`hazimp.aggregate`¶

Aggregating impact data into a chloropleth map.

Module Contents¶

`choropleth`(dframe, boundaries, impactcode, bcode, filename, fields, categories, categorise) → bool	Aggregate to geospatial boundaries using joins on attributes and save to
`aggregate_categorisation`(aggregate, categorise: dict, fields: dict, field_name: str)	Categorise aggregated field values into discrete intervals.
`aggregate_loss_atts`(dframe, groupby=None, kwargs=None)	Aggregate the impact data contained in a pandas.DataFrame

hazimp.aggregate.choropleth(dframe, boundaries, impactcode, bcode, filename, fields, categories, categorise) → bool¶

Aggregate to geospatial boundaries using joins on attributes and save to file.

Parameters:

dframe – pandas.DataFrame containing point data to be aggregated
boundaries (str) – File name of a geospatial dataset that contains geographical boundaries to serve as aggregation boundaries
impactcode (str) – Field name in the dframe to aggregate by
bcode (str) – Corresponding field name in the geospatial dataset.
filename (str) – Destination file name. Must have a valid extension of shp, json or gpkg. See import fiona; fiona.supported_drivers for a complete list of options, but at this time only three have been implemented.
categories (boolean) – Add columns for the number of buildings in each damage state defined in the ‘Damage state’ attribute. This requires that a ‘categorise` job has been included in the pipeline, which in turn requires the bins and labels to be defined in the job configuration.
categorise (dict) – categorise job attributes

NOTE:

Presently, using `categories`=True will not do any categorisation of
the mean damage index for the aggregation areas.

Parameters:: fields (dict) – A dict with keys of valid column names (from the DataFrame) and values being lists of aggregation functions to apply to the columns.

For example:

fields = {'structural': ['mean']}

hazimp.aggregate.aggregate_categorisation(aggregate, categorise: dict, fields: dict, field_name: str)¶

Categorise aggregated field values into discrete intervals.

Parameters:

hazimp.aggregate.aggregate_loss_atts(dframe, groupby=None, kwargs=None)¶

Aggregate the impact data contained in a pandas.DataFrame

Parameters:

dframe – pandas.DataFrame that contains impact data
groupby (str) – A column in the DataFrame that corresponds to regions by which to aggregate data
kwargs (dict) – A dict with keys of valid column names (from the DataFrame) and values being lists of aggregation functions to apply to the columns.

For example:

kwargs = {'REPLACEMENT_VALUE': ['mean', 'sum'],
        'structural': ['mean', 'std']}