1. 1 GIScience and the Big Data Age Yihong Yuan Department of Geography Texas State University

2. 2 About me Yihong Yuan Assistant Professor yuan@txstate.eduyuan@txstate.edu. ELA 366, 512-245-3208 Research Interests –Spatio-temporal data mining –Human mobility and activity patterns –Big data analytics

3. 3 Geography and Big Data GIS –Not only about mapping functions Big Geo-data –Information and communication technologies (ICTs) Greater mobility flexibility A wide range of spatio-temporal data sources Align marketing campaigns to spatial patterns.

4. 4 “Geography is one of the most natural, logical and intuitive ways to discover, visualize, overlay, compare, slice, sort and apply big data to a problem” “GIS used to be about the analysis of relatively static institutional data, but new data streams mean that today’s GIS problems look very much the same as today’s big data problems: extract meaningful information from a fire hose of inputs”

5. 5 Traditional geographic knowledge discovery –e.g., high resolution trajectories Incomplete Spatio-temporal datasets –Low resolution –Few individual attributes –Uncertainty?

6. 6 Past Research Georeferenced mobile phone data analytics –Individual-oriented research –Activity space »Measurements: Radius, Eccentricity, entropy »Correlation between phone usage and activity space –Trajectory and sequence patterns »Time series analysis –Urban-oriented studies Spatial clusters Spatial rhythms Dynamic clustering Functional time points

7. 7 UML Model about Geo-referenced mobile phone data

8. 8 Mobile Phone Connections in 10 cities in northeast China –Time, Duration, and Locations of Mobile Phone Connections in 9 days –Age and Gender Attributes of the Users –Possibility of simulated data Example Mobile Phone Dataset

9. 9 Analysis of Activity space Three measurements –Radius -> Scale eigenvectors of trajectories –Eccentricity -> Shape Range [0,1] Closer to a straight line or a circle –Entropy->Regularity How random the visiting patterns are

10. 10 Correlation between individual activity space and phone usage

11. 11 Results For People with Higher Mobile Phone Usage: –Larger Activity Space –Trajectories are Closer to a Circle –Movement is More Random, Less Predictable

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13. 13 Analysis of trajectory patterns Compare trajectories from phone records –Sequences of cell IDs Edit distance Method –String matching and auto-correction

14. 14 Analysis of trajectory patterns (Cont.) Applications –Identify similar users Clustering analysis –Identify outlier users

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16. 16 The changing clustering of urban area Urban hotspots and clusters WeekdaysWeekends T 2 : 2pm-3pm T 1 :8am-9am T 3: 7pm-8pm

17. 17 Mobility patterns of different population groups –Weekday 2pm-3pm Urban clusters (Cont.) Age: 12-17Age: > 60

18. 18 Provide input for urban infrastructure planning –Are public facilities where people are?? Urban clusters (Cont.) Age: > 60 A park

19. 19 Dynamic Clustering Focus on “rhythms” instead of just “clusters” Various mobility patterns in urban area –How to explore? – time series analysis

20. 20 Dynamic Clustering (Cont.) Methods –Divide study area Voronoi polygon (based on towers) What to compare: 24-hour series for each polygon based on mobility count Outlier detection e.g., traffic congestion

21. 21 Outlier polygons 15 outliers for weekdays and 18 for weekends Weekday Weekends

22. 22 Mobility patterns in outlier areas Outlier Polygon 238 –Night clubs and other leisure facilities –International trading center Outlier Polygon 125 –Several community colleges –Not many night clubs, bars, etc. Polygon 238Polygon 125

23. 23 Current and future research

24. 24 Setting up functional time in cities Standardization of time –Determination of the beginning/end of a day The development of ICT –Real-time activity patterns –More flexibility in time management and activity scheduling i.e., fixed parking hour policy may not be applicable in Central business districts

25. 25 Setting up functional time in cities

26. 26 Cross-country comparison for Social Media websites Flickr data, 100 million records and geo- tagged photos Similarity and dissimilarity of human mobility in various cities –“A tale of many cities”

27. 27 Current and future research Mobility patterns in developing and developed countries –China as a focus Weibo and Twitter check-in data –Comparison study for special time period –Holiday patterns

28. 28 Current and future research Mass media and Social Media –GDELT dataset Geo-tagged news Events from 1970s –Public relations and interaction between countries

29. 29 (a) (b)

30. 30 Big data and GIS jobs… Traditional GIS jobs: GIS Technician/Analyst/consultant GIS manager/researcher …… Where are the positions? Public sector… NGA, USGS, State and local Gov, DOT, planning dept. Private company…Oil&Gas, Mapping companies, Land management, Utility… Non-profit agency… Nature Conservancy, International Crane Foundation Consulting firms…Surveying, Remote Sensing…

31. 31 Example: Private Sector Jobs Mapping Companies Software Developers Utilities Land Development Non-Profits Others

32. 32 Job Skills Project Management Technical Support Report Writing Public Speaking Research/Literature review Programming

33. 33 Software Skills (cont.) GIS software packages ArcGIS, ENVI, GDAL Mobile & Web Technology –Silverlight / Flex /HTML / ASP –Android Dev Python / C#... Database: Access, SQL Server, PostgresSQL

34. 34 Job Postings Company Website –ESRI summer internship program Relevant Employment Websites –General sites: Monster.com / Indeed.com –Linkedin.com –Glassdoor.com –GIS Jobs Clearinghouse (gjc.org) –GISjobs.com & Geojobs.org –GeoCommunity –GIS Café –WI State Cartographers Office http://www.sco.wisc.edu/jobs/jobs.php

35. 35 Job Postings Internal Company Postings Company Website Relevant Employment Websites –GIS Jobs Clearinghouse (gjc.org) –GISjobs.com & Geojobs.org –GeoCommunity –GIS Café –Monster.com

36. 36 Job Postings Internal Company Postings Company Website Relevant Employment Websites –GIS Jobs Clearinghouse (gjc.org) –GISjobs.com & Geojobs.org –GeoCommunity –GIS Café –Monster.com

37. 37 Big data jobs… Spatial data are inherently big data… For GIS major… –Data Scientist This is a more “General” term Focus on big (geo)data analytics Highly competitive salary Graduate degree (MA possible, PhD preferred) Many opportunities… Skill set: Strong statistical background Strong and programming: Python, R, etc,

38. 38 Example positions Data Scientist @ ESRI –http://www.simplyhired.com/job/data-scientist-agriculture- job/esri/5jjxyxjt4b?cid=ntvzgigizsvnqhofbuscopqozjkxqugdhttp://www.simplyhired.com/job/data-scientist-agriculture- job/esri/5jjxyxjt4b?cid=ntvzgigizsvnqhofbuscopqozjkxqugd Research Data Scientist –http://www.americasjobexchange.com/job-detail/job-opening-AJE- 569661132?source=indeed&utm_source=Indeed&utm_medium=cpc&ut m_campaign=Indeedhttp://www.americasjobexchange.com/job-detail/job-opening-AJE- 569661132?source=indeed&utm_source=Indeed&utm_medium=cpc&ut m_campaign=Indeed Other potential groups: Apple geo-group, Twitter geo-group, Facebook data science group

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