Table Of ContentDirectional
analysis
of
surface
artefact
distributions.
A
case
study
from
the
Murghab
Delta,
Turkmenistan
Steven
Markofsky
and
Andrew
Bevan
Postprint
of
2011
paper
in
Journal
of
Archaeological
Science
39.2:
428-‐439
(doi:
10.1016/j.jas.2011.09.031).
Abstract
This
paper
investigates
directional
influences
in
the
distribution
of
Bronze
Age
surface
pottery
in
the
northern
Murghab
Delta,
Turkmenistan.
Drawing
upon
a
continuous
dataset
of
pottery
sherd
counts
obtained
by
intensive
field
survey,
it
examines
the
degree
to
which
we
can
make
sense
of
the
archaeological
processes
at
work
in
a
heavily
obstructed
and
dynamic
landscape.
In
so
doing,
it
makes
use
of
two
analytical
methods
that
have
rarely
been
used
in
archaeology:
a)
geostatistical
analysis
using
variograms
to
investigate
directional
spatial
autocorrelation
in
recorded
sherd
counts,
and
b)
angular
wavelet
analysis
in
evaluating
directional
influences
in
the
sherd
distributions
for
particular
chronological
periods.
While
some
kinds
of
directional
influence
can
be
identified
visually,
a
quantitative
approach
is
particularly
useful
in
deconstructing
such
patterns.
In
this
particular
dataset,
distinct
but
related
directional
processes
can
be
identified
and
measured:
a)
the
impact
of
the
complex
system
of
watercourses
in
the
delta
on
both
settlement
and
post-‐
depositional
processes;
and
b)
recovery
bias
in
the
observations
made
during
survey.
1.
Introduction
Surface
pottery,
often
the
most
accessible
evidence
of
past
archaeological
landscapes,
constitutes
an
awkward
analytical
dataset.
Rather
than
resulting
from
any
single
archaeological
or
post-‐depositional
process,
surface
material
typically
represents
a
palimpsest,
the
spatially
and
temporally
averaged
material
residue
of
a
range
of
processes.
Over
the
past
several
decades,
increasing
recognition
of
this
inherent
dynamism
in
material
distributions
has
fostered
a
shift
in
survey
methodology.
Whereas
traditional
surveys
focused
primarily
on
archaeological
sites
to
explore
settlement
patterns
(e.g.
Adams,
1965,
Adams,
1981),
more
recent
approaches
have
employed
a
broader
range
of
methodologies
designed
to
address
whole
archaeological
landscapes
(e.g.
Bintliff
and
Snodgrass,
1985,
Wilkinson
and
Tucker,
1995,
Cleuziou,
et
al.,
1998,
Van
Leusen,
2002).
A
limitation
of
the
analytical
methods
deployed
by
many
recent
surveys,
however,
has
been
their
tendency
to
consider
rather
simple
isotropic
spaces:
that
is,
ones
in
which
distributional
processes
are
assumed
to
have
equal
influences
in
all
directions
and
to
exist
in
a
uniform
spatial
environment
(Longley
and
Batty,
2003:
311).
This
temptation
to
simplify
can
be
analytically
convenient,
as
disregarding
anisotropic
(i.e.
directionally
varied)
influences
often
makes
it
easier
to
apply
standard
geographic
methods
such
as
Christaller
hierarchies,
catchment
models,
buffers,
networks
or
Voronoi/Thiessen
tessellations
(Evans
and
Gould,
1982,
Conolly
and
Lake,
2006:
212).
Even
when
directional
influences
are
obvious
to
the
archaeologist,
for
example
the
inherent
linearity
that
may
characterise
the
distribution
of
surface
material
along
river
channels,
roads
or
valleys,
such
factors
tend
to
be
addressed
qualitatively
and
often
as
an
afterthought.
Because
anisotropy
has
been
seldom
investigated
formally
in
archaeological
research,
a
potentially
informative
body
of
data
has
been
overlooked.
For
example,
in
archaeological
survey,
directional
influences
may
factor
heavily
in
the
distribution
of
surface
material,
reflecting
not
only
trajectories
of
artefact
deposition
from
settlement-‐derived
and
post-‐depositional
processes,
but
also
recovery
biases
that
may
influence
the
interpretation
of
the
surface
distribution.
To
an
extent,
such
factors
can
be
detected
visually
or
with
the
help
of
GIS-‐based
approaches.
However,
such
methods
only
provide
coarse
data
and
may
overlook
more
subtle
directional
patterns,
as
well
as
ways
in
which
they
may
be
measured
or
quantified.
In
this
paper,
we
investigate
anisotropy
in
a
carefully
recovered
distribution
of
surface
pottery
from
a
dynamic
fluvial
environment.
Using
some
analytical
approaches
that
have
been
rarely
applied
in
archaeology,
we
seek
not
only
to
identify
directional
trends
in
survey
data
that
may
not
be
readily
apparent,
but
also
to
measure
and
interpret
these
trends
in
the
context
of
settlement
dynamics,
post-‐depositional
processes
and
potential
recovery
biases.
2.
Research
Context
The
survey
data
considered
below
is
derived
from
the
Northern
Murghab
Delta
Survey
(NMDS),
an
intensive
field-‐walking
project
conducted
by
the
corresponding
author
in
the
Murghab
river
delta
in
southeastern
Turkmenistan
from
2007-‐2009
(Figure
1).
Since
the
1970s,
the
Murghab
has
been
an
increasing
focus
of
archaeological
enquiry,
prompted
by
the
discoveries
of
major
Bronze
Age
sites
dating
to
the
late
3rd
and
early
2nd
millennium
BC
(Kohl,
1984,
Salvatori,
1998),
of
which
the
best-‐known
is
Gonur
Depe
(Sarianidi,
1990,
Sarianidi,
2005).
Over
the
past
two
decades,
researchers
have
identified
hundreds
of
new
sites
in
the
overall
region
through
extensive
surveys
(Cattani
and
Salvatori,
2008,
Salvatori,
2008).
These
findings
have
led
to
a
new
interpretation
of
the
regional
Bronze
Age
settlement
pattern
which,
it
has
been
argued,
was
characterised
by
continuous
and
widespread
occupation
and
thought
to
be
reflective
of
an
integrated
proto-‐state
structure.
While
not
yet
broadly
accepted,
such
an
interpretation
represents
a
substantial
change
from
previous
ones
that
had
envisioned
settlement
in
a
series
of
discrete
‘micro-‐
oases’,
each
representing
isolated
groups
(Hiebert,
1994:
39).
Figure
1:
Map
of
Central
Asia
(NASA
Blue
Marble)
with
the
survey
area
indicated
by
small
white
square
in
centre
of
image
This
predominant
focus
on
regional
analysis
and
large
sites
in
the
interpretation
of
Murghab
settlement
has
however
precluded
a
more
refined
understanding
of
micro-‐scale
distributions
of
archaeological
material.
While
a
few
small-‐scale
intensive
surveys
have
been
conducted
in
the
region
(e.g.
Cleuziou
et
al.
1998;
Cerasetti
pers.
comm.),
a
continued
under-‐emphasis
on
local
settlement
dynamics
and
material
distributions
has
led
to
a
rather
lopsided
interpretation
of
Murghab
settlement,
drawn
largely
from excavation
of
a
few
large
sites
in
the
context
of
regional
surveys.
This
situation
has
only
recently
begun
to
change,
driven
in
part
by
the
increasing
interest
in
small
sites
in
the
context
of
sedentary/nomadic
relationships
(e.g.
Cattani
et.
al.
2008).
Compounding
this
interpretative
problem
is
the
fact
that,
due
to
geomorphological
and
anthropogenic
processes,
the
recovery
potential
for
archaeological
sites
is
often
extremely
poor.
In
some
cases,
sites
have
been
completely
destroyed
as
a
result
of
an
explosion
in
urban
and
agricultural
development
over
the
past
several
decades,
associated
with
the
construction
of
the
Karakum
Canal.
Elsewhere,
particularly
in
the
central
and
southern
regions
of
the
delta,
aggrading
silts
have
resulted
in
alluvial
deposition
that
may
be
several
metres
deep
(Cremaschi,
1998).
In
the
northernmost
region
of
the
palaeodelta,
where
alluvial
deposits
are
shallower
and
agriculture
less
pervasive,
exposed
sites
are
often
severely
deflated,
susceptible
to
desert
winds
and
identifiable
only
as
nebulous
aggregations
of
surface
pottery.
Intermittent
but
often
heavy
dune
cover
obscures
much
of
the
landscape,
presenting
an
additional
barrier
to
archaeological
visibility
and
hindering
effective
interpretation.
The
northern
margin
of
this
inland
alluvial
fan
effectively
constitutes
a
unique
geomorphology
in
which
a
transitional
zone
exists
between
delta
and
desert
(Figure
2).
Each
of
these
regions
may
be
seen
to
broadly
exhibit
certain
prevailing
anisotropic
trends.
The
desert
morphology
is
represented
in
part
by
dune
ridges
that
tend
to
align
north-‐south,
while
the
prevailing
geomorphology
of
the
delta,
by
contrast,
tends
more
towards
the
northwest.
Where
these
regions
interact,
however,
these
trends
become
much
more
complex,
a
situation
exacerbated
by
relict
watercourses,
roadways
and
gas
pipelines.
Each
of
these
myriad
directional
influences
that
exist
in
the
landscape
has
potentially
influenced
the
present-‐day
orientation
of
surface
pottery.
Figure
2:
Landscapes
of
the
northern
Murghab:
a)
Landscape
of
low
dune
ridges
(left)
and
depressions
(right),
and
b)
Sand
begins
to
accumulate
on
an
old
trackway
A
survey
covering
11
km2
of
this
landscape
was
investigated
using
intensive
methods
in
which
surveyors,
spaced
20
metres
apart,
collected
information
at
20m
intervals
along
individual
transects.
This
process
has
provided
a
dataset
of
observations
that
are
recorded
on
a
20m
x
20m
grid,
each
grid
square
of
which
contains
information
such
as
the
sherd
counts
for
that
square.
For
ease
of
interpretation
and
discussion,
the
survey
area
is
also
divided
into
a
coarser
set
of
analytical
units,
defined
according
to
perceived
similarity
in
geomorphology
and/or
surface
scatter
(Figure
3).
This
archaeological
dataset
is
considered
in
detail
via
the
spatial
analysis
below.
Figure
3.
NMDS
survey
area:
a)
NMDS
survey
boundary
with
analytical
units,
and
b)
Directionality
in
the
sherd
distribution
3.
Methodology
The
objective
of
the
following
analysis
is
to
ascertain
the
degree
to
which
sherd
counts
and
other
landscape
surface
phenomena
exhibit
continuity
in
some
directions
more
so
than
others
(i.e.
are
anisotropic
in
nature).
There
are
a
variety
of
methods
for
considering
such
anisotropy
in
spatial
datasets,
including
directional
quadrat
counts,
nearest
neighbour
bearings,
directional
correlograms
and
Fourier
analysis
(Patterson,
1934,
Bartlett,
1964,
Haynes
and
Enders,
1975,
Rosenberg,
2000),
but
rarely
have
these
been
considered
in
archaeology.
Here
we
use
two
methods
for
understanding
possible
directional
effects
within
the
NMDS
data.
The
first
is
an
aspect
of
geostatistics
that
employs
directional
variograms
and
variogram
surfaces,which
are
graphical
plots
that
can
represent
spatial
variation
over
multiple
distances
and
in
different
directions.
The
second
method,
angular
wavelet
analysis,
examines
angular
variation
between
individual
point
pairs
(e.g.
surface
sherds).
While
geostatistics
have
seen
some
use
in
archaeology
and
have
recently
been
used
for
considering
surface
sherd
counts
(Bevan
and
Conolly,
2009:
258-‐9),
angular
wavelets
have
not
yet
been
considered
in
this
context,
despite
having
produced
very
promising
results
when
applied
to
ecological
data
(e.g.
Rosenberg,
2004).
To
illustrate
each
method,
we
have
created
a
hypothetical
distribution
of
material
over
a
2km
x
2
km
grid
in
which
anisotropy
within
the
point
patterns
(the
heavy
diagonal
striping)
has
been
exaggerated
to
better
illustrate
the
concept
(Figure
4).
The
following
discussion
will
utilise
this
dataset,
first
in
the
context
of
geostatistics
and
then
of
angular
wavelet
analysis,
before
applying
the
methods
to
the
actual
NMDS
survey
data.
Figure
4.
Sample
data
for
a
hypothetical
2km
x
2km
region:
a)
sample
pattern
of
40,000
points,
and
b)
aggregated
Point
pattern.
Legend
indicates
sherd
counts
per
hypothetical
unit
The
first
method,
variogram
analysis,
falls
within
the
discipline
of
geostatistics,
a
body
of
related
concepts
and
methods
that
explore
spatial
variation
in
attribute
values
observed
at
different
point,
line
or
area
locations,
particularly
with
the
purpose
of
spatial
prediction,
simulation
or
sampling
optimisation
(Lloyd
and
Atkinson,
2004).
Geostatistics
typically
assume
an
underlying
continuous
field
of
variation
that
has
been
sampled
at
a
limited
number
of
locations.
Each
sample
point
has
a
distinct
attribute
value
as
well
as
a
location
in
space,
and
unknown
values
may
be
predicted
via
the
interpolation
method
known
as
kriging
(a
well-‐
known
example
concerns
the
prediction
of
continuous
geological
phenomena
based
on
sparsely
sampled
borehole
observations
Journel,
1974).
The
degree
of
similarity
between
individually-‐measured
values
is
referred
to
as
spatial
autocorrelation
or
spatial
dependence
(see
Fortin
and
Dale,
2005:
6-‐10
for
a
useful
distinction
between
the
two),
and
is
a
formal
expression
of
‘Tobler’s
first
law
of
geography’(Tobler,
1970)
which
states
that
the
closer
in
space
that
two
measurements
are,
the
more
similar
those
measurements
are
likely
to
be.
We
can
explore
this
dependence
by
using
experimental
or
empirical
variograms:
graphical
plots
that
summarise
the
average
semi-‐variance
(half
the
variance)
between
the
attribute
values
(e.g.
sherd
counts)
of
sampled
point-‐pairs
as
a
function
of
the
distance
(or
spatial
lag)
between
these
pairs.
A
model
may
then
be
fitted
to
the
plot
in
order
to
interpolate
unknown
values
throughout
the
underlying
continuum.
These
fitted
curves
are
commonly
described
in
terms
of
the
range,
sill
and
nugget.
The
range
is
the
distance
at
which
the
semi-‐variance
values
reach
a
plateau,
indicating
that
the
observed
point
pairs
have
effectively
become
spatially
independent
of
each
other,
and
the
semi-‐variance
at
this
point
is
referred
to
as
the
sill.
The
nugget
effect
is
a
non-‐zero
starting
semi-‐variance
at
extremely
short
distances
that
reflects
indeterminate
local
variability
(Atkinson
and
Tate,
2000).
Here,
we
are
concerned
with
interpretation
rather
than
prediction
and
will
focus
on
the
empirical
variogram,
not
the
fitted
model.
Variograms
may
be
omnidirectional
or
directional.
The
first
type
considers
only
the
distance
between
point
pairs
(i.e.
the
range
of
spatial
autocorrelation)
(Gringarten,
2001).
Directional
variograms,
however,
examine
specific
angles
and
can
therefore
identify
directions
that
exhibit
greater
levels
of
spatial
autocorrelation
than
others
(i.e.
unusual
continuity
in
values),
and
the
spatial
scale
at
which
these
anisotropic
influences
come
into
play.
A
further
measure
of
the
strength
of
anisotropy
may
be
obtained
by
calculating
the
ratio
of
the
ranges
for
the
primary
direction
(major
axis)
of
anisotropy
and
the
perpendicular
direction
(minor
axis),
and
typically
this
is
one
of
the
parameters
that
is
fitted
as
part
of
a
model
variogram
and
for
subsequent
prediction
purposes
(e.g.
Atkinson
and
Lloyd,
2009:
135,163).
However,
the
original
empirical
variogram
retains
more
information
about
the
spatial
range
and
character
of
the
anisotropy
so
we
prefer
it
below.
Referring
to
our
sample
data,
the
omnidirectional
variogram
(Figure
5a)
shows
decreasing
spatial
dependence
as
the
distance
between
observations
increases,
reaching
a
plateau
around
400m,
the
distance
at
which
point
pairs
become
spatially
independent
of
each
other.
Also
visible
is
a
‘hole-‐effect’,
a
characteristic
dip
in
the
plot
that
can
indicate
heterogeneity
and/or
periodicity
in
the
data
(Pyrcz
and
Deutsch,
2003).
The
omni-‐directional
variogram,
however,
provides
little
information
about
anisotropy.
This
is
reflected
more
clearly
in
the
45°
directional
variogram
(Figure
5c,
bottom
right),
in
which
the
semi-‐variance
is
much
lower
than
in
the
omni-‐directional
variogram,
indicating
continuity
in
that
direction.
Also
potentially
useful,
and
sometimes
easier
to
interpret,
are
variogram
maps,
in
which
the
direction
of
maximum
anisotropy
corresponds
with
the
major
axis
of
the
observed
ellipse
(in
Figure
5d,
this
appears
linear
because
of
the
prominence
of
the
35°-‐45°
direction).
The
second
analytical
method
used
in
this
paper
is
angular
wavelet
analysis
(Rosenberg,
2004).
More
commonly
used
in
fields
such
as
mathematics
and
engineering,
for
example
in
the
context
of
signal
processing,
the
procedure
has
been
recently
applied
in
ecological
contexts
to
identify
spatial
patterning
in
different
types
of
vegetation.
An
advantage
of
wavelet
approaches
is
that
they
are
useful
for
recovering
patterns
in
the
presence
of
noise,
for
example
of
the
kind
produced
by
time-‐averaged,
post-‐depositionally
transformed
and
imperfectly
recovered
archaeological
datasets.
Figure
5.
Variograms
for
sample
dataset:
a)
omnidirectional
empirical
variogram,
b)
omnidirectional
empirical
variogram
(as
points
only)
with
fitted
spherical
model
(as
grey
line),
c)
directional
variograms
(0°
indicates
the
N-‐S
direction)
shown
as
black
points
alongside
the
omnidirectional
variogram
shown
as
a
grey
line,
and
d)
a
variogram
map.
The
method
differs
from
geostatistical
approaches
in
that
it
considers
non-‐
valued,
or
unmarked
point
patterns
where
each
point
has
no
intrinsic
value
and
is
merely
identified
by
its
location
in
space.
To
visualise
the
method,
consider
a
spoked
wheel
placed
over
a
distribution
of
points,
and
centred
on
one
of
these
points.
Along
each
angular
transect
(i.e.
1°
spoke),
a
scalable
window,
or
‘wavelet’,
is
fitted
that
measures
the
average
variance
in
the
angles
between
point-‐pairs
(Rosenberg
2004:
278-‐9).
Statistical
significance
is
derived
using
Monte
Carlo
simulation,
which
also
allows
for
the
investigation
of
irregularly
shaped
datasets.
The
method
can
be
applied
either
to
a
single
point,
effectively
assessing
local
anisotropy,
or
can
be
applied
to
multiple
points
simultaneously
to
examine
directional
patterns
more
globally.
Plotted
results
represent
variance
as
a
function
of
angle
measurement
(as
opposed
to
distance)
and
peaks
in
the
graph
indicate
the
direction
of
maximum
anisotropy.
Returning
to
our
sample
data,
we
can
see
that
the
graph
of
observed
variance
(dark
line)
rises
above
the
expected
variance
(dashed
line)
by
a
statistically
significant
margin
from
about
10°
(N-‐S
is
0°),
and
peaks
around
45°
(Figure
6).
This
suggests
that
although
the
anisotropy
is
the
strongest
in
a
SW-‐NE
direction,
several
trends
are
present,
although
the
noise
in
the
data,
and
the
similar
bearing
of
each
‘line’
of
points,
obscures
each
individual
trend.
It
is
worth
noting
an
individual
spike
towards
50°
as
well,
which
is
not
so
apparent
visually
but
may
reflect
additional
influence
in
the
right-‐most
diagonal
line.
Spikes
in
the
eight
cardinal
and
intermediate
directions
(i.e.
N,
NE,
etc.)
are
artefacts
of
the
gridded
pattern
(i.e.
at
shorter
distances,
fewer
angles
are
possible
between
points).
Figure
6.
Angular
wavelet
graph
for
the
sample
dataset.
The
above
dummy
example
suggests
that
each
analytical
method
may
provide
useful
insights
and
might
be
deployed
to
make
sense
of
far
more
noisy,
real
world
datasets.
The
following
analysis
proceeds
by
highlighting
the
directional
trends
that
may
be
perceived
by-‐eye,
first
examining
the
underlying
geomorphology
and
subsequently
the
distribution
of
surface
material.
Once
these
have
been
articulated,
the
two
methods
discussed
above
will
be
used
to
provide
an
integrated
assessment
of
anisotropy
in
the
Murghab
archaeological
landscape.
4.
Data
Analysis
and
Results
4.1
Variography
A
visual
assessment
of
the
northern
Murghab
landscape
reveals
two
prevailing
directional
trends.
The
first
is
the
ridged
topography
of
the
sand
dunes,
broadly
orientated
north-‐south.
A
second
is
the
geomorphology
of
the
palaeodelta
itself,
which
tends
more
towards
the
northwest.
Relict
channels
often,
but
not
always,
follow
a
SSE-‐NNW
trajectory,
influenced
by—but
not
directly
following—this
underlying
geomorphology
of
the
delta
fan.
A
third
anisotropic
influence
is
