This paper presents a resel-based approach to analyzing data from geophysical and surface artifact surveys, emphasizing the application of various processing and analysis techniques. It discusses a case study on an Early Iron Age metalworking site in Northern Britain, detailing the challenges and methods used to correlate surface finds with geophysical data. Furthermore, it highlights established processing techniques adaptable to different datasets, ensuring comprehensive analysis regardless of data structure or configuration.

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