A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Decoding urban landscapes: Google street view and measurement sensitivity
Abstract While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show that the measurement outcomes can vary substantially with changes in GSV acquisition parameter settings, specifically spacing and direction. The sensitivity is found to be particularly high for some measurement targets, including humans, objects, and sidewalks. Some of these elements, such as buildings and sidewalks, also show highly correlated patterns of variation indicating their covariance in the mosaic of urban space.
Highlights The sensitivity of streetscape measures derived from Google Street View (GSV) imagery is analyzed. The measurement outcomes can vary considerably by the intervals and directional settings used in GSV acquisition. The degree of sensitivity differs substantially by streetscape element.
Decoding urban landscapes: Google street view and measurement sensitivity
Abstract While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show that the measurement outcomes can vary substantially with changes in GSV acquisition parameter settings, specifically spacing and direction. The sensitivity is found to be particularly high for some measurement targets, including humans, objects, and sidewalks. Some of these elements, such as buildings and sidewalks, also show highly correlated patterns of variation indicating their covariance in the mosaic of urban space.
Highlights The sensitivity of streetscape measures derived from Google Street View (GSV) imagery is analyzed. The measurement outcomes can vary considerably by the intervals and directional settings used in GSV acquisition. The degree of sensitivity differs substantially by streetscape element.
Decoding urban landscapes: Google street view and measurement sensitivity
Kim, Jae Hong (author) / Lee, Sugie (author) / Hipp, John R. (author) / Ki, Donghwan (author)
2021-03-12
Article (Journal)
Electronic Resource
English
Do-It-Yourself Street Views and the Urban Imaginary of Google Street View
| Taylor & Francis Verlag | 2022
| Taylor & Francis Verlag | 2023