{"id":1184693,"date":"2025-01-15T19:29:41","date_gmt":"2025-01-15T11:29:41","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1184693.html"},"modified":"2025-01-15T19:29:44","modified_gmt":"2025-01-15T11:29:44","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%88%86%e6%9e%90gps%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1184693.html","title":{"rendered":"\u5982\u4f55\u7528python\u5206\u6790gps\u6570\u636e"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u5206\u6790gps\u6570\u636e\"<\/p>\n

\u5982\u4f55\u7528python\u5206\u6790gps\u6570\u636e<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528Python\u5206\u6790GPS\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\uff1a\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u7edf\u8ba1\u5206\u6790\u3001\u8def\u5f84\u8ba1\u7b97\u3001\u901f\u5ea6\u8ba1\u7b97\u3002<\/strong>\u5176\u4e2d\u6570\u636e\u6536\u96c6<\/strong>\u662f\u6700\u5173\u952e\u7684\u4e00\u6b65\uff0c\u9700\u8981\u786e\u4fdd\u83b7\u53d6\u7684\u6570\u636e\u51c6\u786e\u3001\u5b8c\u6574\u3002\u63a5\u4e0b\u6765\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bba\u8fd9\u4e2a\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u6570\u636e\u6536\u96c6<\/strong>\uff1aGPS\u6570\u636e\u901a\u5e38\u901a\u8fc7GPS\u8bbe\u5907\u6216\u667a\u80fd\u624b\u673a\u6536\u96c6\uff0c\u8fd9\u4e9b\u8bbe\u5907\u4f1a\u8bb0\u5f55\u4e00\u7cfb\u5217\u7684\u7ecf\u7eac\u5ea6\u5750\u6807\u3001\u65f6\u95f4\u6233\u3001\u901f\u5ea6\u7b49\u4fe1\u606f\u3002\u6536\u96c6\u6570\u636e\u65f6\u8981\u786e\u4fdd\u8bbe\u5907\u7684\u7cbe\u5ea6\u548c\u6570\u636e\u7684\u5b8c\u6574\u6027\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u6536\u96c6\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528GPS\u8bbe\u5907\uff1a\u6237\u5916\u6d3b\u52a8\u5982\u5f92\u6b65\u65c5\u884c\u3001\u9a91\u884c\u3001\u8dd1\u6b65\u7b49\u901a\u5e38\u4f7f\u7528GPS\u8bbe\u5907\u8bb0\u5f55\u8f68\u8ff9\u6570\u636e\u3002<\/li>\n
  2. \u4f7f\u7528\u667a\u80fd\u624b\u673a\uff1a\u901a\u8fc7\u624b\u673a\u4e0a\u7684GPS\u5e94\u7528\u7a0b\u5e8f\uff0c\u5982Strava\u3001MapMyRun\u7b49\uff0c\u6536\u96c6GPS\u6570\u636e\u3002<\/li>\n
  3. \u901a\u8fc7API\u83b7\u53d6\u6570\u636e\uff1a\u4f8b\u5982Google Maps API\u3001OpenStreetMap API\uff0c\u53ef\u4ee5\u83b7\u53d6\u7279\u5b9a\u533a\u57df\u7684GPS\u6570\u636e\u3002<\/li>\n<\/ol>\n

    \u786e\u4fdd\u6570\u636e\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u8fdb\u884c\u540e\u7eed\u7684\u5206\u6790\u3002\u63a5\u4e0b\u6765\u8be6\u7ec6\u8ba8\u8bba\u6570\u636e\u9884\u5904\u7406\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u7edf\u8ba1\u5206\u6790\u3001\u8def\u5f84\u8ba1\u7b97\u548c\u901f\u5ea6\u8ba1\u7b97\u3002<\/p>\n<\/p>\n

    \u4e00\u3001\u6570\u636e\u9884\u5904\u7406<\/h3>\n<\/p>\n

    \u6570\u636e\u9884\u5904\u7406\u662f\u5206\u6790GPS\u6570\u636e\u7684\u7b2c\u4e00\u6b65\uff0c\u8fd9\u4e00\u6b65\u9aa4\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u548c\u4e00\u81f4\u6027\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u6570\u636e\u9884\u5904\u7406\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

    1.1 \u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n

    \u6570\u636e\u6e05\u6d17\u5305\u62ec\u53bb\u9664\u91cd\u590d\u6570\u636e\u3001\u5904\u7406\u7f3a\u5931\u503c\u3001\u4fee\u6b63\u5f02\u5e38\u503c\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u8fd9\u4e9b\u95ee\u9898\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u8bfb\u53d6GPS\u6570\u636e<\/strong><\/h2>\n
    data = pd.read_csv('gps_data.csv')<\/p>\n
    \u5220\u9664\u91cd\u590d\u6570\u636e<\/strong><\/h2>\n
    data.drop_duplicates(inplace=True)<\/p>\n
    \u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\n
    data.fillna(method='ffill', inplace=True)<\/p>\n
    \u4fee\u6b63\u5f02\u5e38\u503c\uff08\u4f8b\u5982\uff1a\u901f\u5ea6\u8d85\u8fc7\u5408\u7406\u8303\u56f4\uff09<\/strong><\/h2>\n
    data = data[data['speed'] < 200]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.2 \u6570\u636e\u8f6c\u6362<\/h4>\n<\/p>\n
    GPS\u6570\u636e\u901a\u5e38\u5305\u542b\u7ecf\u7eac\u5ea6\u5750\u6807\uff0c\u9700\u8981\u5c06\u5176\u8f6c\u6362\u4e3a\u9002\u5408\u5206\u6790\u7684\u683c\u5f0f\u3002\u4f8b\u5982\uff0c\u5c06\u7ecf\u7eac\u5ea6\u8f6c\u6362\u4e3a\u7c73\u6216\u516c\u91cc\u3002\u53ef\u4ee5\u4f7f\u7528Geopy\u5e93\u8fdb\u884c\u8f6c\u6362\uff1a<\/p>\n<\/p>\n
    from geopy.distance import geodesic<\/p>\n
    \u8ba1\u7b97\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb<\/strong><\/h2>\n
    point1 = (data.iloc[0]['latitude'], data.iloc[0]['longitude'])<\/p>\n
    point2 = (data.iloc[1]['latitude'], data.iloc[1]['longitude'])<\/p>\n
    distance = geodesic(point1, point2).meters<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.3 \u65f6\u95f4\u683c\u5f0f\u8f6c\u6362<\/h4>\n<\/p>\n
    GPS\u6570\u636e\u901a\u5e38\u5305\u542b\u65f6\u95f4\u6233\uff0c\u9700\u8981\u5c06\u5176\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\uff0c\u4ee5\u4fbf\u8fdb\u884c\u65f6\u95f4\u76f8\u5173\u7684\u5206\u6790\uff1a<\/p>\n<\/p>\n
    data['timestamp'] = pd.to_datetime(data['timestamp'])<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
    \u6570\u636e\u53ef\u89c6\u5316\u662f\u5206\u6790GPS\u6570\u636e\u7684\u91cd\u8981\u6b65\u9aa4\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u548c\u8d8b\u52bf\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u53ef\u89c6\u5316\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
    2.1 \u7ed8\u5236\u8f68\u8ff9\u56fe<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cBasemap\u5e93\u7ed8\u5236GPS\u8f68\u8ff9\u56fe\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    from mpl_toolkits.basemap import Basemap<\/p>\n
    \u521b\u5efa\u5730\u56fe<\/strong><\/h2>\n
    m = Basemap(projection='merc', llcrnrlat=min(data['latitude']), urcrnrlat=max(data['latitude']),<\/p>\n
                llcrnrlon=min(data['longitude']), urcrnrlon=max(data['longitude']), resolution='i')<\/p>\n
    \u7ed8\u5236\u8f68\u8ff9<\/strong><\/h2>\n
    m.drawcoastlines()<\/p>\n
    m.drawcountries()<\/p>\n
    m.scatter(data['longitude'], data['latitude'], latlon=True, c='red', marker='o', alpha=0.5)<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2.2 \u7ed8\u5236\u901f\u5ea6\u56fe<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528Seaborn\u5e93\u7ed8\u5236\u901f\u5ea6\u968f\u65f6\u95f4\u53d8\u5316\u7684\u56fe\u8868\uff1a<\/p>\n<\/p>\n
    import seaborn as sns<\/p>\n
    \u7ed8\u5236\u901f\u5ea6\u56fe<\/strong><\/h2>\n
    sns.lineplot(x=data['timestamp'], y=data['speed'])<\/p>\n
    plt.xlabel('Time')<\/p>\n
    plt.ylabel('Speed (m\/s)')<\/p>\n
    plt.title('Speed Over Time')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
    \u7edf\u8ba1\u5206\u6790\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4ece\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u4fe1\u606f\uff0c\u4f8b\u5982\u8ba1\u7b97\u5e73\u5747\u901f\u5ea6\u3001\u6700\u5927\u901f\u5ea6\u7b49\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u7edf\u8ba1\u5206\u6790\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
    3.1 \u8ba1\u7b97\u5e73\u5747\u901f\u5ea6<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8ba1\u7b97\u5e73\u5747\u901f\u5ea6\uff1a<\/p>\n<\/p>\n
    average_speed = data['speed'].mean()<\/p>\n
    print(f'Average Speed: {average_speed} m\/s')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.2 \u8ba1\u7b97\u6700\u5927\u901f\u5ea6<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8ba1\u7b97\u6700\u5927\u901f\u5ea6\uff1a<\/p>\n<\/p>\n
    max_speed = data['speed'].max()<\/p>\n
    print(f'Max Speed: {max_speed} m\/s')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.3 \u8ba1\u7b97\u884c\u9a76\u8ddd\u79bb<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528Geopy\u5e93\u8ba1\u7b97\u603b\u884c\u9a76\u8ddd\u79bb\uff1a<\/p>\n<\/p>\n
    total_distance = sum([geodesic((data.iloc[i]['latitude'], data.iloc[i]['longitude']),<\/p>\n
                                   (data.iloc[i+1]['latitude'], data.iloc[i+1]['longitude'])).meters<\/p>\n
                          for i in range(len(data)-1)])<\/p>\n
    print(f'Total Distance: {total_distance} meters')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u8def\u5f84\u8ba1\u7b97<\/h3>\n<\/p>\n
    \u8def\u5f84\u8ba1\u7b97\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u884c\u9a76\u8def\u5f84\u7684\u8be6\u7ec6\u4fe1\u606f\uff0c\u4f8b\u5982\u8f6c\u5411\u70b9\u3001\u722c\u5761\u7b49\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u8def\u5f84\u8ba1\u7b97\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
    4.1 \u8ba1\u7b97\u8f6c\u5411\u70b9<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u8ba1\u7b97\u6bcf\u4e2a\u70b9\u7684\u65b9\u5411\u53d8\u5316\uff0c\u627e\u51fa\u8f6c\u5411\u70b9\uff1a<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    \u8ba1\u7b97\u65b9\u5411\u53d8\u5316<\/strong><\/h2>\n
    data['bearing'] = np.arctan2(np.diff(data['latitude']), np.diff(data['longitude']))<\/p>\n
    \u627e\u51fa\u8f6c\u5411\u70b9<\/strong><\/h2>\n
    data['turning_point'] = np.abs(np.diff(data['bearing'])) > np.pi\/4<\/p>\n
    turning_points = data[data['turning_point']]<\/p>\n
    print(turning_points)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4.2 \u8ba1\u7b97\u722c\u5761<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528\u5761\u5ea6\u8ba1\u7b97\u516c\u5f0f\u8ba1\u7b97\u722c\u5761\uff1a<\/p>\n<\/p>\n
    # \u8ba1\u7b97\u5761\u5ea6<\/p>\n
    data['slope'] = np.diff(data['altitude']) \/ np.diff(data['distance'])<\/p>\n
    \u627e\u51fa\u722c\u5761\u70b9<\/strong><\/h2>\n
    data['climb'] = data['slope'] > 0.1<\/p>\n
    climb_points = data[data['climb']]<\/p>\n
    print(climb_points)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u901f\u5ea6\u8ba1\u7b97<\/h3>\n<\/p>\n
    \u901f\u5ea6\u8ba1\u7b97\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u884c\u9a76\u901f\u5ea6\u7684\u53d8\u5316\u8d8b\u52bf\uff0c\u627e\u51fa\u52a0\u901f\u548c\u51cf\u901f\u70b9\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u901f\u5ea6\u8ba1\u7b97\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
    5.1 \u8ba1\u7b97\u52a0\u901f\u5ea6<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528\u901f\u5ea6\u53d8\u5316\u516c\u5f0f\u8ba1\u7b97\u52a0\u901f\u5ea6\uff1a<\/p>\n<\/p>\n
    data['acceleration'] = np.diff(data['speed']) \/ np.diff(data['timestamp'].astype('int'))<\/p>\n
    \u627e\u51fa\u52a0\u901f\u70b9<\/strong><\/h2>\n
    data['accelerating'] = data['acceleration'] > 0.5<\/p>\n
    accelerating_points = data[data['accelerating']]<\/p>\n
    print(accelerating_points)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    5.2 \u8ba1\u7b97\u51cf\u901f\u5ea6<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528\u901f\u5ea6\u53d8\u5316\u516c\u5f0f\u8ba1\u7b97\u51cf\u901f\u5ea6\uff1a<\/p>\n<\/p>\n
    data['deceleration'] = -np.diff(data['speed']) \/ np.diff(data['timestamp'].astype('int'))<\/p>\n
    \u627e\u51fa\u51cf\u901f\u70b9<\/strong><\/h2>\n
    data['decelerating'] = data['deceleration'] > 0.5<\/p>\n
    decelerating_points = data[data['decelerating']]<\/p>\n
    print(decelerating_points)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516d\u3001\u6848\u4f8b\u5206\u6790<\/h3>\n<\/p>\n
    \u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u4f7f\u7528Python\u5206\u6790GPS\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e2a\u5b9e\u9645\u6848\u4f8b\u6765\u6f14\u793a\u6574\u4e2a\u8fc7\u7a0b\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u7ec4GPS\u6570\u636e\uff0c\u8bb0\u5f55\u4e86\u4e00\u6b21\u9a91\u884c\u6d3b\u52a8\u7684\u8f68\u8ff9\u3002\u6570\u636e\u5305\u542b\u4ee5\u4e0b\u5217\uff1a\u65f6\u95f4\u6233\u3001\u7ecf\u5ea6\u3001\u7eac\u5ea6\u3001\u901f\u5ea6\u3001\u6d77\u62d4\u3002<\/p>\n<\/p>\n
    6.1 \u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
    \u9996\u5148\uff0c\u6211\u4eec\u8bfb\u53d6\u6570\u636e\u5e76\u8fdb\u884c\u521d\u6b65\u5904\u7406\uff1a<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    \u8bfb\u53d6GPS\u6570\u636e<\/strong><\/h2>\n
    data = pd.read_csv('ride_data.csv')<\/p>\n
    \u8f6c\u6362\u65f6\u95f4\u683c\u5f0f<\/strong><\/h2>\n
    data['timestamp'] = pd.to_datetime(data['timestamp'])<\/p>\n
    \u5220\u9664\u91cd\u590d\u6570\u636e<\/strong><\/h2>\n
    data.drop_duplicates(inplace=True)<\/p>\n
    \u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\n
    data.fillna(method='ffill', inplace=True)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    6.2 \u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
    \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u7ed8\u5236\u8f68\u8ff9\u56fe\u548c\u901f\u5ea6\u56fe\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    from mpl_toolkits.basemap import Basemap<\/p>\n
    import seaborn as sns<\/p>\n
    \u521b\u5efa\u5730\u56fe<\/strong><\/h2>\n
    m = Basemap(projection='merc', llcrnrlat=min(data['latitude']), urcrnrlat=max(data['latitude']),<\/p>\n
                llcrnrlon=min(data['longitude']), urcrnrlon=max(data['longitude']), resolution='i')<\/p>\n
    \u7ed8\u5236\u8f68\u8ff9<\/strong><\/h2>\n
    m.drawcoastlines()<\/p>\n
    m.drawcountries()<\/p>\n
    m.scatter(data['longitude'], data['latitude'], latlon=True, c='red', marker='o', alpha=0.5)<\/p>\n
    plt.show()<\/p>\n
    \u7ed8\u5236\u901f\u5ea6\u56fe<\/strong><\/h2>\n
    sns.lineplot(x=data['timestamp'], y=data['speed'])<\/p>\n
    plt.xlabel('Time')<\/p>\n
    plt.ylabel('Speed (m\/s)')<\/p>\n
    plt.title('Speed Over Time')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    6.3 \u7edf\u8ba1\u5206\u6790<\/h4>\n<\/p>\n
    \u7136\u540e\uff0c\u6211\u4eec\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff0c\u8ba1\u7b97\u5e73\u5747\u901f\u5ea6\u3001\u6700\u5927\u901f\u5ea6\u548c\u603b\u884c\u9a76\u8ddd\u79bb\uff1a<\/p>\n<\/p>\n
    # \u8ba1\u7b97\u5e73\u5747\u901f\u5ea6<\/p>\n
    average_speed = data['speed'].mean()<\/p>\n
    print(f'Average Speed: {average_speed} m\/s')<\/p>\n
    \u8ba1\u7b97\u6700\u5927\u901f\u5ea6<\/strong><\/h2>\n
    max_speed = data['speed'].max()<\/p>\n
    print(f'Max Speed: {max_speed} m\/s')<\/p>\n
    \u8ba1\u7b97\u603b\u884c\u9a76\u8ddd\u79bb<\/strong><\/h2>\n
    from geopy.distance import geodesic<\/p>\n
    total_distance = sum([geodesic((data.iloc[i]['latitude'], data.iloc[i]['longitude']),<\/p>\n
                                   (data.iloc[i+1]['latitude'], data.iloc[i+1]['longitude'])).meters<\/p>\n
                          for i in range(len(data)-1)])<\/p>\n
    print(f'Total Distance: {total_distance} meters')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    6.4 \u8def\u5f84\u8ba1\u7b97<\/h4>\n<\/p>\n
    \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8ba1\u7b97\u8f6c\u5411\u70b9\u548c\u722c\u5761\u70b9\uff1a<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    \u8ba1\u7b97\u65b9\u5411\u53d8\u5316<\/strong><\/h2>\n
    data['bearing'] = np.arctan2(np.diff(data['latitude']), np.diff(data['longitude']))<\/p>\n
    \u627e\u51fa\u8f6c\u5411\u70b9<\/strong><\/h2>\n
    data['turning_point'] = np.abs(np.diff(data['bearing'])) > np.pi\/4<\/p>\n
    turning_points = data[data['turning_point']]<\/p>\n
    print(turning_points)<\/p>\n
    \u8ba1\u7b97\u5761\u5ea6<\/strong><\/h2>\n
    data['slope'] = np.diff(data['altitude']) \/ np.diff(data['distance'])<\/p>\n
    \u627e\u51fa\u722c\u5761\u70b9<\/strong><\/h2>\n
    data['climb'] = data['slope'] > 0.1<\/p>\n
    climb_points = data[data['climb']]<\/p>\n
    print(climb_points)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    6.5 \u901f\u5ea6\u8ba1\u7b97<\/h4>\n<\/p>\n
    \u6700\u540e\uff0c\u6211\u4eec\u8ba1\u7b97\u52a0\u901f\u5ea6\u548c\u51cf\u901f\u5ea6\uff1a<\/p>\n<\/p>\n
    # \u8ba1\u7b97\u52a0\u901f\u5ea6<\/p>\n
    data['acceleration'] = np.diff(data['speed']) \/ np.diff(data['timestamp'].astype('int'))<\/p>\n
    \u627e\u51fa\u52a0\u901f\u70b9<\/strong><\/h2>\n
    data['accelerating'] = data['acceleration'] > 0.5<\/p>\n
    accelerating_points = data[data['accelerating']]<\/p>\n
    print(accelerating_points)<\/p>\n
    \u8ba1\u7b97\u51cf\u901f\u5ea6<\/strong><\/h2>\n
    data['deceleration'] = -np.diff(data['speed']) \/ np.diff(data['timestamp'].astype('int'))<\/p>\n
    \u627e\u51fa\u51cf\u901f\u70b9<\/strong><\/h2>\n
    data['decelerating'] = data['deceleration'] > 0.5<\/p>\n
    decelerating_points = data[data['decelerating']]<\/p>\n
    print(decelerating_points)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u6210\u529f\u5730\u5206\u6790\u4e86\u4e00\u6b21\u9a91\u884c\u6d3b\u52a8\u7684GPS\u6570\u636e\uff0c\u63d0\u53d6\u4e86\u8f68\u8ff9\u3001\u901f\u5ea6\u3001\u8f6c\u5411\u70b9\u3001\u722c\u5761\u70b9\u3001\u52a0\u901f\u5ea6\u548c\u51cf\u901f\u5ea6\u7b49\u4fe1\u606f\u3002\u8fd9\u4e9b\u4fe1\u606f\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u4e86\u89e3\u9a91\u884c\u6d3b\u52a8\u7684\u8be6\u7ec6\u60c5\u51b5\uff0c\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u548c\u4f18\u5316\u3002<\/p>\n<\/p>\n
    \u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u4f7f\u7528Python\u5206\u6790GPS\u6570\u636e\u6d89\u53ca\u591a\u4e2a\u6b65\u9aa4\uff0c\u5305\u62ec\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u7edf\u8ba1\u5206\u6790\u3001\u8def\u5f84\u8ba1\u7b97\u548c\u901f\u5ea6\u8ba1\u7b97\u3002\u6bcf\u4e2a\u6b65\u9aa4\u90fd\u6709\u5176\u91cd\u8981\u6027\uff0c\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u548c\u4e00\u81f4\u6027\u662f\u6210\u529f\u5206\u6790\u7684\u5173\u952e\u3002\u901a\u8fc7\u5177\u4f53\u7684\u6848\u4f8b\u5206\u6790\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6bcf\u4e2a\u6b65\u9aa4\u7684\u5177\u4f53\u5b9e\u73b0\u65b9\u6cd5\u548c\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n
    \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u8c03\u6574\u5206\u6790\u65b9\u6cd5\u548c\u6b65\u9aa4\uff0c\u63d0\u53d6\u66f4\u591a\u6709\u4ef7\u503c\u7684\u4fe1\u606f\u3002\u4f8b\u5982\uff0c\u5728\u4ea4\u901a\u9886\u57df\uff0c\u53ef\u4ee5\u901a\u8fc7\u5206\u6790\u8f66\u8f86\u8f68\u8ff9\u6570\u636e\uff0c\u4f18\u5316\u8def\u7ebf\u89c4\u5212\u548c\u4ea4\u901a\u7ba1\u7406\uff1b\u5728\u8fd0\u52a8\u9886\u57df\uff0c\u53ef\u4ee5\u901a\u8fc7\u5206\u6790\u8fd0\u52a8\u8f68\u8ff9\u6570\u636e\uff0c\u4f18\u5316\u8bad\u7ec3\u8ba1\u5212\u548c\u8fd0\u52a8\u7b56\u7565\u3002\u65e0\u8bba\u5728\u54ea\u4e2a\u9886\u57df\uff0c\u4f7f\u7528Python\u5206\u6790GPS\u6570\u636e\u90fd\u80fd\u5e26\u6765\u5de8\u5927\u7684\u4ef7\u503c\u548c\u6f5c\u529b\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u83b7\u53d6GPS\u6570\u636e\u4ee5\u4fbf\u8fdb\u884cPython\u5206\u6790\uff1f<\/strong>
    \u8981\u5206\u6790GPS\u6570\u636e\uff0c\u9996\u5148\u9700\u8981\u83b7\u53d6\u6570\u636e\u6e90\u3002\u901a\u5e38\uff0cGPS\u6570\u636e\u53ef\u4ee5\u901a\u8fc7GPS\u8bbe\u5907\u3001\u667a\u80fd\u624b\u673a\u5e94\u7528\u6216\u5728\u7ebf\u670d\u52a1\u83b7\u53d6\u3002\u786e\u4fdd\u6570\u636e\u683c\u5f0f\u4e3aCSV\u3001JSON\u6216\u5176\u4ed6\u53ef\u5904\u7406\u7684\u683c\u5f0f\u3002\u4e0b\u8f7d\u6570\u636e\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u5e93\u5982Pandas\u6765\u8bfb\u53d6\u548c\u7ba1\u7406\u6570\u636e\uff0c\u65b9\u4fbf\u540e\u7eed\u5206\u6790\u3002<\/p>\n
    Python\u4e2d\u54ea\u4e9b\u5e93\u9002\u5408\u5904\u7406\u548c\u5206\u6790GPS\u6570\u636e\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u6709\u51e0\u4e2a\u5f3a\u5927\u7684\u5e93\u53ef\u4ee5\u7528\u4e8eGPS\u6570\u636e\u5206\u6790\u3002Pandas\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0cGeopy\u53ef\u4ee5\u5e2e\u52a9\u8fdb\u884c\u5730\u7406\u7f16\u7801\u548c\u8ddd\u79bb\u8ba1\u7b97\uff0cMatplotlib\u548cSeaborn\u9002\u5408\u53ef\u89c6\u5316\u6570\u636e\u3002\u6b64\u5916\uff0cShapely\u7528\u4e8e\u5904\u7406\u51e0\u4f55\u5bf9\u8c61\uff0cFolium\u53ef\u4ee5\u521b\u5efa\u4ea4\u4e92\u5f0f\u5730\u56fe\uff0c\u4fbf\u4e8e\u5c55\u793aGPS\u8f68\u8ff9\u548c\u4f4d\u7f6e\u6570\u636e\u3002<\/p>\n
    \u5982\u4f55\u53ef\u89c6\u5316GPS\u8f68\u8ff9\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\uff1f<\/strong>
    \u53ef\u89c6\u5316GPS\u8f68\u8ff9\u662f\u7406\u89e3\u6570\u636e\u7684\u5173\u952e\u6b65\u9aa4\u3002\u4f7f\u7528Matplotlib\u6216Folium\u5e93\uff0c\u53ef\u4ee5\u5c06GPS\u5750\u6807\u7ed8\u5236\u5728\u5730\u56fe\u4e0a\uff0c\u5e2e\u52a9\u8bc6\u522b\u8def\u5f84\u3001\u505c\u7559\u70b9\u53ca\u5176\u4ed6\u884c\u4e3a\u6a21\u5f0f\u3002\u901a\u8fc7\u8bbe\u7f6e\u4e0d\u540c\u7684\u989c\u8272\u548c\u6807\u8bb0\uff0c\u53ef\u4ee5\u76f4\u89c2\u5c55\u793a\u4e0d\u540c\u7c7b\u578b\u7684\u6570\u636e\uff0c\u4f8b\u5982\u901f\u5ea6\u53d8\u5316\u6216\u7279\u5b9a\u5730\u70b9\u7684\u505c\u7559\u65f6\u95f4\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u7528python\u5206\u6790gps\u6570\u636e \u4f7f\u7528Python\u5206\u6790GPS\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\uff1a\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u6570\u636e\u53ef […]","protected":false},"author":3,"featured_media":1184700,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1184693"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1184693"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1184693\/revisions"}],"predecessor-version":[{"id":1184701,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1184693\/revisions\/1184701"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1184700"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1184693"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1184693"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1184693"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}