Inside 50,000 living rooms: an assessment of global residential ornamentation using transfer learning View Full Text


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Article Info

DATE

2019-02-08

AUTHORS

Xi Liu, Clio Andris, Zixuan Huang, Sohrab Rahimi

ABSTRACT

The global community decorates their homes based on personal decisions and contextual influences of their larger cultural and economic surroundings. The extent to which spatial patterns emerge in residential decoration practices has been traditionally difficult to ascertain due to the private nature of interior home spaces. Yet, measuring these patterns can reveal the presence of geographic culture hearths and/or globalization trends.In this work, we collected over one million geolocated images of interior living spaces from a popular home rental website, Airbnb (http://airbnb.com), and used transfer learning techniques to automatically detect the presence of key stylistic objects: plants, books, decor, wall art and predominance of vibrant colors. We investigated patterns of home decor practices for 107 cities on six continents, and performed a deep dive into six major U.S. cities.We found that world regions show statistically significant variation in decorative element prevalence, indicating differences in geographic cultural trends. At the U.S. neighborhood level, elements were only weakly spatially clustered and found to not correlate with socio-economic neighborhood variables such as income, unemployment rates, education attainment, residential property value, and racial diversity. These results may suggest that American residents in different socio-economic environments put similar effort into personalizing and caring for their homes. More broadly, our results represent a new view of worldwide human behavior and a new application of machine learning techniques to the exploration of cultural phenomena. More... »

PAGES

4

References to SciGraph publications

  • 2018-09-19. Analyzing and predicting the spatial penetration of Airbnb in U.S. cities in EPJ DATA SCIENCE
  • 2018-04-03. Vision-based real estate price estimation in MACHINE VISION AND APPLICATIONS
  • 2014. Microsoft COCO: Common Objects in Context in COMPUTER VISION – ECCV 2014
  • Identifiers

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    http://scigraph.springernature.com/pub.10.1140/epjds/s13688-019-0182-z

    DOI

    http://dx.doi.org/10.1140/epjds/s13688-019-0182-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112019533


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