Spatiotemporal buffer analysis

class pylandstats.SpatioTemporalBufferAnalysis(landscapes, base_mask, buffer_dists, buffer_rings=False, base_mask_crs=None, landscape_crs=None, landscape_transform=None, dates=None, neighborhood_rule=None)[source]

Spatio-temporal buffer analysis around a feature of interest.

__init__(landscapes, base_mask, buffer_dists, buffer_rings=False, base_mask_crs=None, landscape_crs=None, landscape_transform=None, dates=None, neighborhood_rule=None)[source]

Initialize the spatio-temporal buffer analysis.

Parameters:
  • landscapes (list-like) – A list-like of Landscape instances or of strings/file-like/pathlib.Path objects so that each is passed as the landscape argument of Landscape.__init__.
  • base_mask (shapely geometry or geopandas.GeoSeries) – Geometry that will serve as a base mask to buffer around.
  • buffer_rings (bool, default False) – If False, each buffer zone will consist of the whole region that lies within the respective buffer distance around the base mask. If True, buffer zones will take the form of rings around the base mask.
  • base_mask_crs (str, dict or pyproj.CRS, optional) – The coordinate reference system of the base mask. Required if the base mask is a shapely geometry or a geopandas GeoSeries without the crs attribute set.
  • landscape_crs (str, dict or pyproj.CRS, optional) – The coordinate reference system of the landscapes. Required if the passed-in landscapes are Landscape instances, ignored if they are paths to raster datasets that already contain such information.
  • landscape_transform (affine.Affine) – Transformation from pixel coordinates to coordinate reference system. Required if the passed-in landscapes are Landscape instances, ignored if they are paths to raster datasets that already contain such information.
  • dates (list-like, optional) – A list-like of ints or strings that label the date of each snapshot of landscapes (for DataFrame indices and plot labels).
  • neighborhood_rule ({'8', '4'}, optional) – Neighborhood rule to determine patch adjacencies, i.e: ‘8’ (queen’s case/Moore neighborhood) or ‘4’ (rook’s case/Von Neumann neighborhood). Ignored if the passed-in landscapes are Landscape instances. If no value is provided and the passed-in landscapes are file-like objects or paths, the default value set in settings.DEFAULT_NEIGHBORHOOD_RULE will be taken.
compute_class_metrics_df(metrics=None, classes=None, metrics_kws=None, fillna=None)[source]

Compute the data frame of class-level metrics, which is multi-indexed by the buffer distance, class and date.

Parameters:
  • metrics (list-like, optional) – A list-like of strings with the names of the metrics that should be computed in the context of this analysis case.
  • classes (list-like, optional) – A list-like of ints or strings with the class values that should be considered in the context of this analysis case.
  • metrics_kws (dict, optional) – Dictionary mapping the keyword arguments (values) that should be passed to each metric method (key), e.g., to exclude the boundary from the computation of total_edge, metric_kws should map the string ‘total_edge’ (method name) to {‘count_boundary’: False}. The default empty dictionary will compute each metric according to FRAGSTATS defaults.
  • fillna (bool, optional) – Whether NaN values representing landscapes with no occurrences of patches of the provided class should be replaced by zero when appropriate, e.g., area and edge metrics (no ocurrences mean zero area/edge). If the provided value is None (default), the value will be taken from settings.CLASS_METRICS_DF_FILLNA.
Returns:

df – Dataframe with the values computed for each buffer distance, class, distance (multi-index) and metric (columns).
Return type:

pandas.DataFrame
compute_landscape_metrics_df(metrics=None, metrics_kws=None)[source]

Computes the data frame of landscape-level metrics, which is multi-indexed by the buffer distance and date.

Parameters:
  • metrics (list-like, optional) – A list-like of strings with the names of the metrics that should be computed. If None, all the implemented landscape-level metrics will be computed.
  • metrics_kws (dict, optional) – Dictionary mapping the keyword arguments (values) that should be passed to each metric method (key), e.g., to exclude the boundary from the computation of total_edge, metric_kws should map the string ‘total_edge’ (method name) to {‘count_boundary’: False}. The default empty dictionary will compute each metric according to FRAGSTATS defaults.
Returns:

df – Dataframe with the values computed at the landscape level for each buffer distance, date (multi-index) and metric (columns).
Return type:

pandas.DataFrame
plot_landscapes(cmap=None, legend=True, subplots_kws=None, show_kws=None, subplots_adjust_kws=None)[source]

Plot each landscape snapshot in a dedicated matplotlib axis.

Parameters:
  • cmap (str or ~matplotlib.colors.Colormap, optional) – A Colormap instance.
  • legend (bool, optional) – If True, display the legend of the land use/cover color codes.
  • subplots_kws (dict, default None) – Keyword arguments to be passed to matplotlib.pyplot.subplots.
  • show_kws (dict, default None) – Keyword arguments to be passed to rasterio.plot.show.
  • subplots_adjust_kws (dict, default None) – Keyword arguments to be passed to matplotlib.pyplot.subplots_adjust.
Returns:

fig – The figure with its corresponding plots drawn into its axes.
Return type:

matplotlib.figure.Figure
plot_metric(metric, class_val=None, ax=None, metric_legend=True, metric_label=None, buffer_dist_legend=True, fmt='--o', plot_kws=None, subplots_kws=None)[source]

Plot the time series of the metric accross the buffer zones.

Parameters:
  • metric (str) – A string indicating the name of the metric to plot.
  • class_val (int, optional) – If provided, the metric will be plotted at the level of the corresponding class, otherwise it will be plotted at the landscape level.
  • ax (axis object, optional) – Plot in given axis; if None creates a new figure.
  • metric_legend (bool, default True) – Whether the metric label should be displayed within the plot (as label of the y-axis).
  • metric_label (str, optional) – Label of the y-axis to be displayed if metric_legend is True. If the provided value is None, the label will be taken from the settings module.
  • buffer_dist_legend (bool, default True) – Whether a legend linking each plotted line to a buffer distance should be displayed within the plot.
  • fmt (str, default '--o') – A format string for matplotlib.pyplot.plot.
  • plot_kws (dict, default None) – Keyword arguments to be passed to matplotlib.pyplot.plot.
  • subplots_kws (dict, default None) – Keyword arguments to be passed to matplotlib.pyplot.subplots only if no axis is given (through the ax argument).
Returns:

ax – Returns the Axes object with the plot drawn onto it.
Return type:

matplotlib.axes.Axes