Theoretical Framework


Moving from the paradigm of Digital humanity, we introduced our approach on how to define human experience  of cultural dataset. In the context of the developed tool we attempted to bridge the scholar field and the  cultural institution. The rich variety of conditions influence on the state of the museums in the era of the digital environment. Along with that, the scholar field studying cultural datasets also is facing various  challenges – this is what inspired us to explore the framework of the conducted work.

The end of twentieth century is characterized by integration of different types of technologies into a single entity hypermedia. The paradigm of hypermedia assumes the increase of access to new online services for the entire population and in various aspects of life. Thus, the development of technologies contributes to the  democratization of digital services. In this context museums around the world continue widely adopting various technologies aimed to increase art accessibility, both on-site located in the museum and online. This new digital reality raises new functionality prescribed for museums. Today Malraux’s “the imaginary museum” obtains new metamorphoses: digitized collections, mobile applications to access digital archives, and omnichannel strategies to communicate this knowledge.

While mass digitization of archives takes place, many challenges remain in these research field, one of which is how to extract and articulate information structures. Another important challenge of studying cultural datasets is to how identify appropriate methods for studying them and how to recognitize patterns in those datasets.  

Today the rich variety of topographic maps is supplemented by complex semiotics for specific media in order to visualize spatial data. The combination of geospatial communication and the creative process required to understand representation of distorted space. In other words, for maximizing information impact, the cartographic framework for modifying spatial data is not sufficient but requires “creative processes” as complementary basis. Hence, embracing capabilities of spatial analysis, involving creativity and curiosity new solutions can be created for the field of Digital humanity that can museums welcome as their new way to engage with the audience. We believe that the developed tool with enhancing its capabilities has the potential to be replicated for the collections of other museums. By inviting the audience to participate, cultural institutions create a space for a wildly open dialogue related to the subject of the institution. At this stage, inviting to continue explorations of MoMA’s collection on our main webpage.



Project Overview


This website is built in fulfilling Spatial Data Capture module final project requirement. The aim we are perusing placed in the dimension of digital humanities. That means we are reflecting the purposes of defining patterns in the collection though “creative visualization”. To bridge the scholar field and the cultural institution, the objectives of the website is to investigate trends in country level art creation, inclusion, color trends per country/decade and global collaboration in art creation using MoMA’s art collection. The website contains four main features that leverage MoMA’s processed data set that is uploaded on a UCL server and called using API.
More Info:https://github.com/npoladian/SpatialDataCapture.git



MoMa Snapshot


MoMA is one of the largest and most prominent museums in creating and collecting modem arts in the world (Fred and Mamiya, 2015). Established in 1929, MoMA is a home for almost 200,000 art works from expressing 150 years of history. It’s a magnificent collection of paintings, sculpture, printmaking, drawing, photography, architecture, design, film, and media and performance art. Digitally, and what this research will further explore, MoMA’s website contains only 86,000 artworks (Museum of Modern Art, 2020).



Major Reference



MOMA collection:


Data: https://github.com/MuseumofModernArt/collection


Python Libraries:

pandas - https://pandas.pydata.org/
numpy - https://numpy.org/
iGraph - https://igraph.org/redirect.html
validators - https://validators.readthedocs.io/en/latest/
sqlalchemy - https://www.sqlalchemy.org/
re - https://docs.python.org/3/library/re.html
Colorgram - https://github.com/obskyr/colorgram.py

JavaScript Libraries:

Wicket - https://github.com/arthur-e/Wicket
Leaflet - https://leafletjs.com/
Leaflet.curve - https://github.com/elfalem/Leaflet.curve
D3 - https://d3js.org/
Highcharts - https://www.highcharts.com/


External chunks of code:

Ryan Catalani, Creating consistently curved lines on Leaflet - https://medium.com/@ryancatalani/creating-consistently-curved-lines-on-leaflet-b59bc03fa9dc


World polygons:

https://github.com/datasets/geo-countries