## 2 Methodology
### Classification of GDSW Based on Machine Learning
#### 2.4.1 Classification Algorithm
We selected the positive class and negative class in the training set according to the proportion of 2/3-4/5 of their respective numbers. Subsequently, we used the training set as the input information of machine learning to perform the training stage, and after the training was completed, we tested the test set. Finally, we calculated the classification result according to the predicted category and the true category. #### 2.4.2 Evaluation Methodology

When the model training was completed, we used the classifier to predict the categories of all records in the test set and compare the predicted categories with the real categories, so that we could calculate the corresponding confusion matrix and later use the elements of each confusion matrix (true positives _TP_, false negatives _FN_, true negatives _TN_, and false positives _FP_) to evaluate the performance. In this paper, we evaluate the precision, recall, and F1-score. The specific calculation method is as follows:

* _Precision_, also known as positive prediction value, is defined as the percentage of true positive records among all positive predictions: \[Precision=\frac{100\;TP}{TP+FP}\] (5)
* _Recall_, also known as true positive rate, is defined as the percentage of true positive predictions in all true positive records: \[Recall=\frac{100\;TP}{TP+FN}\] (6)
* _F1-score_ is the harmonic average of precision and recall: \[F1\text{-}score=\frac{200 TP}{2 TP\;+\;FP\;+\;FN}\] (7)

The precision and recall are mutually restrained and contradictory, which cannot be increased at the same time. When the threshold value increases, the precision increases, and the recall decreases. When the threshold value decreases, the precision decreases, and the recall increases. The precision-recall curve is a curve calculated by changing the threshold with recall as x axis and precision as y axis, which can better reflect the real performance of classification when the ratio of positive and negative samples is quite different. The ROC curve can reflect the sensitivity and precision of the model when choosing different thresholds. When the distribution of positive and negative samples changes, the shape of the curve can remain unchanged. The curve contains two important values: one is the true positive rate (TPR), and the calculation formula is TPR = \(TP/(TP+FN)\), which describes the proportion of positive instances identified by the classifier to all positive instances; the other is the false positive rate (FPR), calculated as \(FPR=FP/(FP+TN)\), which is the proportion of negative instances that the classifier incorrectly identifies as positive to all negative instances. In the ROC graph, each point is drawn with the corresponding FPR value as the horizontal coordinate and TPR value as the vertical coordinate. The ROC curve needs a quantitative analysis of the model. Here, the area under the ROC curve (AUROC) needs to be introduced. AUROC refers to the area below the ROC curve. To calculate AUROC, it only needs to integrate along the horizontal axis of ROC. In real scenes, the ROC curve is generally above the y = x line, so the value of AUROC is generally between 0.5 and 1. The larger the AUROC value is, the better the performance of the model will be. ## 3 Results and Evaluation

First, we manually marked 2760 original websites. If the corresponding websites provided geospatial data, we marked them as positive; otherwise, we marked them as negative. To perform the classification task, we selected 50% of the records in the datasets as training sets, and the rest as test sets. After that, we trained the classifiers using different classification algorithms and tested the test sets using the classifiers. We randomly performed the extraction of training sets three times, respectively, and averaged all performance results on the three trials (the first set of experiments).