Dataset Information

Research on Constructing a Healing Environment for the Street Spaces of a High-Density City: Using Street Spaces in Macao's Old City Area.

ABSTRACT: It is commonly recognized that street spaces in high-density cities are able to cause negative impacts in terms of residents' physical and mental health. This research intends to investigate and analyze how residents use street spaces in a high-density city in order to construct a healing environment for these street spaces. The research was conducted in Macao's old town by using spatial syntax methods to define the research areas, and implemented on-site observations that evaluated the age of the residents in the space and the conditions of their usage of the space. The study collected data through expert grading and employed the Analytic Hierarchy Process to calculate the weight of each indicator in order to attain accurate and objective research outcomes. The evaluation results indicate that the current Macao street spaces are poor healing environments. By analyzing the effective factors for constructing a healing environment in these street spaces, so that residents can get more space for healing when they use it, the paper aims to provide a model example for those who are involved with city governance, planning and design.

SUBMITTER: Meng L

PROVIDER: S-EPMC7369807 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Research on Constructing a Healing Environment for the Street Spaces of a High-Density City: Using Street Spaces in Macao's Old City Area.

Meng Lingchao L Zhu Chun C Wen Kuo-Hsun KH

International journal of environmental research and public health 20200702 13

It is commonly recognized that street spaces in high-density cities are able to cause negative impacts in terms of residents' physical and mental health. This research intends to investigate and analyze how residents use street spaces in a high-density city in order to construct a healing environment for these street spaces. The research was conducted in Macao's old town by using spatial syntax methods to define the research areas, and implemented on-site observations that evaluated the age of t ...[more]

PMID: 32630722

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Project description:BackgroundStreet imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level.MethodsWe sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011-2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-reported commute shares of four modes (combined walking plus public transport, cycling, motorcycle, and car), as well as APS-reported past-month participation in walking and cycling.ResultsWe found high correlations between GSV counts of cyclists ('GSV-cyclists') and cycle commute mode share (r = 0.92)/past-month cycling (r = 0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r = 0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r = 0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r = 0.81). GSV-car was not correlated with car commute mode share (r = -0.12). However, in multivariable regression models, all outcomes were predicted well, except past-month walking. The prediction performance was measured using cross-validation analyses. GSV-buses and GSV-cyclists are the strongest predictors for most outcomes.ConclusionsGSV images are a promising new big data source to predict urban mobility patterns. Predictive power was the greatest for those modes that varied the most (cycle and bus). With its ability to identify mode of travel and capture street activity often excluded in routinely carried out surveys, GSV has the potential to be complementary to new and traditional data. With half the world's population covered by street imagery, and with up to 10 years historical data available in GSV, further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments.

Dataset Information

Research on Constructing a Healing Environment for the Street Spaces of a High-Density City: Using Street Spaces in Macao's Old City Area.

Publications

Research on Constructing a Healing Environment for the Street Spaces of a High-Density City: Using Street Spaces in Macao's Old City Area.

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