## raw data extracted from the LACD
"all_china.csv" is the raw data extracted from the LACD. The way to mark the taxonimic system of linguistic maps in LACD is illustrated in Section 2.2;
The rows are the 930 localities in China and the columns are 510 linguistic features in LACD;

## WJD distance calculation
To open the codes "wjd_matrix.py" with the IDE of Python and run the codes with the raw data "all_china.csv";
The output entitled "dist_matrix.csv" is the WJD distance among 930 localities;

## MDS and FCM analysis based on the WJD distance matrix
To open the codes "submit.R" with the Rstudio and run the codes;
MDS and FCM analysis are based on the WJD distance matrix;
All the result of MDS and FCM will be stored in one file named "cn_tax.csv";
Based on the "cn_tax.csv", we visualize the results as MDS, RGB and FCM maps;
In order to plot the MDS maps, RGB map, and FCM maps, we need the coordinates of 930 localities which stored in file "cn_xy.csv" and the shape file of the China "China_10-dash_line" and "China_land_territory";
"fill_type1.csv" and "fill_type2.csv" define the color schemes of Type I and Type II FCM maps, respectively;

## weight test
In order to test how weight assignments affect the result of WJD, we calculate four WJD distance matrces in four situations (see the Section 2.4 of paper).
"Type_1_0_form_1_0.csv" is the WJD matrix in  Situation A;
"Type_1_1_form_1_0.csv" is the WJD matrix in  Situation B;
"Type_1_0_form_1_1.csv" is the WJD matrix in  Situation C;
"Type_1_1_form_1_1.csv" is the WJD matrix in  Situation D;

To carry out a correlation analysis among the four WJD matrices, the Pearson's r is as Table 3 in the paper.


