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Presentation given by John Pouncett at the Computer Applications in Archaeology conference in Beijing, April 2011.
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Highs and Lows: A Resel-based Approach to the Analysis of Data
from Geophysical and Surface Artefact Survey
John Pouncett and Emma Gowans
Introduction● Background
Overview the case study on which this paper is based:a) Summary of the nature of data from surface artefact and
geophysical surveyb) 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' scattersb) Geophysical survey – GPS enabled sensors
Case Study● Overview
Early Iron Age metal working site in northern Britain:
a) Slag moundsb) Enclosures
Industry based on exploitation of deposits of bog iron
● Pros/ConsGeology – coversands 'poorly suited' to geophysics Plough damage – truncation and deep ploughingCorrelation between surface finds and geophysics
Non-Invasive Methods
Surface Artefact Survey● Aggregation
Data aggregated by areal unit:a) Grid squareb) Plot of land
Basic unit of analysis = areal unit NOT site or artefact
● Survey dataAreal units represented as:
a) Polygonsb) Centroids
Artefact frequency/density recorded for each areal unitZero data handling and low number of unique values
'Analysis'● Visualisation
Point provenance‘Coloured boxes’
● Point-basedNearest neighbour analysisKernel density estimation
● Cell-basedInterpolation of continuous surfaces from point dataImage processing techniques e.g. thresholding
Geophysical Survey● Sensors
String of readings from one or more sensorsLocation determined from instrument parameters
● Survey dataComposite with gridded valuesX and Y intervals:
a) Regularb) Inequal
Extreme valuesa) Over rangeb) Geology
Processing● Display
Clip – global function● Defect removal
Destripe – zonal functionDestagger –zonal function
● EnhancementDespike – focal functionHigh/Low pass filter – focal function
● AdaptationEstablished techniques applied to 'new' datasets
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 requiredb) Conventional distance-weighted averages computationally
cumbersomeApplied 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 basedGeneralisation of techniques used in image processing based on a spreadsheet analogy
Representation
Regular x and y – dummy values/no dataIrregular x and y – single/contiguous entities
Cell-based Operations● Raster datasets
Regular configuration of cells, each cell has the same:
a) Geometric propertiesb) Number of neighbours
Robust syntax for map algebra based on:
a) Row/column offsets [r,c]b) Kernels (0/1 or weighted)
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
21 22 23 24 25
Point-based Operations
1st order neighbours (light grey)2nd order neighbours (dark grey)
Spatial Relationships● Contiguous polygons
Irregular configuration of areal unitsConceptualisation 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
Topology
● 1st Order NeighboursNeighbours of cell 13ID FID NID Weight1 13 7 02 13 8 103 13 9 04 13 12 105 13 14 106 13 17 07 13 18 108 13 19 00 = Queen's case10 = Rook's case
Adjacency● 2nd Order Neighbours
Neighbours of 1st neighbours# Neighbours 7 1 2 3 6 8 11 12 138 2 3 4 7 9 12 13 149 3 4 5 8 10 13 14 1512 6 7 8 11 13 16 17 1814 8 9 10 13 15 18 19 2017 11 12 13 16 18 21 22 2318 12 13 14 17 19 22 23 2419 13 14 15 18 20 23 24 25Remove duplicates[Remove lower orders]
'Low Density' Scatters● Generalisation
'Continuous' data - frequency of artefacts representative
● ProcessingDefect removal (destripe) – eliminate 'walker' effectsEnhancement (high/low pass) – improve handling of zeros
● AnalysisIncrease in number of unique values (N.B. smoothing)Enables a wider range of approaches e.g. cluster/outlier
GPS Enabled Sensors● Interpolation
Irregular X and YZonal functions - transects
● Thiessen polygonsEach sample representative of adjacent areaFocal functions – resels
● ProcessingPreserves spatial componentEliminates the need for some defect removal techniquesSupports a full range of display and enhancement techniques
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