ArcGIS ModelBuilder Applications for Landscape Development ...

ArcGIS ModelBuilder Applications for Landscape 

Development Planning in the Region of Munich, Bavaria 

1 Introduction 

Joerg SCHALLER and Cristina MATTOS 

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 19 th 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

128 

J. Schaller and C. Mattos 

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).

ModelBuilder Applications for Landscape Development Planning in the Munich Region 129 

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

130 


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.


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.

132 


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).


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.

134 


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


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

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

136 


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)


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. 

5 References 

Ahern, J. (1999): Spatial concepts, planning strategies and future scenarios. In: Klopatek, 

J.M. & R.H. Gardner (eds.): Landscape Ecological Analysis. NY: Springer. p.175-201. 

Buhmann, E. & S. Ervin (eds.) (2003): Trends in Landscape Modeling. Proceedings at 

Anhalt University of Applied Sciences 2003. Heidelberg: Wichmann. 274p. 

Buhmann, E., U. Nothhelfer & M. Pietsch (eds.) (2002): Trends in GIS and Visualization 

in Environmental Planning and Design. Proceedings at Anhalt Univ. Wichmann, 192p. 

Carr, M., P. Zwick (2007): Smart Land-Use Analysis: the LUCIS Model. ESRI Press. 292p. 

Dangermond, J. (2008): GIS—Geography in Action. ArcNews, v.30, n.4, p.6-8. 

ESRI (2000): ModelBuilder for ArcView Spatial Analyst 2. Redlands, CA: ESRI Press, 

18p. 

Goodchild, M.F. (2005): GIS, spatial analysis and modeling overview. In: Maguire, Batty, 

Goodchild (eds.): GIS, Spatial Analysis, and Modeling. Redlands: ESRI Press, p.1-18. 

Kiemstedt, H. (1994): Landscape Planning: Contents and Procedures. The Federal 

Minister of Environment, Nature Protection and Nuclear Safety, Germany. 

Maguire, D., M. Batty & M.F. Goodchild (eds.) (2005): GIS, Spatial Analysis, and 

Modeling. Redlands, CA: ESRI Press. 480p. 

Manegold, J. (2003): Using the ModelBuilder of ArcGIS 9 for Landscape Modeling. In: 

Buchmann, E. & S. Ervin (eds.): Trends in Landscape Modeling. Proceedings at Anhalt 

University of Applied Sciences 2003. Heidelberg: Wichmann, p.240-245. 

Mattos, C. (2007): GIS-based modeling of settlement development scenarios in the Munich 

Region. Freising, Germany: Technical University of Munich TUM. 146p (MSc Thesis). 

Schaller, J. & H.M. Schober (2007): Landschaftsentwicklungskonzept Region München 

(LEK14), Regierung von Oberbayern. Munich, Germany, 612p (Final Project Report).

138 


Schaller, J., T. Gehrke & N. Strout (2009): ArcGIS processing models for regional 

environmental planning in Bavaria. In: Geertman, S. & J. Stillwell (eds.): Planning 

Support Systems: Best Practice and New Methods. Berlin: Springer, p.243-264.

ArcGIS ModelBuilder Applications for Landscape Development ...

Create successful ePaper yourself

Delete template?

Save as template?