Combining Design Patterns and Topic Modeling to Discover Regions That Support Particular Functionality

The problem of discovering regions that support particular functionalities in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design and discovering regions that conform to that knowledge; and bottom-up, us...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2019-09, Vol.8 (9), p.385
Main Authors: Papadakis, Emmanuel, Gao, Song, Baryannis, George
Format: Article
Language:English
Subjects:
Data
Data mining
Design
Dirichlet problem
functional region
Geographic information systems
Knowledge
Learning algorithms
Machine learning
Methods
Metropolitan areas
Modelling
Ontology
patterns
place
Regions
Search engines
Semantics
Shopping
Social networks
topic modeling
Urban areas
Urban environments
Urban planning
Volunteered Geographic Information (VGI)
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Summary:The problem of discovering regions that support particular functionalities in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design and discovering regions that conform to that knowledge; and bottom-up, using data to train machine learning models, which can discover similar regions. Both methodologies face limitations, with knowledge-based approaches being criticized for scalability and transferability issues and data-driven approaches for lacking interpretability and depending heavily on data quality. To mitigate these disadvantages, we propose a novel framework that fuses a knowledge-based approach using design patterns and a data-driven approach using latent Dirichlet allocation (LDA) topic modeling in three different ways: Functional regions discovered using either approach are evaluated against each other to identify cases of significant agreement or disagreement; knowledge from patterns is used to adjust topic probabilities in the learning model; and topic probabilities are used to adjust pattern-based results. The proposed methodologies are demonstrated through the use case of identifying shopping-related regions in the Los Angeles metropolitan area. Results show that the combination of pattern-based discovery and topic modeling extraction helps uncover discrepancies between the two approaches and smooth inaccuracies caused by the limitations of each approach.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi8090385