Sewerage network is an important part of municipal infrastructure for a city. Obstruction of sewer causes street flooding
and affects people's daily life directly. To investigate reasons why some sewage pipes are blocked frequently in
Kunming, China, we employ spatial analysis and data mining technology to analyze the data on the basis of a municipal
sewerage geographic information system of the city. In the GIS, all of map layers and attribute tables are organized and
saved in a relational database with Geodatabase model. First, we combined SQL attribute query with spatial location
query to find out the sewage pipes that are blocked frequently. Then, we carried out buffer analysis and intersect analysis
on the layers of the frequently-blocked pipes and buildings along the streets to extract buildings that are close to these
frequently-blocked pipes. Joining the buildings in the buffer scope and the frequently-blocked pipes forms a big table
prepared for spatial data mining. We used Apriori algorithm to mine spatial association rules from the data in the big
table in order to search implicit reasons of obstruction of the pipes. The results from data mining indicate that strong
spatial and non-spatial associate rules exist between the obstruction and restaurants in the buildings, as well as attribute
slopes and diameters of these sewage pipes.
We used a change detection approach based on support vector machine (SVM) to analyze two remotely sensed images in
order to analyze urban landscape change on high-dense urban use (HDU), medium-dense urban use (MDU) and
low-dense urban use (LDU) in Kunming, China. These two images were subset of a TM image acquired on 16 August
1992 and an ETM+ image acquired on 2 November 2000, respectively. First, we used SVM to classify each subset into
HDU, MDU, and LDU. Then, we compared the label values of classified data pixel by pixel to analyze urban landscape
changes. In order to obtain high quality training data under the circumstance that existing classification products of
sampling area were not available, we proposed a second sampling method to assure obtaining satisfactory training data.
The kernel function of SVM was radial basis function (RBF). Optimal model with the best penalty parameter C and the
kernel parameter gamma was obtained through training samples. We tested the approach in three sites: northern
Kunming, southern Kunming and entire Kunming. Results indicate that the overall urban use has substantially increased
during 1992- 2000, while the substantial growth in high-density urban use was achieved at the cost of low-density urban
use and partially medium- density urban use.
Entities in the real world have non-spatial attributes, as well as spatial and temporal features. A spatial-temporal data
model aims at describing appropriately these intrinsic characteristics within the entities and model them on a conceptual
level so that the model can present both static information and dynamic information that occurs over time. In this paper,
we devise a novel spatial-temporal data model which is based on Geodatabase. The model employs object-oriented
analysis method, combining object concept with event. The entity is defined as a feature class encapsulating attributes
and operations. The operations detect change and store the changes automatically in a historic database in Geodatabase.
Furthermore, the model takes advantage of the existing strengths of the relational database at the bottom level of
Geodatabase, such as trigger and constraint, to monitor events on the attributes or locations and respond to the events
correctly. A case of geographic database for Kunming municipal sewerage geographic information system is
implemented by the model. The database reveals excellent performance on managing data and tracking the details of
change. It provides a perfect data platform for querying, recurring history and predicting the trend of future. The instance
demonstrates the spatial-temporal data model is efficient and practicable.
Lagragian support vector machine (LSVM) is a linearly convergent Lagrangian, which is obtained by reformulating the
quadratic program of a standard linear support vector machine. To investigate the performance of the classifier working
on multispectral images with LSVM as optimizer, we devise a new test based on LSVMs for classifying multispectral
data in this work. First of all, data are preprocessed. To acquire the optimum bands for image classification,
multispectral image is mapped into a two-dimensional feature space to inspect the bands with redundant spectral
information. These extracted data acquired through the feature selection is named data group B relative to the original
data group A for a purpose of comparison. Then, to classify multiclass problem, binary classification is extended to
multiclass classification by pairwise method. Secondly, two groups of data are trained to find models. In this phase,
optimal C values are chosen carefully through trials with different values. Then, classifiers based on LSVMs with
optimal C values are used to yield optimal separating hyperplane (OSH). Lastly, in prediction phase, the two groups of
data are inputted respectively into each classifier for testing. These classifiers include ones with linear kernel and ones
with polynomial kernel of degree 2. The results of the experiment reveal that classifiers with LSVMs as an optimizer
have excellent performances with both linear kernel and polynomial kernel of degree 2. Bias caused by the differentia of
the two groups of data is not obvious.
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