Category Archives: Simulation Modeling

New Paper: Strategic analysis of a water rights conflict in the south western United States

A new paper by PhD Candidate Simone Philpot has just been published in the Journal of Environmental Management! Download a copy here: 

Simone, along with co-authors Dr. Keith Hipel and Dr. Peter Johnson, uses the Graph Model for Conflict Resolution to model the longstanding dispute over water allocation between Nevada and Utah. This modeling process allows for new insights into how different actors perform in different situations. Congrats to Simone for publishing her work in a very prestigious venue!


A strategic analysis of the ongoing conflict between Nevada and Utah, over groundwater allocation at Snake Valley, is carried out in order to investigate ways on how to resolve this dispute. More specifically, the Graph Model for Conflict Resolution is employed to formally model and analyze this conflict using the decision support system called GMCR+. The conflict analysis findings indicate that the dispute is enduring because of a lack of incentive and opportunity for any party to move beyond the present circumstances. Continued negotiations are not likely to resolve this conflict. A substantial change in the preferences or options of the disputants, or new governance tools will be required to move this conflict forward. This may hold lessons for future groundwater conflicts. It is, however, increasingly likely that the parties will require a third party intervention, such as equal apportionment by the US Supreme Court.


  • Trans-boundary resource management;
  • Groundwater;
  • Water rights;
  • Decision support system;
  • Conflict analysis

The impact of climate change on winter ski tourism in the Pyrenees

We all know that climate change is having a major impact on weather patterns around the globe. One industry that is particularly exposed to these changes is the ski industry. Though large mountain/high elevation ski resorts may remain insulated from the impacts of shorter ski seasons and more erratic weather, those ski resorts at low altitude are particularly vulnerable to a changing climate. As a mid-latitude, lower elevation (comparatively) ski region, the Pyrenees are one area where the impacts of a changing climate are pronounced.

I have had the pleasure of working with Marc Pons, a PhD student at Universitat Politècnica de Catalunya (BarcelonaTech), in Spain. Marc has been working on developing an agent-based model (ABM) to explore the climate change impacts on winter ski tourism in Andorra, a key skiing destination in the Pyrenees. Marc’s work has looked at how artificial snowmaking can serve as an adaptation response to extend a marginal ski season, or to ensure adequate snow coverage for peak ski times, such as holidays. The benefit to using an ABM for this type of work is that one can quickly develop and test alternate scenarios. For example, in his work, Marc tests the impact of artificial snowmaking on several different Andorran ski resorts – each with a unique geography and elevation that impacts how effective snowmaking is. Also, Marc has taken into account several different scenarios of climate warming, allowing him to present best/worst case scenarios. I see this rapid scenario development as one of the strengths of ABM, particularly in how it can be used in climate adaptation research. Future work can focus on the skier response to changing snow conditions, helping to determine which resorts, operating in a competitive marketplace, can expect to draw more skiers. There are clear business implications of this research, especially when considering how closely tied a local economy is to a major attraction such as a ski resort. This research will be published very soon in the journal Climate Research, but you can take a look at a pre-print version here.

Why technology adoption constraint research is important

A chapter from my dissertation has been recently published in Environment and Planning B: Planning and Design. This paper, titled “Negotiating constraints to the adoption of agent-based modeling in tourism planning” presents material from a series of interviews that I conducted with tourism planners in Nova Scotia. These planners gave their opinions on the potential for Agent-based modeling as a planning support tool within tourism planning practice and identified several areas of adoption constraint.

This research is actually (and unintentionally) quite timely – there seems to be much made about ‘Geodesign‘ these days and the possibility to connect geospatial analysis tools and approaches within a policy context. Here is a recent meeting from The Centre for Research in Social Simulation at the University of Surrey that discusses the interface between ABM and policy. Everyone seems to be trying to figure out how geotech tools can help decision-makers make better decisions – a noble pursuit, for sure! However, as I (drawing from many others – thanks Helen Couclelis) point out in the paper, there is a fundamental disconnect between the modeler and the planner or policy developer. Modelers (and scientists) thrive on the ability to be wrong about things – a luxury that the policy developer can’t afford (to put it mildly).

When I think about Geodesign, I am excited that GIS and geospatial tools can make an impact within decision-making. But I also hope that this general level of enthusiasm for tools and approaches is accompanied by a similar investment in research that looks to identify and negotiate the adoption constraints associated with technology implementation. As I’m finding out with my current Geoweb research, there are a unique set of constraints created by an organization (community, government, corporation) that can serve as a massive impediment to using any technology. A balanced view going forward should be a research priority!

The desire for a simple model, made with simple tools

The most popular post (by far) on this blog is one where I present a simple version of Butler’s Tourism Area Lifecycle, done up in Excel.

Why is this post so popular? Is it because of the enduring (rightly or wrongly) impact of Butler’s work? Or is it the prospect of actually implementing his theoretical ideas in a model that can be easily shared, manipulated, tweaked, and updated? Certainly I’ll give most of the credit to the 30+ years of TALC research, but I’d like to think that there is demand for an Excel version of this model.

The funny thing is, while I used to link to an actual downloadable spreadsheet, I took it down a long time ago because I wasn’t happy with it. It was too simple, or so I thought. In reality, I think that a simple model, made with simple tools can have a great impact. Perhaps not in a quantitative or research-orientated sense, as I had originally planned, but more as a descriptive tool for tinkering. A way to help tourism students and those interested in the Butler TALC to better understand its assumptions.

So, over the summer I’m going to put in some time to shape up my Excel TALC and make it available for all who want to use it. I’ll try and make it as transparent as possible, with clear instructions both on this blog and embedded in the spreadsheet itself. Actually, another idea would be to host it as a Google Doc, online, and dispense with the need for an Excel license altogether.

Stay tuned!

TourSim chapter published in Planning Support Systems volume

I’m pleased to announce that a chapter describing the development of TourSim, including a scenario on shifting tourist port of entry and identification of adoption constraints, is in the final stages of preparation for publication. This chapter is part of a new book “Planning Support Systems Best Practice and New Methods” published by Springer and edited by Stan Geertman and John Stillwell. I am excited to have my contribution included in this volume, as I believe that the chapters contained cover quite a breadth of the emerging field of PSS research, from a number of top researchers. In preparing the manuscript, I was particularly drawn to the focus that this volume has on identifying and negotiating the constraints that may hinder the adoption of PSS in planning practice. 

This book will be available for purchase early in 2009.

The image below is reproduced in the chapter, and shows an earlier version of TourSim running a port of entry scenario. This scenario can be run here.


TourSim running a port of entry scenario
TourSim running a port of entry scenario

New data sources and experimental options

I’ve made some major alterations to TourSim, both in the data that it relies on, and the types of experimentation it supports. I’m thinking that this is going to make TourSim much more usable for tourism planning, and begins to incorporate many of the ideas of complexity science (such as adaptation) into TourSim.

TourSim model

First, TourSim now uses tourist preference data from the 2004 Nova Scotia Tourist Exit Survey. This survey has a wider range of accommodation and activity options, and the types of categories represented relate much more intuitively to the types of tourism products available in Nova Scotia. Additionally, the number of responses included in the Tourist Exit Survey is considerably larger than the CTS and ITS I have previously been using. The Tourist Exit Survey also segments tourists based on generating market (Atlantic, Quebec, Ontario, Western Canada, New England, Other USA, and International). Each class of tourist has their own range of activity and accommodation preferences, and you can now see the percentage of each market that is arriving in Nova Scotia.

I’ve also improved the destination adaptation function. This is designed to represent destination development in response to high levels of visitation. Several steps are used to model this function:

1) Destination Capacity. Each destination has a maximum capacity for visits, based on occupancy data provided by a mandatory reporting program conducted by the Nova Scotia Department of Tourism, Culture, and Heritage. While this capacity varies considerably from season to season, this occupancy limit represents the maximum accommodation capacity if all accommodations are open.

2) Every month, the destination examines the number of tourists who have visited in that month. If the destination is at 80% of its capacity (this threshold is adjustable by the user), then the destination increases its capacity by 5% (this percentage again can be adjusted by the user).

3) Advertising: This adaptation function also works for destinations that don’t come close to their capacities. If a destination is below 30% of their capacity, the destination “advertises” and raises the likelihood that it will be randomly selected for evaluation by the tourist. Of course, this isn’t exactly how advertising works, but in the simplified world of TourSim, things are a bit different.

All of these variables can be manipulated by you, the user at the start of the model. Like with other versions you can select specific destinations to focus on, and compare simulation results produced with different variables. Check out the new scenario and let me know what you think!



A Simple Tourism Model with Excel

While there is quite a bit of interest in several fields in ABMs as an approach to studying issues such as resilience, and thresholds, their complexity and technical nature is a significant barrier to their use. I’ve had a number of discussions with McGill professor Dr. Garry Peterson about alternate modeling approaches. One technology that he uses in both classes and in his publications is a simple Excel spreadsheet. I’m constantly amazed by the functionality that is included in Excel, and have even seen some simple ABMs built using it.

I’ve put together a simple TALC in Excel and would appreciate any feedback. Although I use the term TALC (tourism area life cycel, after Richard Butler’s seminal work), the only real similarity is that I’ve added a level of capacity to the model. I’m intending this as just a simple toy to facilitate thinking about tourism dynamics, thresholds and resilience. You can experiment with tourist growth rate, decay, threshold, and the effects of the threshold.

Let me know whether you think that this type of tool would be useful as a teaching or research aid? What other dynamics or interactions would be useful to incorporate into a simple model such as this?

You can download the spreadsheet here.

Update: This spreadsheet is one of the more frequently visited parts of my blog, so I’ve decided to put some more effort into it. I’m taking a look at some neat visualization strategies with Google Docs, so expect some changes and a new post very soon.

Simplified TALC