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A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data

Guofeng Cao, Shaowen Wang, Myunghwa Hwang, Anand Padmanabhan, Zhenhua Zhang, Kiumars Soltani

arXiv:1409.2826v1cs.SIphysics.soc-ph

TL;DR

Massive, dynamic, unstructured location-based social-media data challenge conventional spatiotemporal analysis. The paper organizes user activity trajectories in a hierarchical data cube and implements multiscale flow mapping on North American Twitter feeds. The resulting interface supports interactive exploration of movement patterns, while the paper identifies social-network structure and data uncertainty as areas not yet addressed.

  • Problem

    Massive, continuously generated, dynamic, and unstructured location-based social-media data pose representation, modeling, and computational challenges for conventional GIS and spatiotemporal analysis.

    Source: §1 · p. 3

  • Method

    The framework represents users’ activity footprints as space-time trajectories and aggregates them in a hierarchical spatiotemporal data cube for efficient analysis.

    Source: §1 · p. 3, §2 · p. 5, §3 · p. 5

  • Results

    The North American Twitter implementation and interactive flow maps demonstrate exploration of movement dynamics from continent to fine-block scales, including 41 of 73 potential flu-affected travelers from the LAX area traveling to the northeastern United States.

    Source: §4 · p. 12, §5.1 · p. 16, §5.2 · p. 19

  • Takeaways & Limitations

    The data cube provides a structured spatiotemporal data source that can be integrated with conventional GIS and analysis tools for studying large-scale dynamics across different scales.

    Source: §6 · p. 20

    The framework does not explicitly model users’ social-network structure, and uncertainty in the data and model warrants further investigation.

    Source: §6 · pp. 20–21

Abstract

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In the past several years, social media (e.g., Twitter and Facebook) has been experiencing a spectacular rise and popularity, and becoming a ubiquitous discourse for content sharing and social networking. With the widespread of mobile devices and location-based services, social media typically allows users to share whereabouts of daily activities (e.g., check-ins and taking photos), and thus strengthens the roles of social media as a proxy to understand human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space-time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space-time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is developed to allow real-time, and interactive visual exploration of movement dynamics in massive location-based social media at multiple scales.

1. Introduction

Location-based social media can reveal human activity and social dynamics in geographic space, but its scale, speed, and unstructured content challenge conventional GIS. The paper proposes a scalable framework using space-time trajectories and a hierarchical data cube for multiscale analysis.

1. Introduction

  • Location-based social media offers fine-grained individual footprints for studying activity patterns and aggregate population dynamics, with applications including timely influenza surveillance.Prior work reported surveillance capabilities comparable with official CDC reports, but more timely.Source: §1 · pp. 2–3
  • Massive, continuously updated, dynamic, and unstructured social media data exceed mainstream GIS capabilities and challenge conventional static representations and analysis.Retrieving and mining text, photos, and videos are often needed to make these data usable.Source: §1 · p. 3
  • The framework represents individual activity profiles as space-time trajectories and organizes collective dynamics in a hierarchical cube across location, time, and users.The cube defines measures for individual cuboids and pairs of cuboids, including activity counts and travel flows.Source: §1 · p. 3
  • Cuboid measures can be merged or split to support efficient analysis across multiple scales and aggregation boundaries, producing datasets compatible with cyberGIS and conventional GIS workflows.The framework also includes an online flow-mapping interface for near-real-time exploration of distribution and movement dynamics.Source: §1 · p. 4

2. Space-time trajectories

The paper models each social-media user as a continuously moving space-time trajectory approximated by posted location, timestamp, and content footprints. Because these observations are sparse and irregular, it treats the user as remaining at each sampled location until another activity is posted.

2. Space-time trajectories

  • Each user is assumed to have a continuously evolving trajectory, approximated by a series of location, timestamp, and message-content footprints.The model assumes anonymous data collection and allows movement through geographic or regularized network space.Source: §2 · p. 4
  • Social-media trajectory observations are often sparse and irregular, so intermediate positions cannot be reliably reconstructed by interpolation or map-matching.The paper adopts the term “episodic movement data” for this trajectory type.Source: §2 · p. 5
  • The model treats a user as staying at the last observed location until a new activity is posted, when the user moves to the next sampled location.Trajectory measures include home location and radius of gyration, while text mining can estimate attributes such as ILI status.Source: §2 · p. 5

3. A data cube model for location-based social media data analytics

The spatiotemporal data cube organizes users’ space-time trajectories into hierarchical cuboids for analyzing collective dynamics across scales and aggregation boundaries. Its measures capture activity and movement, with aggregation functions supporting queries across cuboid levels.

3. A data cube model for location-based social media data analytics

  • The cube uses spatial, temporal, and human dimensions organized into hierarchical cuboids, enabling analysis at multiple scales and aggregation boundaries.Spatial hierarchies can use merged grid cells or administrative boundaries; temporal intervals can be merged or grouped into calendar periods.Source: §3 · pp. 6–7

3.1. Measures

  • Single-cuboid measures characterize residents, visitors, activities, movements, activity locations, and diagnosed ILI occurrences; pair-cuboid measures capture travel, flu, and migration flows.Cuboids represent contiguous spatial regions and temporal intervals for a fixed user group.Source: §3.1 · pp. 7–8
  • Flow measures are directional, and flows aggregate trajectory points rather than the segments between them.The paper treats flows between a cuboid pair as asymmetric and sets same-cuboid flows to zero.Source: §3.1 · p. 9

3.2. Aggregation functions

  • Aggregation functions derive higher-level measures from sub-cuboids; A and flow measures aggregate directly, while S, I, and O require auxiliary measures.Distinct-user measures R, V, and Vflu are holistic: their higher-level values require raw trajectories, making exact aggregation computationally impractical for massive data.Source: §3.2 · pp. 9–10

4. Implementation

The implementation collects North American Twitter feeds, processes tweet text and locations into user trajectories, and maintains a hierarchical spatiotemporal data cube. It uses a fine grid and hourly base intervals to support cuboid measures and spatial queries.

4. Implementation

  • The system retrieves a 1% sample through Twitter’s streaming API and applies text mining to flag posts associated with ILI symptoms.The paper reports that the API returns almost the complete set of geo-tagged tweets of interest; other data-mining methods can be plugged in.Source: §4 · pp. 11–12
  • Tweets are organized into lifelong user trajectories, with home locations and gyration radii updated as activity footprints arrive.The initial home location is based on the most frequently visited place among the first 50 geolocated tweets; diagnosed users are flagged as infected for a week.Source: §4 · p. 12
  • The framework generates and regularly maintains the spatiotemporal cube from the trajectories database.The paper identifies this cube-building step as the data-warehouse ETL process.Source: §4 · p. 12
  • 1 km spatial cells and hourly intervals form the finest cube granularity across a 13,312 × 9,216 grid for North America.The grid-cell size doubles with each spatial level, reaching a 26 × 18 grid at level 10; spatial queries can aggregate measures for arbitrary regions.Source: §4 · p. 13

5. An on-line mapping interface for exploration of movement dynamics in location-based social media

An interactive flow-mapping interface built on a North America Twitter data cube supports exploring movement at multiple spatial scales. It offers single-source and multi-source views, including examples of travel and potential flu risk.

5. An on-line mapping interface for exploration of movement dynamics in location-based social media

  • The interface uses a North America data cube to enable interactive, near-real-time exploration of Twitter-derived movement across multiple scales.The framework also allows tweet-content mining, including identifying potentially flu-affected users.Source: §5 · p. 15, §5.1 · p. 15

5.1. Single-source flow mapping

  • Single-source maps compare flows from a fixed origin at different scales, from 2-km city cells to 256-km cells across North America.Dots mark cell centroids, and thicker edges represent more flows.Source: §5.1 · p. 16, Figure 7 · p. 17, Figure 8 · p. 17
  • Of 73 potential flu-affected travelers leaving the LAX area, 41 traveled to the northeastern United States.The map overlays a flu-risk surface estimated from flu-affected tweets using kernel density estimation.Source: §5.1 · p. 16, Figure 9 · p. 18

5.2. Multiple-source flow mapping

  • Multi-source mapping adaptively selects important nodes and bundles nearby flows to reduce clutter in all-to-all movement views.Node importance uses incoming and outgoing degree, ranked globally and among neighboring cells; Hadoop supports filtering at scale.Source: §5.2 · pp. 16, 18
  • Regional maps show more Twitter travel between Denver and western cities than between Denver and nearer Salt Lake City.The multi-source maps also show movement patterns among major North American cities at continental scale.Source: §5.2 · p. 19, Figure 10 · p. 19

6. Conclusion and future work

The conclusion presents a maintained hierarchical data cube built from users’ space-time trajectories as the basis for multiscale analysis, with an online flow-mapping service demonstrating group movement from continent to block scales.

  • The framework represents each user as an evolving space-time trajectory and organizes potential paths in a maintained hierarchical data cube for scalable, multiscale analysis.Source: §6 · p. 19
  • Figure 11 maps travel flows from multiple major North American cities over the week spanning January 31 to February 7, 2014.Source: Figure 11 · p. 20
  • An online flow-mapping service uses the cube to visualize group movement from continent scale to fine block levels, including groups such as ILI-affected users.Source: §6 · p. 20
  • Future work could incorporate social-network interactions and relax the step-function trajectory assumption; close friends’ locations are proposed for estimating unsampled positions.Source: §6 · p. 20
  • The authors identify uncertainty across spatiotemporal information, social content, and mining results as requiring further investigation, while noting that other social-media sources could be incorporated.Source: §6 · p. 21

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