Journal ArticleDOI
Subjective or objective measures of street environment, which are more effective in explaining housing prices?
TLDR
Zhang et al. as mentioned in this paper found that subjective measures using visual surveys could capture more subtle human perceptions, thus providing stronger predictive power to housing prices, while the objective view indexes collectively explained more price variances, the five perceptions individually exhibited stronger strength.About:
This article is published in Landscape and Urban Planning.The article was published on 2022-05-01. It has received 36 citations till now. The article focuses on the topics: Predictive power & Ordinary least squares.read more
Citations
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Journal ArticleDOI
Associations between Street-View Perceptions and Housing Prices: Subjective vs. Objective Measures Using Computer Vision and Machine Learning Techniques
TL;DR: In this paper , the authors investigated the effect of subjective and objective street-level perceptions on house price variation in Shanghai using the hedonic price model (HPM) and found that subjective measures could be more effective to describe human perceptions, thus might explain more housing price variations.
Posted ContentDOI
Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai
TL;DR: Wang et al. as mentioned in this paper proposed a new approach for the urbanscale application to quantify both subjectively and objectively measured streetscape scores for six important perception qualities, namely Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity.
Journal ArticleDOI
A comprehensive framework for evaluating the quality of street view imagery
Yujun Hou,Filip Biljecki +1 more
TL;DR: In this article , a framework for describing and assessing the quality of street view imagery (SVI) data is proposed, which is applicable to any source of SVI, including both commercial and crowdsourcing services.
Journal ArticleDOI
Measuring the associations between eye-level urban design quality and on-street crime density around New York subway entrances
Nanxi Su,Wenjing Li,Waishan Qiu +2 more
TL;DR: In this article , the urban design quality of the ground-level environments surrounding subway stations, both how they express (objectively) and how they are perceived (subjectively), is associated with the reported crime density on streets.
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How Are Macro-Scale and Micro-Scale Built Environments Associated with Running Activity? The Application of Strava Data and Deep Learning in Inner London
Hongchao Jiang,Lin Dong,Bing Qiu +2 more
TL;DR: The results revealed the linkages between built environments (on the macro- and micro-scale) and running in Inner London, which can provide practical suggestions for creating running-friendly cities.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition
TL;DR: In this article, a theory of hedonic prices is formulated as a problem in the economics of spatial equilibrium in which the entire set of implicit prices guides both consumer and producer locational decisions in characteristics space.
Journal ArticleDOI
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
TL;DR: Locally weighted regression as discussed by the authors is a way of estimating a regression surface through a multivariate smoothing procedure, fitting a function of the independent variables locally and in a moving fashion analogous to how a moving average is computed for a time series.
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Identification of Endogenous Social Effects: The Reflection Problem
TL;DR: The authors examined the reflection problem that arises when a researcher observing the distribution of behaviour in a population tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group.