def data_boundaries(self, boundary=None, region=None, decode_geom=False,
                        timespan=None, include_nonclipped=False):
        """
        Find all boundaries available for the world or a `region`. If
        `boundary` is specified, get all available boundary polygons for the
        region specified (if any). This method is espeically useful for getting
        boundaries for a region and, with :py:meth:`CartoContext.data
        <cartoframes.context.CartoContext.data>` and
        :py:meth:`CartoContext.data_discovery
        <cartoframes.context.CartoContext.data_discovery>`, getting tables of
        geometries and the corresponding raw measures. For example, if you want
        to analyze how median income has changed in a region (see examples
        section for more).

        Examples:

            Find all boundaries available for Australia. The columns
            `geom_name` gives us the name of the boundary and `geom_id`
            is what we need for the `boundary` argument.

            .. code:: python

                import cartoframes
                cc = cartoframes.CartoContext('base url', 'api key')
                au_boundaries = cc.data_boundaries(region='Australia')
                au_boundaries[['geom_name', 'geom_id']]

            Get the boundaries for Australian Postal Areas and map them.

            .. code:: python

                from cartoframes import Layer
                au_postal_areas = cc.data_boundaries(boundary='au.geo.POA')
                cc.write(au_postal_areas, 'au_postal_areas')
                cc.map(Layer('au_postal_areas'))

            Get census tracts around Idaho Falls, Idaho, USA, and add median
            income from the US census. Without limiting the metadata, we get
            median income measures for each census in the Data Observatory.

            .. code:: python

                cc = cartoframes.CartoContext('base url', 'api key')
                # will return DataFrame with columns `the_geom` and `geom_ref`
                tracts = cc.data_boundaries(
                    boundary='us.census.tiger.census_tract',
                    region=[-112.096642,43.429932,-111.974213,43.553539])
                # write geometries to a CARTO table
                cc.write(tracts, 'idaho_falls_tracts')
                # gather metadata needed to look up median income
                median_income_meta = cc.data_discovery(
                    'idaho_falls_tracts',
                    keywords='median income',
                    boundaries='us.census.tiger.census_tract')
                # get median income data and original table as new dataframe
                idaho_falls_income = cc.data(
                    'idaho_falls_tracts',
                    median_income_meta,
                    how='geom_refs')
                # overwrite existing table with newly-enriched dataframe
                cc.write(idaho_falls_income,
                         'idaho_falls_tracts',
                         overwrite=True)

        Args:
            boundary (str, optional): Boundary identifier for the boundaries
              that are of interest. For example, US census tracts have a
              boundary ID of ``us.census.tiger.census_tract``, and Brazilian
              Municipios have an ID of ``br.geo.municipios``. Find IDs by
              running :py:meth:`CartoContext.data_boundaries
              <cartoframes.context.CartoContext.data_boundaries>`
              without any arguments, or by looking in the `Data Observatory
              catalog <http://cartodb.github.io/bigmetadata/>`__.
            region (str, optional): Region where boundary information or,
              if `boundary` is specified, boundary polygons are of interest.
              `region` can be one of the following:

                - table name (str): Name of a table in user's CARTO account
                - bounding box (list of float): List of four values (two
                  lng/lat pairs) in the following order: western longitude,
                  southern latitude, eastern longitude, and northern latitude.
                  For example, Switzerland fits in
                  ``[5.9559111595,45.8179931641,10.4920501709,47.808380127]``
            timespan (str, optional): Specific timespan to get geometries from.
              Defaults to use the most recent. See the Data Observatory catalog
              for more information.
            decode_geom (bool, optional): Whether to return the geometries as
              Shapely objects or keep them encoded as EWKB strings. Defaults
              to False.
            include_nonclipped (bool, optional): Optionally include
              non-shoreline-clipped boundaries. These boundaries are the raw
              boundaries provided by, for example, US Census Tiger.

        Returns:
            pandas.DataFrame: If `boundary` is specified, then all available
            boundaries and accompanying `geom_refs` in `region` (or the world
            if `region` is ``None`` or not specified) are returned. If
            `boundary` is not specified, then a DataFrame of all available
            boundaries in `region` (or the world if `region` is ``None``)
        """
        # TODO: create a function out of this?
        if isinstance(region, str):
            # see if it's a table
            try:
                geom_type = self._geom_type(region)
                if geom_type in ('point', 'line', ):
                    bounds = ('(SELECT ST_ConvexHull(ST_Collect(the_geom)) '
                              'FROM {table})').format(table=region)
                else:
                    bounds = ('(SELECT ST_Union(the_geom) '
                              'FROM {table})').format(table=region)
            except CartoException:
                # see if it's a Data Obs region tag
                regionsearch = '"geom_tags"::text ilike \'%{}%\''.format(
                    get_countrytag(region))
                bounds = 'ST_MakeEnvelope(-180.0, -85.0, 180.0, 85.0, 4326)'
        elif isinstance(region, collections.Iterable):
            if len(region) != 4:
                raise ValueError(
                    '`region` should be a list of the geographic bounds of a '
                    'region in the following order: western longitude, '
                    'southern latitude, eastern longitude, and northern '
                    'latitude. For example, Switerland fits in '
                    '``[5.9559111595,45.8179931641,10.4920501709,'
                    '47.808380127]``.')
            bounds = ('ST_MakeEnvelope({0}, {1}, {2}, {3}, 4326)').format(
                *region)
        elif region is None:
            bounds = 'ST_MakeEnvelope(-180.0, -85.0, 180.0, 85.0, 4326)'
        else:
            raise ValueError('`region` must be a str, a list of two lng/lat '
                             'pairs, or ``None`` (which defaults to the '
                             'world)')
        if include_nonclipped:
            clipped = None
        else:
            clipped = (r"(geom_id ~ '^us\.census\..*_clipped$' OR "
                       r"geom_id !~ '^us\.census\..*')")

        if boundary is None:
            regionsearch = locals().get('regionsearch')
            filters = ' AND '.join(r for r in [regionsearch, clipped] if r)
            query = utils.minify_sql((
                'SELECT *',
                'FROM OBS_GetAvailableGeometries(',
                '  {bounds}, null, null, null, {timespan})',
                '{filters}')).format(
                    bounds=bounds,
                    timespan=utils.pgquote(timespan),
                    filters='WHERE {}'.format(filters) if filters else '')
            return self.fetch(query, decode_geom=True)

        query = utils.minify_sql((
            'SELECT the_geom, geom_refs',
            'FROM OBS_GetBoundariesByGeometry(',
            '    {bounds},',
            '    {boundary},',
            '    {time})', )).format(
                bounds=bounds,
                boundary=utils.pgquote(boundary),
                time=utils.pgquote(timespan))
        return self.fetch(query, decode_geom=decode_geom)
Find all boundaries available for the world or a `region`. If
        `boundary` is specified, get all available boundary polygons for the
        region specified (if any). This method is espeically useful for getting
        boundaries for a region and, with :py:meth:`CartoContext.data
        <cartoframes.context.CartoContext.data>` and
        :py:meth:`CartoContext.data_discovery
        <cartoframes.context.CartoContext.data_discovery>`, getting tables of
        geometries and the corresponding raw measures. For example, if you want
        to analyze how median income has changed in a region (see examples
        section for more).

        Examples:

            Find all boundaries available for Australia. The columns
            `geom_name` gives us the name of the boundary and `geom_id`
            is what we need for the `boundary` argument.

            .. code:: python

                import cartoframes
                cc = cartoframes.CartoContext('base url', 'api key')
                au_boundaries = cc.data_boundaries(region='Australia')
                au_boundaries[['geom_name', 'geom_id']]

            Get the boundaries for Australian Postal Areas and map them.

            .. code:: python

                from cartoframes import Layer
                au_postal_areas = cc.data_boundaries(boundary='au.geo.POA')
                cc.write(au_postal_areas, 'au_postal_areas')
                cc.map(Layer('au_postal_areas'))

            Get census tracts around Idaho Falls, Idaho, USA, and add median
            income from the US census. Without limiting the metadata, we get
            median income measures for each census in the Data Observatory.

            .. code:: python

                cc = cartoframes.CartoContext('base url', 'api key')
                # will return DataFrame with columns `the_geom` and `geom_ref`
                tracts = cc.data_boundaries(
                    boundary='us.census.tiger.census_tract',
                    region=[-112.096642,43.429932,-111.974213,43.553539])
                # write geometries to a CARTO table
                cc.write(tracts, 'idaho_falls_tracts')
                # gather metadata needed to look up median income
                median_income_meta = cc.data_discovery(
                    'idaho_falls_tracts',
                    keywords='median income',
                    boundaries='us.census.tiger.census_tract')
                # get median income data and original table as new dataframe
                idaho_falls_income = cc.data(
                    'idaho_falls_tracts',
                    median_income_meta,
                    how='geom_refs')
                # overwrite existing table with newly-enriched dataframe
                cc.write(idaho_falls_income,
                         'idaho_falls_tracts',
                         overwrite=True)

        Args:
            boundary (str, optional): Boundary identifier for the boundaries
              that are of interest. For example, US census tracts have a
              boundary ID of ``us.census.tiger.census_tract``, and Brazilian
              Municipios have an ID of ``br.geo.municipios``. Find IDs by
              running :py:meth:`CartoContext.data_boundaries
              <cartoframes.context.CartoContext.data_boundaries>`
              without any arguments, or by looking in the `Data Observatory
              catalog <http://cartodb.github.io/bigmetadata/>`__.
            region (str, optional): Region where boundary information or,
              if `boundary` is specified, boundary polygons are of interest.
              `region` can be one of the following:

                - table name (str): Name of a table in user's CARTO account
                - bounding box (list of float): List of four values (two
                  lng/lat pairs) in the following order: western longitude,
                  southern latitude, eastern longitude, and northern latitude.
                  For example, Switzerland fits in
                  ``[5.9559111595,45.8179931641,10.4920501709,47.808380127]``
            timespan (str, optional): Specific timespan to get geometries from.
              Defaults to use the most recent. See the Data Observatory catalog
              for more information.
            decode_geom (bool, optional): Whether to return the geometries as
              Shapely objects or keep them encoded as EWKB strings. Defaults
              to False.
            include_nonclipped (bool, optional): Optionally include
              non-shoreline-clipped boundaries. These boundaries are the raw
              boundaries provided by, for example, US Census Tiger.

        Returns:
            pandas.DataFrame: If `boundary` is specified, then all available
            boundaries and accompanying `geom_refs` in `region` (or the world
            if `region` is ``None`` or not specified) are returned. If
            `boundary` is not specified, then a DataFrame of all available
            boundaries in `region` (or the world if `region` is ``None``)