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metro's Introduction

###What's new?

The 2016 SODA competition just started from July 12. The metro data is provided again. You're welcome to join. See more information here: Shanghai 2016 SODA


###What's this project?

This is a data visualization project based on the public transportation data (mostly metro data) released by Shanghai government in 2015. With this project, I and my friends participated in the 2015 SODA (Shanghai Open Data Application) competition and won a little prize.

###What the visualization is about?

As a daily metro commuter, I am really interested in knowing how many people are flowing in and out of the metro system at each station. So I created this project just to answer that.

###Give me an example of the visualization.

Check this out. If you clone this project, you can also see some animations.

Shanghai Metro Visualization

###How to see the visualiation?

Follow the following steps, you will see the visualization on your computer as well:

  1. Clone this repo
  2. CD into the directory of the repo
  3. Run a local server on this directory. In my case, I run a local server by typing in "python -m SimpleHTTPServer 8888"
  4. Open your browser and go to localhost:8888
  5. Click some HTML file links

###What are the tools used for this project?

The visualization is built with d3 library. The data are handled by some python scripts while queries are done with SQLServer (not included here).

metro's People

Contributors

icomingi avatar yifili09 avatar

Stargazers

 avatar  avatar Akuyoru avatar yijun xiang avatar delick tang avatar  avatar UNICORNFUSED avatar BereniceAli avatar  avatar  avatar Xiaoxia Lei avatar Ch1y0q avatar Tim Wu avatar nncyc avatar  avatar  avatar Marquez_Jue Hou avatar 鲁新钰 avatar Yuting HAN avatar FrozenMap avatar 小猿猴GISer avatar  avatar  avatar zhen yanjie avatar Joey Xiong avatar  avatar  avatar gssg avatar Yuntao Du avatar  avatar Yanyan Xu avatar Sun Xiaoting avatar  avatar Jiaming avatar  avatar Shiyu Wang avatar Guosen Wang avatar  avatar Yiling avatar  avatar  avatar sentence avatar  avatar  avatar  avatar SHUKE avatar wandergis avatar pingjunpan avatar WANG Leye avatar  avatar HaiSun avatar jiandong avatar Yuanxuan Yang avatar Ethan Koo avatar Dawn Li avatar Jin Yao avatar mahlerzhou avatar Lekton avatar Jason Fan avatar Zhao Guoqi avatar Chenliang Wang avatar Ting Huang avatar  avatar  avatar  avatar Jerry Ji avatar

Watchers

James Cloos avatar  avatar Sun Xiaoting avatar  avatar  avatar Jiang Su avatar Song Xiliang avatar  avatar  avatar  avatar Jerry Ji avatar  avatar Jason Fan avatar  avatar  avatar Jiadong Qian avatar

metro's Issues

[WIP] O-D Table

O D T1 T2
2 3 14:00 14:05
3 11 14:06 14:07

TODO

  • To parse the data and generate the O-D table [NA-Hadoop team support? ]
  • Pre-define the transfer table
100405844,2015-04-01,08:00:46,3号线曹杨路,08:21:18,2号线娄山关路,地铁,3.00,非优惠
 [ records missed 
   3号线中山公园, 2号线中山公园 
   2号线中山公园,2号线娄山关路 ] 
100405844,2015-04-01,12:07:31,2号线娄山关路,12:43:24,11号线祁连山路,地铁,4.00,非优惠

[WIP] Data Strucutre

line_color = {"LINE_NUMBER(KEY)":"LINE_COLOR(VALUE)"}
stations = {"STATION_CODE(KEY)":"{"LAT","LNG"}"}

[WIP] Escalator

Escalators Numbers

The number of escalators suggested for any one direction as follows

escalator_

the result should be calculated round up.

[WIP] TODO

【公共事业】【社交类】【商业价值】

  1. 地铁上班族
    • 流量图
      • 【社交类】有多少的地铁上班(出行)族
      • 【社交类】日常的上班族占据的比例其实并没有那么多
      • 【社交类】 回顾统计去过哪些站,总共多少里程,花了多少时间,总共消费多少时间
      • 【商业价值】广告投放的竞价策略,精准营销
        • break-down 站点,达到投放目标准确
      • _【公共事业 - 安全】 自动扶梯的设备维护和保养_
      • 【公共事业】统计每天出行方式的数据(单纯地铁、混合公交车、P+R出行)
        • 是否能提高运行效率最后一公里的公交线路
    • 动态拥挤度图
      • 【社交类】这些地铁上班(出行)族住在哪里
      • 【社交类】出行高峰,找到合适的上班时间
      • 【社交类】出行时间分类,找出行为差异
        • 按照具体时间,分类出行人群的起始点和到达点
      • 【公共事业 - 安全】依据历史数据,推测今日的拥挤程度,并且给出预警
      • 【公共事业 - 效率】在运营条件的限制下,提高运营效率
      • 【公共事业 - 效率】集中考虑中转的大站台,挖掘具体的数据属性
    • 现场内容
      • 微信公众号,QR扫码,现场互动,
      • 屏幕录制,配乐
      • PPT 修改

2015-11-6 PPT

Topic

  1. 某线,每一站,时间段,入,出总人数
    1. 出入站流量统计
    2. 预估节假日的人流(节假日、早晚高峰、平时、天气、季节)
  2. 同一站,站内人数,车上人数(下车,上车)
    1. 踩踏预警
    2. 是否可以上下班错峰出行
  3. 同一个人 每天在地铁上花费的时间
  4. 如何统计上班族每天的地铁花费时间和成本
  5. 每条线,每一站发生优惠的记录
  6. 统计每天出行方式的数据(单纯地铁或是混合公交车出行)
    1. 未来的展望,不一定要进入作品展示
  7. 单个人的非上班时间的出行习惯
  8. 同一辆车,每站到达时间是否有规律。(人流对列车运行时间的影响)
  9. 单个同一条线路的属性(拥挤度,商业价值)
  10. 同一个人在同一站的等待时间
  11. 统计相同/相似 线路的人流分布情况
    1. 【公共事业】是否考虑增加新的地铁线路或者新的公司地址
    2. 【社交类】追踪遗失的交通卡
    3. 【社交类】追踪我的足迹 (运动量 , 消耗的卡路里, 统计行程量【绕地球几圈】,同行人的统计,竞赛排行榜)
    4. 【公共事业-安全类】 历史停车的时间和进站的人流量是否达到安全的保障,标准
    5. 【社交类】 回顾统计 去过哪些站,总共多少里程,花了多少时间,总共消费多少时间。
    6. 【微信公众号】 当场注册的用户随机分配一个卡号
      1. 公共事业数据展示 ; 个人数据展示
      2. 互动性的微信公众号,【情怀】历史数据

We Want

  • 地铁各个出口的人流统计(闸机口)
  • 卡号信息(是交通一卡通还是零时卡)
  • 每辆车到站的实时数据

Resources

Johnson: Server
Alex: Git repo
Sampledata
Udacity: D3
Python: d3
SQL
.NET
HTML, JS, CSS
MapReduce

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