geoflowkit 0.1.7

A package for geospatial flow analysis and visualization

GeoFlowKit

A Python package for handling and analyzing geographical flow data, extending pandas and geopandas with flow-specific operations.

Overview

GeoFlowKit provides FlowSeries and FlowDataFrame types, which are subclasses of pandas.Series and pandas.DataFrame respectively. They are designed to work with flow data consisting of origin-destination (OD) pairs, similar to how geopandas.GeoSeries and geopandas.GeoDataFrame work with geometries.

Installation

pip install geoflowkit

Or install from source:

pip install .

Dependencies

• shapely
• numpy
• pandas
• geopandas >= 1.0.1
• matplotlib
• scikit-learn
• tqdm
• numba

Quick Start

Creating Flow Objects

import numpy as np
from geoflowkit import Flow, FlowSeries, FlowDataFrame

# Create a single Flow (origin-destination pair)
flow = Flow([[0, 0], [1, 1]])

# Access origin and destination points
print(flow.o)  # POINT (0 0)
print(flow.d)  # POINT (1 1)

Creating FlowSeries

# From a list of Flow objects
fs = FlowSeries([
    Flow([[0, 0], [1, 1]]),
    Flow([[1, 1], [2, 2]]),
    Flow([[2, 2], [3, 3]])
], crs="EPSG:4326")

# From coordinate arrays using flows_from_od
from geoflowkit import flows_from_od

o_points = np.array([[0, 0], [1, 1], [2, 2]])
d_points = np.array([[1, 1], [2, 2], [3, 3]])
fs = flows_from_od(o_points, d_points, crs="EPSG:4326")

Creating FlowDataFrame

# Create a FlowDataFrame with attributes
data = {
    'id': [1, 2, 3],
    'value': [10, 20, 30],
    'geometry': fs
}
fdf = FlowDataFrame(data, crs="EPSG:4326")
print(fdf)

Reading Data from Files

# Read from CSV (specify origin/destination columns)
fdf = read_csv(
    'flow_data.csv',
    use_cols=['ox', 'oy', 'dx', 'dy'],
    crs='EPSG:4326'
)

# Read from GeoPackage
fdf = read_file('flow_data.gpkg', layer='flows')

Core Features

Flow Properties

# Access origin and destination points
origins = fdf.o  # GeoSeries of origin points
destinations = fdf.d  # GeoSeries of destination points

# Flow length and angle
lengths = fdf.length  # Distance from origin to destination
angles = fdf.angle    # Direction of flow (radians)

# Flow density and volume
density = fdf.density  # Flows per unit area
volume = fdf.volume    # Total bounding area

Spatial Operations

# Calculate pairwise distances between flows
from geoflowkit import pairwise_distances

dist_matrix = pairwise_distances(fdf, distance='max')

# Clip flows within a polygon
clipped = fdf.clip(polygon_mask)

# Select flows within bounds
within_bounds = fdf.within(bounds_box)

Spatial Clustering Scale Detection (K/L Functions)

# Calculate K function for spatial clustering
from geoflowkit import k_func, l_func

r_list, kr_list = k_func(fdf, dr=0.1, k=1)
r_list, lr_list = l_func(fdf, dr=0.1, k=1)

# Local L function for individual flows
from geoflowkit import local_l_func

llrs = local_l_func(fdf, r=0.5)

Grid Aggregation

# Divide study area into grid and aggregate flows
gridded = fdf.to_grid(delta_x=0.1, delta_y=0.1)

Visualization

# Plot flows as arrows
ax = fdf.plot(kind='arrow', column='value')

# Plot FlowSeries
ax = fs.plot()

Flow Clustering

# K-medoid clustering
from geoflowkit import kmedoid

labels = kmedoid(fdf, n_clusters=5)

# DBSCAN clustering
from geoflowkit import dbscan

labels = dbscan(fdf, eps=0.5, min_samples=5)

Manifold Learning (FTSNE)

from geoflowkit import FTSNE

# Global interpretability (separate O and D)
transformer = FTSNE(perplexity=200, learning_rate='auto')
X_embedded = transformer.fit_transform(
    fdf,
    identity={'o': 0, 'd': 1}
)

# Local interpretability (union O and D)
X_embedded = transformer.fit_transform(
    fdf,
    union={('o', 'd'): (0, 1)}
)

Location Centrality (I-index)

The I-index quantifies the irreplaceability of a location based on flows, combining flow volume and flow length into a single metric following the H-index principle.

from geoflowkit.spatial import i_index

# Calculate I-index for each zone
result = i_index(fdf, zones)

# Using origin points instead of destination
result = i_index(fdf, zones, od_type='o')

# With custom alpha parameter
result = i_index(fdf, zones, alpha=1000.0)

I-index definition: The I-index of a location is the maximum value of i such that at least i flows with a length of at least α × i meters have reached this location. Higher values indicate more irreplaceable locations that attract many long-distance flows.

Examples

Jupyter notebook examples are available in the examples/ folder:

• basic_usage.ipynb - Basic usage of Flow, FlowSeries, and FlowDataFrame
• clustering.ipynb - K-medoid and DBSCAN clustering for flow data
• kl_function.ipynb - K/L functions for spatial clustering detection
• ft_sne.ipynb - FTSNE manifold learning for flow data
• centrality.ipynb - I-index for location irreplaceability

API Reference

Classes

• Flow: Geometry object representing an origin-destination pair
• FlowSeries: pandas Series subclass for storing Flow objects
• FlowDataFrame: pandas DataFrame subclass with Flow geometry column

Key Functions

• flows_from_od(o, d, crs=None): Create FlowSeries from coordinate arrays
• flows_from_geometry(geometry, crs=None): Create FlowSeries from geometry objects
• read_csv(file_path, use_cols, crs=None, **kwargs): Read flow data from CSV
• read_file(file_path, **kwargs): Read flow data from vector file
• pairwise_distances(fdf, distance='max', ...): Calculate flow distance matrix
• k_func(fdf, dr, k=1, distance='max', ...): K function for spatial clustering detection
• l_func(fdf, dr, k=1, distance='max', ...): L function for spatial clustering detection
• local_l_func(fdf, r, distance='max', ...): Local L function for individual flows
• kmedoid(fdf, n_clusters=5, ...): K-medoid clustering for flows
• dbscan(fdf, eps=0.5, min_samples=5, ...): DBSCAN clustering for flows
• i_index(fdf, zones, alpha=None, od_type='d', ...): I-index for location irreplaceability

License

GeoFlowKit is licensed under the MIT License.

Contact

For questions or feedback: [e-mail]