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