Personal Resilience in Project Management 2 - TV Edit 1a.pdf
Masters Dissertation - Presentation
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Hinweis der Redaktion
WelcomeWelcome to my dissertation presentation which will apply GIS techniques to the world of archaeology!Recreation of the Sutton Hoo helmet found in 1939
Structure of PresentationBit of background, aims and objectives, methodology used, results, conclusions, finally asking whether I achieved my objectives.
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.
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!
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
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.
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.
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
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
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
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
Hotspot Map of all Coin findsMap showing the possible trade route to the NE towards the coast at Great Yarmouth
Hotspot Map of late period commercial and household findsMap showing the possible trade route to the SE towards the port at Felixstowe
Map showing possible trade routesLines show possible routes of trade or communication
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
Map showing VPS sites and buffersLocations of 22 sites 17 in Norfolk 5 in Suffolk
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
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