Assessing Cultural Ecosystem Services: A visual choice...
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Assessing Cultural Ecosystem Services: A visual choice experiment on agricultural landscape preferences from a user perspective in the case study Märkische Schweiz, Germany Kati Häfner Thesis submitted to the University of Potsdam Faculty of Science Institute for Earth and Environmental Sciences for the degree of Master of Science In Geoecology Dr. agr. Ingo Zasada Prof. Dr. rer. nat. Hubert Wiggering
Transcript of Assessing Cultural Ecosystem Services: A visual choice...
AssessingCulturalEcosystemServices:
Avisualchoiceexperimentonagriculturallandscapepreferencesfroma
userperspectiveinthecasestudyMärkischeSchweiz,Germany
KatiHäfner
ThesissubmittedtotheUniversityofPotsdam
FacultyofScience
InstituteforEarthandEnvironmentalSciences
forthedegreeof
MasterofScience
In
Geoecology
Dr.agr.IngoZasada
Prof.Dr.rer.nat.HubertWiggering
Abstract
The provision of natural amenities and the aesthetic quality of agricultural landscapes represent an
important territorial asset for rural tourism and quality of the living environment. The visual value of
a given landscape depends on individual preferences for its structure and composition. A stated
preference survey was conducted in the case study region “Märkische Schweiz” (ca. 580 km2), 30 km
east of the city of Berlin aiming at identifying variances in landscape preferences of local residents
and visitors from Berlin (N=200).
Therefore photorealistic landscape visualisations of four different landscape attributes have been
applied, including green point (e.g. trees) and linear elements (e.g. hedges), crop diversity as a
function of field size and the presence of grazing livestock. Attributes are differentiated into three
levels (low, medium, high) or two levels (present, not present), respectively. A Multinomial‐Logit
Model (MNL) was chosen to estimate the preferences for landscape attributes; a Latent Class
Analysis (LCA) approach to examine possible heterogeneity; and a random parameter (mixed)‐logit
model (RPL) to allow for individual specific values and the socio‐economic influence.
Results of the analysis revealed significant differences in preferences for various landscape
attributes, with a highest general preference for a high level of point elements. Heterogeneity could
be found with 70 % of respondents preferring diverse and highly structured landscapes and about
30 % of respondents having opponent preferences. I also found preferences to be dependent on
individual’s socio‐cultural background, e.g. level of education, gender or attitude and value setting.
The spatial distributions of cumulative preference values were mapped on a regular 100 x 100 m grid,
showing hot and cold spots of aesthetic quality. The results can help to improve the efficiency of the
policy delivery and to identify priority areas for the local landscape management from an aesthetic
value perspective.
Zusammenfassung
Für Tourismus in ruralen Gebieten und die Lebensqualität vor Ort ist die Ausstattung von
Agrarlandschaften mit ästhetischen Qualitäten und die Attraktivität der Natur ein Vorzug. Der
visuelle Wert einer Landschaft hängt von individuellen Präferenzen für Strukturen und Komposition
der Landschaft ab. Eine Präferenzanalyse wurde in der Fallbeispielregion „Märkische Schweiz“
(ca. 580 km2), die etwa 30 km östlich von Berlin liegt, durchgeführt mit dem Ziel Varianzen in
Landschaftspräferenzen von lokalen Einwohnern und Besuchern aus Berlin zu identifizieren
(N = 200). Dafür wurden fotorealistische Landschaftsvisualisierungen vier verschiedener
Landschaftsattribute entwickelt; dazu gehören grüne Punktelemente (z.B. Bäume), grüne lineare
Elemente (z.B. Hecken), Ackervielfalt als Funktion der Feldgröße und die Präsens von weidendem
Vieh. Die Landschaftsattribute wurden in jeweils 3 Level aufgeteilt (niedrig, mittel, hoch) oder 2 Level
(präsent, nicht präsent). Ein Multinomiales Logit Modell (MNL) wurde genutzt, um die Präferenzen
für die Landschaftsattribute zu berechnen; eine latente Klassenanalyse (LCA), um eventuelle
Heterogenität zu untersuchen; und ein sogenanntes Random Parameter Logit Modell (RPL), um
individuell unterschiedliche Werte und sozio‐ökonomische Einflüsse zu berücksichtigen.
Die Ergebnisse der Analyse zeigten signifikante Unterschiede der Landschaftspräferenz für die
verschiedenen Landschaftsattribute. Die höchste Präferenz wurde für ein hohes Level an
Punktelementen ermittelt. Es konnte festgestellt werden, dass es Heterogenität gibt. 70 % der
Befragten präferierten besonders diverse und strukturreiche Landschaften und ca. 30 % zeigten ein
gegenteiliges Präferenzmuster. Auch haben die individuellen Eigenschaften von Befragten Einfluss
auf die Präferenz, z.B. Bildungslevel, Geschlecht oder das Wertebild.
Die räumliche Verteilung von aufsummierten Landschaftspräferenzwerten wurden als Hot Spots und
Cold Spots von Landschaftsattraktivität in einer Karte dargestellt. Die Ergebnisse können helfen die
Effizienz von Politiken zu stärken und Vorrangflächen für das regionale Landschaftsmanagement von
einem ästhetischen Blickwinkel aus zu identifizieren.
TableofContents
1 Introduction ........................................................................................................................... 1
1.1 Context .................................................................................................................................... 1
1.2 State of the Art of Preference Analysis ................................................................................... 2
1.3 Research Objective and Questions .......................................................................................... 3
2 Case Study Area Märkische Schweiz ..................................................................................... 4
2.1 General .................................................................................................................................... 4
2.2 Landscape Structure and Composition ................................................................................... 5
3 Research Design and Methodology ....................................................................................... 7
3.1 Method overview .................................................................................................................... 7
3.2 Choice Experiment .................................................................................................................. 7
3.3 Study Design ............................................................................................................................ 8
3.4 Methodological Steps .............................................................................................................. 9
3.4.1 Development of Landscape Images .......................................................................... 9
3.4.2 Development of Questionnaire ............................................................................... 13
3.4.3 Pre‐testing and selection of choice cards ............................................................... 15
3.4.4 Survey .................................................................................................................... 17
3.5 Statistical Analysis ................................................................................................................. 17
3.5.1 Respondent characteristics ..................................................................................... 17
3.5.2 Preference Analysis (Multinomial Logit model ‐ MNL) ........................................... 18
3.5.3 Analysis of Heterogeneity (Latent Class Analysis ‐ LCA) ......................................... 19
3.5.4 Influence of Explanatory Variables (Random Parameter Logit model ‐ RPL) ......... 19
3.6 Mapping of Landscape Preferences ...................................................................................... 20
4 Results.................................................................................................................................. 22
4.1 Respondent characteristics ................................................................................................... 22
4.2 Preference Analysis (MNL model) ......................................................................................... 24
4.3 Analysis of Heterogeneity (Latent Class Analysis) ................................................................. 25
4.4 Influence of Explanatory Variables on Preferences (RPL model) .......................................... 26
4.5 Mapping of Landscape Values in the Landscape ................................................................... 28
5 Discussion ............................................................................................................................ 30
5.1 Interpretation of results ........................................................................................................ 30
5.2 Methodological Discussion .................................................................................................... 33
5.3 Relevance for Policy and Planning ......................................................................................... 33
6 Conclusion ........................................................................................................................... 34
7 References ........................................................................................................................... 35
8 Annex ................................................................................................................................... 40
ListofFigures
Figure 1 Location of the CSA Märkische Schweiz with county borders .................................................. 4
Figure 2 Land‐use distribution according to CLC2006 (Taken from: Ungaro et al., 2012) ...................... 6
Figure 3 Major landscape elements within agricultural fields (Taken from: Ungaro et al., 2012) ......... 6
Figure 5 Concept of visual representation for the developed images ................................................. 10
Figure 6 Developed base landscape with all attributes set at level 1 ................................................... 11
Figure 7 Developed landscape with all attributes set at level 2, except for livestock at level 1 ........... 11
Figure 8 Developed landscape with linear and point elements, crop diversity and livestock 3321 ..... 12
Figure 9 Developed landscape with all attributes set at their highest level (3 or 2, respectively) ....... 12
Figure 10 Division which parties the respondents voted in the last election ....................................... 23
Figure 11 Origin of the respondents visiting the Märkische Schweiz ................................................... 24
Figure 12 Spatial distribution of the landscape attribute levels in the Märkische Schweiz region ...... 28
Figure 13 Spatial distribution of the landscape utility sum ................................................................... 29
ListofTables
Table 1 Choice experiment set up design ............................................................................................... 8
Table 2 Description of the representation of landscape attribute levels in the visualizations ............. 10
Table 3 Hypotheses about influence of explanatory variable on landscape preference ...................... 15
Table 4 Socio‐economic characteristics of respondents ....................................................................... 22
Table 5 Multinomial logit choice model estimations and attribute ranking ......................................... 25
Table 6 Results of the Latent Class Analysis with three classes ............................................................ 25
Table 7 Results of the Random Parameter Logit model, full and restricted model .............................. 27
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1 Introduction
1.1 Context
There are a lot of benefits for society derived from agricultural landscapes (Power, 2010; van Zanten
et al., 2013; Zhang et al., 2007). These benefits are referred to as ecosystem services. The attention
for ecosystem services has increased over the past years, but mostly focusing on (semi‐) natural
ecosystems (Costanza et al., 1997), neglecting often agricultural landscapes. The main goal in
agricultural landscapes is considered to deliver provisioning services, such as food, fiber, and fuel.
However, they also deliver cultural and recreational services by providing recreational, aesthetic, and
spiritual benefits (Millennium Ecosystem Assessment, 2005b).
How important landscapes are as supplier or provider of cultural ecosystem services is highlighted for
example in law. In the German Federal Nature Conservation Act §1 Nr.3 it is written, that nature and
landscape are to be protected so as to permanently safeguard diversity, characteristic features and
beauty of nature and landscape as well as their recreational value (BNatSchG, 2009). The terms
diversity, characteristic features and beauty are used especially to characterize the landscape
scenery, emphasizing the optical‐aesthetical target of nature protection. With the earlier
amendment to the Federal Nature Conservation Act in 2002 additionally the recreational value was
included in the objectives, highlighting the importance of landscapes for human well‐being.
Many cultural and amenity services are not just of direct importance to human well‐being, but of
indirect as well, as they represent a considerable economic resource, for example through generating
income, jobs, and business opportunities in tourism and related business networks (Millennium
Ecosystem Assessment, 2005a). But better information on the economic importance and value of
these services is needed (ibid.). Valuation (monetary, as well as non‐monetary) of these services
could enable policy makers to address trade‐offs in a rational manner (TEEB, 2010). In rural areas this
is of great importance, because amenity as territorial asset contributes to the regional development,
economy and welfare. It is basically the local community and tourism industry that benefit from
cultural ecosystem services (Madureira et al., 2007). Valorised amenity can result in improvement of
quality of life and attract tourism and ex‐urbanisation (a process of in‐migration of affluent people
into rural settings). However, through indirect benefits and interaction between the benefits from
cultural services and the demand of other services, referred to as second‐order effects, landscape
managers could benefit as well. Increased economic activity in the region, investments and better
facilities benefit the regional society as a whole (van Zanten et al., 2013). Valorising a region’s
amenity can therefore contribute to a positive socio‐economic development.
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On a regional scale this cause‐effect relationship is acknowledged as well. In a local stakeholder
laboratory conducted in the Märkische Schweiz Region, Brandenburg, Germany, farmers were
identified as main actors in the landscape. But regarding services and functions delivered by the
landscape and how the region profits from the landscape ‘landscape aesthetics’ and ‘recreation and
health’ were considered as two main factors (Piorr & Zasada, 2013, unpublished). And in 2007 a
guest survey among 400 visitors of the town Buckow was conducted commissioned by the Culture‐
and Tourist Office Märkische Schweiz (inspektour GmbH, 2008), in which 72.5 % of respondents
stated that they evaluate the landscape as excellent, 25 % as very good and the remaining 3 % as
good. Expressed in school marks the landscape was evaluated in total with 1.3. This is an outstanding
result, if we compare this mark with the evaluation of other characteristics influencing the
attractiveness of the region for visitors: gastronomy (2.5), leisure activities offered (2.6),
entertainment (2.7), historic sites (2.6), opportunity to bath/swim (2.4) and price‐performance ratio
(2.8). This highlights the importance and potential of the landscape aesthetics as factor/ reason for
visiting the region and hence contributing to rural economy. However, which visual characteristics
(hereafter attributes) of landscapes determine landscape aesthetics and therefore the capacity of
landscapes to deliver cultural ecosystem services is not entirely clear (Arnberger & Eder, 2011; Ode
et al., 2009).
1.2 State of the Art of Preference Analysis
The methods of economic valuation of non‐marketed goods can assist to identify, which landscape
attributes foster the cultural function of agricultural landscapes (van Berkel & Verburg, 2013; van
Zanten et al., 2013). The most commonly method used is the stated preference method, also
referred to as choice experiment method. Users of the landscape are directly asked about their
preferences for visual landscape attributes and hence the marginal value of discrete landscape
attributes can be estimated (Arnberger & Eder, 2011; Dachary‐Bernard & Rambonilaza, 2012;
Grammatikopoulou et al., 2012).
Landscape attributes that appeared to be a dominant variable in identifying landscape preference in
earlier studies using different methods are: grazing animals (Grammatikopoulou et al., 2012);
number of land types, number of patches and land type diversity (Dramstad, Tveit, Fjellstad, & Fry,
2006); hedgerows and treelines (Rambonilaza & Dachary‐Bernard, 2007; van Berkel & Verburg,
2013); water (Arriaz et al., 2004; Swanwick, 2009; van Berkel & Verburg, 2013); woodland (Swanwick,
2009; van Berkel & Verburg, 2013); and man‐made attributes like farm buildings or cultural buildings
(Arriaza et al., 2004; Dachary‐Bernard & Rambonilaza, 2012; van Berkel & Verburg, 2013).
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It is unlikely that respondents are a homogeneous group, but rather a group of individuals with
different, even contradictory landscape preferences. This heterogeneity may result from different
socio‐demographic backgrounds of individuals (Grammatikopoulou et al., 2012). Several studies
indicate an influence of socio‐economic, demographic or sociocultural characteristics on preferences
such as age, gender, education or familiarity with the landscape (Aoki, 1999; Arnberger & Eder, 2011;
Kaplan & Kaplan, 1989; Ode et al., 2009; Strumse, 1996; Swanwick, 2009). Also the origin of an
individual is considered to have important influence. Differences were found between farmers,
tourist and residents (Dramstad et al., 2006; Rambonilaza & Dachary‐Bernard, 2007; Swanwick, 2009)
or between urban and rural respondents (Rambonilaza & Dachary‐Bernard, 2007). Arnberger & Eder
(2011) found for example that landscape exposure as a child shaped landscape preferences, but in
contrast to the concept of familiarity. Respondents, who had grown up in the Alps or foothills, an
area heavily forested, preferred more open landscapes while respondents raised on the plains, with
low forest cover, preferred forest‐rich landscapes. It was concluded that there is a desire for
‘different’ landscapes.
Even though these studies aimed to examine the characteristics that account for heterogeneity, a lot
of them state that their findings are not sufficient (Dachary‐Bernard & Rambonilaza, 2012; Dramstad
et al., 2006) and that there is relatively little academic evidence on the influence of socioeconomic
group on landscape preferences (Swanwick, 2009).
1.3 Research Objective and Questions
The aim of this study is the assessment of landscape preferences from an aesthetical point of view. It
will be investigated, which landscape attributes determine landscape preference and therefore also
the capacity to deliver cultural ecosystem services, using a visual choice experiment approach.
My research questions are:
1) What is the contribution of different landscape attributes to the overall landscape
preference in the Märkische Schweiz region?
2) Is there a universal preference pattern or do people differ in their preferences, and if so to
which extend?
3) Which socio‐economic characteristics determine landscape preferences and possible
heterogeneity among respondents?
4) Where are hot and cold spots of landscape aesthetic quality located in the Märkische
Schweiz case study region?
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2 Case Study Area Märkische Schweiz
2.1 General
The case study area “Märkische Schweiz” is a diverse landscape located 25 km east of the city of
Berlin, in the Federal State Brandenburg, Germany. It encompasses ten municipalities (see Figure 1)
and has a total extension of 576.4 km². There are 46 523 people living in that area, of which about 25
500 live in the main town Strausberg and ca. 6 700 in Müncheberg (Amt für Statistik Berlin‐
Brandenburg, 2012). But the number of inhabitants is predicted to decline till 2030 in reference to
2011 (Landesamt für Bauen und Verkehr, 2012). The proximity to Berlin is of great importance for
the region. Many people commute between Berlin and the region for work. And the local tourism,
which is an important economic sector besides the agricultural production, is based mainly on
daytrip visitors from the city (inspektour GmbH, 2008).
Figure 1. Location of the CSA Märkische Schweiz with county borders (dark line) and municipal borders (red lines).
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2.2 Landscape Structure and Composition
Definition
Landscape structure is one of at least three aspects of Landscape ecology: structure, function and change
(Forman & Godron, 1986; Turner, 1989). Structure refers to the spatial heterogeneity and has two components:
The first is the simple number, amount or size/length of different patches or elements within a landscape,
without being spatially explicit, and is known as landscape composition. In other words, landscape composition
encompasses the variety and abundance of patch types or elements, but not the placement or location
(McGarigal & Marks, 1995). Examples are the amount of forest, the length of hedgerows or the density of
elements. The second is the physical distribution or spatial character of patches or elements within the
landscape, known as landscape configuration (ibid.). Examples are arrangement, position, shape and
orientation of elements within a landscape and account for example for ‘edge effects’. In recent literature of
landscape preferences the terminology is used slightly differently, with landscape structure including the
diversity, complexity and pattern of spatial structure, and landscape composition referring to the relative
prevalence of land cover types and landscape elements (van Zanten et al., 2013; Walz, 2011).
The area is characterized by a fragmented, mosaic‐like, semi‐open landscape with hilly terrain, lakes,
forests and farmland. The morphology is the result of quaternary inland glaciations. The cyclic glacial
advances of terrestrial Scandinavian ice sheets and periglacial geomorphologic processes created a
young moraine landscape with heterogeneous natural conditions. A very typical fluvioglacial
landform are kettle holes (german: Sölle), creating small ponds or lakes that are often surrounded by
riverine vegetation. The hilly typography varies from sandy plateaux and sandy moraines, which are
mostly forested, to ground and loamy end moraines, where agriculture is the most present land use
(BfN, 2012; Scholz, 1963). The share of agricultural area, forests and pastures in the total area
according to the Corine Land Cover (EEA, 2007) is about 45 %, 40 % and 5 %, respectively. The two
main crops cultivated in the region are Rye (Secale cereale L.) and Rape (Brassica napus L.) with
about 2000 ha and 1400 ha, respectively (according to agricultural support application for the year
2012, Source: Digitales Feldblockkataster (DFBK), 2012). Part of the case study area is under
environmental protection as Märkische Schweiz Nature Park (205 km²). The core is mainly forested
with adjacent peripheral agricultural areas surrounding it (see Figure 2).The agricultural landscape is
characterized by big farm sizes (average 229 ha arable land per farm holding) determined by its
historical development. Especially the collectivization of agriculture in the former GDR resulted in an
increase of acreage per management unit, which had a dramatic impact on landscape structures and
elements (Philipp, 1997). The field size is ranging from 0.01 ha to 353 ha with an average field size of
22 ha (median = 5 ha).
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The reprivatisation and technical modernization since the reunification of East and West Germany
are now the dominating processes. Non‐agricultural and supra‐regional investors have purchased
many farms (Tietz et al., 2013), hence farming activities are often being carried out by non‐local
personnel with large‐scale machinery. The resulting scale enlargement and intensification of
agricultural practices strongly influence landscape structures and elements, as they are vanishing
(Ungaro et al., 2014). Currently the total length of the linear elements in the agricultural areas sums
up to 85.8 km, including tree rows (23.2 km), hedgerows (60.7 km) and field margins (2.0 km). There
are 390 groups of trees, 49 single trees and 116 ponds (DFBK, MIL, 2012) (see Figure 3 for spatial
distribution).
Figure 2. Land‐use distribution according to CLC2006 (Taken from: Ungaro et al., 2012).
Figure 3. Major landscape elements within agricultural fields (Taken from: Ungaro et al., 2012).
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3 Research Design and Methodology
3.1 Method overview
To reveal landscape preferences of local residents and visitors in the Märkische Schweiz region an
image‐based stated choice survey was applied. Therefore 54 photorealistic landscape visualizations
of four different landscape attributes were developed including green point (e.g. trees) and linear
elements (e.g. hedges), crop diversity as a function of field size and the presence of grazing livestock.
Attributes are differentiated into three levels (low, medium, high) or two levels (present, not
present), respectively. We collected empirical data in July 2013 at touristic spots using the digitally
calibrated images and an additional questionnaire on socio‐economic characteristics. Statistical
analysis included three models that have been applied: (1) a multi nomial logit model (MNL) to
estimate the overall landscape preference of visitors and residents; (2) latent class analysis (LCA) to
account for the heterogeneity of respondents’ choices and (3) a random parameter (mixed) logit
model (RPL) to allow for individual specific values and the socio‐economic influence. The overall
landscape preference of visitors and residents was then translated into maps showing hot spots and
cold spots of landscape aesthetic quality.
3.2 Choice Experiment
The choice experiment method is rooted in the traditional microeconomics theory of consumer
behavior, marketing and preference theory (Louviere, 1988). These techniques are often referred to
as stated preference models, discrete choice models, stated choice analysis or conjoint techniques
(Adamowicz et al., 1994).
The term conjoint analysis as coined by Green and Srinivasan (1978) offers a practical set of methods
for predicting consumer preferences for multiattribute options. It is used to estimate attribute
utilities based on subjects’ responses to combinations of multiple decision attributes. One uses it
when one wants to model consumer decision making and develop measures of consumers’ utilities
(Louviere, 1988). These approaches involve asking respondents to rank or judge attributes or
products or asking respondents to choose from hypothetical choice sets.
The approach I used is a stated choice approach. The decision maker faces a set of alternatives of
which exactly one, the preferred, alternative has to be chosen. In stated choice experiments,
alternatives are defined as combinations of attributes (Louviere et al., 2000). The advantage is that
each alternative is evaluated as a whole and the choices can be modelled as a function of the
attributes of the alternatives (McFadden 1974). As the aesthetic function is determined by landscape
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structure characteristics (van der Zanden et al., 2013), landscape structure and composition are used
as surrogate measures for visual landscape quality.
The strength of this method lies in its ability to predict how people would respond to for example
policy or management changes, that may currently not exist yet and provide insights into trade‐off
behavior of respondents (Arnberger & Eder, 2011). It can be therefore considered an ex‐ante
evaluation approach to investigate possible changes in landscape.
3.3 Study Design
Four attributes (1) presence of grazing livestock; (2) crop diversity as a function of field size; (3) linear
green elements; and (4) green point elements, have been chosen as surrogate measures for visual
landscape quality as they were representative characteristics for the Märkische Schweiz region
(compare with Chapter 2). The attributes were differentiated into three levels (low, medium, high) or
two levels (present, not present) for livestock (see Table 1). The intermediate level is considered to
be representative for the present situation and current state of landscape attributes in the region,
the status quo. The lowest levels represent the abundance of landscape attributes under a future
scenario of field enlargement, vanishing landscape elements and intense agriculture. In opposite the
highest levels represent the abundance of landscape attributes under a future scenario of small scale
practice, sustainable, rather ecological, extensive farming.
Table 1. Choice experiment set up design.
Attribute Levels
Livestock 1 Absent
2 Present
Crop diversity 1 Low
2 Intermediate
3 High
Linear elements 1 Absent
2 Slight presence
3 High presence
Point elements 1 Absent
2 Slight presence
3 High presence
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3.4 Methodological Steps
3.4.1 Development of Landscape Images
The main idea in developing photorealistic landscape visualizations is to control the image content. A
constructed landscape, that is kept constant and in every picture the same, serves as a base.
Following the design plan the different attribute levels are added. They can vary from a low level to a
high level. All pictures have the same light and weather conditions, relief energy, angle/perspective.
All randomness that could occur in elements or image characteristics is excluded from the picture.
Hence all pictures are comparable. Images that have a strict design plan and keep all factors
constant, which are not under control, are called digitally calibrated images (Orland et al., 1994).
The use of landscape visualizations in preference surveys is an established substitute for real
landscapes (Arnberger & Eder, 2011; Ode et al., 2009). All images were created in Adobe®
Photoshop® CS6, a graphics editing program developed and published by Adobe Systems. The
powerful feature is to keep different parts of a picture on different layers.
First the base landscape, representing the characteristics of the region, was designed based on
pictures taken on a photo tour in May 2013 (week 20). It is a composition of different photos and
shows a hilly landscape with agrarian land use and a pond (Söll) in the middleground. The landscape
cover is composed of the two main crops cultivated in the region, Rye (Secale cereale L.) and Rape
(Brassica napus L.), and pasture as base for the livestock attribute. A village in the background
represents the regions rather rural character. Forests were placed in the background, because the
study focuses on agrarian landscapes in the context of the European project CLAIM on supporting the
role of the common agricultural policy in landscape valorization (www.claim‐project.eu).
Then for each landscape attribute level a mask was created, that could be simply hidden or shown
with a click. The representation for each attribute level can be seen in Table 2 and in examples of
developed images (Figure 5 – 8). The full set of pictures can be found in the Appendix.
Following the research design all attribute levels were then combined in all possible combinations,
creating 54 images (2 x 3 x 3 x 3 = 54). Each image contains all the attributes under consideration.
The pictures were composed with focus on an harmonious appearance to create a realistic landscape
view and that no added elements would cover others at any level. No respects was given to the
average length of linear elements or average amount of point elements due to the fact, that
attributes occurring in the foreground are more present anyway; for example 5 m of hedgerow in the
foreground are more recognized than 200 m in the background. Therefore the first and second level
are presented in the back‐ and middleground and the third level in the fore‐, middle‐ and background
(see Figure 4).
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Table 2. Description of the representation of landscape attribute levels in the visualizations.
Attribute Level Representation
Livestock 1 No cattle visible
2 Group of cows on the pasture
Crop diversity 1 3 plots with different landscape coverage
2 6 plots with different landscape coverage
3 10 plots with different landscape coverage
Linear elements 1 No linear elements visible
2 1 Alley of trees, 1 hedgerow
3 1 Alley of trees, 2 hedgerows, 1 tree row
Point elements 1 No point elements visible
2 Riparian vegetation around pond, 1 group of trees, single bushes
3 Riparian vegetation around pond, 3 groups of trees, several
single bushes, 1 solitary tree
Background: containing landscape
characteristics such as forest and
village, as well as landscape
attributes at any level
Middleground: representing
landscape characteristics such
as topography and typical
pond, as well as landscape
attributes at any level
Foreground: representing
landscape attributes on
the third level
Figure 4. Concept of visual representation for the developed images (adapted from van Zanten, 2013 unpublished).
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Figure 5. Developed base landscape with all attributes set at level 1.
Figure 6. Developed landscape with all attributes set at level 2, except for livestock at level 1.
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Figure 7. Developed landscape with linear and point elements set at level 3, crop div. at level 2 and livestock at level 1.
Figure 8. Developed landscape with all attributes set at their highest level (3 or 2, respectively).
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3.4.2 Development of Questionnaire
The questionnaire form developed (see Appendix) is divided into two parts. The first one refers to
the landscape preferences; the answers from the choice situations can be simply ticked there. The
second part is applied to gather background information about the respondent. On the one hand
some information was collected to characterize respondents. On the other hand it was asked for
several socio‐economic factors, which could have explanatory character, as one aim of this study is to
examine what accounts for preference heterogeneity. The selection of variables and developed
hypotheses is based on present research findings (see chapter 1.2), but also try to include variables,
which not have been tested yet and may fill the knowledge gap, which variables determine landscape
preferences. They were partly deduced from discussion with scientists of this field. The expectations
of how the variables influence landscape preference are formulated in Table 3, and in the following I
will explain how the hypotheses were derived.
Dramstad et al. (2006) found that ‘locals’ had higher preferences for more open landscapes than
‘non‐locals’. I therefore hypothesize that visitors will prefer more structured landscapes with higher
attribute levels than residents. As familiarity with landscape influences preferences (Kaplan & Kaplan,
1989; Swanwick, 2009), I assume frequency to have an impact. The Märkische Schweiz region is a
fragmented, mosaic‐like, semi‐open landscape. With visitors knowing the region better and residents
enjoying more frequently the landscape I assume them to have higher preferences for a diverse and
structure rich landscape than visitors and residents not familiar with the region or seldom enjoying it.
Knowledge and awareness are known to alter preferences as well (Kleinhückelkotten & Neitzke,
2011). I therefore included the variable conveyance, which has not been tested before. People using
more eco‐friendly transport modes such as walking, cycling or taking public transport may do this for
environmental reasons, which in turn can affect landscape preferences. Probably the ways used for
these conveyances are also more natural, meaning bike and hike paths, as well as for example trains,
pass through areas, which are less influenced by artificial man made structures as for example streets
and highways. Also this may shape landscape preferences. The same applies for the favorite and
main activity in the landscape. Bikers are often out in the nature, taking different paths. I therefore
assume them to have more awareness for landscapes. For the gender I assume that females have
higher preferences for livestock and higher levels of landscape attributes, as it was found already in
Gao et al. (2013) or other existing knowledge on gendered preferences (Kleinhückelkotten & Neitzke,
2011). The question for the origin of respondents was included because the cultural imprint for
landscape could be different between east (very wide fields, due to the agricultural policy in the
GDR) and west (rather small fields due to inheritance, in German: Realteilung). And because
Arnberger and Eder (2011) found differences between respondents from mountainous and flat areas,
this was included as well. For the age I hypothesize that older people have higher preferences for
14
more structured and diverse landscapes as younger people, as Swanwick (2009) summarized in her
review, that younger people are often detached from nature and landscape. It might be important to
kind of get to know how related or connected people feel to the landscape, but I thought to ask
directly is a difficult question that might require a definition or is not clear. So I choose to ask
indirectly for the connection to the area and included the question, whether people are member of a
registered (non‐profit) association, and if so of what kind (e.g. local voluntary fire brigade etc.) and
how their connection to farming is, whether they are a farmer, grew up on a farm, have family or
friends in the sector or no connection at all. Van den Berg & Koole (2006) found connection to
farming very significant of landscape preferences. To investigate the value setting, whether a
respondent is rather conservative, environmentally interested, liberal or gives more importance to
social concerns, I included the question for which party the respondent voted in the last election. It is
known that preferences for green political parties correspond to higher preferences for wilder
landscapes (Van den Berg & Koole, 2006). I expect conservative and green voters to have a higher
preference for the more structured landscapes due to a preference for conservative farming praxis,
and the knowledge about environmental advantages, respectively. On the other hand it could be
interesting, whether liberal voters have the same preference as other respondents, even though they
would have to expect rather cleared landscapes under liberal policy. Last but not least I hypothesize
that respondents with higher educational level have stronger preferences for diverse and structured
landscapes, as in many studies education, income and social grade were considered strong proxies
(Arnberger & Eder, 2011; Grammatikopoulou et al., 2012; Swanwick, 2009).
Hence, information asked were: whether someone is a resident or tourist; if tourist: trip destination
and activities planned; frequency of visiting or benefitting the landscape, conveyance, favourite/main
activity in nature, gender, residence (postal code), origin (federal state), year of birth, involvement in
an association/union, connection to farming, party voted in the last election, education and
employment.
The formal standardised questionnaire was designed with closed questions (except for the questions
on year of birth and postal code, which were open questions), offering explicit alternatives as
answers. The questions were arranged that more sensitive ones were embedded in more welcomed
and pleasant questions, and potentially very sensitive questions were left to the end, to avoid
respondents breaking off the interview before important information was collected.
15
Table 3. Hypotheses about influence of explanatory variable on landscape preference. The sign “>“ stands for: have higher preferences for more structured and diverse landscapes and hence for higher levels of attributes.
Explanatory information Hypothesis
Visitor/resident Visitors > residents
Frequency Often visiting or benefitting the landscape > seldom visiting or benefitting the landscape
Conveyance environmental‐friendly conveyance (public transport, bike or walk) > car or different
Activity Bike > others
Gender Female > male
Origin Urban > mountainous > lowland; West > east
Age Older > younger
Societal commitment involved in an association or union > not involved
Connection to farming Close relation with farming > intermediate relation > no relation
Attitude and value setting Conservative + environmental > others
Education High education > low/no education
3.4.3 Pre‐testing and selection of choice cards
Before the main survey two pretests were conducted. The aim was to check the readability of the
visualizations and already test, whether the study design and question procedure was applicable and
to make any necessary adjustments.
A first pretest was done with colleagues (n=10) with their background in different specializations and
in different stages of their career, ranging from bachelor student to senior scientist. Respondents
were shown 10 tasks (choice sets) consisting of 3 images (alternatives) and asked to choose the
preferred landscape. Additionally they were asked to evaluate the overall appearance of the images,
and how representative the pictures are for the agrarian landscape of the the Märkische Schweiz
region.
The pretest showed that the livestock as well as the solitary tree appeared to be too big to fit into
the landscape. One respondent criticized that the pond representing the Söll is rather a temporarily
wetted spot and too small to be recognized as a Söll. Additionally it was expressed that it is unlikely
that a Söll or pond appears in the middle of a field as in the representation. But despite these
critiques the overall impression of the images was very good. Following the critiques the tree and
livestock were resized. But the pond representing the Söll was kept as it was, due to emphasis to
keep a harmonious appearance in the image. Making the pond bigger in the image would have
resulted in an inappropriate dominance of it and relocating it in the picture, where it might would
have been more realistic according to the critic, would have resulted in an very unharmonious
16
appearance of the image (no other depression in the relief was so pronounced, as it could have
“taken” a pond).
Regarding the survey procedure it appeared that it is important to show all 3 pictures at once so as
they have the same visibility at a time. It also seemed that 10 tasks in a row were durable, but a
shorter procedure with less than 10 tasks should be favored, because the attention was dropping
towards the end. In a few cases this resulted in people showing different behavior then, such as
choosing a very empty landscape, arguing that this would be now something “different/new/fresh”
and an appreciated change to the rather structured landscape the person preferred all the tasks
before.
The second pretest was done to create a so‐called efficient design for the main survey. Efficient
designs aim to result in data that enables estimation of the parameters with as low as possible
standard errors (ChoiceMetrics, 2012). The pretest was conducted as an online survey using Google
Form, a tool within Google Docs, which is freeware web‐based office suite offered by Google. The
link was send to friends, acquaintances and family of the research group, that have no background in
environmental sciences, come from different areas (urban vs. rural) and covering a wider range of
age (23 – 67 years). The pretest had an orthogonal design (see appendix) generated with the
software Sawtooth. There were 5 versions of questionnaires that included each 10 tasks (choice sets)
with 3 alternatives. The respondents (n = 34) were again asked to choose the preferred landscape.
Based on the outcomes of the second pretest, a MNL model was estimated using NLogit discrete
choice software. Subsequently, the coefficients from the MNL model were used as prior assumptions
to estimate an efficient design in the software Ngene. The underlying idea is to obtain the greatest
knowledge about trade‐offs between different landscape attributes. Showing pictures that are very
likely to be preferred against pictures that are more unlikely to be preferred, does not bear sufficient
information about trade‐offs. But choosing between images that have a similar likelihood to be
chosen, gives greater insight into the trade‐offs made, such as whether a respondent prefers a
landscape with a lot of linear elements over a landscape with a lot of point elements.
In practice, first an attribute level balanced design is created in Ngene by selecting choice situations
from the candidate set. After that, an efficiency error (for details see ChoiceMetrics, 2012) is
computed for this design. If this design has a lower efficiency error than the current best design, the
design is stored as the most efficient design so far, and one continues with the next iteration
repeating the whole process again for a predefined number of iterations (ibid.). Eventually, a design
was created with 6 versions and 8 tasks per version (see Appendix).
17
3.4.4 Survey
In July 2013 (week 27 – 31) the survey was conducted among visitors and residents of the Märkische
Schweiz. People have been approached at several locations:
in the train commuting between Berlin and Kostrzyn (Poland)
at local festivals (hunters festival in Waldsieversdorf and Art & Culture festival in Buckow)
on a beach of the protected lake Großer Klobichsee close to a camping site
in the palace garden in Buckow
At some locations no participants could be found such as the town Altfriedland, the weekly market in
Müncheberg or the tourist information in Neuhardenberg close to the castle Neuhardenberg. This
was maybe due to bad weather but also due to the fact, that some places in the region are
unfrequently visited.
People were asked whether they have 5‐10 minutes time to participate in a survey on landscape for
my master thesis. Surprisingly most people were open to participate, when they saw the pictures.
Before the questionnaire they were informed about seeing first 8 x 3 pictures and afterwards getting
asked person related information. Then they were shown 3 pictures, so that they could see them all
at once and be able to compare them. They were asked to please choose that picture out of three
with the landscape they prefer the most from their aesthetical point of view. If there was interest,
people were told afterwards about the study design, the aim of the study and the context of the
CLAIM project.
In total a number of 200 people responded to the questionnaire, of which 113 were visitors and 87
residents of the Märksiche Schweiz. A lot of them expressed, that it was very nice and even relaxing
to watch the pictures.
3.5 Statistical Analysis
3.5.1 Respondent characteristics
The respondent characteristics were analyzed using the Software R (http://www.r‐project.org/ ) and
Excel. To analyze the origin of the respondents, who are visiting the Märkische Schweiz, a map was
created based on the information about the postal codes. A shapefile with all postal codes of
Germany is available at: http://www.metaspatial.net/download/plz.tar.gz (for information on the
data set without restrictions see: http://arnulf.us/PLZ). Using the Flow Mapper plugin in Quantum
GIS, lines were created to show from which region the visitors come. The first methodological step is
to join geographic coordinates to the postal codes. Each postal code is provided as a polygon. Hence,
to provide exactly one set of X and Y coordinates per postal code, a center point is calculated using
18
the geometry tool ‘Polygon Centroids’. The East and North coordinates can be then calculated and
added to the point layer. Subsequently, the number of visiting respondents per postal code is added;
e.g. 5 for the postal code 10437 in Berlin. All centroids with postal codes, that have been named
minimum one time, are selected and stored in a textfile, which serves as input nodes. The aimed
destination node is defined as Buckow, because it is very much in the center of the Märkische
Schweiz. Given the input nodes, the aimed node Buckow and a matrix of the number of people per
postal code, the Flow Map was created.
3.5.2 Preference Analysis (Multinomial Logit model ‐ MNL)
To analyze the trade‐off behavior of visitors and residents of the Märkische Schweiz a stated choice
approach is used. In stated choice experiments, alternatives are defined as combinations of
attributes (Louviere et al. 2000). The advantage is that each alternative is evaluated as a whole and
the choices can be modelled as a function of the attributes of the alternatives (McFadden 1974).
In a discrete choice experiment (DCE) the decision maker faces a set of alternatives of which exactly
one, the preferred, alternative has to be chosen. The choice made will be affected by observable
influences and by unobservable characteristics of the decision maker. Therefore, following the
random utility theory, the overall utility (Ui ) contains a deterministic component (Vi ) and a
stochastic component (εi). The overall utility of alternativei is represented as (McFadden, 1974):
.
It is assumed that individuals will choose the alternative what yields them the highest utility and will
choose alternativei over any other alternativej only if (Louviere et al., 2000):
; ∀ ∈ , ,
where A is the set of all possible alternatives. With (1) and (2) the probability that alternative i is
chosen from the set of available alternatives A is then:
; ∀ ∈ , .
If one assumes that, for the entire sample, the stochastic elements of the utilities follow a Gumbel
distribution, the multinomial logit (MNL) model can be specified as (Arnberger & Haider, 2005;
Louviere et al., 2000):
∑ ∑.
(1)
(2)
(3)
(4)
19
The maximum likelihood analysis produces regression estimates (usually referred to as part‐worth
utilities), standard errors and t‐values for each attribute level. In our MNL choice models, each
attribute level is dummy‐coded and the minimum levels of all attributes are included as the
reference category. Considering the 4 attributes in this study the resulting model specification for the
deterministic component V associated with alternative i is given by:
, , , , , , , ,
with C2 as dummy for the presence of livestock, D2 and D3 as dummies for medium and high level of
crop diversity, L2 and L3 as dummies for medium and high level of linear elements, P2 and P3 as
dummies for medium and high level of point elements, β1 to β7 are the utility parameters of the
model and are estimated as coefficients. A higher utility parameter or coefficient corresponds to
higher utility and therefore preference of an attribute level.
3.5.3 Analysis of Heterogeneity (Latent Class Analysis ‐ LCA)
Potential heterogeneity in preferences can be examined using Latent Class choice modelling. The
basic assumption of these models is that the observed data can be divided in homogeneous groups,
referred to as classes, and that all heterogeneity of respondents can be represented by a finite
number of classes (Kamakura & Russell, 1989). They are called latent, because the number and size
of groups is not known a priori, as well as, which person belongs to which group (Hillig, 2006). One
can imagine the situation as one, in which the individual resides in a latent class C, which is not
revealed to the analyst (Greene, 2012). With a discrete set of classes, heterogeneity in parameters
across individuals is modeled.
In this study a model with no restrictions was estimated, which means all parameters can differ
across the classes and are not restricted or fixed. Several models with two, three or more classes
were estimated. Every time for each class a latent class probability was estimated and gave the
likelihood to belong to one class. For this probability also a p‐value was estimated as criterion, how
plausible the estimation of probability is. When all estimated classes were likely to occur (significant
at 1 %), the model was chosen as the final model for LCA. For the data in this study a model with
three classes was plausible.
3.5.4 Influence of Explanatory Variables on Preferences (Random Parameter Logit model ‐ RPL)
The random parameter logit (RPL) model, also called mixed logit model, extends the MNL model by
allowing its parameters (β1 to β7) to be random across individuals (Greene, 2012). The explanatory
(5)
20
variables (see Table 3) were dummy‐coded and interacted with the landscape attribute dummy‐
variables in the model. Therefore interaction terms were added to the MNL model. Considering e.g.
just the attribute livestock, but with addition of the explanatory variable ‘visitor’, the resulting model
specification (compare with MNL model specification above, equation 5) for the deterministic
component V associated with alternative i is given by:
, , , ,
with C2 as dummy for the presence of livestock, Vis as visitor‐dummy, and β1 and β1,v as the
parameters. If the interaction would not matter, then , 0. The model would stay the same as
the MNL model without interactions. Hence it would not matter, whether a person is a visitor or not.
The null hypothesis could be therefore formulated as:
: , 0
If the null hypothesis can be rejected, set on a 10 % significance level, then distinguishing between
visitor and resident matters. The procedure, as in this example on livestock and visitors, was applied
to all attributes and all explanatory variables in the full RPL model. To verify the robustness of the
model a stepwise removal of the interactions with the highest p‐values was carried out, until all
interactions were significant on a 10 % significance level.
3.6 Mapping of Landscape Preferences
In order to examine the spatial distribution of hot and cold spots of landscape aesthetics first the
spatial distribution of the levels of the selected four landscape attributes was mapped. Therefore
different methods were applied, as the various attributes distinguished in their occurrence
(present/absent, point and linear shape, or areal extension). Basically the mapping of the levels of
the attributes are based on probability maps for point and linear elements and land use maps for
livestock and crop diversity.
For point and linear elements a fine scale, data‐based, non‐parametric probabilistic approach
(Journel, 1983) was adopted, estimating probabilities of occurrence. The official biotopes’ data base
for Brandenburg has been used as data source for both groups (LUGV, 2011). The approach,
described in detail in Ungaro et al. (2014), encompasses the following steps:
1) stratified random sampling of landscape elements within a regular reference grid (1km x 1km) to
assess the presence or absence of specific landscape features with a 250 m buffers around two
term frommodel so far
interaction term
(6)
(7)
21
randomly selected points within each cell (N=1 344; sampling density 2.3 km‐2); 2) creation of an
indicator data set (0 = absence, 1= presence) followed by experimental variography and indicator
variogram modelling; 3) sequential indicator simulations (Goovaerts, 1997), using variogram models
over a 100 m regular grid (N = 57 657); and 4) post‐processing of simulations results (N = 1 000) to
compute for each grid cell the E‐type estimator (Deutsch & Journel, 1997), i.e. the mean probability
of occurrence of the considered groups of landscape elements.
The three attributes’ levels are then based on the estimated probabilities of occurrence p of the
selected landscape elements, as follows: level 1, p ≤ 0.33; level 2, p> 0.33 and < 0.66; level 3, p≥
0.66. All the geostatistical analyses were carried out with the software Wingslib 1.3.1 (Statios, 2000),
which works in conjunction with the GSLIB90 executables (Deutsch & Journel, 1997).
For the others two landscape attributes, livestock and crop diversity, two different proxies were
used, derived from the information contained in the digital cadastre of Brandenburg (Digitales
Feldblockkataster (DFBK) des Landes Brandenburg, MIL, 2012). For livestock the occurrence of
grasslands, either natural or managed, was used, as grazing livestock is only associated to this specific
land use. This applies to 32% of the field blocks, for a total of 77 782 ha of agricultural land. In the
case of crop diversity, the attribute’s three levels were based on average plot size within field blocks
(N = 1201), as follows: level 1, area ≥ 15 ha (N = 300; 21 224 ha); level 2, area > 5 ha and < 15 ha (N =
275; 6557 ha); level 3, area ≤ 5 ha (N = 626; 21 224 ha).
As the spatial distribution of the levels of the four landscape attributes is mapped, they are joined
with the calculated utilities from the MNL model. If, for example, at one spot the crop diversity is
high, linear and point elements medium and livestock absent, then the coefficients from the MNL
model for high crop diversity, medium linear and medium point elements are attached to this
position. The sum of these coefficients make then the final utility and can be displayed, with higher
values indicating hot spots and low values indicating cold spots of landscape aesthetics.
22
4 Results
4.1 Respondent characteristics
The socio‐economic characteristics of respondents are presented in Table 4. The origin
(visitor/resident), gender and age ranges were evenly distributed. The age of respondents was
ranging from 6 to 84, with an average of 46 years. In comparison the average age in the case study
region is 45 (Statistische Ämter des Bundes und der Länder, 2011). The level of education with 44 %
of respondents having a higher school or university degree is considerably above the German
average with 14 % (Statistisches Bundesamt, 2014). Considering the visitors only, the difference is
even more pronounced, with 47.8 % of them having a higher school or university degree.
Table 4. Socio‐economic characteristics of respondents.
Socio‐economic characteristics % Socio‐economic characteristics %
Origin Visitor 56,5 Federal State grown up in
Brandenburg 35,5
Resident 43,5 Berlin 23,5
Gender Female 55,5 Saxony 6,5
Male 44,5 Lower Saxony 6,0
Age <25 14,6 Baden‐Wbg. 4,5
26‐35 20,2 Others 24,0
36‐45 12,6 Plan to do* Snack 39,5
46‐55 21,7 Eat out 32,0
56‐65 14,1 Overnight‐stay 18,5
>65 16,7 Use health offers 6,5
Education None 3,5 Buy regional products 22,0
Lower secondary education 1,0 Use touristic offers 27,0
Secondary education 4,0 Main activity Hike 47,0
Apprenticeship 32,0 Bike 26,5
Gymnasium (Abitur) 15,5 Water activities 14,0
University 44,0 Others 12,5
Employment In education 15,0 Association/Union Yes 42,0
Employed 47,5 No 58,0
Self‐employed 11,0 Connection** Farmer 7,5
Retired 20,5 Grew up on farm 17,5
Searching 3,5 Family/Friends on farm 36
No relation 45,5
*multiple answers were possible; this question was just asked to visitors **multiple answers were possible, but only the strongest connection is considered in this presentation
59 % grew up in either Berlin or Brandenburg, but in total 75 % grew up in the federal states of East
Germany and just 25 % of West Germany. Of the visitors 18.5 % stated to stay overnight. But a
considerable larger number of visitors stated to plan to take a snack, eat out or purchase regional
products. Of the respondents denying these plans, many stated orally they would take a snack, eat
23
out or purchase regional products, if the offer would be there or bigger. Hiking and Biking are the
main or favorite activities for 73 %. The others stated for example horse riding, gardening, sports,
just relax and motor sports as their favorite activity.
Most of the visitors have been in the region before (82%), a majority even more often than 5 times
(43%). And most of the residents stated that they enjoy the landscape minimum once a week (75 %).
Regarding the main conveyance used, 54 % of the visitors stated they came by car, 44 % by public
transport and 2 % by bike. In contrast 46 % of the residents stated to mainly move in the region by
bike, 28 % by car, 24 % walk and just 1 % used public transport.
The analysis of the postal codes revealed that in total 85 of 113 visitors are from Berlin, which equals
75 %. In Figure 10 it can be seen, that the majority of the people coming from Berlin is from the
eastern part. The remaining 25 % of visitors come from other areas of Brandenburg or other parts of
Germany.
The question, which party has been voted in the last election was the most sensitive one, with 19
people refusing to respond to this question. Still 90 % answered this question. It was sometimes due
to explanation, what the intention behind this question is and asserting the people, that the survey is
anonymous. Excluding the respondents that were too young for voting (≈ 9 %), the voter
participation is calculated as about 70 %. The remaining 30 % are people, who did not vote, refused
to answer or people, who stated others. Considering just the 70 % of people voted, the hypothetical
parliament of the respondents would be composes as in Figure 9, neglecting the electoral threshold
of 5 %.
Figure 9. Division which parties the respondents voted in the last election; excluding respondents that were too young, refusing to answer or not voting in the last election.
30%
25%
17%
10%
5%
4%9%
Bündnis 90/Die Grünen
SPD
Die Linke
CDU
Die Piraten
FDP
Others
24
Figure 10. Origin of the respondents visiting the Märkische Schweiz, the aimed destination node is defined as Buckow, being very much in the center of the Märkische Schweiz.
4.2 Preference Analysis (MNL model)
The results of the estimation of the MNL model, which required five iterations to reach a solution,
are summarized in Table 5. Higher coefficients correspond to higher utilities. All coefficients show a
positive relation between medium and high levels of the considered attributes and the probability of
choice. Hence, the presence of all landscape attributes in these choice experiments is evaluated
positively. All coefficients are statistically significant on a 1 % level, except for medium crop diversity.
The ranks of the coefficients indicate a strong preference for a high level of point elements, which is
by far the most preferred attribute, followed by a high level of linear elements, a medium level of
point elements and high level of crop diversity. The medium levels of linear elements and crop
diversity as well as the presence of livestock are less preferred.
25
Table 5. Multinomial logit model estimations and attribute ranking; higher coefficients correspond to higher preference.
***, **, * ==> Significance at 1%, 5%, 10% level
4.3 Analysis of Heterogeneity (Latent Class Analysis)
As starting point five classes for the first model were randomly chosen. Three classes were
estimated with a p‐value smaller than 0.000, one class with a p‐value of 0.19 and one with 0.32,
indicating three classes are plausible. The stepwise reduction of classes showed: In a model with four
classes (Log‐likelihood: = ‐1252.4) again three classes were plausible and one class with a p‐value of
0.285 was not. The estimation of a model with three classes was then the most likely (Log‐likelihood
= ‐1731.4). In a model with just two classes both classes appeared to be very likely, but the Log‐
likelihood was estimated with ‐1304.9, which is worse than the one of the model with three classes
(remember minimizing the negative log‐likelihood is equivalent to maximum likelihood estimation).
Hence the model with three classes was chosen as final model for the latent class analysis and the
results are presented in Table 6.
Table 6. Results of the Latent Class Analysis with three classes, no parameter was restricted. The outstanding differences between the classes are marked bold.
Class 1 Class 2 Class 3
Latent class probability: 0.35*** 0.29*** 0.36***
Attribute Level Coefficient Rank Coefficient Rank Coefficient Rank
Livestock present 1.24*** 5 0.68*** 2 1.06*** 4
Crop diversity medium 0.51*** 6 ‐0.19** 5 0.09 7
high 2.72*** 1 ‐0.09 4 1.04*** 5
Linear elements medium 0.09 7 ‐0.33*** 7 0.78*** 6
high 2.41*** 2 ‐0.31** 6 3.20*** 2
Point elements medium 1.36*** 4 0.31*** 3 2.62*** 3
high 1.89*** 3 0.98*** 1 4.64*** 1
***, **, * ==> Significance at 1%, 5%, 10% level
Attribute Level Coefficient Rank
Livestock present 0.75*** 5
Crop diversity medium 0.13* 7
high 1.03*** 4
Linear elements medium 0.22*** 6
high 1.38*** 2
Point elements medium 1.18*** 3
high 2.10*** 1
26
The probability to belong to one of the three classes is almost evenly distributed with a latent class
probability of about 33 % per class. The first class has a strong preference for high crop diversity and
high level of linear elements, which rank first, and very low preference for medium crop diversity and
medium linear elements. The second class is characterized by aversion to crop diversity and linear
elements at medium and high level. A more structured and diverse landscape is not favored by
people belonging to this class. Just point elements and livestock are preferred, but do not add that
much value to the landscape as for the other classes. The third class has strong preferences for high
levels of point and linear elements.
4.4 Influence of Explanatory Variables on Preferences (RPL model)
Including all explanatory variables as described in chapter 3.4.2, Table 3, made the model very large
(13 person related categories interacting with 7 landscape attribute levels = 91 interactions). When
one or another explanatory variable was excluded from the model, the estimations changed often
and therefore showed some instability of the model. Characteristics, which appeared to be
significant in the one model, were insignificant in the other, indicating some relations or interactions
in the background, which were not revealed to the analyst. Therefore several models in a big variety
of combinations were estimated, and subsequently the variables that did not show significant
influence, did not add value and optimize the model were excluded from the final RPL model,
namely: being visitor or resident, frequency of visiting the landscape for residents, frequency of
visiting the Märkische Schweiz for visitors, origin (east/west; city/flat/mountainous), and being
member of an association. The variables, which appeared to be significant in most of the tested
models and hence showed explanatory character independent from the other variables, were
included in a final model. These variables are: gender, age, connection to farming, education, value
setting, conveyance and activity. The results of the full model and the restricted model (after
stepwise removal of insignificant interactions) are presented in Table 7. In the restricted model the
background variable education has significant interactions with the all attribute levels, except for
livestock. And, whether a respondent is female or not has a significant effect on four landscape
attributes. The other variables age, connection to farming, political party, conveyance and activity
show one to three significant interactions with the landscape attributes. All socio‐economic variables
chosen show in the restricted model a positive effect on the considered attributes and the
probability of choice, except for females and age. There a negative effect can be observed.
27
Table 7. Results of the Random Parameter Logit model, full and restricted model, showing the effects of individual socio‐cultural background variables on preferences for landscape attributes.
Socio‐economic characteristic
Full Model Restricted Model
Attribute Level Coefficient Coefficient
Female Livestock present 0.52*** 0.53*** Crop diversity medium 0.30** 0.27**
high 0.07Linear elements medium 0.00
high ‐ 0.30* ‐ 0.33** Point elements medium 0.02
high ‐ 0.22 ‐ 0.28*
Age Livestock present 0.09* 0.11** Crop diversity medium 0.00
high ‐ 0.05 ‐ 0.07* Linear elements medium ‐ 0.03
high 0.04Point elements medium 0.11** 0.10***
high 0.03
Connection Livestock present 0.05Crop diversity medium 0.05
high 0.17* 0.16** Linear elements medium 0.03
high 0.12Point elements medium ‐ 0.10
high ‐ 0.15
Education Livestock present 0.04Crop diversity medium 0.15*** 0.16***
high 0.17** 0.19*** Linear elements medium 0.14** 0.13**
high 0.24*** 0.24*** Point elements medium 0.32*** 0.32***
high 0.37*** 0.39***
CDU/Grüne Livestock present 0.19Crop diversity medium 0.16
high 0.61*** 0.44*** Linear elements medium 0.16
high 0.62*** 0.44*** Point elements medium 0.01
high 0.19
Eco‐friendly Transport
Livestock present ‐ 0.07Crop diversity medium 0.01
high 0.10Linear elements medium 0.26* 0.30**
high 0.61*** 0.60*** Point elements medium ‐ 0.02
high ‐ 0.07
Bike Livestock present 0.32Crop diversity medium 0.09
high 0.10Linear elements medium 0.08
high ‐ 0.01Point elements medium 0.59*** 0.53***
high 0.48* 0.37*
***, **, * ==> Significance at 1%, 5%, 10% level
28
4.5 Mapping of Landscape Values in the Landscape
The mapping of the landscape attribute levels revealed the spatial distribution as presented in
Figure 11. As for livestock no data was available, for example where cattle grazes, the map shows the
abundance of pastures. They occur especially in the south‐west of the region, the so called ‘Rotes
Luch’, a reclaimed, drained fen area. The higher levels of crop diversity occur in the center of the case
study region, where the Nature Park Märkisch Schweiz is located, but also on the south‐western
fringe. Also high levels of linear elements occur mainly in the center of the case study area and in the
area of ‘Rotes Luch’, parts of the nature park. The higher levels of point elements are in comparison
distributed more spaciously, occurring in many parts of the case study region, except for parts in the
north‐east and south‐west.
Figure 11. Spatial distribution of the landscape attribute levels in the Märkische Schweiz region; blue areas indicate lowlevels/absence and red areas high levels/presence of the considered attribute; non‐agricultural areas are excluded.
29
The landscape utility sum, which is a sum of the joint of landscape attribute levels and their
respective coefficients from the MNL model, is presented in Figure 12. The highest utilities are
located in the center of the case study region and in the area of the ‘Rotes Luch’, both parts of the
Nature Park Märkische Schweiz. The south western and north‐eastern parts have rather low utilities.
Figure 12. Spatial distribution of the landscape utility sum, which is the sum of the joint of landscape attribute levels with their respective coefficients from the MNL model; non‐agricultural areas are excluded; higher vales correspond to higher preferences.
30
5 Discussion
5.1 Interpretation of results
This study applied a discrete choice experiment with digitally calibrated images of an agricultural
landscape aiming at assessing the landscape preferences of tourists and residents. An MNL model
was estimated to reveal the preferences for landscape attributes; a LCA approach to examine
possible heterogeneity; and a RPL model to allow for individual specific values and the socio‐
economic influence. Additionally the spatial distributions of cumulative preference values were
mapped, showing hot and cold spots of aesthetic quality.
The MNL model showed that a diverse and structured landscape with all attributes at a high level is
the most preferred, which is in line with previous research findings (Dramstad et al., 2006; Kaplan &
Kaplan, 1989). Point elements are the landscape attribute for which respondents have the highest
value, followed by a high level of linear elements and crop diversity. Linear elements and crop
diversity contribute to landscape attractiveness, which has been found also by Dramstad et al.
(2006), Rambonilaza & Dachary‐Bernard (2007) and van Berkel & Verburg (2013). But that point
elements account for landscape preference, and in this dimension, is new.
Medium levels of crop diversity and linear elements rank last. For the medium level of crop diversity
this could be due to the hilly landscape. Relief energy was found to have influence on landscape
preferences (Roser, 2013). It structures the landscape already a bit and offers variance, making the
landscape interesting to the eye. If the landscape would be more flat, the little change in crop
diversity might have been more acknowledged. For the medium level of linear elements, it could be
that they are recognized just when a kind of pattern arises.
Even though the estimated MNL model showed very high significance (already in the pretest with
just 37 respondents) and could therefore lead to the generalization, that there is probably a universal
landscape preference pattern in the Märkische Schweiz among visitors as well as residents, the
result of the latent class model showed that this is not true. Strong heterogeneity was found.
The LCA identified three classes among the respondents based on their preferences. They
considerably differed from each other, with one opposing class. While two out of three classes
favored landscapes with high diversity and a lot of elements, structuring the landscape, the opposing
class was found to dislike these landscape characteristics. This finding of heterogeneity is partly in
line with previous research. Also Arnberger & Eder (2011) and Grammatikopoulou et al. (2012)
found one opposing class, even though they were considerably smaller with 9 % and 21 %,
respectively, whereas the opposing group in this study made 29 % of respondents.
31
The results from the RPL model showed that landscape preference is very dependent on education.
The higher educated the higher the preference for high levels of all landscape attributes, except
livestock. Considering the outcomes of other empirical studies (Arnberger & Eder, 2011;
Grammatikopoulou et al.), as well as of theoretical research (Kaplan & Kaplan, 1989; Swanwick,
2009), the conclusion can be drawn, that education as predictor for landscape preferences can be
generalized.
Females had significantly higher preferences for a medium level of crop diversity and livestock. As
they are more sensitive towards nature concerns and the concept of sustainability (Kleinhückelkotten
& Neitzke, 2011), they maybe recognized the change of crop diversity in the images better and
included their knowledge in their decisions. The high preference for livestock was also found in
earlier studies (Gao et al., 2013; Howley et al., 2012) and could be explained with the scheme of
childlike characteristic. Females were also found to have less preference for high levels of linear and
point elements than men, which can not be explained with recent findings. Probably these outcomes
are resulting from the trade‐offs made. Females might not have less preference for linear or point
elements, but when asked to choose between choice sets, they probably made their decision in favor
to livestock and medium crop diversity. These results expand the existing knowledge on gendered
preferences of landscape features in agricultural landscapes. Acknowledging these differences can
assist to better target customers, thereby increasing their market share and, in turn, strengthening
visitor satisfaction. For example, promotional materials aiming specifically at female consumers could
predominantly portray animals in the landscape (Gao et al., 2013).
The older the people the more they preferred livestock and medium level of point elements (which
was predominantly riparian vegetation around the pond) but less a high level of crop diversity.
Strumse (1996) found that a farmed landscape was more preferred by older respondents in
comparison to younger ones and interpreted it as result of the rarifying of direct experience of
farming practices in our age of rapidly increasing urbanization. The greater familiarity with
agricultural landscapes of older people could be an explanation for the preference pattern found.
The indication that younger respondents are often detached from nature and landscape as stated by
Swanwick (2009) and therefore are less familiar with the landscape and hence have no preference
for diverse and structured landscapes, could not be found.
No difference between visitors and residents or urban and rural population could be found. The
analysis of origin showed that most visitors are from the city of Berlin, and one can therefore equate
visitors with urban population, and residents with rural population. On the one hand one can argue
that no difference was found because of spatial mixture in the last decades. 35.5 % of respondents
grew up in Brandenburg and 23.5 % in Berlin, meaning that about 40 % are from other regions of
Germany. Hence, people living nowadays in Berlin, and considered urban in this study, might grew up
32
in other federal states and in rather rural areas. Also the exchange between Brandenburg and Berlin
due to urbanization, sub‐urbanization and ex‐urbanization, depletes the difference between urban
and rural. On the other hand, it could be also argued, that no difference between visitors and
residents could be found, because structured and diverse landscapes are highly appreciated by
tourists and valued as integral part of the regional identity by residents.
The finding that people with closer connection to farming have a higher preference for a high level of
crop diversity elements is very in line with what I hypothesized and the literature. This applies also to
respondents having a conservative or environmental value setting, as they were found to have higher
preferences for a high level of crop diversity and high level of linear elements.
Respondents choosing eco‐friendly transport had higher preference for both higher levels of linear
elements and respondents, whose favorite or main activity was biking, had higher preferences for
both higher levels of point elements. I can just speculate, why this pattern has been found with no
literature supporting these findings. For the conveyance maybe the higher environmental awareness
of people choosing eco‐friendly transport modes lead to higher preference for linear elements, as
they know about their importance for the environment (buffer strip for nutrients as well as
pesticides, hot spots of biodiversity, wildlife corridor etc.).
In total many respondent characteristics were found to have significant influence on landscape
preferences; some support, some oppose previous research findings and some were discovered new.
Hence, the discussion about which variables determine landscape preferences is ongoing and more
research is needed. Probably no universal explanation can be found, as preferences also vary across
areas and are highly dependent on the territorial asset of each case study area (van Zanten et al., in
preparation).
In general it needs to be considered, that visitors make their decision of coming in the area not only
dependent on the appearance of the agricultural area. The area of the Märkische Schweiz is
considered to be attractive especially for its forests and lakes, which were not considered in this
study, as the focus is on the agricultural landscape. But in general the overall attractiveness of a
landscape is not just dependent on scenery, as many people seek for example tranquility, open space
and fresh air (Swanwick, 2009). We do not know how large the contribution of agricultural landscape
preference to the overall appreciation of the region is. More research on this is required.
The created maps give a good overview on how the landscape attribute levels are distributed in the
area and where hot and cold spots of landscape beauty can be found. The most attractive areas are
mainly found to lie within the Nature Park Märkische Schweiz, indicating that the nature park
management contributes a lot to the attractiveness of the area, also from a landscape aesthetics
point of view.
33
5.2 Methodological Discussion
The outcomes of the MNL model that the medium levels of crop diversity and linear elements are
ranking last could be on the one hand simply due to not much preference, but on the other hand also
influenced by the image set up. Retrospectively, these landscape attribute levels were maybe not
pronounced and visible enough in the picture. Higher contrast or better visibility of both could put
things right.
The LCA model proved to be a good estimation method to account for heterogeneity. But the
methodology used is lacking information, which socioeconomic groups were in which class. This is a
disadvantage. Other studies were able to include respondents characteristics in the LCA, using for
example other software such as Latent GOLD Choice 4.0 (Arnberger & Eder, 2011) or 4.5
(Grammatikopoulou et al., 2012). Their methods could add information on which factors account for
heterogeneity, even using the dataset gathered in this study.
The RPL model appeared to have some instability, when too many background variables or
interactions were included. Hence, a limited number of variables, very clear hypotheses, and
restrictions of the model according to these hypotheses seem to be important for conducting this
analysis. Strong, theory based assumption regarding the source of heterogeneity are needed.
Without strong apriori assumption a LCA model is more appropriate (Grammatikopoulou et al.,
2012).
As an outlook I would consider the following future steps: 1) Taking again pictures in the case study
region Märkische Schweiz, but this time according to the map of hot and cold spots to verify the
method; then conducting again a stated choice survey with the taken pictures. Are hot spots of
landscape scenery really found in the region, where the map displays them? 2) Conducting an
accessibility analysis. Are paths for bicyclists and hikers leading through the beautiful areas according
to their preferences? 3) Based on point 1 and 2, estimate in which areas the landscape could be
valorized the most (cost‐)efficiently (by e.g. suggesting in which areas to enhance landscape structure
and composition, or place a view point etc.).
5.3 Relevance for Policy and Planning
Regarding the current trends in agriculture and landscape management, the results of this study are
of importance as they contribute to the understanding of the multifunctionality of landscape. The
dominating processes are re‐privatization and technical modernization since the reunification of East
and West Germany and non‐agricultural and supra‐regional investors purchasing farms (Tietz et al.,
2013). They lead to scale enlargement and intensification of agricultural practices, which strongly
34
influence landscape structures and elements, as they are vanishing (Ungaro et al., 2014). Field
enlargement and linked clearance of landscape elements would cause negative effects on landscape
preferences and therefore the capacity of landscape to deliver cultural ecosystem services. As
presented in the introduction, this could affect human well‐being, decrease economic activity and
influence socio‐economic development and competitiveness negatively.
The Common Agricultural Policy (CAP) is one of the main drivers for agricultural production,
landscape management and hence for landscape change affecting rural landscapes. It is currently
transforming, known as Greening of the CAP, aiming at greening Pillar I on farmland biodiversity and
reducing greenhouse gas emissions (Westhoek et al., 2014). The implementation of two greening
measures would be highly appreciated from this study point of view: 1) assigning 7 % of agricultural
land as ecological focus areas, and turn those areas into landscape features, buffer strips or
afforested areas; and 2) crop diversification. An establishment of more green linear elements as well
as higher crop diversity would be highly favored by visitors and residents of the Märkische Schweiz
region. It would not just support services from an ecological point of view, but valorize the landscape
in its cultural ecosystem services.
6 Conclusion
By applying a stated choice experiment using photorealistic landscape visualisations, this study
revealed that a diverse and structured landscape with various landscape attributes at a high level is
the most preferred. Significant differences in preferences for landscape attributes were found, with a
highest general preference for a high level of point elements, followed by a high level of linear
elements, medium level of point elements and high crop diversity. Medium levels of crop diversity
and linear elements were ranked along with livestock the lowest.
Heterogeneity could be found with 70 % of respondents preferring diverse and highly structured
landscapes and about 30 % of respondents having opponent preferences, indicating there is no
universal preference pattern. I also found preferences to be dependent on individual’s socio‐cultural
background, namely: gender, age, connection to farming, education, attitude and value setting,
conveyance and main activity in the landscape. The mapped spatial distributions of cumulative
preference values showed that hot spots of aesthetic quality are lying mainly in the region of the
Nature Park Märkische Schweiz. The results can help to improve the efficiency of the policy delivery
and to identify priority areas for the local landscape management from an aesthetic value
perspective.
35
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8 Annex
Developeddigitally calibrated imagesasrepresentationofthelandscape;the4
attributes are arranged in all possible combinations of levels, creating 54
images.Thenumbersunder thepicture represent the levels (1–low/absent; 2‐
medium/present; 3‐high) of the attributes in the following order: Livestock,
CropDiversity,LinearElementsand PointElements.
41
42
43
Questionnaireformusedinthemainsurvey
44
45
Orthogonal design generated with the software Sawtooth for theonline‐based
Second Pretest, which included5 Versionsà10 tasks; each task consisted of3
alternatives, ofwhich one needed to be chosen. The levels for Livestock, Crop
diversity,LinearelementsandPointelementsvaryacrossthealternativesfrom
1 (low;absent)to2 (medium;present)and3 (high),respectively.
Version Task Altern. Level Version Task Altern. Level
Livestock
Crop div.
Linear el.
Point el.
Livestock
Crop div.
Linear el.
Point el.
1 1 1 1 1 1 1 3 6 1 1 1 1 2 1 1 2 2 2 2 3 3 6 2 2 3 2 1 1 1 3 2 3 3 2 3 6 3 1 2 3 3 1 2 1 1 1 3 3 3 7 1 2 2 2 2 1 2 2 1 3 2 1 3 7 2 1 3 3 1 1 2 3 2 2 1 2 3 7 3 2 1 1 3 1 3 1 1 2 3 2 3 8 1 1 2 1 1 1 3 2 2 1 2 1 3 8 2 2 3 3 3 1 3 3 1 3 1 3 3 8 3 1 1 2 2 1 4 1 1 1 2 2 3 9 1 1 1 2 3 1 4 2 2 3 3 1 3 9 2 1 3 1 1 1 4 3 2 2 1 3 3 9 3 2 2 3 2 1 5 1 1 3 2 3 3 10 1 2 1 3 1 1 5 2 2 1 1 2 3 10 2 1 2 1 2 1 5 3 1 2 3 1 3 10 3 2 3 2 3 1 6 1 2 1 3 3 4 1 1 1 3 3 2 1 6 2 1 3 1 2 4 1 2 1 2 2 1 1 6 3 2 2 2 1 4 1 3 2 1 1 3 1 7 1 1 2 1 1 4 2 1 2 1 1 1 1 7 2 1 1 3 3 4 2 2 2 3 2 2 1 7 3 2 3 2 2 4 2 3 1 2 3 3 1 8 1 1 2 2 2 4 3 1 1 3 2 3 1 8 2 2 3 3 1 4 3 2 2 2 1 2 1 8 3 2 1 1 3 4 3 3 1 1 3 1 1 9 1 2 3 1 1 4 4 1 2 1 3 3 1 9 2 1 2 2 3 4 4 2 1 3 1 2 1 9 3 1 1 3 2 4 4 3 2 2 2 1 1 10 1 1 3 1 3 4 5 1 2 2 3 1 1 10 2 2 1 2 2 4 5 2 1 1 2 2 1 10 3 2 2 3 1 4 5 3 2 3 1 3 2 1 1 2 2 1 2 4 6 1 1 3 3 1 2 1 2 2 3 3 3 4 6 2 1 2 1 3 2 1 3 1 1 2 1 4 6 3 2 1 2 2 2 2 1 2 1 2 3 4 7 1 1 1 3 2 2 2 2 1 3 3 2 4 7 2 2 3 2 1 2 2 3 1 2 1 1 4 7 3 1 2 1 3 2 3 1 1 3 2 2 4 8 1 2 3 3 3 2 3 2 2 1 1 1 4 8 2 2 2 1 2 2 3 3 1 2 3 3 4 8 3 1 1 2 1 2 4 1 2 3 1 2 4 9 1 1 3 2 2 2 4 2 1 1 3 1 4 9 2 2 1 1 3 2 4 3 2 2 2 3 4 9 3 1 2 3 1 2 5 1 1 1 1 3 4 10 1 2 3 2 3 2 5 2 2 2 3 2 4 10 2 1 1 1 1 2 5 3 1 3 2 1 4 10 3 2 2 3 2 2 6 1 2 3 3 3 5 1 1 2 2 2 1 2 6 2 1 2 1 2 5 1 2 1 1 3 3 2 6 3 2 1 2 1 5 1 3 2 3 1 2 2 7 1 2 1 3 2 5 2 1 2 1 3 2 2 7 2 2 3 1 1 5 2 2 1 3 1 1 2 7 3 1 2 2 3 5 2 3 1 2 2 3 2 8 1 2 3 2 3 5 3 1 2 1 2 1 2 8 2 1 2 3 1 5 3 2 1 2 1 3 2 8 3 1 1 1 2 5 3 3 1 3 3 2 2 9 1 1 1 3 1 5 4 1 2 3 3 3 2 9 2 1 3 1 3 5 4 2 1 1 2 2 2 9 3 2 2 2 2 5 4 3 2 2 1 1 2 10 1 2 1 3 3 5 5 1 1 2 2 2 2 10 2 2 2 1 1 5 5 2 1 3 3 1 2 10 3 1 3 2 2 5 5 3 2 1 1 3 3 1 1 2 2 2 3 5 6 1 2 3 1 2 3 1 2 2 1 1 2 5 6 2 2 2 3 1 3 1 3 1 3 3 1 5 6 3 1 1 2 3 3 2 1 1 2 1 3 5 7 1 1 2 3 1 3 2 2 2 3 3 2 5 7 2 1 1 1 2 3 2 3 1 1 2 1 5 7 3 2 3 2 3
46
3 3 1 1 1 2 3 5 8 1 1 3 1 1 3 3 2 1 2 3 2 5 8 2 2 2 3 2 3 3 3 2 3 1 1 5 8 3 2 1 2 3 3 4 1 1 3 1 3 5 9 1 2 1 3 1 3 4 2 2 2 2 1 5 9 2 1 3 2 2 3 4 3 2 1 3 2 5 9 3 1 2 1 3 3 5 1 1 3 2 2 5 10 1 1 3 3 3 3 5 2 2 2 3 3 5 10 2 2 1 1 2 3 5 3 2 1 1 1 5 10 3 2 2 2 1
Outcome of the estimatedMNL model from the second pretest (online based)
generatedinNlogit5
|-> RESET ==================================================== ____ ___ _____ /\ / / / / / / / / \ / / / / /___ / / / \/ /___ /___/ /___/ / / ==================================================== O---------------------------------------------------------O | NLOGIT 5 (tm) Jul 02, 2013, 04:36:18PM | | Econometric Software, Inc. Copyright 1986-2012 | | Plainview, New York 11803 | | Registered to UCIT | | VU | | Registration Number 0912-r015702-5nsl | O---------------------------------------------------------O -------Initializing NLOGIT Version 5 (May 1, 2012)-------- ----------------------------------------------------------- |-> read; file=pretest_data_nm_ger.txt;nobs=1020;nvar=9;names=res,card,alt,choice,version,att1,att2,att3,att4 $ |-> sample; all $ |-> create; if(att1=2)cattle=1 $ |-> create; if(att2=1)divlow=1 $ |-> create; if(att2=2)divmed=1 $ |-> create; if(att2=3)divhigh=1 $ |-> create; if(att3=1)linlow=1 $ |-> create; if(att3=2)linmed=1 $ |-> create; if(att3=3)linhigh=1 $ |-> create; if(att4=1)poinlow=1 $ |-> create; if(att4=2)poinmed=1 $ |-> create; if(att4=3)poinhigh=1 $ |-> sample; all $ |-> nlogit; Lhs=choice; Choices=1,2,3; model: u(1)=cattle*cattle+divmed*divmed+divhigh*divhigh+linmed*linmed+linhigh*linhigh+poinmed*poinmed+poinhigh*poinhigh / u(2)=cattle*cattle+divmed*divmed+divhigh*divhigh+linmed*linmed+linhigh*linhigh+poinmed*poinmed+poinhigh*poinhigh / u(3)=cattle*cattle+divmed*divmed+divhigh*divhigh+linmed*linmed+linhigh*linhigh+poinmed*poinmed+poinhigh*poinhigh $ Normal exit: 6 iterations. Status=0, F= 264.6394
47
----------------------------------------------------------------------------- Discrete choice (multinomial logit) model Dependent variable Choice Log likelihood function -264.63943 Estimation based on N = 340, K = 7 Inf.Cr.AIC = 543.3 AIC/N = 1.598 R2=1-LogL/LogL* Log-L fncn R-sqrd R2Adj Constants only -365.2687 .2755 .2680 Response data are given as ind. choices Number of obs.= 340, skipped 0 obs --------+-------------------------------------------------------------------- | Standard Prob. 95% Confidence CHOICE| Coefficient Error z |z|>Z* Interval --------+-------------------------------------------------------------------- CATTLE| .46303*** .14761 3.14 .0017 .17372 .75235 DIVMED| .38733** .17453 2.22 .0265 .04526 .72939 DIVHIGH| 1.05901*** .17319 6.11 .0000 .71957 1.39846 LINMED| .37395** .17798 2.10 .0356 .02512 .72278 LINHIGH| 1.30090*** .17183 7.57 .0000 .96413 1.63768 POINMED| 1.04465*** .19652 5.32 .0000 .65947 1.42983 POINHIGH| 1.85158*** .19681 9.41 .0000 1.46584 2.23731 --------+-------------------------------------------------------------------- ***, **, * ==> Significance at 1%, 5%, 10% level. Model was estimated on Jul 02, 2013 at 04:36:35 PM
EfficientDesigngeneratedwith thesoftwareNgene for themainsurvey,which
included 6 Versions à 8 tasks; each task consisted of 3 alternatives, ofwhich
one needed to be chosen. The levels for Livestock, Crop diversity, Linear
elements and Point elements vary across the alternatives from 1 (low;absent)
to2 (medium;present)and3 (high), respectively.
Alternative 1 Alternative 2 Alternative 2
Version Task Level Level Level
Livestock Crop div.
Linear el.
Point el.
Livestock Crop div.
Linear el.
Point el.
Livestock
Crop div.
Linear el.
Point el.
1 1 2 1 3 3 2 3 3 3 1 3 2 2
1 2 1 1 2 2 1 2 1 1 2 2 1 1
1 3 1 3 1 1 1 1 2 1 1 2 2 2
1 1 2 3 1 1 2 2 2 2 1 1 2 1
1 2 1 3 2 2 1 1 1 2 1 3 1 1
1 3 2 1 2 3 2 3 1 2 2 2 3 2
1 1 1 3 1 2 2 1 3 3 2 3 3 3
1 2 2 3 3 3 2 3 2 2 2 2 2 3
2 3 1 1 2 2 2 3 1 1 1 3 3 1
2 1 1 3 2 1 1 1 3 2 2 1 2 3
2 2 2 1 1 2 2 2 2 1 1 1 1 1
2 3 2 3 2 1 2 2 3 2 2 1 1 3
2 1 2 2 3 1 2 1 3 3 2 2 1 3
2 2 1 3 2 2 1 2 3 1 1 1 1 3
2 3 1 2 3 3 2 3 1 3 1 1 3 2
2 1 1 1 2 3 1 2 3 3 2 2 2 2
3 2 2 1 2 3 1 3 1 3 1 3 2 1
3 3 2 2 3 1 1 1 2 3 1 3 1 2
3 1 2 2 1 3 1 2 2 1 2 1 3 2
3 2 2 2 2 3 1 3 1 2 2 3 3 2
3 3 1 3 1 2 1 2 3 3 2 1 2 3
3 1 2 1 3 1 1 2 1 1 2 2 2 2
3 2 1 2 3 2 1 1 3 1 1 3 2 1
3 3 1 1 3 2 1 1 2 3 1 2 3 3
48
4 1 2 1 1 2 1 1 3 1 1 2 2 2
4 2 1 2 1 1 2 3 2 2 2 3 1 1
4 3 1 1 3 1 2 1 1 3 1 2 2 3
4 1 2 3 3 2 2 2 1 3 1 1 3 1
4 2 1 1 1 3 2 3 1 2 2 2 3 3
4 3 2 2 3 1 2 3 2 2 1 3 1 1
4 1 2 2 2 1 1 2 1 2 1 1 2 1
4 2 2 1 1 3 2 2 3 1 2 3 2 2
5 3 2 1 3 1 2 2 2 2 2 3 1 1
5 1 1 2 1 3 1 2 3 1 1 1 3 3
5 2 1 2 1 1 2 2 2 2 2 1 3 1
5 3 1 2 3 2 1 3 1 3 1 3 3 3
5 1 1 3 1 2 1 1 2 3 1 2 3 2
5 2 1 2 1 3 2 3 3 1 1 1 3 3
5 3 2 3 3 1 1 1 2 2 2 2 1 3
5 1 2 1 2 2 2 1 3 1 2 2 3 2
6 2 1 2 1 1 1 1 2 2 2 2 1 1
6 3 2 2 2 3 2 1 3 1 2 1 1 3
6 1 2 3 2 3 2 3 3 3 2 2 3 2
6 2 1 3 2 3 1 3 2 1 1 1 1 3
6 3 1 2 1 2 1 3 2 1 2 1 1 2
6 1 2 1 3 1 2 3 1 2 2 3 2 1
6 2 1 3 2 3 1 2 1 3 1 3 1 2
6 3 2 3 3 2 2 1 1 3 1 3 2 1
OutcomeoftheestimatedMNL modelfrom themainsurveygenerated inNlogit
5
|-> RESET ==================================================== ____ ___ _____ /\ / / / / / / / / \ / / / / /___ / / / \/ /___ /___/ /___/ / / ==================================================== O---------------------------------------------------------O | NLOGIT 5 (tm) Oct 15, 2013, 03:45:51PM | | Econometric Software, Inc. Copyright 1986-2012 | | Plainview, New York 11803 | | Registered to UCIT | | VU | | Registration Number 0912-r015702-5nsl | O---------------------------------------------------------O -------Initializing NLOGIT Version 5 (May 1, 2012)-------- ----------------------------------------------------------- |-> LOAD;file="C:\Documents and Settings\mke240\my documents\DataIVM\PhD students\Boris van Zanten\2013 CE Boris\CE analysis\Data Germany.lpj"$ Project file contained 4848 observations. |-> sample; all $ |-> nlogit; Lhs=choice; Choices=1,2,3; model: u(1)=cattle*cattle+divmed*divmed+divhigh*divhigh+linmed*linmed+linhigh*linhigh+poinmed*poinmed+poinhigh*poinhigh / u(2)=cattle*cattle+divmed*divmed+divhigh*divhigh+linmed*linmed+linhigh*linhigh+poinmed*poinmed+poinhigh*poinhigh /
49
u(3)=cattle*cattle+divmed*divmed+divhigh*divhigh+linmed*linmed+linhigh*linhigh+poinmed*poinmed+poinhigh*poinhigh $ +------------------------------------------------------+ |WARNING: Bad observations were found in the sample. | |Found 24 bad observations among 1616 individuals. | |You can use ;CheckData to get a list of these points. | +------------------------------------------------------+ Normal exit: 5 iterations. Status=0, F= 1433.722 ----------------------------------------------------------------------------- Discrete choice (multinomial logit) model Dependent variable Choice Log likelihood function -1433.72220 Estimation based on N = 1592, K = 7 Inf.Cr.AIC = 2881.4 AIC/N = 1.810 R2=1-LogL/LogL* Log-L fncn R-sqrd R2Adj Constants only -1748.4057 .1800 .1782 Response data are given as ind. choices Number of obs.= 1616, skipped 24 obs --------+-------------------------------------------------------------------- | Standard Prob. 95% Confidence CHOICE| Coefficient Error z |z|>Z* Interval --------+-------------------------------------------------------------------- CATTLE| .75354*** .08843 8.52 .0000 .58022 .92686 DIVMED| .13291* .06939 1.92 .0554 -.00309 .26891 DIVHIGH| 1.03362*** .09091 11.37 .0000 .85543 1.21180 LINMED| .21605*** .07311 2.96 .0031 .07276 .35935 LINHIGH| 1.37530*** .08614 15.97 .0000 1.20647 1.54414 POINMED| 1.18039*** .08081 14.61 .0000 1.02201 1.33877 POINHIGH| 2.09928*** .11795 17.80 .0000 1.86811 2.33046 --------+-------------------------------------------------------------------- ***, **, * ==> Significance at 1%, 5%, 10% level. Model was estimated on Oct 15, 2013 at 03:45:57 PM -----------------------------------------------------------------------------
OutcomeofLCA withoutrestrictionsand3 classesgeneratedinNlogit4
read; file=Analysis_Germany_test.txt;nobs=4848;nvar=27;names=id,series,alternative,choice,version,att1,att2,att3,att4, F1,F4_1,F4_2,F5,F6,F7,F9_1,F9_2,F10,F11,F12_1,F12_2,F12_3,F12_4,F12_5,F13,F14,F15 $ sample; all $ create; if(att1=2)cattle=1 $ create; if(att2=1)divlow=1 $ create; if(att2=2)divmed=1 $ create; if(att2=3)divhigh=1 $ create; if(att3=1)linlow=1 $
50
create; if(att3=2)linmed=1 $ create; if(att3=3)linhigh=1 $ create; if(att4=1)poinlow=1 $ create; if(att4=2)poinmed=1 $ create; if(att4=3)poinhigh=1 $ --> Sample; all $ --> reject; age=-999 $ --> lclogit ;Lhs=choice ;Choices=a,b,c ;pds=8 ;Maxit=150 ;pts=3 ?number of classes ;model: U(*)=c*cattle+dm*divmed+dh*divhigh+lm*linmed+lh*linhigh+pm*poinmed+ph*pointhigh $ +---------------------------------------------+ | Discrete choice and multinomial logit models| +---------------------------------------------+ +------------------------------------------------------+ |WARNING: Bad observations were found in the sample. | |Found 8 bad observations among 1584 individuals. | |You can use ;CheckData to get a list of these points. | +------------------------------------------------------+ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Jan 22, 2014 at 11:36:09AM.| | Dependent variable Choice | | Weighting variable None | | Number of observations 1576 | | Iterations completed 11 | | Log likelihood function -1415.986 | | Number of parameters 7 | | Info. Criterion: AIC = 1.80582 | | Finite Sample: AIC = 1.80587 | | Info. Criterion: BIC = 1.82964 | | Info. Criterion:HQIC = 1.81467 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | Constants only -1730.7896 .18188 .17587 | | Response data are given as ind. choice. | | Number of obs.= 1584, skipped 8 bad obs. | +---------------------------------------------+ +---------------------------------------------+ | Notes No coefficients=> P(i,j)=1/J(i). | | Constants only => P(i,j) uses ASCs | | only. N(j)/N if fixed choice set. | | N(j) = total sample frequency for j | | N = total sample frequency. | | These 2 models are simple MNL models. | | R-sqrd = 1 - LogL(model)/logL(other) | | RsqAdj=1-[nJ/(nJ-nparm)]*(1-R-sqrd) | | nJ = sum over i, choice set sizes |
51
+---------------------------------------------+ +--------+--------------+----------------+--------+--------+ |Variable| Coefficient | Standard Error |b/St.Er.|P[|Z|>z]| +--------+--------------+----------------+--------+--------+ C|1 | .76904418 .08916051 8.625 .0000 DM|1 | .12164047 .06974273 1.744 .0811 DH|1 | 1.02662038 .09136051 11.237 .0000 LM|1 | .22537857 .07349768 3.066 .0022 LH|1 | 1.38143661 .08679778 15.916 .0000 PM|1 | 1.18753261 .08140183 14.589 .0000 PH|1 | 2.11406392 .11901418 17.763 .0000 Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Latent Class Logit Model | | Maximum Likelihood Estimates | | Model estimated: Jan 22, 2014 at 11:36:12AM.| | Dependent variable CHOICE | | Weighting variable None | | Number of observations 1576 | | Iterations completed 51 | | Log likelihood function -1274.848 | | Number of parameters 23 | | Info. Criterion: AIC = 1.64701 | | Finite Sample: AIC = 1.64747 | | Info. Criterion: BIC = 1.72528 | | Info. Criterion:HQIC = 1.67610 | | Restricted log likelihood -1731.413 | | McFadden Pseudo R-squared .2636952 | | Chi squared 913.1305 | | Degrees of freedom 23 | | Prob[ChiSqd > value] = .0000000 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1731.4130 .26370 .25828 | | Constants only -1730.7896 .26343 .25802 | | At start values -1355.8615 .05975 .05284 | | Response data are given as ind. choice. | +---------------------------------------------+ +---------------------------------------------+ | Notes No coefficients=> P(i,j)=1/J(i). | | Constants only => P(i,j) uses ASCs | | only. N(j)/N if fixed choice set. | | N(j) = total sample frequency for j | | N = total sample frequency. | | These 2 models are simple MNL models. | | R-sqrd = 1 - LogL(model)/logL(other) | | RsqAdj=1-[nJ/(nJ-nparm)]*(1-R-sqrd) | | nJ = sum over i, choice set sizes | +---------------------------------------------+ +---------------------------------------------+ | Latent Class Logit Model | | Number of latent classes = 3 | | Average Class Probabilities | | .337 .279 .344 | | ------------------------------------------- | | LCM model with panel has 198 groups. | | Fixed number of obsrvs./group= 8 | | Discrete parameter variation specified. |
52
| ------------------------------------------- | | Number of obs.= 1584, skipped 8 bad obs. | +---------------------------------------------+ +--------+--------------+----------------+--------+--------+ |Variable| Coefficient | Standard Error |b/St.Er.|P[|Z|>z]| +--------+--------------+----------------+--------+--------+ ---------+Utility parameters in latent class -->> 1 C|1 | 1.24137214 .22587608 5.496 .0000 DM|1 | .51228189 .19028951 2.692 .0071 DH|1 | 2.72133588 .32932763 8.263 .0000 LM|1 | .08577626 .17590880 .488 .6258 LH|1 | 2.40644762 .31059117 7.748 .0000 PM|1 | 1.35976167 .20699638 6.569 .0000 PH|1 | 1.89120339 .28946842 6.533 .0000 ---------+Utility parameters in latent class -->> 2 C|2 | .68414260 .09698815 7.054 .0000 DM|2 | -.18947335 .09318246 -2.033 .0420 DH|2 | -.09325390 .12472551 -.748 .4547 LM|2 | -.32829317 .10971486 -2.992 .0028 LH|2 | -.31103974 .12795584 -2.431 .0151 PM|2 | .31352841 .10300496 3.044 .0023 PH|2 | .97610130 .14625331 6.674 .0000 ---------+Utility parameters in latent class -->> 3 C|3 | 1.05945047 .16043757 6.604 .0000 DM|3 | .09398184 .10495575 .895 .3706 DH|3 | 1.03648745 .14922706 6.946 .0000 LM|3 | .77556934 .12736224 6.089 .0000 LH|3 | 3.20075013 .19793253 16.171 .0000 PM|3 | 2.62330543 .17009218 15.423 .0000 PH|3 | 4.63683081 .28025435 16.545 .0000 ---------+Estimated latent class probabilities PrbCls_1| .35075095 .05349267 6.557 .0000 PrbCls_2| .29117934 .05595597 5.204 .0000 PrbCls_3| .35806970 .05276571 6.786 .0000
OutcomeoftheRPL modelwithoutrestrictionsgenerated inNlogit4
read; file=Results_MS_data_multi_new.txt;nobs=4848;nvar=23;names=id,series,alt,choice,version,att1,att2,att3,att4, F1,F4_1,F4_2,F5,F6,F7,F9_1,F9_2,F10,F11,F12,F13,F14,F15 $
--> sample; all $ --> reject; F10=-999 $ --> create; if(att1=2)cattle=1 $ --> create; if(att2=1)divlow=1 $ --> create; if(att2=2)divmed=1 $ --> create; if(att2=3)divhigh=1 $ --> create; if(att3=1)linlow=1 $ --> create; if(att3=2)linmed=1 $ --> create; if(att3=3)linhigh=1 $ --> create; if(att4=1)poinlow=1 $ --> create; if(att4=2)poinmed=1 $
53
--> create; if(att4=3)poinhigh=1 $ --> create; if(F1=1)visitor=1 $ --> create; if(F1=2)resident=1 $ --> create; if(F5=1|F5=3|F5=4)travel=1 $ --> create; if(F6=3)bike=1 $ --> create; if(F7=1)female=1 $ --> create; if(F9_1=1)east=1 $ --> create; age = F10 $ --> create; connect=F12 $ --> create; if(F13=1|F13=3)CDUgreen=1 $ --> create; edu=F14 $ --> sample; all $ --> reject; age=-999 $ --> calc; ran (1975) $ --> rplogit ;Lhs=choice ;Choices=1,2,3 ;pds=8 ;Halton ;rpl=bike,travel,female,age,connect,cdugreen,edu ;fcn=cattle(c|#1111111),divmed(c|#1111111),divhigh(c|#1111111),linmed(c|#1111111),linhigh(c|#1111111),poinmed(c|#1111111),poinhigh(c|#1111111) ;Maxit=500 ;pts=1 ;model: u(*)=cattle*cattle+divmed*divmed+divhigh*divhigh+linmed*linmed+linhigh*linhigh+poinmed*poinmed+poinhigh*poinhigh $ +---------------------------------------------+ | Discrete choice and multinomial logit models| +---------------------------------------------+ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Start values obtained using MNL model | | Maximum Likelihood Estimates | | Model estimated: Apr 27, 2014 at 04:51:03PM.| | Dependent variable Choice | | Weighting variable None | | Number of observations 1584 | | Iterations completed 11 | | Log likelihood function -1423.365 | | Number of parameters 7 | | Info. Criterion: AIC = 1.80602 | | Finite Sample: AIC = 1.80606 | | Info. Criterion: BIC = 1.82974 | | Info. Criterion:HQIC = 1.81483 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | Constants only -1739.6352 .18180 .16708 | | Response data are given as ind. choice. | | Number of obs.= 1584, skipped 0 bad obs. | +---------------------------------------------+ +---------------------------------------------+ | Notes No coefficients=> P(i,j)=1/J(i). | | Constants only => P(i,j) uses ASCs | | only. N(j)/N if fixed choice set. | | N(j) = total sample frequency for j | | N = total sample frequency. |
54
| These 2 models are simple MNL models. | | R-sqrd = 1 - LogL(model)/logL(other) | | RsqAdj=1-[nJ/(nJ-nparm)]*(1-R-sqrd) | | nJ = sum over i, choice set sizes | +---------------------------------------------+ +--------+--------------+----------------+--------+--------+ |Variable| Coefficient | Standard Error |b/St.Er.|P[|Z|>z]| +--------+--------------+----------------+--------+--------+ CATTLE | .76574793 .08886472 8.617 .0000 DIVMED | .12169151 .06959313 1.749 .0804 DIVHIGH | 1.03202978 .09118825 11.318 .0000 LINMED | .22409152 .07334705 3.055 .0022 LINHIGH | 1.38640010 .08659488 16.010 .0000 POINMED | 1.18364686 .08113550 14.589 .0000 POINHIGH| 2.11018183 .11860010 17.792 .0000 Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Random Parameters Logit Model | | Maximum Likelihood Estimates | | Model estimated: Apr 27, 2014 at 04:51:08PM.| | Dependent variable CHOICE | | Weighting variable None | | Number of observations 1584 | | Iterations completed 60 | | Log likelihood function -1359.103 | | Number of parameters 56 | | Info. Criterion: AIC = 1.78675 | | Finite Sample: AIC = 1.78939 | | Info. Criterion: BIC = 1.97651 | | Info. Criterion:HQIC = 1.85725 | | Restricted log likelihood -1740.202 | | McFadden Pseudo R-squared .2189968 | | Chi squared 762.1974 | | Degrees of freedom 56 | | Prob[ChiSqd > value] = .0000000 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1740.2019 .21900 .20494 | | Constants only -1739.6352 .21874 .20468 | | At start values -1423.3654 .04515 .02797 | | Response data are given as ind. choice. | +---------------------------------------------+ +---------------------------------------------+ | Notes No coefficients=> P(i,j)=1/J(i). | | Constants only => P(i,j) uses ASCs | | only. N(j)/N if fixed choice set. | | N(j) = total sample frequency for j | | N = total sample frequency. | | These 2 models are simple MNL models. | | R-sqrd = 1 - LogL(model)/logL(other) | | RsqAdj=1-[nJ/(nJ-nparm)]*(1-R-sqrd) | | nJ = sum over i, choice set sizes | +---------------------------------------------+ +---------------------------------------------+ | Random Parameters Logit Model | | Replications for simulated probs. = 1 | | Halton sequences used for simulations | | ------------------------------------------- |
55
| RPL model with panel has 198 groups. | | Fixed number of obsrvs./group= 8 | | Random parameters model was specified | | ------------------------------------------- | | Number of obs.= 1584, skipped 0 bad obs. | +---------------------------------------------+ +--------+--------------+----------------+--------+--------+ |Variable| Coefficient | Standard Error |b/St.Er.|P[|Z|>z]| +--------+--------------+----------------+--------+--------+ ---------+Random parameters in utility functions CATTLE | .01484207 .34132424 .043 .9653 DIVMED | -.72700294 .28591589 -2.543 .0110 DIVHIGH | .11793993 .36582891 .322 .7472 LINMED | -.45727936 .29617915 -1.544 .1226 LINHIGH | -.00227777 .33651111 -.007 .9946 POINMED | -.34691552 .32553578 -1.066 .2866 POINHIGH| .81068820 .45503221 1.782 .0748 ---------+Heterogeneity in mean, Parameter:Variable CATT:BIK| .31608198 .23320321 1.355 .1753 CATT:TRA| -.06926454 .19940062 -.347 .7283 CATT:FEM| .51715280 .18509152 2.794 .0052 CATT:AGE| .08678288 .05659601 1.533 .1252 CATT:CON| .05065574 .09678803 .523 .6007 CATT:CDU| .18531044 .22266591 .832 .4053 CATT:EDU| .03860774 .07364108 .524 .6001 DIVM:BIK| .08538667 .18040247 .473 .6360 DIVM:TRA| .01486524 .15693733 .095 .9245 DIVM:FEM| .30228640 .14550307 2.078 .0378 DIVM:AGE| .00012709 .04568998 .003 .9978 DIVM:CON| .04678865 .07838445 .597 .5506 DIVM:CDU| .16073529 .17247223 .932 .3514 DIVM:EDU| .15167502 .06006420 2.525 .0116 DIVH:BIK| .10340725 .23384422 .442 .6583 DIVH:TRA| .09878567 .20654068 .478 .6324 DIVH:FEM| .07067195 .19170675 .369 .7124 DIVH:AGE| -.04729768 .06039961 -.783 .4336 DIVH:CON| .16745388 .10082404 1.661 .0967 DIVH:CDU| .60667765 .22853482 2.655 .0079 DIVH:EDU| .17115711 .07685187 2.227 .0259 LINM:BIK| .07616778 .18756927 .406 .6847 LINM:TRA| .25522465 .16661782 1.532 .1256 LINM:FEM| .00467555 .15435075 .030 .9758 LINM:AGE| -.02848109 .04859963 -.586 .5579 LINM:CON| .02712200 .07879406 .344 .7307 LINM:CDU| .15856974 .17830210 .889 .3738 LINM:EDU| .13506784 .06246857 2.162 .0306 LINH:BIK| -.00961111 .22617847 -.042 .9661 LINH:TRA| .61496719 .19221898 3.199 .0014 LINH:FEM| -.30095835 .18169012 -1.656 .0976 LINH:AGE| .04107653 .05608054 .732 .4639 LINH:CON| .11682932 .09794829 1.193 .2330 LINH:CDU| .62352469 .22258839 2.801 .0051 LINH:EDU| .23663226 .07206442 3.284 .0010 POIN:BIK| .59059647 .21362101 2.765 .0057 POIN:TRA| -.01812235 .18312314 -.099 .9212 POIN:FEM| .01900897 .17096706 .111 .9115 POIN:AGE| .10883431 .05318284 2.046 .0407 POIN:CON| -.09978529 .08797161 -1.134 .2567 POIN:CDU| .01240278 .19937368 .062 .9504
56
POIN:EDU| .31936152 .06933443 4.606 .0000 POI0:BIK| .47909818 .30422700 1.575 .1153 POI0:TRA| -.07413484 .26297341 -.282 .7780 POI0:FEM| -.22277114 .24710496 -.902 .3673 POI0:AGE| .02646242 .07574078 .349 .7268 POI0:CON| -.15039260 .12903211 -1.166 .2438 POI0:CDU| .18514007 .29611576 .625 .5318 POI0:EDU| .36651599 .09671042 3.790 .0002 ---------+Derived standard deviations of parameter distributions CsCATTLE| .000000 ......(Fixed Parameter)....... CsDIVMED| .000000 ......(Fixed Parameter)....... CsDIVHIG| .000000 ......(Fixed Parameter)....... CsLINMED| .000000 ......(Fixed Parameter)....... CsLINHIG| .000000 ......(Fixed Parameter)....... CsPOINME| .000000 ......(Fixed Parameter)....... CsPOINHI| .000000 ......(Fixed Parameter)....... Parameter Matrix for Heterogeneity in Means.
OutcomeoftheRPL modelwith restrictionsgenerated inNlogit4
--> sample; all $ --> reject; age=-999 $ --> calc; ran (1975) $ --> rplogit ;Lhs=choice ;Choices=1,2,3 ;pds=8 ;Halton ;rpl=bike,travel,female,age,connect,cdugreen,edu ;fcn=cattle(c|#0011000),divmed(c|#0010001),divhigh(c|#0001111),linmed(c|#0100001),linhigh(c|#0110011),poinmed(c|#1001001),poinhigh (c|#1010001) ;Maxit=500 ;pts=1 ;model: u(*)=cattle*cattle+divmed*divmed+divhigh*divhigh+linmed*linmed+linhigh*linhigh+poinmed*poinmed+poinhigh*poinhigh $ +---------------------------------------------+ | Discrete choice and multinomial logit models| +---------------------------------------------+ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Start values obtained using MNL model | | Maximum Likelihood Estimates | | Model estimated: Apr 27, 2014 at 04:48:12PM.| | Dependent variable Choice | | Weighting variable None | | Number of observations 1584 | | Iterations completed 11 | | Log likelihood function -1423.365 | | Number of parameters 7 |
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| Info. Criterion: AIC = 1.80602 | | Finite Sample: AIC = 1.80606 | | Info. Criterion: BIC = 1.82974 | | Info. Criterion:HQIC = 1.81483 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | Constants only -1739.6352 .18180 .17477 | | Response data are given as ind. choice. | | Number of obs.= 1584, skipped 0 bad obs. | +---------------------------------------------+ +---------------------------------------------+ | Notes No coefficients=> P(i,j)=1/J(i). | | Constants only => P(i,j) uses ASCs | | only. N(j)/N if fixed choice set. | | N(j) = total sample frequency for j | | N = total sample frequency. | | These 2 models are simple MNL models. | | R-sqrd = 1 - LogL(model)/logL(other) | | RsqAdj=1-[nJ/(nJ-nparm)]*(1-R-sqrd) | | nJ = sum over i, choice set sizes | +---------------------------------------------+ +--------+--------------+----------------+--------+--------+ |Variable| Coefficient | Standard Error |b/St.Er.|P[|Z|>z]| +--------+--------------+----------------+--------+--------+ CATTLE | .76574793 .08886472 8.617 .0000 DIVMED | .12169151 .06959313 1.749 .0804 DIVHIGH | 1.03202978 .09118825 11.318 .0000 LINMED | .22409152 .07334705 3.055 .0022 LINHIGH | 1.38640010 .08659488 16.010 .0000 POINMED | 1.18364686 .08113550 14.589 .0000 POINHIGH| 2.11018183 .11860010 17.792 .0000 Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Random Parameters Logit Model | | Maximum Likelihood Estimates | | Model estimated: Apr 27, 2014 at 04:48:14PM.| | Dependent variable CHOICE | | Weighting variable None | | Number of observations 1584 | | Iterations completed 32 | | Log likelihood function -1366.927 | | Number of parameters 27 | | Info. Criterion: AIC = 1.76001 | | Finite Sample: AIC = 1.76062 | | Info. Criterion: BIC = 1.85150 | | Info. Criterion:HQIC = 1.79400 | | Restricted log likelihood -1740.202 | | McFadden Pseudo R-squared .2145009 | | Chi squared 746.5497 | | Degrees of freedom 27 | | Prob[ChiSqd > value] = .0000000 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1740.2019 .21450 .20775 | | Constants only -1739.6352 .21425 .20749 | | At start values -1423.3654 .03965 .03140 | | Response data are given as ind. choice. | +---------------------------------------------+ +---------------------------------------------+
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| Notes No coefficients=> P(i,j)=1/J(i). | | Constants only => P(i,j) uses ASCs | | only. N(j)/N if fixed choice set. | | N(j) = total sample frequency for j | | N = total sample frequency. | | These 2 models are simple MNL models. | | R-sqrd = 1 - LogL(model)/logL(other) | | RsqAdj=1-[nJ/(nJ-nparm)]*(1-R-sqrd) | | nJ = sum over i, choice set sizes | +---------------------------------------------+ +---------------------------------------------+ | Random Parameters Logit Model | | Replications for simulated probs. = 1 | | Halton sequences used for simulations | | ------------------------------------------- | | RPL model with panel has 198 groups. | | Fixed number of obsrvs./group= 8 | | Random parameters model was specified | | ------------------------------------------- | | Number of obs.= 1584, skipped 0 bad obs. | +---------------------------------------------+ +--------+--------------+----------------+--------+--------+ |Variable| Coefficient | Standard Error |b/St.Er.|P[|Z|>z]| +--------+--------------+----------------+--------+--------+ ---------+Random parameters in utility functions CATTLE | .21377712 .18479835 1.157 .2473 DIVMED | -.61875691 .24817499 -2.493 .0127 DIVHIGH | .27495392 .30717600 .895 .3707 LINMED | -.46154589 .25120871 -1.837 .0662 LINHIGH | .22198494 .29777647 .745 .4560 POINMED | -.40697949 .27321312 -1.490 .1363 POINHIGH| .72191394 .38316511 1.884 .0596 ---------+Heterogeneity in mean, Parameter:Variable CATT:BIK| .000000 ......(Fixed Parameter)....... CATT:TRA| .000000 ......(Fixed Parameter)....... CATT:FEM| .52646851 .18190210 2.894 .0038 CATT:AGE| .10894813 .05215219 2.089 .0367 CATT:CON| .000000 ......(Fixed Parameter)....... CATT:CDU| .000000 ......(Fixed Parameter)....... CATT:EDU| .000000 ......(Fixed Parameter)....... DIVM:BIK| .000000 ......(Fixed Parameter)....... DIVM:TRA| .000000 ......(Fixed Parameter)....... DIVM:FEM| .26642105 .13030634 2.045 .0409 DIVM:AGE| .000000 ......(Fixed Parameter)....... DIVM:CON| .000000 ......(Fixed Parameter)....... DIVM:CDU| .000000 ......(Fixed Parameter)....... DIVM:EDU| .15746559 .05768650 2.730 .0063 DIVH:BIK| .000000 ......(Fixed Parameter)....... DIVH:TRA| .000000 ......(Fixed Parameter)....... DIVH:FEM| .000000 ......(Fixed Parameter)....... DIVH:AGE| -.06797849 .04581445 -1.484 .1379 DIVH:CON| .15669398 .08011814 1.956 .0505 DIVH:CDU| .44305054 .18142347 2.442 .0146 DIVH:EDU| .18584412 .07480086 2.485 .0130 LINM:BIK| .000000 ......(Fixed Parameter)....... LINM:TRA| .29550115 .14940460 1.978 .0479 LINM:FEM| .000000 ......(Fixed Parameter)....... LINM:AGE| .000000 ......(Fixed Parameter).......
59
LINM:CON| .000000 ......(Fixed Parameter)....... LINM:CDU| .000000 ......(Fixed Parameter)....... LINM:EDU| .13439489 .05959322 2.255 .0241 LINH:BIK| .000000 ......(Fixed Parameter)....... LINH:TRA| .59658174 .15764796 3.784 .0002 LINH:FEM| -.33485778 .14784940 -2.265 .0235 LINH:AGE| .000000 ......(Fixed Parameter)....... LINH:CON| .000000 ......(Fixed Parameter)....... LINH:CDU| .44418341 .16820180 2.641 .0083 LINH:EDU| .24441707 .06988274 3.498 .0005 POIN:BIK| .52943308 .18330635 2.888 .0039 POIN:TRA| .000000 ......(Fixed Parameter)....... POIN:FEM| .000000 ......(Fixed Parameter)....... POIN:AGE| .09551111 .03983690 2.398 .0165 POIN:CON| .000000 ......(Fixed Parameter)....... POIN:CDU| .000000 ......(Fixed Parameter)....... POIN:EDU| .32135211 .06624480 4.851 .0000 POI0:BIK| .37449717 .23440708 1.598 .1101 POI0:TRA| .000000 ......(Fixed Parameter)....... POI0:FEM| -.27934669 .17810243 -1.568 .1168 POI0:AGE| .000000 ......(Fixed Parameter)....... POI0:CON| .000000 ......(Fixed Parameter)....... POI0:CDU| .000000 ......(Fixed Parameter)....... POI0:EDU| .38699035 .09194155 4.209 .0000 ---------+Derived standard deviations of parameter distributions CsCATTLE| .000000 ......(Fixed Parameter)....... CsDIVMED| .000000 ......(Fixed Parameter)....... CsDIVHIG| .000000 ......(Fixed Parameter)....... CsLINMED| .000000 ......(Fixed Parameter)....... CsLINHIG| .000000 ......(Fixed Parameter)....... CsPOINME| .000000 ......(Fixed Parameter)....... CsPOINHI| .000000 ......(Fixed Parameter)....... Parameter Matrix for Heterogeneity in Means.
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Ich versichere, die Masterarbeit selbständig und lediglich unter Benutzung der angegebenen
Quellen und Hilfsmittel verfasst zu haben. Alle Stellen, die wörtlich oder sinngemäß aus
veröffentlichten oder noch nicht veröffentlichten Quellen entnommen sind, sind als solche
kenntlich gemacht. Die Zeichnungen oder Abbildungen in dieser Arbeit sind von mir selbst
erstellt worden oder mit einem entsprechenden Quellennachweis versehen.
Ich erkläre weiterhin, dass die vorliegende Arbeit noch nicht im Rahmen eines anderen
Prüfungsverfahrens eingereicht wurde.
Potsdam, den ________________ _________________________________
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