Geographical awareness of Twitter users in Jacksonville, FL.

<p>The size of the circle is proportional to the number of city names mentioned in tweets. Twitter users in Jacksonville, FL mentioned city names 40,039 times from Dec 2013 to Feb 2014. Among them, the users mentioned Jacksonville 33,617 times, which is 84% of the entire city names mentioned in the tweets. The city name, Jacksonville, was excluded in this map. The top three most mentioned city names are Jacksonville, FL, Miami, FL, and Orlando, FL. This map was created using tweets collected from Jacksonville, FL during the collection of dataset 1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.t001" target="_blank">Table 1</a> (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.s003" target="_blank">S3 Table</a>).</p

Global Awareness Index (GAI) at 50 cities in the U.S.

<p>Column D shows the population in each city based on the 2010 census. Column E indicates the rank of population in each city. Column F represents the number of tweets collected in each of the 50 home cities in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.g001" target="_blank">Fig 1</a>. Column G shows the number of tweets containing city names outside the U.S. divided by the total number of tweets. Column H is GAI multiply by 100000. Column I is normalized GAI that ranges between 0 and 1.</p><p>* represents the biggest top 10 cities by population.</p><p>Global Awareness Index (GAI) at 50 cities in the U.S.</p

Four steps of knowledge discovery in cyberspace for geographical awareness (KDCGA).

<p>The first and second steps select tweets containing the city names. The third step is to locate and visualize the tweets on the map. The fourth step is to reveal spatiotemporal patterns by using spatial statistical methods.</p

The awareness of U.S. cities between Twitter users in New York (NY) versus Los Angeles (LA).

<p>The geographical awareness of each group was estimated based on the names of U.S. cities mentioned in their tweets. The map shows the difference in the distributional patterns of the geographical awareness between the two groups. The users in LA are more aware of the red regions (mostly the western U.S.) than those in NY. The users in NY are more aware of the blue regions (mostly the eastern U.S.) than those in LA. This map was created by using tweets collected from LA and NY during the collection of dataset 2 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.t001" target="_blank">Table 1</a> (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.s006" target="_blank">S6</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.s007" target="_blank">S7</a> Tables).</p

Revisiting the death of geography in the era of Big Data: the friction of distance in cyberspace and real space

<p>Many scholars have argued that the importance of geographic proximity in human interactions has been diminished by the use of the Internet, while others disagree with this argument. Studies have noted the distance decay effect in both cyberspace and real space, showing that interactions occur with an inverse relationship between the number of interactions and the distance between the locations of the interactors. However, these studies rarely provide strong evidence to show the influence of distance on interactions in cyberspace, nor do they quantify the differences in the amount of friction of distance between cyberspace and real space. To fill this gap, this study used massive amounts of social media data (Twitter) to compare the influence of distance decay on human interactions between cyberspace and real space in a quantitative manner. To estimate the distance decay effect in both cyberspace and real space, the distance decay function of interactions in each space was modeled. Estimating the distance decay in cyberspace in this study can help predict the degree of information flow across space through social media. Measuring how far ideas can be diffused through social media is useful for users of location-based services, policy advocates, public health officials, and political campaigners.</p

The awareness of global cities between Twitter users in New York (NY) versus Los Angeles (LA).

<p>The geographical awareness of each group was estimated based on the names of international cities mentioned in their tweets. The map shows the difference in the distributional patterns of the geographical awareness between the two groups. The users in LA are more aware of the red regions than those in NY. The users in NY are more aware of the blue regions than those in LA. This map was created by using tweets collected from LA and NY during the collection of dataset 1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.t001" target="_blank">Table 1</a> (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.s004" target="_blank">S4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.s005" target="_blank">S5</a> Tables). Tweets inside the U.S. are excluded to map.</p

Twitter users with regional and local levels of awareness from Jacksonville, FL.

<p>The map shows the central tendency, dispersion and directional trends of the tweets mapped in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.g005" target="_blank">Fig 5</a>. The narrow ellipse shows that the users are more aware of regional and local cities rather than international cities. Map projection: Lambert Conformal Conic. (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.s003" target="_blank">S3 Table</a>)</p

Twitter users with national and international levels of awareness from San Jose, CA.

<p>The map shows the central tendency, dispersion and directional trends of the tweets mapped in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.g004" target="_blank">Fig 4</a>. The widely stretched ellipse shows that the users are well aware of cities belonging to faraway states and international cities. Map projection: Lambert Conformal Conic. (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.s002" target="_blank">S2 Table</a>)</p

The temporal change of global awareness index (GAI) of Twitter users inU.S.

<p>(a) shows the temporal change of the level of geographical awareness of Twitter uesers living in all 50 home cities (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132464#pone.0132464.g001" target="_blank">Fig 1</a>) from December 2013 to February 2014. The temporal change of the level of geographical awareness is also examined at the city level, in New York City (b) and in Houston, TX (c) around the last week of December. The geographical awareness of the Twitter users was highest in late December 2013.</p

Home cities.

<p>Tweets were collected within a 20 mile buffer from each center of the 50 major U.S. cities.</p