Optimizing Spatial Distribution of Retail Shops against Neighborhood Tree Canopy Shade Using Big Data Extracted from Streetscape

Yifeng Liu, Zhanhua Cao, Hongxu Wei () and Peng Guo ()
Additional contact information
Yifeng Liu: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Zhanhua Cao: College of Environment and Bioresources, Dalian Minzu University, Dalian 116600, China
Hongxu Wei: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Peng Guo: College of Environment and Bioresources, Dalian Minzu University, Dalian 116600, China

Land, 2024, vol. 13, issue 8, 1-18

Abstract: The visibility of retail frontages is critical for earning profits from spontaneous traffic visits to retail shops located along a street. The urban tree canopy plays a crucial role in enhancing the street-side environment, yet more is not always better when considering the placement of retail shops behind trees with big canopies. Related evidence in the literature is rarely provided, and an unclear relationship has been reported to exist between the number of shops for a specific retail type and the quantified ratio of the canopy shade in a street view. In this study, both big data crawling and deep learning were employed to unravel this relationship for retail shops in Changchun, Northeast China. The entire study area was divided into 6037 grid cells with a side length of ~0.6 km, wherein the number of shops of five retail types (food and beverage, shopping, life services, entertainment, and hotel) were quantified by computer counting their points of interest (POIs). The canopy shade was evaluated using the green view index (GVI) quantified through the ratio of canopy pixels divided by all the pixels in a street view image obtained through an online map API. A neighboring road network was categorized into four classes: class I road density mainly reduced the number of retail shops, and the road densities of classes III and IV accounted for more retail shops. The relationship between the number of retail shops and the GVI could be fitted with positive skewness curves for class II roads, where the critical peak of the GVI was estimated to be about 3.27%. The optimization scheme indicated that more retail shops should be placed along class I and II roads. In conclusion, more retail shops for food and beverage, shopping, and life services should be placed in the landscape neighboring big canopies.

Keywords: commercial land management; urban forest; neighborhood retail and service; streetscape analysis; point of interest (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/8/1249/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/8/1249/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:8:p:1249-:d:1452924

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().