It also supports a wide range of libraries to simplify writing the code. All these approaches of Python help programmers write logical and clear code for projects of all scales.Īs of now, there are three major versions of Python, Python 3 being the latest. In addition, Python is an object-oriented, reflective, functional, procedural, and structural language. We also try to be at that level with our SaaS tool support. We have latitude and longitude as well as track_id and time.Kinsta spoiled me so bad that I demand that level of service from every provider now. The first five rows of the dataset are shown below. import pandas as pd import geopandas as gpd import plotly_express as px import matplotlib.pyplot as plt !wget !unzip GPSTrajectory.zip df = pd.read_csv(‘GPSTrajectory/go_track_trackspoints.csv’) df.head() Let us read the data with the Pandas library. It also provides unique track_id for each trajectory. It contains GPS points (latitude and longitude) with timestamps. In this tutorial, we use a GPS Trajectory dataset. With the increased use of smartphones, GPS, and location services, geofencing becomes an indispensable tool in location data analytics and intelligence. Simply put, a geofence is a defining virtual boundary around geographic objects or an area, so that every time a user enters or leaves the boundary perimeters, actions or notifications can be triggered. The concept of geofencing is straightforward, yet it is a powerful technique that enhances location applications. The application calculates the congestion charge and tracks the number of vehicles inside the congestion area at a given time. The example in the above GIF shows an app that alerts vehicles based on their location and London’s Congestion Charge Zone (CCZ). Geofencing is often used tool in Geographic data science, especially in marketing, security and zoning applications. Geofencing example from Mapbox: London congestion - Source

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