envision Technical Report Archaeological Prediction Maps Kapiti Coast

Transcription

1 envision Technical Report Archaeological Prediction Maps Kapiti Coast Elise Smith September 2012

2 Contents HYPOTHESIS:... 3 Technical challenges... 3 Alternative methods of analysis considered:... 3 Approach 1:... 3 Approach 2:... 3 Specifications of the spatial layers:... 3 Underlying prediction layers:... 3 METHODS: Geographic Information System methods of analysis... 4 Preparation of data for this analysis:... 4 Key points for Ordinary Least Squares... 6 Try to improve the data for this analysis:... 7 Final Method:... 8 RESULTS and scores:... 8 Alert layer production... 8 Project parameters... 8 Appendix Results of the Arc GIS 10 Spatial Analyst > Extraction > SAMPLE... 9 The 'Ordinary Least Squares' Test Weighted Overlay scaling process Appendix Weighted Overlay Tools The factors included: Overlays Derivation of raster layers used in the final Weighted Overlay Subsets of pre-historic data tested Pa/Pit/Terrace Midden/Oven/Burial Results from the OLS of pseudo-sites - "Approach 1" Appendix Site codes Soil particle size codes 'SAMPLE' results from of all known Maori sites of Pa/Pit/Terrace and environmental variables 'SAMPLE' results from of all known Maori sites Midden/Oven/Burial and environmental variables envision Ltd. 2

3 HYPOTHESIS: Archaeological sites are spatially distributed according to favourability of site, which may be a combination of environmental factors - elevation, slope, soil type, proximity to rivers / lakes / swamps, ridgelines and proximity to the coast. Technical challenges This project noted some of the same technical challenges as Leathwick, J. R. (2000), 4.1, p11. The basis for the GIS analysis is limited by the presence of recorded archaeological sites, rather than true presence/absence data. Absence does not necessarily mean that there is no site, it may not have been recorded. This project first explored the patterns of the data and then ran a regression analysis, Ordinary Least Squares. Alternative methods of analysis considered: 1. A regression using pseudo-absence sites generated as random points and the known Maori sites against five environmental factors. 2. A regression using the known Maori sites to provide the frequency statistics against environmental factors. Approach 1: A layer of random points was created to conform to a presence/absence model. Those points coincident with recorded Maori sites related to the' presences' and those which had no Maori records were taken as 'absences'. The regression Ordinary Least Squares indicated the only statistically significant factor was 'proximity to coast'. See the results in Appendix 2. Create a layer of random sites in the 'Project_Area' Use random points to SAMPLE the Fishnet polygon with Maori sites Display xy data Run Ordinary Least Squares analysis. Results show a significant correlation of known Maori sites and proximity to coast. No other environmental factors were significant. Approach 2: Analysis of the factors underlying only known sites to select likely factors, then a regression analysis of the data, Ordinary Least Squares, to find several correlated variables. The eventual model was chosen through this process, backed by expert local knowledge, providing an acceptable model to identify the major environmental variables influencing site location. Once the main environmental variables were accepted, these were combined as an overlay ('Environmental Factors') to predict pre-historic Maori sites. This layer has been combined with a layer recording 'Human Factors'. Specifications of the spatial layers: The spatial information layers provided by Kapiti Coast District Council is of high resolution, the 5m cell size DEM allowing an analysis to be refined after running the model, testing multiple iterations and different resolutions. An eventual cell size of 10m 2 was chosen for the raster layers used in the analysis. The fishnet created to provide a score of the Maori sites for the analysis was set at 200m 2. The total area of the project was km 2. Underlying prediction layers: 1. Environmental factors: Derived through assessing the effect of 'slope', 'proximity to coast', the 'soil type', 'river zones', 'ridges in the 35m-70m elevation zone', and 'elevation' on the location of a sample of known Maori archaeological sites. 2. Human factors: A layer to classify the alert level of human occupation was derived from all the known Maori sites and historic sites and areas. The current urban area zone was weighted negatively to reduce the alert level in this area. The specific high alert sites ('Value 4' in the District Plan Alert layer) which are paramount are: All the Maori recorded sites (50m buffer) Pataka Moore s polygons of Maori sites Historic town boundaries Railway line (50m buffer) Historic sites (50m buffer envision Ltd. 3

4 METHODS: Geographic Information System methods of analysis ESRI ArcGIS steps -The following lists the general steps to perform overlay analysis: Define the problem. Break the problem into submodels. Determine significant layers. Analyse statistically, determine best modelling approach Reclassify the data within a layer. Weight the input layers. Add or combine the layers. Identify one or more candidate regression models using the Ordinary least Squares regression tool, If the data is normally distributed and suitable, run those models using Geographically Weighted Regression. If the data is not suitable, use the analysis results to seek alternative modelling options. An index of site preference against the different factors was prepared and tested. The index is the percentage of a environmental attribute in the area occupied by the sample sites divided by the percentage of that attribute in the whole area being considered. This was useful background information to assist judgement decisions. See Appendix 1. In the event of the data being biased (insufficient sample data), not normally distributed or showing other problems (such as landscape changes since the sites were established centuries ago), then there are alternatives such as running a Weighted Overlay using the environmental rasters which have been weighted according to the proportions of sites found linked to each environmental variable (use all sites, or each site type). Each environmental raster is thus scaled. When they are all added together (Weighted Overlay) a judgement is made as to if they are all equal in effect, or have different contributions. Produce the following: 1. Output feature classes 2. Supplementary table showing model variables and diagnostic results 3. Prediction output feature class Preparation of data for this analysis: Example of polygon data preparation Rivers: Create meaningful zones incorporating the project area. River/river zones/not near a river Convert river layers from GCS_GRS 1980(IUGG, 1980) to NZTM GCS_NZGD_2000. Export as a new layer using the data frame Remove slivers Objective - Zones to select sites on are: river, 2 river buffer zones and all other land Erase: 'Project area' with 'RivStrm_NZTM2000MultipleRin' = 'landnorivbuffer' and then Erase 'RivStrm_NZTM2000_MultipleRin layer with 'RivStrm_NZTM2000' = 'Norivernobuffer_allotherland' Create a zone field for each layer. 'RivStrm_NZTM2000' (Zone field = 1), ' RivBuffer_noRiv ' (Zone fields = 100, 200) and the 'landnorivbuffer' (Zone field = 3). Then ET GeoWizards tools merge three layers - 'K _River_Buffers_Land' - the river environmental information. Produce RASTER (Arc Toolbox > Conversion Tools > To Raster > Polygon to raster). The statistical layer which forms the basis of analysis was created by finding the number of known Maori sites within a 200m square mesh block. A "Fishnet" grid was created, and populated with the number of the site points within the polygons "countpntsinpolys". Existing rasters and polygons were converted to rasters and standardised: All rasters have a 'Value' field to denote the classes of information (eg band of elevation). The classification must be in numerical ascendency, so 0 really means 0, not 'no data'. '3' cannot be equivalent to "beyond the buffer zone", particularly after a high value "1000" = 'buffer 1000m', as it must be sequential and logical. All rasters to be tested were classified into zones of interest. Each zone was assigned a logical value or marker. Several different configurations of 'zones' were tested during the modelling process. envision Ltd. 4

5 The rasters maintained in a geodatabase: Fishnet200 is a 200m square grid with frequency information about the Maori sites. Aspect DEM_5m classes (elevation) K_soil_PolygonToRaster KSiteDensity (other sites within 500m) Lake_Buffers_land2 River_Buffers_Land2 Ridge_NoRivStrm derived from the DEM, indicating areas of 'no flow', existing streams erased for accuracy Proxcoast_mainland_ras (with intervals of 500, 1000, 1500 etc) ProxcoastALL_6km (with intervals of 1000, 2000, 3000, etc) Project boundary - extent of raster analysis Slope_GWRC_K1grid Results of the 'SAMPLE' analysis were saved to the geodatabase. A test set of Maori data was obtained, to try to reduce bias and to achieve a normal distribution of data. Test set sites - NZAAplus data was used to derive a point layer from those with GPS references, and is as accurate as possible. Factors to consider:- there may have been changes in dune locations and waterways since sites originated. 'UNIQUEID' field name relates to the sorting applied to all NZAA GPS-ed data, e.g. middens are records (84 records). The GPS data had too few sites to analyse e.g. the specifics of horticultural site location. See Appendix 3. MASTER TABLE for analysis: In order to produce a master table with environmental variables, the "Input" location point features was the Maori sites 'test set'. The ESRI Arc GIS Spatial Analyst > Extraction > 'SAMPLE' tool determined the score of each evidence raster layers under an input point. This records what features exist together in a particular spot. The Spatial Statistics analysis program then determines how significant each physical feature is in predicting where a site may be found. SAMPLE tool > table of results. Process: i) CALCULATE FIELD: Create an Unique ID field = UID ii) Display the xy of the points on the map = SampleMiddensGPS_EV Events i) EXPORT Data - to get this into a format that works with OLS - Output feature Class with a suitable name - 'SampleMiddensGPS_EV' ii) Create index on ID (go to Arc Catalogue) iii) Run OLS with a variety of the variables you have on the list eg: DEM, river, proxcoast iv) Input feature class (display xy data of 'SampleMiddensGPS_6variv' as a point file, 'Events' and EXPORT DATA as feature file ' SampleMiddensGPS_6variv Events' v) Dependant variable- the score of how many sites are in each polygon; = Fishnet200_Clip_Middens Run Spatial Autocorrealation Morans I with the residuals field as the input. Score indicates no significance from random distribution. Accept the OLS. envision Ltd. 5

6 Evidence layers: vi) The frequency of the event to be statistically related to a feature needs to be one field. The event occurs, or it does not occur = yes (with a scale of frequency in the 200m 2 square)/ no. vii) Create fields of the environmental variable each with the value of the main dependent variables, use SAMPLE. Spatial Statistics Tools Modelling Spatial Relationships > Ordinary Least Squares regression analysis ESRI Arc Spatial Tool OLS is a global regression model which uses all data from all features. Relationships are fixed. It "performs global Ordinary Least Squares (OLS) linear regression to generate predictions or to model a dependent variable in terms of its relationships to a set of explanatory variables. Results are accessible from the Results window". See the screenshot s in Appendix 1 showing process of refinement to improve the statistical results. Key points for Ordinary Least Squares If these do not all satisfy, the model is not robust. All coefficients need to be positively or negatively related = direction of graph No redundancy among model variables (VIF ) Coefficients are statistically significant * Adjusted R-Square value tends to 1 (0.89 would indicate 89% overlap and a good model). In this Kapiti case the value is very low at 0.15, so we cannot proceed with this model using just distance from rivers and proximity to coast with statistical 'confidence'. We have not found all the factors which explain the location of sites. However, when the OLS is run for different samples (pa, middens or pits), against the environmental variables, the results do show which environmental parameters are of interest for each site type, and offer alternative approaches; using research and knowledge rather than statistics. The high standard errors in this data set made the statistical analysis by this method impossible. Whilst there is some high intercept, the variation is too high to allow interpretation. There are no large VIFs, - so we do not have redundancy (<7.5) There is a negative correlation to rivers - more sites are found further from rivers. Swamp data was removed as it conflicted with rivers (multicolliniarity). The positive correlation with the proximity to the coast shows more sites are found near the coast. Residuals are normally distributed as the Jarque-Bera Statistic is not significant. Koenker Statistic - if statistically significant * - shows regional variation (spatial). If this is so, then use this evidence to improve the model with a Geographically Weighted Regression test, which allows for regional variation. If the explanatory variables can be identified and Koenker Statistic is significant but the Jaques-Bera is not significant, then the Geographical Weighted Regression tool may be used. It is explores local relationships - every feature uses nearby features. Relationships vary across the study area with regional variation. Run GWR and then Morans I for autocorrelation. Use the same variables (fields) as worked for the OLS. Test the OLS 'Residuals' - you do not want 'statistically significant' as this would show clustering. The Morans I in this case shows clustering - this model has problems. This is because we have all data from along the coast, and even after 'thinning' there is a sample problem. envision Ltd. 6

7 Try to improve the data for this analysis: Improve the raster layers used in the OLS Try to get rid of distorting clustering by using a sample points tool - Hawth r.sample Sample Features: Creates a random or stratified random sample of records in a feature Table Reduce sites - Sample these against site density for a score. Need to erase polygon of known site density against study area. Then merge the erased background = 1 and the other scores. Run Morans I with zone of 10,000 and 'Inverse distance' and a 'Row' standardisation - clustered sites, sample not normally distributed. De-cluster, remove items which were producing autospatialcorrelation conflicts. Correct any errors - One site in water - no soil = 0 ( #291) Reclassify the categories of the layers to more meaningful zones of influence using 'Spatial Analyst > Reclass >Reclassify' tool; e.g. elevation bands reclassified and tested from 5m bands to 10m intervals or 15m intevals. Remove redundant explanatory variables: Ridges were not included in the general analysis, given the closeness and prevalence of ridges in the coastal area, and the historically transient nature of the dunes causing more confusion for an analysis, and no solutions. A portion of the ridges in the elevation zone 35m-70m was used in the final model Weighted Overlay after consultation with expert opinion. The layers indicating effect of proximity to lakes and swamps were also discarded from the model, as was 'Site Density', as they were of no significance as explanatory variables. Output: Tested with improved rasters, which produced improved OLS result. However, the variables did not provide all the explanatory variables as the R 2 was too low. See Appendix 1 results. Despite this, the data was tested using the Geographically Weighted Regression tool which creates a coefficient surface for each of the explanatory variable and explains the relationship with a dependent factor. It confirmed how the main environmental variables affected site distribution. But the method was not statistically reliable as a prediction for this dataset, and not pursued. It was decided that as the statistical confidence for OLS and GWR was low, that the information needed to be used adaptively to produce a broader, likely, and acceptable 'alert' layer to identify where archaeological sites may be found in the Kapiti Coast area. envision Ltd. 7

8 Final Method: The pre-historic Maori site dataset was assigned environmental values by running it against the raster layers using the SAMPLE tool. The resulting data was used in an Ordinary Least Squares to find major variables. These results did not provide an R 2 tending to 1 (we are really looking for a 0.85 level (85%) overlap of factors to say the model is robust), hence it is not significant, and further spatial statistics tools were not used. However, certain features such as swamps and lakes could be removed from the analysis as having no effect. Other features such as soils, elevation, ; instead the proportions of each environmental factor that were under each archaeological point were determined by using the table properties tools, and SUMMARY STATISTICS, or Microsoft Excel to analyse the table. In this latter case, export the table as a text file, import this to Excel as a delimited file with tab and commas. Exploration of the proportions of the features associated with sites gave a clear indication that some zones are of importance RESULTS and scores: General results from the pooled data is seen in Appendix 1. Site-type investigation: Two further analyses were run to determine if the Pa/Pit/Terraces and the Midden/Oven/Burial sites could be distinguished and predicted. Analysis was done without ridge information as the Kapiti Coast District has distinctly different geography to Greater Wellington. In Kapiti, of 131 sites only 58 were actually on an existing ridgeline (generally spanning 30macross). Buffering the ridgelines by 20m would effectively mean selecting the entire area below 40m elevation. Therefore ridges were not included in the analysis. Data sets of Pa/Pit/Terrace and Midden/Oven/Burial were extracted, SAMPLED against the main raster layers described above, and analysed. The results are in Appendix 2, "Subsets of pre-historic data tested" The results of the OLS when used to produce a Weighted Overlay are obviously too simplistic to be used for a definitive prediction. Reclassify the raster datasets to a common scale: 10 = high likelihood, 0 = very low, based upon the proportion of sites found in each environmental type. Each environmental factor was analysed to see what proportion of pre-historic Maori sites fell within discrete environmental zones. Each was then converted to a 1-10 scale so that they could be weighted according to expert opinion and combined. Appendix 1, Weighted Overlay scaling process. Alert layer production Include all sites - NZAA and Iwi sites combined and buffered for 50m (some of which fall beyond the project boundary, as at Waikanae Estuary) Pataka Moore's alert layer of polygons Historic sites combined and buffered for 50m - includes old railways and features and town boundaries Weighted overlay for the environmental variables. Existing urban area incorporated into the Weighted Overlay and given a negative weighting. See the final maps in Appendix 2 and Final Alert Layer. Project parameters Arc GIS 10 "Spatial Analyst ' and 'Spatial Statistics' tools were was used for data analysis and modelling, stored in a geodatabase, with relevant final layers organised into the groups of 'Final layers', 'Working layers' and Calculations' (OLS results). The Geoprocessing Environment Settings were set at: NNGD_200_Transverse_Mercator Metres, cell size 10, 10 Processing extent = Kapiti Coast District Council boundary Snap raster = Kapiti Coast District Council boundary Raster analysis cell size 10m Mask = Kapiti Coast District Council boundary envision Ltd. 8

9 Appendix 1 Kapiti Coast District Archaeological Prediction - Technical Report, September 2012 Results of the Arc GIS 10 Spatial Analyst > Extraction > SAMPLE Point layer of pre-historic Maori sites with the underlying environmental factors envision Ltd. 9

10 The 'Ordinary Least Squares' Test 1. Results of OLS of test sites on initial explanatory variables (coefficients) 2. Results of OLS with modified explanatory variables Only the explanatory variables of river and coast proximity are statistically significant Adjusted R 2 15% All explanatory variables are statistically significant Adjusted R 2 14% overlap explained by the variables chosen, not strong. Koenker, relationships do not vary significantly across the study area Jarque-Bera shows residuals are normally distributed. Koenker, relationships do not vary significantly across the study area Jarque-Bera shows residuals are not normally distributed. 3. Back-test by sampling test sites on layers of the final model which have been decided with expert opinion 4. Explanatory key taken from ESRI "Regression Analysis Basics in Arc GIS 9.3" Coefficients are all statistically significant Adjusted R 2 still weak, at 12%. The model does not statistically provide all explanatory variables Koenker, relationships do not vary significantly across the study area Jarque-Bera shows that residuals are normally distributed envision Ltd. 10

11 Weighted Overlay scaling process Selected Environmental Variables to assist in judgement decisions and the weighting of the raster overlays Coast proximity zone Slope River buffer Elevation Soil frequency percent factor to scale scale adjusted scale for Weighted Overlay (river mouth) envision Ltd. 11

12 Appendix 2 Kapiti Coast District Archaeological Prediction - Technical Report, September 2012 Weighted Overlay Tools The factors included: The underlying environmental prediction is a combination of: A. Environmental factors: A layer of likelihood of archaeological site was derived through assessing the effect of slope, proximity to coast, the soil type, river zones, ridges in the 35m-70m elevation zone, and elevation. B. Human factors: A layer to classify the alert level of human occupation was derived from the Maori sites and historic sites, with a negative effect from the current urban area zone reducing the alert level. The specific high alert sites (value 4) which overlay the environmental and human factors are: All the Maori recorded sites (50m buffer) Pataka Moore s polygons Historic town boundaries Railway line (50m buffer) Historic sites (50m buffer Screen shot of the Weighted Overlay tool The Weighted Overlay tool will allow changes in the influence of the layer, or the category, e.g. distance from coast, to be varied with input from interested parties who have local knowledge. Overlays 1. The final map indicating all known sites at a Level 4 alert, combining the human factors and the environmental factors alert layers. envision Ltd. 12

13 2. The weighted overlay of human and environmental predictors in four classes. 3. The combined weighted overlay in twelve classes showing the six selected 'environmental factors' combined with the three 'human factors' (the known Maori and historic sites; and existing urban extent). envision Ltd. 13

14 4. The layer resulting from combining and weighting the three selected 'human factors': all the Maori sites (40%), all the historic sites (40%), and the current urban extent (20% and given a negative -8 weighting). 5. The weighted overlay of the six selected 'environmental factors' in ten classes. envision Ltd. 14

15 6. The Environmental Factors to be combined. The layer indicating 'Proximity to rivers' was altered to reflect the change noted with distance from the coast. The layer of 'Ridges between 35m - 70m' was created as a response to expert opinion requiring the Otaki Valley with ridge-lines to be incorporated to provide a higher level in the alert layer. Derivation of raster layers used in the final Weighted Overlay Final layer 7912_d1 with twelve classes was derived from the combination of known sites, Maori and Historic, and the Environmental Variables in the following Weighted Overlay combination of values and percent weightings: 'MaoiHisPopv3' 20 'VALUE' (-2-10; 0 0; 2 2; 4 4; 6 2; 8 8;NODATA NODATA); 'wo_6912_d' 80 'VALUE' (0 0; 1 0; 2 1; 3 1; 4 4; 5 5; 6 6; 7 7; 8 8; 9 9;NODATA NODATA)); Range of weightings of original layers were: The layer of known Human Influence 'MaoiHisPopv3' was a combination of: 'All_Maori_rec' 40 'VALUE' (0 0; 1 10;NODATA NODATA); 'All_Hist_rec' 40 'VALUE' (0 0; 1 10;NODATA NODATA); 'Kapiti_pop' 20 'VALUE' (0 0; 1-10;NODATA NODATA)); Range of weightings of original layers were: The Environmental Variables layer ('wo_6912_d') was derived from with the following weightings: 'Slope_4_15_30_60_rescored' 13 'Value' (0 0; 1 1; 2 2; 3 3; 10 10;NODATA NODATA); 'ProxCoast_6km_Reclass_scaled' 16 'Value' (0 0; 1 1; 3 3; 4 4; 10 10;NODATA NODATA); 'Soil_rescored' 16 'Value' (0 0; 1 1; 2 2; 10 10;NODATA NODATA); 'RivBuff_Zonb' 10 'VALUE' (0 0; 1 1; 8 8; 10 10;NODATA NODATA); 'RidgesScored' 20 'VALUE' (0 1; 10 10;NODATA NODATA); 'Elev_Rec_060912' 25 'Value' (0 0; 1 1; 3 3; 10 10;NODATA NODATA)); Range of weightings of original layers were: Area of the project: Project area "ID" Area m km 2 envision Ltd. 15

16 Subsets of pre-historic data tested Two set of data extracted from the pre-historic sites were tested for correlation of variables using the Ordinary Least Squares and the results indicated that few of the main variables determining site location have been identified. More work could be done on grouping classes of data, but would need expert opinion input. Pa/Pit/Terrace The OLS indicates that only elevation and proximity to coast are significant, with R 2 =0.05. A Weighted Overlay showing these two variables shows that more discussion and refinement is required for a prediction. WO_PaPitTc_5 Midden/Oven/Burial The OLS indicates significant soil type (77% of known sites are on sandy soil) and proximity to coast (66% of known sites within 1km of the coast). R 2 = gives a better model, a starting point for discussion. WO_MidOvBu_1 envision Ltd. 16

17 Results from the OLS of pseudo-sites - "Approach 1" Regression using pseudo-absence sites with all sites generated as random points. The underlying layer shows the scores on the 'Fishnet of Maori sites', 200m square. The 'Ordinary Least Squares' results indicate poor relationships, with only 'proximity to coast' significant. The main variables determining site location have therefore not been identified in this process. A Geographically Weighted Regression cannot be used. envision Ltd. 17

18 Appendix 3 Keys to tables used and coding Site codes The number of sites with GPS accuracy to be used for accurate prediction was very low, e.g. there were no Maori horticulture sites available to test for site specific environmental variables. NZAA_GPS_Features_10m2 Combined sites NZAAplus_nowiwi Code "OBJECTID" "Count_" Value Count Unknown Agricultural/ pastoral Artefact find Burial/cemetery Cement/ lime works Church Commercial Defensive - Island/ swamp pa Defensive - Military Defensive - Pa Fishing Forestry Historic - domestic Industrial Kainga Maori horticulture Marae Memorial Midden/Oven Pit/Terrace Religious 23 Traditional site Transport/communication Whaling Station Soil particle size codes 1 lake 2 Loam over clay 3 Loam over sand 4 Loam over skeletal 5 Loamy 6 Loamy peat 7 Peat 8 Recent 9 river 10 Sand over sketetal 11 Sandy 12 Silty 13 Silty clay 14 Silty sand 15 Silty skeletal 16 Skeletal envision Ltd. 18

19 'SAMPLE' results from of all known Maori sites of Pa/Pit/Terrace and environmental variables n=128 frequency percent factor to scale scale Coast proximity zone Slope River buffer (use River Buffer Zone b layer instead!) Elevation Soil code (river mouth) envision Ltd. 19

20 'SAMPLE' results from of all known Maori sites Midden/Oven/Burial and environmental variables n = 227 frequency percent factor to scale scale adjusted scale for Weighted Overlay Coast proximity zone Slope River buffer (use River Buffer Zone b layer instead!) Elevation Soil - significant OLS add the layer of ridges scored for general corrected WO keep standard river Weighted Overlay weights this confuses the weighting of the land to the "interior" of the District (river mouth) envision Ltd. 20