The spatial data in GIS-T database is huge and complicated, discovery knowledge from this database is very
important, region traffic network evaluation is one of the important contents. In this paper the author referred
to an integrated algorithm combined Ant colony algorithm with FCM to cluster the traffic data of 15 regions
of Hubei Province, then used the method of maximizing deviation to arrange the clustering result. From the
result we can evaluate the traffic conditions of the 15 regions.
The base land value evaluation plays an important role in urban land value system for a long time. To establish a simple
and feasible method to evaluate the urban land value is always the target for scholars. According to the traditional
method of base land value evaluation, combining the theory of K-means clustering and trend surface analysis, this paper
proposes a new method for base land value evaluation, which fully considers the spatial correlation, inherent similarity
and regional continuity of the land value samples. Additionally, in this method, we propose some measurement indices of
evaluation results, which are wanting in traditional methods. At last, we evaluate the industry land value in Shanghai
with this method for instance.
In GIS for Transportation (GIS-T), how to discover knowledge from complex traffic data is very vital. This paper focuses
on the regional traffic to evaluate the traffic condition in a certain region, which can provide decision-making support for
leadership. Nowadays, there are multitudinous regional traffic network evaluation models, most of which are based on a
single item of index. It is difficult to give a satisfying evaluation result to the whole regional traffic condition. In this paper,
we establish a regional traffic evaluation system for traffic network based on the theory of fuzzy clustering and maximizing
deviation, and evaluate the traffic networks of 15 regions in Hubei province in 2004.