Topology-Guided Vis isual Exploratory ry Analysis Harish - PowerPoint PPT Presentation

Topology-Guided Vis isual Exploratory ry Analysis Harish Doraiswamy NYU Center for Data Science

Scan All Fish 2

Scan All Fish 3

TopoAngler 4

Data Exhaust from Cities 5

Data Exhaust from Cities Infrastructure Environment People 6

Open Urban Data 7

NYC Taxi Data • Yellow cab trips • ~175 million trips / year • Spatial-Temporal • 2 spatial attributes • 2 temporal attributes • Other attributes • Fare, tip • Distance • Duration • … 8

2011 2012 9

Idea: Use Topology of the Data 10

Idea: Use Topology of the Data 11

Exploring Features • Several features per time step • Group similar features within a larger time interval • Represents “macro” events • Similarity • Graph similarity: Shape • Persistence / Volume: Topological similarity • Key for each group • Average shape and volume • Efficient search 12

Guiding Users towards Interesting Events • Properties of Macro Events Frequent occurance An event that occurs every hour during a week Rare occurence of these events Two micro-events that occur on consecutive weeks Two micro-events that occur on consecutive days Two micro-events that occur on consecutive hours 13

Rare and Interesting Features - Hourly • October Halloween Parade 14

Daily • October 1. Hispanic Day Parade (Oct 9 2011) 2. Columbus Day Parade (Oct 10 2011) 15

Weekly • August • No. of weeks = 3 • NYC Summer Streets 16

Dominican Day Parade 2011 (14 August 2011) 5 Borough Bike Tour 2012 (6 May 2012) Query Dominican Day Parade 2012 (12 August 2012) 5 Borough Bike Tour 2011 (1 May 2011) Gaza Solidarity Protest NYC (18 November 2012) 17

Frequent Features • Taxi hotspots • Filter over time General trends Night time trends 18

Frequent Features • Maxima: Taxi hotspots • Filter over time General trends Night time trends Using Topological Analysis to Support Event-Guided Exploration in Urban Data , TVCG 2014 . 19

How to compare cities? • Design of public spaces • Understand what works / doesn’t work in one city • Use this to improve design in another city • Understand properties of neighborhoods • Compare “activity” between neighborhoods with similar properties • Compare properties between neighborhoods with similar “activity” 20

How to analyze / compare different properties of a city? • How do cities behave during different times? • Summer vs. Winter • Weekdays vs. Weekends • Data sets about different cultural communities in a city • What patterns do the different communities follow? • How do these patterns compare? 21

Urban Pulse • Flickr activity in New York City 7:00 am 1 0 22

Urban Pulse • Flickr activity in New York City 7:00 am 11:00 am 1 0 23

Urban Pulse • Flickr activity in New York City 7:00 am 7:00 pm 1 0 24

Urban Pulse • Flickr activity in New York City 7:00 am 11:00 pm 1 0 25

Temporal Resolutions • Compute functions along 3 resolutions Time of Day Day of Week Month of Year 26

1. Identify Locations 2. Quantify Pulse Step 1: Identify Pulse Locations • Set of scalar functions over time • Identify all maxima • Location of prominent pulses • is a high persistent maxima in at least 1 time step • is a high persistent maxima in at least 1 resolution 27

1. Identify Locations 2. Quantify Pulse Step 2: Quantifying Pulse • 3 Beats to quantify the pulse at each location • Significant Beats • Is the location a high persistent maximum? B s 28

1. Identify Locations 2. Quantify Pulse Step 2: Quantifying Pulse • 3 Beats to quantify the pulse at each location • Maxima Beats • Is the location a maximum? B s B m 29

1. Identify Locations 2. Quantify Pulse Step 2: Quantifying Pulse • 3 Beats to quantify the pulse at each location • Function Beats B f • Variation of the function values B s B m B f 30

1. Identify Locations 2. Quantify Pulse Step 2: Quantifying Pulse Month of Year Day of Week Time of Day 31

1. Identify Locations 2. Quantify Pulse Step 2: Quantifying Pulse B 1 Signature B 2 B 3 Data B 4 Oblivious B 5 B 6 B 7 Rank B 8 B 9 32

1. Identify Locations 2. Quantify Pulse Step 2: Quantifying Pulse B 1 Signature B 2 B 3 Data B 4 Oblivious B 5 B 6 B 7 Compare B 8 B 9 33

Urban Pulse Interface 34

Use Case: Understanding Public Spaces Rockefeller Center Bryant Park Union Square • Typically classified together as being similar 35

Use Case: Understanding Public Spaces Rockefeller Center Bryant Park Union Square 36

NYC Taxi Data • Yellow cab trips • ~175 million trips / year • Spatial-Temporal • 2 spatial attributes • 2 temporal attributes • Other attributes • Fare, tip • Distance • Duration • … 37

Topology-Guided Vis isual Exploratory ry Analysis https://www.github.com/harishd10 https://github.com/ViDA-NYU Work done together with Alex Bock, Theodoros Damoulas, Nivan Ferreira, Juliana Freire, Bruno Gonçalves, Mondrian Hsieh, Marcos Lage, Fabio Miranda, Claudio Silva, Adam Summers, Luc Wilson, Kai Zhao 38