1. FACULTY OF SCIENCE
MSc DEGREE
IN
Applied Geographical Information Systems
Edward James Kemp
K1047325
A spatial analysis of East Anglian
Anglo Saxon artefacts using a GIS
14 September 2011
Kingston University London

2. A SPATIAL ANALYSIS OF
EAST ANGLIAN
ANGLO SAXON
ARTEFACTS USING A GIS
STUDENT ID: K1047325
NAME: EDWARD JAMES KEMP
COURSE: MSc APPLIED GEOGRAPHICAL INFORMATION SYSTEMS
DATE: 14 SEPTEMBER 2011
MODULE: GGM122: RESEARCH PROJECT
WORD COUNT: 25,410
PROJECT SUPERVISOR: IAN GREATBATCH

3. Abstract
Most people would associate archaeological research with the organised excavations that take
place in areas of historical or cultural interest. This is of a course a very valuable part of the
discipline and has led to some notable discoveries such as the Viking burial ground at Sutton Hoo
in Suffolk. But there is now a growing trend for the use of metal detectors by the general public to
compliment such projects through field walking exercises.
This has helped create large online databases that hold detailed information on each
archaeological find. Organisations such as the Portable Antiquities Scheme (PAS) have helped in
verifying and categorising the finds, meaning the database can be searched according to a period
of British history. Studies such as the VASLE (Viking and Anglo Saxon Lifestyle and Economy)
VASLE project undertaken by John Naylor in 2009 have sought to understand the databases East
Anglian Anglo Saxon finds within the context of known archaeological sites. As spatial references
are one of the pieces of information held on the database a GIS can be used to analyse and
explore the spatial properties of the archaeological finds. Which in turn can aid our understanding
of how, when, and where people lived at various points in English history.
The 2009 project did not use a Geographical Information System (GIS) as part of its method, and it
is for this reason that this project uses such techniques to analyse the PAS finds from the East
Anglian Anglo Saxon period. Its aim wasn’t to challenge the results of the 2009 project, but to look
at the database of finds from a GIS and spatial analysis perspective.
Cluster, hotspot and buffering techniques were used to explore and interrogate the Anglo Saxon
finds, which were divided up into logical object type groupings and possible point in time they
were last used. These techniques revealed a lot more about the data than would have been
possible through a mere visual inspection.
Cluster analysis revealed how jewellery and clothing may have been stored, and how horse
breeding may have been a highly specialised local occupation in the later Anglo Saxon period.
Hotspot analysis revealed possible trade routes through parts of Norfolk and Suffolk, and key
towns that may have played a role in tax collection or the manufacture of certain goods. Buffering
analysis allowed the reappraisal of 22 sites identified in 2009 project as being ‘productive’ it also
helped build a picture indicating which sites may have been important and when.
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4. GIS techniques have allowed a more in depth analysis of the PAS archaeological data, and have
highlighted areas and sites that could be investigated further through GIS or field work projects. It
definitely has huge potential to help us understand our past and is an ideal complimentary
technology to sit alongside archaeological digs in muddy fields.
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5. Acknowledgements
I would like to thank the following for help and contribution to this project.
My parents Liz and John Kemp for all their love, support, advice and hours of proofreading work.
My partner Jana Cerny for all her love and support throughout the project. My partner’s parents
Moira and Zdenek Cerny for their support and advice. My project tutor Dr Ian Greatbach and
course director Dr Mike Smith for their help and advice at varying stages of the project. Finally Dr
John Naylor of the Ashmolean Museum in Oxford, who gave valuable historical as well as
archaeological advice at the initial stages of the project.
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6. Table of Contents
Abstract ................................................................................................................................i
Acknowledgements ......................................................................................................................iii
Table of Contents ......................................................................................................................... iv
Table of Figures ............................................................................................................................ ix
Chapter 1: Introduction ...........................................................................................................1
Chapter 2: Literature Review ...................................................................................................3
2.1 The issues surrounding the use of metal detected finds in archaeological analysis ..........4
2.1.1 Bias in archaeological finds found using metal detectors .........................................4
2.1.2 The use of volunteered geographic Information in GIS (VGI) ...................................5
2.1.3 Building useable datasets from metal detected archaeological information ............6
2.1.4 Dealing with gaps in spatial data .............................................................................7
2.2 Defining the productive site ............................................................................................8
2.2.1 Productive sites and Anglo Saxon towns and communication routes .......................9
2.3 Spatial Analysis Techniques .......................................................................................... 10
2.3.1 Spatial autocorrelation of archaeological data ...................................................... 10
2.3.2 Cluster analysis of spatial data .............................................................................. 11
2.3.3 Hotspot analysis of spatial data ............................................................................. 12
2.4 Conclusions .................................................................................................................. 13
Chapter 3: Materials and methods ........................................................................................14
3.1 Data collection.............................................................................................................. 15
3.2 Data cleaning and manipulation.................................................................................... 15
3.2.1 Cleaning and manipulating the PAS dataset .......................................................... 16
3.2.2 Dating of PAS finds ................................................................................................ 16
3.2.3 Classification of PAS finds...................................................................................... 17
3.2.4 Cleaning and manipulating the Ordnance Survey datasets .................................... 18
3.3 Creating a geodatabase from all the datasets ............................................................... 18
3.4 Cluster analysis of PAS finds.......................................................................................... 19
3.5 Hotspot analysis of PAS finds ........................................................................................ 21
3.6 VASLE productive site comparison analysis ................................................................... 23
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7. 3.7 Overview of study area and PAS finds dataset............................................................... 25
3.7.1 Overview of study area ......................................................................................... 25
3.7.2 Overview of PAS finds dataset............................................................................... 26
Chapter 4: Results and discussion ..........................................................................................29
4.1 Cluster analysis of PAS finds.......................................................................................... 29
4.1.1 Average nearest neighbour (ANN) results ............................................................. 29
4.1.1.1 East Anglian Anglo Saxon towns ANN results ..................................................... 33
4.1.1.2 Summary of average nearest neighbour (ANN) results ...................................... 33
4.1.2 Global Morans I (GMI) results ............................................................................... 34
4.1.2.1 East Anglian Anglo Saxon towns GMI results ..................................................... 36
4.1.2.2 Summary of Global Morans I (GMI) results ........................................................ 36
4.1.3 Ripley’s K function results ..................................................................................... 37
4.1.4 Discussion of cluster analysis of PAS finds ............................................................. 39
4.2 Hotspot analysis of PAS finds ........................................................................................ 42
4.2.1 Hotspot analysis of all PAS finds at all time periods ............................................... 43
4.2.2 Hotspot analysis of global early, middle and late period finds ............................... 45
4.2.2.1 Hotspot analysis for the global early Anglo Saxon period finds (400 – 600AD) ... 45
4.2.2.2 Hotspot analysis for the global middle Anglo Saxon period finds period
(601 – 800AD) ..................................................................................................... 46
4.2.2.3 Hotspot analysis for the global late Anglo Saxon period finds period
(801 – 1066AD) ................................................................................................... 46
4.2.3 Hotspot analysis for the 6 global finds object groups hotspots .............................. 47
4.2.4 Hotspot analysis for the clothing object group through the early, middle and
late Anglo Saxon periods ....................................................................................... 49
4.2.5 Hotspot analysis for the coins object group through the middle and late Anglo
Saxon periods ....................................................................................................... 50
4.2.6 Hotspot analysis for the horse items object group through the early, middle
and late Anglo Saxon periods ................................................................................ 51
4.2.7 Hotspot analysis for the commercial and household object group through the
early, middle and late Anglo Saxon periods ........................................................... 52
4.2.8 Hotspot analysis for the jewellery object group through the early, middle and
late Anglo Saxon periods ....................................................................................... 53
4.2.9 Hotspot analysis for the pins object group through the early, middle and late
Anglo Saxon periods.............................................................................................. 54
4.2.10 Hotspot analysis for the Anglo Saxon towns of East Anglia .................................... 55
4.2.11 Discussion of PAS finds hotspot analysis ................................................................ 56
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8. 4.3 Analysis of VASLE productive sites ................................................................................ 58
4.3.1 Buffer analysis on the 22 VASLE and 22 control sites ............................................. 59
4.3.1.1 Summary of the buffer analysis results for the 22 VASLE productive sites (VPS). 59
4.3.1.2 Summary of buffer analysis results for the 22 control sites ................................ 60
4.3.2 Comparison of the 22 VPS and 22 control sites through buffer analysis ................. 61
4.3.3 Analysis of the 22 individual VPSs using the total VPS finds dataset ....................... 61
4.3.3.1 Overview of the total VPS finds dataset ............................................................. 62
4.3.3.2 Individual VPS buffering analysis results ............................................................ 63
4.3.3.2.1 Burgh Castle VPS ......................................................................................... 63
4.3.3.2.2 Barham VPS ................................................................................................ 64
4.3.3.2.3 Burnham Market VPS .................................................................................. 65
4.3.3.2.4 Caister St Edmunds VPS ............................................................................... 66
4.3.3.2.5 Coddenham VPS .......................................................................................... 67
4.3.3.2.6 Colkirk VPS .................................................................................................. 68
4.3.3.2.7 Congham VPS .............................................................................................. 69
4.3.3.2.8 East Rudham VPS ........................................................................................ 70
4.3.3.2.9 East Walton VPS .......................................................................................... 71
4.3.3.2.10 Freckenham .............................................................................................. 72
4.3.3.2.11 Hindringham VPS....................................................................................... 73
4.3.3.2.12 Ixworth VPS ............................................................................................... 74
4.3.3.2.13 Lackford VPS ............................................................................................. 75
4.3.3.2.14 Middle Harling VPS .................................................................................... 76
4.3.3.2.15 Narborough VPS ........................................................................................ 77
4.3.3.2.16 Rockland All Saints VPS.............................................................................. 78
4.3.3.2.17 Rockland St Peter VPS ............................................................................... 79
4.3.3.2.18 Tibenham VPS ........................................................................................... 80
4.3.3.2.19 West Rudham VPS ..................................................................................... 81
4.3.3.2.20 Whissonsett VPS ....................................................................................... 82
4.3.3.2.21 Wormegay VPS.......................................................................................... 83
4.3.3.2.22 Discussions from the individual VPS buffering analysis .............................. 84
5. Conclusions ..........................................................................................................88
References .............................................................................................................................90
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9. APPENIDIX A .............................................................................................................................99
1. All PAS finds all time periods hotspot map ...................................................................... 100
2. Early period global finds (400 – 600AD) hotspot map...................................................... 101
3. Middle period global finds (601 – 800AD) hotspot map .................................................. 102
4. Late period global finds (801 – 1066AD) hotspot map ..................................................... 103
5. Clothing global finds hotspot map .................................................................................. 104
6. Coins global finds hotspot map ....................................................................................... 105
7. Horse items global finds hotspot map............................................................................. 106
8. Commercial and household global finds hotspot map ..................................................... 107
9. Jewellery global finds hotspot map ................................................................................. 108
10. Pins global finds hotspot map ..................................................................................... 109
11. Clothing early period finds (400 – 600AD) hotspot map .............................................. 110
12. Clothing middle period finds (601 – 800AD) hotspot map ........................................... 111
13. Clothing late period finds (801 – 1066AD) hotspot map .............................................. 112
14. Coins middle period finds (601 – 800AD) hotspot map ................................................ 113
15. Coins late period finds (801 – 1066AD) hotspot map................................................... 114
16. Horse items early period finds (400 – 600AD) hotspot map......................................... 115
17. Horse items middle period finds (601 – 800AD) hotspot map...................................... 116
18. Horse items late period finds (801 – 1066AD) hotspot map ........................................ 117
19. Commercial and Household early period finds (400 – 600AD) hotspot map ................ 118
20. Commercial and Household middle period finds (601 – 800AD) hotspot map ............. 119
21. Commercial and Household late period finds (801 – 1066AD) hotspot map ................ 120
22. Jewellery early period finds (400 – 600AD) hotspot map ............................................. 121
23. Jewellery middle period finds (601 – 800AD) hotspot map.......................................... 122
24. Jewellery late period finds (801 – 1066AD) hotspot map............................................. 123
25. Pins middle period finds (601 – 800AD) hotspot map .................................................. 124
26. Pins late period finds (801 – 1066AD) hotspot map ..................................................... 125
27. All Anglo Saxon towns hotspot map ............................................................................ 126
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10. APPENDIX B ...........................................................................................................................127
1. Breakdown of total PAS dataset by object group and VPS ............................................... 128
2. Breakdown of total PAS dataset by Anglo Saxon period and VPS .................................... 129
3. Breakdown of the coins object group within the total PAS dataset by Anglo Saxon
period and VPS ............................................................................................................... 130
4. Breakdown of the clothing object group within the total PAS dataset by Anglo Saxon
period and VPS ............................................................................................................... 131
5. Breakdown of the horse items object group within the total PAS dataset by Anglo
Saxon period and VPS ..................................................................................................... 132
6. Breakdown of the commercial and household object group within the total PAS
dataset by Anglo Saxon period and VPS .......................................................................... 133
7. Breakdown of the jewellery object group within the total PAS dataset by Anglo Saxon
period and VPS ............................................................................................................... 134
8. Breakdown of the pins object group within the total PAS dataset by Anglo Saxon period
and VPS .......................................................................................................................... 135
APPENDIX C 136
1. Breakdown of control sites dataset by object group ....................................................... 137
2. Breakdown of control sites dataset by Anglo Saxon period ............................................. 138
3. Breakdown of control sites dataset by coin object group and Anglo Saxon period .......... 139
4. Breakdown of control sites dataset by clothing object group and Anglo Saxon period .... 140
5. Breakdown of control sites dataset by horse items object group and Anglo Saxon
period............................................................................................................................. 141
6. Breakdown of control sites dataset by commercial and household object group and
Anglo Saxon period......................................................................................................... 142
7. Breakdown of control sites dataset by jewellery object group and Anglo Saxon period ... 143
8. Breakdown of control sites dataset by pins object group and Anglo Saxon period .......... 144
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11. Table of Figures
Figure 1: Location map of the East Anglia study area .................................................................... 26
Figure 2: Percentage distribution of total PAS data ...................................................................... 28
Figure 3: Percentage distribution of PAS objects through the Anglo Saxon periods ....................... 28
Figure 4: Results of the ANN statistical test on the PAS data......................................................... 32
Figure 5: Results of the ANN statistical test on the East Anglian Anglo Saxon towns data ............. 33
Figure 6: Results of the GMI statistical test on the PAS data ......................................................... 35
Figure 7: Results of the GMI statistical test on the East Anglian Anglo Saxon towns data .............. 36
Figure 8: Results of the Ripley’s K statistical test on the PAS data ................................................. 38
Figure 9: Results of the Ripley’s K statistical test on the East Anglian Anglo Saxon towns data ...... 39
Figure 10: ANN results for the global object groups...................................................................... 40
Figure 11: GMI results for the global object groups ...................................................................... 40
Figure 12: Hotspot analysis map of the early period Anglo Saxon finds ......................................... 43
Figure 13: Hotspot analysis map of the early period Anglo Saxon finds ......................................... 45
Figure 14: Hotspot analysis map of the middle period Anglo Saxon finds...................................... 45
Figure 15: Hotspot analysis map of the late period Anglo Saxon finds .......................................... 45
Figure 16: Hotspot analysis map of global clothing Anglo Saxon finds........................................... 47
Figure 17: Hotspot analysis map of global coin Anglo Saxon finds................................................. 47
Figure 18: Hotspot analysis map of global horse items Anglo Saxon finds ..................................... 47
Figure 19: Hotspot analysis map of global commercial and household Anglo Saxon finds ............. 47
Figure 20: Hotspot analysis map of global jewellery Anglo Saxon finds ......................................... 47
Figure 21: Hotspot analysis map of global pin Anglo Saxon finds .................................................. 47
Figure 22: Hotspot analysis map of early period clothing Anglo Saxon finds ................................. 49
Figure 23: Hotspot analysis map of middle period clothing Anglo Saxon finds .............................. 49
Figure 24: Hotspot analysis map of late period clothing Anglo Saxon finds ................................... 49
Figure 25: Hotspot analysis map of middle period coin Anglo Saxon finds .................................... 50
Figure 26: Hotspot analysis map of late period coin Anglo Saxon finds ......................................... 50
Figure 27: Hotspot analysis map of early period clothing Anglo Saxon finds ................................. 51
Figure 28: Hotspot analysis map of middle period clothing Anglo Saxon finds .............................. 51
Figure 29: Hotspot analysis map of late period clothing Anglo Saxon finds ................................... 51
Figure 30: Hotspot analysis map of early period commercial and household Anglo Saxon finds .... 52
Figure 31: Hotspot analysis map of middle period commercial and household Anglo Saxon finds . 52
Figure 32: Hotspot analysis map of late period commercial and household Anglo Saxon finds...... 52
Figure 33: Hotspot analysis map of early period commercial and household Anglo Saxon finds .... 53
Figure 34: Hotspot analysis map of middle period commercial and household Anglo Saxon finds . 53
Figure 35: Hotspot analysis map of late period commercial and household Anglo Saxon finds...... 53
Figure 36: Hotspot analysis map of middle period pins Anglo Saxon finds .................................... 54
Figure 37: Hotspot analysis map of late period pins Anglo Saxon finds ......................................... 54
Figure 38: Hotspot analysis map of all Anglo Saxon towns ............................................................ 55
Figure 39: Possible location of new productive site within 2.5 miles of Hoxne .............................. 57
Figure 40: Location of the 22 VASLE productive sites .................................................................... 58
Figure 41: Location of the 22 control sites .................................................................................... 58
Figure 42: Percentage breakdown of unique finds across all 22 VPSs ............................................ 59
Figure 43: Percentage breakdown of unique finds across all 22 control sites ................................ 60
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12. Figure 44: Possible area of pin production or trade in Anglo Saxon East Anglia ............................. 86
Figure 45: Possible trade routes in Anglo Saxon East Anglia.......................................................... 87
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13. Chapter 1: Introduction
Geographical Information Systems (GIS) can be applied to a wide range of disciplines that have a
spatial dimension to them. There are however some areas of research that have not forfilled their
potential use of GIS.
Early England has a rich history from the invasion of the Romans in 5AD to the Anglo Saxon period
and the Norman Conquest of 1066. In between, the Vikings and Danes also tried to stake their
claim on parts on various parts of the country with varying degrees of success. These competing
settlers have all left behind their mark in some way or another. Much of the evidence of their
occupation is no longer visible, but through archaeological exploration finds, sometimes of great
importance and value, can be unearthed; this helps us to piece together what life might have
been like in what some describe as the ‘dark ages’.
Archaeological digs investigating all parts of English history have become very popular in the last
20 years or so partly due to television programs such as Time Team. Recent headline grabbing
discoveries such as the Staffordshire hoard, which is estimated to be worth £3.2 million, have also
added to the public’s interest. This has inspired a growing number of hobby archaeologists to not
only go on organised digs but also use ‘metal detectors’ to look for archaeological items in fields
and pastures across the UK.
In an effort to better organise the finds recording process as well as provide analysis for any finds
the Portable Antiquities Scheme (PAS) was set up in 1997. Central to its operations was an online
database that could be accessed by any member of the public to record their finds, a team of
regional finds officers could then authenticate and offer further advice on the finds. By 2011 there
were over 450,000 items on the database ranging from gold rings to copper spoons, and dating
from the Roman period to the modern day. Attributes such as dimensions and date were stored
for each item but more importantly from a GIS perspective there was a spatially referenced
location on the earth’s surface in the form of northings and eastings that could be used for GIS
analysis.
John Naylor from the University of Oxford had carried out an investigation into these finds in 2009
called the (Viking and Anglo Saxon Landscape and Economy) VASLE project (Naylor et al, 2009).
He investigated the finds and their relationship with 22 Anglo Saxon productive sites in and
around the East Anglian area. He used a ‘fingerprint’ method to assess how many of each of the
PAS finds were located near the productive sites in order to better understand their use and levels
of activity over the Anglo Saxon period. Naylor did not use any GIS techniques for this project
1

14. except to create a few maps of the find locations; no focus was put on using the wealth of spatial
analysis, techniques such as cluster and hotspot analysis available in modern GIS packages like
ArcMap 10. These techniques are widely used in other disciplines such as crime mapping (Block
and Block, 1995) but have been little used in archaeology.
This means that there is a great opportunity to explore the large quantities of data available in the
PAS database using spatial analysis techniques. The resulting analysis could help us understand
how people lived at various points in history as well as highlighting patterns within groups of
certain items such as jewellery or household items. It could also highlight areas that could need
further investigation.
It is for these reasons that this project will take a sample of data from the PAS database dating
from the Anglo Saxon period and apply GIS and spatial analysis techniques to explore and
investigate the data. The results of the 2009 project by John Naylor will be used to judge the
success of some of the spatial analysis techniques within the archaeological discipline.
It will not try to rewrite the work that John and his team did as they firstly have a vastly superior
knowledge of archaeology than the author of this project and secondly they had access to
additional archaeological datasets that are not available to the public. This project proposes to
compliment the work done by the VASLE project by performing spatial analysis techniques to help
further understand the PAS data found in this area.
This dissertation will be broken down into four sections. Following this introduction there will be a
thorough literature review covering the areas of archaeology and spatial analysis relevant to this
project. After this there will be a presentation of the materials and methods used to undertake
the GIS and spatial analysis followed by a discussion of the results that were achieved. Finally
there will be a summary of the conclusions made and recommendations for possible further
study.
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15. Chapter 2: Literature Review
Geographical Information Systems (GIS) and advanced spatial analysis techniques have been used
in archaeology since the late 1970’s (Matsumoto, 2007). This is because archaeologists have
always understood the value of analysing the spatial data they find through fieldwork
investigations (Seibert, 2007) (Wheatly and Gillings, 2002). Haining (2003) suggests that
archaeology has become a subfield of geography and its spatial processes. ESRI the company
behind leading GIS software package ArcMap 10 has created a ‘Best Practice’ document for the
use of GIS in archaeological projects (Brett et.al, 2009).
GIS related technologies are now widely used to collect archaeological data supplementing the
more traditional methods of field walking (Medlycott, 2006) (Foard, 1978). Such technologies
include remote sensing in the multispectral and thermal bands; this technique has been widely
used to understand the structure of the ancient Mayan civilization, (Estrada-Belli and Koch, 2007)
(Sever et.al, 2007). Ariel photography has also been used in a similar way but at a higher
resolution (Matheny, 1962) (Gilman, 1999).
Other technologies involve devices that measure the soil’s resistivity to electric currents Ground
Penetrating Radar (GPR) (Basile et.al, 2000) or magnetic characteristics (Geo Physical Surveys)
(Bevan, 1991). These are particularly useful when other visual surveys reveal no obvious activity
and when linked with GPS devices can provide another perspective of an archaeological site.
One collection method that has already added a great deal of the knowledge to the field of
archaeology is metal detecting (Thomas and Stone, 2009) (Kidd, 2008) (Cool, 2000). Finds made by
the general public can be given a GPS location and then be imported into GIS software for further
analysis. This analysis can greatly improve the knowledge where and how people lived ‘VASLE
Project’ (Naylor et al, 2009) (Chester-Kadwell, 2009) (Ulmschneider, 2000). These projects all
centred around the analysis of metal detected finds from East Anglia dated to the Anglo Saxon
period 400 – 1066 AD.
The vast amounts of metal detected data held on databases such as that of the Portable
Antiquities Scheme (PAS) means that there is a great opportunity to utilise the spatial analysis
functions of GIS software such as ArcMap 10 (Gill, 2002). Unfortunately whilst some projects
utilise GIS functionality (Moyes, 2002) (Kay and Witcher, 2009) the projects undertaken utilising
metal detected data have yet to fully exploit these functions, concerning themselves more with
basic mapping and visualisation of the finds and so called ‘productive sites’ across study areas.
Johnson
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16. (2002) cites a possible reason for this as the fact GIS has not fully been embraced by the
archaeological community, reflected by the lack of peer reviewed material involving the two
disciplines. This gap in the material shows that there is justification in undertaking a GIS project
such as this.
The body of this review will be divided into three sections; the first will outline the issues
surrounding the use of metal detected finds in archaeological analysis; the second will discuss the
definition of an archaeological ‘productive site’ as this will have an impact on the hypothesises
and methodology for the project; finally, the third will discuss, using other projects, the required
spatial analysis techniques that can be employed to interpret the archaeological data.
2.1 The issues surrounding the use of metal detected finds in
archaeological analysis
Before discussing the use of spatial analysis techniques in archaeological projects it is important
to understand the issues surrounding the use of data derived from metal detectors. This is
important as Dobinson and Denison (1995) concluded that metal detecting has been responsible
for some major advances in archaeological knowledge. This section will summarise the key
literature for each of the main issues and explain how their effects can be minimised or mitigated
against to maximise the accuracy of any spatial analysis work that is carried out.
2.1.1 Bias in archaeological finds found using metal detectors
Due to fact that metal detecting is so popular amongst the general public (Paynton, 2002) there is
going to be a certain amount of bias in the locations of many finds, this is over and above the
obvious bias against pottery and ferrous objects (Naylor and Richards, 2007). This subject is not
widely acknowledged in current literature, some account for the extensive urban areas in the UK
which prevent metal detecting (Naylor et al, 2009), but most rarely account for the way find
distributions are influenced by the metal detectorist.
Studies have shown the most suitable land type for metal detecting is agricultural land with short
stubble (Gurney, 2003), woodland and open pasture are also preferred over inaccessible areas
with unsuitable surfaces such as concrete urban areas. Kershaw (2009) however states that these
very sites also cause bias because modern activity has re distributed them from their original
locations leading spurious findspot locations. Sites known to have been previously rich in finds will
4

17. also attract a higher than normal level of surveying. The metal detectorist community is highly
active and news of a finds rich site will travel quickly (Pflum, 2011).
Analysis has also shown that metal detecting may also be biased towards accessible areas, such as
whether the field or wood is close to a main road or urban settlement. Suitable areas near these
locations may therefore be surveyed preferentially (Ulmschneider, 2000) (Naylor et al, 2009).
Further study could indicate whether this bias is indeed the case, but it is beyond the scope of this
project.
Most experts also specify that search patterns need to be properly structured; sites that are
randomly searched will not provide a detailed picture of the distribution of potential finds.
Searching via a grid or cross based pattern is advisable to obtain the best results (Ulst, 2010)
(Gurney, 2003) (Foard, 1972).
It is clear therefore that this potential bias must be taken into consideration when looking at the
spatial distribution of metal detected archaeological finds. For the purpose of this report the data
must be taken for what it is with the allowance and acceptance that there will be some bias, how
this will be dealt with will be discussed further in the methodology section.
2.1.2 The use of volunteered geographic Information in GIS (VGI)
The spatial information associated with metal detected finds made by the public can be seen as
being volunteered (Goodchild, 2007), i.e. non-proprietary data as opposed to commercially
obtained data. Literature has focused on comparing the advantages and disadvantages of each
source (Zielstra and Zipf, 2005) its advantages have been highlighted in disaster management
situations (Goodchild and Glennon, 2010) (Zook et.al 2010) some have directly compared the
software VGI data it is based on (Haklay et.al, 2009) (Mooney and Corcoran, 2011) (Kounadi,
2009).
The quality of VGI data has been called into question as the hobby geographer does not always
have the skills of an academically trained one (Brando and Bucher, 2010). The completeness of
VGI data has also been called into question (Haklay and Ellul, 2010) these shortcomings could
possibly detract from the usefulness of VGI in archaeological analysis. Subsequent research has
specifically developed systems using fuzzy sets theory to overcome the inherent vagueness in VGI
(De Longueville et.al, 2009).
5

18. Studies have described that good quality data improves the outcome of any project (Naylor,
2005). Others have tried to define what makes good quality data and how end users decide
whether they should use it or not depending on its quality (Van Oort, 2006). The next section will
describe some of the inconsistencies that are unique to using metal detected data for an
archaeological spatial analysis project.
2.1.3 Building useable datasets from metal detected archaeological information
The use of publically collected spatial information for use in archaeological analysis brings a set of
issues that must also be understood; these are partly due to the bias that was outlined in section
2.1.1 but also UK law and the ability of the metal detectorist to accurately document their
findings. These issues are summarised in much of the archaeological literature (Wheatly and
Gillings, 2002) (Greene and Moore, 2010) (McAdams and Kocaman, 2010).
The locational quality of data is paramount for any accurate spatial analysis to take place. Where
finds are recorded in situ by trained archaeologists this is not a problem as the use of GPS devices
to fix locations of finds is commonplace (Tripcevich, 2004). This method means that accurate
spatial analysis can then be carried out such as in the work by Niknami and Amirkhiz (2009) and
Sommer (2011).
Unfortunately, privacy and inconsistent use of GPS devices mean that the locations of metal
detected data is not consistently accurate (Naylor et al, 2009); sometimes the locational
information is withheld altogether, this is to protect the privacy of the landowner. The database
held by the PAS has accuracy to a six figure easting and northing reference meaning the find can
be pinpointed to a 100m x 100m area of land. Greater detail is held but is not made available to
the public (Richardson, 2011). Some literature has proposed that metal detecting be regulated to
prevent the unauthorised removal of finds (Ulst, 2010) this idea is supported by TV personalities
such as Tony Robinson (Highfield, 2008).
Another important area covered in the literature is how artefacts should be grouped and dated
prior to their analysis within a GIS a process described as ‘epochization’ (Tobler, 1974). As the
finds contained within the PAS database have not been collected by trained archaeologists, an
approximate date range has been used. Naylor et al (2009) suggests that any range of 250 years
or less is suitable for dating an artefact to a particular time period. Other archaeological
6

19. techniques such as serration are also commonly used to sequence the date of artefacts from a
particular site, usually graves (O’Brien, 2002).
Grouping of finds appears to have a large degree of subjectivity among the literature, Naylor et.al
(2009) highlights that many researchers apply inconsistent schemes that can affect the results of a
project. A full discussion of the classification and dating of archaeological finds is beyond the
scope of this review but it is important to discuss the literature within a GIS context. There are
numerous technical papers on the subject of classification and coding of archaeological finds,
(Camiz, 2004) (Rouse, 1960) and specific objects buckles (Geake, 1997) and pins (Hinton, 1996)
these help reduce the subjectivity somewhat. Regarding the use of GIS within archaeology, Rivett
(1997) states that the quality of data and database structure is fundamental.
There is little literature however on the subject of classifying archaeological finds for use in a GIS
or to carry out their subsequent spatial analysis. Naylor et al (2009) use a ‘fingerprint’ system to
define the proportions of each artefact type found in the UK; this distribution is not used in a GIS
beyond a mapping capacity. This is the case with many other projects of this type (Ulmschneider,
2000). Some studies have used GIS for the spatial analysis of artefacts (Tomaszewski and Smith,
2007) although these have not used any form of finds classification.
From the literature reviewed it is clear that although the drawbacks and bias involved with using
publicly sourced data in a GIS are well known. Conversely little work has been carried out in how
to prepare large collections of finds for spatial analysis in a GIS. A robust and quantifiable
classification scheme is important as it means any spatial analysis can show the distribution of
types of finds at different points in time.
2.1.4 Dealing with gaps in spatial data
Due to the constraints discussed in section 2.1.1 there will be areas where there is no recorded
spatial data. To perform the spatial analysis tasks for this project a continuous surface needs to be
created from the find points. Wheatly and Gillings (2002) caution against using interpolation as
any resulting surface would be wholly artificial. The text by Conolly and Lake (2006) does not
make any mention of this downside of using interpolation and suggests using it to fill in any gaps
in archaeological data.
7

20. A better method is to create a density surface (Herzog, 2006) (Smith et.al, 2009) (Chou, 1997).
ESRI have produced an excellent summary on how basic density surfaces can be created (Zeiler,
1999). Longley et.al, (2008) agree with this stating that density estimation only makes sense from
a discrete object perspective.
A range of techniques can be employed to create a density surface, the simplest being the gridded
quadrat method (Bailey and Gatrell, 1995) (Wheatly and Gillings, 2002). It has been used widely in
the field of Ecology to estimate species distributions (Kenney, 1990) (Krebs and Foresman, 2007).
Other methods include kernel density estimation (KDE) which eliminates the problems with
quadrat grid sizes (Rogerson, 2010). Herzog (2006) summarises these and other less common
methods in his paper and concludes that KDE is the most accurate but the analyst must be careful
in choosing the size of the bandwidth of the kernel (Akpinar and Usul, 2004). O’Sullivan and
Unwin (2003) and Meane (2011) also agree that this method produces the smoothest results.
Once a surface has been created, further spatial analysis can be carried out. However there
remains a gap in the literature surrounding how significant clusters of artefacts can be correlated
with other significant features in the landscape (Baxter and Beardah, 1997). The last part of this
literature review will discuss the spatial analysis techniques that could be used to for fill this task
within a GIS.
2.2 Defining the productive site
One of the most contentious issues that have arisen in the field of spatial archaeology is the
definition of the so called ‘productive site’. It is well known that Anglo Saxon ‘wics’ or ‘emporia’
were centres for trade at the time (Loseby, 2000) mention Anglo Saxon Southampton (Hamwic) as
a good example. Debates have centred on what criteria are needed to define productive sites
found in the rural hinterland as well as clearer definition for the term. Even after conferences
were convened to discuss the subject, no concrete definition could be found (Brookes, 2001).
Critics have also argued, however, that the phrase is out of date and only indicates an area where
multiple finds by many metal detectorists have been found (Richards, 1998).
Most academics agree though that it is important to define what makes a productive site as it can
help in the interpretation through spatial analysis of archaeological finds data (Naylor and
Richards, 2007) (Ulmschneider, 2000). This is especially the case in numismatics and can greatly
aid our understanding of Anglo Saxon communities (Hutcheson, 2009) (Naylor, 2007).
Ulmschneider (2000) states that the most likely use of productive sites are either as a minster or a
8

21. monastery. Hutcheson (2006) however proposes that early productive sites were tax collecting
and administrative centres.
Most of the literature surrounding productive sites has been undertaken by Kathrin
Ulmschneider, her 2003 book Markets in Early Medieval Europe: Trading and Productive Sites,
650-850 is one of the most comprehensive productive site projects from the period. It uses the
distribution of metal detected coins to build a picture of communication and trade between Anglo
Saxon towns Ulmschneider and Pestell (2003). The presence of coins at sites is backed up by
Hutcheson’s (2006) view that early productive sites were tax collecting and administrative
centres.
Further research has been carried out on productive sites by John Naylor; his work on Anglo
Saxon coins in Northern England (Naylor, 2007) also states that the nature of productive sites
could change over time as the Anglo Saxon period ranges from 400 – 1100AD. His largest work on
the subject, the Viking and Anglo Saxon Landscape and Economy (VASLE) (Naylor et.al, 2009),
creates a ‘fingerprint’ for artefact date, artefact type, artefact metal type and coin dates for each
of the previously identified productive sites. This has been done through previous excavations and
local records. From these fingerprints the social use and date of peak activity can be assessed for
each site.
2.2.1 Productive sites and Anglo Saxon towns and communication routes
Another area of literature relevant to this project is the relationship between sites that have
shown to be productive and the locations of Anglo Saxon towns and the Roman roads that
connect them. Little literature is present on directly analysing the relationships, although most
authors state that productive sites are well connected to lines of communication whether or not
they were of Roman origin (Ulmschneider, 2002) this backs up earlier discussion that these sites
were used for trade and tax collection Hutcheson (2006).
Studies have also shown that the Anglo Saxons probably failed to maintain many of the Roman
roads left behind after 410 AD (Vince, 2001) possibly due to the fact the Anglo Saxons didn’t live
the same urban lifestyle as the Romans (Witcher, 2009). This is backed up by some key Anglo
Saxon towns such as Nottingham and Northampton not being connected by roads of Roman
origin (Vince, 2001). Other studies have revealed a number of smaller Roman roads that may have
connected forts and smaller towns (Frere, 2000); what, if any, connection these have with Anglo
Saxon productive sites is therefore of great interest.
9

22. The relationship between productive sites and known Anglo Saxon towns that appeared in such
records as the Doomsday Book of 1068 has mostly been studied on a site by site basis. The
method employed by Naylor et.al (2009) in his VASLE project was to correlate existing Anglo
Saxon settlements with finds from the PAS database. This lead to a better understanding of the
activities, and importance of certain towns during the Anglo Saxon period.
The main drawbacks to this study were that, firstly, not all the Anglo Saxon towns were taken into
account and, secondly, there was a certain amount of subjectivity on the part of the author in
choosing the sites that he did. There is therefore scope to perform a spatial analysis on the region
as a whole searching for a possible relationship with clusters of PAS finds and Anglo Saxon towns;
this process may highlight new areas of interest ‘hotspots’ as well as areas that could be searched
further ‘coldspots’.
2.3 Spatial Analysis Techniques
2.3.1 Spatial autocorrelation of archaeological data
Many pieces of literature have been written summarising the methods and techniques used in
spatial autocorrelation (Goodchild, 1986) (Baxter et.al, 1995) (Griffith, 2000). Spatial
autocorrelation techniques are of use to archaeologists as they explain the intensity in clustering
of any finds (Smith et.al, 2009). This in turn may pinpoint intense clusters of finds where people
may have been living and therefore what they may have been doing (Al-Shorman, 2006). Tobler
(1974), however, underlines the purely exploratory role that spatial auto correlation techniques
provide in archaeology. (Smith et.al, 2009) also state that correlation does not imply causation
and care should be taken before drawing any conclusions.
The two most commonly used spatial auto correlation indices used today are Moran’s I (Moran,
1948) and Geary’s C Index (Geary, 1954) these methods are used in a variety of fields as well as
archaeology, including epidemiology (Pefeiffer et.al, 2009) and ecology (Schneider, 1989) (Liebold
and Sharov, 1998). Lasaponara and Masini (2010) also used the local versions of the two indices’
to pinpoint areas of looting from ceremonial sites in Peru.
These two techniques are available in most GIS packages such as ARCView (Fischer and Getis,
1997) (Smith et.al, 2009) but also in range of more specialist statistical packages (Legendre, 1993).
ARCView has the benefit of having a specific tool to measure at what distance the clustering is
10

23. most intense, this distance band can then be used in the spatial autocorrelation or hotspot
calculation.
Specific literature involving spatial autocorrelation in an archaeological context are limited, some
of which use spatial statistics in an inappropriate way (Hurst Thomas, 1978). Hodder (1977)
theorizes that the lack of literature may be due to unreliable or scant nature of archaeological
data. 35 years later with the advent of the internet and use of GPS to record data this assessment
seems a little out of date. There is clearly a gap in the use of spatial auto correlation techniques in
the field of archaeology and the availability of datasets such as that of the PAS make projects such
as this timely.
2.3.2 Cluster analysis of spatial data
From the find spots of the PAS database it is hard to see where if any clustering may be occurring,
spatial autocorrelation techniques discussed in the previous section can give an indication of the
degree of clustering but not physically show them to the analyst. Cluster analysis techniques can
be used to group find spots together.
There are three common indices for cluster analysis the first is average nearest neighbour (ANN)
(Wong and Lane, 1983) (Cherni, 2005). Luxburg et.al (1981) proposes that this technique does not
try to find the optimum divisions within the sample but in the underlying space. Smith et.al (2009)
highlight that defining the study space is very important and can affect the results greatly,
however, Whallon (1974) seems to disagree stating that this method is not limited by the size and
shape of the area under investigation. Another disadvantage is that NN does not take into
account local variations in clustering which could have occurred (Mitchell, 2009).
Secondly there is k means clustering (Moyes, 2002) (Whitley and Clark, 1985). The analyst defines
the number of clusters required a priori; this can however cause problems as there is no optimum
number of clusters (Everitt, 1979) (Grubesic, 2006). Work has been carried out to try and
constrain the clustering to simplify the process and thus remove the possibility of clusters forming
with no points in them (Bradley et.al, 2000). Others have tried to refine the locations of the initial
‘seed’ points thus meaning the points of a data set will converge at a better local minimum
(Bradley and Fayyad, 1998) (Khan and Ahmad, 2004). Even after this, most academics recommend
running the procedure multiple times to check the stability of the clusters (O’Sullivan and Unwin,
2003) (Smith et.al, 2009).
11

24. The third is Ripley’s K function (Ripley, 1981) where multiple distances are used to indicate
dispersion or clustering based on observed and expected patterns (Dixon, 2006) it also has the
benefit of displaying the size and separation of any clusters (O’Sullivan and Unwin, 2003). There is
literature showing the use of the k function in archaeological analysis beyond the investigation of
settlement patterns (Winter-Livneh et.al, 2010). The k function suffers greatly from edge effect
(Briggs, 2010) although algorithms have been developed to reduce this (Francois and Raphael,
1999).
Although the functions outlined so far can create clusters they cannot provide a detailed
summary of the clustering (Smith et.al, 2009). It also doesn’t show why some clusters may be
more significant i.e. why hot and cold areas are grouping together (Grubesic, 2004?).
2.3.3 Hotspot analysis of spatial data
Hotspot analysis is most commonly used to analyse crime data (Block and Block, 1995) (Eck et.al,
1995) but has also found use in anthropology (Mayes, 2010) and traffic analysis (Clevenger et.al
2006). The ability for archaeologists to determine whether clustering is significant or not is
important as it means an element of confidence can be added to any results (Smith et.al, 2009). It
looks at each feature in the context of neighbouring features to identify clusters with higher
values than you would expect by random chance (Rosenshein and Scott, 2011). An example of this
algorithm is the Getis-Ord Gi* method which is used ArcView. The other technique available is
Anselin Local Morans I (Anselin, 1995) (Zhang et.al, 2008).
The weight could be the density of the artefact under investigation or the number of crimes per
unit area (Gonzales et.al, 2005) or teenage birth rates (Mayes, 2010). It is important to aggregate
incident point data such as the co-incidental find spot locations of artefacts from the same group
such as brooches (Smith et.al, 2009). The benefit of conducting this form of analysis is that the
resulting z and p scores can tell the analyst whether they reject or accept the null hypothesis with
a certain level of significance.
Finding hotspots in clusters of archaeological data may indicate increased activity and the location
of a productive site, it could indicate a popular area for metal detectorists. What is more
interesting are the cold spots and their relationships to Anglo Saxon towns and lines of
communication (section 2.2.1) this could indicate as yet undiscovered finds and hoards.
12

25. 2.4 Conclusions
This literature review has latest thoughts and discussion surrounding the use of advanced spatial
analysis techniques in order to better understand the wealth of publically collected archaeological
data. It has explored the issues surrounding the collection and manipulation of the raw finds data.
Although there has been much research into allocating ‘fingerprints’ of the PAS data to previously
identified productive sites, none of the advanced spatial analysis detailed in this review have been
used.
Thorough planning and preparation of the data needs to be carried out before any analysis is
carried out as this will mean that the subsequent analysis is both accurate and objective. The
pitfalls of using VGI data have been discussed but the growth in all fields acquiring data through
this route means that efficient and effective ways of dealing with such data should be found.
With the inclusion of more and more spatial analysis techniques in commercial as well as open
source GIS software, the opportunity to analyse this kind of data in this way is becoming easier.
Add to this the unique way in which software such as ArcMap 10 can visualise these relationships
projects such as this can only add to archaeologies knowledge base. We can better understand
the distributions of finds their relationships with other features as well as highlighting possible
gaps in any metal detector searches.
13

26. Chapter 3: Materials and methods
The aim of this project is to analyse through the use of GIS and advanced spatial analysis
techniques the vast amounts of metal detected data held on databases such as that of the
Portable Antiquities Scheme (PAS), more specifically those dated to the Anglo Saxon period of
English history. From the literature review it is clear that there is a gap in this area of research and
as a result this is the main focus for this methodology. Some of the questions that need to be
answered are how GIS and spatial analysis techniques can be used to explain the patterns such as
clustering and statistically significant hotspots as well as comparing the results of the 2009
productive sites VASLE project (Naylor et al, 2009) with results gained through spatial analysis
techniques. Appropriate hypotheses will be drawn up which can then be accepted or rejected
based on the spatial analysis work.
The PAS database will be the primary source of data for this project, access is through a free
registration process. The general public can log metal detected finds they have made onto the
database by filling in the appropriate database fields, after this a group of PAS experts verify the
descriptions and make any comments necessary. The database is divided up into broad time
periods in history and is searchable on many fields such as object type, size and the material it is
made of and so on. Some of the problems at this stage of the project could be the authors own
lack of knowledge in archaeological finds and this period in history, as a result several pieces of
literature have been reviewed to expand this knowledge such as the excellent books by Stenton
(1971) and Fleming (2010).
Converting the data for use in a GIS will also be a challenge because the PAS database is in a flat
format with many gaps, anomalies and ambiguities; unfortunately this is often the case with VGI
data. Converting and cleaning the data could present a real challenge, especially, as specific
archaeological categorisation and dating techniques may need to be used. This process will need
to be done carefully if any resulting spatial analysis is to be accurate and valid. Again relevant
literature was consulted to aid the author such as by (Wheatly and Gillings, 2002) and (Greene
and Moore, 2010).
This methodology will now go through the preparation of data and choice of analytical procedures
that will be used to answer each of the questions outlined above with the goal being the overall
aim of the project. The first section will deal with the initial collection and preparation of the data
14

27. as well as loading it into the GIS software. The next three sections will discuss each of the
analytical and spatial analysis procedures used to explore and interrogate the PAS dataset.
ArcMap 10 will be the software used to carry out the analytical and spatial analysis procedures;
this will be supplemented by further Excel data exploration. ArcMap 10 is available under the
Kingston University student license agreement, therefore there are no additional costs to the
author, but there is however other freely available GIS software such as Quantum GIS which will
perform any tasks in a similar way.
3.1 Data collection
The main source of data for the project came from the PAS database (Portable Antiquities
Scheme, 2011) the raw data is able to be downloaded in a variety of formats including the .csv
format making it suitable to import into ArcMap 10. The searchable database was used to extract
the PAS records dating from between 400AD – 1066AD, the period of Anglo Saxon rule over
England. This was a large document containing 2617 records detailing a wide variety of items
from brooches to tweezers.
The second source of data was Digimap (Digimap, 2011). This site provided the maps, towns and
county boundarys needed to put the location of PAS finds into context as well as interpret the
locations of hotspots and productive sites. Digimap was able to provide the 1:50,000 scale
Ordnance Survey (OS) raster tiles as well as the county boundaries and gazetteer for the towns in
the East Anglia area. As a registered student at Kingston University, the data was free to
download and use for academic purposes.
3.2 Data cleaning and manipulation
Once the PAS and OS data had been collected it needed to be tided and organised into a suitable
format for GIS analysis. Dealing with the OS was a relatively straightforward process, however,
the PAS data required several carefully considered preparation stages before it could be used for
analysis.
15

28. 3.2.1 Cleaning and manipulating the PAS dataset
The large .csv file downloaded from the PAS website contained 2617 records each with 47
attributes. The first task was to narrow down the number of attributes and identify the ones that
would be useful for the analysis aims of the project. The attributes that were chosen were:
 Object Type: Such as brooch, pin, bracelet, stirrup, hooked tag etc.
 Period From: This is the earliest date the find can be dated too.
 Period Too: This is the latest date the find can be dated too.
 Easting: This is the easting co-ordinate locating the findspot of the artefact.
 Northing This is the northing co-ordinate locating the findspot of the artefact.
The data held in these attributes could be manipulated and organised further in order to carry out
a more detailed analysis. Undertaking spatial analysis on just the total PAS dataset would not help
reveal some of the patterns and distributions unique to each of the different types of finds;
breaking the finds further down into periods of Anglo Saxon England will help reveal further
patterns.
The cleaning tasks were carried out on the dataset to standardise the entries made by the general
public; this included adjusting the spelling and description of the finds. Also, records were
discarded if there was insufficient data present, such as insufficient dating information or the PAS
experts could not verify the find described.
3.2.2 Dating of PAS finds
The first task was to date the individual finds. The attributes provided on the PAS database had an
earliest possible date and a latest possible date, these could range from 0 – 1000 years. The main
aim in dating the finds was to again allocate each to a time period in Anglo Saxon England either
early, in the middle or towards the end of the period from 400AD – 1066AD. These groups would
be called Early, Middle and Late.
In order to work out which group each find belonged to, a number of criteria were used: any find
with a date range from earliest to latest of greater than 250 years was to be excluded from the
analysis. Naylor et.al (2009) used this as benchmark in his project and, as part of this project is to
compare the techniques used to analyse the PAS data, it was appropriate to follow the same
16

29. criteria here. Secondly a ‘mid’ point between the earliest and latest date was found, for example,
if the dates were ‘600AD’ and ‘850AD’ then the mid date would be ‘725AD’. Thirdly a range of
dates was created to define the: ‘Early’, ‘Middle’ and ‘Late’ Anglo Saxon periods, the dates used
were: 400AD – 600AD, 601AD – 800AD and 801AD – 1066AD respectively. Finally, each find was
allocated a date range based on its ‘mid’ point date; if there was an overlap, the category with
more than 50% of the finds date range would be chosen.
3.2.3 Classification of PAS finds
Once the data had been cleaned and dated the remaining finds needed to be classified into larger
‘object groups’. The following criteria were drawn up based on classifications researched in the
literature review together with the project’s constraints such as time and computing power. The
first task was to allocate each of the 89 unique ‘object type’ find entries to a broader ‘object
group’. This group should be large enough to provide useful GIS analysis whilst accurately
representing the objects within it. The groupings must also be such that in higher densities they
could represent increased types of human activity at that point in time.
The first attempt at object groupings were as follows: Coins, Commercial, Clothing, Jewellery,
Household, Horse Items, Military, Burial and Pins. On populating these object groups they were
found to have, 186, 23, 362, 1010, 195, 683, 30, 16 and 162 finds respectively. It was decided that
the Commercial, Military and Burial groupings did not have a sufficient number of finds to carry
out effective GIS analysis. Therefore the Commercial and Household groupings were combined
due to the relationship between some of their objects. The Military and Burial groupings were
considered to be too distinct from the other groups to be integrated and were therefore
discarded from the analysis phase of the project, leaving six object groups in all. A more specialist
grouping of the finds could have been undertaken using more complex archaeological techniques
but, as has been discussed, that is beyond the scope of this project. Each item was now part of an
object group and time period. There were however two categories which did not have any finds
falling into the early category these were Pins and Coins, it was decided that the middle and late
categories would remain as they contained a large number of finds that would contribute a lot
during the analysis. A full presentation and discussion of the cleaned and dated PAS dataset will
follow later in this section.
17

30. 3.2.4 Cleaning and manipulating the Ordnance Survey datasets
The Digimap website provided all the 1:50,000 scale Ordnance Survey (OS) raster tiles as well as
the county boundaries and gazetteer for the towns in the East Anglia area. No cleaning of this
data was necessary but it did need to be manipulated through cutting out areas and towns not
relevant to the analysis. ArcMap 10 was used to select the counties Norfolk and Suffolk from the
national dataset as well as the OS raster tiles that covered the same area; this used the simple
select by attribute process. The gazetteer provided all the towns for the UK and again had to be
manipulated by cutting out the towns and villages not in the East Anglia area. This was done by
selecting just the points which fell within the Norfolk and Suffolk polygons.
As all the finds being analysed were from the Anglo Saxon period, only the towns and villages
from this period were required for the analysis. An online database detailing the towns and
villages present at the time of the Domesday Book in 1085 was consulted to select the relevant
towns from the gazetteer dataset (Domesday Book Online, 2011). Out of the 878 towns in Suffolk
319 could be dated back to 1085 and out of the 1050 in Norfolk 440 could be. Modern records of
some of the towns detailed in the Doomsday Book could not be found but it was thought that 759
locations would be sufficient to carry out the necessary analysis. We cannot be certain these
towns existed throughout the Anglo Saxon period, but the Doomsday Book is the best record we
have of the towns active at this period in history.
3.3 Creating a geodatabase from all the datasets
Once the datasets had been cleaned and manipulated they were loaded into ArcMap 10 a
commercially available piece of GIS analytical software. ARC provides a wide range of powerful
spatial analysis functions that will be required to carry out the rest of this projects methodology.
Firstly, a personal database was created to hold all the files and provide a single access point for
all the subsequent files that would be created as a result of the analysis process. The finds were
brought into ArcMap 10 using the Easting and Northing values held on the PAS database. In most
cases this will not be the exact location of the find but an approximate area such as the centre of
the field the find was found in or the centre of the owners land; this is to protect the privacy of
the landowner and prevent further looting or illegal metal detecting. It is not possible to say
which locations are exact and which are not. The PAS has accurate locational data for all the finds
18

31. but they were not made available for this project. The 2009 VASLE project did however have
access to this additional data; this must also be taken into account when comparing any results.
All the shapefiles, points and raster tiles were projected in the British National Grid (OSGB 1936)
co-ordinate system. This was important as any spatial analysis will rely on every feature class
being spatially related to each other in the same way.
Additional fields were added to the finds attribute table to incorporate the attributes ‘object
group’ and ‘Anglo Saxon time period’ to each find. This meant that new feature classes could be
created for the global dataset for each object group and as well 3 further feature classes could be
created for the finds of each object group that fell into each time period (early, middle and late).
Further feature classes could be created for the total PAS dataset and the total number of finds
that fell in the early, middle and late periods irrespective of object group. Finally the VASLE
productive sites located during the 2009 project, Norfolk and Suffolk Anglo Saxon towns were also
selected from the East Anglia town’s database and set in their own feature classes. The next three
sections of the methodology will detail the analytical and spatial analysis procedures used to
explore and interrogate the PAS dataset.
3.4 Cluster analysis of PAS finds
To further investigate the PAS data it important to see whether the finds are randomly dispersed
across the East Anglia area, as stated in the null hypothesis, or if they show possible clustering.
This investigation can be extended further to examine at which distances the clustering is greatest
and whether the level of clustering changes over distance. This analysis covers the whole of East
Anglia not just the areas covered by the productive sites found by the VASLE analysis. This could
help show whether object groups are clustering in East Anglia and at what distances.
For the purpose of this project there are three techniques that will be used they are average
nearest neighbour, Ripley’s K function and Global Morans I. Global Morans I will be used to give
an indication of the spatial auto correlation and seek to find if nearby points have similar or
dissimilar values; this, again, will demonstrate whether the finds are clustering, random or
dispersed. A range of tests will be employed as each has distinct advantages and disadvantages
depending on the data being used. Results can be compared from each technique which will
hopefully lead to better conclusions about the distribution of PAS finds.
19

32. The average nearest neighbour (ANN) technique considers the distance between the points; this
can however be its disadvantage (Mitchell, 2009) as if some of the points are in the same location
than the distances calculated can be smaller than they should be. Unfortunately ArcMap 10 does
not give you the option to perform the analysis using k-order neighbours and so the use of this
technique was probably compromised in some way due to this. ANN also suffers from ‘edge
effect’ whereby if there are too many points located towards the edge of the study area results
could become biased (Conolly and Lake, 2006). A visual inspection of the PAS data mapped over
East Anglia shows that are indeed areas where points cluster around the edges, notably in the
west around Newmarket here there are also several points within very short distances from each
other. One final consideration when conducting the ANN test is the size of the study area. This
must be fixed and identical for each analysis, a bounding box corresponding to the extent of the
points would be different each time. Therefore, the size of the East Anglia study area as well as
the 2 counties must be calculated first using the calculate area function in ArcMap 10 the results
can then be entered as a variable during the ANN test. The ANN analysis will provide a
benchmark set of results to compare against the other two techniques.
The Global Morans I test will provide a different way of looking at possible clustering to the ANN
test. The Global Morans I test can indicate whether nearby points have similar or dissimilar
values. This does not however indicate whether these values are high or low that will be covered
by the hotspot analysis outlined in the next section. Within the ArcMap 10 function the variable
‘conceptualisation of spatial relationships’ will be set to inverse distance squared as the influence
of finds nearer the target feature should be greater than those further away, the threshold
distance was also set to ‘0’ because of this.
Due to the fact that the finds are represented as points in ArcMap 10 and they have no associated
values that can be compared a density map must first be created to produce a ‘pixel value’ that
can be assigned to each find point. The kernel density method will be used to create the density
surface for each object group; kernel density is more preferable to the simpler point density
method as it produces smoother more accurate results as discussed in the literature review. An
output cell size of quarter of a mile was chosen as it provides a detailed surface without being too
computationally intensive. The resulting pixel values are then assigned to each find point through
the extract values to points function in ArcMap 10, this now gives each point a value that can be
compared via the input field within the Global Morans I test.
20

33. The Ripley’s K function will also be used as it provides several useful outputs for the project.
Firstly it describes the degree of clustering, randomness or dispersal at varying distances over the
entire East Anglia area. The function will be used to determine the point at which the clustering
becomes most intense, and where the finds cease to be clustered and become dispersed. This
information is useful as it helps to generate a more accurate hotspot analysis through the use of
the distance where the clustering is most intense.
This process will be carried out for each of the 16 groups of finds together with the overall finds
dataset and the Anglo Saxon towns of both Norfolk and Suffolk. Ripley’s’ K function in ArcMap 10
gives you the option to input the number of bands and the distances that will be set between
them; for this project each feature will be given the initial options of 100 bands at 500m intervals,
the results can be displayed visually to aid analysis. Confidence levels can be attached to any
results by selecting the number of permutations the test undertakes. This can be either: 9, 99 or
999 permutations equating to 90%, 95% and 99% levels respectively; each permutation plots a
series of random points across the study area in order to calculate the K values. Due to
restrictions on computing power 99 permutations may not be possible and so results may need to
calculated to the 90% confidence level. Areas of interest can then be focused on at smaller
distances in order to pinpoint the distance where clustering is most intense. A summary of the
results can be entered into Excel for further analysis and discussion.
3.5 Hotspot analysis of PAS finds
The third and final spatial analysis technique that will be used is a hotspot analysis of the PAS
finds data. This will be able to show where there are statistically significant hot or cold spots in
the locations of the metal detected finds data. The results will be used firstly to help analyse the
VASLE productive sites, as the resulting hotspots can be overlaid for comparison. Secondly, any
statistically significant coldspots can be highlighted as areas that may need further investigation
for certain object groups such as jewellery or clothing. These spots can be analysed further by
overlaying them over the OS raster tiles; roads, towns and features can be picked out and possible
links made to the hot and cold spots. Lastly a hotspot analysis of the Anglo Saxon towns can be
compared to the hot and cold spots of the finds; again, visual analysis could uncover trends and
links between the two.
The Getis-Ord-Gi* method will be used within ArcMap 10 to perform the hotspot analysis as this
is the more appropriate version of the two Getis-Ord statistics, it also includes the value of the
21

34. target feature since its value contributes to the occurrence of the cluster (Mitchell, 2009). The
Getis-Ord-Gi* method can be optimised if the user has knowledge of where the features are at
their peak clustering; the output from the Ripley’s K analysis will be used for this purpose.
Another issue with the Getis-Ord-Gi* method for hotspot analysis is that it is recommended that
there are at least 30 points as input to the analysis. All the 14 of the groups plus the Anglo Saxon
towns have 30 or points in them the only two which don’t are early and middle Anglo Saxon horse
items; they only have 13 and 19 respectively. Care must be taken when interpreting the output
from these two hotspot analyses as the results could be suspect (Mitchell, 2009). Comparisons
will have to be made with the late and global horse finds groups to determine whether the results
for these two groups are valid.
As with the Global Morans I test outlined earlier, the Getis-Ord-Gi* function requires input values
to compare, to do this the points that have had the pixel values extracted to them are used as the
input feature class and the pixel values as the input field. The Getis-Ord-Gi*method will be carried
out for each of the 22 feature classes. As with the Global Morans I test, the conceptualization of
spatial relationships field will be set to inverse distance squared as the influence of finds nearer
the target feature should be greater than those further away. The threshold distance will be set at
the distance of peak clustering as found in the Ripley’s K analysis.
The resulting z scores that will be assigned to each of the find points can then be interpolated
across the entire East Anglia area using the IDW method. The IDW method was chosen over other
forms of interpolation because it again puts more weight on the points that are nearest the target
feature than would be the case with the points and their corresponding hotspots. A power of 3
was specified to give less influence to points that are further away and an output cell size of a
quarter of a mile (402.336 metres) was used to give consistency to the earlier density maps. There
will be gaps in the interpolated surface as the bounding box used to encompass the find points
will not always cover the entire East Anglia area.
The resulting surface can then be reclassified according to the z scores and their levels of
statistical significance breaks will be created at 1.645, 1.96 and 2.576 and -1.645, -1.96 and -2.576
to represent the 90%, 95%, and 99% confidence levels. These can be symbolised in shades of blue
and red with all other values between 1.645 and -1.645 receiving a neutral beige colour indicating
a random pattern. This choice of shading was chosen as red and blue are most associated with hot
and cold values respectively. The interpolated hotspot surfaces will then be clipped to the outline
22

35. of East Anglia using the extract my mask function in ArcMap 10. This removes any unnecessary
information outside the study area.
3.6 VASLE productive site comparison analysis
There were 22 ‘productive’ sites identified by Naylor and his team in 2009. The aim of this project
was to compare the results of the ‘non-GIS’ techniques he used with the spatial analysis functions
available in ArcMap 10. Although the ‘fingerprint’ technique he used was useful in representing
the spread of finds over different categories and time periods this technique wasn’t based on the
recognised spatial analysis techniques that are detailed in this section.
The primary technique used to interrogate the VASLE productive sites is through buffering.
Buffers can be created around any point or polygon feature in ArcMap 10, the user can specify the
radius of the buffer circle that will surround the feature. The intersect function can then be used
to identify which of the PAS finds falls within each buffer. As productive sites don’t have any
definitions, they don’t have any indication as to how large they should be; studies rarely give
detailed maps of the sites just points on a map (Ulmschneider, 2000).
This meant that criteria had to be drawn up as to how large the buffer would be that surrounded
each of the VPSs. It was decided to make the buffering reasonably large to encompass enough
finds to make the analysis worthwhile, as the locations of the VPSs and PAS finds will remain the
same, any spatial analysis would be fair and unbiased.
The buffers should be large enough to encompass enough PAS finds to enable a proper and
thorough analysis of the data but not too large so that they become meaningless. Also, the
dispersed nature of Anglo Saxon settlements meant that finds belonging to each VPS must be
considered to have come from a similarly disperse area surrounding it. This should also take into
account the possible movement of finds to and from the site and in the immediate local area due
to the movement of people goods and services. Some sites may have higher quantities of one or
more object groups especially coins because they specialise in the trading or manufacture of
certain items. One of the features of productive sites is an increase in the trading of goods which,
in turn, creates wealth and an increase in the variety of finds found within the site (Ulmschneider,
2000). This also covers the accidental loss of items which can occur in a large radius around the
actual productive site. It is for this reason that a 2.5 mile buffer was chosen as it covered all these
criteria without being too large.
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36. A 2.5 mile buffer meant that some of the sites that were close together such Rockland All Saints
and Rockland St Peter, only 0.6 miles apart, would share some of the same PAS finds. This is
because there is no attribute field in the PAS database linking finds to any particular VPS and so it
cannot be determined which site any of the finds would have belonged too. Therefore the
productivity of each site would be judged on the number of PAS finds that fall within its buffer,
the productivity of the entire set of VASLE sites would be judged on the number of unique PAS
finds detected.
As this project only had access to a fraction of the data that was available to the VASLE project the
VPSs productivity would be judged on the PAS data only. This meant that direct comparison of the
projects was not possible; however, a productivity comparison could be made on whether the
finds found as a result of the buffering analysis came from a wide range of the 6 object groups as
well as the 3 time periods. An average number of total finds from the 6 object groups and 3 time
periods would also give an indication of productiveness.
To judge whether the sites found by Naylor were indeed productive a further 22 ‘control’ sites
were picked at random from across East Anglia to see what results they gave. ArcMap 10 has a
place random points’ function that allows the user to randomly place as many points as they wish
across a defined area. An additional field containing the number of each control site (1 -22) was
added to aid identification during further analysis. A comparison would then be made with the 22
control sites using the same methods to help put the comparison of the VASLE and the spatial
analysis results into perspective.
Buffers of 2.5 miles would therefore be created around each of the 22 VASLE and 22 control sites.
In order to reveal which finds fall within these areas the intersect function can be used to analyse
which find points fall within each of the buffers. The resulting attribute table will carry all the data
from the original tables such as object group, time period and so on. This means that a spatially
analysed fingerprint can be generated for each of the VASLE and control sites making comparisons
and analysis possible. Any duplicate items will then be removed to determine how many of the
finds are unique to 22 VASLE sites and 22 control sites creating a two more attribute tables for
analysis of the unique finds.
To carry out the analysis individually on each of the points would take a very long time and
possibly lead to slower processing time and possible system crashes. Therefore, the model builder
function was utilised to automate the tasks needed to carry out this analysis. This meant that the
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37. buffering and intersect functions could be carried out on each point in one go improving
consistency and efficiency.
The outputs of the buffering and intersect analysis process were 22 tables of finds data for the
VASLE sites and 22 table of data for the random sites, these could then be transferred to Excel for
further analysis and presentation. As a final comparison the results of the hotspot analysis can be
overlaid onto the VASLE productive sites to see if any fall within a statistically significant hot or
cold spot.
3.7 Overview of study area and PAS finds dataset
Before presenting and discussing the results of the data it is useful to introduce both the study
area and the constitution of the PAS finds dataset.
3.7.1 Overview of study area
The map in figure 1 shows the location of the study area used in this project. The counties of
Norfolk and Suffolk comprise an area of approximately 3540 square miles; the land is
predominantly flat and low lying with the highest point being 338ft, significant areas are actually
below sea level. The key towns seen in figure 1 are important as administrative or commercial
reasons. Most of the land in East Anglia is used for farming and the settlement patterns are rural
away from the large towns of Norwich and others shown on the map. This make the area a prime
destination for metal detecting and other archaeological work.
25

38. Figure 1: Location map of the East Anglia study area
3.7.2 Overview of PAS finds dataset
This section will look at the global PAS dataset and present the quantities of finds that fell into
each of the object groups as a result of the classification schemes outlined in the methodology
section. This will provide a background to the spatial analysis techniques to follow which will
explain the finds distribution across the East Anglia area. As we can see from Figure 2 there were
a total of 2617 PAS finds present after cleaning and classification.
The majority of the finds in the dataset are made up of jewellery and horse items that account for
64.7% of the data. The largest proportion of the jewellery dataset (90%) is made up of brooches
with the rest being made up of beads (1%) and rings (4%). The majority of the jewellery items
(93%) were made of copper alloy but 9 items were made of gold indicating a very high status
piece indeed, however only one was found within the 2.5 mile buffer of a Narborough VASLE
productive site and that was a late period finger ring. Based on this analysis there was an
argument that brooches could have been made into an object group in their own right but this
26

39. would have meant the remaining items would have been insufficient in number to make up a
secondary jewellery object group. The horse items group was made up of strap ends (40%),
stirrups (24%) strap fittings (18%), harness fittings (8%) and bridle fittings (7%). The rest being
made up of small numbers of spurs and cheekpieces.
The number of coins recorded on the PAS database for this area is relatively small making up only
7% of the data this reflects the fact that the early medieval corpus (EMC) holds the majority of
Anglo Saxon coins found in England, unfortunately this data was not made available for this
project as the grid references of the find spots were deemed too sensitive. There were also no
coins from the early period (figure 3) we cannot say that this meant that there was no economic
activity from this time period as the EMC data may contain a large number of finds from this time.
The clothing, pins and commercial and household object groups were all of a similar size. The
clothing group was made up of: sleeve clasps (40%), buckles (37%), hooked tags (10%) and girdle
hangers (8%). The remainder of the groups was made up of small numbers of buckle frames and
other clasps. The commercial and household object group was made up of items that could have
been used in the businesses and homes of Anglo Saxon England, the biggest groups of finds were
mounts (24%) these could appear on bowls and other items requiring handles, vessels (22%)
many of which were made of pottery and ceramic, tweezers (11%) and weights (7%). The pins
object group had no finds from the early period but interestingly had the majority of its finds from
the middle period, the only object group to show this distribution. Looking at the split of the finds
between the 3 Anglo Saxon periods in figure 2 the largest numbers of finds (48%) come from the
later period of Anglo Saxon rule.
The chart in figure 3 shows the quantities of each object group that were found in each time
period. It is clear that some finds are more present in certain periods of Anglo Saxon history, for
instance 95% of horse items come from the late period. This result ties in with work undertaken
by Neville (2006) who states that the use of horses became popular in warfare towards the end of
Anglo Saxon times possibly through Danish influence. Both jewellery and commercial and
household have the largest percentage of their object group found in the early period whereas
the majority of pins come from the middle period. Only clothing and commercial and household
could be considered to have large percentages of their finds from each time period. This shows
that there was Anglo Saxon activity within the East Anglia region throughout the Anglo Saxon
period.
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40. All % Grand
Finds Total
Clothing 362 13.83
Coins 184 7.03
Commercial + Household 218 8.33
Horse Items 683 26.10
Jewellery 1010 38.59
Pins 160 6.11
Grand Total 2617 100
Early Anglo Saxon – (400 – 600AD) 974 37.22
Middle Anglo Saxon – (601 – 800AD) 384 14.67
Late Anglo Saxon – (801 – 1066AD) 1259 48.11
Grand Total 2617 100
Figure 2: Percentage distribution of total PAS data
100
90
80
70
60
50
40
30
20
10
0
Coins
Pins
Clothing
Jewellery
Cml and Hhld
Horse Items
Early Anglo Saxon Middle Anglo Saxon Late Anglo Saxon
Figure 3: Percentage distribution of PAS objects through the Anglo Saxon periods
28

41. Chapter 4: Results and discussion
This section will now present, discuss as well as compare and contrast the results from each of the
three spatial analysis processes performed on the PAS archaeological finds. The aims of this
project were to explain the distribution and structure of the PAS data together with comparing
the results of the 2009 VASLE project conducted by John Naylor and his team.
The first section will put forward null hypothesises to describe the distribution of each of the
object groups finds these will either be accepted or rejected and reasons why given. The results
showing the degree of clustering will then be presented and discussed alongside this distribution
analysis.
The second section will analyse the hot and cold spots for the PAS data. The results will be
compared as a whole, by object group and by time period. An OS 1: 50,000 scale map will be used
to locate any underlying features and towns and use these to interpret any patterns that may be
found in the data such as areas that may warrant further investigation.
The third section will use a combination of the first two sections’ results and the results of the
buffering analysis of the 22 VASLE productive sites to establish whether GIS and spatial analysis
techniques can produce comparable results to the non GIS techniques employed by the VASLE
team. Each site will be analysed separately with the results of the VASLE fingerprint technique
being compared to the buffering results and the 22 random sites that were created as a control
set of sites. Further analysis will then be undertaken to compare the locations of the VASLE and
control sites to the hotspot of all the object groups and the locations of the Anglo Saxon towns.
4.1 Cluster analysis of PAS finds
4.1.1 Average nearest neighbour (ANN) results
Figure 4 shows the results of the ANN statistical test on the PAS data. It shows the nearest
neighbour index and Z score. If the nearest neighbour ratio is a value less than 1 then the points
are tending towards clustering and if it is greater than 1 then the points are tending towards
being dispersed. A null hypothesis was proposed that the distribution of finds within each of the
object groups was due to a random process. With the aid of a Z score the ANN would either
approve or disprove these hypotheses with an associated confidence level of either: 90%, 95% or
29