The document summarizes a study that clusters major topics on Twitter by space and time to understand popular topic patterns. It analyzed over 31,000 tweets from June to July 2016, clustering hashtags by time (grouping tweets into 56 3-hour timeframes per weekday) and geographic location (aggregating data within a 10x10 global grid system). Preliminary analysis found borough, career, travel/NYC, and #NYC as top clustered topics. Future work could use topic patterns to suggest advertising times/audiences or link traffic/incident updates to tweets to improve safety and reduce congestion.

1. SPATIO-­TEMPORAL SIMILARIITIES
BETWEEN MAJOR TOPICS ON TWITTER
Team9
MEMBERS: HENRY,FRANCIS, ISABELLA, XING, JIANGHAO, KAI
City Challenge Week CUSP
September 1, 2016

2. GOAL AND OBJECTIVES
Cluster twitter topics (#hashtag) by time and space
To understand the pattern of most popular topics

3. DATASET, TOOLS AND LIMITATION
Data size:
Ø Number of 31940 twitter data
Ø From June 16th, 2016 to July 10th, 2016
Ø Attributes: Timestamp, Tweets, (lat,lon)
Tools:
Ø Carto for visualization
Ø Scikit-­learn for clustering and PCA
Ø Pandas for data manipulation
Limitations:
Ø Natural Language Processing (sentiment / stemming… etc)
Ø Size of dataset
Ø Number of retweet
Ø Socio-­economic characteristic

4. number'of'
319431'tweets'
Time'series'Data'
#
Select'
Top'50'
hashtags
Spa=al'Data'
Time'series'Clustering'
Spa=al'Clustering
K"means(Clustering
Principle(Component
METHODOLOGY
Text:
Stemming, exclude punctuation
Time:
8 times 3hours grouping per weekday => 56 timeframes
Geographic:
Set up global grid system in 10 X 10 size and aggregate data within each grid

5. ANALYSIS
BOROUGH
CAREER
TRAVEL/NYC

6. #NYC
NEXT STEPS & EXAMPLE
l Use text and time pattern to suggest when/who to advertise
l Link traffic and incident updates to twitter to improve safety
and reduce congestion
#HiringDIABETES HEAT MAP

7. FUTURE WORK