Metadata-Version: 2.1
Name: transbigdata
Version: 0.2.4
Summary: A tool for transportation big data
Home-page: https://github.com/ni1o1/transbigdata
Author: Qing Yu
Author-email: qingyu0815@foxmail.com
License: BSD
Project-URL: Bug Tracker, https://github.com/ni1o1/transbigdata/issues
Description: English [中文版](README-zh_CN.md)
        
        # TransBigData
        
        <img src="https://github.com/ni1o1/transbigdata/raw/main/docs/source/_static/logo-wordmark-dark.png" style="width:550px">
        
        [![Documentation Status](https://readthedocs.org/projects/transbigdata/badge/?version=latest)](https://transbigdata.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/transbigdata.svg)](https://badge.fury.io/py/transbigdata) ![PyPI - Downloads](https://img.shields.io/pypi/dm/transbigdata) ![GitHub commit activity](https://img.shields.io/github/commit-activity/m/ni1o1/transbigdata) [![bilibili](https://img.shields.io/badge/bilibili-%E5%90%8C%E6%B5%8E%E5%B0%8F%E6%97%AD%E5%AD%A6%E9%95%BF-green.svg)](https://space.bilibili.com/3051484)  
        
        
        **Main Functions**
        
        TransBigData is a Python package developed for transportation spatio-temporal big data processing and analysis. TransBigData provides fast and concise methods for processing common traffic spatio-temporal big data such as Taxi GPS data, bicycle sharing data and bus GPS data. It includes general methods such as rasterization, data quality analysis, data pre-processing, data set counting, trajectory analysis, GIS processing, map base map loading, coordinate and distance calculation, and data visualization.
        
        **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.
        
        
        For more details please see the [documentation](https://transbigdata.readthedocs.io/en/latest/)
        
        
        ## Installation
        
        Before installing TransBigData, make sure that you have installed the available geopandas package: https://geopandas.org/index.html
        If you already have geopandas installed, run the following code directly from the command prompt to install it
        
            pip install -U transbigdata
        
        
        ## Usage
        
        The following example shows how to use the TransBigData to extract Origin-Destination(OD) information of taxi trips from taxi GPS data:
        
            import transbigdata as tbd
            #Read the data    
            import pandas as pd
            data = pd.read_csv('TaxiData-Sample.csv',header = None) 
            data.columns = ['VehicleNum','time','slon','slat','OpenStatus','Speed'] 
            data
        
        <img src="https://github.com/ni1o1/transbigdata/raw/main/docs/source/_static/WX20211021-192131@2x.png" style="height:300px">
        
        Use the tbd.taxigps_to_od method and pass in the corresponding column name to extract the trip OD:
        
            #Extract OD information from GPS
            oddata = tbd.taxigps_to_od(data,col = ['VehicleNum','time','slon','slat','OpenStatus'])
            oddata
        
        <img src="https://github.com/ni1o1/transbigdata/raw/main/docs/source/_static/WX20211021-190104@2x.png" style="height:300px">
        
        Aggregate OD into grids:
        
            #Defining study area
            bounds = [113.6,22.4,114.8,22.9]
            #Input the bounds for the study area and generates the rasterization parameters
            params = tbd.grid_params(bounds = bounds,accuracy = 1500)
            #Rasterized OD and aggregate them into grids, this function will also generates a GeoDataFrame of the OD, which contains the counts of the aggregation.
            od_gdf = tbd.odagg_grid(oddata,params)
            od_gdf.plot(column = 'count')
        
        <img src="https://github.com/ni1o1/transbigdata/raw/main/docs/source/_static/WX20211021-190524@2x.png" style="height:300px">
        
        ## Related Links
        
        * Bilibili:  https://space.bilibili.com/3051484
        * Data analytics course for beginner https://www.lifangshuju.com/#/introduce/166  
        * Transportation Big Data analysis course： https://www.lifangshuju.com/#/introduce/154  
        * Data Visualization course： https://www.lifangshuju.com/#/introduce/165  
        * Github for this project： https://github.com/ni1o1/transbigdata/  
        * Bug report： https://github.com/ni1o1/transbigdata/issues  
        
        ## Citation
        
        And if you want to reference this GitHub repository, you can use the following bibtex.
        
        ```
        @misc{transbigdata,
          author = {Qing Yu},
          title = {TransBigData},
          year = {2021},
          publisher = {GitHub},
          journal = {GitHub Repository},
          howpublished = {\url{https://github.com/ni1o1/transbigdata}},
        }
        ```
        
Platform: UNKNOWN
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Framework :: Matplotlib
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Utilities
Classifier: Topic :: Internet
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
