Connecting Music and Place: Exploring Library Collection Data Using Geo-visualizations

Abstract

Objectives -This project had two stated objectives: 1) to compare the location and concentration of Saskatchewan-based large ensembles (bands, orchestras, choirs) within the province, with the intention to draw conclusions about the history of community-based musical activity within the province; and 2) to enable location-based browsing of Saskatchewan music materials through an interactive search interface.Methods -Data was harvested from MARC metadata found in the library catalogue for a special collection of Saskatchewan music at the University of Saskatchewan. Microsoft Excel and OpenRefine were used to screen, clean, and enhance the dataset. Data was imported into ArcGIS software, where it was plotted using a geo-visualization showing location and concentrations of musical activity by large ensembles within the province. The geo-visualization also allows users to filter results based on the ensemble type (band, orchestra, or choir).Results -The geo-visualization shows that albums from large community ensembles appear across the province, in cities and towns of all sizes. The ensembles are concentrated in the southern portion of the province and there is a correlation between population density and ensemble location. Choral ensembles are more prevalent than bands and orchestras, and appear more widely across the province, whereas bands and orchestras are concentrated around larger centres.Evidence Based Library and Information Practice 2017, 12.2 37 Conclusions -Library catalogue data contains unique information for research based on special collections, though additional cleaning is needed. Using geospatial visualizations to navigate collections allows for more intuitive searching by location, and allow users to compare facets. While not appropriate for all kinds of searching, maps are useful for browsing and for locationbased searches. Information is displayed in a visual way that allows users to explore and connect with other platforms for more information.

A Survey on Visualizations for Musical Data

“…For hypothesis verification and generation, many visualizations follow Shneiderman's Information Seeking Mantra [Shn96] to provide an overview of the biographical database at hand, and means of zooming and filtering are to be used to analyse patterns of interest, to explore clusters of musicians having similar characteristics and to detect outliers. Typically, biographical information of various types given in databases is used to generate an overview of the data [AWR*07, KJ16, Doi17, Jän18]. Khulusi et al .…”

“…Other works take fewer biographical data into account. Doi [Doi17] focuses on music bands and the locations of their performances. Those are plotted on an interactive map that supports analysing the popularity of places.…”

“…This includes person‐describing information like name, life span or denomination, career knowledge like work span, practiced professions or related institutions and relational information, for example, to other musicians or to music‐related objects like musical pieces and instruments. Biographical information, typically available in a text‐based form, is extracted from databases offering the present research state in musicology [Foc19] (used by [JFS16, KJ16, JF17, Jän18, KJKF19]), from digital library catalogues [AWR*07, AGC*17, Doi17] or automatically derived from crowdsourced data [CK04, GZL05, Vav17, Spo19].…”

musiXplora: Visual Analysis of a Musicological Encyclopedia

“…Some projects focus on a prosopographical inspection of persons. While generic visualization tools for persons (Leskinen et al, 2017) are used to show domain-independent features such as networks (Lu and Akred, 2018;Miller et al, 2012;Gleich et al, 2005;Vavrille, 2017;Crauwels and Crauwels, 2018), places (Doi, 2017) or temporal data (André et al, 2007), musicological research often requires domainspecific, contextual information. For example, regarding musical instruments (objects), typical visualizations use three-dimensional data, gained through CT (Borman and Stoel, 2009;den Bulcke et al, 2017;Hopfner, 2018) or other techniques (Heller, 2017;Konopka et al, 2017) to generate volume or surface renderings (Tuniz et al, 2012).…”