Mapping for Transformation

There is a constant flow of narrative streams competing for our attention. Within a market based economy this attention is a scarce resource, which creates an economy of attention derives from sophisticated and seductive marketing practices.

Alternative approaches - here are plenty of them, called TAPAs - include experiments striving to meet people's needs beyond market and State while generating and formulating patterns of self-sufficiency and cyclic metastability. ... to sustain an enlivement's mere capability to remain adaptable. [I TRIED TO edit as far as possible, but could not make sense of the last part "... to sustain...." - SIlke)

Hélas, these alternatives remain widely under the radar of the multitudes/masses/common people and decision makers. We map them to make TAPAs easier to find, enjoy, use and transform the economy and society towards a free-fair and sustainable world.


… formerly branded as Mapping the Transformations

… Twitter hashtag #commonswsf

… derived from sketchpad

Plenty of alternatives to dominant economic market practices exist. Countless mapping projects around the world are committed to enhance their visibility. However, they receive only small attention in mainstream media and culture.

At a fringe meeting in the context of the World Social Forum 2016's Commons Space, members of Transformap, RIPESS and Greenmap decided to convene for Mapping the Alternatives during a Winter Camp late 2016, early 2017. The First Step - invite a core group of 15-20 people to a Deep Dive about this topic. Application: People and Institutions to be involved: TransforMap Greenmap Ripess Representatives from Foundations

reports to inform

open to report


Merging maps is an instructory visualisation about simple geospatial reasoning and combination of multiple data sources into a single map view. Learnings from understanding it led to the discovery of spaces in Montréal that act as laboratories of well-being communities.

Merging graphs displays a demonstratory TransforMap data categorisation pattern language. Six individual patterns, representing layers of different qualities, are subsequently fed into a combining visualisation to learn from an inferred perspective.