Nicolas is invited to hold a public lecture and a participate to a workshop about cartography and cartographical representation in the context of the project ‘Planetary Urbanization in a Comparative Perspective’.
The research project consists of an experimental comparison of urbanisation processes in 8 urban mega regions. It investigates the sharp increase in speed, scale and scope of urbanisation as well as the surprising forms of difference, diversity and variation within and between urban areas. (Pearl River Delta, Tokyo, Kolkata, Istanbul, Lagos, Paris, Mexico City and Los Angeles). The major aim of the project is to develop adequate definitions of these processes, which means to capture both global and local dimensions alike.
Cartographic representations are today widely applied in architecture, urban design, planning, and social sciences. However, moments of critical reflection on methodological foundations,and political consequences of mapping are rare. This transdisciplinary symposium has the aim to engage a critical discussion on mapping and cartography, by inviting four specialists in geography, cartography, visual representation, arts and graphic design for a presentation of their experiences in mapping. The lecture and workshop will particularly focus on mapping practice as a device for critical reflection of urbanization processes and investigate the notions of time, clarity and intuitive imprecision in the maps.
– discussion of different forms and means of cartographic representation and their effects.
– possible consequences of producing visibility through mapping exercises
– limits of the cartographic method (cartographic silences, mapping the non-mappable?)
– hierarchies of representation (e.g. center of the map, projection, orientation).
– differentiation between analysis of built environment and analysis of urbanization processes;
– differentiation between temporal and spatial level of interpretation
– legibility of maps and reasonable density of information despite superimposition of different levels of data