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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Published on: February 25, 2013

Spatial point pattern and urban morphology: Perspectives from entropy, complexity, and networks.

Hoai Nguyen Huynh¹

¹Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore.

Physical Review. E

|October 3, 2019

Summary

This study introduces a new framework using entropy maximization to quantify urban spatial patterns. It reveals distinct urban morphologies based on geographical factors and development status.

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Area of Science:

Urban Planning and Geography
Complex Systems Science
Spatial Analysis

Background:

The spatial organization of cities significantly impacts their social functioning.
Mathematical quantification of urban spatial patterns is crucial for system analysis.

Purpose of the Study:

To propose a novel framework for characterizing urban spatial patterns using entropy maximization.
To introduce quantifiable metrics for comparing the spatial organization of different cities.

Main Methods:

Developed a framework based on entropy maximization to analyze urban spatial patterns.
Calculated three distinct spatial length scales.
Introduced mass decoherence and space decoherence as quantitative measures.

Main Results:

Quantified urban spatial patterns using mass and space decoherence.
Enabled comparative analysis of different global cities.
Identified diverse urban morphologies linked to geography and development.

Conclusions:

The proposed framework effectively quantifies urban spatial patterns.
Urban morphology is influenced by geographical background and development status.
Mass and space decoherence offer valuable insights into urban system dynamics.