探秘京东外卖幕后：地图&轨迹技术探寻

from qgis.core import QgsApplication, QgsProject
# 初始化 QGIS 应用程序qgs = QgsApplication([], False)qgs.initQgis()
# 加载项目，如果有特定项目文件可以替换为项目文件路径project = QgsProject.instance()# 这里假设项目文件名为 example.qgs，可根据实际情况修改project.read('example.qgs')  
# 获取所有图层layers = project.mapLayers().values()
# 遍历每个图层并输出相关信息for layer in layers:    print(f"图层名称: {layer.name()}")    print(f"图层类型: {'矢量图层' if layer.type() == 0 else '栅格图层'}")    if layer.type() == 0:  # 矢量图层        print(f"要素数量: {layer.featureCount()}")        print(f"几何类型: {layer.geometryType()}")        fields = layer.fields()        field_names = [field.name() for field in fields]        print(f"字段名称: {', '.join(field_names)}")    print("-" * 50)
# 释放 QGIS 资源qgs.exitQgis()

from osgeo import ogr
# 打开Shapefile数据源in_ds = ogr.Open('input.shp')in_layer = in_ds.GetLayer()
# 创建GeoJSON输出数据源driver = ogr.GetDriverByName('GeoJSON')out_ds = driver.CreateDataSource('output.geojson')out_layer = out_ds.CopyLayer(in_layer, 'output_layer')
# 释放资源del in_ds, out_ds

import pandas as pdimport numpy as npfrom scipy.signal import savgol_filter

# 读取GPS轨迹数据，假设数据存储在CSV文件中，包含'latitude', 'longitude', 'time'等列data = pd.read_csv('gps_trajectory.csv')
# 去除重复的轨迹点data = data.drop_duplicates(subset=['latitude', 'longitude', 'time'])
# 去除异常点：通过计算经纬度的z - score来识别异常值z_score_lat = np.abs((data['latitude'] - data['latitude'].mean()) / data['latitude'].std())z_score_lon = np.abs((data['longitude'] - data['longitude'].mean()) / data['longitude'].std())# 设定阈值，这里以3为例，即z - score大于3的认为是异常点threshold = 3data = data[(z_score_lat < threshold) & (z_score_lon < threshold)]
# 平滑轨迹：使用Savitzky - Golay滤波器对经纬度进行平滑处理window_length = 5  # 窗口长度，必须是奇数polyorder = 2  # 多项式阶数data['latitude_smoothed'] = savgol_filter(data['latitude'], window_length, polyorder)data['longitude_smoothed'] = savgol_filter(data['longitude'], window_length, polyorder)
# 查看处理后的数据前几行print(data.head())

# 使用 folium 库绘制简单地图轨迹示例
import foliumimport pandas as pd
# 假设我们的轨迹数据存储在一个CSV文件中，文件包含'latitude'（纬度）、'longitude'（经度）和'time'（时间）等列data = pd.read_csv('gps_trajectory.csv')
# 创建一个地图对象，初始中心位置设置为轨迹数据的起始点# 这里假设轨迹数据不为空，否则需要添加一些错误处理逻辑m = folium.Map(location=[data.iloc[0]['latitude'], data.iloc[0]['longitude']], zoom_start=10)
# 遍历轨迹数据，为每个轨迹点添加标记for index, row in data.iterrows():    folium.Marker(        location=[row['latitude'], row['longitude']],        popup=f"时间: {row['time']}"    ).add_to(m)
# 如果轨迹数据包含连续的点，我们可以添加一条线来连接这些点# 这里假设轨迹数据是按时间顺序排列的if 'latitude' in data.columns and 'longitude' in data.columns:    coordinates = list(zip(data['latitude'], data['longitude']))    folium.PolyLine(        locations=coordinates,        color='blue',        weight=2.5,        opacity=1    ).add_to(m)
# 保存地图为HTML文件m.save('trajectory_map.html')

# 使用 Python 的 geopandas 库进行缓冲区分析示例
import geopandas as gpd
# 读取包含工厂位置的矢量数据factory_data = gpd.read_file('factory.shp')# 设置缓冲区距离为5000米（假设数据单位为米）buffer_distance = 5000buffered_factory = factory_data.buffer(buffer_distance)# 将缓冲区结果保存为新的文件buffered_factory.to_file('factory_buffer.shp')

import heapq
def dijkstra(graph, start):    distances = {node: float('inf') for node in graph}    distances[start] = 0    queue = [(0, start)]    while queue:        cur_dist, cur_node = heapq.heappop(queue)        if cur_dist > distances[cur_node]: continue        for neighbor, weight in graph[cur_node].items():            dist = cur_dist + weight            if dist < distances[neighbor]:                distances[neighbor] = dist                heapq.heappush(queue, (dist, neighbor))    return distancesgraph = {    'A': {'B': 1, 'C': 4},    'B': {'A': 1, 'C': 2, 'D': 5},    'C': {'A': 4, 'B': 2, 'D': 1},    'D': {'B': 5, 'C': 1}}start_node = 'A'print(dijkstra(graph, start_node))

import networkx as nximport matplotlib.pyplot as plt
G = nx.DiGraph()nodes = ['A', 'B', 'C', 'D', 'E']G.add_nodes_from(nodes)edges = [('A', 'B', 2), ('A', 'C', 4), ('B', 'C', 1), ('B', 'D', 7), ('C', 'D', 3), ('D', 'E', 2)]G.add_weighted_edges_from(edges)
start_node = 'A'end_node = 'E'try:    shortest_path = nx.dijkstra_path(G, source=start_node, target=end_node)    shortest_path_length = nx.dijkstra_path_length(G, source=start_node, target=end_node)    print(f"从 {start_node} 到 {end_node} 的最短路径: {' -> '.join(shortest_path)}")    print(f"最短路径长度: {shortest_path_length}")except nx.NetworkXNoPath:    print(f"从 {start_node} 到 {end_node} 无路径。")
pos = nx.spring_layout(G)nx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=500, font_size=12)edge_labels = nx.get_edge_attributes(G, 'weight')nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)plt.title("简单路径规划图")plt.show()