ArcGIS ModelBuilder Applications for Landscape Development Planning in the Region of Munich, Bavaria

Joerg SCHALLER and Cristina MATTOS

1 Introduction

Regional landscape development planning is a complex multidisciplinary task aimed at the sustainable use and management of a region’s natural and cultural resources, including the protection of unique and rare features, controlled use of limited resources, impact mitigation, management of landscape changes, and placement of human development in suitable locations (KIEMSTEDT 1994, AHERN 1999). The planning methods and instruments may vary considerably depending on the planning goals and objectives, time frame and scale, availability of data, political support, public participation, and main driving issues, but some approaches and technologies are often used, such as Geographic Information Systems (GIS) and models. The use of GIS for Landscape Planning goes as far back as Warren Manning and Ian McHarg, both landscape architects who, in the 19th and 20th centuries, already used map overlays as the framework for ecology-based land-use planning, setting the basic fundaments of GIS (DANGERMOND 2008). Since then, GIS tools have evolved and diversified, and thanks to their powerful geoprocessing and modeling capabilities, have greatly contributed to the areas of environmental planning, design and engineering, and have been increasingly used for Landscape Architecture, particularly digital landscape modeling and visualization (BUHMANN & ERVIN 2003). The GIS technology has become particularly useful to create and implement models for problems of spatial nature (BUHMANN ET AL. 2002, MAGUIRE ET AL. 2005). GIS tools help not only to process, analyze, and combine spatial data, but also to organize and integrate spatial processes into larger systems that model the real world (ESRI 2000). As abstractions and simplifications of complex systems and processes, these spatial models can be powerful tools for prediction, forecasting and planning, used to assess scenarios and reduce uncertainties about the future (GOODCHILD 2005). The Region of Munich (or Planning Region 14) is one of the fastest growing in Europe. Comprised by the city of Munich and 186 municipalities in Bavaria, Southern Germany, it has experienced stark population growth and settlement development in the past 150 years that have resulted in pressures on the region’s natural and cultural resources, intensification of land use, and threats to the quality of life. Since 2003, coordinated efforts from ESRI Germany, the GIS Laboratory of the Chair of Landscape Ecology—Technical University of Munich, the Chair of GIS Science—University of Redlands (USA), the LDP Group (Landscape Development Plan for the Munich Region), and the Schober/Schaller Planning & Consulting Group have focused on

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the development of GIS processing tools and models to support development planning and assessment in the Munich Region, as an attempt to provide regional and local authorities with a framework of data and tools to help planning and implementing measures for sustainable regional development (MATTOS 2007, SCHALLER & SCHOBER 2007, SCHALLER ET AL. 2009). This paper presents an overview of the results from these efforts, including GIS model frameworks built with ESRI’s ModelBuilder technology and geoprocessing applications, which demonstrate the benefits of GIS-based modeling for decision support and scenario analysis in face of this region’s complex planning issues.

2 The GIS Modeling Approach

Regional planning involves intensive spatial data processing, where logical sequence of tasks (geoprocessing workflows) must be performed and documented. GIS technology is essential in this effort, providing geoprocessing tools to perform the necessary spatial analyses. GIS spatial models make the process easier—they automate the workflows by connecting tasks and processes together; they allow executing a workflow, modifying it, and repeating it multiple times with one click of the mouse, making it easier to manage the workflow and increasing the efficiency of geoprocessing (GOODCHILD 2005). GIS models for development planning of the Munich Region were created using ESRI’s ModelBuilder, a ArcGIS application to create, edit, and manage models. In ModelBuilder, the model is represented by a flux diagram in a graphic user interface that facilitates to create, visualize, edit, and execute geoprocessing workflows, to use and reuse them, to share and apply them to different geographic areas (ESRI 2000, MANEGOLD 2003) (Figure 1). The model diagram represents a geoprocessing workflow with one or multiple processes strung together. Each process consists of a tool and its parameter values (e.g., input and output data, reclassification table). The model components, or elements, are represented in the ModelBuilder interface by different symbols (Figure 1): inputs (geographic data, values or SQL expressions) as blue ovals, outputs (geographic data or values generated when the model runs) as green ovals, tools (operations to be performed on the input data) as orange rectangles, connecting arrows that indicate the processing sequence, and text labels that explain the model. The geoprocessing tools may be the ready-to-use ArcGIS system tools available in the ArcToolbox, scripts created with Python or other COM-compliant language, or embedded models in the case of complex processes within processes. The model can be easily built by dragging-and-dropping the model elements onto the ModelBuilder window and connecting them with arrows according to the processing sequence. Model parameters (labeled as ‘P’) are information that must be defined by the user before running the model. ModelBuilder features also include wizards to automate model creation or change, layout configuration and drag-and-drop tools, property sheets to modify the model components’ properties, and the possibility to save the entire model as a XML file or script (ESRI 2000).

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Fig. 1: ModelBuilder graphical interface—model processes and elements

The Munich Region models were fully documented, including descriptions, help pages, and comments, in order to make them clear and easy to use by anybody; to be shared or used as templates for different applications or planning regions.

3 ModelBuilder Applications for the Region of Munich

3.1 The Landscape Development Concept (LDC) The LDC is a comprehensive regional landscape development concept for nature protection and landscape management. It was created by request of the Government of Upper Bavaria, taking into consideration the region’s characteristics, potentials and limitations, past and future development trends, development guidelines and goals, and complex planning challenges. It is a decision support system based on environmental planning methods, GIS and modeling technologies, including automated processing and documentation of geoprocessing workflows with the ArcGIS ModelBuilder (SCHALLER & SCHOBER, 2007). The LDC addresses issues and questions faced by the planners in the Munich Region, such as: the quality and sensitivity of natural resources, impacts of regional development, potential development scenarios, how to mitigate impacts with planning measures, and how to efficiently communicate complex issues to decision makers and the public. The LDC main goals were: protection and sustainable use of natural resources; protection and development of nature and landscape as compensation for intensively used urban areas; development of open-spaces and green-belts as foundations for maintenance of the living quality; elaboration of proposals for sustainable land use development, especially for urban

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uses (commercial, residential, industrial), transportation, and water management uses, besides agriculture, forestry, and recreation. The work started with the creation of a ArcGIS geodatabase with all necessary data to address those issues and goals, including data on soil, water, climate/air, species, biotopes, landscape scenery, historical/cultural landscape, and planning (e.g., the Regional Plan, landscape plans, infrastructure plans, water management plans, etc.). The following step was the assessment of natural resources, their current state and quality, their actual and potential functions (e.g., groundwater recharge, floodplain water retention, energy exchange, ventilation, species diversity and biotope network, wildlife preservation, recreation), and their development potential. ‘Potential maps’ (resource assessment maps or function maps) were created for each of the resources using models implemented with ArcGIS geoprocessing tools and ModelBuilder, e.g. soils and potential agricultural yield map, current land use map, ground and surface water map, species protection and habitats map (Figure 2). The potential conflicts and impacts of actual and planned land uses were then assessed using GIS models implemented with ModelBuilder. Simple conceptual models were initially developed to describe the relationships between resource potentials and sensitivity of impacts. Each conceptual model was detailed, incorporating scientific assessment criteria from literature and expert ratings. Examples are the flooding risk, groundwater recharge, soil erosion hazard, and noise impact models (Figure 3). The ‘potential maps’ were overlaid with the land use map to identify and quantify conflicts between land use and resource functions. ‘Goal maps’ were created based on the intensity of conflicts identified in relation to the pre-defined development goals.

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Fig. 2: Example of potential map—soils

Fig. 3: Example of LDC model—connection of open spaces

An overall landscape concept was developed based on the results obtained and on the existing Regional Plan and development guidelines. The concept includes safeguard instruments and suggestions on how to implement development goals through actions. The complete database, models and maps generated, concept and suggestions constitute a Decision Support System for the region’s planning and decision making, which can be altered, updated and used for assessment of different development alternatives. This system has already been adjusted and detailed for assessment of specific development aspects in the Munich Region, such as identification of suitable areas for urban land use.

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The LDC database, models and maps have been delivered to the regional planning authority and shall be published on the Web this year. The results (thematic maps and sustainable development goals) have been discussed with the City of Munich Parliament and Planning Department, and with the assembly of mayors of the 186 municipalities that constitute the Planning Region 14. The LDC results shall be used for the update of the Regional Development Plan; it is up to the planning authority (Regionaler Planungsverband München - RPV), including the mayors, to decide which content of the LDC will be incorporated into the final Regional Plan. 3.2 Settlement development models After the development of the LDC, a special framework of models was created focusing specifically on the issue of population growth and consequent settlement development in the Munich Region. Using ModelBuilder, the LCD geodatabase, and updated population data, models were developed to support the assessment of settlement suitability and potential future settlement development scenarios (MATTOS 2007). Cluster analysis of regional statistical data showed intra-regional differences and allowed to identify four groups of municipalities with distinct characteristics and development trends and goals. Time series analysis of population data were performed for each group, providing forecasts until 2025. The main development goals and principles, and in particular the spatial aspects related to settlement development, were identified through a review of the existing relevant development plans, guidelines, and norms, complemented by interviews with planning authorities. The following ten settlement development models were then designed and implemented with ArcGIS ModelBuilder:

• Eight “partial” suitability models identify the favorable locations for settlement development (Figure 4): 1) exclusions, or forbidden areas for settlement, such as protected areas; 2) restrictions to settlement development set by plans and laws; 3) physical environment suitability, based on soil types and slope; 4) land cover/land use suitability; 5) socio-economic suitability, based on employment and education; 6) proximity to infrastructure; 7) proximity to recreation; and 8) scenery beauty.

• One main suitability model combines and weights the outputs of the eight partial models, generating a final settlement suitability map (Figures 5 and 6). This model was adjusted to the characteristics and development goals of the four intra-regional groups of municipalities previously identified through cluster analysis.

• One final “dynamic” model adds a time scale to the main model to assess potential future settlement development scenarios based on the suitability map and on prognoses of population and urban growth (Figures 7 and 8).

The nine static suitability models were implemented through a typical suitability modeling approach (CARR & ZWICK 2007, ESRI 2000), using ready-to-use ArcGIS analysis tools—reclassification, arithmetic and weighted overlays, distance analyses, and map algebra (Figure 4). The “dynamic” model is a more complex ‘process model’ with no pre-defined methodology to follow, and was implemented using map algebra expressions and Python scripting (Figure 7).

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Sensitivity analysis was performed by varying model input parameters and variables (relative weights of model criteria) and observing the relative change in model response in terms of settlement suitability values (range) and their distribution in the Munich Region. An example is shown in Figure 9, where two considerably different suitability maps are obtained from the restrictions model by using different model criteria weights. The users may alter any model’s inputs and variables, and their respective weights, in the model dialog box (example in Figure 5), adjusting them as they wish to different development goals or scenarios. Three potential scenarios of future development for the Munich Region considered by regional and local planners were assessed with this model framework. For each scenario, the “dynamic” model calculates how much new settlement area will be needed to accommodate the forecasted future population and then allocates this area according to the final settlement suitability map—the most suitable land is taken first, followed by areas of decreasing suitability: 1) the high migration scenario, with 11% population growth until 2023 that results in approximately 13000 ha of new settlements; 2) the stagnation scenario, with 6% population growth until 2023 and 7000 ha of new settlements; and 3) the airport expansion scenario (i.e., construction of a third runway at the Munich International Airport), which results in about 2000 ha of new settlements until 2020 in 62 municipalities nearby the airport.

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Fig. 4: Example of suitability model—graphical representation of the proximity to

recreation model

Fig. 5: Customized dialog box of the final suitability model with explanations about its

purpose and instructions to the user

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Fig. 6: Output of the main suitability model for settlement development

Fig. 7: Graphical representation of different versions of the ‘time’ model

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Fig. 8: Results of the ‘time’ model calculated for 5-year time intervals in the City of

Munich

Fig. 9: Results of the restrictions model with higher weights to parameters related to

landscape protection (left); and with higher weights to parameters related to air & road traffic noise (right)

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4 Conclusions

The model frameworks and applications described in this paper exemplify the potential benefits of using GIS models for regional landscape planning, as tools to help planners identify suitable areas for desired development, locate and quantify the consequences of alternative scenarios, and reduce uncertainties about the future. Although some processes cannot be fully automated (e.g., data collection and selection, expert rating), and despite limitations of time modeling in GIS, the models created for the Munich Region allowed to integrate a great volume of data and information in a systemic way, to automate geoprocessing workflows, to produce quantified, georreferenced, and visual outputs, helping to optimize and speed up the planning process. The models also provided an easily understandable language between those involved in the planning process. With the ModelBuilder graphical environment it was easy to create, modify, run and re-run the models. These models can also be replicated and adapted to other areas, development goals, and scales. Future developments to increase the benefits of these models include the use of GIS Server technology to implement the models on the Web, land use map update, 3D modeling, and agent-based dynamic models.

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