SensePlace3: a geovisual framework to analyze place–time–attribute information in social media

<p>SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted <i>about</i>, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources.</p

Centrality values between districts in Kenya.

<p>Centrality values between districts in Kenya.</p

Map illustrating the connectivity between districts and Eigenvector value illustrating the level of influence of each district.

<p>For clarity we have only illustrated linkages with more than 100 connections and Eigenvectors greater than 0.50. Map created using ArcGIS 10.2.</p

Connectivity between locations and human movement within Kenya.

<p>(A) Distances travelled daily, monthly and in total by each user and (B) the proportion of user’s radius of gyration (solid line).</p

Maps illustrating (A) the distribution of geo-located tweets in the study area for users who crossed-borders (N = 770) (B) connections between Kenya and the surrounding countries and (C) a flow map showing the connectivity between different geographic locations by travel distance.

<p>Map created using ArcGIS 10.2.</p

A framework and associated data sources useful for capturing human mobility in time and space.

<p>Movements are characterized in terms of their spatial and temporal scale, which are defined in terms of physical displacement (<i>spatial</i>) and time spent (<i>temporal</i>, frequency and duration) (Source: adapted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129202#pone.0129202.ref045" target="_blank">45</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129202#pone.0129202.ref057" target="_blank">57</a>]).</p

Movement patterns captured at different temporal scales illustrate connectivity between districts in Kenya within a 24-hour time period (N = 90,645 tracks) and during a ten month time period (N = 17,900).

<p>Each line represents a movement segment. The long distance tracks indicates population movements by plane or by train within the country. Maps created using ArcGIS 10.2.</p

Summary of Twitter data used in this analysis that was collected for Kenya between June 2013 and March 2014 (N_{unique users} = 28,335; N_tweets = 720,149).

<p>Summary of Twitter data used in this analysis that was collected for Kenya between June 2013 and March 2014 (N_{unique users} = 28,335; N_tweets = 720,149).</p