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1 Using GIS to Understand Behavior Patterns of Twitter Users Yue Li M.S. Civil/Geomatics Engineering Purdue University Committee: Dr.Jie Shan (Chair), Dr.Nicole Kong, Dr.James Bethel
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2 Introduction Volunteered Geographic Information (VGI) 1 −Emergency management, event detection, tourist behavior, knowledge discovery… Twitter −The most popular micro-blogging site −Tweets with longitude and latitude −A gold mine for scholars in geography, linguistics, sociology, economics, health, and psychology 2 −Marketing, advertising, regulation,…
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3 Research Goal To discover the spatio-temporal pattern of tweets To infer the human mobility patterns behind the tweets To understand the lifestyle of college students
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4 Study Area College town/city, Big Ten Universities West Lafayette, IN −Most densely populated city in IN −Home of Purdue University Ann Arbor, MI −University of Michigan Bloomington, IN −Indiana University, Bloomington Columbus, OH −Ohio State University
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5 Data Geo-tagged tweets downloaded with Twitter Streaming API With longitude and latitude at time of posting Nov 18, 2013 to April 2, 2014 −West Lafayette : 59,238 −Ann Arbor: 220,117 −Bloomington :247,202 −Columbus: 1,936,238
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6 Methods Pure Spatial −Point density analysis Pure Temporal Spatio-Temporal −Tweets in Land Use −Event/Anomaly detection −Individual twitter user patterns
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7 Tweets in West Lafayette
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8 Tweets in Ann Arbor
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9 Tweets in Bloomington
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10 Tweets in Columbus
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11 Tweets by Hour
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13 Tweets and Land Use Land use in Ann Arbor, MI −Industrial −Mixed Use −Office −Public/Education −Recreation −Residential −Transportation −Vacant Spatially join the tweets with land use
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14 Tweets and Land Use
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15 1 - Commercial; 2- Industrial; 3- Mixed Use; 4- Office; 5- Public/Education; 6 – Recreation; 7- Residence; 8- Transportation; 9- Vacant/River Tweets and Land Use
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16 Event Detection Spatially and temporally aggregated −Football game, concert, festival,… Use Purdue shooting on Jan 21, 2014 as an example −Lockdown from around 12-14pm Temporally −710 tweets in 12-14pm Jan 21, 231 unique users −7443 tweets in 12-14pm in the whole datasets, 1080 unique users Spatially −How to measure spatial anomaly?
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17 Hypotheses Challenge: Inhomogeneous/clustered process even outside lockdown period −Were tweets more significantly clustered during lockdown than average? Intensity of tweets is correlated with distance to campus buildings Extent of clustering is positively correlated with chi- sqare value
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18 Covariate: Purdue Buildings Purdue Building Shapefile converted to tesselation R libraries: maptools, sp, spatstat Functions: as.mask → im → tess
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19 Randomization Test Algorithm (by Ken Kellner): 1. Select 710 random tweets from dates 1/16/14 - 1/26/14 and hours 12am - 14pm without replacement 2. Call quadratcount() and quadrat.test() on new random dataset 3. Save chi-square value 4. Repeat 1000 times to obtain distribution of chi-square values 5. Compare actual chi-square value obtained on 1/21/14 with distribution 6. Quasi-p value: proportion of values more extreme than obtained value Assumption: greater chi-square value = more inhomogenous/clustered Tested with simulation
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20 Randomization Test Result Chi-square: 85162.85 Quasi-p value: 0.038 We were able to detect a change in the pattern of tweets during the lockdown, when presumably more people were stuck in Purdue buildings than average.
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21 Event Detection We can see anomaly from Twitter data both temporally and spatially However, we are still looking for a complete and integrated algorithm, and apply it to other events To be cont’d
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22 Frequent Twitter Users Top 10 Twitter users with the most tweets in Ann Arbor Plot the tweets of individual Twitter user Four typical patterns −Work-Home −Work-Road-Home −Work-Home-Short Visit −Multiple Clusters
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23 Frequent Twitter Users
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25 Future Work On-going research Complete analysis in all 4 study areas, and compare the patterns Develop/Find an algorithm for event detection … Any suggestions are welcomed!
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26 References 1. Goodchild, M. F., 2007. Citizens as sensors: The world of volunteered geography, GeoJournal, 69, 211- 221. 2. Ghosh, D., and R. Guha, 2013. What are we ‘tweeting’ about obesity? Mapping tweets with topic modeling and Geographic Information System, Cartography and Geographic Information Science, 40(2), 90-102.
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27 QUESTIONS?
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