Integration of GIS, Remote Sensing and Statistical Technologies for Marine Fisheries Management Jianjun Wang University of Aberdeen Introduction Fisheries resources need to be properly managed for sustainable exploitation of the world’s living aquatic resources . It has been realized that the traditional fisheries management, which considers the target species as independent, self-sustaining populations, is insufficient EAF: Ecosystem Management for Sustainable Marine Fisheries has been becoming popular. However, it has been realized that, a working ecosystem approach management depends on a boarding of data and information on environmental, biological and social aspects, analysis and modeling technologies. Data Data Data Data Data Data Remote Sensing Technology Remote sensing has gained increasing importance in studies of marine systems, for extracting oceanographic information, and monitoring the dynamics of oceanic environment GIS Technology GIS technology has proven to be an indispensable tool for integrating, managing and visualising spatially distributed data, discovering hidden patterns that other numerical methods could not find, and providing maps. Statistical technology Statistical and geo-statistical analyses and modelling have been widely used to provide quantitative description and predictions about living marine resources However, the success of such approaches has been limited due to the complex nature of the four-dimensional marine environment and fish distribution, the complex spatio-temporal relations between them – and the occurrence of anomalies in distribution and abundance caused by anomalies in environmental conditions. Projects: Cephalopod Resources Dynamics: Patterns in Environmental and Genetic Variation(CEC FAIR programme, 1997-2000 ) Environmental Influences on the Distribution of Commercial Fish Stocks (NERC small grant project, 1999) Data collection for assessment of the main finfish stocks in the Patagonian shelf (SW Atlantic). (CEC DG Fisheries Study Project, 2000-01) Department of Trade and Industry Strategic Environmental Assessment: An Overview of Cephalopods Relevant to the SEA4 area. Geotek Ltd, 2003 Promoting higher added value to a finfish species rejected to sea (ROCKCOD). (CEC DG Fisheries CRAFT project, 2003-04) Cephalopod Stocks in European Waters: Review, Analysis, Assessment and Sustainable Management (CEPHSTOCK). (CEC Framework 5 Concerted Action, 2002-05 ) The area covered by the projects 140° 130° 120° 110° 100° 70° 60° 50° 40° 30° 20° 10° 0° 10° 20° 30° 40° 50° 60° 70° 80° 90° 100° 110° 60° 50° 50° 40° 40° 30° 30° 20° 20° 10° 10° 0° 0° 62° 57° 10° 52° Haul locations (Spanish data, 1989 - 1999) 20° 41° 30° 1989 # 1990 # 1991 # 1992 # 1993 # 1994 # 1995 # 1996 # 1997 # 1998 # 1999 Depth (m) 200m 3000m # # ### ## ## # ## # # # # # # ## # # # ## ## # ## ## # ## ## ## # ## ## ## ### ## ## # ## # # # ## ## ## # # # ## # ## # ##### ## ## # ## ### # ## # # # ## ## # # ## # # # ## # ## # ## ## # ## ## ## ## ## # ## ## # # # # ### ## ## # # # ## # # # # ## ## # # # # # # # # # # ## # ## # # ## # ## # # ## # # ## # ## ## # ### ## # # ## ## ## # # # ## ## # # # ## ## ## ## # ## # ## # # ## # # ## ## ## # #### ## ## # ## ## # ## # # ## # # # ## # ## ## # ## ## ## # ## # # # # # # # # # ## # # # ## # # # ## ## # # # # ## # ## ## ## ## ## ## ## # # # ## # #### ## # #### ## # ## # ## ## ## ## ## ## ## ## # ## ## ## ## ## # # ## ## ## # ## ## # # # # # # # # ## # # ## # # # #### # # # ## # # ## # ## # # ## ## ## # ## ## ## # # ## #### # # # ## # ## # ## ## ## # # # # ## ## ## ## # ## # # # ## ## # # # # # ## ## # # # ## # # # ## # ## # ## ## # # # ## # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # ## ### ## # ## ## ### ## # ####### # # ## ## ## ## ## # # # # # ## # ### ## # ## ### ####### # # # # # # # ## # # ## # # # ## # # #### # ## # ## ### # ## # # # # # # # # #### # 40° # # # ## # ## ## ## ## # ### ## ## ### ## ##### ## # ## ### ## ## ###### # ## ### ### # ## ### ### # ## ## ## ## ### # ## ## # ### ##### ## ### ##### ### ## ## ## # # ## ## ## ## ## # ### # ## #### ## ### ### # ### ### # ### ## ## # ## ## ### #### ##### ## ## ## # ## ## ## ## # # # ## ## ## ## ## ## ## # #### ## ## ## ### # ## # # # ## # # # # # # # # # # # # ### ## # # # # # # # ### # # # ## # # # # ######## # ## ## # # # # # # # # # ### ### #### ### ######### ## ##### ### #### ## ## ## ##### ## ## ###### ###### ## # ### ## ## ## ######## ## ##### ####### # ##### ### ## #### ## ### ## ## # ## ## ### # ### ### ## # # ########### ## # ## ###### # ## # # # ### ## ### ### #### # ## # ## ## # ### # ##### ######## ## ## ####### ## # #### # # # # # # # # # # # # # # # # # # # ## ## # # # # # # ### ################# ## ### ### ## ######### ## ##### #### # ## ### # ## # ## ## ####### ### ###### ############## ########## #### ##### ### ## # # ## ## ##### # # ## #### ## ## # # ## ## ### ### # ## # # ### # ### # ## ## #### ## # # # # ## # ### # # # # # # ## ## ### # # # # # # # # # # # # # # # # # # # # # # # ########## ### ######### ## # ## ## #### # # ## #### ## # #### ## ## ## ### ## # # ## ## ## ## # ##### ### # ## ### ############## # # # ## ## ## ### ## ## ## ## # # # ## ### ### ## ## ## ## ## ## #### ## ## # ## #### ## # #### ## ### ## ## # # ## # ## ## # ### #### ### #### # # # # # # # # # # # # # # # # # # #### # # # ## ## ## # # # # # # # # # # # # # # # # # ## # ### # # # # # # # # # ## # ##### ### ## ### ## ### ## # ## ### ## ### ## ##### ## #### ##### ##### ## # ## ## ## ### ## ## ## ## ## ## ## ## ## ### ## ##### ## ## ## ## ## # ## ## ## ## ## ## ### ## ## ### ## ### ### # # # # # # # ## ## ## # # # # ## ## ### ## ## ## ## ## ## ## ## ## ### ## # # # ## ## ## # # ## ## ## ### ### # ## # # ### # # ## ## # # ## ## ## # # ## # # ## ## ## #### # ## ## # # ## # ### ## ## ## ## # ## # # # # # # ## ## ## # ## ## ## ## # #### ## # ### # ## ## # # # ### # ## # ### # ## # # ## ## # ## ## ## ## # # # ## # # # # ## ## ### # # ## ## ## # # ## # # # ## # ## ## # # # # # # # # # # # # # # # # # # # # # # # ## ## # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## ####### # ## # # # ## ## #### # ##### ## ### ## ## ## # ###### ## ### ######### ### ## # ## #### ### ## ## # ## # ### # ## ### ## ### ### ##### #### ## ## ## ## ## ## ## # # # # # ## ## # ## ### ## ## ## # ### ## ## ## ## # ## # # ## ## # # ##### # # ## ### ## ### ###### # # ## ## ## # ### ######### #### ### ## # # # # ## ## ## ## # # # ## ### #### ### # ## # ### ## # # ## # ##### # ###### ## # ### # ## ### ## ### ## ## ## # # ## ## ### ## ## ### ### # # # # # # # # # # # # # # # # # # # # ### ## # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # ### ## ## #### ########### ### # ##### ### ## ###### # # # ## ## ##### ### ## # ## # ## ### ## ## ## ## ### ## ## ### ## ## ### ## ### # ###### ####### ### ## ## ## ## # ### ## ## ### ## ####### ### ## # # ## ## ## ## ## ## # # ## ######## #### ## # ### # ### ## ## ## ## # ## ### #### # ## # ## ### ## ## ## ###### ### ## # # ## # # # ### ## ## ## ### ## ## ## ## # ## # ## ## ## ## ## ##### ##### # ### # # # ## ## ## # ## # # ## # # # # # # # # # ### # # ## ## # # # # # # ## # # # ### ## # # ##### ## # # # # # # ## # #### # # # #### # # # # # ## ## # # # # # # ### # # # ## # # # # ## # # # # # # # # ## # # # ## # # # # # # # # ### ## ### ## ### ## ## ## ## # # # # ## ## # # # # # # # # ## ## ### ## ## ### ## ## ## ## ## ## ## ## ## # ## # ### # # # # ## ## ## # # # # # # # # ## ## ## # ## ## # # ## ## # ## # # # # # # # # # # # # # # # ## # ## ## ## # # # # ## ## # ## # # # # # # ## # # # # # # # # # # # # # ## # ## # ## # ## # # # ## #### ## ## ## ## ## ## ## ## ## ## ####### #### ## ##### ### ## ## #### ## ## ## ### # ### ## # ## # ## ## ## ## ## ## ### ## ## # ## ## # ### ## ## ### # # ### ## ## ## ## ## # # ###### # # # ## ## # ## # ## # ## ### ###### ### ## #### ## #### #### ## ## ## # # # # ## ## ## # ## #### # ## # ## # # ## 50° 60° 67° 62° 20° 41° Year ## ## ## ## ## # ## ## ## ## ## ## # ## ### ### # # ## ## # ## # ## ## # # # ## # # ## ## ### # ## # ## ## #### #### ## ## ## ## # ## # ## ## ## ### ## ## # # ## #### ## 51° 80° 60° 10° 67° 46° 90° 30° 46° 40° 50° 51° # ## 60° 57° 52° 70° 70° 140° 130° 120° 110° 100° 90° 80° 70° 60° 50° 40° 30° 20° 10° 0° 10° 20° 30° 40° 50° 60° 70° 80° 90° 100° 110° A schematic diagram of the system The GIS based on PC ArcView with user-friendly interface The GIS based on UNIX Arc/Info with user-friendly interface The front page of a database based on MS Access Spatio-temporal analysis and modelling Spatial / temporal Analysis and modelling Visual analysis Data explanatory analysis Correlation Classification Refine Auto-correlation Spatial Correlograms Variogram Statistics GIS Modelling …… Refine Outputs Visual analysis base on GIS: The distribution of cuttlefish abundance and the influence of sea surface temperature Cuttlefish abundance (LPUE, kg/hr) density and SST (1990) 9.0 10.0 11.0 10 .0 >0 - 5 11.0 11.0 5 - 10 12.0 10 - 15 12.0 12.0 12.0 LPUE (k g/hr) .0 10.0 0 9. 11.0 10.0 10.0 11.0 1990 Warm year 10 .0 10 9.0 9.0 9.0 9.0 10.0 15 - 20 13.0 13.0 13.0 13.0 20 - 25 14.0 25 - 30 Jan Fe b Mar Ap r 30 - 35 13.0 35 - 40 17. 0 12.0 .0 12 40 - 45 45 - 50 14.0 18.0 16.0 50 - 55 15.0 19.0 15.0 16.0 19 .0 19 .0 May 65 - 70 .0 21 20 .0 18.0 .0 17 60 - 65 20.0 18.0 17.0 16.0 55 - 60 17.0 Jun 22 .0 14.0 Jul 70 - 75 Au g 75 - 80 12.0 9.0 16.0 13.0 90 - 95 95 - 100 12.0 16.0 18.0 > 100 14.0 17.0 19.0 DEP TH 13.0 15.0 High fish abundance appeared after warm hatching season 20.0 15.5 18.0 .5 15 Oct Sep 200m Nov De c Cuttlefish abundance (LPUE, kg/hr) density and SST (1991) 9.0 8.0 8.0 LPUE (k g/hr) 9.0 7.0 9.0 9.0 8.0 9.0 7.0 8.0 10.0 6. 0 >0 - 5 10. 0 9.0 10.0 5 - 10 10.0 11.0 11.0 10 - 15 11.0 15 - 20 11.0 12.0 12 .0 12.0 20 - 25 12.0 25 - 30 Jan Fe b Mar Ap r 16.5 .0 11 16 .0 .0 10 16.0 45 - 50 17.0 50 - 55 55 - 60 16.0 14.0 18.0 60 - 65 1 7.0 15.0 19.0 .0 18 .0 20 19 17 .0 May 65 - 70 2 0.0 .0 1 6.0 .0 13.0 35 - 40 15.0 13.0 12 .0 30 - 35 40 - 45 12 .0 70 - 75 21 11. 0 Jun Jul Au g 75 - 80 14.0 12.5 5 1 5. 11.0 80 - 85 85 - 90 .0 10 12.0 11.0 .5 12 16.5 17.0 9. 0 11.5 17.5 90 - 95 95 - 100 18.0 15.0 16.0 20.0 .0 21 Sep DEP TH 14.0 17 .0 22.0 > 10 0 12.0 13.0 19.0 15.0 1991 Cold year 80 - 85 85 - 90 13.5 15 .5 11.0 11.0 .0 10 .0 13 13 .5 15.5 15.0 17.0 9.0 The centre of high abundance located further north in warmer year than in cold year 13.0 Oct 13.0 200m Nov De c Very low fish abundance appeared after cold hatching season Statistical tests Tests to look at relationship 50 0 50 -1.0-0.50.0 0.5 1.0 rho 0 100 150 200 250 January: Distance (n.m.) rho 100 150 200 250 July: Distance (n.m.) 0 50 100 150 200 250 Aprl: Distance (n.m.) 0 50 100 150 200 250 October: Distance (n.m.) -1.0-0.50.0 0.5 1.0 rho rho Spatial empirical correlograms (rho) for long-term average LPUE in 4 months in different seasons -1.0-0.50.0 0.5 1.0 Tests to look at spatial correlation -1.0-0.50.0 0.5 1.0 The correlation between cuttlefish abundance and sea surface temperature (SST) Spatial classification Spatial classification of squid Loligo spp. abundance in the NE Atlantic Water 12 monthly long-term averaged LPUE (landings per unit effort (kg/h) variables Principal components analysis (PCA) was used to reduce the complexity of the data, and to remove the correlation Cluster analysis was used to define areas with similar spatio-temporal patterns of LPUE, and LPUE level. Display and refine the result SEASONAL CHANGES OF LPUE (Kg/Hr) IN FIVE AREAS 0 1 LPUE (Kg/Hr) 2 3 4 5 Area1 Area2 Area3 Area4 Area5 2 4 6 Time in Month 8 10 12 Spatial modelling Generalized additive model (GAM) g(x) = + f1(x1) + f2(x2) + fi(xi) where is a constant intercept, each of the xi are the predictors and the fi are functions of the predictors or terms Modelling Squid abundance in relation with environmental variables in the Northern North Sea The response: LPUE The initial predictor variables with Norway the input terms: 1. sea surface temperature (SST) 2. sea bottome temperature (SBT) 3. sea surface salinity (SSS) 4. sea bottom salinity (SBT) 5. Depth in the terms of lineal, splines smoother with 4A degree of freedom from 2 to 4, e.g. Loligo spp. (January, 1997) 1+SST+s(SST,2)+s(SST,3)+s(SST,4) LPUE (kg/h) Depth (m) Scotland Fitted LPUE (kg/h) 200 The final optimum model is: lpue ~ s(sst, 4) + s(sbs, 4) + depth 5° 3° 1° 1° 3° 5° 62° 62° 60° 60° 58° 58° 5° 3° 1° 1° 3° 5° Temporal analysis and modelling – The temporal distribution pattern of hake abundance in SW Atlantic Data explanatory analysis: Train-based model: Visual analysis: Long-term monthly average LPUE (Common Hake, Merluccius hubbsi) and SST (1989 - 1999) SST (C d eg ree) Long-term monthly average LPUE (Common Hake, Merluccius hubbsi) and SST (1989 - 1999) 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 25 25.5 26 26.5 27 SST (C d eg ree) Long-term monthly average LPUE (Common Hake, Merluccius hubbsi) and SST (1989 - 1999) 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 25 25.5 26 26.5 27 SST (C d eg ree) Long-term monthly average LPUE (Common Hake, Merluccius hubbsi) and SST (1989 - 1999) Feb Jan Mar Feb Jan Apr Mar Feb Jan Feb Jan May Apr Mar Jun May Mar Aug Jul Jun Apr Aug Jul LP UE (kg/hr) May Jun May Sep Jun Oct Sep Nov Oct Sep Sep Dec Nov Oct 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 25 25.5 26 26.5 27 d) Dec Nov Oct Aug Jul SST (C d eg ree) 0 >0 - 5 0 50 - 10 0 100 - 1 50 150 - 2 00LP UE (kg/hr) 200 - 2 50 0 250 - 3 00 >0 - 5 0 300 - 3 50 50 - 10 0 350 - 4 00 100 - 1 50 400 - 4 50 150 - 2 00LP UE (kg/hr) 450 - 5 00 200 - 2 50 0 500 - 5 50 250 - 3 00 >0 - 5 0 550 - 6 00 300 - 3 50 50 - 10 0 600 - 6 50 350 - 4 00 100 - 1 50 650 - 7 00 400 - 4 50 150 - 2 00LP UE (kg/hr) 700 - 7 50 450 - 5 00 200 - 2 50 750 - 8 00 0 500 - 5 50 250 - 3 00 800 - 8 50 >0 - 5 0 550 - 6 00 300 - 3 50 850 - 9 00 50 - 10 0 600 - 6 50 350 - 4 00 100 - 1 50 900 - 9 50 650 - 7 00 400 - 4 50 950 - 1 000 150 - 2 00 700 - 7 50 450 - 5 00 >10 00 200 - 2 50 750 - 8 00 500 - 5 50 250 - 3 00 800 - 8 50 550 - 6 00 300 - 3 50 850 - 9 00 600 - 6 50 350 - 4 00 900 - 9 50 650 - 7 00 400 - 4 50 950 - 1 000 700 - 7 50 450 - 5 00 >10 00 750 - 8 00 500 - 5 50 800 - 8 50 550 - 6 00 850 - 9 00 600 - 6 50 900 - 9 50 650 - 7 00 950 - 1 000 700 - 7 50 >10 00 750 - 8 00 Aug Jul Apr 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 25 25.5 26 26.5 27 Dec Nov Dec 800 - 8 50 850 - 9 00 900 - 9 50 950 - 1 000 >10 00 4 Integration and use of remotely sensed data The first order oceanic data: Ocean colour SST …… Surface height Roughness Second order oceanic data: Define local relative SST variability (RV) and gradients 66° 44° Local relative SST variability (RV) 65° 60° 55° 50° 45° 64° 62° 60° T $ T $ T $ T$ $ T T $ T $ T $ T$ $ T T $ T $ T$ $ T T $ 46° 40° 48° T $ 50° 30° 50° 45° $$ T T T $ T$T $ T $ 0.1 - 500 500 - 1000 T $ 1000 - 2000 T $ 2000 - 3000 T $ 3000 - 6000 SST 35° -2 - - 1 12 - 12. 5 12. 5 - 1 3 -0 .5 - 0 13 - 13. 5 0 - 0 .5 13. 5 - 1 4 0.5 - 1 14 - 14. 5 1 - 1 .5 14. 5 - 1 5 1.5 - 2 15 - 15. 5 2 - 2 .5 15. 5 - 1 6 2.5 - 3 16 - 16. 5 3 - 3 .5 16. 5 - 1 7 3.5 - 4 17 - 17. 5 Depth (m ) 200 500 54° SST re lativ e Var iety 0.1 - 0.2 0.2 - 0.4 0.4 - 0.6 0.6 - 0.8 0.8 - 1 30° 55° 500 17. 5 - 1 8 55° 1000 18 - 18. 5 2000 18. 5 - 1 9 3000 19 - 19. 5 4000 65° 19. 5 - 2 0 20 - 20. 5 60° 55° 50° 45° 35° 20. 5 - 2 1 66° 4.5 - 5 5 - 5 .5 5.5 - 6 40° 6 - 6 .5 T $ T $ T $ T$ $ T T $ T $ 50° 52° T $ TT$ $ T T $ $ T $ T T$ $ T $ T$ $ T T $ T $ T $ T $ T $ T$ T$ T T$ $ T$ T $ T $ T$ $ T$ $ T T 54° 64° 62° 60° 58° CPUE with background of RV 4 - 4 .5 40° 52° Depth (m ) 200 -1 - - 0.5 50° 40° Sea surface temperature, day 256 - 261, 1989 SST (c degrees) T $ $ T T $ T $ 48° 45° T $ 55° T $ T $ T $ T $ T $ CPUE 60° 46° T $ T $ T $ T $ 40° 50° 65° 44° T $ T $ Relative SST variety in grid and SST in contour lines (day 256 - 261, 1989) 45° 70° 58° M. hubbsi CPUE and SST relative variety (day 256 - 262, 1989) 6.5 - 7 7 - 7 .5 66° 7.5 - 8 64° 62° 60° 58° 8 - 8 .5 8.5 - 9 44° 9 - 9 .5 9.5 - 10 10 - 10. 5 45° 45° 10. 5 - 1 1 M. hubbsi CPUE and SST gradient (day 256 - 262, 1989) 44° 11 - 11. 5 T $ 11. 5 - 1 2 T $ 65° 50° 60° 55° 50° 50° T $ 46° 65° 60° 55° 50° 45° T $ T $ 48° 48° T $ 40° $$ T T 45° T $ 0.1 - 500 500 - 1000 T $ 1000 - 2000 T $ 2000 - 3000 T $ 3000 - 6000 T $ 50° T $ T $ T $ CPUE 45° 50° 50° 52° Gradients T $ $ T T $ T $ 40° 55° 70° 46° T $ T $ T $ T $ T $ T $ T $ 40° 55° T $ T $ T $ T$ $ T T $ T $ T $ T$ $ T T $ T $ T$ $ T 45° SST gradients in grid and SST in contour lines (day 256 - 261, 1989) 55° 54° 65° 60° 55° 50° 45° T $ 50° Depth (m ) 200 500 52° TT$ $ T T $ $ T $ T T$ $ SST gradient 0 - 50 50 - 100 100 - 200 200 - 300 300 - 400 66° T $ T$ $ T T $ T $ 55° T $ T $ T $ T$ $ T $ T $ T $ T T $ T $ T$ T$ T $$ T T$ T $ T $ T$ $ T$ $ T T 54° 64° 62° 60° 58° CPUE with background of SST gradients The relationship between RV and fish abundance Spearman’s test Is it reliable? Let’s see… Tree-based models South area: April Middle area: April Middle area: July The model based on GIS: An example: A cephalopod migration model based on GIS The optimum path and corridor between spawning ground and the catch location Chl-a Criteria and weight Spawning ground SST SBT Current Depth Model based on GRID Catch Location Discussion 1. GIS provides a good tool for integration and management of spatially distributed data, and for fishery resources management. 2. As field measurement data are limited, remote sensing is the only solution for getting regionally covered, time-series environmental data. In marine environment: First order data: Surface temperature, surface elevation, roughness, … Second order data: regional and local oceanic circulation features 3. The combination of GIS and statistical technologies, provides a convenient and flexible way for data analysis and modelling. GIS: Unique visualization functions, grid-based module Less powerful statistical analysis and modelling Statistical technology: Powerful quantitative analysis and modelling functions Lack of visualisation functions and grid-based module THANKS