Fundamentals of Map Trajectory Technology and GIS Applications

Introduction

Recent announcements about JD's entry into the food‑delivery market highlight the use of precise map‑trajectory technology to display real‑time rider locations, illustrating the growing importance of GIS in everyday services.

Geographic Coordinate Systems

WGS84 is the global standard for GPS positioning, while the UTM projection divides the earth into 60 zones for detailed local calculations, each with its own conversion challenges.

Map Projections and Coordinate Transformations

Common projections include Gauss‑Krüger for regional maps and Mercator for maritime navigation; coordinate transformations bridge different systems such as WGS84 to local planar coordinates.

Basic GIS Software Operations

ArcGIS offers comprehensive spatial analysis tools but at a higher cost, whereas QGIS is free, extensible, and suitable for beginners. Example Python code for accessing QGIS project data:

from qgis.core import QgsApplication, QgsProject
qgs = QgsApplication([], False)
qgs.initQgis()
project = QgsProject.instance()
project.read('example.qgs')
layers = project.mapLayers().values()
for layer in layers:
    print(f"Layer name: {layer.name()}")
    print(f"Layer type: {'Vector' if layer.type() == 0 else 'Raster'}")
    if layer.type() == 0:
        print(f"Feature count: {layer.featureCount()}")
        print(f"Geometry type: {layer.geometryType()}")
        fields = layer.fields()
        field_names = [field.name() for field in fields]
        print(f"Field names: {', '.join(field_names)}")
    print('-' * 50)
qgs.exitQgis()

Map Data Formats and Processing

Common formats include Shapefile, GeoJSON, and others. GDAL can convert between formats, and both ArcGIS and QGIS provide GUI tools for import/export.

from osgeo import ogr
in_ds = ogr.Open('input.shp')
in_layer = in_ds.GetLayer()
driver = ogr.GetDriverByName('GeoJSON')
out_ds = driver.CreateDataSource('output.geojson')
out_layer = out_ds.CopyLayer(in_layer, 'output_layer')
del in_ds, out_ds

GPS and Trajectory Data

GPS determines position via trilateration from at least four satellites. Other GNSS systems (GLONASS, Galileo, BeiDou) offer regional advantages. Raw GPS data often contain noise; preprocessing steps like duplicate removal, outlier filtering, and smoothing improve quality.

import pandas as pd
import numpy as np
from scipy.signal import savgol_filter

data = pd.read_csv('gps_trajectory.csv')
data = data.drop_duplicates(subset=['latitude','longitude','time'])
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())
threshold = 3
data = data[(z_score_lat < threshold) & (z_score_lon < threshold)]
window_length = 5
polyorder = 2
data['latitude_smoothed'] = savgol_filter(data['latitude'], window_length, polyorder)
data['longitude_smoothed'] = savgol_filter(data['longitude'], window_length, polyorder)
print(data.head())

Real‑Time vs. Historical Trajectories

Real‑time trajectories update continuously, requiring high‑frequency data transmission; they are crucial for ride‑hailing, logistics, and emergency response. Historical trajectories store past movement records for analysis in urban planning, marketing, health, and security.

Latest Developments

AI‑driven platforms (e.g., cloud‑based trajectory computation services) integrate large‑scale spatiotemporal models. Cross‑domain fusion with autonomous driving, digital twins, and traffic perception large models expands capabilities. High‑precision maps combined with trajectory data enhance navigation and traffic flow prediction.

Conclusion and Outlook

Map trajectory technology is evolving from basic navigation to intelligent, data‑driven applications across transportation, urban management, and commerce. Continued advances in AI, big data, and IoT will further improve accuracy, automation, and integration, driving smarter and more efficient future services.