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Urban Modeling Using Big Data

Vahid Moosavi

Researcher at Future Cities Laboratory

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

26 August 2013

Outline

• Our understanding of Big Data and its applications

• My research interests related to Big Data

• Possible Applications

What Can We Do With Big Data? Comparing Two Modeling Extremes

Issues Classic Modeling paradigm (Theory (model) driven)

Emerging Modeling Paradigm (Data Driven)

Scope of applications (complexity as a function of number of aspects and the relations between elements of the system. (e.g. a wooden

chair vs. a city)

Simple systems Complex Systems Simple systems Complex Systems

Fully Applied Limited

Application Fully Applied Can be Applied

Primary element of modeling process

Theory (Model) Data

Observing, sensing and data gathering

Expensive (Low Volume, Velocity and Variety)

Cheap, Pervasive and Ubiquitous (High Volume, Velocity and Variety)

Form of Data Structured Unstructured and Structured

Data management Designed DBs

(e.g. RDBMS, SQL) Complex Event Processing,

Cloud Computing

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

Some of the Possible Applications

1- Modeling Urban Traffic Dynamics Using Urban Data Streams

Vahid Moosavi, Ludger Hovestadt, Urban Computing, 2013

GPS Trajectory of Taxicabs, Beijing

Road communities based on movement of the cars not just the physical road network

highly critical areas for the whole traffic flow

Areas with high potential of traffic jam

The conceptual set up

2- 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

Conclusion

• Big Data can be considered as a new capability to revolutionize the concept of modeling and decision making in many fields. But for that a new data literacy is required.

• My main research interest is to learn and develop generic modeling technologies, using a specific category of mathematical and computational modeling techniques which are only feasible in coexistence with Big Data.

• And finally to apply this generic set up into different practical applications

Thanks!