Find spatial outliers from FinBIF simple data format

spatial_outliers.py

# Made wth ChatGPT / GPT-4
# Finds spatial ouliers by clcularing SD of locations
# Input: FinBIF data of a single species in simple data format 

import pandas as pd
import numpy as np

# Load the data (Replace 'file_path' with your actual file path)
file_path = 'your_data_file.csv'  # Change this to your file path
bird_data = pd.read_csv(file_path, sep='\t')

# Extracting latitude and longitude
latitude = bird_data['WGS84 N']
longitude = bird_data['WGS84 E']

# Calculating mean and standard deviation
mean_latitude = np.mean(latitude)
std_latitude = np.std(latitude)
mean_longitude = np.mean(longitude)
std_longitude = np.std(longitude)

# Defining a threshold for outliers (e.g., 2 standard deviations from the mean)
threshold = 2
min_lat, max_lat = mean_latitude - threshold * std_latitude, mean_latitude + threshold * std_latitude
min_lon, max_lon = mean_longitude - threshold * std_longitude, mean_longitude + threshold * std_longitude

# Identifying outliers
outliers = bird_data[(latitude < min_lat) | (latitude > max_lat) | (longitude < min_lon) | (longitude > max_lon)]

# Print or export the outliers
print(outliers)