Tag Archives: agent

Vespucci Summer Institute 2010

The eighth edition of the Vespucci Summer Institute for the Advancement of Geographic Information Science was held from June 14th to June 18th, near beautiful Florence, Italy. The goals and purpose of the Summer Institute are outlined on the Vespucci website (www.vespucci.org):
“The Summer Institute is aimed at researchers from the university, commercial, and government sectors. It provides an inspiring and productive opportunity for peer-to-peer interaction with leading international experts in the field. Participants will:
  • Learn the state of the art in the topic areas
  • Understand and explore tomorrow’s research and market challenges
  • Be challenged to think laterally outside their daily work setting
  • Present their own work and ideas to receive feedback and advice
  • Get one-on-one access to experts in a relaxed and productive setting
  • Improve presentation and team work skills
  • Return home refreshed and newly motivated”
Family photo overlooking Florence
The themes for this year’s Summer Institute was “Interfacing Social and Environmental Modeling” with presentations by two teams, the first led by Gilberto Camara, from the Brazilian National Institute for Space Research (INPE), who focused on the use of spatial models to combat perceived misconceptions about the degree and extent of Amazonian deforestation, and the second was led by Henk Scholten and Eduardo Dias representing Vrije University, who focused on integrating scientific wildfire models with emergency response systems.
Both instruction teams framed their respective presentations around specific models that integrated social and environmental variables. In his lectures, Dr. Camara drew from many classic examples, such as Schelling’s segregation model, to demonstrate a basic approach to modeling with cellular automata. He then presented TerraME (www.terrame.org), an open-source GIS/modeling environment developed by the Brazilian National Institute for Space Research. Students were given time to follow a tutorial using sample data to familiarize themselves with the benefits and constraints of TerraME. Students then worked in groups to develop a hypothetical approach to modeling change dynamics in the Amazon.
Similarly, Dr. Scholten and Dr. Dias built their lectures on the use of the FARSITE fire model (www.farsite.org). Participants were given a self-paced tutorial to familiarize themselves with FARSITE. Then, working in groups, students developed a hypothetical model to represent the flow of information and decision steps used in an emergency management system. Dr. Scholten and Dr. Dias then presented their own comprehensive work on developing an emergency management system in the Netherlands, using the EAGLE system developed in collaboration with Microsoft.
I attended this event as one of 28 participants. The majority of attendees came from the United States, with 18 representatives from a variety of universities. The remaining 10 students hailed from institutions in Canada (2), Brazil (2), Germany (5), and the European Community Joint Research Centre (1). The experience level of attendees was varied, including recent master’s graduates, PhD students of all levels, and a small handful of postdoctoral and industry researchers. For me, the greatest benefit to attending the Vespucci Summer Institute was the opportunity to meet these other students and discuss the similarities and differences between our research. These conversations occurred informally over breaks for espresso and lunch, as well as at the two formal group dinners. I was struck by the incredible diversity represented in the group of attendees – in addition to many typical “GIScientists”, there were individuals with backgrounds in fields such as political science, sociology, robotics, engineering, computer science, public health, and ecology. I found that each participant had an important viewpoint and the opportunity to access this “collective intelligence”, even just in an informal setting, was one of the most valuable aspects of the week. I have no doubt that I will stay in touch with many of these fellow participants, and look forward to future collaboration as our careers progress.
Statue of Amerigo Vespucci near the Uffizi gallery, Florence
The 2010 Vespucci Summer Institute was a very memorable experience. The setting was very comfortable and the technologies presented by the instructors showed important applications of GIScience tools in diverse areas. Most important however, were the unstructured networking opportunities that occurred between students and also between students and instructors. Considering that I spend much of my work time in front of a computer screen, it is easy to forget the benefits that come from face-to-face communication. For this reason, the Vespucci Summer Institute was a strong reminder that one conversation over coffee can produce much more than dozens of emails.
I would like to thank the Summer Institute organizers Michael Gould, Max Craglia, David Mark, and Werner Kuhn, for their efforts in making the Vespucci Institute happen. I would also like to thank the generosity of the GEOIDE Network in providing funding without which I would not have been able to attend.

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!



Adaptative Destination Model

For quite a while now I’ve been trying to expand the types of adaptive behaviour included in TourSim. Currently the tourists display a type of adaptive behaviour, as they move to destinations that satisfy their preferences. In the Baddeck Hotel Development model, tourists would adapt to the development of a new hotel, as more tourists visited Baddeck because of this new type of accommodation.

To take this a bit further, what I wanted to include was destination development in terms of tourist capacity. Of course, this is the classic measure used in Butler’s Tourism Area Life Cycle, one of the most widely cited models in tourism research. What I’ve added to TourSim is a measure of destination capacity. If a destination attracts enough tourists per day to be within 80% of their capacity, they add 20% more capacity. This is process is triggered once a month. You can take a look at my new adaptation model and test this out (or head to the Tourism Scenarios tab for more information). I’ve included a toggle-type button that will turn adaptation on and off, so you can compare the two types of scenarios. As always, let me know what you think. I’ve already gotten some great feedback, but there is always room for more.