AbstractUnprecedented urbanization in China has directly resulted in residential vacancies, which has seriously stunted sustainable development, a pa
A spatial-compositional feature fusion convolutional autoencoder for geochemical anomaly recognition
ABSTRATThe spatial structural features and compositional relationships of multivariate geochemicals are influenced by complex geological processes (e.
AbstractEmpirical data are limited to decipher where people live and work in large cities; however, neighborhood information, such as street view imag
AbstractPrevious literature has examined the relationship between the amount of green space and perceived safety in urban areas, but little is known a
Awareness is mounting that urban greenspace is beneficial for residents’ health. While a plethora of studies have focused on greenspace quantity, scant attention has been paid to greenspace quality. Existing methods for assessing greenspace quality is either highly labor-intensive and/or prohibitively time-consuming. This study develops a new machine learning method to assess greenspace quality based on street view images collected from Guangzhou, China. It also examines whether greenspace exposure disparities are linked to the neighbourhood socioeconomic status (SES). The validation process indicated that our scoring system achieved high accuracy for predicting street view-based greenspace quality outside the training data. Results also show that there were marked differences in spatial distribution between aggregated NDVI (Normalized Difference Vegetation Index), street view greenness quantity and quality. Regression models show that neighbourhood SES is not associated with NDVI. Although neighbourhood SES is associated with both street view greenness quantity and quality index value, street view greenness quality is more sensitive to the change of neighbourhood SES. Our work suggests that policymakers and planners are advised to pay more attention to greenspace quality and greenspace exposure disparities in urban area.
AbstractThe rapid development of information technology and location techniques not only leads to an increasing growth of massive geospatial big data
AbstractWith the acceleration of the process of building a national-level central city in Wuhan, the landscape pattern of the city has undergone treme