A Python package develop for transportation spatio-temporal big data processing, analysis and visualization.

Last update: Jan 03, 2023

Overview

TransBigData

Introduction

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)

Target Audience

The target audience of TransBigData includes:

Data science researchers and data engineers in the field of transportation big data, smart transportation systems, and urban computing, particularly those who want to integrate innovative algorithms into intelligent trasnportation systems
Government, enterprises, or other entities who expect efficient and reliable management decision support through transportation spatio-temporal data analysis.

Technical Features

Provide 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.

Main Functions

Currently, TransBigData mainly provides the following methods:

Data Quality: Provides methods to quickly obtain the general information of the dataset, including the data amount the time period and the sampling interval.
Data Preprocess: Provides methods to clean multiple types of data error.
Data Gridding: Provides methods to generate multiple types of geographic grids (Rectangular grids, Hexagonal grids) in the research area. Provides fast algorithms to map GPS data to the generated grids.
Data Aggregating: Provides methods to aggregate GPS data and OD data into geographic polygon.
Data Visualization: Built-in visualization capabilities leverage the visualization package keplergl to interactively visualize data on Jupyter notebook with simple code.
Trajectory Processing: Provides methods to process trajectory data, including generating trajectory linestring from GPS points, and trajectory densification, etc.
Basemap Loading: Provides methods to display Mapbox basemap on matplotlib figures

Installation

It is recommended to use Python 3.7, 3.8, 3.9

Using pypi

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

Using conda-forge

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

Contributing to 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.

Examples

Example of data visualization

Visualize trajectories (with keplergl)

Visualize data distribution (with keplergl)

Visualize OD (with keplergl)

Example of taxi GPS data processing

The following example shows how to use the TransBigData to perform data gridding, data aggregating and data visualization for taxi GPS data.

Read the 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

Data pre-processing

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'])

Data gridding

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')

Triangle and Hexagon grids & rotation angle

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')

Data Visualization(with basemap)

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()

Griding framework offered by TransBigData

Here is an overview of the gridding framework offered by TransBigData.

See This Example for further details.

Citation information

Please cite this when using TransBigData in your research. Citation information can be found at CITATION.cff.

Introducing Video (In Chinese)

Comments

[JOSS] Installation & Dependencies
In order to make it most clear for users to install geopandas, the link in README and the install docs should be: https://geopandas.org/en/stable/getting_started.html#installation

Also, the following packages are used in the docs/tutorials and should be added to dependencies list:

CoordinatesConverter>=0.1.4

leuvenmapmatching

igraph (optional)

osmnx (optional)

plot_map

rtree

seaborn (optional)

shapely

xref: openjournals/joss-reviews#4021
opened by jGaboardi 7
mapbox图片不显示
```
Package Version

appnope 0.1.2 argon2-cffi 21.1.0 attrs 21.2.0 backcall 0.2.0 bleach 4.1.0 blis 0.7.7 catalogue 2.0.7 certifi 2021.10.8 cffi 1.15.0 chardet 4.0.0 click 8.0.4 click-plugins 1.1.1 cligj 0.7.2 commonmark 0.9.1 cycler 0.11.0 cymem 2.0.6 d2l 0.17.3 debugpy 1.5.1 decorator 5.1.0 defusedxml 0.7.1 entrypoints 0.3 filelock 3.6.0 Fiona 1.8.21 geopandas 0.10.2 huggingface-hub 0.5.1 idna 2.10 ipykernel 6.4.2 ipython 7.29.0 ipython-genutils 0.2.0 ipywidgets 7.6.5 jedi 0.18.0 jellyfish 0.9.0 Jinja2 3.0.2 joblib 1.1.0 jsonschema 4.1.2 jupyter 1.0.0 jupyter-client 7.0.6 jupyter-console 6.4.0 jupyter-contrib-core 0.3.3 jupyter-contrib-nbextensions 0.5.1 jupyter-core 4.9.1 jupyter-highlight-selected-word 0.2.0 jupyter-latex-envs 1.4.6 jupyter-nbextensions-configurator 0.4.1 jupyterlab-pygments 0.1.2 jupyterlab-widgets 1.0.2 keplergl 0.3.2 keybert 0.5.1 kiwisolver 1.3.2 langcodes 3.3.0 lxml 4.8.0 mapclassify 2.4.3 MarkupSafe 2.0.1 matplotlib 3.3.3 matplotlib-inline 0.1.3 mistune 0.8.4 multi-rake 0.0.2 munch 2.5.0 murmurhash 1.0.6 nbclient 0.5.4 nbconvert 6.2.0 nbformat 5.1.3 nest-asyncio 1.5.1 networkx 2.8 nltk 3.7 notebook 6.4.5 numpy 1.18.5 packaging 21.2 pandas 1.2.2 pandocfilters 1.5.0 parso 0.8.2 pathy 0.6.1 pexpect 4.8.0 pickleshare 0.7.5 Pillow 8.4.0 pip 22.0.4 plot-map 0.3.7 preshed 3.0.6 prometheus-client 0.12.0 prompt-toolkit 3.0.21 ptyprocess 0.7.0 pycld2 0.41 pycparser 2.20 pydantic 1.8.2 pygeos 0.12.0 Pygments 2.10.0 pyparsing 2.4.7 pyproj 3.3.1 pyrsistent 0.18.0 python-dateutil 2.8.2 pytz 2021.3 PyYAML 6.0 pyzmq 22.3.0 qtconsole 5.1.1 QtPy 1.11.2 regex 2022.3.15 requests 2.25.1 rich 12.2.0 Rtree 1.0.0 sacremoses 0.0.49 scikit-learn 1.0.1 scipy 1.7.2 segtok 1.5.11 Send2Trash 1.8.0 sentence-transformers 2.2.0 sentencepiece 0.1.96 setuptools 56.0.0 Shapely 1.8.1.post1 six 1.16.0 sklearn 0.0 smart-open 5.2.1 spacy 3.2.4 spacy-legacy 3.0.9 spacy-loggers 1.0.2 srsly 2.4.2 summa 1.2.0 tabulate 0.8.9 terminado 0.12.1 testpath 0.5.0 thinc 8.0.15 threadpoolctl 3.0.0 tokenizers 0.12.1 torch 1.8.1 torchvision 0.9.1 tornado 6.1 tqdm 4.64.0 traitlets 5.1.1 traittypes 0.2.1 transbigdata 0.4.5 transformers 4.18.0 treelite 2.2.2 treelite-runtime 2.2.2 typer 0.4.1 typing_extensions 4.1.1 urllib3 1.26.8 wasabi 0.9.1 wcwidth 0.2.5 webencodings 0.5.1 widgetsnbextension 3.5.2 xgboost 1.5.2 yake 0.4.8
opened by java2python 4
line, stop = tbd.getbusdata('深圳', ['2号线']) 报错：No such busline
Describe the bug A clear and concise description of what the bug is.

To Reproduce Steps to reproduce the behavior:

Go to '...'

Click on '....'

Scroll down to '....'

See error

Expected behavior A clear and concise description of what you expected to happen.

Screenshots If applicable, add screenshots to help explain your problem.

Desktop (please complete the following information):

OS: [e.g. iOS]

Browser [e.g. chrome, safari]

Version [e.g. 22]

Smartphone (please complete the following information):

Device: [e.g. iPhone6]

OS: [e.g. iOS8.1]

Browser [e.g. stock browser, safari]

Version [e.g. 22]

Additional context Add any other context about the problem here.
opened by MissChengLX 4
[JOSS] Missing dependencies after conda install

I followed the conda install instructions

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

and it seems that not all dependencies are resolved because I get

ModuleNotFoundError: No module named 'scipy'

xref: openjournals/joss-reviews#4021

opened by anitagraser 4
[JOSS] moving pandas citation

I seem to have made in mistake (618f7c8e5d5aecdc3e4dce08d0b3e6a5d3ddc366) in the MovingPandas citation that omits the journal title (see line 100 here).

opened by jGaboardi 4

Example 1-Taxi GPS data processing 运行失败

运行到5大步骤： Aggregate OD into polygons 出错

# Aggragate OD data to polygons 
# without passing gridding parameters, the algorithm will map the data 
# to polygons directly using their coordinates
od_gdf = tbd.odagg_shape(oddata, sz, round_accuracy=6)
fig = plt.figure(1, (16, 6), dpi=150) # 确定图形高为6，宽为8；图形清晰度
ax1 = plt.subplot(111)
od_gdf.plot(ax=ax1, column='count')
plt.xticks([], fontsize=10)
plt.yticks([], fontsize=10)
plt.title('OD Trips', fontsize=12);

报错信息： ImportError: Spatial indexes require either rtree or pygeos. See installation instructions at https://geopandas.org/install.html

版本：

Package                           Version
--------------------------------- -----------
appnope                           0.1.2
argon2-cffi                       21.1.0
attrs                             21.2.0
backcall                          0.2.0
bleach                            4.1.0
blis                              0.7.7
catalogue                         2.0.7
certifi                           2021.10.8
cffi                              1.15.0
chardet                           4.0.0
click                             8.0.4
click-plugins                     1.1.1
cligj                             0.7.2
commonmark                        0.9.1
cycler                            0.11.0
cymem                             2.0.6
d2l                               0.17.3
debugpy                           1.5.1
decorator                         5.1.0
defusedxml                        0.7.1
entrypoints                       0.3
filelock                          3.6.0
Fiona                             1.8.21
geopandas                         0.10.2
huggingface-hub                   0.5.1
idna                              2.10
ipykernel                         6.4.2
ipython                           7.29.0
ipython-genutils                  0.2.0
ipywidgets                        7.6.5
jedi                              0.18.0
jellyfish                         0.9.0
Jinja2                            3.0.2
joblib                            1.1.0
jsonschema                        4.1.2
jupyter                           1.0.0
jupyter-client                    7.0.6
jupyter-console                   6.4.0
jupyter-contrib-core              0.3.3
jupyter-contrib-nbextensions      0.5.1
jupyter-core                      4.9.1
jupyter-highlight-selected-word   0.2.0
jupyter-latex-envs                1.4.6
jupyter-nbextensions-configurator 0.4.1
jupyterlab-pygments               0.1.2
jupyterlab-widgets                1.0.2
keplergl                          0.3.2
keybert                           0.5.1
kiwisolver                        1.3.2
langcodes                         3.3.0
lxml                              4.8.0
mapclassify                       2.4.3
MarkupSafe                        2.0.1
matplotlib                        3.3.3
matplotlib-inline                 0.1.3
mistune                           0.8.4
multi-rake                        0.0.2
munch                             2.5.0
murmurhash                        1.0.6
nbclient                          0.5.4
nbconvert                         6.2.0
nbformat                          5.1.3
nest-asyncio                      1.5.1
networkx                          2.8
nltk                              3.7
notebook                          6.4.5
numpy                             1.18.5
packaging                         21.2
pandas                            1.2.2
pandocfilters                     1.5.0
parso                             0.8.2
pathy                             0.6.1
pexpect                           4.8.0
pickleshare                       0.7.5
Pillow                            8.4.0
pip                               22.0.4
plot-map                          0.3.7
preshed                           3.0.6
prometheus-client                 0.12.0
prompt-toolkit                    3.0.21
ptyprocess                        0.7.0
pycld2                            0.41
pycparser                         2.20
pydantic                          1.8.2
Pygments                          2.10.0
pyparsing                         2.4.7
pyproj                            3.3.1
pyrsistent                        0.18.0
python-dateutil                   2.8.2
pytz                              2021.3
PyYAML                            6.0
pyzmq                             22.3.0
qtconsole                         5.1.1
QtPy                              1.11.2
regex                             2022.3.15
requests                          2.25.1
rich                              12.2.0
sacremoses                        0.0.49
scikit-learn                      1.0.1
scipy                             1.7.2
segtok                            1.5.11
Send2Trash                        1.8.0
sentence-transformers             2.2.0
sentencepiece                     0.1.96
setuptools                        56.0.0
Shapely                           1.8.1.post1
six                               1.16.0
sklearn                           0.0
smart-open                        5.2.1
spacy                             3.2.4
spacy-legacy                      3.0.9
spacy-loggers                     1.0.2
srsly                             2.4.2
summa                             1.2.0
tabulate                          0.8.9
terminado                         0.12.1
testpath                          0.5.0
thinc                             8.0.15
threadpoolctl                     3.0.0
tokenizers                        0.12.1
torch                             1.8.1
torchvision                       0.9.1
tornado                           6.1
tqdm                              4.64.0
traitlets                         5.1.1
traittypes                        0.2.1
transbigdata                      0.4.5
transformers                      4.18.0
treelite                          2.2.2
treelite-runtime                  2.2.2
typer                             0.4.1
typing_extensions                 4.1.1
urllib3                           1.26.8
wasabi                            0.9.1
wcwidth                           0.2.5
webencodings                      0.5.1
widgetsnbextension                3.5.2
xgboost                           1.5.2
yake                              0.4.8

opened by java2python 3

[JOSS] Language of status and error messages

I've been going through Example 1-Taxi GPS data processing.ipynb and found that some messages are in Chinese:

Considering a potentially global user base, I think the messages should be in English.

xref: openjournals/joss-reviews#4021

opened by anitagraser 3
FileNotFindError in plotmap.py
There were some problems when I was trying to set imgsavepath, it seems like “filepath = searchfile('mapboxtoken.txt')” in line 68 doesn't work. when I typing the following code in my jupyternotebook.

import transbigdata as tbd tbd.set_imgsavepath(r'/home/liqi/CodeSpace/Map_tile/')

I don't know if it was caused by miniconda virtual environment or something else.

Screenshots

OS: Ubuntu20.04LTS
opened by liq77 2
Update README (and docs?)

This PR provides several minor corrections in README.md. I wanted to make the same changes to the docs, but it seems they have to be made in transbigdata-docs-en repo?

opened by jGaboardi 2
[JOSS] target audience?
Regarding the Statement of Need, the docs and manuscript clearly state what problems are being solved, but there is no mentioned of who the target audience is.

This should be added to:

the actual manuscript

README.md

the docs site

For reference:

Documentation Do the authors clearly state what problems the software is designed to solve and who the target audience is?

Software paper Does the paper have a section titled 'Statement of Need' that clearly states what problems the software is designed to solve and who the target audience is?

xref: openjournals/joss-reviews#4021
opened by jGaboardi 2
[JOSS] State of the field?

Currently no other similar software packages are mentioned in the manuscript, which is a requirement. There should be an overview of comparable packages, even/especially if they are not written in Python. Further, if no other packages exist this should be mentioned.

xref: this comment from @anitagraser.

opened by jGaboardi 2

Releases(0.4.17)

0.4.17(Dec 25, 2022)

fix bug to support shapely 2.0.0
Source code(tar.gz)
Source code(zip)
0.4.16(Nov 16, 2022)
Add activity.py to analysis human activity

Entropy to calculate Entropy and Entropy rate

Confidence ellipse to calculate and plot confidence ellipse

Activity plot to plot Activity

Update function tbd.mobile_plot_activity rename it to tbd.plot_activity

Add parameter fontsize to control fontsize of xticks and yticks

Add parameter yticks_gap to control yticks

Add parameter xticks_rotation and xticks_gap to control xticks

Use column group to control the color of the bars

Source code(tar.gz)
Source code(zip)
0.4.15(Oct 31, 2022)
rename the tbd.mobile_stay_duration method name

fix bug in tbd.mobile_stay_move: some stay can not correctly identified.

fix bug in tbd.mobile_plot_activity: add the shuffle parameter and fix the norm function to control the color display

Source code(tar.gz)
Source code(zip)
0.4.14(Oct 6, 2022)
Update function clean_taxi_status. Sort the VehicleNum and Time columns before clean taxi status

Add error info in amap getadmin function

Fix error info in bounds setting in grid.py

Source code(tar.gz)
Source code(zip)
0.4.13(Sep 11, 2022)
Improve plotmap:

Change the way of creating file path in plotmap to solve the error not reading local base map in some system environment.

Expose the read_imgsavepath and read_mapboxtoken function

Source code(tar.gz)
Source code(zip)
0.4.12(Sep 8, 2022)
Improve the test coverage to 100%

Require the geopandas version 0.10.2 to avoid some potential errors

Support python 3.6 and 3.10

Add the timeout parameter in crawler.py

Use requests instead of urllib in data fetching

Source code(tar.gz)
Source code(zip)
0.4.11(Jul 19, 2022)

Use ConfigParser to store Mapbox settings, mapboxtoken and imgsavepath are now store at config.ini in transbigdata source code folder.
Source code(tar.gz)
Source code(zip)
0.4.10(Jul 8, 2022)
Update the mobile phone data processing function, See example for detail usage. Add functions:

transbigdata.mobile_stay_move: Input trajectory data and gridding parameters, identify stay and move.

transbigdata.mobile_stay_dutation: Input the stay point data to identify the duration during night and day time.

transbigdata.mobile_identify_home: Identify home location from mobile phone stay data. The rule is to identify the locations with longest duration in night time.

transbigdata.mobile_identify_work: Identify work location from mobile phone stay data. The rule is to identify the locations with longest duration in day time on weekdays(Average duration should over minhour).

transbigdata.mobile_plot_activity: Plot the activity plot of individual.

Source code(tar.gz)
Source code(zip)
0.4.9(Jul 1, 2022)
Update the metro model, add functions:

transbigdata.metro_network: create metro network

transbigdata.get_shortest_path: Obtain the shortest path with given OD

transbigdata.get_k_shortest_paths: Obtain the k shortest path with given OD

transbigdata.get_path_traveltime: Obtain the travel time of the path

See example for detail usage: https://transbigdata.readthedocs.io/en/latest/gallery/Example%205-Modeling%20for%20subway%20network%20topology.html
Source code(tar.gz)
Source code(zip)
0.4.8(May 21, 2022)

update plot_map, now the basemap support input self-define mapbox style.
Source code(tar.gz)
Source code(zip)
0.4.7(Apr 25, 2022)

The tbd.plot_map function is added OpenStreetMap as the style 0, which do not need access token any more. imgsavepath and access_token are not neccessarily required now.
Source code(tar.gz)
Source code(zip)
0.4.6(Apr 25, 2022)

update grid_params_optimize method, this algorithm now is changed into PSO to speed up the optimizing process.
Source code(tar.gz)
Source code(zip)
0.4.5(Apr 20, 2022)

fix bug in geohash_decode
Source code(tar.gz)
Source code(zip)
v0.4.4(Apr 15, 2022)

fix bug in getadmin
Source code(tar.gz)
Source code(zip)
v0.4.1(Mar 27, 2022)
Add the Triangle and hexagon gridding methods, the methods are vectorized and fast:

Triangle grids: GPS_to_grids_tri and gridid_to_polygon_tri

Hexagon grids: GPS_to_grids_hexa and gridid_to_polygon_hexa

See Example for details.
Source code(tar.gz)
Source code(zip)
v0.3.12(Mar 25, 2022)
v0.3.12:

Modify code format to fit the standard of flake8.

Fix bug in getbusdata, now it work perfectly.

Source code(tar.gz)
Source code(zip)
v0.3.11(Mar 17, 2022)

fix bug in getbusdata
Source code(tar.gz)
Source code(zip)
v0.3.10(Mar 16, 2022)

Add method grid_params_optimize to optimize the grid params. Drop method getbusdata because it produce uncorrect data.
Source code(tar.gz)
Source code(zip)
v0.3.9(Mar 6, 2022)
Add two functions for isochrone download:

get_isochrone_mapbox: Obtain the isochrone from mapbox isochrone.

get_isochrone_amap: Obtain the isochrone from Amap isochrone.

Grids are now support rotate with given angle:

grid params are now support the fifth parameter theta to represent the rotation angle.

GPS_to_grids,grids_centre,rect_grids,gridid_to_polygon,regenerate_params are rewrite to support grids with angle.

Source code(tar.gz)
Source code(zip)
v0.3.7(Feb 23, 2022)

The method grid_from_params is similar with the method rect_grids, so its function is merged into rect_grids.
Source code(tar.gz)
Source code(zip)
v0.3.6(Feb 22, 2022)

Add two functions:

transbigdata.regenerate_params(grid): Regenerate gridding params from grid. transbigdata.grid_from_params(params,location): Generate grids from params and bounds or shape.
Source code(tar.gz)
Source code(zip)
v0.3.5(Feb 1, 2022)

TransBigData v0.3.5 Integrate plot_map and CoordinatesConverter, it nolonger depends on these two packages. This is also the first version on conda-forge
Source code(tar.gz)
Source code(zip)
0.3.3(Jan 28, 2022)

Published at https://pypi.org/project/transbigdata/0.3.3/
Source code(tar.gz)
Source code(zip)

Owner

Qing Yu

Python, JavaScript, Spatio-temporal big data, Data visualization

GitHub Repository https://transbigdata.readthedocs.io/en/latest/

A Python package develop for transportation spatio-temporal big data processing, analysis and visualization.

Related tags

Overview

TransBigData

Introduction

Target Audience

Technical Features

Main Functions

Installation

Using pypi

Using conda-forge

Contributing to TransBigData

Examples

Example of data visualization

Visualize trajectories (with keplergl)

Visualize data distribution (with keplergl)

Visualize OD (with keplergl)

Example of taxi GPS data processing

Read the data

Data pre-processing

Data gridding

Triangle and Hexagon grids & rotation angle

Data Visualization(with basemap)

Griding framework offered by TransBigData

Citation information

Introducing Video (In Chinese)

Comments

Releases(0.4.17)

0.4.17(Dec 25, 2022)

0.4.16(Nov 16, 2022)

0.4.15(Oct 31, 2022)

0.4.14(Oct 6, 2022)

0.4.13(Sep 11, 2022)

0.4.12(Sep 8, 2022)

0.4.11(Jul 19, 2022)

0.4.10(Jul 8, 2022)

0.4.9(Jul 1, 2022)

0.4.8(May 21, 2022)

0.4.7(Apr 25, 2022)

0.4.6(Apr 25, 2022)

0.4.5(Apr 20, 2022)

v0.4.4(Apr 15, 2022)

v0.4.1(Mar 27, 2022)

v0.3.12(Mar 25, 2022)

v0.3.11(Mar 17, 2022)

v0.3.10(Mar 16, 2022)

v0.3.9(Mar 6, 2022)

v0.3.7(Feb 23, 2022)

v0.3.6(Feb 22, 2022)

v0.3.5(Feb 1, 2022)

0.3.3(Jan 28, 2022)

Owner

Qing Yu

Type hints support for the Sphinx autodoc extension

DeltaPy - Tabular Data Augmentation (by @firmai)

Pyoccur - Python package to operate on occurrences (duplicates) of elements in lists

Legacy python processor for AsciiDoc

Collection of Summer 2022 tech internships!

Python bindings to OpenSlide

Collections of Beautiful Latex Snippets

Crystal Smp plugin for show scoreboards

NetBox plugin that stores configuration diffs and checks templates compliance

Code and pre-trained models for "ReasonBert: Pre-trained to Reason with Distant Supervision", EMNLP'2021

An awesome Data Science repository to learn and apply for real world problems.

Count the number of lines of code in a directory, minus the irrelevant stuff

Fun interactive program to sort a list :)

A Python library that simplifies the extraction of datasets from XML content.

PyPresent - create slide presentations from notes

A next-generation curated knowledge sharing platform for data scientists and other technical professions.

freeCodeCamp Scientific Computing with Python Project for Certification.

Compare two CSV files for differences. Colorize the differences and align the columns.

Python code for working with NFL play by play data.

Sphinx-performance - CLI tool to measure the build time of different, free configurable Sphinx-Projects