Dataset Information

Toward liveable commercial streets: A case study of Al-Karada inner street in Baghdad.

ABSTRACT: Nowadays, liveability is a major consideration in upgrading public spaces, especially more sustainable streets. Considerable research has also been conducted to determine the most relevant indicators for more vibrant commercial streets. This paper sheds light on a specific case study in which terrorist attack led to the loss of vitality and ability to attract visitors in one of the most important commercial areas in Baghdad, Al-Karada inner street. The paper's aim is to identify indicators for reviving the companionable and sociable life of a street. As a result, it presents many indicators that can be applied to upgrade commercial street vitality by way of a significant, inclusive checklist, which incorporates many aspects. This checklist serves as a comprehensive framework to present needed information, diagnose problems, and identify the strengths and weaknesses of a commercial street's performance to enhance its liveability.

SUBMITTER: Ghazi NM

PROVIDER: S-EPMC6529690 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Toward liveable commercial streets: A case study of Al-Karada inner street in Baghdad.

Ghazi Noor Mazin NM Abaas Zaynab Radi ZR

Heliyon 20190520 5

Nowadays, liveability is a major consideration in upgrading public spaces, especially more sustainable streets. Considerable research has also been conducted to determine the most relevant indicators for more vibrant commercial streets. This paper sheds light on a specific case study in which terrorist attack led to the loss of vitality and ability to attract visitors in one of the most important commercial areas in Baghdad, Al-Karada inner street. The paper's aim is to identify indicators for r ...[more]

PMID: 31193437

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Project description:BackgroundActive School Travel (AST) initiatives align with the Ottawa Charter for Health Promotion, which calls for 'creating supportive environments' and 'strengthening community action.' However, their reliance on volunteers poses sustainability challenges. The main objectives of this study were to document the motivations, satisfaction, and experiences of volunteers involved in sustaining two AST initiatives in Ontario for an entire school year.MethodsTwo volunteer-led School Street initiatives in Kingston, Ontario successfully operated during pick-up and drop-off times of each school day. The first initiative operated for the entire 2021-2022 school year, and the second operated for the entire 2022-2023 school year. These initiatives were the first of their kind in the province of Ontario, Canada. Volunteers from both sites (n = 56) participated in online surveys and their motivations, satisfaction, and experiences of their role were compared using the 2-sided Fisher's Exact Test.ResultsOver 80% of volunteers were highly motivated to promote safety and over 70% of volunteers were highly motivated to disrupt the status quo of unsupportive, car-centric urban environments by reimagining how streets can be used. By taking collective action to re-shape the environment around these public schools to support healthy, active living, our findings reveal that over 90% of volunteers were highly satisfied. Of the volunteers, 87% felt they contributed to child safety and 85% felt they had developed stronger community connections. They appreciated the short (i.e., 40 minute) time commitment of each shift, weekly email communications by the community organization leading the initiative, and the volunteer schedule. They also appreciated the positive social interactions during volunteer shifts, which they felt outweighed the minimal resistance they experienced.ConclusionsThis research demonstrates the importance of logistical, motivational, and social factors in recruiting and retaining volunteers for community-led School Streets. Our findings support appealing to prospective volunteers' influence in achieving School Street objectives (e.g., improved safety) in recruitment efforts, as well as highlighting School Streets' innovative approach. Communicating with volunteers throughout School Street planning and implementation processes and limiting traffic in the closed street zone (i.e., by excluding the school staff parking lot and private driveways from the scope) are additional recommendations based on the findings of this study.

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

Toward liveable commercial streets: A case study of Al-Karada inner street in Baghdad.

Publications

Toward liveable commercial streets: A case study of Al-Karada inner street in Baghdad.

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