ewha-parking 0.1.0

Analyze frequent and illegal parking patterns using trajectory and GIS data

README.md

## ewha-parking

A Python package for analyzing frequent parking and illegal parking using trajectory data and GIS layers.

• Detects Frequent Parking spots from trajectory data.
• Identifies Illegal Parking candidates by comparing detected spots against sidewalks, crosswalks, and yellow line buffer zones.

## Installation

After uploading to PyPI: pip install ewha-parking

For local development: pip install -e .

## Usage

import time

import pandas as pd

from ewha_parking.frequent_parking import FrequentParking

from ewha_parking.illegal_parking import IllegalParking

# Load input data

df, road_df

start = time.perf_counter()

# 1. Detect frequent parking spots

frequent_parking = FrequentParking(df.copy(), road_df)

frequent_result = frequent_parking.call()

# 2. Detect illegal parking

illegal_parking = IllegalParking(

  zip_path="illegal_parking.zip", # ZIP archive containing SHP files

  extract_dir="illegal_parking_extracted" # Directory to extract SHP files

)

illegal_result = illegal_parking.call(frequent_result)

end = time.perf_counter()

print(f"Execution time: {end - start:.2f} seconds")

# Example output DataFrame columns:

# ['CCTV_ID', 'time', 'Geometry', 'Leaving_time', 'Traj_ID', 'Duration', 'ufid']

## Requirements

• Python >= 3.9
• pandas
• numpy
• geopandas
• shapely (>= 2.0 recommended)

Note: SHP layers are assumed to use EPSG:4326 (WGS84).

If your data uses a different CRS, please convert it before analysis.

## License

MIT License (?)

## Author

• Name: Jiwon Kim
• Email: [e-mail]