Geospatial Analytics with Big Data:

Transcription

1 Geospatial Analytics with Big Data: Five Steps for Creating Business Value Fern Halper TDWI Director of Research, Advanced October 28, 2015 David Stodder TDWI Director of Research,

2 Sponsor

3 Speakers Fern Halper Research Director, Advanced Analytics David Stodder Research Director, Business Intelligence Sunil Venkayala Sr. Product Technical Manager, HP 3

4 Agenda Geospatial s evolution toward big data Geospatial data s use for analytics Five tips for geospatial analytics and big data Concluding recommendations HP s View Q&A

5 Geospatial s Evolution Pre-computer era mapping of data Mapping clusters of cholera cases in London, 1854 Advances in data mapping techniques: color, shapes, contours, etc., to spot data relationships Geographical information systems (GIS) and applications Digitized mapping, computer graphics, and data visualization come together; layered views Spatio-temporal location, plus other variables that can be located 5

6 Now, This is Geospatial Analytics Source : 6

7 This is Geospatial,Too! Magic bands Semi-autonomous vehicles 7

8 Geospatial is Changing GIS/Geospatial Maps Remote sensing GPS Location aware Sensors Devices Etc. 8

9 Another Way to Look at it 9

10 Net Result: Big Data, Big Potential Discrete coverage of earth in inches; a digital inventory of the surface of the earth every structure, vehicle, road, tree, rock (DigitalGlobe). Using data to tell everything you know about the inventory and place Customer geo analytics for personalization; integrating spatial and customer data views Real time incident location tracking to know where you need to pay attention 10

11 Use for Analytics Descriptive Visualization Data discovery Advanced Predictive Streaming 11

12 Example 1: Risk Identification Mapping is used to identify areas at risk flooding, storm surge, etc. Geolocation data can also be married with other data to predict risk of high payout Studying scenarios Predictive modeling 12

13 Example 2: Crime Prediction Police use layered maps with variables like incident type, date, time, location, pts of interest to understand crime Viewing crime locations, not just data tables Mapping calls for service, suspicious activity Data with known outcomes can be used to predict certain kinds of crime 13

14 Example 3: Target Marketing Geospatial data can be combined with demographic data to target promotions for stores In store beacons can be used to target promotions to customers while in stores 14

15 Case Example: Planet Fitness Finds Retail Sweet Spot Using data with maps to choose right locations and geoanalyze customer patterns Avoiding siloed views: All of this comes together to paint you a picture of a story that is in fact already there in the data. Matt Felton, Pres. of Datastory Consulting Bringing multiple variables to bear on finding the location sweet spot and evaluate risk of cannibalization Geospatial analysis of the potential for Planet Fitness club membership cannibalization among facilities in the same region. 15

16 Example 4: Asset Maintenance State and local governments can manage, visualize, analyze, and communicate information about assets more easily Understand vulnerable sites in case of storm E.g. network outages, work crew dispatch Enhance with preventive maintenance 16

17 Geospatial Data and Analytics Gaining Traction Now and Planned Planned (Source: Emerging Technologies and Platforms TDWI BPR, 2015) 17

18 Five Tips for Big Geospatial Analytics Think outside of the box Deal with volume and data management Enrich your data Consider bigger analytics Security and governance matters 18

19 Think Outside the Box Consider new use cases Make sure the project maps to a business problem 19

20 Volume and Data Management Volume is a number 1 issue for geospatial data Diverse data sources, most of which are big Avoiding disparate silos; integrated views are critical. Solving metadata chaos Performance matters Parallel, distributed Faster access: Columnar 20

21 Enriching Data for Business Objectives Enriching data beyond raw levels: refining, improving quality, cleansing Marrying geospatial with other data types Metadata integration, virtualization, master data management Consider the whole analysis: who needs the data and why 21

22 Big Data Analytics Consider newer kinds of analysis Classification models Stream analytics Social media/text analytics 22

23 Security and Governance Security Protect sensitive data, including about customers, patients, etc. Uncontrolled access to geospatial data could violate regulations, including for privacy Governance Establish data stewardship that includes business and IT Governance can help guide and fund - projects 23

24 Wrap-up Geospatial data and analysis is changing and growing more data more sources bigger analytics Companies are gaining value with geospatial big data However, there is a need to think it through 24

25 Poll Question Are you using geospatial data today? Yes we are now and it is big geospatial data Yes we are now No, but we are planning to use it in the next 6 months No, and we have no plans to do so 25

26 SEIZE THE DATA HP Vertica Geospatial Analytics at Big Data Scale

27 HP Vertica - Columnar Database Built for Speed What 1000% means: Use to take Now takes 1 hour 3.6 Seconds 8 hours (overnight) Under 30 seconds "When we did the first queries, they were done so fast, we thought they were broken. - Michael Relich, Guess?

28 HP Vertica Place 2 8 In-database spatial data types Geometry Geography Ingest and store spatial objects in industry standard formats Enrich location analytics Combine with business data Discover new insights by combining spatial metrics with other data using SQL More than 50 spatial functions OGC standard functions Easy migration to big data spatial analytics using well known standards Find actionable insights Machine learning Sentiment analytics Gain competitive edge by combining location analytics with sentiment and predictions Fast and scalable spatial joins Analyze billions of geolocations Perform large scale spatial joins using parallelized geospatial indexing Visual analytics JDBC/ODBC Most geospatial visual tools and BI platforms support JDBC/ODBC

29 HP Vertica Spatial Functions OGC-standard Functions: ST_* Measurement ST_Area ST_Distance ST_Length Predicate ST_Intersects ST_IsValid ST_Equals Operator ST_Buffer ST_Boundary ST_Envelope Input/Output ST_GeomFromText ST_GeographyFromText Custom Functions (improved performance): STV_* Intersection STV_Create_Index STV_Intersect Loader STV_ShpCreateTable STV_ShpSource STV_ShpParser Other STV_PolygonPoint STV_NN

30 SECONDS Parallel Geospatial Indexes 80 GeoSpatial join using STV_Intersect with polygons I million 2 million 4 millon 8 million 16 million 32 million NUMBER OF POINTS 7.1SP2 Intersect (sec)

31 Retail Banking Use Case Optimize Branch and ATM location

32 Retail Banking 1. Which branches are over and under served?

33 Retail Banking 2. What s the customer reach of a branch?

34 Retail Banking 3. What if I close under serving branch?

35 Retail Banking 4. Where do I have new branch opportunities?

36 Advanced analytics behind the graphs Customer demographic distribution Age Income Education Traffic patterns On the way to work/home Shopping centers Commercial and office locations Customer service usage patterns Competitive branch statistics Predictive models Sentiment Analysis

37 Summary HP Vertica Place - Include geospatial data in your analysis - Spatial data types to store points/lines/polygons, plus (OGCstandard/custom) functions - Available as separate package Online documentation Download the package from my.vertica.com my.vertica.com > Download > Vertica Analytics Extensions GitHub

38 Thank you!

39 QUESTIONS? 39

40 Contact Information If you have further questions or comments: Fern Halper TDWI Research Director for Advanced Analytics David Stodder TDWI Research Director for BI Sunil Venkayala Sr. Technical Product Manager, HP Vertica 40