A Journey through theSpatial Data Mining andGeographic KnowledgeDiscovery JungleDr. Kam Tin Seong PhDAssociate Professor o...
Content Motivations Interactive exploratory analysis Distribution analysis Geographic data visualisation Visualising ...
Motivations Availability of massive, high dimensional, and complex  geospatially-referenced data General lack of spatial...
Taxi Travel Log Case Study One day taxi travel log – 278676 trips Number of variables: 53                               ...
Initial Data exploration: Univariate Overview of the data Detect outliers Missing data Identify new variables         ...
Initial Data exploration: Bivariate                                                                             6         ...
Data Cleaning and Transformation Data cleaning Derive new variables: Time interval, travel time etc                     ...
Geographic Data Visualisation                                                                           8               Co...
Visualising and Detecting Spatio-temporalPatterns with Interactive Brushing                                               ...
Visualising and Detecting Spatio-temporalPatterns with Animated Map                                                       ...
Visualising and Detecting Spatio-TemporalPatterns with Trellis Maps                                                       ...
Visualising and Detecting Spatio-TemporalPoint Patterns                                                                   ...
Visualising and Detecting Spatio-TemporalPoint Patterns with Density Map                                                  ...
Q&ACopyright © 2011, SAS Institute Inc. All rights reserved.
Upcoming SlideShare
Loading in...5
×

A journey through the spatial data mining and geographic knowledge discovery jungle

829

Published on

Presented at SAS Business Analytics 2011 event in Singapore.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
829
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

A journey through the spatial data mining and geographic knowledge discovery jungle

  1. 1. A Journey through theSpatial Data Mining andGeographic KnowledgeDiscovery JungleDr. Kam Tin Seong PhDAssociate Professor of Information Systems (Practice)School of Information SystemsSingapore Management UniversityE-mail: tskam@smu.edu.sg Copyright © 2011, SAS Institute Inc. All rights reserved.
  2. 2. Content Motivations Interactive exploratory analysis Distribution analysis Geographic data visualisation Visualising and detecting spatio-temporal patterns 2 Copyright © 2011, SAS Institute Inc. All rights reserved.
  3. 3. Motivations Availability of massive, high dimensional, and complex geospatially-referenced data General lack of spatial data visualisation and analysis functions in data analysis software General lack of data analytics techniques in conventional GIS There is an urgent need for effective and efficient methods to visualise and detect unknown and unexpected information from these massive datasets 3 Copyright © 2011, SAS Institute Inc. All rights reserved.
  4. 4. Taxi Travel Log Case Study One day taxi travel log – 278676 trips Number of variables: 53 4 Copyright © 2011, SAS Institute Inc. All rights reserved.
  5. 5. Initial Data exploration: Univariate Overview of the data Detect outliers Missing data Identify new variables 5 Copyright © 2011, SAS Institute Inc. All rights reserved.
  6. 6. Initial Data exploration: Bivariate 6 Copyright © 2011, SAS Institute Inc. All rights reserved.
  7. 7. Data Cleaning and Transformation Data cleaning Derive new variables: Time interval, travel time etc 7 Copyright © 2011, SAS Institute Inc. All rights reserved.
  8. 8. Geographic Data Visualisation 8 Copyright © 2011, SAS Institute Inc. All rights reserved.
  9. 9. Visualising and Detecting Spatio-temporalPatterns with Interactive Brushing 9 Copyright © 2011, SAS Institute Inc. All rights reserved.
  10. 10. Visualising and Detecting Spatio-temporalPatterns with Animated Map 10 Copyright © 2011, SAS Institute Inc. All rights reserved.
  11. 11. Visualising and Detecting Spatio-TemporalPatterns with Trellis Maps 11 Copyright © 2011, SAS Institute Inc. All rights reserved.
  12. 12. Visualising and Detecting Spatio-TemporalPoint Patterns 12 Copyright © 2011, SAS Institute Inc. All rights reserved.
  13. 13. Visualising and Detecting Spatio-TemporalPoint Patterns with Density Map 13 Copyright © 2011, SAS Institute Inc. All rights reserved.
  14. 14. Q&ACopyright © 2011, SAS Institute Inc. All rights reserved.

×