Presentation given by John Pouncett at the Computer Applications in Archaeology conference in Beijing, April 2011.

1. Highs and Lows: A Resel-based
Approach to the Analysis of Data
from Geophysical and Surface
Artefact Survey
John Pouncett and Emma Gowans

2. Introduction
● Background
Overview the case study on which this paper is based:
a) Summary of the nature of data from surface artefact and
geophysical survey
b) Overview of the techniques used to process and
analyse/interpret those datasets
● Resels
Introduce the concept of a resel (resolution element)
Implement/extend Tobler's proposal for a resel-based GIS:
a) Surface artefact survey – 'low density' scatters
b) Geophysical survey – GPS enabled sensors

3. Case Study
● Overview
Early Iron Age metal working
site in northern Britain:
a) Slag mounds
b) Enclosures
Industry based on exploitation
of deposits of bog iron
● Pros/Cons
Geology – coversands 'poorly
suited' to geophysics
Plough damage – truncation
and deep ploughing
Correlation between surface
finds and geophysics

4. Non-Invasive Methods

5. Surface Artefact Survey
● Aggregation
Data aggregated by areal unit:
a) Grid square
b) Plot of land
Basic unit of analysis = areal
unit NOT site or artefact
● Survey data
Areal units represented as:
a) Polygons
b) Centroids
Artefact frequency/density
recorded for each areal unit
Zero data handling and low
number of unique values

6. 'Analysis'
● Visualisation
Point provenance
‘Coloured boxes’
● Point-based
Nearest neighbour analysis
Kernel density estimation
● Cell-based
Interpolation of continuous
surfaces from point data
Image processing techniques
e.g. thresholding

7. Geophysical Survey
● Sensors
String of readings from one or
more sensors
Location determined from
instrument parameters
● Survey data
Composite with gridded values
X and Y intervals:
a) Regular
b) Inequal
Extreme values
a) Over range
b) Geology

8. Processing
● Display
Clip – global function
● Defect removal
Destripe – zonal function
Destagger –zonal function
● Enhancement
Despike – focal function
High/Low pass filter – focal
function
● Adaptation
Established techniques applied
to 'new' datasets

9. Resels
● Spatial Averages (Tobler & Kennedy 1985)
Epidemiological and political data is often aggregated spatially:
a) Interpolation – assign an average to the location(s) for which
data is required
b) Conventional distance-weighted averages computationally
cumbersome
Applied to both point-based and resel-based datasets
● Resel-based GIS (Tobler 1995)
Typical user doesn't know or care whether a system is raster or vector
based
Generalisation of techniques used in image processing based on a
spreadsheet analogy

10. Representation
Regular x and y – dummy values/no data
Irregular x and y – single/contiguous entities

11. Cell-based Operations
● Raster datasets 1 2 3 4 5
Regular configuration of cells,
6 7 8 9 10
each cell has the same:
a) Geometric properties 11 12 13 14 15
b) Number of neighbours
16 17 18 19 20
Robust syntax for map algebra
based on: 21 22 23 24 25
a) Row/column offsets [r,c]
b) Kernels (0/1 or weighted)

12. Point-based Operations
1st order neighbours (light grey)
2nd order neighbours (dark grey)

13. Spatial Relationships
● Contiguous polygons
Irregular configuration of areal
units
Conceptualisation of spatial
relationships:
a) Rook's case - shared
edges
b) Queen's case - shared
edges and nodes
Spatial weights e.g. length of
shared edge

14. Topology

15. Adjacency
● 1st Order Neighbours ● 2nd Order Neighbours
Neighbours of cell 13 Neighbours of 1st neighbours
ID FID NID Weight # Neighbours
1 13 7 0 7 1 2 3 6 8 11 12 13
2 13 8 10 8 2 3 4 7 9 12 13 14
3 13 9 0 9 3 4 5 8 10 13 14 15
4 13 12 10 12 6 7 8 11 13 16 17 18
5 13 14 10 14 8 9 10 13 15 18 19 20
6 13 17 0 17 11 12 13 16 18 21 22 23
7 13 18 10 18 12 13 14 17 19 22 23 24
8 13 19 0 19 13 14 15 18 20 23 24 25
0 = Queen's case Remove duplicates
10 = Rook's case [Remove lower orders]

16. 'Low Density' Scatters
● Generalisation
'Continuous' data - frequency of
artefacts representative
● Processing
Defect removal (destripe) –
eliminate 'walker' effects
Enhancement (high/low pass) –
improve handling of zeros
● Analysis
Increase in number of unique
values (N.B. smoothing)
Enables a wider range of
approaches e.g. cluster/outlier

17. GPS Enabled Sensors
● Interpolation
Irregular X and Y
Zonal functions - transects
● Thiessen polygons
Each sample representative of
adjacent area
Focal functions – resels
● Processing
Preserves spatial component
Eliminates the need for some
defect removal techniques
Supports a full range of display
and enhancement techniques

18. Concluding Remarks
● Common processing
Techniques applied to any
dataset regardless of the:
a) Data structure used to
encode data
b) Configuration of the areal
units/samples
c) Geophysical or surface
artefact data
Robust syntax for applying
processing techniques