sas business analytics forum 2011 kam

Copyright © 2011, SAS Institute Inc. All rights reserved.

A Journey through the Spatial Data Mining and Geographic Knowledge Discovery JungleDr. Kam Tin Seong PhD Associate Professor of Information Systems (Practice)School of Information SystemsSingapore Management UniversityE-mail: [email protected]

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Content

Motivations

Interactive exploratory analysis

Distribution analysis

Geographic data visualisation

Visualising and detecting spatio-temporal patterns

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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

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Taxi Travel Log Case Study

One day taxi travel log – 278676 trips

Number of variables: 53

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Initial Data exploration: Univariate

Overview of the data

Detect outliers

Missing data

Identify new variables

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Initial Data exploration: Bivariate

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Data Cleaning and Transformation

Data cleaning

Derive new variables: Time interval, travel time etc

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Geographic Data Visualisation

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Visualising and Detecting Spatio-temporal Patterns with Interactive Brushing

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Visualising and Detecting Spatio-temporal Patterns with Animated Map

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Visualising and Detecting Spatio-Temporal Patterns with Trellis Maps

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Visualising and Detecting Spatio-Temporal Point Patterns

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Visualising and Detecting Spatio-Temporal Point Patterns with Density Map


Q & A

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sas business analytics forum 2011 kam

Technology

initial data exploration

detecting spatio

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temporalpatterns

variables

visualising