spacenet.pcf.weighted_pair_correlation_function#
- spacenet.pcf.weighted_pair_correlation_function(spatial_network, labels_for_objects_B, object_indices_A=None, object_indices_B=None, spatial_kernel_bandwidth=10, spatial_kernel_n=2, r_min=0, r_max=100, r_step=10, marker_kernel_bandwidth=0.2, marker_kernel_n=1, marker_min=0, marker_max=1, marker_step=0.1, edge_weight_name='Distance', return_confidence_interval=False, confidence_interval_kwargs={}, low_memory=False, verbose=True, n_jobs=1)#
Computes the weighted pair correlation function between two populations of objects (A and B) using a continuous label on objects B over a spatial network.
- Parameters:
- spatial_networknetworkx.Graph
The spatial network on which to compute the pair correlation function. Edges should have a weight attribute corresponding to the distance between nodes.
- labels_for_objects_Barray-like
A continuous label for each object in population B. This should be an array of the same length as object_indices_B, where each entry corresponds to the label of the respective object in population B.
- object_indices_Aarray-like, optional
The indices of the nodes in population A. If None, all nodes in the network will be considered as part of population A. Default is None.
- object_indices_Barray-like, optional
The indices of the nodes in population B. If None, all nodes in the network will be considered as part of population B. Default is None.
- spatial_kernel_bandwidthfloat, optional
The bandwidth parameter for the spatial kernel function. This controls the smoothness of the pair correlation function. Default is 10.
- spatial_kernel_nint, optional
The exponent parameter for the spatial kernel function. This controls the shape of the kernel. Default is 2 (which corresponds to a Gaussian-like kernel).
- r_minfloat, optional
The minimum distance to consider when computing the pair correlation function. Default is 0.
- r_maxfloat, optional
The maximum distance to consider when computing the pair correlation function. Default is 100.
- r_stepfloat, optional
The step size for the distance bins when computing the pair correlation function. Default is 10.
- marker_kernel_bandwidthfloat, optional
The bandwidth parameter for the marker kernel function. This controls the smoothness of the weighting based on the continuous labels of objects B. Default is 0.2.
- marker_kernel_nint, optional
The exponent parameter for the marker kernel function. This controls the shape of the kernel for weighting based on the continuous labels of objects B. Default is 1 (which corresponds to a Laplacian-like kernel).
- marker_minfloat, optional
The minimum value of the continuous label for objects B to consider when computing the weighted pair correlation function. Default is 0.
- marker_maxfloat, optional
The maximum value of the continuous label for objects B to consider when computing the weighted pair correlation function. Default is 1.
- edge_weight_namestr, optional
The name of the edge attribute in the network that corresponds to the distance between nodes. Default is ‘Distance’.
- return_confidence_intervalbool, optional
Whether to compute and return confidence intervals for the pair correlation function using spatial bootstrapping. Default is False.
- low_memorybool, optional
Whether to use a low-memory implementation of Dijkstra’s algorithm that computes distances in batches. This can be useful for large networks that do not fit in memory. Default is False.
- verbosebool, optional
Whether to print progress messages during computation. Default is True.
- n_jobsint, optional
The number of parallel jobs to run when computing contributions. If n_jobs > 1, the contributions will be computed in parallel across multiple CPU cores. Default is 1 (no parallelization).
- Returns:
- taunumpy.ndarray
The target marker values at which the pair correlation function was computed.
- radiinumpy.ndarray
The radii at which the pair correlation function was computed.
- gnumpy.ndarray
The values of the pair correlation function at the corresponding radii and target marker values. Shape is (len(tau), len(radius)).
- confidence_intervalnumpy.ndarray (if return_confidence_interval is True)
If return_confidence_interval is True, this will be a numpy array (2,num_mark_targets,num_radii) containing the confidence intervals for the pair correlation function at each mark target and radii. The CI[0,:,:] corresponds to the lower bounds of the confidence intervals, and CI[1,:,:] corresponds to the upper bounds. If return_confidence_interval is False, this will not be returned.
Notes
For details, see the reference paper…