Analysing Scenarios for Cultural Ecosystem Services Using MENE Data


The first phase of the UKNEA made a significant contribution to our understanding of cultural ecosystem services (CES) and how to characterise them, by developing the notion of an 'environmental setting'. Settings were defined as 'locations and places where humans interact with each other and nature that give rise to cultural goods and benefits that people obtain from ecosystems'.

On this website we use the Monitor of Engagement with the Natural Environment (MENE) dataset of Natural England, to explore people.s activities and the settings they use in more detail. The aim is to find ways of operationalizing the use of these kinds of data, to develop indicators of CES and eventually to model the impacts of the NEA scenarios upon them.

The tool used here is that of a Bayesian Belief Network (BBN). Such networks can be used to show the main types of relationship found in a dataset like MENE, and can allow users to interact with these data to explore the causal relationships that exist. The BBN developed during the first stages of NEAFO is shown below; a full account of the work is provided by (Haines-Young et al. 2013).

The BBN presented here is based on the hypothesis that the settings people use are determined by the activities they wish to pursue. The states for both these nodes is represented by different types of activity and different types of place recorded in MENE, and the arrow (or edge) that goes between them indicates that activities influence choice of setting. The network also hypothesises that people at different 'life stages' and in different 'socio-economic groups' will undertake different kinds of activity. It also assumes that activities and settings determine both the distance travelled and duration of the visit, as well as the feelings people derived from the trip.

Making the BBN operational

For the initial phase of this work we have used the MENE data for the three years from 2009 through to 2012; from the 160,000 records a subset has been extracted covering those respondents who both made a visit of some kind, and who were interviewed in detail to find out what they did and where they went. This subset contains roughly 50,000 records. The work has focussed on understanding the current situation using these data before the scenario aspect is developed.

In a BNN the state that each node is in is shown as a probability bar. These bars should be read as the probability that a variable (say 'setting') is in this state, given the evidence available. On the page below we have organised the nodes to show the socio-demographic characteristics of people who were surveyed on the left and the details of the visit on the right.

When the network is first opened or 'reset' the probabilities essentially reflect the frequencies of the different states as recorded by MENE. By clicking once on any probability bar users can see what the effect is when they are certain that they are, say, dealing with 'an empty nester'. Similarly, users can select an activity or setting and see what kind of people are likely to use it.



Composite Socio Demographic and Locational Characteristics of Visitors


Social-economic status

Locational Characteristics

Disaggregated Demographic Characteristics of Visitors

In the BBN Lifestage is assumed to be dependent on a number of other characteristic; you can see their influence by changing

Respondent age

Household Size

The presence of children in the household

Work status


Disaggregated Locational Characteristics of Visitors

In the BBN Locational Characteristics is assumed to be dependent on a number of other factors; you can see their influence by changing any of these components

The index of multiple deprivation of their location where they live

Whether they live in an urban area

Settings and Activities and Trip Characteristics

Distance travelled

Region in which setting is visited

Trip duration

Transportation Used

Was the site visit designated?

Feelings about the visit were rated on a 5 point scale, with 5 indicating the strongest feelings






Close to Nature


At present the BBN has been calibrated using the data from MENE, which obviously shows the current situation. In future work we will develop it further to allow users to explore what might happen if, for example, the socio-demographic composition of the population changed, as described under the different NEA scenarios. Already users could explore this in an informal way simply by trying various combinations of the input variables.

For example, under the NEA scenario 'World markets', it is projected that the dominance of London will grow and, amongst other things, transform large parts of the South East. You might like to inspect the effect of changing the Region node shown above to see the effect it has on the types of setting selected by people.

References and Links

Further details about the development of this network can be found in: Haines-Young, R. et al. (2013) Operationalizing scenarios in the UK National Ecosystem Assessment Follow-on. Interim report, Centre for Environmental management, School of Geography, University of Nottingham.

More details about the UK NEA Follow-on can be found by following this link, and details of MENE can be found here.

Useful references for those interested in BBN include:

Haines-Young, R. (2011) Exploring ecosystem service issues across diverse knowledge domains using Bayesian Belief Networks. Progress in Physical Geography 35(5): 681-699.

Landuyt, D. et al. (2013) A review of Bayesian Belief Networks in ecosystem service modelling. Environmental Modelling and Software, 46, 1-11.

Kjærulff, U. B. and Madsen, A.L (2013) Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Springer, Second Edition.

Contact information

Jamie Tratalos, Centre for Environmental management, School of Geography, University of Nottingham,

Roy Haines-Young, Centre for Environmental management, School of Geography, University of Nottingham,

Marion Potschin, Centre for Environmental management, School of Geography, University of Nottingham,