Inferring storefront vacancy using mobile sensing images and computer vision approaches

IF 7.1 1区 地球科学 Q1 ENVIRONMENTAL STUDIES
Yan Li , Ying Long
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引用次数: 0

Abstract

Storefront vacancy has been a widespread and worldwide phenomenon, raising concerns about the changing characteristic of the retail landscape, loss of community vitality, and hollowing out of cities. Although the causes leading to this phenomenon have been extensively debated, little granular data are available to evaluate the issue in a timely manner. Therefore, this study aims to develop a data-driven approach to capture the commercial structure of vacant storefronts on a store-by-store basis as well as to analyze their evolution patterns. First, street-level images were collected using mobile sensing in a low-cost, large-scale and efficient manner; then, a storefront vacancy estimation model was developed using computer vision techniques to infer the storefront location, operation status, business category, and vacancy rates. Three volunteers spent five days collecting street-level images from an urban area of 964 km2 in the case city of Xining, China. As a result, 93,069 stores were identified in the city in March 2022, of which 25,488 were vacant. Moreover, the storefront vacancy rate increased significantly after the epidemic, from 21.8% in 2018 to 30.0% in 2022. Stores in shopping, catering, and life services had the maximum vacancies. The factors that had the greatest impact on storefront vacancy were, in order of importance, far from commercial zonings, low population density, and far from the urban center. However, these factors influenced the vacancy in diverse and complex ways, and in the future, urban planning strategies to address vacancy issues should be well considered and differentiated.

利用移动传感图像和计算机视觉方法推断店面空置情况
店面空置一直是一个普遍存在的世界性现象,引发了人们对零售业态特征变化、社区活力丧失和城市空心化的担忧。尽管导致这一现象的原因已被广泛讨论,但很少有细化数据可用于及时评估这一问题。因此,本研究旨在开发一种数据驱动的方法,逐店捕捉空置店面的商业结构,并分析其演变模式。首先,利用移动传感技术以低成本、大规模和高效率的方式收集街道图像;然后,利用计算机视觉技术开发店面空置估算模型,以推断店面位置、经营状况、商业类别和空置率。三名志愿者花了五天时间在中国西宁市 964 平方公里的城市区域内收集街道图像。结果,在 2022 年 3 月,该市共识别出 93069 家店面,其中 25488 家为空置店面。此外,疫情过后,店面空置率大幅上升,从 2018 年的 21.8%上升到 2022 年的 30.0%。其中,购物、餐饮和生活服务业的店面空置率最高。对店面空置影响最大的因素依次是远离商业区、人口密度低、远离城市中心。然而,这些因素对空置率的影响是多样的、复杂的,未来解决空置率问题的城市规划策略应充分考虑并区别对待。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
13.30
自引率
7.40%
发文量
111
审稿时长
32 days
期刊介绍: Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.
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