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Geospatial Analysis

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Python Programming Learning Circle
Python Programming Learning Circle
May 14, 2025 · Big Data

Fetching and Visualizing 30‑Minute Traffic Isochrones with Python and Amap API

This tutorial explains how to retrieve Amap traffic‑life‑circle data via its public API, decode district IDs, and use Python with the Folium library to visualize 30‑minute reachable areas on an interactive map, providing a practical workflow for geospatial data analysis.

Amap APIGeospatial AnalysisPython
0 likes · 13 min read
Fetching and Visualizing 30‑Minute Traffic Isochrones with Python and Amap API
Python Programming Learning Circle
Python Programming Learning Circle
Apr 24, 2024 · Big Data

Using the TransBigData Python Library for Mobile Signaling Data Processing, Analysis, and Visualization

This article introduces the TransBigData Python package, explains how to install it, read mobile signaling data with pandas, preprocess and grid the data, identify stay and move events, determine home and work locations, and visualize individual user activity using built‑in functions.

Big DataGeospatial AnalysisMobile Data
0 likes · 7 min read
Using the TransBigData Python Library for Mobile Signaling Data Processing, Analysis, and Visualization
Python Programming Learning Circle
Python Programming Learning Circle
Apr 17, 2024 · Big Data

Comparative Analysis of Starbucks and Luckin Coffee Store Distribution in China Using Python Data Visualization

Using Python data visualization and geospatial analysis, this article compares the nationwide distribution of Starbucks and Luckin Coffee stores in China, revealing differences in regional concentration, proximity patterns, and statistical insights such as average Luckin stores within 500 m of each Starbucks location.

Big DataGeospatial AnalysisPython
0 likes · 11 min read
Comparative Analysis of Starbucks and Luckin Coffee Store Distribution in China Using Python Data Visualization
Python Programming Learning Circle
Python Programming Learning Circle
Feb 1, 2024 · Big Data

Analyzing the Distribution and Competition Between Starbucks and Luckin Coffee Using Python Data Visualization

Using Python and the Shapely library, this article visualizes and compares the nationwide store distribution of Starbucks and Luckin Coffee, revealing that Starbucks concentrates in coastal first‑tier cities while Luckin is more dispersed, with an average of 0.6 Luckin stores within 500 m of each Starbucks location.

Geospatial AnalysisLuckin CoffeePython
0 likes · 10 min read
Analyzing the Distribution and Competition Between Starbucks and Luckin Coffee Using Python Data Visualization
Qunar Tech Salon
Qunar Tech Salon
Jan 23, 2018 · Artificial Intelligence

Intelligent Business Zone Planning for Super Bus Service Using DBSCAN Clustering and Convex Hull

The article describes how the Super Bus platform leverages unsupervised DBSCAN clustering and a Graham‑scan convex‑hull algorithm, combined with a data‑center and distributed processing framework, to automatically generate compliant service zones that match user demand while improving efficiency and scalability.

ClusteringDBSCANGeospatial Analysis
0 likes · 8 min read
Intelligent Business Zone Planning for Super Bus Service Using DBSCAN Clustering and Convex Hull