Today, SO! continues its series reconsidering the life and work of Alan Lomax in his centenary year, edited by Tanya Clement of The University of Texas at Austin. We started out with Mark Davidson‘s reflections on what it means to raise questions about the politics behind Lomax’s efforts to record and collect folk music, and continued a few weeks later with Parker Fishel‘s consideration of Lomax’s famous “Southern Journey” and how it has been appropriated by musicians more recently.
With Clement’s own article below, the series begins to rethink Lomax as a touchstone in current and continuing drives to collect, measure and compute sonic cultures, something that seems hot all of a sudden (see, for instance, coverage of recent digital analysis of trends in pop music at Queen Mary University of London). In her thoughtful, illuminating and inspiring article below, Clement challenges us to consider the politics behind these efforts to search, retrieve and analyze audio, something that the case of Lomax throws into stark relief.
— Special Editor Neil Verma
When the Association for Cultural Equity, an organization that Alan Lomax founded in 1983, announced the release of 17,000 music tracks from Lomax’s fieldwork collections, the New York Times heralded the release as a manifestation of Lomax’s Global Jukebox project, a computational experiment for accessing and studying his vast multimedia collection of the world’s culture. The Times piece likens Lomax’s project to Pandora, which allows the listener to search for music “like” music she has already found. Lomax’s biographer, John Szwed, also makes this comparison but modifies his description by proclaiming that unlike Pandora’s recommendations which are “based on personal taste” and “tend to lead sideways . . . to production style,” Lomax’s Global Jukebox idea held the potential to point a listener to “deeper principles of cultural and musical organization” (The Man Who Recorded the World 391).
Gobsmacked by whizbang possibilities, neither the Times nor Szwed discuss the deeper principles behind Lomax’s attempt to represent culture as a global search engine. In the context of the powerful work being accomplished in the Music Information Retrieval (MIR) community and my own project (HiPSTAS) to develop software for making sound collections searchable and accessible, In this article I will argue that how we build systems for searching and retrieving and browsing cultural artifacts as data is a profoundly political act. Recognizing such politics suggests that Lomax’s Global Jukebox project serves as a cautionary tale for how social and cultural contexts — or what Donna Haraway calls our “ways of being” — are reflected in the systems we develop.
The Singer with the Song
The year that Alan Lomax was born (1915), his father John Alan Lomax published a landmark piece heralding seven new types of American ballads for study. American ballads, he argues “reveal the mode of thinking, the character of life, and the point of view, of the vigorous, red-blooded, restless Americans, who could no more live life contented shut in by four walls than could Beowulf and his clan, who sailed the seas around the coasts of Norway and Sweden” (“Some Types of American Folk-Song”, 3). Unlike any other collection of ballads, John’s “American ballad” included the ballads of the miner, the lumbermen, the inland sailor, the soldier, the railroader, “the ballads of the negro; and the ballads of the cowboy . . . [and] the songs of the down-and-out classes, — the outcast girl, the dope fiend, the convict, the jail-bird, and the tramp” (3). Governed by a laudable goal to record the songs of folk cultures at the fringes of mainstream society, the senior Lomax’s view of the communities where he would collect his songs (including jails and state farms), was complex, and can fairly be called both progressive as well as racist (Porterfield 170).
John and Alan went on seven collecting trips together between 1934 and 1936 and co-authored five books on their return. On these trips, they collected songs from people on the street in cities like New Orleans and people in the country, from both church-goers and prisoners. While John held romanticized views of the “noble” southern black man, Alan, on the other hand, indicated a more nuanced understanding of the complexities inherent to his father’s attempt to generalize patterns of “folk” for study. Alan linked “the singer with the song” and was interested in the politics behind prisoners made to sing with guns at their backs and in the cultural lives of people that were so poor in means but so rich in “beautiful harmony, with enormous volume, with total affection” (Szwed 49). While Alan maintained that he was interested in the individual’s story, John believed that “a genuine ballad has no one author. It is therefore the expression of no one mind: it is the product of the folk . . . It might have been written by any one” (“Some Types of American Folk-Song”, 1).
The Global Jukebox project demonstrates an almost complete reversal in Alan’s concerns. The studies behind the Global Jukebox include Alan’s Cantometrics and Choreometrics, in which he produces taxonomies for studying song and dance and his Parlametrics project, an “experiment in metalinguistics,” which Alan and his collaborators describe as a taxonomy of “patterns of style” in speech based on dynamic changes in pitch, loudness, speed, spacing, rhythm, and timbre (“A stylistic analysis of speaking”). These taxonomies show that Alan’s early consideration for the individual performer gave way to a desire to make folk study more scientific as a cultural mapping like what his father espoused rather than what Szwed and others have seen as Alan’s concerns with the situated politics of individuals.
Alan’s Parlametric study serves as good example. Approaching delegates from the United Nations and soliciting mail-in samples from regions not covered by the U.N. volunteers, Alan and his team collected representative recordings of 114 languages. Then, in order to study the “generally neglected meta-communicational level” in these recordings, the team designed a rating system including 50 codes that (1) “described the distinctive features of each recording,” and (2) “tended to cluster the recordings into sets of similars” that Alan maintains anyone could “readily use” to record “salient differences in conversation style” (19). These clusters pointed to 14 factors that Alan and his team would use to categorize the cultures from which they received samples:
- Speech length
- Descending cadence
Using these factors, Alan makes some broad assertions. The association of clear syllabification” (the degree to which syllables run together) “is most strongly predicted among gardeners with domesticated animals” and “[t]he association of clear syllabification to feminine autonomy is suggested by the discovery that this mode of speaking predicts and is predicted by permissive rather than restrictive premarital sexual mores” (27). Further, “Dominance vs. Sharing of conversation space” is strongly correlated with settlement size and severity of sexual sanctions,” a statement that Alan immediately rationalizes by noting that “this relation between a more crowded social space, high sexual tension and increased rate of interaction seems to make good sense, even if it does not account for every possibility” (31).
These spurious and broad generalizations were what Lomax hoped to facilitate for all with his Global Jukebox as the access point for “the first numerical models of the full range of global cultural variation in holistic form” for “the scientist, the layman, and the student to explore, experience, and manipulate the broad universe of culture and creativity in a systematic fashion, with audio-visual illustrations at every turn of the road” (“The Global Jukebox,” 318). By leveraging his taxonomies of song, dance, and speech in the computer age, Alan could suddenly associate and differentiate cultures holistically and en masse.
Machinic Methods / Humanistic Questions
As someone who works in the liminal spaces between the humanities and technology, between cultural studies and critique and the machines that increasingly function both as access points and barriers to our cultural artifacts, I see Alan’s switch to generalizable taxonomies as par for the course in the digital age. My own >HiPSTAS project’s primary objective is to develop a virtual research environment in which users can better access and analyze spoken word collections of interest to humanists. We understand that in order for us to search digital sound artifacts, we have to create taxonomies, metadata, keywords and other generalizable frameworks that facilitate discovery.
At the same time that we are using machinic methods, however, we can still ask humanistic questions that open up rather than close down debates and dialogues. In a recent test for the HiPSTAS project, for example, we used machine learning to analyze the recordings in the UT Folklore Center Archives, which comprises 219 hours of field recordings collected by John and Alan Lomax, Américo Paredes, and Owen Wilson, among others (UT Folklore Center Archives, ca. 1928-1981, Dolph Briscoe Center for American History, University of Texas at Austin, Box 2.325/R). In our attempt to predict the presence of different sonic patterns including instrumental music, singing, and speech, the results of our analysis are noteworthy as the visualization shown in this brief movie demonstrates.
from Tanya Clement on Vimeo
Within the results, we see a visualization of how many seconds comprise each file (in blue) and how many of those seconds for each file our software has predicted the presence of instruments (green), speech (red), and song (purple). A subtle yet striking difference emerges in the comparison between the Lomax recordings (created 1926-1941), which are the oldest in the collection, and the others, which were created up until 1968. The Lomax recordings (primarily created by John Lomax) consistently contain the least amount of speech in comparison to what the other files contain.
Of course, there are a number of ways you can read these results. Given the conversation above, one could hypothesize that perhaps the Lomaxes were primarily interested in their participants’ songs rather than their stories. One could also think about it in terms of recording capabilities across time. When the Lomaxes were first recording, John Lomax writes, “The amplifier weighed more than one hundred pounds; the turntable case weighed another one hundred; two Edison batteries weighed seventy-five pounds each. The microphone, cable, the tools, etc., accounted for sufficient weight to make the total five hundred pounds. . . . In order to carry them in the car I tore out the back seat . . .” Even in 1967, forty years later, good recorders still weighed 70 pounds and required a car battery, but tapes were longer and costs were less. More tape and more time at less cost both financially and physically had a big impact on what researchers recorded. At the same time, the data shows that the later recordings are not much longer, but do seem to have more seconds of speech.
There is a danger in these kinds of machine-generated generalities. We employed taxonomies (instrumental, sung, speech) to teach the machine to categorize these patterns, but why these patterns? Are there others? Or did I choose these based on what I already wanted to say about the Lomaxes’ practices? And, I haven’t even mentioned here the subjective practices inherent to choosing algorithms for such work.
These kinds of questions require more research, and more contextualization than this aggregated data set can show. Just as the ballads that John and Alan Lomax once collected were written and sung by someone, so were the communities that Alan interpreted through his Parlametrics made up of individuals, not types. Perhaps Alan’s desire “to record the world” was just and Google, the collector, categorizer, and interface for all things on the Internet, isn’t evil. But the Global Jukebox Project serves as a cautionary tale about the politics behind the speed and efficiency that machinic methods seem to promise, a politics that needs to be far less opaque about its deeper principles and problems.
Tanya Clement is an Assistant Professor in the School of Information at the University of Texas at Austin. She has a PhD in English Literature and Language and an MFA in fiction. Her primary area of research is scholarly information infrastructure. She has published widely on digital humanities and digital literacies as well as scholarly editing, modernist literature, and sound studies. Her current research projects include High Performance Sound Technologies in Access and Scholarship (HiPSTAS).
Featured image: “Day 21 – Waveform” by Flickr user evil_mel, CC BY-NC 2.0
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This September, Sounding Out! challenged a #flawless group of scholars and critics to give Beyoncé Knowles-Carter a close listen, re-examining the complex relationship between her audio and visuals and amplifying what goes unheard, even as her every move–whether on MTV or in that damn elevator–faces intense scrutiny. In the coming Mondays, you will hear from Priscilla Peña Ovalle (English, University of Oregon), Liana Silva (Editor, Women in Higher Education, Managing Editor, Sounding Out!), Regina Bradley (writer, scholar, and freelance researcher of African American Life and Culture), and Madison Moore (Research Associate in the Department of English at King’s College, University of London and author of How to Be Beyoncé). Today, we #wakeuplikethis with Kevin Allred (Women and Gender Studies, Rutgers), who refuses to let us fast forward past the understated and discomfiting video track “No Angel.”–Editor-in-Chief Jennifer Stoever
Michael Brown, an unarmed black teenager, was shot to death by white police officer Darren Wilson in Ferguson, Missouri on August 9, 2014, for no apparent reason [see author’s note below]. While the fact that the death of unarmed black men (and women and trans people) at the hands of those in power is, sadly, no unique occurrence, as Melissa Harris-Perry – among countless others – have pointed out, Brown’s death has sparked nation-wide outrage and protests about police brutality, racial injustice, and severely decreased resources for impoverished communities.
In the New York Times’ reportage on Brown’s death, reporter John Eligon found it appropriate to characterize the teenager as “no angel,” as if any amount of alleged wrongdoing or possible character flaws on Brown’s part somehow justified his murder. Both Eligon and the Times were called out on social media and in traditional publications such as the Atlantic, and, while the press quickly apologized, the phrase’s familiar interpellation of victims – especially when they are people of color – as “no angels” lingered: a journalistic gaze born of sympathy with white authority that invalidates an individual’s life, tarnishes their memory, and even rewrites histories.
A powerful counter discourse challenges these racist narratives, however; one we can hear in the dissonant sonics of Beyoncé Knowles-Carter’s “No Angel,” a song that anticipates–and adamantly refutes–the racist notion that people who live, and quite possibly die as Michael Brown did, are anything but valuable.
Released almost 9 months before Brown was killed, Beyoncé’s “No Angel” cannot explicitly be called a response, but the ghosts of black men–living and dead, unfairly judged and mistreated–haunt the entire song. “No Angel” begins with a slow-burning 4-count beat; a low, synthesized, slightly off-kilter kick drum sets the tone for the song while an eerily-lilting high-pitched synthetic warbling noise simultaneously pours out of the speakers. The sound, reminiscent of feedback, drifts between pitches for four painstakingly slow measures before Beyoncé’s vocals kick in. When they do, what we hear is unusual. Her voice reverberates in a breathy, almost too-high falsetto that sounds nothing like the soaring powerhouse vocals of “Halo” or “I Was Here;” nothing like the precise, punctuated staccato of “Single Ladies” or “Run The World (Girls);” and certainly nothing like any of the other songs on BEYONCÉ. Sonic and musical contradictions abound. Neither the kind of impressive falsetto showing a wide range, nor necessarily immediately pleasing to the ear, Beyoncé’s voice is forced. . .and it haunts. Further, the vocals seem to cut the time signature of the song in half, effectively doubling its pace. Its tempo, cadence and vocal dynamics signal “No Angel” as a very different kind of Beyoncé song, one forwarding a sonic and political statement over radio-readiness.
Admittedly, “No Angel” was not initially one of my favorite songs on Beyoncé’s new album. In fact, when teaching this song, most of my students – even the hardcore Beyoncé fans – admit sheepishly to skipping through it because of a feeling of discomfort mainly related to Beyoncé’s vocal delivery. But after a few listens, something clicks: “No Angel” is not supposed to be pleasant, easy listening, but rather it jars and unsettles, just like the impact of news of another unarmed black person killed by white police. We are neither supposed to immediately identify with the audio of “No Angel,” nor possibly even like it.
All about dissonance, the song’s innovation fuels itself with inconsistency and contradiction. Vocally speaking, there are incongruities between the highs and the lows (the bass and the treble or the bass and the soprano), and the abrupt shifts in pacing—the slow pace both in opposition to and contained within the faster pace of the song. Even the stretched and breathy falsetto Beyoncé puts on for most of the song strikes a dissonant chord. Simply put, dissonance is tension, and neither the sonic nor the political tension ends here.
Here’s the thing: we know Beyoncé’s voice doesn’t usually sound like it does on “No Angel.” It is a conscious manipulation. And when coupled with the refrain:
You’re no angel either baby
her voice even becomes a kind of accusation. If Michael Brown is “no angel;” if Beyoncé herself, as the singer and also as a black woman in a racist U.S. society, is also initially “no angel” then, Eligon in the NYT as Brown’s accuser and, by extension, we as the listeners, become “no angel either.” What’s more, Beyoncé ‘s delivery is so deliberately overdone that she can be heard taking deep breaths in between each word of the chorus – something most singers would attempt to hide. Beyoncé chooses instead to push it to the forefront of the recording, audibly projecting each word toward the listener; in effect, slapping us in the face with each syllable, driving that message/accusation straight home.
However, while this forum may be dedicated to the sonic qualities of Beyoncé’s work, I don’t believe we can completely divorce the sonic from the visual for “No Angel,” or anything on BEYONCÉ, as it was specifically marketed and publicized as audio and visual album. BEYONCÉ visual music experience is nothing knew to Beyoncé herself. As far back as Dangerously In Love, Beyoncé forwarded her belief “that harmonies are colors” on an interlude toward the end of the album. For her latest album, she went so far as to strike an exclusive deal with iTunes in which the album was only available as a complete package for the first week of its release – singles could not be purchased separately nor without videos (and vice versa). It would be a disservice to ignore the visual depiction of “No Angel,” directed by @LilInternet and its synaesthetic fusion of sound and sight.
Just as the sonic qualities of the song provoke discomfort, unexpected imagery confronts the eye. First, the video is quite simply not about Beyoncé. She is rarely shown. Instead, Houston street culture and its struggles and poverty, represented largely through the faces of people of color, takes center stage. The video features mainly black men, although there are some images of black women throughout; a stark moment showing black women– raced and sexualized as “no angels”–working as strippers reveals an industry driven not by the agency/desire of the strippers themselves, but rather by the failures of capitalism that often offer few profitable choices to poor women other than sex work (as evidenced in the highlighting money shots/literal shots of money). But the video celebrates the lives of all theses alleged non-angels by showing small moments of happiness – a father smiling with his son; young boys flashing the peace sign with their fingers; sweet glances between lovers, community members proud to show off their jewelry and cars for the camera–redirecting the viewer’s attention to the structural inequality framing many of the images, provoking more voyeuristic viewers to reflect on their own responsibility and complicity in the systems that create poverty and segregation in the first place. Simultaneously, members of the Houston community–as well as diasporic Houstonians–can see themselves represented, even celebrated in the video, as opposed to the usual cinematic vilification and denigration of these very same bodies as “no angels.” Many Houston hop-hop legends appear in the video as well: Bun B, Scarface, Willie D, Z-Ro, and of course Beyoncé herself.
When the camera does linger on Beyoncé, it juxtaposes her iconic image–associated with the wealth and power of her celebrity status–with everyday people of her Houston hometown, including its most impoverished. Beyoncé, the residents, and the city itself stare the viewer down – actively challenging us to see both life and death, both living people and memorials to those, such as Michael Brown, who have died too young. The dissonant tension between life and death, encapsulated in Beyoncé’s voice and the video’s mirroring visuals: the high and the low; the rich and the poor; and the fraught gap in between.
Beyoncé tries to bridge this gap, both visually, through her self-representation in Houston, and sonically, particularly the moment when the refrain “you’re no angel, either. . .baby” finishes and she swings her voice down out of falsetto, into her more powerful full-out throat voice. It is assuredly no coincidence that during those moments she repeats the word “no,” over and over again. With that “no,” she objects to mainstream news vilification, anticipating and objecting to Michael Brown’s characterization as “no angel.” Her voice shifts from the high-falsetto – a sound serving the dual purpose of exposing the above dissonance AND acting as a caress to black communities in Houston, and Ferguson, and on and on – and moves into the heavy throat-voiced complaint and objection. “No, you will not treat my people this way. No, you will not treat ANY people this way,” her sound testifies. And, for viewers not represented in the video, that “you” is them–and she means that realization to feel uncomfortable. Beyoncé’s vocal dissonance and visual images force a confrontation with this accusation: it is not Michael Brown and those represented and celebrated in the video who are “no angels,” but the rest of us who fall short.
So, if we are “no angels, either” what do we do about it? Do we skip the track? Do we sit in front of our computer with the visual evidence of the failure of the American Dream staring us down as we groove to Beyoncé’s vocals? Do we turn off the news coverage in Ferguson, MO and pretend it’s not happening? Beyoncé’s voice provokes these uncomfortable questions and more: What does inequality sound like? What does hope and/or resolution/reparation sound like? Are they the same sound? This is a Beyoncé more in line with artists such as Nina Simone, who famously used her voice to express political critique, and not always in the most “pleasant” ways. “No Angel” is Beyoncé’s audio-visual “Strange Fruit,” positioning her alongside some of the most political and innovative protest singers of our time.
Author’s Note: At the time of Brown’s death, I had already been submitted this article. Due to the eerie coincidence between Beyoncé’s song “No Angel” and Eligon’s characterization of Michael Brown in the New York Times noted during the review process, I reworked the article to bring the two into conversation – which, in fact, they always were, even though I didn’t know it at the time.
Kevin Allred is a musician, activist, and teacher, currently at Rutgers University, where he teaches a signature course he created: “Politicizing Beyoncé.” He lives in Brooklyn, NY with his boyfriend and two elderly dachshunds. Join the Politicizing Beyoncé community at www.facebook.com/politicizingbeyonce. You can also find Kevin at www.kevinallredmusic.com and www.politicizingbeyonce.com.
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“It’s a city, not a cemetery. You can’t tell everybody to go around wearing earplugs.”
In 1905, a New York Times article declared New York City “the noisiest city on Earth.” More than a century later—this summer, to be exact—The New York Times ran a series on noise in New York City titled “What? The Long War on Loud” that proved that this city is still trying to figure out its relationship to sound. (One of the gems of that series? “New York’s War on Noise” timeline.) As a displaced New Yorker, some of my most vivid memories of the city are aural. Although New York City isn’t the only loud city out there, there are many reasons it’s called “The City That Never Sleeps”—and sound has a lot to do with it, depending on which neighborhood you call home.
Now you can see what neighborhoods are allegedly noisiest, and where all that noise comes from. Brooklyn designer Karl Sluis created the 2012 Manhattan Noise Complaints maps (click for full image), in which Sluis correlated the data on 311 noise complaints made during the year 2012 (40, 412 complaints, to be exact) that he obtained from the NYC Open Source site with Manhattan’s geographical coordinates. He used circles of various sizes to a) create an aural tracing of the island of Manhattan, sitting in a sea of turquoise blue b) showcase the number of complaints in an area. The bigger the circle, the larger the number of complaints.
The maps Sluis has created are helpful for visualizing the complaints on a broad scale, but they paint an incomplete picture of what noise means in New York City. The demographics of each neighborhood are absent from each map, a slight that can perhaps be traced to the 311 data available, but in order to better understand how New Yorkers define “noise” those stats must be included. Both Sluis and John Metcalfe from The Atlantic Cities discuss notable findings, but neither takes into account the fact that some of the areas with a higher concentration of noise complaints are not just densely populated but densely populated with racial and ethnic minorities. Indeed, comparing the maps’ noisy hotspots to a map of Manhattan racial demographics reveal how urban racial dynamics intersect with ideas about sound and power: who can make sound, who must be chastised for making noise, who can complain and whose complaints are actually being heard.
Mapping noise complaints gives a spatial dimension to noise, and it renders noise palpable, in a way. Sluis points out, “Noise complaints reveal the concentration of activity in the city as well as many smaller stories, such as the construction of the Second Avenue subway line, idling buses on the Upper East Side, and the homes of the loudest dogs (or the least patient neighbors).” He reminds us that the data comes from complaints and not necessarily decibels; in other words, it represents local ideas of what counts as sound and what counts as noise.
While Metcalfe correctly describes the thousands of 311 complaints about noise from 2012 as “the entire year’s expression of mass annoyance,” Sluis’s map does not go far enough toward figuring out whose annoyance, exactly. We must remember that annoyance oftentimes stems not just from physical reactions to noise but rather one’s perceptions about noise, what Jennifer Stoever-Ackerman deems “the listening ear.” How we hear others, Stoever-Ackerman argues, is not as natural as it seems. For example, whom we deem as noisy may stem from our community, our parents, and/or social conditioning. Accounting for race/ethnicity in noise maps will show how the listening ear conditions neighbors to categorize and react to certain sounds.
For the purpose of this analytic exercise, I compared Sluis’s maps and the Center for Urban Research, CUNY Graduate Center’s 2010 map of block-by-block demographic changes in New York City, in order to illustrate how population density and racial/ethnic demographics play a role in concentrated pockets of noise complaints. Drawn from 2010 census data, the CUNY map clearly delineates neighborhoods and color-codes the groups in each neighborhood per block: blue for whites, green for Latino, orange for black, purple for Asian, and grey for “Other.” Although the Center for Urban Research, CUNY Graduate Center’s maps cannot be superimposed on Sluis’s maps, they help give a general idea as to where neighborhoods are located in addition to racial demographics.
From the maps illustrating changing race/ethnicity patterns, I gathered what neighborhoods were predominantly white (West Village, Lincoln Square, Yorkville, Upper West Side), predominantly Latino (Washington Heights, East Harlem) predominantly black (Central Harlem, parts of Hamilton Heights), and predominantly Asian (Chinatown, blocks of the Lower East Side). When one compares Sluis’s overall noise map of Manhattan to the racial demographic maps of Manhattan, what stands out is that the major circles of noise complaints are also places where there are different racial and ethnic groups mingling (for example, Times Square) or places that are populated by mostly minorities (Hamilton Heights). Whereas Sluis flattens out the noise complaints, demographic stats point to the racial/ethnic contours of each neighborhood. Sluis’s maps focus on number of complaints; unfortunately this assumes everyone complaining is the same and that everyone making the noise is the same—a level aural playing field if you will. Bringing demographics into the equation underscores how not all complainers are equal and how not all complaints carry the same heft.
The city may be noisy, but “noisy” is relative. Sluis’s map shows some predictably noisy areas for those of us familiar with Manhattan’s soundscape (Union Square, Times Square) but it also draws attention to other areas not as predictable in the mainstream imagination (East Harlem South, Hamilton Heights). However, the maps by the Center for Urban Research, CUNY Graduate Center help us better understand the context for the high or low number of complaints in certain areas. For example, one of the biggest circles on Sluis’s general map of Manhattan is located in the Hamilton Heights/Washington Heights area; the Center for Urban Research, CUNY Graduate Center’s map of Manhattan above 110th Street show that these areas are densely populated by blacks and Latinos/as. This is key information because it reminds viewers that this neighborhood is a lot more ethnically diverse than other neighborhoods with a smaller number of complaints. It brings to mind: what role does race play in these complaints, in terms of those who complain and those who are the focus of the complaints? Although more people might mean more complaints, the prevalence of complaints like “loud talk” in East Harlem (Spanish Harlem) are nevertheless connected racialized ideas about people of color being “loud.” This doesn’t assume that the people complaining are white, but that they are complaining about groups that are characterized as loud, noisy, rowdy.
These noise maps, when put into conversation with demographic data, also indicate what areas are priorities in urban planning—the sounds of gentrification. The visualizations of the complaints by section (under the main map), combined with CUNY’s maps, are even more telling because they break down the number of complaints by category. The aforementioned northern tip of Manhattan, for example, is also where many of the complaints are concentrated. At a glance, loud parties, loud people, and loud car stereos seem to be the major complaints in those areas, according to Sluis’s visualizations. Meanwhile, noises of “urban growth,” such as construction and jackhammers, are less prevalent in these areas, whereas they are more prevalent below Central Park North, in now mostly-white neighborhoods.
Sluis’s maps of the 311 noise complaints data allow readers to see differences in terms of neighborhoods: who complains the most? what do they complain about? However, one thing to keep in mind is that first question: who makes the complaints. This is where the data falls short. Can it be assumed that those who are calling about the noise are mostly people who live in the neighborhood? Are Upper Manhattan neighbors less or more tolerant of noise? The answers to these questions, although they’re not found in Sluis’s map, point to how ideas of who is noisy or who can make noise are at play here.
I do not mean to downplay the usefulness of Sluis’s map. I instead call for the necessary addition of key missing factors to future noise maps in order to give us a more complex picture of noise complaints in Manhattan and elsewhere. Although it may not be possible to gather who the 311 callers are, including factors such as race and class may lead to very different noise maps. For example, what would a noise map of Manhattan look like if researchers brought income into the equation? Income inequality, especially in Manhattan where that imbalance is starkly on display, matters for the purpose of sound mapping. The more affluent neighborhoods are also the ones with less complaints and are the ones that are mostly inhabited by whites. Wealthier communities are more spread out and have more ability to couch themselves from noise, not to mention that it probably takes fewer complaints to get a response.
Gentrification is another factor: what kind of analysis could we do if we considered what neighborhoods have been gentrified in the past ten years? It is possible that as whites move into neighborhoods where people of color have historically lived, suddenly they find them noisy—hence, complaints. It is fitting to consider, for example, the tension between an established group of drummers in Marcus Garvey Park in Harlem and the inhabitants of a new highrise (characterized as “young white professionals”) who wanted the 30-years and running drum circle shut down, as reported in The New York Times in 2008. Moreover, if we accounted for the history of zoning in the neighborhoods that have the most or the least complaints it would add another layer of analysis to the data. Are some of these neighborhoods used as entertainment zones, for example? Is it easier to open up bars there than elsewhere in the city?
With these questions in mind, the maps go from beautiful renditions of data, to opening up a bigger conversation about the arbitrariness of noise. The demographical and sociological context of these noise complaints must accompany the raw data, especially when it comes to sound. The analysis also points to the source of the data: 311 calls. I wonder if this is the only way that people in Manhattan (and New York City at large) are dealing with noise. I’m sure that after a century of being “the noisiest city on Earth,” folks have gotten creative about it.
Featured image: ” Stranger 10/100 Johano” by Flickr user MichaelTapp, CC BY-ND 2.0
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The Noise You Make Should Be Your Own–Scott Poulson-Bryant