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TransBigData
is a Python package developed for transportation spatio-temporal big data processing, analysis and visualization. TransBigData
provides fast and concise methods for processing common transportation spatio-temporal big data such as Taxi GPS data, bicycle sharing data and bus GPS data. TransBigData
provides a variety of processing methods for each stage of transportation spatio-temporal big data analysis. The code with TransBigData
is clean, efficient, flexible, and easy to use, allowing complex data tasks to be achieved with concise code.
For some specific types of data, TransBigData
also provides targeted tools for specific needs, such as extraction of Origin and Destination(OD) of taxi trips from taxi GPS data and identification of arrival and departure information from bus GPS data. The latest stable release of the software can be installed via pip and full documentation
can be found at https://transbigdata.readthedocs.io/en/latest/. Introduction PPT can be found here and here(in Chinese)
The target audience of TransBigData
includes:
TransBigData
is clean, efficient, flexible, and easy to use, allowing complex data tasks to be achieved with concise code.Currently, TransBigData
mainly provides the following methods:
Here is an overview of the gridding framework offered by TransBigData
.
See This Example for further details.
Here is an overview of the Trajectory processing framework offered by TransBigData
.
See This Example for further details.
TransBigData
support Python >= 3.6
TransBigData
can be installed by using pip install
. Before installing TransBigData
, make sure that you have installed the available geopandas package. If you already have geopandas installed, run the following code directly from the command prompt to install TransBigData
:
pip install transbigdata
You can also install TransBigData
by conda-forge
, this will automaticaly solve the dependency, it can be installed with:
conda install -c conda-forge transbigdata
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. A detailed overview on how to contribute can be found in the contributing guide on GitHub.
The following example shows how to use the TransBigData
to perform data gridding, data aggregating and data visualization for taxi GPS data.
import transbigdata as tbd
import pandas as pd
#Read taxi gps data
data = pd.read_csv('TaxiData-Sample.csv',header = None)
data.columns = ['VehicleNum','time','lon','lat','OpenStatus','Speed']
data
VehicleNum | time | lon | lat | OpenStatus | Speed | |
---|---|---|---|---|---|---|
0 | 34745 | 20:27:43 | 113.806847 | 22.623249 | 1 | 27 |
1 | 34745 | 20:24:07 | 113.809898 | 22.627399 | 0 | 0 |
2 | 34745 | 20:24:27 | 113.809898 | 22.627399 | 0 | 0 |
3 | 34745 | 20:22:07 | 113.811348 | 22.628067 | 0 | 0 |
4 | 34745 | 20:10:06 | 113.819885 | 22.647800 | 0 | 54 |
... | ... | ... | ... | ... | ... | ... |
544994 | 28265 | 21:35:13 | 114.321503 | 22.709499 | 0 | 18 |
544995 | 28265 | 09:08:02 | 114.322701 | 22.681700 | 0 | 0 |
544996 | 28265 | 09:14:31 | 114.336700 | 22.690100 | 0 | 0 |
544997 | 28265 | 21:19:12 | 114.352600 | 22.728399 | 0 | 0 |
544998 | 28265 | 19:08:06 | 114.137703 | 22.621700 | 0 | 0 |
544999 rows × 6 columns
Define the study area and use the tbd.clean_outofbounds
method to delete the data out of the study area
#Define the study area
bounds = [113.75, 22.4, 114.62, 22.86]
#Delete the data out of the study area
data = tbd.clean_outofbounds(data,bounds = bounds,col = ['lon','lat'])
The most basic way to express the data distribution is in the form of geograpic grids. TransBigData
provides methods to generate multiple types of geographic grids (Rectangular grids, Hexagonal grids) in the research area. For rectangular gridding, you need to determine the gridding parameters at first (which can be interpreted as defining a grid coordinate system):
#Obtain the gridding parameters
params = tbd.area_to_params(bounds,accuracy = 1000)
params
{'slon': 113.75, 'slat': 22.4, 'deltalon': 0.00974336289289822, 'deltalat': 0.008993210412845813, 'theta': 0, 'method': 'rect', 'gridsize': 1000}
The gridding parameters store the information of the initial position, the size and the angle of the gridding system.
The next step is to map the GPS data to their corresponding grids. Using the tbd.GPS_to_grid
, it will generate the LONCOL
column and the LATCOL
column (Rectangular grids). The two columns together can specify a grid:
#Map the GPS data to grids
data['LONCOL'],data['LATCOL'] = tbd.GPS_to_grid(data['lon'],data['lat'],params)
Count the amount of data in each grids, generate the geometry of the grids and transform it into a GeoDataFrame:
#Aggregate data into grids
grid_agg = data.groupby(['LONCOL','LATCOL'])['VehicleNum'].count().reset_index()
#Generate grid geometry
grid_agg['geometry'] = tbd.grid_to_polygon([grid_agg['LONCOL'],grid_agg['LATCOL']],params)
#Change the type into GeoDataFrame
import geopandas as gpd
grid_agg = gpd.GeoDataFrame(grid_agg)
#Plot the grids
grid_agg.plot(column = 'VehicleNum',cmap = 'autumn_r')
TransBigData
also support the triangle and hexagon grids. It also supports given rotation angle for the grids. We can alter the gridding parameter:
#set to the hexagon grids
params['method'] = 'hexa'
#or set as triangle grids: params['method'] = 'tri'
#set a rotation angle (degree)
params['theta'] = 5
Then we can do the GPS data matching again:
#Triangle and Hexagon grids requires three columns to store ID
data['loncol_1'],data['loncol_2'],data['loncol_3'] = tbd.GPS_to_grid(data['lon'],data['lat'],params)
#Aggregate data into grids
grid_agg = data.groupby(['loncol_1','loncol_2','loncol_3'])['VehicleNum'].count().reset_index()
#Generate grid geometry
grid_agg['geometry'] = tbd.grid_to_polygon([grid_agg['loncol_1'],grid_agg['loncol_2'],grid_agg['loncol_3']],params)
#Change the type into GeoDataFrame
import geopandas as gpd
grid_agg = gpd.GeoDataFrame(grid_agg)
#Plot the grids
grid_agg.plot(column = 'VehicleNum',cmap = 'autumn_r')
For a geographical data visualization figure, we still have to add the basemap, the colorbar, the compass and the scale. Use tbd.plot_map
to load the basemap and tbd.plotscale
to add compass and scale in matplotlib figure:
import matplotlib.pyplot as plt
fig =plt.figure(1,(8,8),dpi=300)
ax =plt.subplot(111)
plt.sca(ax)
#Load basemap
tbd.plot_map(plt,bounds,zoom = 11,style = 4)
#Define colorbar
cax = plt.axes([0.05, 0.33, 0.02, 0.3])
plt.title('Data count')
plt.sca(ax)
#Plot the data
grid_agg.plot(column = 'VehicleNum',cmap = 'autumn_r',ax = ax,cax = cax,legend = True)
#Add scale
tbd.plotscale(ax,bounds = bounds,textsize = 10,compasssize = 1,accuracy = 2000,rect = [0.06,0.03],zorder = 10)
plt.axis('off')
plt.xlim(bounds[0],bounds[2])
plt.ylim(bounds[1],bounds[3])
plt.show()
Please cite this when using TransBigData
in your research. Citation information is as follows:
@article{Yu2022,
doi = {10.21105/joss.04021},
url = {https://doi.org/10.21105/joss.04021},
year = {2022},
publisher = {The Open Journal},
volume = {7},
number = {71},
pages = {4021},
author = {Qing Yu and Jian Yuan},
title = {TransBigData: A Python package for transportation spatio-temporal big data processing, analysis and visualization},
journal = {Journal of Open Source Software}
}