tools for graphical representation of multidimensional data in...

Tools for graphical representation ofmultidimensional data in SIATSENSOR Project Deliverable Report 4.4.1 and 4.4.2

SENSOR REPORT SERIES 2008/04

SENSOR Sustainable Impact Assessment: Tools for Environmental, Social and Economic Effects of Multifunctional Land Use in European Regions www.ip-sensor.eu

Title Tools for graphical representation of multidimensional data in SIAT

Authors Snizek B, Jørgensen I (LIFE); Acevedo R, Biber P, Seifert S (TUM); Paar P(Lenné3D); Sieber S (ZALF); Jones L (NERC)

Date March 2008

Category Project Deliverable Report

Deliverable title D 4.4.1 Methodology report on tools for graphical representation of multidi-mensional data in SIAT, andD 4.4.2 Tools for graphical representation of multidimensional data in SIAT

Submission date March 2007

SENSOR Project The Integrated EU project SENSOR aims to develop ex-ante SustainabilityAssessment Tools (SIAT) to support policy making regarding multifunctionalland use in European regions. Land use represents a key human activitywhich drives socio-economic development in rural regions and manipulatesstructures and processes in the environment. At the European level, policiesrelated to land use intend to support the efficient use of natural resources andto improve socio-economic developments. The project is financed by the EU6th Framework Programme. Project duration is four years, starting inDecember 2004. The project is carried out by a consortium of research insti-tutes, led by the Leibniz-Centre for Agricultural Landscape Research (ZALF).

This document contributes to the development of the modelling approach inSIAT, in particular the visualisation of synthetic landscapes by using a novelconcept of spatial embodiment.

Keywords Sustainability Impact Assessment Tools, 3D visualisation, landscape genera-tor, spatial embodiment

Correct Reference Snizek B et al. (2008) Tools for graphical representation of multidimensional data in SIAT. In: Helming K, Wiggering H (eds.): SENSOR Report Series 2008/4, www.sensor-ip.eu, ZALF, Germany

Prepared under contract from the European CommissionContract no 003874 (GOCE)

EU FP6 Integrated Project Priority Area 1.1.6.3 "Global Change and Ecosystems"December 2004 - December 2008

This publication has been funded under the EU 6th Framework Programme for Research, TechnologicalDevelopment and Demonstration, Priority 1.1.6.3. Global Change and Ecosystems (European Commission, DGResearch, contract 003874 (GOCE)). Its content does not represent the official position of the European Commission and is entirely under the responsibility of the authors.The information in this document is provided as is and no guarantee or warranty is given that the information isfit for any particular purpose. The user thereof uses the information at its sole risk and liability."

SENSOR Report Series 2008/04

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Executive Summary 41 Introduction 62 The visualisation approach related to SIAT 73 The 3D Landscape Generator 104 3D Landscape Visualisation 24

4.1 Photo-realistic 3D visualisation L-VIS 244.2 Schematising 3D Landscapes 25

5 Colour Schemes for SIAT 295.1 Short overview over results 295.2 Maps in SIAT 305.3 Colour Schemes for SIAT 37

6 Visualisation Modules 426.1 Visualisation Matrix 426.2 General design 426.3 The 3D Scene Dialogue 456.4 Thematic Maps – Google Map Dialogue 466.5 Explanatory overlays 47

7 Conclusions 488 References 49

Table of Contents


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Tools for graphical representation of multidimensional data in SIAT

Bernard Snizek / Forest & Landscape Denmark (LIFE) ([email protected])

Executive SummaryIn SENSOR it was decided to present complex modelling results not only as numbers, chartsand maps but via advanced visualisations, graphical data mining tools and enhanced mappingtools which shall connect back to the real-world landscapes the scenarios calculate results for.Therefore a modelling approach was taken in which after a step of Landscape Creation, thesynthetic landscapes get visualised. The visualisations are then embedded into SIAT via easyto use, graphical comparison tools. Alongside the 3D photorealistic and schematic presentationof SIAT results, different kind of alternative visualisations are proposed and described: aVisualisation Matrix and Thematic Maps utilising a novel concept of Spatial Embodiment.Application-wide Colour Scheme advices are given to push SIAT towards a user friendly, intuiti-ve and appealing application.

Based on the baseline scenario and selected indicator results the 3D Landscape Generatorfirst builds a closest-to-reality Artificial Digital Elevation Model of the baseline scenario, thendistributes landscape elements (i.e. arable land, artificial surfaces, forests, pastures etc.) withinheight zones and in the end modifies the distribution of landscape elements to match the policylandscape scenario. These synthetic landscapes are prepared in different numbers per mem-ber state and then taken as input for two types of visualisations.

Two Visualisation Approaches have been developed and applied within SENSOR, given thegenerated landscape models as inputs: a Photo-realistic 3D Visualisation and a SchematisedVisualisation. The photo-realistic one was based on the visualisation system L-VIS (TUM) andoriginated from the field of forest visualisation. Therefore it has been historically centred aroundthe entity of a single tree which was extended to also comprise and render different other land-scape elements like pastures, arable land etc.The Schematised Visualisations were created by Biosphere3D, a product of Lenné3D GmbH,which was well adapted to fulfil SENSOR requirements: Using Satellite Imagery, DigitalElevation Models, Aerial Photography and vector-based vegetation data it can in real-time crea-te 3D visualisations in different levels of detail, accessing huge libraries containing realistic 3Dplants. To avoid the dilemma of generating visualisations which might be difficult to distinguishfrom photographs of real landscapes, None Photorealistic Rendering Techniques were applied,which inspired by artistic variation in graphical styles, generated images of visual artificialnature. Both visualisation approaches described were to be implemented as web services inorder to retrieve the required images for SIAT.

Communicating complex model results in an intrinsic way is a difficult task. To avoid that SIAT’soutput data just become numbers and figures in a chart which are far from a users imaginarycapabilities, several visualisation, data-mining and mapping tools are introduced. A visualisationmatrix shows all indicators of all member states at the same time in a grid using the globaldiverging colour scheme of SIAT. The possibility to order the member states in several differentways (alphabetically, by size, year of entry, geographic location, population and area) enablesthe user to interactively change the grid and thereby visually identify order and distribution ofscenario results. A data inspector explains each cell of the grid in terms of absolute and relativenumbers as well as the units of the indicators are depicted by icons. From several places of thevisualisation matrix, the indicator fact sheets can be retrieved. In addition the matrix shows thenumber of visualised scenes per member states and provides with links to the 3D Dialogue andthe Thematic Map Dialogue. Purpose of the 3D Dialogue is to provide the user with a tool toinspect landscape changes between the baseline scenario and the policy case scenario as wellas to visually identify and mine through the indicators used for 3D image generation. Again aData Inspector helps with understanding the real sizes of indicators as well as the nature of


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indicators. The Thematic Map Dialogue presents a novel concept of being able to experiencethe sizes of area-based indicators by providing with a Spatial Embodiment Tool: A small squarecan be pulled upon the map window and will – once positioned within a member state’s boun-daries – show the relevant indicator’s spatial extent in the given member state. Thereby anembodiment of abstract numbers takes place – the user can relate areal measures to knownplaces.

Based on thorough analysis of SIAT Prototype I, map colour schemes were developed. SIATdevelopers are advised to stick to only one, diverging orange-purple colour scheme centred atzero for all indicator variable results in the application. A red-green scheme would be mostappropriate for sustainability maps and charts. Icons, explaining the units of mapped indicators(e.g. person/person or kg/ha) might speed up map understanding. It is necessary to underlinethat, having only one, application-wide colour scheme and in general keeping things simple willadd immensely to the user-friendliness and thereby the success of SIAT.

The present report aims at describing a set of techniques and tools developed to obtainvisualisation of scenario results of the Sustainability Impact Assessment Tools (SIAT).


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1 IntroductionThis report is considered as a description of the complimentary approach on visualisation tech-niques of SENSOR results beyond standard solutions presented in Deliverable 4.3, which pre-sents the prototypes of the Sustainability Impact Assessment Tool (SIAT). Communicating thecomplex outcome of SIAT calculation in an optimal way, demands representations beyond stan-dard ways of presenting results as for examples by charts and tables.

Within SIAT different approaches of representing, visualising and contextualising data shall,side by side, contribute to a better understanding of modelled sustainability impact assessmentsrelated to land use changes. Alongside a series of measures are proposed in this deliverablereport, which will enhance the readability and result interpretation of complex data of a wideset of indicators of different scale, quality, nature and unit, images of landscape change andgeographically related data.

Section 1 will describe the achievements, which can be put into three groups: 3D LandscapeGeneration, Visualisation of 3D Landscape Scenarios, Visualisation Tools and overallCommunicational Advices for SIAT.

Section 2 explains the visualisation approach within SIAT, it lays out both how the advancedvisualisation technologies and modules fit within SIAT’s architecture and work together to pre-sent SIAT results.

Section 3 will discuss an approach in generating three-dimensional representations of landsca-pes as basis for all kinds of 3D visualisation in SENSOR based on SIAT output and intermedia-te variables. These landscape models will then be used to render images of landscape changes:both photo-realistically (see section 3.1) and in a schematised way (section 3.2).

Sections 5 and 6 will go in depth with communicational aspects. First a SIAT-wide colourlanguage will be introduced that will support the end user in quickly grasping the essence ofSIAT data, then three modules will be described, which when plugged into SIAT will presentdata in alternative and supplementary ways – schematically and geographically – and willembed the landscape visualisations described in section 6 into SIAT.


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2 The visualisation approach related to SIATThe objective of the visualisation approach is the improved visualisation of simulation results bymeans of advanced techniques, applications and guidelines that are necessary complementaryadd-ons to standard SIAT-illustrations. The visualisation of simulations results comprise (i) inte-grated improvements within standard SIAT-solutions (e.g. Google applications) as well as (ii)new stand-alone approaches as parallel technical developments to SIAT (e.g. photorealisticvisualisation).

The visualisation approach consists therefore of a set of components that interact with the SIATapproach as well as among each other (see above). The visualisation applications are linked with the SIAT, based on Adobe Flex technology, of which the data base contains user defined

policy parameter only. The connection is ensured using live link via web server (soap) inthe form of jpg-image that get 3D Image on specific constellations of policy parametersp (see figure 1).

the SIAT spatial relational data base, of which a live link to the remote database ensuresGeodata-provision as Spatial Window with data identification I (see figure 1).

Fig 1 Integrated linkages of the visualisation approach into SIAT


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The End User Tool of WP 4.3 is the final instrument provided to the end user for SIAT applica-tion. It consists among other of an impact identification component and a valuation componentof the resulting economic, environmental and social effects. Based on the impact identificationand a valuation component various additional tool interfaces will be included for the visualisa-tion of results:

(1) The Visualisation Matrix summarises results on a member state level. It indicates overallimpacts as trend indications (increasing vs. decreasing indicator values). Indicator scores aretranslated into an ordinal system, of which each is determined by a break even point with anequal number of classes for each indicator. The matrix serves as a quick-scan overview for sub-sequent in-depth analysis.

Consistent use of colour schemes is essential across all SIAT components. Colour interpretationfor indicator indications are discussed and transformed into colour guidelines which apply forSIAT as a whole. A commonly applied system should avoid misinterpretations on indicator results.

(2) Another component of the visualisation task is landscape representation on a 5 x 5 km area.This is accomplished by different tools but their common basis is a newly devolved 3DLandscape Generator. This tool generates virtual landscapes based on the SIAT output andselected intermediate variables. These landscapes are represented by 3D raster maps (20 x 20 mresolution) and show horizontal and vertical distributions of key landscape elements, typical forthe region of interest and the respective baseline and policy scenario at year 2025.The raster maps are intended to be usable by different visualisation tools. In SENSOR this will beL-VIS and Lenné3D but other tools can be easily adapted. The raster maps will be accessiblethrough a standard interface via the internet. The generation methods are described in section F.

(3) The 3D photorealistic landscape visualisation component uses the raster maps provided bythe 3D Landscape Generator and thus depicts SIAT results intuitively, i.e. as three-dimensionalgeotypical impressions of the landscape scenery. The photorealistic landscape visualisationsoftware L-VIS (Seifert, 1998) which was originally designed for forest dominated landscapesis being modified in order to interpret the information from SIAT as transported by theLandscape Generator. As well as the raster maps from (2) these photorealistic landscape viewswill be accessible through a standard web service.

(4) The output of the 3D Landscape Generator (figures 2 and 3) will be interpreted throughschematic landscape illustrations by porting desktop vegetation modelling and landscapevisualisation system (Werner et al., 2005) to a server. The visualisation server is extended withan interface for a web-rendering service that allows indicator interpretation through schematicillustrations. This OGC (Open GIS Consortium) interface for standardised perspective viewsdelivers the images to SIAT assuming that the user is online. This includes graphical symboli-sation of indicators to be able to visualise sets of SIAT-indicators that do not have an on-sitespatial impact.

(5) SIAT map overlays will be developed in order to compare mapped SIAT results with satelli-te images using Google map as platform. Based on this view graphic tools that enhance theinterpretation of the data in a landscape context will be developed. Each indicator/land use mapcan be projected on member state level on top of aerial photographs by using Google Map(Thematic Map Dialogue). Below the Google Map map area, indicator values of those coun-tries are shown, that are currently visible within the map area. Zooming in and out on the map,dynamically changes the number of countries shown. Relative and absolute indicator valuescan be retrieved (if available) and spatial indicators get a special feature: Spatial Comparators,which make it possible to drag a polygon (typically a rectangle) onto the map area.


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The polygon will then adjust to the current map scale and facilitating the user to compare aspatial SIAT indicator result to a geographically known area (embodiment).

In summary, the improvements beyond SIAT-standard applications take place at various levels.The attributes to the SIAT visualisation to be accomplished within deliverable 4.4.2 are definedas visualising impacts result of the Sustainability Impact Assessment that are developed beyondthe SIAT-standard solutions (interactive mapping, spider, tables). The latter cases are all inte-grated into the SIAT software architecture. In all, three major applications are to be integratedin SIAT that cover at the same time different scales: (1) the visualisation matrix at nationallevel summarises impacts regarding its tendencies, (2) additional layers as Google maps atnational level (3) the 5x5 km landscape views consisting of both photorealistic visualisationsand schematisations of landscape elements as “landscape interpretation” with colouring and / ordifferent structure and patterns.

Fig 2 The three different layers as additional visualisation applications in SIAT

Having summarised the relation of the visualisation applications of SIAT, the next sections des-cribe the up-to-date detailed technical solutions and applied methodologies in order to imple-ment the developed techniques within SIAT.


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3 The 3D Landscape GeneratorThe sustainability impact assessment tool of the EU project SENSOR is a complex modellingframework that presents results not only as numbers, charts and maps but via advanced photo-realistic and schematised visualisations of synthetic landscapes. To generate such visualizationsit was necessary to create a three dimensional landscape generator (3DLG), a tool able to inter-pret numerical values related to topography and land use of a region and convert them into syn-thetic landscapes reflecting the land use situation. The 3DLG is a spatial simulation softwaredesigned to represent landscapes based either on regional information of land use: a standingalone tool; or to use information from other landscape dynamics spatial models: part of a model-ling system. The ASCII grid output format of the 3DLG allows the results to be easily interpre-ted by different tools like ArcGIS, R and for example photorealistic or schematised visualisationsoftware in situations where particular visualisations of landscapes are needed.

For SENSOR, the generated landscapes reflect the impact of policy cases on land uses on speci-fic regions of Europe. Based on baseline scenario information and selected SENSOR modellingresults of a given region the 3DLG first uses a controlled random midpoint displacement fractalalgorithm to generate a close-to-reality Artificial Digital Elevation Model. Afterwards, the tooldistributes land use elements (e.g. arable land, artificial surfaces, forests, etc.) on the syntheticsurface by using order theory, controlled cellular automata and multicriteria optimizationmethods to represent the impact of baseline and policy-cases in landscape scenarios. In thisdocument the tools description as well as a set of results will be shown.

The following definitions on used visualisation terms have been elaborated for communicating acommon language and thus better understanding of the visualisation tool: A landscape element is the physical material at the surface of the earth, including artifi-

cial surfaces, arable land, forest, heterogeneous agricultural areas, open spaces with littleor no vegetation, pastures, permanent crops, shrubs & herbaceous vegetation and waterbodies.

A landscape comprises the visible features of an area of land, including physical ele-ments such as land-forms, elevations and living elements of flora and fauna; and humanelements, for instance human activity or the built environment (artificial surfaces, arableland, etc.). Thus, landscape is understood as the arrangement of landscape elements on aregion.

Land use is the human modification of natural environments into built environmentsused for self benefit, such as artificial surfaces, agricultural areas, pastures, permanentcrops. Thus, land use is part of landscape elements.

In SENSOR the impact of EU policy decisions affecting land use sustainability is assessedregionally, therefore a Spatial Regional Reference Framework (SRRF) was developed by meansof stratifying European territory into relatively homogeneous regions, integrating biophysical,socioeconomic and regional administrative aspects, The result of the stratifications are NUTS xregions, that are afterwards clustered in thirty Europe regions, see Renetzeder et al. [2006]. Forthe current work, the 3DLG was fitted with an initial input set of one landscape per clusterincluding the current policy cases CAP209 and CAP219.

In the SIAT modelling system five sectors related to land use are modelled individually: Forestry(models EFISCEN, DYNA-CLUE), agriculture (models CAPRI, DYNA-CLUE), urban land use(model SICK), transport infrastructure (TIM) and tourism (B&B), see Jansson et al. [2008,2009]. The first step when designing the 3DLG was to look at the output information providedby SIAT, from the above mentioned models, the decision factor on what variables to use wastheir output scale. Therefore just chosen variables, from EFISCEN, CAPRI and DYNA-CLUE,with output values at nutsx level are used as inputs for the 3DLG.


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In addition to the variables modelled within SIAT, information concerning terrain type andanother land uses for the nutsx region is obtained from the SENSOR CLUSTER-regions report,see Renetzeder et al. [2006] based on CORINE Land cover and LANMAP2. These variablesare used by the 3DLG to represent the following land uses on the landscape:

Arable LandArtificial SurfacesForestOpen Spaces with little or no vegetationPasturesPermanent CropsSemi-natural VegetationShrubs & Herbaceous VegetationWater Bodies

The following Sub lists of land uses were included for Arable Land, Pastures, Permanent Cropsand Forest:

Acreage of Soft wheat (ha)Acreage of Durum wheat (ha)Acreage of Rye and meslin (ha)Acreage of Barley (ha)Acreage of Oats and summer cereal mixes without triticale (ha)Acreage of Grain maize (ha)Acreage of Other cereals including triticale (ha)Acreage of Paddy rice (ha)Acreage of Rape (ha)Acreage of Sunflower (ha)Acreage of Soya (ha)Acreage of Olives for oil (ha)Acreage of Other oils (ha)Acreage of Pulses (ha)Acreage of Potatoes (ha)Acreage of Sugar beet (ha)Acreage of Flax and hemp (ha)Acreage of Tobacco (ha)Acreage of Other industrial crops (ha)Acreage of Tomatoes (ha)Acreage of Other vegetables (ha)Acreage of Applespears and peaches (ha)Acreage of Other fruits (ha)Acreage of Citrus fruits (ha)Acreage of Table grapes (ha)Acreage of Table olives (ha)Acreage of Wine (ha)Acreage of Other wine (ha)Acreage of Nurseries (ha)Acreage of Flowers (ha)Acreage of Other crops (ha)Acreage of Fodder maize (ha)Acreage of Fodder root crops (ha)Acreage of Fodder other on arable land (ha)Acreage of Gras and grazings (ha)Acreage of Set aside idling (ha)Acreage of Non food production on set aside (ha)Acreage of Fallow land (ha)


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Forest area in EFISCEN in broadleaved dominated forests (ha)Forest area in EFISCEN in conifer dominated forests (ha)Forest area in EFISCEN in mixed forests (ha)

The 3DLG thus is able to represent up to 46 land uses when the information is provided by SIAT.

The land use models of SIAT produce information on the impact of different policies on landuse for every NUTS x region in Europe. The type of policy chosen will determine the land useon the NUTS x region on the year 2025. Thus two types of scenarios representing policy casesare modelled, the baseline and the policy cases scenarios: Baseline scenarios are scenarios calculated for every NUTS x region of year 2025 which

represent the autonomous developments, in the absence of policy changes, see Kuhlmanet al. [2006]. The information on baseline scenarios generated by the above mentionedmodels is taken as input to create synthetic baseline landscapes with the 3DLG.

Policy case scenario is a description of a possible EU policy, idem. Currently only theinformation generated by the Common Agricultural Policy (CAP) scenario is used asinput to create synthetic policy landscapes with the 3DLG as it is the only policy casewith finished results from SIAT. For the purposes of the 3DLG, just two simulationexperiments of the CAP are used: CAP simulation ID 209: with 5% of direct subsidies, no market support and no re-investment in R&D, idem CAP simulation ID 219: with 5% of direct subsidies, no market support and withre-investment in R&D

Thus, every NUTS x region represented with the 3DLG has three landscapes reflecting theimpact of policy cases: a baseline landscape, a policy ID 209 landscape and a policy ID 219landscape.One of the key points when deciding what kind of output data the 3DLG shall produce forSENSOR is the working scale of the visualization software L-Vis and Lenné3D. Photorealisticand schematized visualizations will turn to be very demanding in terms of computer require-ments when dealing with landscapes representing areas greater than 5*5km, and yet the inputvariables coming from SIAT models Dyna-CLUE, CAPRI, EFISCEN have a minimum com-mon scale at NUTS x level. Therefore, it was decided to use mean values of fractions of landuse of every NUTS x region to generate a preliminary set of visualizations. Thus, the syntheticlandscape generated is an ideal ‘mean’ landscape from the NUTS x region.

Fig 3 The three different layers as additional visualisation applications in SIAT


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Fig 4 Cluster information: Elevation (left) and Landscape elements percentage (right)

The 3DLG ConceptThis section describes the approach taken in generating three-dimensional representations oflandscapes using regional information of land uses and topography as input. The discussiongoes in depth with the description of technical aspects used in order to generate a close-to-rea-lity synthetic landscape. First an artificial digital terrain model generator that allows the genera-tion of surfaces on a grid will be introduced, and then the mathematical models used to distri-bute land uses on the terrain will be described.

In order to generate realistic looking-like landscapes, the 3DLG is equipped with a set oftuning tools called controllers allowing the user (for SENSOR the tool is parameterized with aset of bydefault values) to have relative control over the final landscape to be represented. Foreach of the initial thirty landscape generated for SENSOR the controllers are calibrated bycomparing empirically actual land maps of the region, like CORINE land cover maps andGoogle satellite images, with the 3DLG generated landscapes of the same region, these cali-brated values are intended to match in the nearest possible way patch size, patch distributionand first order landscape metrics of the nutsx land uses; this method however will be improvedby using multicriteria optimization techniques. The controllers are defined as parameters whichvalues can be assigned by the user within a space of possible values, the parameters used are: Euclidean distance to developing center slope height difference with respect to a developing center neighbours land use influence other characteristics

ComponentsThe 3DLG can be understood as the combination of two models, the artificial digital terrainmodel generator and the landscape distributor.

The Artificial digital terrain model generator (ADTM)

The ADTM algorithm is a function mapping from a two dimensional to a three dimensionalEuclidean space, where a pair describes the position of the cell on a plane and the third compo-nent represents altitude above mean sea level. The ADTM algorithm designed for the 3DLGis a spatial network version of the random midpoint displacement fractal algorithm that generateshighly realistic height maps and that was conceived to work on square surfaces with dimensions


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(2^n+1)*(2^n+1), where n is taken in this context as 8, so 257*257 grid cells are generated.1

In the original version of the algorithm, see Fournier et al. [1982], Sala et al. [2000], Brouste et al. [2007] the corners of the grid are assigned with predefined elevation values2 and then themidpoint, the cell just in the center of the grid, obtains its elevation by averaging the grid’s corners elevation and adding up a uniformly distributed random value that will produce a fractal Brownian surface, Fournier et al. [1982], the interval of the random value added isdecreased on every iteration, see equation 1, until the whole surface is produced. As the original version of the algorithm needs just the elevation of the grid’s corners to start the proce-dure, the final surface generated is unpredictable as the elevation of the cells is the result of adeterministic function and a Brownian added value:

E= average(a,b,c,d) + random(k) (1)

where a,b,c,d are the elevations correspongind to the corners of the grid, E is the final elevati-on, random(k) ∈ [-k,k] and k := pow(2,-r), and r is the roughness constant.

In SENSOR mean elevation values of a nutsx region are given as fraction of lowland, hills,mountains, high mountains and Alps see Renetzeder et al. [2006]; of course to find a digitalelevation model in Europe fulfilling these fractions is impossible as the goal is to generate alandscape based on mean values of a NUTS x region. As the elevations produced with the ori-ginal random midpoint displacement algorithm are uncontrollable, see Saunders et al. [2005],Sala et al. [2000] a spatial network version of the algorithm was developed to control heightson the terrain and to represent the fractions of elevations required by SENSOR. In the Spatialnetwork version of the random midpoint displacement algorithm, a subset of cells equally dis-tributed on the grid (cellnetwork) is assigned with elevation values according to the NUTS xregion. Elevations are assigned to a cell every 555m approx, see Figure (5). Thus, the networkwill be a set of 81 cells with predefined elevations. Afterwards the algorithm is forced to takethese elevation values as predetermined for the given cell-network, i.e. to assign a cell elevati-on if and only if the cell does not belong to the network; this elevation is calculated with the‘midpoint displacement’ but with the current average including the previously assigned elevati-on values of the network cells. The random value added and its interval will change accordingto the original version of the algorithm, see equation (1).

1 The grid size was chosen based on computer memory and algorithm performance, with n=8, 66049 cells areneeded, with n=10, more than a million cells are needed.

2 This elevation values of the four corners of the grid give very few degree of control on the final generatedsurface.

Fig 5 Nodes of the cell-network with assigned elevation values on the grid, b) surfaces generated with the spatial network random midpoint algorithm


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The Landscape distributorAfter the artificial digital elevation model is built, and every single cell on the grid has an ele-vation value, the Landscape distributor model allocates available land uses of a given NUTS xregion into the grid. In the model it is assumed that a set of characteristics, measured at cellscale, constrains land uses to be more/less suitable for certain cells; more specifically: themodel maps a land use from a finite set of available nutsx-land-uses to subsets of suitable cellswithin the grid. To assign suitability values to every single cell on the grid a ranking modelbased on set theory is built. Every characteristic studied is assigned with a linear transformati-on, obtained by comparing all cells under the same characteristic, mapping from the field ofReals to a finite subset of it. The advantage of such an approach is that a single characteristicof a cell can be based on metric space theory, econometrics, statistics, fuzzy logic, etc. and atthe end all of them will be joint in a mathematical model that ranks every cell, based on it cha-racteristics, into a partially ordered set. Hence, the model can incorporate the variables that theuser considers convenient for his modelling process.

For modelling purposes the artificial-surfaces land use is thought as the ‘independent land use’of the landscape, i.e. all other land use allocations are influenced by how many patches of arti-ficial surfaces exist, their location, size, etc. However, the term ‘independent land use’ meansonly that its allocation is not influenced by other land uses, but just by its own constraint locati-on-variables explained below, while other land uses depend on their own constraint location-variables that are calculated with respect to the developing center, i.e. the center of the land useartificial-surfaces.

Definition 1: The landscapes are to be represented on a grid of 5x5km composed of squaredcells of 20x20m. A cell is defined as an element of the finite set G (i.e. the grid). Let c_i be ann-tuple:

c_i =: (x_i, y_i, z_i, suit_c_i, ch_i1, ch_i2,....., ch_ik,),where k= n-4 is the number of measured characteristics of a cell.and:

x_i, y_i, z_i are location coordinates of cell i in the R^3 Euclidean space,suit_c_i: total suitability of the cell i, defined below.ch_i1 := Euclidean distance of cell i to the nearest developing centerch_i2 := slope of cell i w.r.t its range3 one neighbouring cellsch_i3 := height difference of the cell i w.r.t the height of nearest developing centerch_i4 := land use of range one neighbours of cell i. .ch_ik:= measured characteristk k of cell i.

Definition 2: let CH_k := { ch_jk | j=1,...,m}, with #CH_k = #G = m, be the set of measuredvalues of the characteristic k of all cells on the grid G, thus CH_k ⊂ ¼ .

Generation of partially ordered sets (poset)

Definition 3: let CH_k_poset := (CH_k, <=) be a poset with maximal element max_ch_k andminimal element min_ch_k.

Axiom 1: suitability of cell i to characteristic k (suit_c_i_ch_k). Let S_ch_k be a lineartransformation mapping from CH_k_poset to the interval [0,1/k]; with S_ch_k(min_ch_k) :=1/k; and S_ch_k(max_ch_k) := 0. Therefore, for every cell i on G, exist a real on [0,1/k] calledsuit_c_i_ch_k that represent the suitability of cell i to characteristic k. The procedure is perfor-med for all k characteristics of every cell on the grid.

3 Range one of a Moore neighborhood


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Definition 4: Total suitability of cell i (suit_c_i) is a weighted function over all k suitabilities ofcell i:

suit_c_i := weight_ ch_1 * suit_c_i_ch_1 +…+ weight_ ch_k * suit_c_i_ch_kwhere weight_ ch_1 +…+ weight_ ch_k := k.and weight_ch_k is defined as the weighting parameter of characteristic k. By definition, whenall weighting parameters weight_ ch_k have value one, the total suitability of a ‘perfect suitablecell’, i.e. a cell with all k suit_c_i_ch_k= 1/k has suit_c_i = 1.

Axiom 2: Based on the previous definitions a cell c_i in G can be reduced to its form:c_i =: (x_i, y_i, z_i, suit_c_i)

Axiom 3: As every single cell on G has a total suitability value between [0,1], the pair:SUIT_G:={ suit_c_i | 1<=i<=m},

and the relation <= form the poset (SUIT_G, <=). Thus, every cell on the grid has a rankingvalue, within a partially ordered set, that makes it more suitable for certain land uses than othercells based on the k characteristic studied.

ControllersThe set of weighting parameters {weight_ ch_j | 1 <= j <= k} is the actual set of tuning tools(controllers) the 3DLG is equipped with. The user can adjust the weighting value of certain(s)characteristic(s) to give it more/less relative importance within the set of characteristics andthus change the cells ranking order, i.e. for every set of weighting values assigned by the enduser exist a unique poset SUIT_G that will allow specific control to the user over the finalshape of the landscape generated.

Land use suitability-intervalsAs the cells were assigned with composed suitability values that give them a rank within aposet (SUIT_G, <=), the land uses can be constraint, based on SIAT modelling purposes, bysuitability intervals were they can be allocated; hence, the user generated poset and the con-straints given to land uses will generate a unique landscape for a given NUTS x region, thegoal is to find a proper set of values to the controllers so real looking-like landscapes can begenerated. In order to assign constraints to the land uses the following assumptions, constraintsand specific land use constraints were assigned to the land uses.

Land use typesThe following list of input values for artificial-surfaces must be defined by the user in order toassign suitability-intervals to the land uses. As artificial-surfaces is generated independently ithas a unique set of intervals, the other land uses share other interval values as well assigned bythe user.

Artificial-surfaces4 suitability intervals:1. Maximum and minimum terrain-isoline where the developing center(s) can be allocated2. Maximum allowed Euclidean distance of possible artificial-surfaces cells to the develo-

ping center(s)3. Maximum difference in elevation of potential artificial-surfaces cells with respect to the

developing center(s) height4. Maximum cell slope

4 As mentioned above, the only land use that is independently generated is artificial-surfaces.


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Other land use variables and constraint-parameters:1. Maximum Euclidean distance of cells to the nearer developing center(s)2. Maximum difference in cell elevation with respect to the nearer developing center(s)

height3. maximum cell slope4. minimum number of range one neighbours sharing the same land use.

The following assumptions/constraints lists on specific land uses allocation were made basedon SIAT modelling results and empirical analysis of the 30 cluster regions with satelliteimages. This list will help the user when defining the suitability-intervals:

Water Bodies Constraint: allocated on the lower heights of the gridFrom SIAT models outputs for baseline and policy cases this land use always keeps its sizeconstant.

Artificial surfacesAssumption: this land use is independent from other land uses.Constraints: Preference to be allocated on lower heights of the grid, preference to be allocatedon cells with less slope values, preference to be allocated in cells with less difference in heightwith respect to the developing center(s).From SIAT models outputs for policy cases: this land use always keeps its size constant.

ForestAssumptions: can be allocated in all possible terrain slopes, can be allocated in all possibledifference in height (up to the tree line5) with respect the developing center(s), can be allocatedin all possible distances to artificial surfaces (as it is not strictly first source of subsistence).From SIAT models outputs for baseline and policy cases this land use does not decrease its size.

Semi-Natural vegetationConstraints: following SIAT methodology this land use will be transformed into forest, thereforeit is allocated on the nearest available locations to forest. This land use was previously shrubsand herbaceous vegetation and it is the product of agricultural abandonment and marginalizati-on, so it is allocated on cells with greater slope (first areas to be abandoned from agriculture)and greater distance to developing center(s) than shrubs and subsequently than arable land, per-manent crops and pastures. Only forest can have a greater distance to artificial surfaces.From SIAT models outputs for policy cases this land use does not decrease its size.

Shrubs and herbaceous vegetationConstraints: following SIAT methodology this land use is the immediate product of agriculturalabandonment therefore it is allocated on the nearest available locations to agricultural land uses(arable land, permanent crops, pastures). Cells located near agricultural land use but with greaterdistance to developing center(s) and greater slope values than those of agricultural land use.Only forest and Semi-Natural vegetation may hold greater distance to developing center(s).From SIAT models outputs for policy cases this land use does not decrease its size.

5 tree line: http://en.wikipedia.org/wiki/Tree_line


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Agricultural Land (Permanent Crops, Pastures and Arable Land)Assumptions: three land uses with relative equal importance with respect to the developingcenter(s).Constraint: allocated on cells with lower slopes (as a result of facility to be used) and cells nea-rer to developing center(s) transport facilities.From SIAT models outputs for policy case Arable Land never increases its size whilePermanent.Crops and Pastures either increase or decrease their size.

Open spaces with little or no vegetationConstraints: when the grid has elevations higher than the Tree line (see forest above) then itcan be allocated on these elevations. When the grid has elevations lower than the Tree line,there is no clear pattern of appearance (owed to bare rock formations or bad land), a uniformdistribution is assigned to give equal probability of appearance on the Landscape.From SIAT models outputs for policy cases: this land use increases or decreases its size.

The algorithmsWhen the necessary inputs from SIAT are provided; the weighting parameters and the suitabili-ty intervals are assigned by the user, the 3DLG calculates the most likely land use distributionwithin the given set of constraints and assumptions. The allocation algorithm is describedbelow:

Algorithm for baseline landscape generation1. the chosen land use variables are used as input for the 3DLG2. The user assigns values to the controllers (cells weighting parameters and to land use

suitability intervals), defining thus the space of cells suitability and land use suitability.3. Based on cells suitability and land use suitability intervals, the land uses present in the

region are allocated on the grid by creating a poset where cells with higher ranks willbe assigned with land uses with greater priorities followed by land uses with fewerpriorities until reach the minimal element in the poset.

4. an empirical comparison with real satellite images is performed5. the controllers are tuned and steps 1 to 5 are repeated until a close-to-really landscape

(step 4) is generated.

Algorithm for Policy case landscape generationFor the policy landscapes the baseline landscape generated on the previous algorithm is takenas departure, the values of the policy case from SIAT modelling process are taken as input:1. Determine which grid-cells are due to change their status: because they represent a land

use that decrease it size in the SIAT modelling process. The cells with the lowest suita-bility values of the poset, for that decreasing land use, will be made available to changestatus, the other ones will keep representing the same land use.

2. From the land uses that increase their size, determine what conversions are most likelyto occur based on SIAT outputs.

3. steps 3 to 5 of previous algorithm will be used to determine the final distribution onland use on available cells

The 3DLG program code, based in the algorithms shown below, is written in C++ (MicrosoftVisual C++) running on windows XP OS. The generated landscapes can be interpreted by toolslike L-Vis, Lenné 3D, ArcGis, R, and any software able to interpret raster ASCII data (GIS for-mats).


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3DLG ResultsThe software 3DLG is a tool adapted to interpret sustainability impact assessment modellingoutputs of SIAT in order to generate an initial set of thirty synthetic landscapes for baseline andpolicy cases including ten land uses each. The output of the 3DLG is a set of ascii-grid filesthat can be interpreted by software like L-Vis and Lenné3D. The already generated landscapesproduced by the 3DLG for SENSOR are currently being “visualised” by the other partners ofthe M4 visualisation group, i.e. by L-Vis and Lenné3D software. As the final visualisationsfrom L-Vis and Lenné3D are currently under construction the landscapes generated by 3DLGare visualised here using the freely available R software. With the preliminary set of generatedlandscapes an empirical analysis has been done to test software performance in terms of resultsreliability, resource usage, processing time, parameter-tuning and output resemblance withrespect to real landscapes. In this work an empirical comparison of results resemblance isshown for four NUTS x generated landscapes.

The outputs of the 3DLG are two raster ASCII files, one corresponding to the represented base-line and the second to the policy case landscape. The landscapes shown below belong to theregions Galiati (Romania), Malta, Tübingen (Germany) and Värmlands (Sweden) that, for thesynthetic cases, were visualised6 using the open source program R, package ‘rgl’ see Adler[2007], for 2D and 3D cases, figures 6 to 13. Table (1) shows percentages of land use genera-ted by the 3DLG for the corresponding landscapes under baseline and policy_ id_209 impact.Thus the only quantitative landscape metric used so far is land use percentage; other metricsdiscussed were compared empirically. All landscapes, synthetic and Google maps satelliteimages in figures 6 to 9 and the 3D figures 10 to 13, are represented as 5*5Km.

In figure (6) the NUTS x RO024, Galati (Romania) is represented. The left image is the 3DLGgenerated synthetic-landscape and visualised as 2D with R, the image at the right is a satelliteimage of the same region obtained with Google maps. From a visual comparison both landsca-pes have similar patch richness of five land uses, see Table (1). Both landscapes show predomi-nance of agricultural land (permanent crops, arable land and pastures). Artificial surfaces tendto be similar in patch size and number of patches representing it. The percentage of forest inboth cases is similar, approx 4% (satellite) and 6.5% but in the satellite image the patch sizeseems to be bigger than in the synthetic. Patch shape in forest and artificial-surfaces are appa-rently correctly modelled. Patch distribution in both landscapes show resemblance. Artificialsurfaces and forest are surrounded by agricultural land in both landscapes.

In figure (7) NUTS x MT00, Malta, is represented as 2D. Both images show predominance ofartificial-surfaces land use and hold relative similar patch size, shape and distribution. Patchrichness is similar in both landscapes including four main land uses (arable land, pastures, seminatural vegetation, and artificial surfaces). Artificial-surfaces land use is surrounded by agricul-tural land and semi natural vegetation in both images but patch size in the latter land use appe-ars smaller and with greater number of patches in the satellite image. Patch distribution hassimilar aspect in both landscapes for most land uses. In the synthetic landscape forest tends tobe over represented.

In figure (8) NUTS x DE14, Tübingen (Germany) is represented as 2D. Predominance of forestis observed in both landscapes followed by arable land and pastures. For these three land usessimilarities in patch size, shapes and distribution are observed. Patch size shape and density ofartificial surfaces show resemblance in both landscapes. In the satellite image however there isno clear difference between arable land and pastures that appears in the synthetic landscape.

6 These are not the final visualisations that will be used in SIAT. L-Vis and Lenne 3D will be produce SIATvisualisations


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In figure (9) NUTS x SE061, Värmlands (Sweden) is represented as 2D. Clear predominanceof forest is seen in both landscapes followed by water bodies, for both land uses similarities inpatch size, shapes and distribution is apparent. Patch size of artificial surfaces seems to be overrepresented in the synthetic case. Patch size and shape of pastures and crops appear similar inboth cases.

In figure (10 and 11) a 3D representation of baseline and policy cases for the NUTS x Galatiand Malta is visualised with R. As can be appreciated, as well in Table (1), there is no changebetween baseline and policy case representations in terms of land use for both NUTS x.

In figure (12 and 13) the 3D representation shows a slight change between baseline and policycases. For the three NUTS x region in the policy case, abandoned arable land areas are takenby shrubs and herbaceous vegetation, following thus SIAT methodology. The changed cells arehighlighted with dark blue colour representing ‘increased shrubs for policy 20’, see Table (2).

NUTS x / Land use RO024, Galiati MT00, Malta, DE14,Tübingen SE061 VärmlandsBaseline

%Policy

%Baseline

%Policy

%Baseline

%Policy

%Baseline

%Policy

%Arable Land 70.3 70.3 13.8 13.8 31.9 30.3 8.4 7.4Forest 6.5 6.5 0.9 0.9 33.2 33.4 71.8 71.5Pastures 6.8 6.8 18.1 18.1 25.5 25.7 2.8 2.3Shrubs, Herb.Vegetation 0 0 1.4 1.4 0 1.6 0 2.3Semi-natural Vegetation 0.6 0.6 30.0 30.0 0.5 0.6 0 0Permanent Crops 6.4 6.3 0.9 0.9 1.3 0.8 2.0 1.7Open Spaces, little veget. 0 0.0 0 0 0 0 0 0Water Bodies 0 0 0 0 0.7 0.7 12.0 12.0Artif.Surfaces 9.3 9.3 34.8 34.8 6.8 6.8 3.0 2.7

Table 1 Percentage of represented land uses with the 3DLG for baseline and policy_209 and 219 scenarios.

Table 2 Land use signatures for simulated landscapes of figures (3 - 10)


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Fig 6 NUTS x RO024, Galati, Romania: generated with the 3DLG and represented as 2D with R (left), and satellite image from Google maps.(right). The percentages of land use generated are shown in table (Results %)

Fig 7 NUTS x MT00, Malta: generated with the 3DLG and represented as 2D with R (left), and satellite image from Google maps (right)

Fig 8 NUTS x DE14, Tübingen, Deutschland: generated with the 3DLG and represented as 2D with R (left), and satellite image from Google maps (right)


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Fig 9 NUTS x SE061, Värmlands, Sweeden: generated with the 3DLG and represented as 2D with R (left), and satellite image from Google maps (right)

Fig 10 NUTS x RO024 Galati, Romania: Baseline (left) and policy_209 landscape (right) generated with the 3DLG and represented as 3D with R.

Fig 11 NUTS x MT00, Malta: Baseline (left) and policy_209 landscape (right) generated with the 3DLG and represented as 3D with R.


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Fig 12 NUTS x DE14, Tübingen, Germany: Baseline (left) and policy_209 landscape (right) generated with the 3DLG and represented as 3D with R.

Fig 13 NUTS x SE061, Värmlands, Sweden: Baseline (left) and policy_209 landscape (right) generatedwith the 3DLG and represented as 3D with R.


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4 3D Landscape Visualisation4.1 Photo-realistic 3D visualisation L-VISThe landscape visualisation system L-VIS has been developed at TUM. Originally designed todisplay forest based scenes, it is currently extended to display arbitrary landscapes. The designgoal was to build a program that can directly display the output of landscape models. Thestructured element of forest landscapes are the singe tree. Thus the visualisation is also basedon elements like the single tree. As to be a scientific visualisation, not only simple clones ofsuch objects are displayed, but every object has its own dimensions and proportions. For exam-ple each tree of a scene has its own diameter, height, crown diameter and even time dependentvariation of those dimensions. To be able to realise such a detailed presentation, the objectsneed to be simple enough not to consume too much computing power and to be able to adapt tothe dimensions given from simulation models but also to look realistic enough. The objects inL-VIS are no structural explicit models but only adaptive aggregations of texture planes. Thismakes them light weight, realistic looking and easy adaptable to different regions, see figure14. This flexibility allows an interactive display of trees, houses, streets and other elements ofthe landscape, see figure 15.

L-VIS has been extended to display the different landscape elements as delivered from the alsoin SENSOR developed landscape generator. Currently the regional typical textures, distributi-ons and structures for the cluster regions are collected which will be incorporated into L-VIS tointerpret and display the generated landscapes optimally. The goal is to deliver a web-servicewhich takes the description of the landscape from the generator, some additional parameterslike observer position and return a realistic looking image of the corresponding scene. All thecomplexity of structure generation and visualisation is hidden behind the web-service interface.

Fig 14 L-VIS output for a forest scene


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Fig 15 L-VIS output for a forest scene and tree details

4.2 Schematising 3d-LandscapesIn order to fulfil the innovative requirements, Lenné3D GmbH has rewritten its desktop vegeta-tion modelling and landscape visualisation system to platform independent solution that caneasily be ported to other modern operation systems. This framework is a state-of-the-art, modu-lar landscape visualisation system called Biosphere3D. Currently, Windows XP and Vista versi-ons for both 32 and 64 bit are supported. This interactive visualisation system is focusing onreal-time rendering of vegetation, and landscapes in different scales (Paar and Clasen, 2007).

The new system supports multiple scales on a virtual globe reflecting our thoughts on themaximum extent of a landscape. Unlimited terrain can be visualised due to the spherical terrainmodel and the efficient data management (Clasen and Hege 2007, 2006). Satellite images,raster digital elevation models (DEM), and aerial views of multiple terabyte can be combinedwith vegetation plots based on vector shapes and biological sample data to create photorealisticviews. Since no pre-calculation is required, the data can be edited and reloaded to enable quickresponse, e.g. cycles and semi-interactive participation processes. Biosphere3D is compatible toLenné3D’s plant models, permitting access to one of the largest databases of realistic 3D plants(Rekittke and Paar, 2006). Lenné3D has extended its plant library to common Mediterranean orfrequently grown tree species to represent south European landscapes see figures 16 and 17.These 3D plant symbols are used in the visualisations of landscape sections.


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Fig 16 Example of an orange tree 3D plant symbol

Fig 17 Example of an eucalyptus tree 3D plant symbol

In order to model and visualise land cover, landscapes elements of the 3D Landscape Generatorhave to be matched with regionalised vegetation and settlement patterns. Therefore, we definecharacteristic plant communities of the 30 SRRF Clusters. GIS coverage of the cluster regionsare overlaid with the GIS coverage of the natural potential vegetation of Europe (BfN, 2004).The intersecting areas are exported into a dBASE database file, which then is imported into aspreadsheet program. Using the Pivot Table function, areas for each plant community withineach cluster are summarised and then sorted.

Thereby we gain a list of the most common plant communities and associated species withineach cluster region. For the following steps we consider simply the most common 3 plant com-munities (or more until the represented area is at least 75% of the cluster regions area). Thecharacteristic species of each plant community are listed in a Relevé plot / inventory (Braun-Blanquet, 1964) using an oik XML file (Röhricht, 2005) to calculate prototypical spatial plantdistribution patterns For each species one or more virtual trees, shrubs, grasses, and largeperennials are assigned from the Lenné3D Flora3D plant model library. The resulting file istested for its syntax using the plant distributor software oik (ibid.). oik’s drilling and matrix dis-tribution functions are used for crops and plantations.


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Generic 3D buildings and churches are modelled for each of the 30 Cluster regions. We useGoogle and Flickr image search for Cluster region names, “Building”, “House”, and “Church”etc. and regional tourism websites. Approx. 10 images were used for each region up to 7 3Dmodels.

Figures (18)and (19) illustrate the workflow from particular constructions to generic 3DSketchUp models.

Fig 18 Example constructing a generic church and generic multi-storey buildings for Cluster region 10

Fig 19 Example constructing generic buildings for Cluster region 10

Nowadays, computer visualisation enables the rendering of landscape scenes that look convin-cing, even authentic. The following dilemma often arises in consequence: in order to recreatethe impression of a photorealistic image, it is often unavoidable that missing data must befabricated or existing data manipulated in order to make them fit (Orland et al., 1997).Sheppard (2001) comments that sometimes correctly prepared landscape data is missing, butthis cannot be discerned from the extremely realistic visualisation. Both paradoxical and fasci-nating is the fact that an image may be de facto non-realistic, yet at the same time photoreali-stic. The depicted reality may appear very similar without necessarily having any correlation inthe physical world. Photorealistic computer simulations fundamentally entail exactness, perfec-tion and authenticity – yet these aspects may be purely superficial (Sheppard, 2001).Lenne3D’s visualisation prototype is extended to transform TUM’s raster-based data sets oflandscape scenarios into non-photorealistic landscape perspectives – as one of SIAT’s 3D opti-ons. Non-photorealistic rendering (NPR) is an area of computer graphics that focuses on enab-ling a wide variety of expressive styles for digital art. In contrast to traditional computer gra-phics, which has focused on photo-realism, NPR is inspired by artistic styles such as painting,drawing, technical illustration, and animated cartoons (source:http://en.wikipedia.org/wiki/Nonphotorealistic_rendering).

During the SENSOR project we have also considered and experimented with an in-situ visuali-sation, integrating geo statistical data (e.g. indicator values) with landscape scenery. Figure (20)depicts this concept of scientific visualisation mapping discrete or scalar fields onto the 3Dscene to express qualitative properties or the spatial distribution of an indicator.


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Fig 20 Embedded data visualization

Unfortunately, in SIAT there is not such fine grain indicator data available. Nevertheless, thiscould be still an interesting concept for future version of SIAT. Then we have focussed on sket-chy landscape scenery visualisation. Figure 21 and 22, based on the same 3d scene model, andprototype of a forest distribution, both rendered with Lenné3D-Player show an implementationof a NPR algorithm, which is part of a recent dissertation (Coconu, 2008). A vague depiction ofthe landscape sections, i.e. a graphic reduction can provide a safeguard against over-interpreta-tion of details by SIAT users. Lenné3D aims for a representation looking more like an illustrati-on than photography but does not rely on artistic license. Still the renditions support theimpression of being there.

Fig 21, 22 Examples of photorealistic rendering (top) and of non-photorealistic rendering (bottom) based on the same 3d scene model, and prototype of a forest distribution, both rendered with Lenné3D-Player


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5 Colour Schemes for SIATWell and carefully designed maps and consistent use of map colour schemes can enhance theeffectiveness of a map regarding result interpretation, i.e. reducing the amount of time betweenthe moments of a first glance on the map to a better understanding of the content or data to becommunicated. When maps are presented via a website or an application a colour languagedefined by consistent use of map colours can be introduced. This will ease the interpretation ofthe single map. This section will analyse data types and maps in use within the SIAT prototypeI (Verweij et al 2006), (SIAT, 2007), (and recommendations based on Brewer’s (Brewer, 1994)colour use guidelines will be given at two levels: at application and map level.

5.1 Short overview over results5.1.1 Application Level Colours are used to express change Quantitative data is expressed by diverging colour schemes to show a positive versus

negative change Two diverging colour schemes are proposed to express the difference between land use

and indicator values Units are not expressed by colours but should be explained by icons A traffic light colour scheme is proposed for the quality estimation indicator A binary colour scheme is proposed to indicate sustainability Sustainability dimensions are not expressed by colours Sectors are not expressed by colours but might be shown by icons

5.1.2 Map LevelAfter having analysed the SIAT’s maps and application windows containing maps we can lineup several possibilities of map colour schemes and other measures to enhance the readability ofSIAT data:A Step 2, Policy Settings. Expert (see section 5.3.2)

a. Recommendation binary black/white scheme

B Step 3, Impact Indicators. Land use change map (see section 5.3.2.4.)a. Recommendation

diverging green white brown colour scheme 5 or 6 classes

C Step 3, Impact Indicators. Indicators (see section 5.3.2.3)a. Recommendation

diverging purple white orange scheme 5 or 6 classes

b. If an indicator is split into sectors (forestry, industry, etc.) like for example SOC 1.2 an icon indicating the sector might be considered

c. The quality estimation indicator, currently part of the map, might be moved outof the latter. It is suggested to apply a red/green scheme to it (see section 5.4.4)

D Step 4, Sustainability. sustainability risk (see section 5.3.2.7)a. Recommendation: binary red/green scheme (see section 5.4.3.)

E Step 4, Sustainability – threshold fact sheet (5.3.2.8.)a. Diverging scheme, centred at zero, symmetrical thresholds (increase/decrease),

same colour scheme as B and C for land use changes and impact indicators respectively

b. Icon, explaining the nature of the indicatorAs the nature of the indicators i.e. the maxima, minima, distribution etc. is not known at themoment, it was not possible to work out principles for data classing. The persons responsiblefor indicators may currently be the best source of information for that kind of work.


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5.2 Maps in SIATThis section gives an overview over maps used in the current SIAT proposal based on thesystem description of SIAT in the Deliverable Report 4.3.4 (Verweij et al, 2006) and the availa-ble prototype (SIAT, 2007).

The current version of SIAT (SIAT 2007) contains two types of maps: static and dynamicmaps. Static maps on the one hand are images which the user cannot manipulate, they have astatic extent, and one cannot zoom. Basically this representation corresponds to a paper map.Dynamic maps on the other hand are maps which can be altered interactively by the user. Thedegree of interactivity can differ from basic interactions like changing the zoom level and theextent, requesting feature information, reclassifying the colour scheme, changing the featuredata by selecting from different drop down boxes to manipulating features and writing themback to a geoserver.

According to Deliverable Report M4 4.3 (Verweij, 2006), five maps are shown in four applica-tion windows within the simulation section of the application.

Discussed in section

Map Where Static / Dynamic

5.3.2. Expert Step 2 – Policy Settings Interactive – selection, zoom, pan

5.3.2.4. Land Use Changes Step 3 – Impact Indicators Interactive – zoom, pan

5.3.2.5. Impact Indicators Step 3 – Impact Indicators Interactive – zoom, pan

5.3.2.7. Sustainability Indicators Step 4 – Sustainability Interactive – zoom, pan

5.3.2.8. Threshold Fact Sheet Step 4 – Sustainability Fact Sheet Static

Table 3 Map – location – map type

5.2.1 EU Countries and the rest of EuropeCurrently the maps implemented in the SIAT prototype do not reflect a difference between EUcountries and non-EU countries. Switzerland and Iceland are shown equally with the EU NUTSx regions. According to ec.europa.eu (2007) neither of the two countries is mentioned in the listof NUTS countries. SENSOR (2007) refers to EU 25 where again neither Switzerland norIceland are included.

Measures to visually mark countries which are not part of the SENSOR project are highlyrecommended. Colouring water areas might as well be a good idea to emphasise the fact thatthe EU is actually part of a bigger Europe and to acquire a more complete map.

5.2.2 Step 2 - “Policy settings” – Expert

Fig 23 Policy Settings – Expert


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AnalysisThe expert map is an interactive map that facilitates the selection of single NUTS x regionswhich then are included into calculations. The user can select regions by countries and add orremove single NUTS x regions. Currently the selected NUTS x-regions are indicated by agreen fill.

Design proposalSelected regions on a map should not convey any other values than selected or not selected or1 and 0. Using a green fill might therefore not be optimal. We would suggest a black/whitebinary colour scheme (see 5.4.3.).

Step 3 - “Impact Indicators”SIAT's step 3 contains selection elements, a map area containing an element displaying anestimation of indicator quality. Two choice elements let the user select either Land UseChanges or Indicators or Impact Indicators.

Land Use ChangesLand Use Changes shows changes within 8 land use classes measured in hectares. Currently agreen to orange transition is used spreading from 0 million to 10 million hectares. It was agreedto classify land use into the following eight land use classes:1. Agriculture grasslands2. Agriculture arable3. Forestry/Nature (as an option these can be separated)4. Residence and recreation5. Business and transport6. Abandoned land (resultant of the others)7. Other (considered constant)8. Water bodies (considered constant)

Deliverable report 2.3.2/3 (Konkoly Gyuró, 2007) does not tell us too much about land use, butthe current SIAT prototype (SIAT 2007) indicates, that at least some land use classes are divi-ded into further sub-classes: the possibility of selecting arable land – energy crops and arableland – general in the current SIAT prototype is an indicator for a finer graduation than eightclasses.

Changes in land use can be positive, neutral and negative. Positive and negative values canappear in one land use class or one land use class’ values can be negative while the others canbe positive. It could be imagined, that areas of Residence and Recreation (1) grow while areasof Arable Agriculture (2) would shrink. As the graphical user interface (GUI) is built today, itenables a quick switch between the different land use classes by selecting the upper drop downbox. The maps being shown subsequently are therefore part of a system where the drop downbox acts as an element of interactivity on a level with map controls (pan, zoom …).

Colours can be used here to express different aspects of the data:(1) Data content – one colour scheme per land use class(2) Data type – one colour scheme for all land use classes – same data type throughout

classes(3) Unit – one colour scheme for all land use classes – same data unit (ha)


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Deriving a colour scheme from only one land use class or in other words one colour schemeper land use class (1) might make it difficult for the user to understand the mapped data at aglance. In addition, this might result in a series of different colour schemes which might confu-se the user more than clarify an affiliation with a certain sector. Using the same colour schemefor all indicators (2 and 3) will make it easier to understand a land use class in relation to anot-her. On the other hand the differences between the individual land use classes might be quitebig and therefore details could be lost.

When using one colour scheme for all indicators within land use changes, an iconic representa-tion of at least the sector (factory for industry, tree for forestry, etc. ) may contribute to visuallegibility. The colour scheme of the map should not visually interfere with the colours used forthe quality impact indicator.

We recommend a diverging green white brown scheme plus icons indicating at least sec-tors for any indicator within land use changes. See section 5.4.1.1. for a description of thecolour scheme.

Impact IndicatorsAnalysisThe maps on Sustainability Impact Indicators show, depending on the selection in the lowerselect box, a series of different indicators.

Before investigating specific data that are to be presented within the visualisation framework ora SIAT application (simulation run), we have a look at the taxonomy the indicators are placedwithin. Indicators are part of Impact Issues like Air quality, which again are part of three bigclasses, the Sustainability Dimensions: Environment, Economy and Social.

Group name

Number of items

Sustainability dimensions

3

Impact Issues

12+11+12=35

Indicators

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Indicator Units

?

kg, ha, individuals,...

Table 4 Taxonomy of SENSOR terms

Units of indicatorsAt a quick look indicators are measured in spatial (ha), weight (tons or kg), monetary (€) andsingular/nominal ( people, birds, etc.) units: the indicators within the environmental sustainabi-lity dimension are predominantly measured in kg and hectares, the ones within the dimensionof economy in Euro and the ones of the social dimension in people. Table 5 gives you someoverview of possible indicators.


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Table 5 Indicators and their units (current state of information)

At a closer look some indicators are expressed via different relational measures like for exam-ple 10.1 Social Pressure. This “indicator group” actually consists of two indicators: SocialTourism Pressure which is expressed in people / people and Recreational Tourism Pressureexpressed in people / ha. In the following section, we will elaborate on a solution to communi-cate these relations in an easy way.


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Design proposalAs this map view shows maps based on indicator data expressed in diverse units and relations,several approaches are possible:1. Character scheme. Dimension: one colour scheme for each sustainability dimension

(ENV, SOC, ECO)2. Character scheme. Unit: a colour scheme for each unit (ha, kg, individuals, items, etc.) as

well as for relational data (%, people/people, people / area, etc.)3. Keep it simple: just one, diverging colour scheme which focuses on change and therefore

illustrates positive and negative values. If an indicator only has only positive or negativevalues, we extrapolate the colour scheme and cut it off respectively above or below zero.

We consider visual clarity and simplicity as more important than expressing affiliation with eitherdimension or unit by applying different colour schemes. Therefore, we recommend applying thesame colour scheme to every impact indicator. A diverging colour scheme with zero as a centrewould be able to cover any indicator; even binary or qualitative data could be expressed through adiverging colour scheme. The colour scheme for indicators should not be identical to the colourscheme for land use classes. In addition we have to take care, that the map colours do not interfe-re with the colours of the quality estimation indicator. The orange white purple colour sche-me, proposed in section G.4.1. was carefully selected to live up to these constraints.

In addition, we recommend inserting an icon, which explains the nature of the impact indicator:Person/person, ., weight, rectangle etc. would stand for simple units, person / person, person/rec-tangle etc. for indicators expressing relations. These icons will clarify questions about the natureof an indicator by simple means. Besides they could be easily implemented into the application.Figure 24 shows some examples of icons for simple units (Figure 24, top) and indicators expres-sed through a relation of units (Figure 24, bottom).

Fig 24 Example of icons for simple indicators (top): hectares and people and relational indicators (bottom): Euros/hectare, kilogram/hectare, individuals/hectare and individuals/individuals)

Sectorial information could as well be indicated by using icons.


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The Quality Estimation Indicator

The quality estimation indicator is located at the bottom of the sustainability indicator and landuse change maps. According to Verweij 2006 its values can be good, moderate and weak andcurrently a red/orange/green colour scheme is proposed. We recommend a red/yellow/greencolour scheme, see section 5.4.4. for more about this scheme.

As the quality estimation indicator applies to the data-set of one indicator in whole, we recom-mend moving it out of the map and letting it form a graphical element of its own. Thereby thechance of visually relating the colour of quality indicator to areas on the map gets minimised.

Sustainability IndicatorsAnalysis

Fig 25 Quality estimation indicator

Fig 26 Step 3 – Sustainability

The Sustainability Indicators map compares indicator values with thresholds of critical limitsbased on both quantitative and qualitative calculation methods. [...] A slider enables to changethe critical limit and makes SIAT flexible towards specific end user target. (Verweij et al,2006). Users can select an indicator and investigate the effect of different cut-offs for thres-holds. Currently a binary scheme is applied: red shows a sustainability risk, green indicates norisk.

Design proposalThe map colours selected communicate the message quite well. Maybe saturated colours like abright red and a bright green might convey an even stronger message. The proposed trafficlights within other parts of the application should follow the same colour scheme.See section 5.4 for the actual colour values.


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The Map on the Threshold Fact SheetAnalysisPresently, the maps shown in the fact sheet are non-interactive or static maps showing the spa-tial distribution of thresholds within the sustainability view/window. 4 shows the currentlyimplemented indicators.

Design proposalThe maps within this window are apparently currently dummies. Looking at the differentthresholds currently implemented, one can see three types: Relative increase / decrease (5,6) Absolute values (4) Relative increase (1,2,3)

We would recommend diverging schemes in this part of the application too following the samerules as the proposed colour schemes for land use classes and indicators (see 5.4.1.).

1 Threshold name unit colour scheme range

2 SOC – Employment/labour –unemployment rate

% b/w w=no unemployment, b=1% increase

3 SOC – Inclusion – Gini coefficient % b/w w = max. 2% to higher class b = max.10% to higher increase

4 ENV – Water quality – NitrogenSurplus

kg /ha b/w w = 50 kg/hab = 150 kg/ha

5 ECO – Macro Economy – GDPgrowth / capita

% b/w w = 4% increase b = 3% decrease

6 ECO – Sector Related – Grossvalue added (agriculture)

% b/w w = 4% increaseb = 3% decrease

Table 6 Currently implemented indicators

Fig 27 Map on the threshold fact sheet


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5.3 Colour schemes for SIATWhen designing colour schemes one might bear in mind that they might look differently whenviewed on different media and/or hardware. SIAT will be used on laptops and PCs, be shownvia beamers and maybe looked at by colour blind people. We will therefore have an eye on thefriendliness of each selected colour scheme towards those criteria.Colour schemes described in this section are based on one byte per channel (red/green/blue orrgb). It is recommended to view the colour schemes on a screen as different (not calibrated)printers might render colours not properly.

5.3.1 Diverging schemesA diverging scheme shows data bi-directionally from a centre outwards. When creating diver-ging schemes, the number of classes has an impact on friendliness towards colour blindness,photocopier and LCD projector friendliness. The higher the number of classes, the lower is thenumber of compatibility with the friendliness classes. In the selection process, we tried toemphasise the importance of CRT, laptop (LCD), LCD projector colour blind friendliness.Figure 28 shows the four colour schemes that match best to these criteria. Increasing the num-ber of classes results in a radical decrease in the number of friendliness’s.

Fig 29 Difference between the greens of the green white browndiverging scheme (top 4) and the saturated green of the red / greenscheme (bottom)

Fig 28 Diverging schemes: (a) purple white orange, (b) green white brown, (c) blue white red (d) blue yellow red

Taking the above into consideration, we could actually choose from 4 schemes with five clas-ses but as the quality estimation is shown in red-yellow-green, the latter turns out to be thelimiting factor for the selection of colour schemes. It is therefore not advisable to use thesecolours in either the land use change maps or the indicator maps. Map colours could be mixedup with quality indicator colours.

Therefore the only colour schemes left are the green white brown scheme which weassign to the land use change maps and the purple white orange scheme which we assignto the sustainability indicator map. As shown in figure 29 the greens of the first colour schemeis quite different from the pure, saturated green of the quality estimation.

Splitting the colour ramps into five classes results in the highest number of friendliness’ butmight be too coarse to reproduce data detail. We therefore extrapolate the colour scheme to eit-her nine or eleven classes which results in the actual availability of five or six classes for onemap. Figure 30 shows an example of four colour ramps when classing into eleven classes andvisualising different data sets.


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A non scientific, quick visual evaluation on two different screens showed, that differences bet-ween classes when classing into five classes could be perceived properly, the ability to distin-guish differences between adjacent classes might not be as clear and difficult to distinguish onthe map.

Green White BrownA green white brown scheme is proposed for SIAT’s step 3 / impact indicators / land usechange maps.

Fig 30 Actual display of the colour ramp in SIAT for data with negative, positive, symmetrically negative and positive, negative and positive with shift towards negative (from left to right)

Fig 31 green white brown 9 (5) class colour sche-me with RGB codes

Fig 32 green white brown 11 (6) class colour sche-me with RGB codes


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Purple White OrangeA purple white orange colour scheme is proposed for SIAT’s step 3 impact indicator maps.

Fig 33 purple white orange 9 (5) class colour sche-me with RGB codes

Fig 34 purple white orange 11 (6) class colour sche-me with RGB codes

5.3.2 Binary schemesBinary schemes are used to communicate qualitative data which consist only of two values: yesor no, warm or cold, member or not member or like in within the context of SIAT sustainableor not sustainable or selected or not selected.

Binary, single value schemes can therefore be built using two colours with different hues orwith the same hue but different lightness’s. If one class is regarded as more important than theother it should be darker, if no difference needs to be expressed two hues with similar lightnessshould be selected.

5.3.3 Black/white schemeThe policy settings – expert map in step two lets the user decide which regions SIAT shouldinclude into calculations. A black/white colour scheme is considered appropriate for that task.

Fig 35 Black - white colour scheme with rgb colour codes


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5.3.4 Red - green schemeThe sustainability risk assessment map in step 4 indicates whether the changes within one indi-cator are regarded as sustainable or not sustainable. In the current prototype of SIAT, areas witha sustainability risk are shown in a red tone while those without in a green tone. This can beconsidered as appropriate; a little improvement might be acquired though by choosing saturatedcolours.

Fig 36 Red - green binary scheme with rgb colour codes

This colour scheme was daltonized7, i.e. the vision of a colour blind was simulated, atwww.vischeck.com with a red/green stretch factor of 0.5, a luminance correction of 0.5 and ablue/yellow correction of 0.5.

5.3.5 Traffic light schemeThe quality estimation indicator in step 3 gives an indication on the reliability of the calculateddata (maps) and methodology. Originally a red/orange/green colour scheme was proposed(Verweij 2006) which might conflict with the purple white orange scheme of the indicatorvalues. We therefore propose a red / yellow / green colour scheme.

Fig 37 Red-yellow-green colour scheme with rgb colour codes

7 www.vischeck.com, online daltonizing here: http://www.vischeck.com/daltonize/runDaltonize.php

8 ColorBrewer is a piece of online software which facilitates design and generation of colour schemes.http://www.personal.psu.edu/cab38/ColorBrewer/ColorBrewer.html

9 http://www.apache.org/licenses/LICENSE-2.0

10 GeoTools is an open source JAVA GIS toolkit, read more here: http://udig.refractions.net/docs/api-geotools/org/geotools/brewer/color/package-tree.html

11 See JAVADOC here: http://udig.refractions.net/docs/apigeotools/org/geotools/brewer/color/package-tree.html

12 This chapter is a contribution of Laurence Jones, NERC and taken from his unpublished working paper(Jones, 2007)

5.3.6 Copyright and Implementation into SIATThe diverging colour schemes presented above were generated with ColorBrewer8. They aresubject to copyright under the Apache 2.0 license9 and credit should be given somewhere in theSIAT application as follows: Colours from www.ColorBrewer.org by Cynthia A. Brewer,Geography, Pennsylvania State University.

SIAT development might consider, that Brewer’s colour schemes actually are implemented intoGeoTools10 in the package org.geotools.brewer.color11.

5.3.7 Dividing up the scale12

The appropriate ways of achieving this depend on what we want to achieve. Different possibili-ties are described below, and the method of dividing up the scale may vary with the indicator.These are decisions that have to be made for each indicator anyway for mapping purposes andthis approach just attempts to formalise that, with the advantage that all indicators will then berepresented in a standard way:


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Fig 38 Dividing range into equal classes.

The simplest is to divide the maximum potential range of values into equal classes (Fig. 38).However, this is a fairly arbitrary distinction and takes no account of the social, economic orecological relevance of the actual outputs, and is likely to be skewed by outlier regions. Aslightly more sophisticated method might define a range within which to divide classes equally,and above or below which we decide that values do not require new classes and these justfall into the end classes. Yet another option may be to look at the frequency distributionof values and divide classes according to some measure of standard deviation around an avera-ge or percentiles around the median. What these approaches fail to do is incorporate anyknowledge about what a particular value of the indicator actually means. Skewed distributionsof results around the threshold may also be an issue.

Dividing range into equal clas-ses of damage, for a non-linearrelationship.

In a non-linear relationship, we may be more interested in equal classes of damage or impact,and in this case we need to know the approximate form of the relationship (e.g. Eq. 5.1). Thisis preferable to the first option, but its applicability is limited for most indicators by how wellwe understand the underlying processes.

Fig 39, 40 Manual divisions providing greater resolution near to the threshold (left) and Manual divisions emphasising consequences of threshold exceedance.

Manually deciding on the boundaries for each class provides the greatest flexibility. We maywant to give greater resolution to detecting change near the threshold (Fig. 39) or decide that itis important to incorporate knowledge about the consequences and reversibility of exceedingthe threshold, for example with climate change where exceeding some system threshold hassevere consequences and is effectively irreversible (Fig. 40) In this example, exceeding thethreshold would change the value from ‘0’ straight to ‘-3’.


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6 Visualisation ModulesThis section introduces a series of modules that when implemented into SIAT will contribute toSIAT’s ability to communicate results to the end user. They provide with enhanced data miningfeatures that will shed light on the absolute and relative nature of indicators, will give an over-all picture of all indicators at a same time, provide with features for regional comparison andembed the visualisations described in section F.

6.1 Visualisation MatrixThe purpose of the visualisation matrix is to give a quick overview over all indicators for allmember states at the same time, to facilitate the end user with simple but powerful data miningfeatures, show the number of generated and visualised landscapes and provide links to the 3DScene Dialogue and the Google Map Dialogue.

6.2 General design

Fig 41 The Visualisation Matrix

The Visualisation Matrix consists of three elements: the data area, the colour ramp and the datainspector. Within the data area information is envisioned by allocating values within the dataarea in two dimensions:, horizontally by member states and vertically by indicators. The latterare grouped into the three SENSOR sustainability dimensions Social, Environment andEconomy (column most left). Due to limited horizontal space the matrix cannot show the fullindicator name, but only a label, containing an ID of the indicator. The full indicator name aswell as a hot-link to the Fact Sheet of the indicator is revealed when the end user moves thecursor over the indicator label: it slides out.


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Fig 42 Indicator label slide out

Clicking on either the indicator ID label or the full indicator name in the slide-out will open theGoogle Map Dialogue.

The first row of the data area is special; it contains information about the available 3D scenes.Depending on the size of the member state a different number of visualisations will be calcula-ted creating images of different landscapes within one member state. The small squares withineach tile indicate the number of visualisations. By clicking on a tile within the 3D row, the 3DVisualisation Dialogue will open.

6.2.1 The Country Order Selector - Data Mining Features

Fig 43 Country order selector


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The country order selector is located to the right of the member state row. A context menu willappear on mouse over and make it possible to order the columns of the matrix in differentways: alphabetically, by year of EU entry, geographically north-south and east-west (the cen-troids of the member state serves as ordering criteria here), by population size and by area.Thereby a quick and easy possibility is created for visually analysing data and its distributionacross the member states in relation to geography, area etc. In addition to the ordering optionsdescribed here, further possibilities could be added.

6.2.2 The Data Inspector

Fig 44 The Data inspector

The data inspector, allocated on the right hand side of the matrix provides the end user withnumerical information, a pictogram explaining the unit of the indicator as well as the possibili-ty to call country-indicator specific fact sheets. By leading the cursor over the matrix, theinspector will show the following information: the value in percent, the value of relativechange between 2025 with and without the selected scenario and the absolute value. Figure 6.4illustrates the data inspector showing the results of the Social Tourism Pressure Indicator,which has a result of -80% comparing the 2025 base scenario with the modelled scenario. Thismeans that there are eight tourists less which leaves the country with 2 tourists in total.


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6.3 The 3D Scene Dialogue

Fig 45 T3D Scene Dialogue

The purpose of the 3D Scene Dialogue is to highlight landscape change and provide with asolid and understandable connection between data and images of landscapes. This dialogueconsists mainly of a country selector bar, the 3D visualisation area, a scene selector, a datainspector and two switches. One of them toggles between the two types of visualisations: pho-torealistic and schematic visualisations, the other between representations of the base scenarioand the policy scenario. Below the visualisation area, indicator values are presented in twolevels. The upper bar shows the indicators used to calculate the synthetic landscapes, the lowerone the other indicator values. Every indicator value is shown by a widget, holding the indica-tor colour code and a slide out widget: full indicator titles won’t fit on the display, thereforeonly the indicator id or number is shown, a full indicator is revealed on mouse over. Indicatorwidgets allocated on the right hand side of the dialogue point and slide the other way, in orderto avoid the full title sliding out over application or screen edges. The functionality of the datainspector within this dialogue is the same as in the Visualisation Matrix showing detailed infor-mation on mouse over at the indicator widgets.


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6.4 Thematic Maps – Google Map Dialogue

Fig 46 Thematic Map

The Thematic Map dialogue gives a geographic overview of only one indicator at a time andconsists of a Google Map Area and a data area. The Google Map area contains the standardGoogle Map controls: the background map selector, and the zoom control. The data area belowshows the indicator values of the countries shown on the map - clicking on the data field shiftsbetween displaying absolute and relative numbers.

Regional EmbodimentRelative changes in data can be quite possible to relate to; therefore a spatial comparator forindicators expressed in square meters is introduced. After having pulled the little square alloca-ted next to the indicator value onto the map area it will adjust according to the indicator value

and the current map scale and show the spatialextents of the indicator value. Thereby the enduser can relate the indicator value to a knownarea.

Fig 47 Spatial Embodiment Regional Comparator


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6.5 Explanatory overlaysApplications with complex design might be quite difficult to navigate, especially end users newto the application might find the application complicated to use. Different techniques are possi-ble to apply, mouse over with context help and help screens are two of them. For this applicati-on a concept of explanatory overlays was developed which explains all interactive and infor-mative elements of an application screen. By clicking on the question mark, allocated at thetop-right corner of the window, a semitransparent, white layer is superimposed the applicationand explicative texts are shown.

Fig 48 Explanatory Overlay – Matrix

Fig 49 Explanatory Overlay – Thematic MapsFig 50 Explanatory overlay – 3D Visualisation

Dialogue


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7 ConclusionsThe current work of the TUM team focuses on the description of the software 3DLG used togenerate a set of 30 representative NUTS x landscapes, including the baseline and policy casesCAP209 and CAP219. The results presented here are visualized using R (free software envi-ronment for statistical computing and graphics) as the produced landscapes are currently beingvisualized by L-Vis and Lenné3D and not yet available in their final version.

Further tasks of Lenné3D regarding schematised visualisations are: Development of plant and landscape objects distributions (algorithms) for TUM’s raster

cells according to land-use and cluster regions. Connection to TUM’s interface to access raster-based data sets. Calculation (rendering) of all visualisations (renderings)

LIFE is now co-operating with ALTERRA to develop and implement the visualisation modulesinto the next prototype of SIAT and support the SIAT development team in developing theGraphical User Interface. At a point in time a decision regarding classing of indicator valueswill be expected – without having classes or thresholds, the general concept of theVisualisation Matrix will need to be reconsidered as well as the indicator widgets of the twoother visualisation dialogues. The work on colour schemes is finished and is awaiting reflectionby the members of M4 / M1.


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Main partners involved in this publications are:Technische Universität München, DELeibniz Zentrum für Agrarlandschaftsforschung, DELenné3D GmbH, DEForest and Landscape, DK

This report was edited by theLeibniz-Centre for Agricultural Landscape Research Contact: [email protected]

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