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Understanding Individual-Space Relationships to Inform and Enhance Location-Based Applications

Licia Amichi, Gautam Malviya Thakur, Carter Christopher

TL;DR

Understanding the link between individuals and their spaces, the paper introduces two data-quality metrics $\mu_T$ and $\mu_S$ to ensure reliable mobility data and uses a multi-dimensional visit-characterization approach. It employs Gaussian Mixture Model clustering to per-user PoIs, selecting seven components via $\mathrm{BIC}$ and $\mathrm{AIC}$, to define seven visitation motifs (G1–G7) that span exploratory to anchored patterns. Evaluations on Singapore and Beijing reveal city-specific anchored patterns and semantic shifts (e.g., Singapore ties anchored visits to recreation, Beijing to residential/business/education), illustrating the role of geography and culture in mobility. The framework supports improved personalization of location-based services, urban planning, and public-health interventions by providing a nuanced view of how people relate to places over semantic, temporal, and spatial dimensions.

Abstract

Understanding the complex dynamics of human navigation and spatial behavior is essential for advancing location-based services, public health, and related fields. This paper investigates the multifaceted relationship between individuals and their environments (e.g. location and places they visit), acknowledging the distinct influences of personal preferences, experiences, and social connections. While certain locations hold sentimental value and are frequently visited, others function as mere transitory points. To the best of our knowledge, this paper is the first to exploit visitation patterns and dwell times to characterize an individual's relationship with specific locations. We identify seven key types of spatial relationships and analyze the discrepancies among these visit types across semantic, spatial, and temporal dimensions. Our analysis highlights key findings, such as the prevalence of anchored-like visits (e.g. home, work) in both real-world Singapore and Beijing datasets, with unique associations in each city -Singapore's anchored-liked visits include recreational spaces, while Beijing's are limited to residential, business, and educational sites. These findings emphasize the importance of geographic and cultural context in shaping mobility and their potential in benefiting the precision and personalization of location-based services.

Understanding Individual-Space Relationships to Inform and Enhance Location-Based Applications

TL;DR

Understanding the link between individuals and their spaces, the paper introduces two data-quality metrics and to ensure reliable mobility data and uses a multi-dimensional visit-characterization approach. It employs Gaussian Mixture Model clustering to per-user PoIs, selecting seven components via and , to define seven visitation motifs (G1–G7) that span exploratory to anchored patterns. Evaluations on Singapore and Beijing reveal city-specific anchored patterns and semantic shifts (e.g., Singapore ties anchored visits to recreation, Beijing to residential/business/education), illustrating the role of geography and culture in mobility. The framework supports improved personalization of location-based services, urban planning, and public-health interventions by providing a nuanced view of how people relate to places over semantic, temporal, and spatial dimensions.

Abstract

Understanding the complex dynamics of human navigation and spatial behavior is essential for advancing location-based services, public health, and related fields. This paper investigates the multifaceted relationship between individuals and their environments (e.g. location and places they visit), acknowledging the distinct influences of personal preferences, experiences, and social connections. While certain locations hold sentimental value and are frequently visited, others function as mere transitory points. To the best of our knowledge, this paper is the first to exploit visitation patterns and dwell times to characterize an individual's relationship with specific locations. We identify seven key types of spatial relationships and analyze the discrepancies among these visit types across semantic, spatial, and temporal dimensions. Our analysis highlights key findings, such as the prevalence of anchored-like visits (e.g. home, work) in both real-world Singapore and Beijing datasets, with unique associations in each city -Singapore's anchored-liked visits include recreational spaces, while Beijing's are limited to residential, business, and educational sites. These findings emphasize the importance of geographic and cultural context in shaping mobility and their potential in benefiting the precision and personalization of location-based services.

Paper Structure

This paper contains 13 sections, 2 equations, 10 figures.

Table of Contents

  1. Introduction
  2. Mobility Data Processing
  3. Datasets
  4. Data quality assessment metrics
  5. Temporal completeness
  6. Spatial completeness
  7. Quality assessment
  8. Proposed Methodology
  9. Visitation motifs
  10. Semantic patterns
  11. Temporal patterns
  12. Spatial patterns
  13. Related Works

Figures (10)

  • Figure 1: Temporal and Spatial Completeness in Singapore and Beijing.
  • Figure 2: BIC and AIC values for GMM with varying numbers of clusters and different covariance types. Both subfigures share the same legend.
  • Figure 3: Visits characterization Singapore (GMM).
  • Figure 4: Visits characterization Beijing (GMM).
  • Figure 5: Singapore visitation patterns.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Definition 1: Mobility Record
  • Definition 2: Mobility Trace