Data Visualization

visualization_data(data[, col, accuracy, ...])

The input is the data points, this function will aggregate and then visualize it

visualization_trip(trajdata[, col, zoom, height])

The input is the trajectory data and the column name.

visualization_od(oddata[, col, zoom, ...])

The input is the OD data and the column.

Visualization Settings in Jupyter

The TransBigData package also provide one-click data organization and visualization methods based on the visualization plugin provided by kepler.gl to .
To use this feature, please install the keplergl package for python first.
pip install keplergl

If you want to display the visualization results in jupyter notebook, you need to check the jupyter-js-widgets (which may need to be installed separately) and keplergl-jupyter plugins

_images/jupytersettings.png

Visualization of data point distribution

transbigdata.visualization_data(data, col=['lon', 'lat'], accuracy=500, height=500, maptype='point', zoom='auto')

The input is the data points, this function will aggregate and then visualize it

Parameters:

  • data (DataFrame) – The data point

  • col (List) – The column name. The user can choose a non-weight Origin-Destination (OD) data, in the sequence of [longitude, latitude]. For this, The aggregation is automatic. Or, the user can also input a weighted OD data, in the sequence of [longitude, latitude, count]

  • zoom (number) – Map zoom level (Optional). Default value: auto

  • height (number) – The height of the map frame

  • accuracy (number) – Grid size

  • maptype (str) – Map type, ‘point’ or ‘heatmap’

Returns:

vmap – Visualizations provided by keplergl

Return type:

keplergl.keplergl.KeplerGl

Trajectory Visualization

transbigdata.visualization_trip(trajdata, col=['Lng', 'Lat', 'ID', 'Time'], zoom='auto', height=500)

The input is the trajectory data and the column name. The output is the visualization result based on kepler

Parameters:

  • trajdata (DataFrame) – Trajectory points data

  • col (List) – The column name, in the sequence of [longitude, latitude, vehicle id, time]

  • zoom (number) – Map zoom level

  • height (number) – The height of the map frame

Returns:

vmap – Visualizations provided by keplergl

Return type:

keplergl.keplergl.KeplerGl

OD Visualization

transbigdata.visualization_od(oddata, col=['slon', 'slat', 'elon', 'elat'], zoom='auto', height=500, accuracy=500, mincount=0)

The input is the OD data and the column. The output is the visualization result based on kepler

Parameters:

  • oddata (DataFrame) – OD data

  • col (List) – The column name. The user can choose a non-weight Origin-Destination (OD) data, in the sequence of [origin longitude, origin latitude, destination longitude, destination latitude]. For this, The aggregation is automatic. Or, the user can also input a weighted OD data, in the sequence of [origin longitude, origin latitude, destination longitude, destination latitude, count]

  • zoom (number) – Map zoom level (Optional). Default value: auto

  • height (number) – The height of the map frame

  • accuracy (number) – Grid size

  • mincount (number) – The minimum OD counts, OD with less counts will not be displayed

Returns:

vmap – Visualizations provided by keplergl

Return type:

keplergl.keplergl.KeplerGl