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Report By Xinshuang Wang
Visual Exploration of Shanghai Subway Data Report By Xinshuang Wang - - PowerPoint PPT Presentation
Visual Exploration of Shanghai Subway Data Report By Xinshuang Wang - - PowerPoint PPT Presentation
Visual Exploration of Shanghai Subway Data Report By Xinshuang Wang 01 Introduction and Related work Outline 02 Three views 03 Explore the influence of weather on passenger flow Introduction and Related Work Introduction of Visualization
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Introduction and Related Work
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4 Introduction of Visualization
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Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data
[2] Chen S, Yuan X, Wang Z, et al. Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data[J]. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1): 270-279.
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Track people’s movements with social media data with geotags.
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Visualizing Mobility of Public Transportation System
[1] Zeng W, Fu C W, Arisona S M, et al. Visualizing mobility of public transportation system[J]. IEEE Transactions
- n Visualization and Computer Graphics, 2014, 20(12): 1833-1842.
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Visualize and explore passenger mobility in a public transportation system with subway system data and bus network data.
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Visual Exploration of Big Spatio-Temporal Urban Data A Study of New York City Taxi Trips
[3] Ferreira N, Poco J, Vo H T, et al. Visual exploration of big spatio-temporal urban data: A study of new york city taxi trips[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 2149-2158.
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Propose a model that allows users to visually query taxi trips.
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Three views
Passenger Flow Volume View Running View Lines & Stations View
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Passenger Flow Volume View
Passenger Flow Volume Time: Apr 1 – Apr 30 2015 14 Subway Lines Passenger Flow Volume in particular time (mouse hover)
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Visual analytics
Weekly periodic variation Qingming Festival: 3 days’ holiday on Apr 4 - 6 2015 Three Hot Lines:
Line 2 Line 1 Line 8
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Visual analytics
Two Peaks: Morning Peak (07:00 – 09:00) & Evening Peak (16:00 – 18:00) Morning Peak is more agminated; Evening Peak is more dispersive Line 16 Morning peak comes earlier Evening peak comes later
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Apr 16 2015 Line 1
Running View
Time Time
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Lines & Stations View
Lines 14 Lines (Line 1 – Line 12, Line 16) Stations 366 Stations Length 617km
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Explore the influence of weather on passenger flow
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Explore the influence of weather on passenger flow
Scenic resign Sunny: --- Raining and windy: Fog and haze: Entertainment district Sunny: --- Raining and windy: Fog and haze: Residential district Sunny: --- Raining and windy: --- Fog and haze: --- Business district Sunny: --- Raining and windy: --- Fog and haze: --- 3 datasets: Shanghai subway smartcard data weather data AQI (air quality index)
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Station Apr 7 (Tues) Apr 8 (Wed) Apr 8 – Apr 7 (Apr 8 – Apr 7) / Apr 8
豫园 36547 41615 5068 12.1783% 人民广场 193788 216861 23073 10.6395% 上海动物园 12152 13327 1175 8.8167% 徐家汇 148406 159163 10757 6.7585% 南京西路 96842 101707 6865 6.7498% 金科路 66335 69324 2989 4.3116% 张江高科 68070 70769 2699 3.8138% 唐镇 27334 28220 886 3.1396% 浦东国际机场 17738 17262
- 476
- 2.7575%
上海火车站 200811 189518
- 11293
- 5.9588%
Explore the influence of weather on passenger flow
Simply validate the influence of weather
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THANK YOU
2016.04.20
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