2,038 research outputs found
Computer vision uncovers predictors of physical urban change
Which neighborhoods experience physical improvements? In this paper, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements—an observation that is compatible with the economic literature linking human capital and local success. Second, neighborhoods with better initial appearances experience, on average, larger positive improvements—an observation that is consistent with “tipping” theories of urban change. Third, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods—an observation that is consistent with the “invasion” theories of urban sociology. Together, our results provide support for three classical theories of urban change and illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities
An interpretable machine learning framework for measuring urban perceptions from panoramic street view images
The proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their end-to-end structure and “black-box” nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability. By utilizing the MIT Place Pulse data, the developed framework can systematically extract six dimensions of urban perceptions from the given panoramas, including perceptions of wealth, boredom, depression, beauty, safety, and liveliness. The practical utility of this framework is demonstrated through its deployment in Inner London, where it was used to visualize urban perceptions at the Output Area (OA) level and to verify against real-world crime rate
An interpretable machine learning framework for measuring urban perceptions from panoramic street view images
The proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their end-to-end structure and "black-box" nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability. By utilizing the MIT Place Pulse data, the developed framework can systematically extract six dimensions of urban perceptions from the given panoramas, including perceptions of wealth, boredom, depression, beauty, safety, and liveliness. The practical utility of this framework is demonstrated through its deployment in Inner London, where it was used to visualize urban perceptions at the Output Area (OA) level and to verify against real-world crime rate
Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals
We propose a new form of plausible counterfactual explanation designed to explain the behaviour of computer vision systems used in urban analytics that make predictions based on properties across the entire image, rather than specific regions of it. We illustrate the merits of our approach by explaining computer vision models used to analyse street imagery, which are now widely used in GeoAI and urban analytics. Such explanations are important in urban analytics as researchers and practioners are increasingly reliant on it for decision making. Finally, we perform a user study that demonstrate our approach can be used by non-expert users, who might not be machine learning experts, to be more confident and to better understand the behaviour of image-based classifiers/regressors for street view analysis. Furthermore, the method can potentially be used as an engagement tool to visualise how public spaces can plausibly look like. The limited realism of the counterfactuals is a concern which we hope to improve in the future
Exploring age-related patterns in internet access: Insights from a secondary analysis of New Zealand survey data
About thirty years ago, when the Internet started to be commercialised,
access to the medium became a topic of research and debate. Up-to-date evidence
about key predictors, such age, is crucial because of the Internet's
ever-changing nature and the challenges associated with gaining access to it.
This paper aims to give an overview of New Zealand's Internet access trends and
how they relate to age. It is based on secondary analysis of data from a larger
online panel survey with 1,001 adult respondents. The Chi-square test of
independence and Cramer's V were used in the analysis. The study provides new
evidence to understand the digital divide. Specifically, it uncovers a growing
disparity in the quality of Internet connectivity. Even though fibre is the
most common type of broadband connection at home, older adults are less likely
to have it and more likely to use wireless broadband, which is a slower
connection type. Additionally, a sizable majority of people in all age
categories have favourable opinions on the Internet. Interestingly, this was
more prevalent among older people, although they report an increased concern
about the security of their personal information online. The implications of
the results are discussed and some directions for future research are proposed.Comment: 15 pages, 5 table
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