Loop city workshop
h3. Bill Hillier: Cities are movement economies
h3. In the city there are
* space explorers: children, homeless, vendors, skateboarders,
* space utilisers: commuters, workers,
h3. Two ways of looking at the city
* exocentric: external, connected
* egocentric: centred, point of view,
h3. Spatial organisation
* Large, diverse research field.
* Abler, Ronald Adams: ‘Spatial organisation: the geographer’s view
of the world’
h3. Relative space
* Expressing thematic data through spatial differentiation
h3. Scaling areas according to non-geographic data
* Political maps based on size of army
* Map of USA based on Elvis concerts
h3. Time space
* Irina Vasiliev: ‘Design issues for mapping time’
* Time as a way of measuring space (one conclusion: world is
h3. Taxicab geography
* Grid systems make diagonal movement problematic
* There is study of movement in grid spaces, showing multiple optimum routes: a big L shape is the same distance as a zig-zag.
* The grid is no longer in Euclidian space
h3. Social space
* Philip Thiel: Spatial annotation methods
h3. John S. Adams:
* Human geographer
h3. mapped human interaction over 1 day
* vertical axis: time
* horizontal axis: distance
* made 3D diagrams of this multi-dimensional space, showing relative
distances travelled and communicated with over 1 day.
* Social network maps
h3. Mental mapping
* spatial representations of the brain or memory
* In some ways the analysis by Lynch and others has failed, because
they focused on trying to know everything about people’s mental
maps of the city.
* Richard Long: walking project
h3. Imagined cities
* Norman Klein: History of forgetting
* Fictional writers form mental models of cities
* Dietmar recreated the shape of LA by phoning people and asking
* PML maps
h3. Single parameter mapping
* Boylan height maps: Denis Wood
* Maps of Halloween lanterns in an area
h3. Multiple parameter mapping
* Correlating space
* Chernoff faces: iconographic representations of faces, with
expressions that map to different social conditions
* Eugene Turner
* Correlating socio-economic factors is common
h3. Mapping as a game
* Raoul Bunschoten
h3. Narrowed the analysis of space down to very simple
* Mapped results as a synthesis?
h3. Photographic / media mapping
* Tokyo Nobody
* Images with text removed, replaced with a textmap
* Text / image project… ?
* Graffiti archaeology project
* Time lapse as a tool: mapping crowds
* Threshold linear key as a tool: RCA project…
h3. Diagrammatic / information mapping
* Information diagrams representing time, space, actions, events,
people, cause/effect etc.
h3. Collaborative mapping
* multiple authorship over shared themes
* Presented her NY Green space project, in which access to green
space is correlated with socio-economic factors. Refer to Social
design notes weblog.
h3. Some ideas for mapping
* Children’s tactile book: sandpaper for Asphalt, felt for grass.
* Litter, sky cover, text, colours, people, edges, boundaries, nodes
* Use gps and digital camera. Use a compass to always orient the
camera to North, or relevant reference. Then map the space with
textures or sky cover (down or up). Could make a great map.
* A method for collaborative presentation might be to use a projector
to trace physical space onto a wall or large open space, then to
layer drawn annotations. A public presentation could be achieved by
projecting digital data (photos, textures, movement) onto this
annotated area, for interesting layered correlations.
* Everyone has their own agenda when approaching a space: personal
ways of looking, awareness, attractions and unnatractions. Could
try to map what a space makes you think instantly, from one vantage
point, or multiple, correlated vantage points.
* Bluetooth mapping of devices. Our personal ‘Auras’ are becoming
public and this might be useful for mapping.
h3. What kind of data can we collect about the city and it’s usage,
that is really reliable and plentiful? The audioscrobbler mapping
example shows how really simple data can be mapped into
extraordinary useful spatial representations, just because it’s
high quality and plentiful.
* Geographic data is potentially plentiful, because there is a lot of
effort put into mapping space.
* What other things are mapped with effort, or easily?