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Related Experiment Video

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Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

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Intrinsic dimensionality of human behavioral activity data.

Luana Fragoso¹, Tuhin Paul¹, Flaviu Vadan¹

¹Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

|June 28, 2019

Summary

Analyzing smartphone data reveals spatial behavior patterns. Fractal-based intrinsic dimensionality shows these complex datasets can be represented in approximately two dimensions, simplifying analysis.

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The HoneyComb Paradigm for Research on Collective Human Behavior

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Published on: January 19, 2019

Area of Science:

Data Science
Human Mobility Analysis
Complex Systems

Background:

Spatial behavior patterns influence infrastructure use, social interactions, and access to opportunities.
Commodity smartphone data offers a rich source for understanding human mobility, driving innovation in academia and industry.
Massive datasets generated from smartphones necessitate methods for understanding data scope and correlations.

Purpose of the Study:

To investigate the intrinsic dimensionality of smartphone-collected spatial behavior data.
To explore the potential of fractal-based methods for quantifying data complexity.
To guide data collection strategies and identify actionable features by understanding data correlations.

Main Methods:

Applied fractal-based intrinsic dimensionality analysis.
Analyzed four distinct smartphone datasets.
Evaluated seven input dimensions relevant to spatial behavior.

Main Results:

Empirically demonstrated that smartphone spatial behavior datasets exhibit an intrinsic dimension of approximately two.
Showcased the effectiveness of fractal-based intrinsic dimensionality in characterizing complex datasets.
Provided a concise measure of data complexity for large-scale mobility data.

Conclusions:

Smartphone spatial behavior data, despite its complexity, can be effectively represented in a low-dimensional space (approximately two dimensions).
Intrinsic dimensionality offers a valuable tool for understanding data scope, guiding data collection, and developing new analytical models.
This finding has implications for efficient data management and feature engineering in human mobility studies.