Mapping for the Digital Humanities DHIB 2017

Mapping for the Digital Humanities

Digital Humanities Institute – Beirut in March 2017 (5 hours).
@DJWrisley

Description:

This is a five-hour course that introduces basic elements of modeling spatial data for the humanities, data creation with gazetteers and making simple interactive maps with a symbology appropriate to the data.

Outcomes: 

Participants who complete this workshop will

    • understand the basics of spatial data (formats, types, accuracy).
      gain a basic appreciation for the concept of data modeling
    • learn where they can get spatial data appropriate for humanities inquiry, or how they can create it themselves.
    • gain a basic appreciation of the critical, interpretative side of making a map.
    • experiment with extracting locations from text.
    • appreciate different kinds of spatial data curation (manual, semi-automatic and automatic).
    • use geolocation services on their smartphones to generate some basic data.
    • learn to make a basic interactive map using Carto (and within a web hosting, if skill level permits).

Outline: 

(1) What are spatial data, that is, the data we need to make basic maps?  In what formats, do such data come?

(2) Where can we obtain spatial data? How can we create spatial data?  What is a gazetteer?  What is a spatial repository?

(3) Examples of digital maps projects: Edmonton Pipelines, Mapping Dante, Year of the Riot, Harlem 1935,  London Chatty Map, Slave Revolt in Jamaica, Going to the Show, Mapping the Lake District: A Literary GIS, Linguistic Landscapes of Beirut, Digital Karnak, NYT’s pick, Wandering Rocks, NoSweatShakespeare map, (LOTRLife of Maya Angelou, Novel City Maps), Photogrammar, Literary Geographies of Christine de Pizan, Digital Archaeological Archive of Comparative Slavery, Mapping the Mahjar

(4) Two hands-on examples:

a. Making a map from a text in three “flavors”:  (1) manually (2) semi-automatically using TopoText and (3) automatically using a basic python script (adapted from here).

b. Making a map using data captured with smartphone apps.

(5) How can we stylize those maps and share them with others?

 

See the other spatial humanities workshops and courses I have given. And this bibliography from 2015.

Linguistic Landscapes at Scale

Linguistic Landscapes at Scale: Affordances and Limitations of a Mobile Data Collection Approach
David Joseph Wrisley

Abstract – 2016 LAUD Conference

Institut für Fremdsprachliche Philologien, Fach Anglistik
Universität Koblenz-Landau
April 2016

A pilot study of the linguistic landscape of Beirut began in the Fall of 2015. The data has been collected by mobile crowdsourcing: a group of fifteen Lebanese language students who use the resultant dataset in a course project. At the time of writing this abstract, the total dataset has reached 600 samples from the city of Beirut and its suburbs. After initial analysis, the data model will be refined and the project will continue, running until the Spring of 2017. The linguistic situation of the Lebanese Republic–and in particular the capital city Beirut–is complicated by a number of factors: (1) differing patterns of multilingual practice marked by class and communitarianism, (2) the orthographic possibilities of alternating between Arabic and Latin scripts and Oriental and Western numerals and (4) what I called „audience-specific multilingual non-equivalence.“ Can LL studies say something about the intended audience of different national communities of varied multilingual practices? Unlike notable LL studies that examine the imprint of immigrant communities in specific neighborhoods of diverse metropolitan environments (Blommaerts), this project describes „indigenous“ multilingualisms, without restricting the zones in which data is collected and without prescribing a level of granularity of the data. Instead of qualitative ethnography focusing on deep local context of a small contained area, the project resituates the notion of ethnography in a data-driven, geospatial frame. Geosemantics, and discourses in place (Scollon/Scollon) are replaced with the possibility of spatially-bound multilinguistic patterns.

Written language samples are captured by mobile application and the image samples are human tagged with metadata based on a boundary vocabulary of contextual and linguistic features specific to the Lebanese language situation. The platform employed permits live visualization of the aggregate of the data. It also allows access to a growing database of geotagged images of written samples of language, and their visualization of them on a map interface using queries based on their metadata. The scope of data achieved in a small amount of time produces interesting visual results, but ones that we must „read“ with care. We must create a continuum, including which kinds of language samples are not location-based, which ones might be location-specific, as well as those which are most susceptible to revealing spatial patterns. The overwhelming amount of bilingual samples collected were unsurprisingly English/Arabic. In the paper I will discuss some of the others patterns in LL that the study has found about languages in contact in the Beirut area (Arabic/French, Latin transliteration of Arabic, minority languages as Armenian and Tagalog) as well as refinement needed for the metadata.

Analysis in a data-driven LL study recalls debates in literary studies between distant and close reading, having both affordances and limitations. Larger sets of data reveal patterns that were not previously visible and abstract representations of them (like a map) provide new avenues for contextualizing such data. There is, however, a need for flexible visualization possibilities to keep all scales in mind. Interface design, for example, facilitates an explorative close-distant readings with the possibility to visualize language sample and its metadata in the information box.

Keywords: LL, mobile data collection, map visualization, spatial humanities, digital humanities, GIS, Arab world, network analysis