Python出租车轨迹数据分析：热点交互网络可视化

本问题需要对出租车轨迹数据进行处理和分析，并绘制热力图来展示热点交互网络。下面是一份基本的Python代码示例，供参考：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# 读取轨迹数据
df = pd.read_csv('path/to/data.csv', parse_dates=['timestamp'])

# 计算轨迹点的密度
df['density'] = 1
df['density'] = df['density'].rolling('5min', on='timestamp').sum()

# 选择需要分析的时间范围和区域范围
start_time = pd.Timestamp('2019-01-01 00:00:00')
end_time = pd.Timestamp('2019-01-01 23:59:59')
min_lat, max_lat, min_lon, max_lon = (lat1, lat2, lon1, lon2)

# 筛选出在时间范围和区域范围内的轨迹点
df = df[(df['timestamp'] >= start_time) & (df['timestamp'] <= end_time)]
df = df[(df['latitude'] >= min_lat) & (df['latitude'] <= max_lat)]
df = df[(df['longitude'] >= min_lon) & (df['longitude'] <= max_lon)]

# 绘制热力图
fig, ax = plt.subplots(figsize=(10, 8))
heatmap, xedges, yedges = np.histogram2d(df['latitude'], df['longitude'], bins=100)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
im = ax.imshow(heatmap.T, extent=extent, origin='lower', cmap='YlOrRd')
cb = fig.colorbar(im, ax=ax)
cb.set_label('Density')
ax.set_xlabel('Latitude')
ax.set_ylabel('Longitude')
plt.show()

该代码首先读取出租车轨迹数据，并计算轨迹点的密度。然后，根据所选时间范围和区域范围筛选出符合条件的轨迹点，并绘制热力图来展示热点交互网络。