Combining Design Patterns and Topic Modeling to Discover Regions That Support Particular Functionality
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Department of Geoinformatics—Z_GIS, University of Salzburg, Schillerstr. 30, 5020 Salzburg, Austria
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Department of Geography, University of Wisconsin, Madison, WI 53706, USA
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Department of Computer Science, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 385; https://doi.org/10.3390/ijgi8090385
Received: 20 July 2019
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Revised: 26 August 2019
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Accepted: 30 August 2019
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Published: 3 September 2019
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.
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Keywords:
functional region; place; patterns; topic modeling; urban planning; Volunteered Geographic Information (VGI)
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MDPI and ACS Style
Papadakis, E.; Gao, S.; Baryannis, G. Combining Design Patterns and Topic Modeling to Discover Regions That Support Particular Functionality. ISPRS Int. J. Geo-Inf. 2019, 8, 385. https://doi.org/10.3390/ijgi8090385
AMA Style
Papadakis E, Gao S, Baryannis G. Combining Design Patterns and Topic Modeling to Discover Regions That Support Particular Functionality. ISPRS International Journal of Geo-Information. 2019; 8(9):385. https://doi.org/10.3390/ijgi8090385
Chicago/Turabian StylePapadakis, Emmanuel, Song Gao, and George Baryannis. 2019. "Combining Design Patterns and Topic Modeling to Discover Regions That Support Particular Functionality" ISPRS International Journal of Geo-Information 8, no. 9: 385. https://doi.org/10.3390/ijgi8090385
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