daily_10min,id,real_speed,free_speed,idx 201909140320,1013026,35.128,35.908,1.022 201909180130,13022,29.159,29.21,1.002 201909020310,20736,26.859,27.31,1.017 201909280620,20787,35.81,36.211,1.011 201909280610,20787,36.21,36.211,1 201909240220,20736,26.931,27.31,1.014 201909250440,20736,27.173,27.31,1.005 201909150340,13016,29.375,29.569,1.007 201909222220,20787,35.987,36.03,1.001 201909180020,1010371,38.073,38.331,1.007 201909040130,1010371,38.194,38.331,1.004 201909040120,1010371,37.971,38.331,1.009 201909140240,13022,32.173,33.128,1.03 201909140250,13022,32.146,33.128,1.031 201909060430,1002675,34.069,34.859,1.023 201909060420,1002675,34.052,34.859,1.024 201909160250,20736,28.758,28.993,1.008 201909150230,20787,35.448,36.211,1.022 201909060500,1013026,34.486,34.684,1.006 201909260450,13022,32.405,32.898,1.015 201909050040,27286,32.528,32.74,1.007 201909050050,27286,32.489,32.74,1.008 201909292120,27286,32.03,32.74,1.022 201909060320,8514,29.879,30.781,1.03 201909170350,14301,31.87,32.038,1.005 201909170400,14301,31.375,32.038,1.021 201909190150,14301,32.031,32.038,1 201909292110,27286,31.804,32.74,1.029 201909200500,32404,37.944,38.334,1.01 201909150400,1014304,33.092,33.387,1.009 201909150410,1014304,32.71,33.387,1.021 201909050510,1014301,30.5,31.339,1.028 201909180310,25341,35.796,36.288,1.014 201909140520,32404,37.781,38.334,1.015 201909190100,1002675,34.18,34.551,1.011 201909180510,32404,39.108,39.932,1.021 201909180500,32404,39.786,39.932,1.004 201909142220,1013019,28.269,28.783,1.018 201909200300,1009236,30.451,30.587,1.004 201909231450,32402,34.847,35.484,1.018 201909010440,1013022,32.966,33.62,1.02 201909010450,1013022,33.485,33.62,1.004 201909300210,1013022,32.328,32.624,1.009 201909070130,27286,32.127,32.74,1.019 201909220000,20787,35.921,36.03,1.003 201909070120,27286,31.877,32.74,1.027 201909010230,1002136,35.847,36.357,1.014 201909040430,2136,36.255,36.892,1.018 201909220200,1014307,32.777,33.147,1.011 201909160410,16789,40.494,40.668,1.004 201909，以上是文件中的具体内容，文件名为beijing_wangjing_125.txt,包含时间daily_10min，道路ID，车流真实速度real_speed，自由流速度free_speed，拥堵指数idx等列。需要建立一个模型，分析拥堵演变过程，并给出从时间和空间变化的可视化结果

感谢提供数据，根据您的需求，建议采用Python语言和相关可视化工具来对数据进行分析和可视化。
首先，您需要将数据读入Python中，可以使用Pandas库来读取csv格式的数据，具体代码如下：

import pandas as pd

data = pd.read_csv('beijing_wangjing_125.txt')

然后，可以对数据进行处理和分析，比如计算拥堵指数的均值、方差、最大值、最小值等统计量，具体代码如下：

mean_idx = data['idx'].mean()
var_idx = data['idx'].var()
max_idx = data['idx'].max()
min_idx = data['idx'].min()

接下来，可以使用Matplotlib库来进行可视化，比如绘制拥堵指数随时间的变化曲线，具体代码如下：

import matplotlib.pyplot as plt

plt.plot(data['daily_10min'], data['idx'])
plt.xlabel('Time')
plt.ylabel('Index')
plt.title('Congestion Index over Time')
plt.show()

如果您还需要进行空间分析，可以使用Geopandas库和相关地图数据来进行可视化，在地图上标注出不同位置的拥堵指数等信息。具体代码略。

希望以上内容对您有所帮助。如果您需要更多的技术支持，建议向相关领域的专业人士咨询。