Deep mapping gentrification in a large Canadian city using deep learning and Google Street View

Gentrification is multidimensional and complex, but there is general agreement that visible changes to neighbourhoods are a clear manifestation of the process. Recent advances in computer vision and deep learning provide a unique opportunity to support automated mapping or 'deep mapping' o...

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Bibliographic Details
Published in:PloS one Vol. 14; no. 3; p. e0212814
Main Authors: Ilic, Lazar, Sawada, M., Zarzelli, Amaury
Format: Journal Article
Language:English
Published: United States Public Library of Science 13.03.2019
Public Library of Science (PLoS)
Subjects:
Analysis
Artificial neural networks
Biology and Life Sciences
Building permits
Canada
Cities - statistics & numerical data
Computer and Information Sciences
Computer vision
Deep Learning
Digital mapping
Earth Sciences
Ecology and Environmental Sciences
Engineering and Technology
Geographic Mapping
Geospatial data
Humans
Metropolitan areas
Research and Analysis Methods
Residence Characteristics
Social Change
Social Sciences
Spatial Analysis
Canada
Cities
United Kingdom
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Gentrification is multidimensional and complex, but there is general agreement that visible changes to neighbourhoods are a clear manifestation of the process. Recent advances in computer vision and deep learning provide a unique opportunity to support automated mapping or 'deep mapping' of perceptual environmental attributes. We present a Siamese convolutional neural network (SCNN) that automatically detects gentrification-like visual changes in temporal sequences of Google Street View (GSV) images. Our SCNN achieves 95.6% test accuracy and is subsequently applied to GSV sequences at 86110 individual properties over a 9-year period in Ottawa, Canada. We use Kernel Density Estimation (KDE) to produce maps that illustrate where the spatial concentration of visual property improvements was highest within the study area at different times from 2007-2016. We find strong concordance between the mapped SCNN results and the spatial distribution of building permits in the City of Ottawa from 2011 to 2016. Our mapped results confirm those urban areas that are known to be undergoing gentrification as well as revealing areas undergoing gentrification that were previously unknown. Our approach differs from previous works because we examine the atomic unit of gentrification, namely, the individual property, for visual property improvements over time and we rely on KDE to describe regions of high spatial intensity that are indicative of gentrification processes.
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Current address: Institut National de l’Information Géographique et Forestière (IGN), Saint-Mandé, France
Competing Interests: The authors have declared that no competing interests exist.
LI and MS are joint senior authors on this work.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0212814