microplexes - very small complex systems can show us how to grow large urban systems
TRANSCRIPT
8/8/2019 Microplexes - Very Small Complex Systems Can Show Us How to Grow Large Urban Systems
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Micr
This is
as part
Univers
researc
the for
Hello.
It’s a pr
systems
Today I
scale.
Fig. 1 Sc
This is
seemed
discipli
cross-sc
I want t
systems
spatial
oplexe
transcript
of a series
ity College
h aims and
at it was d
y talk is ca
esentation
.
want to tal
ales of knowle
ne of the fi
a bit throw
es, but its
alar manne
o show you
at disparat
ystem and
of a talk de
f seminars
London. Th
their conce
elivered her
led micropl
n how very
about scal
ge (a very ro
st sketches
way to me
ore messag
r in my ow
how networ
e scales, ho
hat the li
livered at t
held by the
is is my firs
tual under
e.
exes.
small comp
. It strikes
gh sketch fro
I put in my
t the time,
e of scales o
research.
k topologie
network s
itations of t
e Bartlett s
Center for
t academic
innnings.
lex systems
e that mo
my noteboo
notebook
and of cour
f knowledg
give us a
cience gives
hat vocabu
chool of Ar
dvanced S
seminar, in
ou can see
can show u
ern knowle
).
hen I starte
se it isn’t ve
holds I thi
eans of an
us a vocab
ary might b
hitecture o
atial Anal
which I lay
the origina
s how to gr
dge is inten
d my PhD 6
y accurate
k. I’m tryi
lysing and
lary for tal
e.
March 24
sis (CASA)
out some o
l presentati
w large urb
sely special
months ag
in how it lin
g to work i
omparing s
ing about a
th 2010,
at
f my
on in
an
sed by
. It
ks
a
patial
ny
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The cell biologist and the ecologist will tell you that they work in different fields of the same
branch of knowledge, invoking the kind of tree taxonomy of knowledge sketched by Diderot in
the 18th Century.
Fig. 2 Tree taxonomy of knowledge from Diderot’s 18th Century Encyclopédie. Knowledge is structured into memory,
reason, imagination.
[ source ]
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Fields a
from Di
knowle
nd branche
derot to co
ge structur
are, of cou
struct a ‘fig
es.
rse, metaph
urative’ syst
ors borrow
em of kno
d from nat
ledge, an in
re. Here’s
formation v
nother atte
isualisation
mpt
of
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Fig. 3 Pie
Keepin
Gilles D
tubular
can giv
picture
Fig. 4 Fr
rre Mouchon’s
to this trad
eleuze, bett
root which
rise to ma
you are loo
sh rhizome of
information vi
ition of bio
er thought
as a non-h
y inputs an
ing at is th
a Cimicifuga
sualisation of
ogical meta
f in terms o
ierarchical,
d outputs, s
t of a rhizo
acemosa. [so
nowledge str
phor I see
f rhizomes.
decentralis
pawning ne
e.
urce ]
cture for the E
nowledge, l
The potato
d structure
w growths i
ncyclopédie o
ke the fren
is a rhizom
of tubers, s
multiple l
f 1780. [ sourc
h philosop
. A rhizom
hoots and s
ocations. T
e ]
er
is a
ems. It
e
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Of cour
can acc
particul
is both i
and tha
Fig. 5a Ill
e, network
mmodate
ar type of n
ntensely ho
t there is a
ustration of m
can be no
any inputs
twork. Sin
rizontally n
ichotomy b
sh and tree to
-hierarchic
and output
e we live in
etworked as
etween thes
pologies.
l, and, dep
s, so we can
a network
well as arb
e two topol
nding on t
think of kn
ociety, it is
orescent, cl
gies, the m
e rules of e
owledge, or
intuitive to
stered as
esh and the
ngagement,
the rhizom
us that kno
ell as casca
tree.
nodes
, as a
ledge
ing,
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one hig
exists i
Very di
assertio
this by l
Fig. 5b T
A mixtu
take a
ly decentr
many com
ferent topol
n of mine is
ooking at t
op-level topolo
re of the de
icroscope t
lised and t
lex networ
ogies often
that compl
e rhizome s
gy of a rhizom
dritic stru
o a shoot in
e other hig
ks, not just
exist at diff
x systems
tructure its
e.
ture associ
the rhizom
ly hierarch
our systems
rent scales
re characte
lf. At its to
ted with tr
it’ll look a
ical. I will tr
of knowled
of a comple
rised by sca
-level, its t
e topologie
little like th
y to show h
ge.
x network.
les of conne
pology loo
s and a mes
s.
ow this inte
principle
ctivity, we
s a bit like
hwork. But
rplay
an see
his.
if we
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Fig. 6 Va
[ source ]
Here w
deform
area bet
optimis
membr
the cell we’re lo
stem vi
Import
interco
we mod
this.
Fig. 7 To
scular bundle
see a vasc
tions prod
ween cell
ed for two t
nes via os
boundaries,oking at all
diffusion i
ntly, if we l
nected wit
el each cell
pology of a va
f a fern rhizo
lar bundle
ced by con
embranes
ansport sce
osis and a
a highly po w water an
duced by g
ook at the t
its closest
as a node, a
cular bundle
e. Microscopi
f cells, a de
ractile forc
s well as a l
narios – on
other to all
rous structd sugars to
radients.
pology of t
neighbours,
nd contact
f cells in a fer
c cross-sectio
nse packin
s and gradi
arge diversi
e to permit
w material
re. The xylermeate th
is cellular
there are n
ith other c
rhizome.
of xylem and
forming co
ents. We se
ty of passag
the diffusio
s to percola
m and phloe cell tissue
tructure, w
o long rang
lls as a link
phloem cells
ncentric cir
an optimis
s between
of materia
e through t
em cells inand travel
see that e
connectio
, the topolo
y Michael Cla
cles, we see
ation of sur
cells, a mor
l through c
he system b
this fern rhiown the rh
ch cell is o
s in this sy
gy looks a b
yton.
face
hology
ll
etween
zomeizome
ly
tem. If
it like
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To a m
dendrit
degrees
The dis
happen
this pro
Decentr
assemb
So even
see ho
the rhiz
appare
Fig. 8 Vis
thematicia
c system, t
of connecti
ribution he
to be. We
perty is kno
alised mes
ing, self-or
in a rhizom
scales of co
ome, the str
t.
ualisation of
it looks lik
ere is no tr
ity, where
re is based
all this a hi
wn as triadi
topologies,
anising co
e itself, Del
nnectivity
ucture is su
ikipedia page
e a Delauna
e, just a me
node’s de
n cell size a
ghly cluster
c closure a
gradients a
plex syste
uze’s prop
re characte
fficiently co
linkage netwo
y triangulat
shwork. W
ree denotes
nd the radi
ed topology
d was disco
nd high clu
s, of whic
sed metap
istic of co
mplex that
rk in a small s
ion. There a
say there i
the numbe
s of the vas
because it’s
vered in so
stering app
more later.
or for the s
plex syste
e need a
bgraph of pag
re no hubs,
a narrow d
r of other n
cular bundl
full of link
ial network
ar to be ch
ructure of
s. It’s just t
icroscope t
es, by Ian Pe
and there i
istribution
des it is lin
e at which t
d triplets o
s.
racteristic
nowledge,
hat in the c
make this
rce. [ source ]
no
of
ked to.
he cell
nodes,
f self-
e can
se of
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My co
structur
renderestraight
Fig. 9 W
ments on t
e through t
d by Ian Peaway both
ighted, directe
e rhizome
e analysis
rce showinubs and de
d graph of onl
ren’t entire
f documen
g connectivinse clusteri
ine citation net
ly speculati
linkage in
ty in a few tng, the inte
work, by Rosv
n. We have
wikipedia.
housand wiplay of star
all & Bergstro
some insig
ere’s a sm
kipedia pagand mesh t
. [ source ]
ht as to kno
ll subgraph
es. You canopologies.
wledge
see
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Rosvall
networ
widths
in that t
the app
form a r
So in th
connect
Throug
dispara
vascula
I’d like
I consid
the mic
& Bergstro
between s
nd colours,
opic by rea
ied science
ing-like top
ese knowle
ivity.
these exa
e systems,
bundle of
o look now
er to be the
ochip.
rendered
veral thous
the size of
ers. In this
directed at
ology of cit
ge network
ples I seek
oth spatial
ells.
at some mi
smallest ex
this directe
and academ
he nodes c
analysis th
a smaller s
tions betw
s we see dif
to show ho
and virtual,
roscopic sp
ample of or
, weighted
ic papers.
mmunicate
y found hie
et of natura
en themsel
erent topol
networks
systems as
atial syste
anised co
graph visua
he volume
s the amou
rarchies of
sciences. T
es.
gies at diff
give us a to
diverse as
s. Let’s beg
plexity in
ising an on
f citations i
t of time s
opics, with
hese natura
rent scales
lkit to skip
uman kno
n with a hu
hat we call
line citation
s expressed
ent surfing
many flows
l sciences i
, we see scal
between th
ledge and
man artefa
the built la
in link
papers
from
turn
es of
se
tiny
t which
dscape:
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Microc
Fig. 10 In
This is
silicon s
of a £2
essenti
Fig. 11 T
hips
tel’s dual-core
ontecito, I
ubstrate ab
oin. Let’s l
lly contains
p-level layout
Montecito mic
ntel’s dual
out 2×2cm
ok at the s
a mirror i
of Intel’s Mon
ro-processor.
ore chip fro
n dimensio
atial logic o
age around
ecito micro-pr
[ source ]
m 2006, wi
ns. The sur
f this chip.
a horizont
cessor. [ sou
th 1.7 billio
ace area of
You can tell
l axis.
ce ]
transistors
ontecito i
it’s dual cor
fused onto
smaller th
e because i
a
n that
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As you
small c
caches.
speedie
instruct
by an intype of
nodes.
The co
are stri
caches
Current
Microc
Floorpllower le
an see a mi
re of centra
There are a
r the cache
ions. Along
terface to tetwork top
ote that po
ponents in
tly sequent
nd the core
travels bet
ips are laid
nning, pac vel configur
crochip is e
l processin
out four-le
ccess. Thes
both sides
e rest of thology in wh
wer manag
a microchi
al within a
, as these o
een these
out in this
ing and opations. Mic
sentially a
units, whi
vels of cach
e are low ac
ou see a sp
hardware iich one cent
ment is a c
are synchr
ingle core.
cur multipl
omponents
onfiguratio
imisation aochip desi
agglomera
h are colou
s in Monte
tivity areas
awl of low-
n the formral link ser
mpact uni
onised usin
The chip is
e times duri
on the scal
n using mu
lgorithms cners model
tion of me
red in black
cito, the mo
designed si
speed mem
of an I/O Bes to trans
just off cen
g a clock su
optimised f
ng the mos
of nano se
tiple phase
eate this tothe chip as
ory, colour
. Memory c
re intense t
ply to stor
ry. The wh
s. A Bus deort inform
ter, coloure
system an
r round tri
rudimenta
onds.
of heuristi
p-level strua network,
d in red, ar
mes in the
e colourin
e data in be
ole chip is b
scribes a pation across
d in yellow
signal dyn
s between
ry of instru
c design.
ture as welrouping th
ound a
form of
, the
tween
ounded
rticulara lot of
ere.
mics
he
tions.
l as the
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billions
are hig
compo
connect
So in a
circuit.
footpri
of integ
would s
designe
a static,
Here is
image o
Fig. 12 R
This mcompac
Foster
surpris
agglom
order to
from th
levels o
safe to s
artefact
of compone
. The top-le
ents but at
ivity here.
icrochip
In this case,
t of compo
ration and d
ay the spati
rs acting as
non-living
an example
f a micro-pr
endering of M
ster-plan,t, highly int
Partners e
to see the
rations aro
become m
eir cores, w
low activit
ay that we s
, and that i
nts into jus
vel topolog
lower levels
e see an int
optimisati
ents is carr
ensity crea
l outcome
a centralise
ystem.
of what a
ocessor.
sdar master-
asdar, waserlinked, w
xplicitly sta
icrochip i
und cores.
re efficient
ere the vas
bands in t
ee a numbe
both we se
t hundreds
of a micro
, it can look
ense logic o
n of conne
ied out algo
ing high en
s one of or
d agency. In
icrochip cit
lan by Foster
proposed blkable urba
e the micro
voked in su
hey too ha
processors
t majority o
e form of r
r of echoes
the effects
f logical u
hip is cent
like a highl
integratio
tivity lengt
rithmically.
ergy efficie
anised co
this sense,
y might loo
& Partners in
y Foster &nisation of
chip as desi
ch a way. A
e become p
of their ow
f their activ
sidential z
of urban m
of organis
its by ident
red around
clustered
, as eviden
s, energy c
The microc
cy in a com
plexity, org
they can be
like if you
bu Dhabi. [ so
artners in50,000 rev
gn inspirati
ter all, our
olycentric,
. They too
ty takes pla
nes. Howe
rphology in
d complexi
fying wher
a small nu
raph. Agai
ed by its na
nsumption
hip is an ex
plex signal
anised that
considered
planned an
urce ]
bu Dhabi.lving aroun
on for the p
ities too ha
r multicore
ave optimi
ce. They too
er imperfe
this micros
y.
clustering
ber of core
we see sca
me, the int
and the sp
ample of hi
rocessing s
is, by micro
master-pla
urban syste
t’s a high-dd a busines
roject. It’s
ve grown a
, over time,
ed transpo
contain m
t the analo
copic hum
levels
les of
grated
tial
h levels
ystem. I
chip
ners of
m in the
nsity,core.
o
in
t to and
ltiple
y, it is
n
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Now let
but wit
Neural
Fig. 13 Ill
A huma
but a br
transist
a transi
Fig. 14 V
’s look at an
a self-orga
Networks
ustration of a t
n neuron a
ain contain
ors, about 1
tor, which
isualisation of
other infor
nising dyna
etanus neuron
d a transist
an order o
0 billion. N
an be cons
lustering in a
ation proc
mic, the ne
. [ source ]
or on avera
f magnitude
te that the
dered simp
neuron culture
essing netw
ral networ
e have dim
more neur
neuron is si
ly as a switc
of rat hippoc
ork with co
that descri
ensions of
ns than co
gnificantly
h.
mpal neurons.
ponents o
bes our brai
bout 1 micr
temporary
ore compl
[ source ]
n a similar s
in.
on, or 10-6
microchips
ex in topolo
cale,
eters,
possess
gy than
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Both th
are radi
decentr
have ar
logic ga
propert
A brain
networ
using n
quantit
propert
latter fe
Fig. 15 Ill
[ source ]
adult hum
cally differe
alised proce
und 10,00
te rarely tak
ies highligh
then, is ana
of microch
twork repr
tive manne
ies as well a
ature.
ustration of sm
an brain an
nt in terms
ssing archit
synaptic li
es more th
s the core d
logous not t
ips, someth
sentations
r. We can s
s connectivi
all world netw
d Montecito
of processi
ecture. It’s
nks to other
n 3 inputs.
ifferences b
o a microch
ing like the
we can mak
y that both
ty features
rk connectivit
consume a
g and topol
ften very h
neurons in
This dispari
etween the
ip, which is
World Wid
e these co
the brain a
eminiscent
y properties, b
bout the sa
ogy. The br
ighly cluste
a human b
ty in degree
e two infor
serialised a
Web, anot
parisons ac
d the inter
of small-w
y Watts & Stro
e energy, 1
in is a mas
ed. It’s nor
ain, where
distributio
ation proc
nd centralis
er self-org
ross vast sp
et display
rld networ
gatz.
00 watts, b
sively parall
al for a ne
s a transist
and cluste
essing netw
ed, but to a
nised syste
atial scales i
cale-free d
s. Let’s loo
t they
elised,
ron to
r or
ring
orks.
large
m. By
n a
gree
at this
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A small
diagra
is chara
a shortc
what is
world s
accessi
mobilit
were fir
Fig. 16 E
-world netw
) and yet c
cterised by
ut if you wil
known as a
ate. From a
ility from a
if you will.
st observed
pithelial cell ne
ork exhibit
ntains high
ots of local
l. Only a fe
phase trans
spatial net
y given no
We see sm
in social ne
tworking. [ so
low mean
clustering
connectivit
of these lo
ition, from
ork standp
e to any ot
ll-world pr
works by
rce ]
ath length
oefficients
between n
nger links
relatively
oint, small
er node, a
perties pri
atts & Stro
between a
(C on the di
odes, as wel
re required
oorly conn
worlds mea
homogenou
arily in sel
atz.
y two arbit
gram). Yo
l as the occ
in order to
cted global
high level
s distributi
f-organised
ry nodes (
can see he
sional long
tip the netw
state into a
of mean
n of high le
networks.
on the
e that it
r link,
ork, in
small-
vels of
hey
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Comingthat it i
self-org
develop
formin
Let’s lo
Morph
Here w sponta
Fig. 17 E
The net
self-org
unsupe
may see
period
are not
back to neboth a self
anising stru
ing its dend
new synap
k more clo
ogenesis
can see epieously for
pithelial cell ne
works they
anised netw
vised grow
it in the de
f time, part
present.
ral networassembling
cture, as ne
rites, probi
es.
ely at this
thelial cellsing networ
tworking vide
roduce via
ork formati
h of self-int
elopment
icularly wh
s. If we loostructure,
urons deter
g into its s
echanism
in a collages via rando
.
this mecha
on is not oft
erested age
f linkages b
re regional
at the gros neurons
ine which
rrounding
f biological
n substratem motion a
ism are str
en seen in
nts, or node
etween urb
policies rel
th of the bnd dendrit
other neur
in search o
growth.
doing a simnd outreac
ongly meshl
rban growt
s. I will allo
an centers t
ting to co
ain, or neus grow and
ns to conne
f neighbour
ilar thing to.
ike in topol
h as it requi
myself to
at prolifer
pactness or
ogenesis, w reproduce,
ct to, each
ing neuron
neurons;
ogy. This ki
res the
speculate t
te over a lo
urban cont
e seeand a
euron
and
d of
at you
ng
ainment
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The probing we see here due to cell tip growth is a form of morphogenesis. The term
morphogenesis describes the various processes that result in the development of form in cellular
biological systems. Let’s look at more morphogenetic processes, because they can show us some
spatial outcomes of self-organisation.
Here’s one of the most fundamental, mitosis,
Fig. 18 Mitotic processes in a one-hour old fruit fly embryo imaged via confocal microscopy. A Histone protein is tagged
with a substance that allows us to track cell nuclei.
It is interesting to note that mitosis creates very dense but essentially decentralised
morphologies. We can say they’re decentralised because a centrality analysis of the network
representation will show us that no cell in the system is especially important from the point of
view of the global connectivity of the system. Cellular systems provide us with a good
demonstration of this fact, that high-density form is not the same as centralised form.
In this mesh topology, cell integration is not as good as that evinced in small-world networks,
because, as with the vascular bundle, long range shortcuts are not available. Note the
synchronisation of various phases of mitotic processes across all the cells.
Lastly here are epithelial ducts developing a complex network structure through a process known
as branching morphogenesis. This movie depicts a fragment of epithelium growing in a 3D gel of
extracellular matrix proteins. New ducts initiate, elongate, bifurcate, and stop. This
morphogenetic process underpins the fractal cardiovascular structures in our own bodies. It
creates self-similar, dendritic structures, a tree topology.
Fig. 19 Branching Morphogenesis in epithelial cells.
The Sabin & Jones architecture studio has examined the effects of branching morphogenesis in
endothelial cells and how the attractive forces between the cells deform the substrate, which is
modelled as an extra-cellular matrix, or ECM,
Fig. 20 Model showing ECM deformations produced by spontaneous network formation in enothelial cells, by Sabin &
Jones. [ source ]
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My own
propose
pattern
Fig. 21 A
of Papilio
[ source ]
research in
d by Alan T
formation i
lan Turing’s re
Dardanus.
to morpho
uring in the
organism
action-diffusio
enesis has r
50s. He wa
, from butt
model for pat
evolved aro
s trying to f
rfly wing p
tern formation
und the rea
ind a mathe
tterns to z
has successf
ction-diffus
matical mo
bra stripes.
lly been appli
ion dynami
el to explai
d to the wing
s
n
patterns
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He pro
period
be blac
forms.
gene exgenerat
Fig. 22 G
osed the di
f time, the
or white a
iologists w
ression. I’ive graphics
enerative mod
fusion of a
oncentrati
cording to t
uld call the
e used thes.
el showing ho
activator a
n gradients
he concentr
se activator
reaction-d
reaction-diff
nd inhibito
dictating c
ation of a
s morphoge
iffusion dy
sion dynamic
through a
ll differenti
hite-cell ac
ns, as these
amics to ex
can produce
evolving c
ation, ie a z
ivator at th
are the pro
plore morp
gradients in a
llular syste
ebra skin ce
e point whe
teins that r
ology usin
matrix of lines.
over a
ll can
it
gulate
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In my g
investig
activatoand dep
deformi
result.
no two
Fig. 23 G
raphics I’ve
ate what ki
r and inhibilete over ti
ng lines an
radients in
patial elem
enerative mod
used a gro
d of format
tor, hiddene. In this g
affecting li
morphoge
ents alike.
el showing ho
ing matrix
ions this ki
from view,raphic you
ne growth.
esis are cap
reaction-diff
of straight l
d of dyna
are placed ran see a gr
No two vert
able of crea
sion dynamic
nes, one ad
ics can pro
andomly ondient oper
ical slices of
ting this ki
can produce
ded at each
uce. My m
the canvas.ting from l
this image
d of hetero
pattern formati
time step, t
rphogens,
. They react,ft-to-right,
are the sam
enous spac
ion in a matrix
n
diffuse
e as a
e, with
of lines.
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In this
of shap
Fig. 24 G
ne you can
by a force
enerative mod
see pattern
f attractio
el showing ho
formation i
induced b
an uninhibite
n organic lo
the activat
d attractor ca
oking tendr
r.
produce inte
iled structu
sities in a mat
es, lines cu
rix of lines.
rved out
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In this
appear
diffusiocompo
parame
influen
In the u
mechan
exampl
residen
Reggia
influenstreet
ne you can
round the l
n functionsents, there
tric images
e evolving
rban analo
ism, in the
, you can t
ial ‘genes’ i
i of UCL ha
es populatiatrix, defor
see what ha
ocation of t
and reactiois a large p
s a means
rban form
y, you can t
orm of a m
ink of plan
cells as th
s used react
n distributmed by bot
ppens whe
he activator
kinetics y ase space o
f visual thi
n a setting
hink of thes
rphogen, t
ed retail or
concentra
ion-diffusio
ion in just sattractors
the inhibit
, which aga
u choose apotential
king on th
f decentral
e models as
at activate
business lo
ion of the a
n dynamics
uch a mannand cell int
or has a lo
n acts as an
d the proporphologie
potential o
ised urban
based on s
or inhibits
cations as p
ctivator diff
to model h
er. You canractions.
concentrat
attractor.
rty of yours. I’ve rend
f morphoge
olicy.
lf-organisi
certain cell
otential act
uses throug
w the cost
also think o
ion. Intensi
epending o
graphicalred many s
netic dyna
g cells and
behaviours
ivators, acti
h space. Au
of transport
f the ECM
ies
n the
uch
ics to
a policy
. For
vating
ra
ation
s a
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It shoul
express
morpho
Before
micelle
Fig. 25 M
Sodium
sheets,
physics
I want t
multitu
the city.
living t
some fo
Fig. 26 C
d be noted t
gradients. I
genetically
oving on I’
,
icelle formatio
oleate mol
s a result o
and chemis
o skip to th
e of compl
This 4-way
non-living
rm of centr
omplex syste
hat networ
nformation
induced for
d like to bri
n in sodium ol
cules self-o
f hydrophili
try acting o
urban scal
x systems
chart show
and self-or
lised plann
s 4-way chart
representa
is invariabl
s into net
efly show y
ate molecules
rganise into
c and hydro
molecules
now finall
as allowed
some of th
anised to o
ing.
tions of spa
y lost durin
orks.
u one mor
. [ source ]
a variety o
phobic mol
— the syste
, to articul
me to prop
complex s
ganised. T
ce, being di
the proces
self-organi
forms, incl
ecular ends.
m is not ali
te some of
rly situate
stems I’ve l
e term org
continuous
s of convert
sing micros
ding spher
Crucially t
e.
y researc
y system o
ooked at, si
nised in thi
, can’t accu
ing
copic syste
es, circles a
ough, this i
aims. Loo
f interest, n
tuated on a
s case refer
ately
,
nd
s just
ing at a
amely
axis of
ing to
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You can
systems
left – th
middle
environ
terms o
dynami
south o
So, to
One of
Growi
Fig. 27 A
see microc
and social
ese are non
of these axe
ment andin
f all the uns
c will vary d
the Wester
y research
y aims is t
g Cities
rchigram’s plu
ips and po
etworks in
-living, self-
s. They are l
frastructure
pervised d
epending o
n post-met
ims.
grow citie
-in city (1964)
t-modern
the top left,
organised c
iving syste
. In part or
ecisions ma
whether y
opolis.
.
. [ source ]
rchitecture
and micell
omplex syst
s characte
anised by c
de by indivi
u’re lookin
in the botto
s and the
ems. Cities
ised by soci
entral agen
dual develo
g at say, inf
m right, br
orld Wide
however, fa
al interacti
ies, in part
ers. This o
rmal urba
ins, cellula
Web in the
ll squarely i
n within a
self-organis
rganisation
ism in the
ottom
n the
uilt
ing, in
l
lobal
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This tra
Archigr
multituof this a
arise ou
The ma
Fig. 28 C
dition start
am proposi
e of cranesdaptive fra
t of heuristi
with the c
edric Price (19
d out in ar
g this plug
. It was theiework of
cs.
gar and the
64). [ source ]
hitecture in
in city, arra
r assertion tulti-storey,
detachable
the 60s, wi
nged withi
hat organicpluggable
collar in th
th the Bartl
a huge sca
, complex grchitectura
s image,
ett’s own ar
folding and
owth pattel units, that
chitectural
presided o
ns could ar‘organicnes
roup
er by a
ise outs’ could
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is Cedri
his ideahis Fun
site on t
Fig. 29 C
c Price. He i
s relating fePalace proj
he River Le
edric Price’s F
ntroduced
edback cybct, a propo
a.
un Palace. [ s
he term ge
rnetic systesed cultural
urce ]
erator into
ms to respocenter to b
architectur
nsive architlocated on
al practice i
ectures. Thwhat is no
n the 60s, a
ese ideas w London’s
s part of
nt intolympic
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In my o
ruleset
I’m con
heyday
for the
excelle
Fig. 30 T
1960. [ s
wn researc
esigned to
cious in thi
of systems
ity. Here ar
t book fro
ransport netwo
urce ]
, I’m using
describe gr
s mode of e
lanning, th
e just four t
that era.
rks for theoret
Price’s term
wth rules.
nquiry of h
60s, whic
heoretical t
any of thes
ical settlement
, generator,
rking back
saw syste
ansport sys
are based
s (A) Kohl, 18
to describe
to what Pet
s analysts
tems compi
n fairly red
0 (B) Christall
a bottom-u
r Hall has
ropose ma
led in Hagg
uctive shor
er, 1933 (C) L
p urban pol
escribed as
y idealised
ert & Chort
est path an
osch, 1954 (D)
icy
the
forms
ey’s
lysis.
Isard,
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A mode
interest
to mod
Hillier iplacem
Fig. 31 C
rn approac
ed in growi
l environm
n the 80s, hnt rules for
ellular automa
to urban g
g emergen
ntal, trans
e attemptebuildings.
a model for gr
owth mode
forms usin
ort or econ
to reprodu
owing French
ls makes a
g bottom-u
omic policy
ce the com
hamlets, by Bi
ignificant d
rulesets, o
. In this cell
lex form of
ll Hillier (1989)
eparture, in
r generator
lar autom
French vill
[ source ]
that we are
, which can
ta model b
ges using
be used
Bill
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My sup
work ouse dyn
Fig. 32 C
rvisor Mik
the dual uramics. Here
rystal city gro
Batty’s ow
ban evolutiare some o
th, variations
research a
nary modef his attemp
ith stochastic
lso contain
l, or DUEMts to model
noise, by Mik
a variety o
, designed acrystal gro
Batty. [ sourc
growth mo
s a predictith systems
e ]
dels, includ
e model fowith noise,
ing his
land
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We can
aspects
incorpo
environ based o
Fig. 34 T
broadly cat
of real worl
rate develo
mental polin Mathieu
hree stages of
gorise gro
d urban sys
ment rules
y and analelie’s polic
growth in Mat
th models i
ems. Secon
ts, or gene
se their resrulesets,
ieu Helie’s co
nto two typ
dly, a breed
ators, to m
ults. One suhich are de
mplex grid. [ s
es. First tho
of growth
del new fo
ch algorithigned to ha
urce ]
se that atte
odel that a
ms of econ
ic model isrness self-o
pt to mod
ttempts to
mic, trans
the complerganisation
l
ort or
x grid,.
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The thr
1. Any s
networ
anyone,
2. The
order to
3. Stree
acciden
The rul
develop
develop
Fig. 35 M
e principle
ize of urban
. This ensu
no matter t
etwork mu
participate
ts must not
ts are encou
sets allow
er replace t
ment can p
athieu Helie’s
Helie stat
growth is a
es that the
heir econo
t not beco
in large-sc
grow too lo
raged.
rivate deve
e part used
oduce wide
complex grid.
s as the bas
llowed as lo
city has the
ic importa
e so compli
le activities
g without i
lopment on
up by exte
r roads, whi
[ source ]
is of his gro
ng as the n
economic fl
ce, to cont
cated that i
and a cultu
nterruption
any availab
ding the n
ch you can
wth model
w growth e
exibility of
ibute to th
t becomes i
re of conge
in such a w
le part of th
twork arou
see in yello
re:
tends the b
he medieva
city’s grow
possible t
tion.
ay that spee
e network s
d the new
.
oundary of
l city and al
th.
move arou
ding and tr
o long as th
lock. Com
the
lows
nd in
ffic
unity
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Policy i
produc
Throug
growth
in dicta
based o
are esseThey ar
But by
instruct
organis
contem
One of
organis
urban f come in
tervention
more of a s
this mode
through cos
ing precisel
n dense urb
ntially behae also still si
uilding soft
ionalDNA a
tion can cr
orary citie
y research
tion by mo
rms and prterms of a
are allowe
mall-world
Helie atte
tly, specula
y how grow
an form. It’
vioural momplistic.
ware mode
s opposed t
ate urban
and comp
aims is to p
deling grow
oviding anetwork an
, only for t
connectivit
pts to sho
ive infrastr
th occurs. T
not withou
els for plan
s with thes
the agent
nvironmen
re those dif
roduce algo
th accordin
nalytical tolysis of thei
e widenin
model.
how polic
cture inve
he outcome
t its compli
ners and co
kind of gr
f a total, ce
ts with diffe
ferences.
rithmic urb
to these p
olkit to anar connectivi
of existing
can reduce
tment, and
, or so he cl
cations how
ntain ideol
wth rules,
ntralised vi
rent connec
an growth
licy genera
lyse the emty properti
links into ri
its predicti
reduce its o
ims, is sus
ever, as the
gical state
here policy
ion, we ca
tivity prope
odels whic
ors, investi
rgent morps. These m
ng roads th
ve role in pr
rganisation
ainable gro
se kind of
ents about
acts like
analyse ho
rties to our
h harness s
gating the r
hologies. Tdels will
t
ojecting
al role
wth
odels
policy.
w self-
lf-
esultant
is will
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necessa
analyst
rulesets
My sec
include
Urban
Let’s lo
this wo
Fig. 36-4
Here is
the low
represe
further
From a
general
tertiary
rily produc
in the 60s,
.
nd researc
bus, rail an
Connectiv
k at scales
k.
Increasing s
a segment o
st end of o
ted as das
up the scale
systems vie
y not be de
roads,
far more h
as we begi
aim is a sy
tube netw
ity
f connectiv
ales of conne
f East Lond
r hierarchy
ed lines. Y
. We can sa
point, this
endent on
terogenous
to approxi
thetic net
rks.
ity in the ur
tivity in a 3km
on, the nort
of scales, w
u can see it’
it’s poorly
strikes one
upper layer
form than
ate morp
ork analysi
ban contex
slice of East
hern tip of
e have the
’s fragment
decoupled
immediatel
in the syst
the idealise
ogenetic dy
s of London
as a means
ondon, produ
he City and
edestrian n
d and inco
rom other
y as poor d
m. Up the
forms pro
namics thro
’s public tra
to underst
ed using Clou
Hoxton, ab
etwork of f
plete. It’s
etworks an
sign, as lo
cale we ha
duced by sy
ugh botto
nsport syst
nding the b
dmade [ sourc
out 3km wi
otpaths
eliant on n
d not self-re
er layers sh
e residenti
tems
-up
ms, to
asis of
e ]
e. At
tworks
liant.
ould
l and
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These t
a set of
o form arc
incoherent
ipelagos. Y
ragments.
ou can see a single resi ential lattice up near t e top but t e rest is
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The pri
cohere
longer l
These li
have lo
ary and A
t form out
inks of the
nks are obv
g range rai
roads, by c
f all the sca
ndergroun
ously deco
way links,
ntrast, for
les so far, id
system,
pled from
a strong
entifiable a
ll the other
esh of arter
s a single n
s. And at th
ies. They h
twork. Fina
highest co
ve the mos
lly we have
nnectivity s
the
ale, we
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As we h
systems
also the
level of
terms o
It’s part
to analy
interve
charact
movem
I’ve star
rendere
Fig. 41 V
ave seen, a
. It helps th
basis for s
complexity
f the mobili
of my rese
se represen
tions, in th
ristic path
nt potentia
ted with an
d for now.
isualisation of
iversity of
em to deal
all-world p
hrough org
y of its inh
rch aims to
tations of t
form of li
engths whi
l at the nei
analysis of
ere is Lon
London’s 24,6
cales of co
ith a multi
roperties in
anised gro
bitants.
use an und
e public tra
k reconfigu
st retaining
hbourhood
he bus net
on’s morph
0 bus stops.
nectivity is
licity of tr
networks.
th, and in
erstanding
nsport syst
ration, dele
the high cl
scale.
ork. I’ll jus
ology as ex
ata from NP
a key prope
nsport scen
y hypothe
oing so it c
f scales of
m in Lond
tion or addi
stering pro
t show you
ressed thro
DR.
rty of self-o
arios. Scale
is is that a
n improve
onnectivity
n, with a vi
ion, which
perties that
ome quick
ugh its 25,0
rganised co
s of connect
city can ach
its efficienc
and small-
ew to propo
improve glo
maximise
visualisatio
00 bus sto
plex
ivity are
ieve this
in
orlds
sing
bal
s I’ve
s.
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In this
Fig. 42 V
isualisatio
isualisation of
you can se
London’s 734
the netwo
bus routes. Da
k represent
ta from NPTD
ation of ove
R.
r 700 bus r utes.
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novel analysis and novel techniques for identifying low-cost reconfigurations of transport links
based on these insights.
I’ve touched on how creating generative growth models for cities that embrace self-organisation
through bottom-up policy rules, or generators, can provide insights into how to produce more
sustainable urban form. Even our cursory glance at cell biology gives us clear examples of very
dense yet decentralised spatial formations which allow for effective transport of nutrients
through porous structures and boundary diffusion. These kind of insights into density,
morphology and transport can contribute to the discourse on sustainable urban form and growth.
Lastly, I’ve introduced several concepts from morphogenetic systems, such as cells, gradients,
extra-cellular matrix, motility and morphogens, which are useful when talking about emergent
morphologies, a vocabulary that can be employed in the kind of urban growth models I am
working with.
Thank you.
See Also: Network , Infrastructure, Cybernetics, Emergence, Morphogenesis, Space Syntax
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Revised on October 4, 2010 20:23 by Anil Bawa-Cavia