08 - Exploring Semantic¶
1. Imports¶
import pymove as pm
from pymove.semantic import semantic
2. Load Data¶
move_df = pm.read_csv('geolife_sample.csv')
move_df.head()
| lat | lon | datetime | id | |
|---|---|---|---|---|
| 0 | 39.984094 | 116.319236 | 2008-10-23 05:53:05 | 1 |
| 1 | 39.984198 | 116.319322 | 2008-10-23 05:53:06 | 1 |
| 2 | 39.984224 | 116.319402 | 2008-10-23 05:53:11 | 1 |
| 3 | 39.984211 | 116.319389 | 2008-10-23 05:53:16 | 1 |
| 4 | 39.984217 | 116.319422 | 2008-10-23 05:53:21 | 1 |
Detect outlier points considering distance traveled in the dataframe
outliers = semantic.outliers(move_df)
outliers[outliers['outlier']]
VBox(children=(HTML(value=''), IntProgress(value=0, max=2)))
| id | lat | lon | datetime | dist_to_prev | dist_to_next | dist_prev_to_next | outlier | |
|---|---|---|---|---|---|---|---|---|
| 148 | 1 | 39.970511 | 116.341455 | 2008-10-23 10:32:53 | 1452.319115 | 1470.641291 | 71.088460 | True |
| 338 | 1 | 39.995042 | 116.326465 | 2008-10-23 10:44:24 | 10.801860 | 10.274331 | 1.465144 | True |
| 8133 | 1 | 39.991075 | 116.188395 | 2008-10-25 08:20:19 | 5.090766 | 6.247860 | 1.295191 | True |
| 10175 | 1 | 40.015169 | 116.311045 | 2008-10-25 23:40:12 | 23.454754 | 24.899678 | 3.766959 | True |
| 13849 | 1 | 39.977157 | 116.327151 | 2008-10-26 08:13:53 | 11.212682 | 10.221164 | 1.004375 | True |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 216877 | 5 | 39.992096 | 116.329136 | 2009-03-12 15:57:42 | 7.035981 | 6.182086 | 1.909349 | True |
| 216927 | 5 | 39.998061 | 116.326402 | 2009-03-12 16:02:17 | 16.758753 | 19.151449 | 4.051863 | True |
| 217456 | 5 | 40.001983 | 116.328414 | 2009-03-19 04:35:52 | 179.564668 | 191.030434 | 15.276237 | True |
| 217465 | 5 | 40.001433 | 116.321387 | 2009-03-19 04:41:02 | 77.928727 | 75.686512 | 16.676141 | True |
| 217479 | 5 | 40.003626 | 116.317695 | 2009-03-19 05:02:52 | 9.725231 | 7.573682 | 2.463175 | True |
383 rows × 8 columns
move_df.get_bbox()
(22.147577, 113.548843, 41.132062, 121.156224)
Detect points outside of a bounding box
fake_bbox = (20, 110, 40, 120)
out_bbox = semantic.create_or_update_out_of_the_bbox(move_df, fake_bbox)
out_bbox[out_bbox['out_bbox']]
| lat | lon | datetime | id | out_bbox | |
|---|---|---|---|---|---|
| 415 | 40.000026 | 116.322214 | 2008-10-23 10:48:31 | 1 | True |
| 416 | 40.000082 | 116.322072 | 2008-10-23 10:48:33 | 1 | True |
| 417 | 40.000164 | 116.321996 | 2008-10-23 10:48:37 | 1 | True |
| 418 | 40.000245 | 116.321964 | 2008-10-23 10:48:40 | 1 | True |
| 419 | 40.000312 | 116.321921 | 2008-10-23 10:48:45 | 1 | True |
| ... | ... | ... | ... | ... | ... |
| 217643 | 40.000205 | 116.327173 | 2009-03-19 05:45:37 | 5 | True |
| 217644 | 40.000128 | 116.327171 | 2009-03-19 05:45:42 | 5 | True |
| 217645 | 40.000069 | 116.327179 | 2009-03-19 05:45:47 | 5 | True |
| 217646 | 40.000001 | 116.327219 | 2009-03-19 05:45:52 | 5 | True |
| 217651 | 40.000015 | 116.327433 | 2009-03-19 05:46:17 | 5 | True |
104787 rows × 5 columns
Detects points with no gps signal, given by the time between adjacent points
deactivated = semantic.create_or_update_gps_deactivated_signal(move_df)
deactivated[deactivated['deactivated_signal']]
VBox(children=(HTML(value=''), IntProgress(value=0, max=2)))
| id | lat | lon | datetime | time_to_prev | time_to_next | time_prev_to_next | deactivated_signal | |
|---|---|---|---|---|---|---|---|---|
| 147 | 1 | 39.978068 | 116.327554 | 2008-10-23 06:01:57 | 5.0 | 16256.0 | 16261.0 | True |
| 148 | 1 | 39.970511 | 116.341455 | 2008-10-23 10:32:53 | 16256.0 | 7.0 | 16263.0 | True |
| 960 | 1 | 40.013803 | 116.306531 | 2008-10-23 12:04:28 | 2.0 | 41796.0 | 41798.0 | True |
| 961 | 1 | 40.013867 | 116.306473 | 2008-10-23 23:41:04 | 41796.0 | 2.0 | 41798.0 | True |
| 3088 | 1 | 39.977899 | 116.327063 | 2008-10-24 06:35:50 | 2.0 | 61695.0 | 61697.0 | True |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 216997 | 5 | 40.007003 | 116.323674 | 2009-03-13 13:29:06 | 30157.0 | 5.0 | 30162.0 | True |
| 217054 | 5 | 40.010537 | 116.322052 | 2009-03-13 13:34:01 | 5.0 | 57426.0 | 57431.0 | True |
| 217055 | 5 | 40.009639 | 116.322056 | 2009-03-14 05:31:07 | 57426.0 | 5.0 | 57431.0 | True |
| 217452 | 5 | 39.990464 | 116.333510 | 2009-03-14 06:47:12 | 2.0 | 424105.0 | 424107.0 | True |
| 217453 | 5 | 40.001467 | 116.326665 | 2009-03-19 04:35:37 | 424105.0 | 5.0 | 424110.0 | True |
420 rows × 8 columns
Detects points with jumps, defined by the maximum distance between adjacent points
jump = semantic.create_or_update_gps_jump(move_df, )
print(jump[jump['gps_jump']].shape)
jump[jump['gps_jump']].head()
VBox(children=(HTML(value=''), IntProgress(value=0, max=2)))
(46, 8)
| id | lat | lon | datetime | dist_to_prev | dist_to_next | dist_prev_to_next | gps_jump | |
|---|---|---|---|---|---|---|---|---|
| 3088 | 1 | 39.977899 | 116.327063 | 2008-10-24 06:35:50 | 0.140088 | 4361.216241 | 4361.148665 | True |
| 3089 | 1 | 40.013812 | 116.306483 | 2008-10-24 23:44:05 | 4361.216241 | 7.587244 | 4358.356247 | True |
| 12434 | 1 | 39.974821 | 116.333828 | 2008-10-26 03:27:37 | 1.358606 | 4536.318481 | 4536.121843 | True |
| 12435 | 1 | 39.976599 | 116.387014 | 2008-10-26 03:45:46 | 4536.318481 | 4.280041 | 4535.822332 | True |
| 23936 | 1 | 39.978222 | 116.327002 | 2008-10-31 08:06:33 | 10.665636 | 4328.751469 | 4318.102530 | True |
Determines if a point belongs to a short trajectory.
short = semantic.create_or_update_short_trajectory(move_df)
short[short['short_traj']]
VBox(children=(HTML(value=''), IntProgress(value=0, max=2)))
VBox(children=(HTML(value=''), IntProgress(value=0, max=2)))
VBox(children=(HTML(value=''), IntProgress(value=0, max=2)))
| id | lat | lon | datetime | dist_to_prev | time_to_prev | speed_to_prev | tid_part | short_traj | |
|---|---|---|---|---|---|---|---|---|---|
| 148 | 1 | 39.970511 | 116.341455 | 2008-10-23 10:32:53 | 1452.319115 | 16256.0 | 0.089340 | 2 | True |
| 18244 | 1 | 39.993663 | 116.325751 | 2008-10-27 23:49:47 | 233.351618 | 4.0 | 58.337905 | 18 | True |
| 18795 | 1 | 39.983927 | 116.309349 | 2008-10-28 13:21:25 | 480.465717 | 8147.0 | 0.058975 | 22 | True |
| 26941 | 1 | 39.982361 | 116.330762 | 2008-11-01 06:02:13 | 270.452069 | 3.0 | 90.150690 | 39 | True |
| 27878 | 1 | 40.017806 | 116.307530 | 2008-11-02 09:44:34 | 454.090137 | 19527.0 | 0.023254 | 44 | True |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 217471 | 5 | 40.001707 | 116.318926 | 2009-03-19 04:42:22 | 36.849631 | 5.0 | 7.369926 | 404 | True |
| 217472 | 5 | 40.001784 | 116.318965 | 2009-03-19 04:42:27 | 9.183862 | 5.0 | 1.836772 | 404 | True |
| 217473 | 5 | 40.002218 | 116.320306 | 2009-03-19 04:42:32 | 123.999483 | 5.0 | 24.799897 | 404 | True |
| 217474 | 5 | 40.004917 | 116.314376 | 2009-03-19 04:49:37 | 587.526625 | 425.0 | 1.382416 | 404 | True |
| 217475 | 5 | 40.004955 | 116.313697 | 2009-03-19 04:49:42 | 57.987365 | 5.0 | 11.597473 | 404 | True |
1151 rows × 9 columns