International Journal of Japan Association
for Management Systems (IJAMS)
AAAAAa*, BBBBBb, and CCCCCc
a
Department of **********, ******* University, **********, Tokyo, 111-1111 Japan
Department of **********, ******* University, **********, Osaka, 222-2222 Japan
cb
Department of **********, ******* University, **********, Kyoto, 333-333 Japan
b
Abstract
The spatial interaction model is regarded as a region choice model such as which region a
tourist patronizes from a tourist viewpoint. A cultural resource tour is modeled by regarding a
selection of region in a spatial interaction model as a satisfaction function. On the other hand,
from a resource viewpoint, a spatial interaction model can be recognized as an influence area
model of target resource. Recently, efficient utilization of tourism resources tends to be
important. In order to promote cultural resources, based on an entropy maximization principle
both the spatial interaction model and the discrete choice model are discussed. In this paper of
a tour route planning, in case of multiple tourists and a competition environment, a model that
each tourist visits cultural resource will be proposed under conditions that points distribute on a
network. We propose connectivity of cultural resources derived from spatial interaction model,
and attractive degree of visit place based on satisfaction of tourist measured by dynamic statics.
Key words: Spatial Interaction Model, Dynamic Statics, Attractive Degree, Connectivity, Cultural
Resources.
1. Introduction
Recently, tourism has become one of popular leisure
activity. People do travel because of predominantly
recreational or relaxation purposes or the provision of
services. Tourists was defined as people who "travel to
and stay in places outside their usual environment for
not more than one consecutive year for leisure,
business and other purposes not related to the exercise
of an activity remunerated from within the place
visited" by world tourism organization [1]. Industries
relating to sightseeing become large one. These service
industries include transportation services such as buses,
trains and taxis, accommodation such as hotels and
entertainment venues, and other hospitality industry
services such as resorts.
This paper is organized as follows. The next section
devotes to introduce the explanation of concept of
connectivity based on a spatial interaction model. Also
usual concept of geographical advantage is explained.
In section 3, we formulate a Temporal Difference (TD)
learning model containing a discrete choice model. An
*
corresponding author: aaaaaa@bbb.cccccc.ac.jp
attractive function is defined in the model, it is a value
reflecting emphasis of tourist such as promotion,
setting, facilities, service, host and management in
target resource [10]. The relations among a attractive
degree and connectivity are shown and we give an
estimation algorithm to derive them by using
nonparametric method.
2. Derivation of connectivity among
cultural resources from spatial data
In this section we introduce the derivation of
connectivity among cultural resources from spatial data.
The data assumed to be consisted as graph. The value
of geographical advantage is derived as the index of
such geographical convenience. When we think the
geographical condition about a store and a house we
use the word like a geographical convenience. We first
consider a graph which represents the cross as a node
and the road as an arc in target area; it is natural that
each geographical advantage is different since the
connection situation is different.
That is, the value of geographical advantage of node
i becomes large if the number of connected node
increases. The value of geographical advantage can be
transformed into the adjacency matrix. It satisfies
 oi d j  1 if the node connects each other, otherwise
 o d  0 . The row sum and column sum are given by
i
j
Voi    oi d j ,
j
U d j    oi d j .
(1)
(2)
i
In usual geographical advantage has been derived
from network structure, however our connectivity and
attractive degree which will be given in next section
have been obtained from dynamical statics in special
interaction model (see Figure 3).
Figure 3 Difference between geographical advantage
and connectivity.
3. Attractive degree of cultural
resource
In order to formulate an organically-combined
weight about cultural resource tourism, we consider the
control is a real-valued vector with same dimension to
the number of state [11]. The discounted infinite-
horizon optimal value function is solution to the
following Bellman equation:
4. Conclusion
In this paper, we explained the concept of the
connectivity in the spatial interaction model based on
an entropy maximization principle. The model
containing a discrete choice model was applied to
define the connectivity from dynamic statics data. In
order to estimate those properties the iterative
algorithm using TD learning was applied. In addition
we formulated the attractive degree about cultural
resources as the reward in dynamic programming.
References
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