Deep Mapping in Small Scale

Philomena Vida
Professur
Architekturgeschichte und kuratorische Praxis
Seminararbeit

Depending on who you are (identity), where you live (largest scale being the continent), how you live (smallest scale being the room) and what you do, the perception of the subject changes to its surroundings. City does not influence the subject but the subject’s general situation influences the objective perception of the city.

The aim is the development of a programmatic or parametric deep mapping method trying to collect all data involved in an exhaustive manner – with the goal to allow both subjectivity and objectivity depending on the user.

Shannon Mattern’s “Deep Mapping the Media City” illustrates in great detail how complex and all-encompassing the attempt, to find a method that makes it possible to represent the city in its conglomerate of influences and relationships, is. A very good approach of research is to borrow the competences from other scientific topics such as archeology in order to apply them to this topic and thus to find new methods to cover the broad spectrum. In my opinion, another successful way to do this is to approach it on different scales and to work in an interdisciplinary manner. “One field of industry“ will not be able to grasp the complexity, but with the help of technology, I am thinking here of parametric or programmatic data collection, the knowledge of various fields of work can be gathered. By combining competences of different fields of work it is not necessary to become an expert in the areas involved but to simply create a tool that simplifies data collection and also aids to understand the mass of information. By using parametric data collection systems relationships and (co-)dependencies can be made visible.

In the first part of this work, data collection tools such as destatis.de and data representation tools like kaggle.com (and many more) will be analyzed thoroughly in order to understand all the parameters involved and to get familiar with already existing data collection methods with the aim of improving them. After having collected sufficient data (the claim being completeness) in a next step parametric of programmatic tools will be tested – in a “trial and error” manner – until, hopefully, an adequate tool is found or developed which enables the reader to access data on all scales in an interacting manner.

Mapping in Small Scale: Case Study Munich. (cc)
Digging deeper than the usual Map: Gathering Data from various Fields of Knowledge.