Dunham's Data: Katherine Dunham and Digital Methods for Dance Historical Inquiry, 26 Countries, 1950-1953 (ICPSR 37698)

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The purpose of this study is to invesitgate the use of data analysis in dance history through a project that centers on the case study of African American choreographer Katherine Dunham (1909-2006). While digital methods have altered the landscape of most humanities and arts disciplines, the field of dance studies has yet to fully identify how it can benefit from these analytic approaches. Through the specific line of research ragarding Dunham, the project addresses the original problems and questions of dance history that can be advanced through an innovative, critical, mixed-methods approach that includes geographical mapping and network analysis. Using digital research methods and data visualization in the context of dance history can catalyse a better understanding of how dance movements are shared and circulated among people and continents, and the networks of support and influence that undergird artistic and economic success.

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Everyday Itinerary 1950-53 Data: This manually curated itinerary catalogues where Katherine Dunham was every day from 1950-53. In these four years alone, Dunham passed through at least 71 cities over 134 trips across four continents. This dataset tracks geographic location and, less comprehensively, the accommodation in which Dunham stayed each night (the theatres, nightclubs, television studios, and other places she and the company performed), the modes of transportation used when travel occurred, additional transit cities through which she passed, and whether or not Dunham was likely to be in rehearsals or giving public performances. Writing and datasets are equally co-authored by Harmony Bench and Kate Elswit; name order is alphabetical. This data can be explored as an interactive timeline visualization on the Dunham's Data website.

Everyday Itinerary 1950-53 Grouped Cities Data: This is an adaptation of the main dataset "Everyday Itinerary Dataset, 1950-53". The only difference with respect to the original version is that locations are grouped by proximity. If the geodesic distance between two or more locations is less than a specific threshold, we grouped them in one single location with a common name. This threshold was calculated empirically for this particular dataset and set to 15 km / 9.32 miles. We considered distance, terrain, and geopolitical situation of the considered areas. The resulting grouped areas are: Port-au-Prince and Petion-Ville under the common denomination "Port-au-Prince". Los Angeles, Hollywood, and Beverly Hills under the common denomination "Los Angeles area".

Stay-Lengths 1950-53 Data: This dataset is based on "Everyday Itinerary Dataset, 1950-53". It groups every single day of Dunham's itinerary into stays. A stay is a visit to a place and its length is calculated as the number of consecutive nights spent in that place. Basic columns: CITY & COUNTRY: City visited and its country. START_DATE: Arrival and departures dates for this stay in the city. N_ROWS: Number of consecutive rows (each row represents a day) in which this city is the value for the column "CITY1" on the previous "Everyday Itinerary 1950-53 Grouped Cities Data". LAST_MOMENT: Last likely moment spent in the city with respect to the last night spent. The two possible values are: "Probably that night" if "CITY2" is blank on the last row. We assume they stay overnight if we have no certainty that they travelled. In this case, the last row corresponds to the last night spent in the city. "The next morning" if the value for "CITY2" is different from "CITY1". The second-to-last row corresponds to the last night spent on the city, while the last row indicates they travelled the following day in the morning. Due to the uncertainty created by the information (or lack of it) contained on "CITY2", the stay length ranges between two values: MIN_NIGHTS: Certain number of nights spent in the city, regardless the value for "CITY2". It is always equal to either N_ROWS (when "CITY2" is not blank on the last row and the LAST_MOMENT was "The next morning") or N_ROWS minus 1 (when "CITY2" is blank on the last row and the LAST_MOMENT was "Probably that night", meaning they travelled in the morning and did not spent the night on their last day). MAX_ROWS: Likely number of nights spent in the city, under the assumption that a blank value for "CITY2" on the last row means they probably stayed that night. Other columns: LATITUDE & LONGITUDE: Geographic coordinates of the city.

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2020-07-30

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