Applied GIS Masters Dissertation

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Applied GIS Masters Dissertation

  1. 1. FACULTY OF SCIENCE MSc DEGREE INApplied 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. 2. A SPATIAL ANALYSIS OF EAST ANGLIAN ANGLO SAXON ARTEFACTS USING A GISSTUDENT ID: K1047325NAME: EDWARD JAMES KEMPCOURSE: MSc APPLIED GEOGRAPHICAL INFORMATION SYSTEMSDATE: 14 SEPTEMBER 2011MODULE: GGM122: RESEARCH PROJECTWORD COUNT: 25,410PROJECT SUPERVISOR: IAN GREATBATCH
  3. 3. AbstractMost people would associate archaeological research with the organised excavations that takeplace in areas of historical or cultural interest. This is of a course a very valuable part of thediscipline and has led to some notable discoveries such as the Viking burial ground at Sutton Hooin Suffolk. But there is now a growing trend for the use of metal detectors by the general public tocompliment such projects through field walking exercises.This has helped create large online databases that hold detailed information on eacharchaeological find. Organisations such as the Portable Antiquities Scheme (PAS) have helped inverifying and categorising the finds, meaning the database can be searched according to a periodof 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 EastAnglian Anglo Saxon finds within the context of known archaeological sites. As spatial referencesare one of the pieces of information held on the database a GIS can be used to analyse andexplore the spatial properties of the archaeological finds. Which in turn can aid our understandingof 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 itis for this reason that this project uses such techniques to analyse the PAS finds from the EastAnglian Anglo Saxon period. Its aim wasn’t to challenge the results of the 2009 project, but to lookat the database of finds from a GIS and spatial analysis perspective.Cluster, hotspot and buffering techniques were used to explore and interrogate the Anglo Saxonfinds, which were divided up into logical object type groupings and possible point in time theywere last used. These techniques revealed a lot more about the data than would have beenpossible through a mere visual inspection.Cluster analysis revealed how jewellery and clothing may have been stored, and how horsebreeding 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 keytowns that may have played a role in tax collection or the manufacture of certain goods. Bufferinganalysis allowed the reappraisal of 22 sites identified in 2009 project as being ‘productive’ it alsohelped build a picture indicating which sites may have been important and when. i
  4. 4. GIS techniques have allowed a more in depth analysis of the PAS archaeological data, and havehighlighted areas and sites that could be investigated further through GIS or field work projects. Itdefinitely has huge potential to help us understand our past and is an ideal complimentarytechnology to sit alongside archaeological digs in muddy fields. ii
  5. 5. AcknowledgementsI 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 parentsMoira and Zdenek Cerny for their support and advice. My project tutor Dr Ian Greatbach andcourse director Dr Mike Smith for their help and advice at varying stages of the project. Finally DrJohn Naylor of the Ashmolean Museum in Oxford, who gave valuable historical as well asarchaeological advice at the initial stages of the project. iii
  6. 6. Table of ContentsAbstract ................................................................................................................................iAcknowledgements ......................................................................................................................iiiTable of Contents ......................................................................................................................... ivTable of Figures ............................................................................................................................ ixChapter 1: Introduction ...........................................................................................................1Chapter 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 .................................................................................................................. 13Chapter 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 iv
  7. 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............................................................................... 26Chapter 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 v
  8. 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 .............................. 845. Conclusions ..........................................................................................................88References .............................................................................................................................90 vi
  9. 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 vii
  10. 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 .......................................................................................................................... 135APPENDIX 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 viii
  11. 11. Table of FiguresFigure 1: Location map of the East Anglia study area .................................................................... 26Figure 2: Percentage distribution of total PAS data ...................................................................... 28Figure 3: Percentage distribution of PAS objects through the Anglo Saxon periods ....................... 28Figure 4: Results of the ANN statistical test on the PAS data......................................................... 32Figure 5: Results of the ANN statistical test on the East Anglian Anglo Saxon towns data ............. 33Figure 6: Results of the GMI statistical test on the PAS data ......................................................... 35Figure 7: Results of the GMI statistical test on the East Anglian Anglo Saxon towns data .............. 36Figure 8: Results of the Ripley’s K statistical test on the PAS data ................................................. 38Figure 9: Results of the Ripley’s K statistical test on the East Anglian Anglo Saxon towns data ...... 39Figure 10: ANN results for the global object groups...................................................................... 40Figure 11: GMI results for the global object groups ...................................................................... 40Figure 12: Hotspot analysis map of the early period Anglo Saxon finds ......................................... 43Figure 13: Hotspot analysis map of the early period Anglo Saxon finds ......................................... 45Figure 14: Hotspot analysis map of the middle period Anglo Saxon finds...................................... 45Figure 15: Hotspot analysis map of the late period Anglo Saxon finds .......................................... 45Figure 16: Hotspot analysis map of global clothing Anglo Saxon finds........................................... 47Figure 17: Hotspot analysis map of global coin Anglo Saxon finds................................................. 47Figure 18: Hotspot analysis map of global horse items Anglo Saxon finds ..................................... 47Figure 19: Hotspot analysis map of global commercial and household Anglo Saxon finds ............. 47Figure 20: Hotspot analysis map of global jewellery Anglo Saxon finds ......................................... 47Figure 21: Hotspot analysis map of global pin Anglo Saxon finds .................................................. 47Figure 22: Hotspot analysis map of early period clothing Anglo Saxon finds ................................. 49Figure 23: Hotspot analysis map of middle period clothing Anglo Saxon finds .............................. 49Figure 24: Hotspot analysis map of late period clothing Anglo Saxon finds ................................... 49Figure 25: Hotspot analysis map of middle period coin Anglo Saxon finds .................................... 50Figure 26: Hotspot analysis map of late period coin Anglo Saxon finds ......................................... 50Figure 27: Hotspot analysis map of early period clothing Anglo Saxon finds ................................. 51Figure 28: Hotspot analysis map of middle period clothing Anglo Saxon finds .............................. 51Figure 29: Hotspot analysis map of late period clothing Anglo Saxon finds ................................... 51Figure 30: Hotspot analysis map of early period commercial and household Anglo Saxon finds .... 52Figure 31: Hotspot analysis map of middle period commercial and household Anglo Saxon finds . 52Figure 32: Hotspot analysis map of late period commercial and household Anglo Saxon finds...... 52Figure 33: Hotspot analysis map of early period commercial and household Anglo Saxon finds .... 53Figure 34: Hotspot analysis map of middle period commercial and household Anglo Saxon finds . 53Figure 35: Hotspot analysis map of late period commercial and household Anglo Saxon finds...... 53Figure 36: Hotspot analysis map of middle period pins Anglo Saxon finds .................................... 54Figure 37: Hotspot analysis map of late period pins Anglo Saxon finds ......................................... 54Figure 38: Hotspot analysis map of all Anglo Saxon towns ............................................................ 55Figure 39: Possible location of new productive site within 2.5 miles of Hoxne .............................. 57Figure 40: Location of the 22 VASLE productive sites .................................................................... 58Figure 41: Location of the 22 control sites .................................................................................... 58Figure 42: Percentage breakdown of unique finds across all 22 VPSs ............................................ 59Figure 43: Percentage breakdown of unique finds across all 22 control sites ................................ 60 ix
  12. 12. Figure 44: Possible area of pin production or trade in Anglo Saxon East Anglia ............................. 86Figure 45: Possible trade routes in Anglo Saxon East Anglia.......................................................... 87 x
  13. 13. Chapter 1: IntroductionGeographical Information Systems (GIS) can be applied to a wide range of disciplines that have aspatial dimension to them. There are however some areas of research that have not forfilled theirpotential use of GIS.Early England has a rich history from the invasion of the Romans in 5AD to the Anglo Saxon periodand the Norman Conquest of 1066. In between, the Vikings and Danes also tried to stake theirclaim on parts on various parts of the country with varying degrees of success. These competingsettlers have all left behind their mark in some way or another. Much of the evidence of theiroccupation is no longer visible, but through archaeological exploration finds, sometimes of greatimportance and value, can be unearthed; this helps us to piece together what life might havebeen like in what some describe as the ‘dark ages’.Archaeological digs investigating all parts of English history have become very popular in the last20 years or so partly due to television programs such as Time Team. Recent headline grabbingdiscoveries such as the Staffordshire hoard, which is estimated to be worth £3.2 million, have alsoadded to the public’s interest. This has inspired a growing number of hobby archaeologists to notonly go on organised digs but also use ‘metal detectors’ to look for archaeological items in fieldsand pastures across the UK.In an effort to better organise the finds recording process as well as provide analysis for any findsthe Portable Antiquities Scheme (PAS) was set up in 1997. Central to its operations was an onlinedatabase that could be accessed by any member of the public to record their finds, a team ofregional finds officers could then authenticate and offer further advice on the finds. By 2011 therewere over 450,000 items on the database ranging from gold rings to copper spoons, and datingfrom the Roman period to the modern day. Attributes such as dimensions and date were storedfor each item but more importantly from a GIS perspective there was a spatially referencedlocation on the earth’s surface in the form of northings and eastings that could be used for GISanalysis.John Naylor from the University of Oxford had carried out an investigation into these finds in 2009called 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 andaround the East Anglian area. He used a ‘fingerprint’ method to assess how many of each of thePAS finds were located near the productive sites in order to better understand their use and levelsof activity over the Anglo Saxon period. Naylor did not use any GIS techniques for this project 1
  14. 14. except to create a few maps of the find locations; no focus was put on using the wealth of spatialanalysis, techniques such as cluster and hotspot analysis available in modern GIS packages likeArcMap 10. These techniques are widely used in other disciplines such as crime mapping (Blockand 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 thePAS database using spatial analysis techniques. The resulting analysis could help us understandhow people lived at various points in history as well as highlighting patterns within groups ofcertain items such as jewellery or household items. It could also highlight areas that could needfurther investigation.It is for these reasons that this project will take a sample of data from the PAS database datingfrom the Anglo Saxon period and apply GIS and spatial analysis techniques to explore andinvestigate the data. The results of the 2009 project by John Naylor will be used to judge thesuccess 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 superiorknowledge of archaeology than the author of this project and secondly they had access toadditional archaeological datasets that are not available to the public. This project proposes tocompliment the work done by the VASLE project by performing spatial analysis techniques to helpfurther understand the PAS data found in this area.This dissertation will be broken down into four sections. Following this introduction there will be athorough literature review covering the areas of archaeology and spatial analysis relevant to thisproject. After this there will be a presentation of the materials and methods used to undertakethe GIS and spatial analysis followed by a discussion of the results that were achieved. Finallythere will be a summary of the conclusions made and recommendations for possible furtherstudy. 2
  15. 15. Chapter 2: Literature ReviewGeographical Information Systems (GIS) and advanced spatial analysis techniques have been usedin archaeology since the late 1970’s (Matsumoto, 2007). This is because archaeologists havealways understood the value of analysing the spatial data they find through fieldworkinvestigations (Seibert, 2007) (Wheatly and Gillings, 2002). Haining (2003) suggests thatarchaeology has become a subfield of geography and its spatial processes. ESRI the companybehind leading GIS software package ArcMap 10 has created a ‘Best Practice’ document for theuse of GIS in archaeological projects (Brett et.al, 2009).GIS related technologies are now widely used to collect archaeological data supplementing themore traditional methods of field walking (Medlycott, 2006) (Foard, 1978). Such technologiesinclude remote sensing in the multispectral and thermal bands; this technique has been widelyused 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 higherresolution (Matheny, 1962) (Gilman, 1999).Other technologies involve devices that measure the soil’s resistivity to electric currents GroundPenetrating 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 activityand 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 ofarchaeology is metal detecting (Thomas and Stone, 2009) (Kidd, 2008) (Cool, 2000). Finds made bythe general public can be given a GPS location and then be imported into GIS software for furtheranalysis. This analysis can greatly improve the knowledge where and how people lived ‘VASLEProject’ (Naylor et al, 2009) (Chester-Kadwell, 2009) (Ulmschneider, 2000). These projects allcentred around the analysis of metal detected finds from East Anglia dated to the Anglo Saxonperiod 400 – 1066 AD.The vast amounts of metal detected data held on databases such as that of the PortableAntiquities Scheme (PAS) means that there is a great opportunity to utilise the spatial analysisfunctions of GIS software such as ArcMap 10 (Gill, 2002). Unfortunately whilst some projectsutilise GIS functionality (Moyes, 2002) (Kay and Witcher, 2009) the projects undertaken utilisingmetal detected data have yet to fully exploit these functions, concerning themselves more withbasic mapping and visualisation of the finds and so called ‘productive sites’ across study areas.Johnson 3
  16. 16. (2002) cites a possible reason for this as the fact GIS has not fully been embraced by thearchaeological community, reflected by the lack of peer reviewed material involving the twodisciplines. This gap in the material shows that there is justification in undertaking a GIS projectsuch as this.The body of this review will be divided into three sections; the first will outline the issuessurrounding the use of metal detected finds in archaeological analysis; the second will discuss thedefinition of an archaeological ‘productive site’ as this will have an impact on the hypothesisesand methodology for the project; finally, the third will discuss, using other projects, the requiredspatial analysis techniques that can be employed to interpret the archaeological data.2.1 The issues surrounding the use of metal detected finds in archaeological analysisBefore discussing the use of spatial analysis techniques in archaeological projects it is importantto understand the issues surrounding the use of data derived from metal detectors. This isimportant as Dobinson and Denison (1995) concluded that metal detecting has been responsiblefor some major advances in archaeological knowledge. This section will summarise the keyliterature for each of the main issues and explain how their effects can be minimised or mitigatedagainst to maximise the accuracy of any spatial analysis work that is carried out.2.1.1 Bias in archaeological finds found using metal detectorsDue to fact that metal detecting is so popular amongst the general public (Paynton, 2002) there isgoing to be a certain amount of bias in the locations of many finds, this is over and above theobvious bias against pottery and ferrous objects (Naylor and Richards, 2007). This subject is notwidely acknowledged in current literature, some account for the extensive urban areas in the UKwhich prevent metal detecting (Naylor et al, 2009), but most rarely account for the way finddistributions are influenced by the metal detectorist.Studies have shown the most suitable land type for metal detecting is agricultural land with shortstubble (Gurney, 2003), woodland and open pasture are also preferred over inaccessible areaswith unsuitable surfaces such as concrete urban areas. Kershaw (2009) however states that thesevery sites also cause bias because modern activity has re distributed them from their originallocations leading spurious findspot locations. Sites known to have been previously rich in finds will 4
  17. 17. also attract a higher than normal level of surveying. The metal detectorist community is highlyactive 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 aswhether the field or wood is close to a main road or urban settlement. Suitable areas near theselocations 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 thisproject.Most experts also specify that search patterns need to be properly structured; sites that arerandomly 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 thespatial distribution of metal detected archaeological finds. For the purpose of this report the datamust be taken for what it is with the allowance and acceptance that there will be some bias, howthis 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 asbeing volunteered (Goodchild, 2007), i.e. non-proprietary data as opposed to commerciallyobtained data. Literature has focused on comparing the advantages and disadvantages of eachsource (Zielstra and Zipf, 2005) its advantages have been highlighted in disaster managementsituations (Goodchild and Glennon, 2010) (Zook et.al 2010) some have directly compared thesoftware 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 alwayshave the skills of an academically trained one (Brando and Bucher, 2010). The completeness ofVGI data has also been called into question (Haklay and Ellul, 2010) these shortcomings couldpossibly detract from the usefulness of VGI in archaeological analysis. Subsequent research hasspecifically developed systems using fuzzy sets theory to overcome the inherent vagueness in VGI(De Longueville et.al, 2009). 5
  18. 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 decidewhether they should use it or not depending on its quality (Van Oort, 2006). The next section willdescribe some of the inconsistencies that are unique to using metal detected data for anarchaeological spatial analysis project.2.1.3 Building useable datasets from metal detected archaeological informationThe use of publically collected spatial information for use in archaeological analysis brings a set ofissues that must also be understood; these are partly due to the bias that was outlined in section2.1.1 but also UK law and the ability of the metal detectorist to accurately document theirfindings. These issues are summarised in much of the archaeological literature (Wheatly andGillings, 2002) (Greene and Moore, 2010) (McAdams and Kocaman, 2010).The locational quality of data is paramount for any accurate spatial analysis to take place. Wherefinds are recorded in situ by trained archaeologists this is not a problem as the use of GPS devicesto fix locations of finds is commonplace (Tripcevich, 2004). This method means that accuratespatial analysis can then be carried out such as in the work by Niknami and Amirkhiz (2009) andSommer (2011).Unfortunately, privacy and inconsistent use of GPS devices mean that the locations of metaldetected data is not consistently accurate (Naylor et al, 2009); sometimes the locationalinformation is withheld altogether, this is to protect the privacy of the landowner. The databaseheld by the PAS has accuracy to a six figure easting and northing reference meaning the find canbe pinpointed to a 100m x 100m area of land. Greater detail is held but is not made available tothe public (Richardson, 2011). Some literature has proposed that metal detecting be regulated toprevent the unauthorised removal of finds (Ulst, 2010) this idea is supported by TV personalitiessuch as Tony Robinson (Highfield, 2008).Another important area covered in the literature is how artefacts should be grouped and datedprior to their analysis within a GIS a process described as ‘epochization’ (Tobler, 1974). As thefinds contained within the PAS database have not been collected by trained archaeologists, anapproximate date range has been used. Naylor et al (2009) suggests that any range of 250 yearsor less is suitable for dating an artefact to a particular time period. Other archaeological 6
  19. 19. techniques such as serration are also commonly used to sequence the date of artefacts from aparticular 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 aproject. A full discussion of the classification and dating of archaeological finds is beyond thescope of this review but it is important to discuss the literature within a GIS context. There arenumerous 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 GISor to carry out their subsequent spatial analysis. Naylor et al (2009) use a ‘fingerprint’ system todefine the proportions of each artefact type found in the UK; this distribution is not used in a GISbeyond 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 usingpublicly sourced data in a GIS are well known. Conversely little work has been carried out in howto prepare large collections of finds for spatial analysis in a GIS. A robust and quantifiableclassification scheme is important as it means any spatial analysis can show the distribution oftypes of finds at different points in time.2.1.4 Dealing with gaps in spatial dataDue to the constraints discussed in section 2.1.1 there will be areas where there is no recordedspatial data. To perform the spatial analysis tasks for this project a continuous surface needs to becreated from the find points. Wheatly and Gillings (2002) caution against using interpolation asany resulting surface would be wholly artificial. The text by Conolly and Lake (2006) does notmake any mention of this downside of using interpolation and suggests using it to fill in any gapsin archaeological data. 7
  20. 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 froma discrete object perspective.A range of techniques can be employed to create a density surface, the simplest being the griddedquadrat method (Bailey and Gatrell, 1995) (Wheatly and Gillings, 2002). It has been used widely inthe field of Ecology to estimate species distributions (Kenney, 1990) (Krebs and Foresman, 2007).Other methods include kernel density estimation (KDE) which eliminates the problems withquadrat grid sizes (Rogerson, 2010). Herzog (2006) summarises these and other less commonmethods in his paper and concludes that KDE is the most accurate but the analyst must be carefulin choosing the size of the bandwidth of the kernel (Akpinar and Usul, 2004). O’Sullivan andUnwin (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 thereremains a gap in the literature surrounding how significant clusters of artefacts can be correlatedwith other significant features in the landscape (Baxter and Beardah, 1997). The last part of thisliterature review will discuss the spatial analysis techniques that could be used to for fill this taskwithin a GIS.2.2 Defining the productive siteOne of the most contentious issues that have arisen in the field of spatial archaeology is thedefinition 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) asa good example. Debates have centred on what criteria are needed to define productive sitesfound in the rural hinterland as well as clearer definition for the term. Even after conferenceswere 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 wheremultiple 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 canhelp in the interpretation through spatial analysis of archaeological finds data (Naylor andRichards, 2007) (Ulmschneider, 2000). This is especially the case in numismatics and can greatlyaid 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. 21. monastery. Hutcheson (2006) however proposes that early productive sites were tax collectingand administrative centres.Most of the literature surrounding productive sites has been undertaken by KathrinUlmschneider, 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 thedistribution of metal detected coins to build a picture of communication and trade between AngloSaxon towns Ulmschneider and Pestell (2003). The presence of coins at sites is backed up byHutcheson’s (2006) view that early productive sites were tax collecting and administrativecentres.Further research has been carried out on productive sites by John Naylor; his work on AngloSaxon coins in Northern England (Naylor, 2007) also states that the nature of productive sitescould change over time as the Anglo Saxon period ranges from 400 – 1100AD. His largest work onthe 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 eachof the previously identified productive sites. This has been done through previous excavations andlocal records. From these fingerprints the social use and date of peak activity can be assessed foreach site.2.2.1 Productive sites and Anglo Saxon towns and communication routesAnother area of literature relevant to this project is the relationship between sites that haveshown to be productive and the locations of Anglo Saxon towns and the Roman roads thatconnect them. Little literature is present on directly analysing the relationships, although mostauthors state that productive sites are well connected to lines of communication whether or notthey were of Roman origin (Ulmschneider, 2002) this backs up earlier discussion that these siteswere used for trade and tax collection Hutcheson (2006).Studies have also shown that the Anglo Saxons probably failed to maintain many of the Romanroads left behind after 410 AD (Vince, 2001) possibly due to the fact the Anglo Saxons didn’t livethe same urban lifestyle as the Romans (Witcher, 2009). This is backed up by some key AngloSaxon towns such as Nottingham and Northampton not being connected by roads of Romanorigin (Vince, 2001). Other studies have revealed a number of smaller Roman roads that may haveconnected forts and smaller towns (Frere, 2000); what, if any, connection these have with AngloSaxon productive sites is therefore of great interest. 9
  22. 22. The relationship between productive sites and known Anglo Saxon towns that appeared in suchrecords as the Doomsday Book of 1068 has mostly been studied on a site by site basis. Themethod employed by Naylor et.al (2009) in his VASLE project was to correlate existing AngloSaxon settlements with finds from the PAS database. This lead to a better understanding of theactivities, 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 intoaccount and, secondly, there was a certain amount of subjectivity on the part of the author inchoosing the sites that he did. There is therefore scope to perform a spatial analysis on the regionas 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 searchedfurther ‘coldspots’.2.3 Spatial Analysis Techniques2.3.1 Spatial autocorrelation of archaeological dataMany pieces of literature have been written summarising the methods and techniques used inspatial autocorrelation (Goodchild, 1986) (Baxter et.al, 1995) (Griffith, 2000). Spatialautocorrelation techniques are of use to archaeologists as they explain the intensity in clusteringof any finds (Smith et.al, 2009). This in turn may pinpoint intense clusters of finds where peoplemay 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 techniquesprovide in archaeology. (Smith et.al, 2009) also state that correlation does not imply causationand 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 asarchaeology, including epidemiology (Pefeiffer et.al, 2009) and ecology (Schneider, 1989) (Lieboldand 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. 23. most intense, this distance band can then be used in the spatial autocorrelation or hotspotcalculation.Specific literature involving spatial autocorrelation in an archaeological context are limited, someof 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 archaeologicaldata. 35 years later with the advent of the internet and use of GPS to record data this assessmentseems a little out of date. There is clearly a gap in the use of spatial auto correlation techniques inthe field of archaeology and the availability of datasets such as that of the PAS make projects suchas this timely.2.3.2 Cluster analysis of spatial dataFrom 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 thedegree of clustering but not physically show them to the analyst. Cluster analysis techniques canbe 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 nottry 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 andshape of the area under investigation. Another disadvantage is that NN does not take intoaccount local variations in clustering which could have occurred (Mitchell, 2009).Secondly there is k means clustering (Moyes, 2002) (Whitley and Clark, 1985). The analyst definesthe number of clusters required a priori; this can however cause problems as there is no optimumnumber of clusters (Everitt, 1979) (Grubesic, 2006). Work has been carried out to try andconstrain the clustering to simplify the process and thus remove the possibility of clusters formingwith 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 recommendrunning the procedure multiple times to check the stability of the clusters (O’Sullivan and Unwin,2003) (Smith et.al, 2009). 11
  24. 24. The third is Ripley’s K function (Ripley, 1981) where multiple distances are used to indicatedispersion or clustering based on observed and expected patterns (Dixon, 2006) it also has thebenefit of displaying the size and separation of any clusters (O’Sullivan and Unwin, 2003). There isliterature showing the use of the k function in archaeological analysis beyond the investigation ofsettlement 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 detailedsummary of the clustering (Smith et.al, 2009). It also doesn’t show why some clusters may bemore significant i.e. why hot and cold areas are grouping together (Grubesic, 2004?).2.3.3 Hotspot analysis of spatial dataHotspot 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.al2006). The ability for archaeologists to determine whether clustering is significant or not isimportant as it means an element of confidence can be added to any results (Smith et.al, 2009). Itlooks at each feature in the context of neighbouring features to identify clusters with highervalues than you would expect by random chance (Rosenshein and Scott, 2011). An example of thisalgorithm is the Getis-Ord Gi* method which is used ArcView. The other technique available isAnselin 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 perunit area (Gonzales et.al, 2005) or teenage birth rates (Mayes, 2010). It is important to aggregateincident point data such as the co-incidental find spot locations of artefacts from the same groupsuch as brooches (Smith et.al, 2009). The benefit of conducting this form of analysis is that theresulting z and p scores can tell the analyst whether they reject or accept the null hypothesis witha certain level of significance.Finding hotspots in clusters of archaeological data may indicate increased activity and the locationof a productive site, it could indicate a popular area for metal detectorists. What is moreinteresting are the cold spots and their relationships to Anglo Saxon towns and lines ofcommunication (section 2.2.1) this could indicate as yet undiscovered finds and hoards. 12
  25. 25. 2.4 ConclusionsThis literature review has latest thoughts and discussion surrounding the use of advanced spatialanalysis techniques in order to better understand the wealth of publically collected archaeologicaldata. 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 previouslyidentified productive sites, none of the advanced spatial analysis detailed in this review have beenused.Thorough planning and preparation of the data needs to be carried out before any analysis iscarried out as this will mean that the subsequent analysis is both accurate and objective. Thepitfalls of using VGI data have been discussed but the growth in all fields acquiring data throughthis 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 opensource 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 relationshipsprojects such as this can only add to archaeologies knowledge base. We can better understandthe distributions of finds their relationships with other features as well as highlighting possiblegaps in any metal detector searches. 13
  26. 26. Chapter 3: Materials and methodsThe aim of this project is to analyse through the use of GIS and advanced spatial analysistechniques the vast amounts of metal detected data held on databases such as that of thePortable Antiquities Scheme (PAS), more specifically those dated to the Anglo Saxon period ofEnglish history. From the literature review it is clear that there is a gap in this area of research andas a result this is the main focus for this methodology. Some of the questions that need to beanswered are how GIS and spatial analysis techniques can be used to explain the patterns such asclustering and statistically significant hotspots as well as comparing the results of the 2009productive sites VASLE project (Naylor et al, 2009) with results gained through spatial analysistechniques. Appropriate hypotheses will be drawn up which can then be accepted or rejectedbased on the spatial analysis work.The PAS database will be the primary source of data for this project, access is through a freeregistration process. The general public can log metal detected finds they have made onto thedatabase by filling in the appropriate database fields, after this a group of PAS experts verify thedescriptions and make any comments necessary. The database is divided up into broad timeperiods in history and is searchable on many fields such as object type, size and the material it ismade of and so on. Some of the problems at this stage of the project could be the authors ownlack of knowledge in archaeological finds and this period in history, as a result several pieces ofliterature 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 flatformat with many gaps, anomalies and ambiguities; unfortunately this is often the case with VGIdata. Converting and cleaning the data could present a real challenge, especially, as specificarchaeological categorisation and dating techniques may need to be used. This process will needto be done carefully if any resulting spatial analysis is to be accurate and valid. Again relevantliterature was consulted to aid the author such as by (Wheatly and Gillings, 2002) and (Greeneand Moore, 2010).This methodology will now go through the preparation of data and choice of analytical proceduresthat will be used to answer each of the questions outlined above with the goal being the overallaim of the project. The first section will deal with the initial collection and preparation of the data 14
  27. 27. as well as loading it into the GIS software. The next three sections will discuss each of theanalytical 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 theKingston University student license agreement, therefore there are no additional costs to theauthor, but there is however other freely available GIS software such as Quantum GIS which willperform any tasks in a similar way.3.1 Data collectionThe main source of data for the project came from the PAS database (Portable AntiquitiesScheme, 2011) the raw data is able to be downloaded in a variety of formats including the .csvformat making it suitable to import into ArcMap 10. The searchable database was used to extractthe PAS records dating from between 400AD – 1066AD, the period of Anglo Saxon rule overEngland. This was a large document containing 2617 records detailing a wide variety of itemsfrom brooches to tweezers.The second source of data was Digimap (Digimap, 2011). This site provided the maps, towns andcounty boundarys needed to put the location of PAS finds into context as well as interpret thelocations of hotspots and productive sites. Digimap was able to provide the 1:50,000 scaleOrdnance Survey (OS) raster tiles as well as the county boundaries and gazetteer for the towns inthe East Anglia area. As a registered student at Kingston University, the data was free todownload and use for academic purposes.3.2 Data cleaning and manipulationOnce the PAS and OS data had been collected it needed to be tided and organised into a suitableformat 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 foranalysis. 15
  28. 28. 3.2.1 Cleaning and manipulating the PAS datasetThe large .csv file downloaded from the PAS website contained 2617 records each with 47attributes. The first task was to narrow down the number of attributes and identify the ones thatwould 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 outa more detailed analysis. Undertaking spatial analysis on just the total PAS dataset would not helpreveal 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 furtherpatterns.The cleaning tasks were carried out on the dataset to standardise the entries made by the generalpublic; this included adjusting the spelling and description of the finds. Also, records werediscarded if there was insufficient data present, such as insufficient dating information or the PASexperts could not verify the find described.3.2.2 Dating of PAS findsThe first task was to date the individual finds. The attributes provided on the PAS database had anearliest possible date and a latest possible date, these could range from 0 – 1000 years. The mainaim in dating the finds was to again allocate each to a time period in Anglo Saxon England eitherearly, in the middle or towards the end of the period from 400AD – 1066AD. These groups wouldbe called Early, Middle and Late.In order to work out which group each find belonged to, a number of criteria were used: any findwith a date range from earliest to latest of greater than 250 years was to be excluded from theanalysis. Naylor et.al (2009) used this as benchmark in his project and, as part of this project is tocompare the techniques used to analyse the PAS data, it was appropriate to follow the same 16
  29. 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 ofdates was created to define the: ‘Early’, ‘Middle’ and ‘Late’ Anglo Saxon periods, the dates usedwere: 400AD – 600AD, 601AD – 800AD and 801AD – 1066AD respectively. Finally, each find wasallocated a date range based on its ‘mid’ point date; if there was an overlap, the category withmore than 50% of the finds date range would be chosen.3.2.3 Classification of PAS findsOnce 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 theliterature review together with the project’s constraints such as time and computing power. Thefirst task was to allocate each of the 89 unique ‘object type’ find entries to a broader ‘objectgroup’. This group should be large enough to provide useful GIS analysis whilst accuratelyrepresenting the objects within it. The groupings must also be such that in higher densities theycould 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 werefound to have, 186, 23, 362, 1010, 195, 683, 30, 16 and 162 finds respectively. It was decided thatthe Commercial, Military and Burial groupings did not have a sufficient number of finds to carryout effective GIS analysis. Therefore the Commercial and Household groupings were combineddue to the relationship between some of their objects. The Military and Burial groupings wereconsidered to be too distinct from the other groups to be integrated and were thereforediscarded from the analysis phase of the project, leaving six object groups in all. A more specialistgrouping of the finds could have been undertaken using more complex archaeological techniquesbut, as has been discussed, that is beyond the scope of this project. Each item was now part of anobject group and time period. There were however two categories which did not have any findsfalling into the early category these were Pins and Coins, it was decided that the middle and latecategories would remain as they contained a large number of finds that would contribute a lotduring the analysis. A full presentation and discussion of the cleaned and dated PAS dataset willfollow later in this section. 17
  30. 30. 3.2.4 Cleaning and manipulating the Ordnance Survey datasetsThe Digimap website provided all the 1:50,000 scale Ordnance Survey (OS) raster tiles as well asthe county boundaries and gazetteer for the towns in the East Anglia area. No cleaning of thisdata was necessary but it did need to be manipulated through cutting out areas and towns notrelevant to the analysis. ArcMap 10 was used to select the counties Norfolk and Suffolk from thenational dataset as well as the OS raster tiles that covered the same area; this used the simpleselect by attribute process. The gazetteer provided all the towns for the UK and again had to bemanipulated by cutting out the towns and villages not in the East Anglia area. This was done byselecting 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 villagesfrom this period were required for the analysis. An online database detailing the towns andvillages present at the time of the Domesday Book in 1085 was consulted to select the relevanttowns from the gazetteer dataset (Domesday Book Online, 2011). Out of the 878 towns in Suffolk319 could be dated back to 1085 and out of the 1050 in Norfolk 440 could be. Modern records ofsome of the towns detailed in the Doomsday Book could not be found but it was thought that 759locations would be sufficient to carry out the necessary analysis. We cannot be certain thesetowns existed throughout the Anglo Saxon period, but the Doomsday Book is the best record wehave of the towns active at this period in history.3.3 Creating a geodatabase from all the datasetsOnce the datasets had been cleaned and manipulated they were loaded into ArcMap 10 acommercially available piece of GIS analytical software. ARC provides a wide range of powerfulspatial 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 forall the subsequent files that would be created as a result of the analysis process. The finds werebrought into ArcMap 10 using the Easting and Northing values held on the PAS database. In mostcases this will not be the exact location of the find but an approximate area such as the centre ofthe field the find was found in or the centre of the owners land; this is to protect the privacy ofthe landowner and prevent further looting or illegal metal detecting. It is not possible to saywhich locations are exact and which are not. The PAS has accurate locational data for all the finds 18
  31. 31. but they were not made available for this project. The 2009 VASLE project did however haveaccess 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 classbeing spatially related to each other in the same way.Additional fields were added to the finds attribute table to incorporate the attributes ‘objectgroup’ and ‘Anglo Saxon time period’ to each find. This meant that new feature classes could becreated for the global dataset for each object group and as well 3 further feature classes could becreated 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 findsthat fell in the early, middle and late periods irrespective of object group. Finally the VASLEproductive sites located during the 2009 project, Norfolk and Suffolk Anglo Saxon towns were alsoselected from the East Anglia town’s database and set in their own feature classes. The next threesections of the methodology will detail the analytical and spatial analysis procedures used toexplore and interrogate the PAS dataset.3.4 Cluster analysis of PAS findsTo further investigate the PAS data it important to see whether the finds are randomly dispersedacross 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 greatestand whether the level of clustering changes over distance. This analysis covers the whole of EastAnglia not just the areas covered by the productive sites found by the VASLE analysis. This couldhelp 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 averagenearest neighbour, Ripley’s K function and Global Morans I. Global Morans I will be used to givean indication of the spatial auto correlation and seek to find if nearby points have similar ordissimilar values; this, again, will demonstrate whether the finds are clustering, random ordispersed. A range of tests will be employed as each has distinct advantages and disadvantagesdepending on the data being used. Results can be compared from each technique which willhopefully lead to better conclusions about the distribution of PAS finds. 19
  32. 32. The average nearest neighbour (ANN) technique considers the distance between the points; thiscan however be its disadvantage (Mitchell, 2009) as if some of the points are in the same locationthan the distances calculated can be smaller than they should be. Unfortunately ArcMap 10 doesnot give you the option to perform the analysis using k-order neighbours and so the use of thistechnique was probably compromised in some way due to this. ANN also suffers from ‘edgeeffect’ whereby if there are too many points located towards the edge of the study area resultscould become biased (Conolly and Lake, 2006). A visual inspection of the PAS data mapped overEast Anglia shows that are indeed areas where points cluster around the edges, notably in thewest around Newmarket here there are also several points within very short distances from eachother. One final consideration when conducting the ANN test is the size of the study area. Thismust be fixed and identical for each analysis, a bounding box corresponding to the extent of thepoints would be different each time. Therefore, the size of the East Anglia study area as well asthe 2 counties must be calculated first using the calculate area function in ArcMap 10 the resultscan then be entered as a variable during the ANN test. The ANN analysis will provide abenchmark 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 ANNtest. The Global Morans I test can indicate whether nearby points have similar or dissimilarvalues. This does not however indicate whether these values are high or low that will be coveredby 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 influenceof finds nearer the target feature should be greater than those further away, the thresholddistance 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 associatedvalues that can be compared a density map must first be created to produce a ‘pixel value’ thatcan be assigned to each find point. The kernel density method will be used to create the densitysurface for each object group; kernel density is more preferable to the simpler point densitymethod as it produces smoother more accurate results as discussed in the literature review. Anoutput cell size of quarter of a mile was chosen as it provides a detailed surface without being toocomputationally intensive. The resulting pixel values are then assigned to each find point throughthe extract values to points function in ArcMap 10, this now gives each point a value that can becompared via the input field within the Global Morans I test. 20
  33. 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 theentire East Anglia area. The function will be used to determine the point at which the clusteringbecomes most intense, and where the finds cease to be clustered and become dispersed. Thisinformation is useful as it helps to generate a more accurate hotspot analysis through the use ofthe 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 findsdataset and the Anglo Saxon towns of both Norfolk and Suffolk. Ripley’s’ K function in ArcMap 10gives you the option to input the number of bands and the distances that will be set betweenthem; 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 anyresults by selecting the number of permutations the test undertakes. This can be either: 9, 99 or999 permutations equating to 90%, 95% and 99% levels respectively; each permutation plots aseries of random points across the study area in order to calculate the K values. Due torestrictions on computing power 99 permutations may not be possible and so results may need tocalculated to the 90% confidence level. Areas of interest can then be focused on at smallerdistances in order to pinpoint the distance where clustering is most intense. A summary of theresults can be entered into Excel for further analysis and discussion.3.5 Hotspot analysis of PAS findsThe third and final spatial analysis technique that will be used is a hotspot analysis of the PASfinds data. This will be able to show where there are statistically significant hot or cold spots inthe locations of the metal detected finds data. The results will be used firstly to help analyse theVASLE productive sites, as the resulting hotspots can be overlaid for comparison. Secondly, anystatistically significant coldspots can be highlighted as areas that may need further investigationfor certain object groups such as jewellery or clothing. These spots can be analysed further byoverlaying them over the OS raster tiles; roads, towns and features can be picked out and possiblelinks made to the hot and cold spots. Lastly a hotspot analysis of the Anglo Saxon towns can becompared to the hot and cold spots of the finds; again, visual analysis could uncover trends andlinks between the two.The Getis-Ord-Gi* method will be used within ArcMap 10 to perform the hotspot analysis as thisis the more appropriate version of the two Getis-Ord statistics, it also includes the value of the 21
  34. 34. target feature since its value contributes to the occurrence of the cluster (Mitchell, 2009). TheGetis-Ord-Gi* method can be optimised if the user has knowledge of where the features are attheir 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 thatthere are at least 30 points as input to the analysis. All the 14 of the groups plus the Anglo Saxontowns have 30 or points in them the only two which don’t are early and middle Anglo Saxon horseitems; they only have 13 and 19 respectively. Care must be taken when interpreting the outputfrom these two hotspot analyses as the results could be suspect (Mitchell, 2009). Comparisonswill have to be made with the late and global horse finds groups to determine whether the resultsfor these two groups are valid.As with the Global Morans I test outlined earlier, the Getis-Ord-Gi* function requires input valuesto compare, to do this the points that have had the pixel values extracted to them are used as theinput feature class and the pixel values as the input field. The Getis-Ord-Gi*method will be carriedout for each of the 22 feature classes. As with the Global Morans I test, the conceptualization ofspatial relationships field will be set to inverse distance squared as the influence of finds nearerthe target feature should be greater than those further away. The threshold distance will be set atthe 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 interpolatedacross the entire East Anglia area using the IDW method. The IDW method was chosen over otherforms of interpolation because it again puts more weight on the points that are nearest the targetfeature than would be the case with the points and their corresponding hotspots. A power of 3was specified to give less influence to points that are further away and an output cell size of aquarter of a mile (402.336 metres) was used to give consistency to the earlier density maps. Therewill be gaps in the interpolated surface as the bounding box used to encompass the find pointswill not always cover the entire East Anglia area.The resulting surface can then be reclassified according to the z scores and their levels ofstatistical significance breaks will be created at 1.645, 1.96 and 2.576 and -1.645, -1.96 and -2.576to represent the 90%, 95%, and 99% confidence levels. These can be symbolised in shades of blueand red with all other values between 1.645 and -1.645 receiving a neutral beige colour indicatinga random pattern. This choice of shading was chosen as red and blue are most associated with hotand cold values respectively. The interpolated hotspot surfaces will then be clipped to the outline 22
  35. 35. of East Anglia using the extract my mask function in ArcMap 10. This removes any unnecessaryinformation outside the study area.3.6 VASLE productive site comparison analysisThere were 22 ‘productive’ sites identified by Naylor and his team in 2009. The aim of this projectwas to compare the results of the ‘non-GIS’ techniques he used with the spatial analysis functionsavailable in ArcMap 10. Although the ‘fingerprint’ technique he used was useful in representingthe spread of finds over different categories and time periods this technique wasn’t based on therecognised 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 theradius of the buffer circle that will surround the feature. The intersect function can then be usedto identify which of the PAS finds falls within each buffer. As productive sites don’t have anydefinitions, they don’t have any indication as to how large they should be; studies rarely givedetailed 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 surroundedeach of the VPSs. It was decided to make the buffering reasonably large to encompass enoughfinds to make the analysis worthwhile, as the locations of the VPSs and PAS finds will remain thesame, any spatial analysis would be fair and unbiased.The buffers should be large enough to encompass enough PAS finds to enable a proper andthorough analysis of the data but not too large so that they become meaningless. Also, thedispersed nature of Anglo Saxon settlements meant that finds belonging to each VPS must beconsidered to have come from a similarly disperse area surrounding it. This should also take intoaccount the possible movement of finds to and from the site and in the immediate local area dueto the movement of people goods and services. Some sites may have higher quantities of one ormore object groups especially coins because they specialise in the trading or manufacture ofcertain 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 theactual productive site. It is for this reason that a 2.5 mile buffer was chosen as it covered all thesecriteria without being too large. 23
  36. 36. A 2.5 mile buffer meant that some of the sites that were close together such Rockland All Saintsand Rockland St Peter, only 0.6 miles apart, would share some of the same PAS finds. This isbecause there is no attribute field in the PAS database linking finds to any particular VPS and so itcannot be determined which site any of the finds would have belonged too. Therefore theproductivity 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 PASfinds detected.As this project only had access to a fraction of the data that was available to the VASLE project theVPSs productivity would be judged on the PAS data only. This meant that direct comparison of theprojects was not possible; however, a productivity comparison could be made on whether thefinds found as a result of the buffering analysis came from a wide range of the 6 object groups aswell as the 3 time periods. An average number of total finds from the 6 object groups and 3 timeperiods would also give an indication of productiveness.To judge whether the sites found by Naylor were indeed productive a further 22 ‘control’ siteswere picked at random from across East Anglia to see what results they gave. ArcMap 10 has aplace random points’ function that allows the user to randomly place as many points as they wishacross a defined area. An additional field containing the number of each control site (1 -22) wasadded to aid identification during further analysis. A comparison would then be made with the 22control sites using the same methods to help put the comparison of the VASLE and the spatialanalysis 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 analysewhich find points fall within each of the buffers. The resulting attribute table will carry all the datafrom the original tables such as object group, time period and so on. This means that a spatiallyanalysed fingerprint can be generated for each of the VASLE and control sites making comparisonsand analysis possible. Any duplicate items will then be removed to determine how many of thefinds are unique to 22 VASLE sites and 22 control sites creating a two more attribute tables foranalysis of the unique finds.To carry out the analysis individually on each of the points would take a very long time andpossibly lead to slower processing time and possible system crashes. Therefore, the model builderfunction was utilised to automate the tasks needed to carry out this analysis. This meant that the 24
  37. 37. buffering and intersect functions could be carried out on each point in one go improvingconsistency and efficiency.The outputs of the buffering and intersect analysis process were 22 tables of finds data for theVASLE sites and 22 table of data for the random sites, these could then be transferred to Excel forfurther analysis and presentation. As a final comparison the results of the hotspot analysis can beoverlaid onto the VASLE productive sites to see if any fall within a statistically significant hot orcold spot.3.7 Overview of study area and PAS finds datasetBefore presenting and discussing the results of the data it is useful to introduce both the studyarea and the constitution of the PAS finds dataset.3.7.1 Overview of study areaThe map in figure 1 shows the location of the study area used in this project. The counties ofNorfolk and Suffolk comprise an area of approximately 3540 square miles; the land ispredominantly flat and low lying with the highest point being 338ft, significant areas are actuallybelow sea level. The key towns seen in figure 1 are important as administrative or commercialreasons. Most of the land in East Anglia is used for farming and the settlement patterns are ruralaway from the large towns of Norwich and others shown on the map. This make the area a primedestination for metal detecting and other archaeological work. 25
  38. 38. Figure 1: Location map of the East Anglia study area3.7.2 Overview of PAS finds datasetThis section will look at the global PAS dataset and present the quantities of finds that fell intoeach of the object groups as a result of the classification schemes outlined in the methodologysection. This will provide a background to the spatial analysis techniques to follow which willexplain the finds distribution across the East Anglia area. As we can see from Figure 2 there werea 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 for64.7% of the data. The largest proportion of the jewellery dataset (90%) is made up of broocheswith 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 statuspiece indeed, however only one was found within the 2.5 mile buffer of a Narborough VASLEproductive site and that was a late period finger ring. Based on this analysis there was anargument that brooches could have been made into an object group in their own right but this 26
  39. 39. would have meant the remaining items would have been insufficient in number to make up asecondary 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 beingmade 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 only7% of the data this reflects the fact that the early medieval corpus (EMC) holds the majority ofAnglo Saxon coins found in England, unfortunately this data was not made available for thisproject as the grid references of the find spots were deemed too sensitive. There were also nocoins from the early period (figure 3) we cannot say that this meant that there was no economicactivity 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. Theclothing group was made up of: sleeve clasps (40%), buckles (37%), hooked tags (10%) and girdlehangers (8%). The remainder of the groups was made up of small numbers of buckle frames andother clasps. The commercial and household object group was made up of items that could havebeen used in the businesses and homes of Anglo Saxon England, the biggest groups of finds weremounts (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 pinsobject group had no finds from the early period but interestingly had the majority of its finds fromthe middle period, the only object group to show this distribution. Looking at the split of the findsbetween the 3 Anglo Saxon periods in figure 2 the largest numbers of finds (48%) come from thelater period of Anglo Saxon rule.The chart in figure 3 shows the quantities of each object group that were found in each timeperiod. It is clear that some finds are more present in certain periods of Anglo Saxon history, forinstance 95% of horse items come from the late period. This result ties in with work undertakenby Neville (2006) who states that the use of horses became popular in warfare towards the end ofAnglo Saxon times possibly through Danish influence. Both jewellery and commercial andhousehold have the largest percentage of their object group found in the early period whereasthe majority of pins come from the middle period. Only clothing and commercial and householdcould be considered to have large percentages of their finds from each time period. This showsthat there was Anglo Saxon activity within the East Anglia region throughout the Anglo Saxonperiod. 27
  40. 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 data100 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

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