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      Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning

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      PLoS ONE
      Public Library of Science

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          Abstract

          This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R 2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.

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          Spatial Econometrics: Methods and Models

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            Review on urban vegetation and particle air pollution – Deposition and dispersion

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              Bagging predictors.

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2017
                2 May 2017
                : 12
                : 5
                : e0176684
                Affiliations
                [1 ]Department of Geography & Planning, University of Liverpool, Liverpool, United Kingdom
                [2 ]Research in Spatial Economics (RiSE-group), Department of Economics, Universidad EAFIT, Medellín, Colombia
                University of Vermont, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: DAB JP JD.

                • Data curation: DAB JP JD.

                • Formal analysis: DAB JP JD.

                • Investigation: DAB JP JD.

                • Methodology: DAB JP JD.

                • Project administration: DAB JP JD.

                • Resources: DAB JP JD.

                • Software: DAB JP JD.

                • Supervision: DAB JP JD.

                • Validation: DAB JP JD.

                • Visualization: DAB JP JD.

                • Writing – original draft: DAB JP JD.

                • Writing – review & editing: DAB JP JD.

                Author information
                http://orcid.org/0000-0002-6274-1619
                Article
                PONE-D-16-37984
                10.1371/journal.pone.0176684
                5413026
                28464010
                2358a6f0-43e6-4d19-a2a1-aefb66ec2922
                © 2017 Arribas-Bel et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 September 2016
                : 16 April 2017
                Page count
                Figures: 4, Tables: 4, Pages: 25
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Ecology and Environmental Sciences
                Terrestrial Environments
                Urban Environments
                Computer and Information Sciences
                Geoinformatics
                Remote Sensing Imagery
                Earth Sciences
                Geography
                Geoinformatics
                Remote Sensing Imagery
                Research and Analysis Methods
                Imaging Techniques
                Remote Sensing Imagery
                Engineering and Technology
                Remote Sensing
                Remote Sensing Imagery
                Engineering and Technology
                Management Engineering
                Decision Analysis
                Decision Trees
                Research and Analysis Methods
                Decision Analysis
                Decision Trees
                Engineering and Technology
                Remote Sensing
                Medicine and Health Sciences
                Health Care
                Quality of Life
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Machine Learning Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Machine Learning Algorithms
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Machine Learning Algorithms
                Social Sciences
                Economics
                Economic Analysis
                Econometrics
                Social Sciences
                Economics
                Mathematical Economics
                Econometrics
                Physical Sciences
                Mathematics
                Mathematical Economics
                Econometrics
                Custom metadata
                All data and code files required to reproduce the analysis in the paper are hosted at the Github repository “satellite_led_liverpool”, available at the url: https://github.com/darribas/satellite_led_liverpool.

                Uncategorized
                Uncategorized

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