Exploring thresholds of built environment characteristics for walkable communities: Empirical evidence from the Seoul Metropolitan area

Eom, Hyunjoo; Cho, Gi-Hyoug

doi:10.1016/j.trd.2015.07.005

Cited by 43 publications

(18 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 studies found results to depend on different groups of people (Hanabuti et al, 2011, Inoue et al, 2010, Sugiyama et al, 2014, Shigematsu et al, 2009. Secondly, two studies identified a non-linear relationship between density and walking (Tanishita andAsada, 2013, Eom andCho, 2015). A third conclusion is that some studies have only a small sample size, especially some studies focusing at specific neighborhoods.…”

Section: An Overview Of Literaturementioning

confidence: 99%

“…Areas with well-mixed, well-connected, beautiful aesthetics and safe, attract specific groups of people (e.g. Eom and Cho, 2015).…”

Section: An Overview Of Literaturementioning

confidence: 99%

“…In addition, in densely populated areas, taller buildings with elevators might also reduce the number of steps. Two recent studies in Asia showed a non-linear (convex) relationship between population density and walking (Tanishtia and Asada, 2013 and Eom and Cho, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impact of regional population density on walking behavior

Tanishita

Wee

2017

Transportation Planning and Technology

View full text Add to dashboard Cite

Land use can influence walking (measured by the number of steps) and so the health of people. This paper presents the result of empirical research on the impact of regional population densities (inhabitants per inhabitable area) on the number of steps (all steps, outdoors and indoors). We collected data of almost 11,000 respondents in 148 Japanese regions and estimate polynomial regression models, the total number of steps being the dependent variable and densities being the main independent variable. Regional population density significantly affects the number of steps after controlling for individual and household attributes. The estimated population density that maximizes the number of steps is around 11,000 persons/km2. Increasing densities, up to levels of around 11,000 inhabitants/km2 will increase walking and consequently the health of inhabitants. The population density elasticity of the number of steps is 0.046-0.049 in a simple log linear regression model without a peak.

show abstract

Section: An Overview Of Literaturementioning

confidence: 99%

“…Areas with well-mixed, well-connected, beautiful aesthetics and safe, attract specific groups of people (e.g. Eom and Cho, 2015).…”

Section: An Overview Of Literaturementioning

confidence: 99%

See 1 more Smart Citation

Impact of regional population density on walking behavior

Tanishita

Wee

2017

Transportation Planning and Technology

View full text Add to dashboard Cite

show abstract

“…Almost all accounts of emergentism involve a form of irreducibility to the lower level. As an example, an urban fabric (e.g., inner city) created by multiple actor layers, incrementally developed with a diversity of building types, scales, and functions, is often seen as having the attributes of a more intense and livelier street lives (Eom & Cho, 2015;Jacobs, 1961;Merlino, 2011). Overall, the evolutionary resilience approach to urban planning seems to deliver the characteristic features of data-driven smart sustainable cities with respect to the compact, ecological, and technological dimensions of their landscape.…”

Section: Cities As Quintessential Examples Of Complex Systems and Wicked Problemsmentioning

confidence: 99%

Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning

Bibri

2021

Comput.Urban Sci.

View full text Add to dashboard Cite

Sustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

show abstract

“…The conception of spatial scaling is predicated on the assumption that urban transformations, whether emergent or designed urban developments, represent outcomes of processes. For example, an urban fabric (e.g., inner city) created by multiple actor layers, incrementally developed with a diversity of building types, scales, and functions, is often seen as having the attributes of a more intense and livelier street lives (Eom and Cho 2015;Merlino 2011). However, like urban form, spatial scale is both the frame that shapes process and the frame that emerges from a process.…”

Section: Urban Forms and Spatial Scales As Processual Outcomes Of Urbanizationmentioning

confidence: 99%

Data-Driven Smart Sustainable Cities of the Future: New Conceptions of and Approaches to the Spatial Scaling of Urban Form

Bibri

2021

Future Cities and Environment

View full text Add to dashboard Cite

IntroductionThe increased pressure on cities has lead to a stronger need to build sustainable cities that last. Designing sustainable cities of the future, educated by the lessons of the past and anticipating the challenges of the future, entails articulating a multi-scalar vision and following key principles-energy, ecology, infrastructure, waste, water, livability, mobility, accessibility, economy, and culturewhile responding to macro-shifts along the way. These principles are at the core of urban sustainability, which represents an ideal outcome in the sum of all the goals of development planning, on which there is widespread consensus with trade-offs and conflicts when it comes to making decisions. Indeed, research in the field of sustainable urban form, especially compact cities and eco-cities, has, over the last two decades, produced contradictory, uncertain, weak, non-conclusive, and questionable results (e.g.,

show abstract

Exploring thresholds of built environment characteristics for walkable communities: Empirical evidence from the Seoul Metropolitan area

Cited by 43 publications

References 31 publications

Impact of regional population density on walking behavior

Impact of regional population density on walking behavior

Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning

Data-Driven Smart Sustainable Cities of the Future: New Conceptions of and Approaches to the Spatial Scaling of Urban Form

Contact Info

Product

Resources

About