svm@arch.ethz.ch SEC

http://www.ad-exchange.fr/

Urban Modeling Using Big Data

Vahid Moosavi

Researcher at Future Cities Laboratory

PhD Student at Chair for Computer Aided Architectural Design (CAAD), ETH Zurich, Ludger Hovestadt

23 September 2013

Outline

• Our understanding of Big Data and its applications

• My research interests related to data driven modeling

• Possible Collaborations

Big Data Landscape and My Research Interests (How to go beyond simple data analytics toward a new data literacy)

Data-Infrastructures

Data Management and Data Processing

Complex Event Processing

Data-Driven Modeling Technologies

Clustering and Grouping

Signal Processing /Time Series Forecasting Prediction and Classification

Information Visualization

Data Management

• My research focus areas with red color

Practical Application Domains

Self Organizing Map (SOM) : A generic setup, playing with symbolic indexes

• SOM acts as a multidimensional sorting system

Informational Space

Physical Space (Imaginary)

Teuvo Kohonen

Somehow similar to the idea of Cartesian Dualism

SOM as a social Machine

5

Specific Modeling with a specific curve Pre-specific modeling with any potential curve

SOM: Formal products

Then, we can do any kind of prediction/classification, pattern recognition, time series analysis

6

Some projects…

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Case 1: Pre-Specific City Modeling

Footprint of buildings in Orchard area, Singapore

Similar buildings are in the same area of SOM

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Matthias Standfest, Vahid Moosavi , Design Modelling Symposium Berlin 2013

Case 2: Data Driven Modeling of a Manufacturing Process

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Designed Experiments

(Data Farming)

A practical look up table for the operator

How to optimize the process variables for different product specs?

Saraee, Mohamad, Vahid Moosavi, and Shabnam Rezapour. "Application of Self Organizing Map (SOM) to model a machining process."Journal of Manufacturing Technology Management 22.6 (2011): 818-830.

Case 3: Data Driven Modeling of Urban Air Quality (Rochor Area, Singapore)

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Building Structural Features

Land Use Features

Urban Network Features

Different Pollutants

241 measurement points in the city More than 3000 points without measurements

Case 3: Data Driven Modeling of Urban Air Quality (Rochor Area, Singapore)

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Finding the main Clusters of similar points

Center of the clusters as the potential measurement stations

Predicting the pollution level without any direct measurement

241 measurement points More than 3000 points without measurements

Some of the Potential Applications for collaboration

1- A Generic Setup for Text Modeling in Association With Other Data Streams (How to gain insight from lots of low-economic value data?)

Search APIs

Text Streams

Text Modeling

Quantitative Data

movement traces Stock Market

Smart Grid

Real estate

SOM

Text Processing

Markov Chain

Sentiment Analysis

Pattern recognition

Prediction and classification

Decision Making

Enterprise Knowledge Modeling Using Text Archives (As a potential software solution)

1. Using Historical reports through digital archives

2. Finding the main (similar) knowledge pools in the organizations

3. Modeling the strategic trends and strategic focus of the company

4. Finding the informal (hidden) networks of potential collaborations between human resources

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Urban Energy Modeling (Singapore Case)

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Energy Consumptions

Building Information

Design

Land Use

History

Smart Meter records

Gas and Electricity

Urban Information

Land use

Weather

Demand Forecasting

Customer Segmentations

Analytics

Just as a sample

Future Steps (Smart Grid Applications)

• Load Forecasting • Theft Detection • Dynamic Pricing • Distribution

Optimization • And Capacity Planning

Household Demographics

Conclusion

• Some applications of data driven modeling

• Potential collaborations

– A generic text modeling framework : Enterprise Knowledge Modeling

– Urban Energy Modeling

Thanks!