how to use the DBSCAN to do the taxi passengers hot spot recognition with taxi GPS data?

Question

I am doing a research about taxi passengers hot spot recognition recently,but I am new in programming, so could anyone provide some Python programming code on how to use DBSCAN to identify hotspot areas for cab loads?thank you very much

I am doing a research about taxi passengers hot spot recognition recently,but I am new in programming, so could anyone provide some Python programming code on how to use DBSCAN to identify hotspot areas for cab loads?

Answer 1

Here is the sample code, for the DbScan,

import numpy as np
import pandas as pd
from sklearn.cluster import DBSCAN
from sklearn.metrics import silhouette_score
import matplotlib.pyplot as plt

# Sample data: coordinates of pickup locations
pickup_locations = np.array()  

# Parameter settings for DBSCAN
epsilon = 1  
min_samples = 2  

# Initialize DBSCAN
dbscan = DBSCAN(eps=epsilon, min_samples=min_samples)

# Fit DBSCAN to the data
dbscan.fit(pickup_locations)
labels = dbscan.labels_

# Number of clusters in labels, ignoring noise if present
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

print('Estimated number of clusters: %d' % n_clusters_)

# Visualizing the clusters
unique_labels = set(labels)
colors = [plt.cm.Spectral(each)
          for each in np.linspace(0, 1, len(unique_labels))]

for k, col in zip(unique_labels, colors):
    if k == -1:
        col = [0, 0, 0, 1]

    class_member_mask = (labels == k)

    xy = pickup_locations[class_member_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=14)

plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()