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Spatial Analysis of East AnglianAnglo Saxon Archaeology,[object Object],Edward Kemp,[object Object],MSc Applied Geographical Information Systems ,[object Object],Wednesday 7th September 2011,[object Object]
Presentation Structure,[object Object],Provide a background to the project.,[object Object],Explain the projects aims and objectives.,[object Object],Introduce the study area and data.,[object Object],Briefly explain the methods used.,[object Object],Share some results and conclusions.,[object Object],Did we achieve our objectives?,[object Object],I mages from:  Culture 24 www.culture24.org.uk,[object Object]
Background,[object Object],Dissertation topic inspired by a project from 2006 by John Naylor at Ashmolean Museum in Oxford,[object Object],Uses of an online database of archaeological finds found using metal detectors by members of the public,[object Object],Started in 1997 now has over 450,000 unique objects on the searchable database,[object Object],Run by the Portable Antiquities Scheme (PAS) in conjunction with the British Museum ,[object Object]
Aims and Objectives,[object Object],Explore archaeological data using spatial analysis techniques,[object Object],Explore distributions and patterns in the data  with the aim of better understanding the development of Anglo Saxon East Anglia,[object Object],Assess the ‘productivity’ (level/type of activity) at previously identified archaeological sites using spatial analysis techniques and the PAS database,[object Object],Combine the results highlighting areas that could be investigated further through fieldwork or additional spatial analysis,[object Object]
Study Area,[object Object]
Preparing PAS Data,[object Object],Remove inconstancies in data,[object Object],Remove any unclassified or incomplete items,[object Object],Object types assigned object groups,[object Object],Finds assigned a period in Anglo Saxon history,[object Object],Plotted using easting and northing grid references,[object Object]
Assigning Categories,[object Object],Object groups and time periods were assigned to each item,[object Object],One of six object groups were assigned to each item:,[object Object],Clothing	 		(buckle, hooked tag, sleeve clasp),[object Object],Coins			(single coins, ingots),[object Object],Horse Items	 		(stirrup, strap ends, bridle bit, harness mount),[object Object],Commercial and Household:	(spoon, bowl, tweezers, hinge, gaming piece),[object Object],Jewellery			(brooch, ring, bracelet, bead, armlet),[object Object],Pins			(hair pin, linked pin),[object Object],One of three time periods were also assigned to each item:,[object Object],Early:			400 – 600AD,[object Object],Middle:			601 – 800AD,[object Object],Late:			801 – 1066AD,[object Object],Each object group was broken down further by time period,[object Object]
Spatial Analysis Techniques,[object Object],Average Nearest Neighbour:,[object Object],Compare the distance between feature locations,[object Object],Global Morans I:,[object Object],Compares the distance between feature values,[object Object],Are similar or dissimilar values clustering,[object Object],Getis – Ord G* Hotspot Analysis:,[object Object],Identifies whether features with high values or features with low values tend to cluster together in a study area(forming ‘hot’ or ‘cold’ spots),[object Object],All results were accompanied by a level of significance ,[object Object],Either 99%, 95% or 90%,[object Object],Use to accept or reject the null hypothesis,[object Object],Points/ values were located as a result of a random process,[object Object]
Average Nearest Neighbour Findings,[object Object],Results gave an insight into the distribution of some object groups,[object Object],Overall finds showed less tendency to cluster over time ,[object Object],Jewellery items showed the highest tendency to cluster,[object Object],Commercial and household items the least,[object Object],Both at a 99% confidence level,[object Object]
Global Morans I Findings,[object Object],Provided slightly different picture,[object Object],Finds again showed less tendency to cluster of time ,[object Object],Clothing items showed the highest tendency to cluster at a 99% confidence level,[object Object],Commercial and household the least but with no confidence level attached,[object Object]
Getis –Ord G* Hotspot Analysis Findings,[object Object],No statistically significant cold spots were found,[object Object],Significant hotspots move around the study area over time,[object Object],The central area around the town of Hoxne seems to be important in the early – middle period,[object Object],The shift is then to the north and west of Norfolk,[object Object],Patterns in two of the maps indicate possible trade or communication routes – aid of underlying OS map,[object Object],One running from the VASLE site at Freckenham NE to Great Yarmouth and the other running SE to Felixstowe,[object Object]
All ,[object Object],Coin Finds,[object Object]
Commercial ,[object Object],and Household,[object Object],Late Period,[object Object],Finds,[object Object]
Possible trade,[object Object],routes,[object Object]
VASLE Productive Site (VPS) Analysis,[object Object],Aim to assess the productivity of the 22 VPS using buffering and intersection functions,[object Object],No specific definition of what a productive site is so buffer size and criteria were based on the 2006 project:,[object Object],2.5 mile buffer chosen because of:,[object Object],Dispersed settlement pattern,[object Object],Increased distribution through trade as time goes on,[object Object],Take into account disposal and accidental loss,[object Object],Productivity would  be assessed by counting the number of object groups and time periods returning above average numbers of finds from the PAS dataset,[object Object],More than 4 out 6 object groups AND 2 out of 3 time periods and the VPS would be deemed productive throughout the Anglo Saxon period,[object Object]
22 VPS,[object Object],+ 2.5 mile buffers,[object Object]
VASLE Productive Site (VPS) Buffer Analysis Results,[object Object],7 out of 22 sites were deemed to be productive as a result of the spatial analysis process,[object Object],Gave an indication as to when some sites developed ,[object Object],Gave an indication of each sites primary function ,[object Object],Pin manufacture, tax and trade.,[object Object],Timings for the possible trade routes could be given, northern route earlier, southern route later,[object Object]
Conclusions – Did we achieve out objectives?,[object Object],Spatial analysis techniques can be successfully used in conjunction with complex archaeological data ,[object Object],Patterns are revealed that can tell a story about a town or area,[object Object],These would not be apparent using purely visual inspection,[object Object],Each technique complimented each other,[object Object],The project has set the scene for future possible GIS projects    ,[object Object]
THANK YOU ,[object Object],FOR LISTENING,[object Object],ANY QUESTIONS?,[object Object]

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Masters Dissertation - Presentation

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Hinweis der Redaktion

  1. WelcomeWelcome to my dissertation presentation which will apply GIS techniques to the world of archaeology!Recreation of the Sutton Hoo helmet found in 1939
  2. Structure of PresentationBit of background, aims and objectives, methodology used, results, conclusions, finally asking whether I achieved my objectives.
  3. BackgroundInspiration came from a project undertaken in 2006 by a guy called John Naylor.Make use of the finds made by metal detector usersThe online database designed to hold the information on their finds now has over 450,000 objects on it. Run by the Portable Antiquities Scheme in conjunction with the British Museum.
  4. Aims and ObjectivesInvestigate a complex set of archaeological data using spatial analysis techniques . Reveal patterns in the data that otherwise would be unseenAssess the levels of activity at 22 previously identified sites using spatial analysisMay even be able to pinpoint where the next coin hoard worth £1,000’s may be!
  5. Map of Study AreaMap of study area which encompassed the counties of Norfolk and Suffolk in the east of England3500 sq miles rich in Anglo Saxon finds
  6. Preparing DataTook ages! Initial searches revealed over 3000 finds for the study areaWhen dealing with data supplied by the general public inconstancies everywhere.I broke down the mass of data into logical object groups Dated everything to an AS period because every item only had a possible date range. Plotted using e + n in the database.
  7. Assigning CategoriesEach of the 2617 items were assigned an object group and time periodHere are the 6 categories and 3 time periods and an example of some of the finds placed in each of the object categories.
  8. Spatial Analysis Techniques3 different clustering techniques used in the projectAverage Nearest Neighbour – Compare distance between feature locationsGlobal Morans I - Compare the distance between feature valuesGetis Ord G* Hotspot Analysis – shows whether features with high values or features with low values tend to cluster together. Thus forming hot or cold spotsZ Score significance 99% -90%Used to approve or disprove the null hypothesis that all the points were distributed as the result of a random process
  9. Average Nearest Neighbour FindingsClustering tended to be less over timepossible reason:Anglo Saxon lifestyles became more rural over time Jewellery was found to cluster the most.Tend to be located togetherSafe storage Commercial and household least clustered.Less care taken over storage and disposal of these items
  10. Global Morans I FindingsSlightly different picture, some object groups showed different degrees of clusteringClothing showed the greatest tendency to cluster this timeAgain storage a probable causeClothes and related items tend to be kept in closets or chests Commercial again the least clustered but no confidence level attached this time
  11. Hotspot Analysis FindingsNo coldspots were foundAreas of high value clusters moved over time from central – north west of study areaSome object groups clustered in certain areas: Example:Coins in and around the town of Freckenham which was one of the sites identified in 2006Corridors of higher value clusters shown in the coins and commercial object groups can be seen stretching away NE and SE from FreckenhamThis could indicate possible trade or communication routesRoman roads underneathCouple of maps to illustrate this
  12. Hotspot Map of all Coin findsMap showing the possible trade route to the NE towards the coast at Great Yarmouth
  13. Hotspot Map of late period commercial and household findsMap showing the possible trade route to the SE towards the port at Felixstowe
  14. Map showing possible trade routesLines show possible routes of trade or communication
  15. VASLE Site AnalysisFinal piece of analysis aimed to evaluate the productivity of 22 sites identified by the 2006 projectUse buffering and intersect techniques to see how many finds fell within a 2.5 mile radius of each siteAs there is no definition of what a productive site is criteria based on the 2006 project were used. Buffer size criteria were unique to this projectProductivity would be based on whether there was an above or below average number of finds falling within an object group or time period.The numbers of object groups and time periods showing above average would indicate whether the site was productive or not
  16. Map showing VPS sites and buffersLocations of 22 sites 17 in Norfolk 5 in Suffolk
  17. VASLE Site Analysis Results7 out of the 22 sites were deemed to be productive as a result of the buffering processFurther analysis of the finds within each buffer gave an indication of when and how each of the sites developedMost developed as time went on others started strong and weakened over timeHigh quantities of some object groups indicated possible functions of some of the sitesCoins indicated trade or tax collection functions such as in the town of FreckenhamIt also helped assign timings to the possible trade routes
  18. Did we achieve our objectives?The project has proved that you can successfully use spatial analysis techniques on complex archaeological datasetsIt’s shown that you can reveal which areas were important and when as well as which towns played a key role The spatial analysis techniques complimented each other helping build a bigger picture for the projectSet the scene for further analysis either through fieldwork or other GIS analysis
  19. Thanks for listening any questions?