Merge branch 'master' of https://github.com/ipeaGIT/geobr

Author: rafapereirabr

README.md

@@ -11,7 +11,7 @@ The package is currently available in [**R**](https://CRAN.R-project.org/package
 
 | ***R*** | ***Python*** | ***Repo***|
 |-----|-----|----|
-| [![CRAN/METACRAN Version](https://www.r-pkg.org/badges/version/geobr)](https://CRAN.R-project.org/package=geobr) <br /> [![CRAN/METACRAN Total downloads](http://cranlogs.r-pkg.org/badges/grand-total/geobr?color=blue)](https://CRAN.R-project.org/package=geobr) <br /> [![CRAN/METACRAN downloads per month](http://cranlogs.r-pkg.org/badges/geobr?color=yellow)](https://CRAN.R-project.org/package=geobr) <br /> [![Codecov test coverage](https://codecov.io/gh/ipeaGIT/geobr/branch/master/graph/badge.svg)](https://codecov.io/gh/ipeaGIT/geobr?branch=master)  <br /> [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable) | [![PyPI version](https://badge.fury.io/py/geobr.svg)](https://badge.fury.io/py/geobr) <br />  [![Downloads](https://pepy.tech/badge/geobr)](https://pepy.tech/project/geobr) <br />  [![Downloads](https://pepy.tech/badge/geobr/month)](https://pepy.tech/project/geobr/month)  <br /> [![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) |<img alt="GitHub stars" src="https://img.shields.io/github/stars/ipeaGIT/geobr.svg?color=orange"> <br /> [![R build status](https://github.com/ipeaGIT/geobr/workflows/R-CMD-check/badge.svg)](https://github.com/ipeaGIT/geobr/actions) <br /> [![Travis-CI Build Status](https://travis-ci.org/ipeaGIT/geobr.svg?branch=master)](https://travis-ci.org/ipeaGIT/geobr)  <br />  [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) |
+| [![CRAN/METACRAN Version](https://www.r-pkg.org/badges/version/geobr)](https://CRAN.R-project.org/package=geobr) <br /> [![CRAN/METACRAN Total downloads](http://cranlogs.r-pkg.org/badges/grand-total/geobr?color=blue)](https://CRAN.R-project.org/package=geobr) <br /> [![CRAN/METACRAN downloads per month](http://cranlogs.r-pkg.org/badges/geobr?color=yellow)](https://CRAN.R-project.org/package=geobr) <br /> [![Codecov test coverage](https://codecov.io/gh/ipeaGIT/geobr/branch/master/graph/badge.svg)](https://codecov.io/gh/ipeaGIT/geobr?branch=master)  <br /> [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable) | [![PyPI version](https://badge.fury.io/py/geobr.svg)](https://badge.fury.io/py/geobr) <br />  [![Downloads](https://pepy.tech/badge/geobr)](https://pepy.tech/project/geobr) <br />  [![Downloads](https://pepy.tech/badge/geobr/month)](https://pepy.tech/project/geobr/month)  <br /> [![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) |<img alt="GitHub stars" src="https://img.shields.io/github/stars/ipeaGIT/geobr.svg?color=orange"> <br /> [![R build status](https://github.com/ipeaGIT/geobr/workflows/R-CMD-check/badge.svg)](https://github.com/ipeaGIT/geobr/actions) <br />  [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) |
 
 
 
@@ -168,11 +168,11 @@ As of today, there is another R package with similar functionalities: [simplefea
 - Brazil: [geobr](https://ipeagit.github.io/geobr/)
 - Canada: [cancensus](https://mountainmath.github.io/cancensus/index.html)
 - Chile: [chilemapas](https://pacha.dev/chilemapas/)
+- Czech Republic: [RCzechia](https://github.com/jlacko/RCzechia)
 - Finland: [geofi](https://ropengov.github.io/geofi/)
 - Uruguay: [geouy](https://github.com/RichDeto/geouy)
 - USA: [tigris](https://github.com/walkerke/tigris)
--  Global (political administrative boundaries): [rgeoboundaries](https://dickoa.gitlab.io/rgeoboundaries/)
-
+- Global (political administrative boundaries): [rgeoboundaries](https://dickoa.gitlab.io/rgeoboundaries/)
 
 -----
 

python-package/CHANGELOG.md

@@ -1,6 +1,13 @@
 # log history of geobr package development in Python
 
 -------------------------------------------------------
+
+# 0.1.9
+* Adds read_schools
+* Adds read_comparable_areas
+* Adds read_urban_concentrations
+* Adds read_intermediate_region
+* updates read_health_region
 # v0.1.7
 
 * Adds read_health_region.py

python-package/geobr/__init__.py

@@ -20,4 +20,9 @@
 from .read_weighting_area import read_weighting_area
 from .read_neighborhood import read_neighborhood
 from .read_health_region import read_health_region
-from .read_pop_arrangements import read_pop_arrangements
+from .read_pop_arrangements import read_pop_arrangements
+from .lookup_muni import lookup_muni
+from .read_intermediate_region import read_intermediate_region
+from .read_urban_concentrations import read_urban_concentrations
+from .read_schools import read_schools
+from .read_comparable_areas import read_comparable_areas

python-package/geobr/lookup_muni.py

@@ -1,17 +1,14 @@
-try:
-    import utils
-except ModuleNotFoundError:
-    import geobr.utils as utils
+from geobr import utils
 
 
 def lookup_muni(name_muni=None, code_muni=None, verbose=False):
-    """ Lookup municipality codes and names.
+    """Lookup municipality codes and names.
 
     By default, it looks for all municipalities. You can also use 'all' to in
-    `name_muni` or `code_muni` to get all municipalities. 
+    `name_muni` or `code_muni` to get all municipalities.
 
-    Input a municipality NAME or CODE and get the names and codes of 
-    the municipality's corresponding state, meso, micro, intermediate, and 
+    Input a municipality NAME or CODE and get the names and codes of
+    the municipality's corresponding state, meso, micro, intermediate, and
     immediate regions. You should not select both code_muni and name_muni
 
     Parameters

python-package/geobr/read_comparable_areas.py

@@ -0,0 +1,75 @@
+from geobr.utils import select_metadata, download_gpkg
+
+
+def read_comparable_areas(
+    start_year=1970, end_year=2010, simplified=True, verbose=False
+):
+    r"""Download spatial data of historically comparable municipalities
+
+    This function downloads the shape file of minimum comparable area of
+    municipalities, known in Portuguese as 'Areas minimas comparaveis (AMCs)'.
+    The data is available for any combination of census years between 1872-2010.
+    These data sets are generated based on the Stata code originally developed by
+    \doi{10.1590/0101-416147182phe}{Philipp Ehrl}, and translated
+    into `R` by the `geobr` team.
+
+    Years available:
+        1872,1900,1911,1920,1933,1940,1950,1960,1970,1980,1991,2000,2010
+
+    Parameters
+    ----------
+    year : int, optional
+        Year of the data, by default
+    simplified: boolean, by default True
+        Data 'type', indicating whether the function returns the 'original' dataset
+        with high resolution or a dataset with 'simplified' borders (Default)
+    verbose : bool, optional
+        by default False
+
+    Returns
+    -------
+    gpd.GeoDataFrame
+        Metadata and geopackage of selected states
+
+    Raises
+    ------
+    Exception
+        If parameters are not found or not well defined
+
+    Example
+    -------
+    >>> from geobr import read_comparable_areas
+
+    # Read specific state at a given year
+    >>> df = read_comparable_areas(year=)
+    """
+
+    years_available = [
+        1872,
+        1900,
+        1911,
+        1920,
+        1933,
+        1940,
+        1950,
+        1960,
+        1970,
+        1980,
+        1991,
+        2000,
+        2010,
+    ]
+
+    if (start_year not in years_available) or (end_year not in years_available):
+        raise ValueError(
+            "Invalid `start_year` or `end_year`."
+            f"It must be one of the following: {years_available}"
+        )
+
+    metadata = select_metadata("amc", year=start_year, simplified=simplified)
+
+    metadata = metadata.query(f'download_path.str.contains("{start_year}_{end_year}")')
+
+    gdf = download_gpkg(metadata)
+
+    return gdf

python-package/geobr/read_health_region.py

@@ -1,46 +1,51 @@
 from geobr.utils import select_metadata, download_gpkg
 
 
-def read_health_region(year=2013, simplified=True, verbose=False):
-    """ Download official data of Brazilian health regions as an sf object.
-    
-     @param year Year of the data (defaults to 2013, latest available)
- @param simplified Logic FALSE or TRUE, indicating whether the function returns the
-  data set with 'original' resolution or a data set with 'simplified' borders (Defaults to TRUE).
-  For spatial analysis and statistics users should set simplified = FALSE. Borders have been
-  simplified by removing vertices of borders using st_simplify{sf} preserving topology with a dTolerance of 100.
- @param showProgress Logical. Defaults to (TRUE) display progress bar
- @param tp Argument deprecated. Please use argument 'simplified
-
-    Parameters
-    ----------
-    year : int, optional
-        Year of the data, by default 2013
-    simplified: boolean, by default True
-        Data 'type', indicating whether the function returns the 'original' dataset 
-        with high resolution or a dataset with 'simplified' borders (Default)
-    verbose : bool, optional
-        by default False
-    
-    Returns
-    -------
-    gpd.GeoDataFrame
-        Metadata and geopackage of selected states
-    
-    Raises
-    ------
-    Exception
-        If parameters are not found or not well defined
-
-    Example
-    -------
-    >>> from geobr import read_health_region
-
-    # Read specific state at a given year
-    >>> df = read_health_region(year=2013)
+def read_health_region(year=2013, macro=False, simplified=True, verbose=False):
+    """Download official data of Brazilian health regions as an sf object.
+
+    Health regions are used to guide the the regional and state planning of health services.
+    Macro health regions, in particular, are used to guide the planning of high complexity
+    health services. These services involve larger economics of scale and are concentrated in
+    few municipalities because they are generally more technology intensive, costly and face
+    shortages of specialized professionals. A macro region comprises one or more health regions.
+
+     Parameters
+     ----------
+     year : int, optional
+         Year of the data, by default 2013
+     macro: If `False` (default), the function downloads health regions data.
+            If `True`, the function downloads macro regions data.
+     simplified: boolean, by default True
+         Data 'type', indicating whether the function returns the 'original' dataset
+         with high resolution or a dataset with 'simplified' borders (Default)
+     verbose : bool, optional
+         by default False
+
+     Returns
+     -------
+     gpd.GeoDataFrame
+         Metadata and geopackage of selected states
+
+     Raises
+     ------
+     Exception
+         If parameters are not found or not well defined
+
+     Example
+     -------
+     >>> from geobr import read_health_region
+
+     # Read specific state at a given year
+     >>> df = read_health_region(year=2013)
     """
 
-    metadata = select_metadata("health_region", year=year, simplified=simplified)
+    if macro:
+        metadata = select_metadata(
+            "health_region_macro", year=year, simplified=simplified
+        )
+    else:
+        metadata = select_metadata("health_region", year=year, simplified=simplified)
 
     gdf = download_gpkg(metadata)
 

python-package/geobr/read_intermediate_region.py

@@ -0,0 +1,61 @@
+from geobr.utils import select_metadata, download_gpkg
+
+
+def read_intermediate_region(
+    code_intermadiate="all", year=2019, simplified=True, verbose=False
+):
+    r"""Download spatial data of Brazil's Intermediate Geographic Areas
+
+    The intermediate Geographic Areas are part of the geographic division of
+    Brazil created in 2017 by IBGE. These regions were created to replace the
+    "Meso Regions" division. Data at scale 1:250,000, using Geodetic reference
+    system "SIRGAS2000" and CRS(4674)
+
+       Parameters
+       ----------
+       code_intermadiate: str or int, by default "all"
+            4-digit code of an intermediate region. If the two-digit code or a
+            two-letter uppercase abbreviation of a state is passed,
+            (e.g. 33 or "RJ") the function will load all intermediate regions of that
+            state. If `code_intermediate="all"` (Default), all intermediate regions of
+            the country are loaded.
+       year : int, optional
+           Year of the data, by default 2019
+       simplified: boolean, by default True
+           Data 'type', indicating whether the function returns the 'original' dataset
+           with high resolution or a dataset with 'simplified' borders (Default)
+       verbose : bool, optional
+           by default False
+
+       Returns
+       -------
+       gpd.GeoDataFrame
+           Metadata and geopackage of selected states
+
+       Raises
+       ------
+       Exception
+           If parameters are not found or not well defined
+
+       Example
+       -------
+       >>> from geobr import read_intermediate_region
+
+       # Read specific state at a given year
+       >>> df = read_intermediate_region(year=2019)
+    """
+
+    metadata = select_metadata("intermediate_regions", year=year, simplified=simplified)
+
+    gdf = download_gpkg(metadata)
+
+    if code_intermadiate == "all":
+        return gdf
+
+    for col in ["abbrev_state", "code_state", "code_intermediate"]:
+        if code_intermadiate in gdf[col].unique():
+            return gdf[gdf[col] == code_intermadiate]
+    else:
+        raise ValueError(
+            f"Invalid value to argumet `code_intermadiate`: {code_intermadiate}"
+        )

python-package/geobr/read_schools.py

@@ -0,0 +1,44 @@
+from geobr.utils import select_metadata, download_gpkg
+
+
+def read_schools(year=2020, verbose=False):
+    r"""Download geolocated data of schools
+
+    Data comes from the School Census collected by INEP, the National Institute
+    for Educational Studies and Research Anisio Teixeira. The date of the last
+    data update is registered in the database in the column 'date_update'. These
+    data uses Geodetic reference system "SIRGAS2000" and CRS(4674). The coordinates
+    of each school if collected by INEP. Periodically the coordinates are revised
+    with the objective of improving the quality of the data. More information
+    available at \url{https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/inep-data/catalogo-de-escolas/}
+
+    Parameters
+    ----------
+    year : int, optional
+        Year of the data, by default 2020
+    verbose : bool, optional
+        by default False
+
+    Returns
+    -------
+    gpd.GeoDataFrame
+        Metadata and geopackage of selected states
+
+    Raises
+    ------
+    Exception
+        If parameters are not found or not well defined
+
+    Example
+    -------
+    >>> from geobr import read_schools
+
+    # Read specific state at a given year
+    >>> df = read_schools(year=2020)
+    """
+
+    metadata = select_metadata("schools", year=year, simplified=False)
+
+    gdf = download_gpkg(metadata)
+
+    return gdf

python-package/geobr/read_urban_concentrations.py

@@ -0,0 +1,46 @@
+
+from geobr.utils import select_metadata, download_gpkg
+
+
+def read_urban_concentrations(year=2015, simplified=True, verbose=False):
+    r""" Download urban concentration areas in Brazil
+    
+     @description
+ This function reads the official data on the urban concentration areas (Areas
+ de Concentracao de Populacao) of Brazil. Original data were generated by the
+ Institute of Geography and Statistics (IBGE)  For more information about the
+ methodology, see details at \url{https://www.ibge.gov.br/apps/arranjos_populacionais/2015/pdf/publicacao.pdf}
+
+    Parameters
+    ----------
+    year : int, optional
+        Year of the data, by default 2015
+    simplified: boolean, by default True
+        Data 'type', indicating whether the function returns the 'original' dataset 
+        with high resolution or a dataset with 'simplified' borders (Default)
+    verbose : bool, optional
+        by default False
+    
+    Returns
+    -------
+    gpd.GeoDataFrame
+        Metadata and geopackage of selected states
+    
+    Raises
+    ------
+    Exception
+        If parameters are not found or not well defined
+
+    Example
+    -------
+    >>> from geobr import read_urban_concentrations
+
+    # Read specific state at a given year
+    >>> df = read_urban_concentrations(year=2015)
+    """
+
+    metadata = select_metadata('urban_concentrations', year=year, simplified=simplified)
+
+    gdf = download_gpkg(metadata)
+
+    return gdf