry = T,
      showProgress = F
    )

## Spatial distribution of jobs

    ggplot() +
      geom_sf(data=df, aes(fill=T001), color=NA, alpha=.9) +
      scale_fill_distiller(palette = "YlOrRd", direction = 1) +
      labs(title='Spatial distribution of jobs', fill="N. of jobs") +
      theme_void()

## Spatial distribution of schools

In this case below, elementary schools with the column`E003`.

    ggplot() +
      geom_sf(data=df, aes(fill=factor(E003)), color=NA, alpha=.9) +
      scale_fill_brewer(palette = "PuBuGn", direction = 1) +
      labs(title='Spatial distribution of elementary schools', fill="N. of schools") +
      theme_void()

## Spatial distribution of healthcare

In this example, we mape high-complexity health care facilities (column `S004`).

    ggplot() +
      geom_sf(data=df, aes(fill=factor(S004)), color=NA, alpha=.9) +
      scale_fill_brewer(palette = "YlGnBu", direction = 1)+
      labs(title='Spatial distribution of hospitals', fill="N. of hospitals") +
      theme_void()

## Map Centers for social assistance (CRAS)

    ggplot() +
      geom_sf(data=df, aes(fill=factor(C001)), color=NA, alpha=.9) +
      scale_fill_brewer(palette = "RdPu", direction = 1)+
      labs(title='Spatial distribution of CRAS facilities', fill="N. of CRAS") +
      theme_void()