Analysing the space-time dynamics of urban structure change using employment density and distribution data

Moghadam, Alireza Salahi; Soltani, Alì; Parolin, Bruno; Alidadi, Mehdi

doi:10.1016/j.cities.2018.04.009

Cited by 28 publications

(14 citation statements)

References 67 publications

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“…This factor originally refers to the index of distance from other regions/destinations, which has an impact on the development of urban areas. Proximity to the city centre [18,30,73,79,88,90,96] and distance from built-up areas [10,94], accessibility to public facilities such as public transport stations [30,32], access to education and research centres (such as colleges, universities, school, etc.) [1,9,20,56,85], commercial/leisure centre/ park [1,30,56,68,[96][97][98], hotel [1,13,57], neighboring effects [16,99], medical care e.g., hospital [1,9] are all considered to be crucial in driving urban growth.…”

Section: Accessibilitymentioning

confidence: 99%

“…Proximity to the city centre [18,30,73,79,88,90,96] and distance from built-up areas [10,94], accessibility to public facilities such as public transport stations [30,32], access to education and research centres (such as colleges, universities, school, etc.) [1,9,20,56,85], commercial/leisure centre/ park [1,30,56,68,[96][97][98], hotel [1,13,57], neighboring effects [16,99], medical care e.g., hospital [1,9] are all considered to be crucial in driving urban growth. This factor refers not only to the physical distance of one region/destination from another, but also to the functional distance or distance to access a region/destination.…”

Section: Accessibilitymentioning

confidence: 99%

See 1 more Smart Citation

Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review

Allan

Soltani

Abdi

et al. 2022

Land

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This paper is based on reviewing the literature in the past 10 years on the drivers of land use and land cover change (LULCC) in urban areas. It combines quantitative and qualitative keyword analysis of papers drawn out from the Scopus database. The analysis is primarily based on the number of mentions of keywords in the titles and abstracts of the papers, in addition to the number of keywords appearing in the papers. On the basis of content analysis, a three-level structural categorization of the driving factors was developed. These are presented in a schematic diagram, where the contextual factors are shown as influencing economic and financial factors and policy and regulation, which in turn influences transportation investments and availability, and industrial and residential location choices. Transportation availability was seen as the most frequent factor identified in the literature. This research contends that LULCC is mostly determined by interactions among these four themes in a three-level structure, and on this basis, a model is presented that illustrates LULCC drivers based on local circumstances across the globe.

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Section: Accessibilitymentioning

confidence: 99%

Section: Accessibilitymentioning

confidence: 99%

Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review

Allan

Soltani

Abdi

et al. 2022

Land

Self Cite

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“…Therefore, a reasonable spatial weight matrix is a key step for the spatial econometric analysis. Generally, the contiguity-based weight matrix: if two locations that are geographically adjacent are 1, if they are not adjacent are 0, was often used in paper [66]. However, this adjacent effect cannot reflect a proximity effect of air pollution if these locations are close enough.…”

Section: Study Area Data and Methodsmentioning

confidence: 99%

Do Compactness and Poly-Centricity Mitigate PM10 Emissions? Evidence from Yangtze River Delta Area

Tao

Wang

et al. 2019

IJERPH

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The Yangtze River Delta (YRD) region is one of the most densely populated and economically developed areas in China, which provides an ideal environment with which to study the various strategies, such as compact and polycentric development advocated by researchers to reduce air pollution. Using the data of YRD cities from 2011–2017, the spatial durbin model (SDM) is presented to investigate how compactness (in terms of urban density, jobs-housing balance, and urban centralization) and poly-centricity (in terms of the number of centers and polycentric cluster) affect PM10 emissions. After controlling some variables, the results suggest that more jobs-housing-balanced and centralized compactness tends to decrease emissions, while poly-centricity by developing too many centers is expected to result in more pollutant emissions. The effect of high-density compactness is more controversial. In addition, for cities with more private car ownerships (>10 million within cities), enhancing the polycentric cluster by achieving a more balanced population distribution between the traditional centers and sub-centers could reduce emissions, whereas this mitigated emissions effect may be limited. The difference between our study and western studies suggests that the correlation between high-density compactness and air pollution vary with the specific characteristics and with spatial planning implications, as this paper concludes.

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“…Research has found that only 16% of jobs in the United States were within about five kilometers of CBDs in the early 1990s (Glaeser & Kahn, 2001). A growing body of empirical studies shows multiple employment subcenters emerging in major American, European, and Australian metropolises, such as Chicago, Dallas-Fort Worth, Los Angeles, and Sydney (Craig & Ng, 2001;Fujii & Hartshorn, 1995;Garcia-Lopez & Muniz, 2010;McMillen, 2003;McMillen & McDonald, 1998;Moghadam et al, 2018;Shukla & Waddell, 1991). Recent studies on Western metropolitan cities have documented evidence supporting the "generalized dispersion" view of employment concentration (Angel & Blei, 2016;Arribas-Bel et al, 2015;Lee, 2007), claiming that the vast majority of jobs are currently located outside CBDs and employment subcenters (Angel & Blei, 2016;Hajrasouliha & Hamidi, 2016).…”

Section: Introductionmentioning

confidence: 99%

Understanding spatial and compositional dynamics of employment centers in urban China: Empirical evidence from Nanjing

et al. 2021

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Analysing the space-time dynamics of urban structure change using employment density and distribution data

Cited by 28 publications

References 67 publications

Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review

Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review

Do Compactness and Poly-Centricity Mitigate PM10 Emissions? Evidence from Yangtze River Delta Area

Understanding spatial and compositional dynamics of employment centers in urban China: Empirical evidence from Nanjing

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