Built environment and violent crime: An environmental audit approach using Google Street View

He, Li; Páez, Antonio; Liu, Desheng

doi:10.1016/j.compenvurbsys.2017.08.001

Cited by 93 publications

(41 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Urban street is a vital medium for urban residents to thrive-its perceptual quality has significant impacts on residents' behaviors and quality of life (Dubey et al, 2016). On the one hand, physical disorder with the street (e.g., broken windows, abandoned housings, graffiti, and decayed street lighting) correlates to crime, decreases resident's sense of safety, and consequently lowers residents' willingness to live (He et al, 2017;Naik et al, 2014a). On the other hand, well-designed and maintained street environment increases residents' physical activities, lessen their stress, improve their health (Brownson et al, 2004;Jackson, 2003) and perceived safety (Dubey et al, 2016).…”

Section: Street Environment and Property Valuesmentioning

confidence: 99%

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Qiu¹,

Li²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

The relationship between the street environment and the health, education, mobility, and criminal behaviors of its citizens has long been investigated by economists, sociologists and urban planners. Home buyers were found to pay a premium for better street appearance. Prior studies considering streetscapes mainly focus on objective measures such as the number of nearby trees, the tree canopy area, or the view index of physical features such as greenery, sky or building. However, subjective perceptions may have complex or subtle relationships to physical features, individual physical features or simply summing them up do not capture people’s comprehensive perception. In contrast, this study proposed a new approach for the urban-scale application to quantify both subjectively and objectively measured streetscape scores for six important perception qualities, namely Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity. Built on prior quantitative studies in urban design quality and emerging applications in deep learning and open source street view imagery for urban perceptions, we integrated existing frameworks to (1) effectively collect and evaluate both subjectively and objectively- measured perceptions; (2) investigate the coherence and divergence in ML-predicted subjective scores and formula-derived objective scores; and (3) compare their effects in affecting house prices taking Shanghai as a case study using a large-scale dataset on home transactions. The results implied: first, the percentage increase in sales price attributable to street scores is significant for both subjective and objective measurements. In general subjective scores explained more variance over structural attributes and objective scores in hedonic price model. Particularly, objective Greenness, subjective Safety and Imageability scores positively affected house prices. Second, for Greenness and Imageability scores, the subjective and objective measures exhibited opposite signs in affecting house prices, which implied that there might be mechanisms related to the psychological, social-demographical characteristics of street users that have not been fully incorporated by objective measures that taking view indices or recombination of them. In addition, certain objective measure might outperform subjective counterpart when the connotation of the perception is self-evident and not complicated, for example the Greenness. For those concepts were not familiar to the average person, subjective framework exhibits better performance. This is the first study comprehensively expanding hedonic price method with both subjectively and objectively measured streetscape qualities. It suggested that city authorities could levy a street environment tax to compensate the public budget invested in street environment where developers secured benefits from a price premium. This study enriches our understanding of the economic values of the subjective and objective measures street qualities. It sheds light on promising future study areas where the coherence and divergence of the two measurements should be further stressed.

show abstract

Section: Street Environment and Property Valuesmentioning

confidence: 99%

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Qiu¹,

Li²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, urban perception is closely related to the amenities of the urban visual environment. For instance, residential parcels filled with rubbish and painted with graffiti can make pedestrians feel unsafe and further negatively affect residents’ willingness to live in the area [6,7]. Some studies have shown that the perceptual elements in the urban environment have a vital influence on people’s mental and physical health [8–10].…”

Section: Introductionmentioning

confidence: 99%

Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning

et al. 2019

View full text Add to dashboard Cite

Many studies have explored the relationship between housing prices and environmental characteristics using the hedonic price model (HPM). However, few studies have deeply examined the impact of scene perception near residential units on housing prices. This article used house purchasing records from FANG.com and open access geolocation data (including massive street view pictures, point of interest (POI) data and road network data) and proposed a framework named “open-access-dataset-based hedonic price modeling (OADB-HPM)” for comprehensive analysis in Beijing and Shanghai, China. A state-of-the-art deep learning framework and massive Baidu street view panoramas were employed to visualize and quantify three major scene perception characteristics (greenery, sky and building view indexes, abbreviated GVI, SVI and BVI, respectively) at the street level. Then, the newly introduced scene perception characteristics were combined with other traditional characteristics in the HPM to calculate marginal prices, and the results for Beijing and Shanghai were explored and compared. The empirical results showed that the greenery and sky perceptual elements at the property level can significantly increase the housing price in Beijing (RMB 39,377 and 6011, respectively) and Shanghai (RMB 21,689 and 2763, respectively), indicating an objectively higher willingness by buyers to pay for houses that provide the ability to perceive natural elements in the surrounding environment. This study developed quantification tools to help decision makers and planners understand and analyze the interaction between residents and urban scene components.

show abstract

“…The flux information of the street light fixtures was collected from the local government and street light fixture provider. The urban form of the street canyons was derived from building height model and the publicly accessible Google Street View (GSV) panoramas, which have been used in urban analysis, ranging from crime and the physical features of cities (He et al, 2017) to shade provision .…”

Section: Introductionmentioning

confidence: 99%

Analyzing the obstruction effects of obstacles on light pollution caused by street lighting system in Cambridge, Massachusetts

Ratti

2019

Environment and Planning B: Urban Analytics and City Science

View full text Add to dashboard Cite

Artificial light has transformed urban life, enhancing visibility, aesthetics, and increasing safety in public areas. However, too much unwanted artificial light leads to light pollution, which has a negative effect on public health and urban ecosystems, as well as on the aesthetic and cultural meanings of the night sky. Some of the factors interfering with the estimation of light pollution in cities are urban features, such as the presence of trees, road dimensions, and the physical characteristics of buildings. In this study, we proposed a simplified model for unwanted upward light coming from street luminaires based on a building height model and the publicly accessible Google Street View images. We simulated and analyzed the obstruction effects of different street features on the light pollution caused by the street lighting system in Cambridge, Massachusetts. By providing quantitative information about the connections between the streetscape features and the amount of unwanted upward artificial light, this study provides reference values to inform policies aimed at curbing light pollution.

show abstract

Built environment and violent crime: An environmental audit approach using Google Street View

Cited by 93 publications

References 68 publications

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning

Analyzing the obstruction effects of obstacles on light pollution caused by street lighting system in Cambridge, Massachusetts

Contact Info

Product

Resources

About