Visualizing the Relational Spaces of Hurricane Sandy

Nearly a year ago, Hurricane Sandy made landfall on the eastern seaboard of the US, wreaking havoc on the lives of millions of people in its path. At the time, we threw together some quick maps of where Sandy was being talked about on Twitter, and how the geographies of Sandy-related tweets were both intensely connected to the material impacts of the storm, but also somewhat incongruent.

Since then, we've been putting the finishing touches on a paper that extends our initial interest in the data shadows of Hurricane Sandy to a more comprehensive look at how we can use Sandy-related tweeting to understand the multidimensionality of the geographies of social media activity. All too often, a one-to-one connection is made between the location of a geotagged tweet or other piece of social media content and the content of that tweet [1]. We have instead been attempting to understand how we can think through, and then visualize, how geotagged tweets reflect and produce much more complex socio-spatial relations, which include both intense connections to the places where such content is produced, as well as much more physically distant locations which are brought closer in relational space through such informational flows. The rest of this post is adapted from our paper-in-progress, and outlines how we can map and measure the relational spaces of Hurricane Sandy.

Using T-100 Domestic Market data from the Research and Innovative Technology Administration (RITA) on flights and the number of passengers between city pairs in 2012, we determined the 50 cities that have the most passenger traffic with New York City, ranging from Chicago (3.5 million passengers back and forth) to Kansas City (175,000 passengers). Since operations and activities at some airports close to New York were directly affected by Sandy’s landfall, we exclude any airport within 500 kilometres of Manhattan in this analysis. For the remaining airports we used a buffer of 5km to collect all Hurricane Sandy related tweets and calculated the lower bound of the odds-ratio (or location quotient).  This metric measures the level of Hurricane Sandy tweets relative to overall Twitter activity . If relational networks did not play a significant role in Sandy-related tweeting, one would expect to see a direct distance decay effect: as the distance from New York City increases the odds-ratio should decrease.

Twitter Activity vs. Physical Distance

Our map shows, however, that physical distance has no significant relationship with the relative level of tweeting activity about Hurricane Sandy as is evidenced by both the scatterplot and the map (Spearman’s rho is -0.05). The map uses an azimuthal equidistant projection with New York City as the center, where the size of each airport is proportional to its odds ratio. Airports that are equally distant in physical terms from New York have widely diverging measures of Sandy-related Twitter activity. In addition, the average odds ratio in each 1000km zone does not decrease the further away one travels from New York.

In contrast, a slightly altered version of our map shows that the number of passengers between each city and New York City exhibits a much stronger positive correlation with the odds-ratio metric of Twitter activity (Spearman’s rho is 0.34). This figure preserves the directional bearing of each city with respect to New York City, but instead uses an inverse of the number of passengers to recalculate the relational distance between the cities. Airports are thus no longer displayed according to their physical distance from New York City, but rather based on the intensity of air traffic between the two cities. Since the bearing has remained the same, airports with a higher intensity will move closer to New York along that line, and vice versa. In addition to the correlation coefficient, we can also visually determine that cities with a lower odds-ratio, such as Pittsburgh and Memphis, have a tendency to move towards the outer circles while cities with a higher odds-ratio, such as San Francisco and Los Angeles, move relatively closer.

Twitter Activity vs. Air Traffic Interactivity

In other words, it is the relational connection to New York, measured by number of air travelers, not physical distance, which better explains the level of concern with Hurricane Sandy as expressed via Twitter. This concern, however, can vary within metropolitan territories depending upon the scale of analysis; some parts of an urban area may have much stronger relational ties to distant cities, while other parts are largely disconnected from such global flows.

To test the extent to which the data shadows of Sandy-related tweeting are a localized phenomenon within certain parts of metropolitan areas (rather than a more generalized territorial phenomenon), we increased the initial buffer around each airport from 5km to 25km. Thus, rather than just capturing neighborhoods that are spatially proximate to the airport, this measure captures a much wider swath of each metropolitan area. With this larger buffer, there is a near-reversal of the correlations illustrated in our first map, as Pearson’s rho for total number of passengers is now 0.06 (rather than 0.34), while the distance effect starts to emerge (rho is -0.15). In other words, even though the sociospatiality of a phenomenon like Sandy is expressed partly through a network of connections between territories, these connections are very much bounded by the locally-specific practices of place. This once again highlights the complex ways in which the digital data shadows of a material event are manifest through the intertwinement of different dimensions of social space.

As evidenced by these examples, Sandy’s data shadows are not evenly distributed through the continental United States. They are instead quite intense in some locations, while hardly reaching others at all, demonstrating the multiple spatial dimensions of social processes such as the response to Hurricane Sandy.
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[1] We're as guilty of this as anyone.

The Urban Geographies of Hurricane Sandy in New York City

Following our two earlier posts showing how discussion of Hurricane Sandy were reflected on Twitter, we present another representation of tweets, focused specifically on how New York City -- the center of both the storm's effects and the media attention around it -- tweeted about the storm.

The following map includes a broader temporal range of tweets dating back to last week on October 24th, up to approximately 1:22pm on Tuesday, October 30th, as the storm was starting to subside and damage be more closely assessed. Tweets included in this dataset contain direct reference to "Sandy" and include more-or-less precise latitude/longitude coordinates (as opposed to being geocoded to less specific scales such as the city or neighborhood level), allowing a greater level of precision, despite sacrificing a significant number of tweets in order to do so, though still leaving us with nearly 16,000 individual observations to work with. In order to show density as opposed to individual points, tweets were then aggregated to the level of census blocks.

Although we definitely see some larger clusters, it is remarkable how spatially dispersed the tweeting about Sandy was. The majority of tweets are located in midtown Manhattan, which was not only the location of the last open Starbucks in the city, but was also hit by widespread power outages. The concentration of tweets around the southern tip of Central Park are likely caused by the infamous dangling crane (and subsequent evacuations) at 57th Street.

While some areas that were hit by flooding see a pattern of increased tweet activity -- for example Battery Park, Dumbo, LaGuardia and Hudson River Park -- it is surprising how few tweets we find in areas that were hit especially hard or where significant events happened. In Breezy Point (not included in the map) a fire destroyed more than eighty homes, but only a handful of tweets come from that same location. Similarly, Sandy inflicted very significant damage to large parts of Rockaway and Coney Island with very little mention in these places on Twitter. Other major events covered by the media, such as the evacuation of the NYU Medical Center just north of Stuyvesant Town or the explosion at ConEd's power station on 14th Street, also see only a few tweets in the immediate vicinity, though perhaps owing to the fact that individuals in these locations would be more concerned about safety than tweeting.

It seems that, when zooming in on the urban scale, the location and density of tweets does not necessarily correlate with areas most effected by Sandy. As the hurricane brought the city to a grinding halt, with businesses and schools closing ahead of the storms, Sandy appears to have been tweeted from the -- relatively -- safe confines of the home, as opposed to the many locations throughout the city which were hard hit, but relatively unrepresented in this virtual representation.

Ultimately, we're left wondering whether Hurricane Sandy represents a case distinct from that of Hurricane Katrina? Though the areas that were the most tweeted from in this case represent both the most densely populated and most well-off, areas such as Harlem don't mirror the experience of Katrina in being devastated by the storm and then wiped off of the virtual representation of the event. Or, as Mark indicated in his earlier post, is it simply difficult to ascertain much from such finely-grained data in cities? Or, as the relative lack of discussion about the devastation Sandy has caused in the Caribbean indicates, has the location of the storm in arguably the world's most important city simply deflected media attention away from other locations?

We don't offer these as definitive conclusions, but instead as provocations, as much deeper analysis needs to be undertaken to more fully understand the relationship between such intensely material events as Hurricane Sandy and virtual representations of them through platforms like Twitter.

For a good reference on areas hard hit by the storm, see this from the New York Times: http://www.nytimes.com/interactive/2012/10/30/nyregion/hurricane-sandys-aftermath.html

Hurricane Sandy and the Geographies of Flooding on Twitter

With the worst of Hurricane Sandy now past, we wanted to build on our initial map of references to "Frankenstorm" and construct a fuller picture of how the storm was represented and discussed on Twitter. The first alternative representation we offer visualizes how Twitter discussed the most obvious impact of the storm, the massive flooding (felt particularly acutely in New York City) that has not only disrupted the every functioning of the city, but also had likely long-lasting impacts on many individual lives and the way we prepare for and attempt to manage such 'natural' disasters.

To begin, we have been collecting tweets containing the terms "flood" and "flooding" in order to examine how Twitter usage might reflect lived experiences of the storm. By examining the digital data shadows of an intensely material event, we can hope to gain some understanding of how the intertwining and interfacing of virtual and material spaces apart from the immediate consequences of this particular event.

An interactive version of this map is available at:

http://oxfordinternetinstitute.github.com/InteractiveVis/usmap/

The map reveals a few important findings. First, like the map of references to Frankenstorm, tweets referencing flooding are almost exactly where you would expect them to be; in other words, the vast majority of tweets were located in the path of the hurricane. Nonetheless, it is interesting that so few people elsewhere in the US are tweeting about the unprecedented flooding and resulting damage taking place on the East Coast. In this sense, the geography of data shadows drawn from Twitter appear to be quite effective at reflecting experiences of the storm. The hurricane, in essence, leaves a digital trail.

Second, we are able to see that these data become significantly less useful if we want to draw insights at a scale finer than the county level. Until noon GMT on Tuesday, October 30th, there were only 5,209 geocoded tweets about flooding, a fairly small number over such a broad area. We even initially intended to map references in both English and Spanish to reflect the potential differences in experience between different linguistic groups affected by the storm, but despite the millions of Spanish-speakers undoubtedly affected, we were only able to collect five Spanish-language tweets!

In other words, it is the absences on this map that are almost more interesting than the mapped results. The lack of published content in Spanish means that we are necessarily only including published content from English speakers in these representations. The absences in the rest of the country are also revealing. Why are so few people in Kentucky, Missouri, Wisconsin, etc. tweeting about East Coast flooding? Is it because the act of tweeting about such an event is only really likely to be performed by people in situ, experiencing the storm? Are people outside the direct path of the hurricane interested in other impacts apart from flooding (for instance, the significant snowfall in parts of central Appalachia)? Are they interested at all? Or does the necessarily limited representation offered by Twitter constrain any possible explanations?

Mapping the Frankenstorm on Twitter

As some of us hunker down in our fortified bunker in Worcester, Massachusetts awaiting the Frankenstorm, and others hang out in the California sunshine, we thought we'd contribute our collective two cents to the discussion of the ongoing storm via mapping -- from Google's crisis map to the New York Times' map of the hurricane's expected path -- in the form of a visualization of Twitter activity around the storm [1]. 

It didn't take long for the term "Frankenstorm" to catch on. Shortly after the National Oceanic and Atmospheric Administration first used the term this past Thursday, the first geotagged tweet was created by @SStirling, a data journalist for the Star-Ledger newspaper in Newark, NJ, around 11:06am that day.

Since then, well over 7,000 geotagged tweets referencing the Frankenstorm have been created in North America. The dataset used here includes exactly 7,056 geotagged tweets collected from DOLLY, from the very first mentioned above until approximately 12:36pm EST on Monday, October 29, just as the storm was starting to pick up along the east coast [2].

Mapping the Frankenstorm

After aggregating the tweets to the county level, a quick glance reveals some striking, if not unsurprising, patterns. Despite being a major national news event, Twitter activity around the storm has been incredibly concentrated along the east coast where the storm is expected to hit the hardest, demonstrating a clear connection to the places in the path of the storm. While itself not surprising, the precise level of concentration is a bit more startling. Indeed, over 40% of the total number of geotagged tweets referencing Frankenstorm in this sample come from just eight counties along the east coast.

And while these counties represent four of the ten largest metropolitan areas in the United States, and the four largest along the east coast, the concentration within these areas demonstrates the extent to which areas which might be just as hard hit -- such as rural Vermont during 2011's Hurricane Irene -- are relatively underrepresented in the virtual reflection of events such as these. But perhaps more interesting than the cluster of references along the east coast is the anomalous concentration of references all the way across the country in southern California.

Frankenstorm Hot Spots

Just 23 counties across the United States had more tweets referencing the Frankenstorm than Los Angeles, which had 46 tweets, breaking up what would otherwise represent a clear effect of distance decay in predicting the number of tweets referencing the Frankenstorm. In contrast to the relatively concentrated pattern discussed above, a cluster of references comparably significant to areas of Maine, Pennsylvania and Virginia pops up around over 2000 miles away from the path of the storm, while areas in between in the American south and midwest show no such clusters.

Though L.A.'s large population makes this concentration of activity somewhat less surprising, the city's position within the national (and global) urban hierarchy offers a somewhat more interesting (at least to geographers!) explanation. When considering L.A.'s centrality within the global air transportation system and the fact that thousands of flights have been affected by the storm, there emerges a range of alternative explanations emphasizing the relationship between Los Angeles and the cities along the east coast more directly affected by the storm. For instance, at least a handful of tweets, like those below from @robyntomlin and @paulhogarth, specifically reference air travel from Los Angeles to the east coast and into the path of the impending Frankenstorm.

So while the analysis presented here is more a confirmation than a revelation, it clearly shows the persistent connections between space and place in online networks like Twitter, as well as how geotagged Twitter content represents a promising way of demonstrating these connections between the virtual and the material. With the worst of the Frankenstorm still yet to come -- as are thousands more tweets, we're sure -- we hope everyone continues to stay safe and dry in the coming days... and that someone goes ahead and starts working on the next ridiculous name for a major storm so that we can do this again in the future!
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[1] Despite there being a range of possible keywords possible here -- such as "Hurricane Sandy" itself -- we, in typical Floatingsheep fashion, chose only to map the more ridiculous "Frankenstorm". As such, the analysis here is tempered by this limitation.
[2] No members of the Floatingsheep collective were harmed in the making of this map. Taylor did, however, bravely venture out into the Frankenstorm to make it to the office in order to produce these maps, and his pants were appropriately drenched for this effort.