Identifying urban spatial structure and urban vibrancy in highly dense cities using georeferenced social media data

Chen, Tingting; Hui, Eddie C.M.; Wu, Jiemin; Lang, Wei; Li, Xun

doi:10.1016/j.habitatint.2019.102005

Cited by 128 publications

(50 citation statements)

References 62 publications

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“…With a rise in income and enrichment in material wealth, people have begun to reconsider previous city development patterns and have called for creating vibrant and energetic urban space [2]. By providing viable places for sociability, connections, and involvement in multiple activities, the vibrant urban environment is beneficial to the economy and people's social and economic lives [3,4].…”

Section: Urban Vitalitymentioning

confidence: 99%

Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China

Han

Zhu

2019

Sustainability

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This study investigates the association between urban vitality and street centrality in Wuhan, China. Urban vitality was measured with social network review data. Street centrality was evaluated in terms of closeness, straightness, and betweenness in walking and driving mode. We constructed a square mesh to convert datasets of street centrality (segments) and urban vitality (points) into one unit for analysis. Geospatial visualization, a chi-square test, and correlation analysis were first employed to obtain an initial understanding of the spatial coupling relationship between urban vitality and street centrality. Then spatial regression models were applied to evaluate the significances and directions of the influences of street centrality on urban vitality. A geographical detector technique was utilized to further evaluate the magnitudes of these influences. The results suggest that street centrality plays an important role in shaping the spatial organization of urban vitality, and various street centralities presented marked differences in their association with urban vitality. More specifically, when considering street centrality in walking mode, betweenness affected urban vitality the most, followed by closeness and straightness. When considering street centrality in driving mode, straightness had the greatest influence on urban vitality, followed by closeness and betweenness.

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Section: Urban Vitalitymentioning

confidence: 99%

Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China

Han

Zhu

2019

Sustainability

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“…Then, the 3 Ds were extended to the 4 Ds, the 5 Ds, and the 6 Ds, where the importance of destination value, the distance to transit, and the demand management were successively proposed [30][31][32]. Generally, density is regarded as the key concept in the description of urban spatial structure [33], and it is also the key factor in forming city centers and subcenters [4]. Diversity represents mixed land use.…”

Section: Tod Concept and Urban Structurementioning

confidence: 99%

“…Due to the high population mobility and the social diversity, it is necessary to measure different types of human behaviors around the whole urban area instead of in fixed areas only. With the development of data-acquisition techniques, dynamic data sources such as smart card data [42], Point of Interest (POI) data [43], social media data [4], and remote sensing data [44] are organized to study the urban agglomeration. Those datasets show different types of human activities at different places, such as entertainment, dining, and shopping.…”

Section: Tod Concept and Urban Structurementioning

confidence: 99%

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Identifying Urban Structure Based on Transit-Oriented Development

et al. 2019

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The fast development of urbanization has led to imbalances in cities, causing congestion, pollution, and urban sprawl. In response to the growing concern over the distribution of demand and supply, a more coordinated urban structure is addressed in comprehensive planning processes. In this study, we attempt to identify urban structure using a Network–Activity–Human model under the Transit-Oriented Development (TOD) concept, since TOD is usually regarded as an urban spatial planning tool. In order to explore the strengths and weaknesses of the urban structure, we define the TOD index and unbalance degree and then classify the urban areas accordingly. We take the city of Beijing as a case study and identify nine urban types. The results show a hierarchical urban structure: the city center covers most of the hotspots which display higher imbalances, the surroundings of the city center are less developed, and the city edges show higher potentials in both exploitation and transportation development. Moreover, we discuss the extent to which the spatial scale influences the unbalance degree and apply a sensitivity analysis based on the goals of different stakeholders. This methodology could be utilized at any study scale and in any situation, and the results could offer suggestions for more accurate urban planning, strengthening the relationship between TOD and spatial organization.

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“…The distribution and location of healthcare services related to transport networks have been investigated in several previous studies (e.g., Chen, Hui, Lang, & Tao, 2016; Guagliardo, 2004; Lang, Chen, Chan, Yung, & Lee, 2019; Paez, Mercado, Farber, Morency, & Roorda, 2010). The environmental conditions were classified as spatial factors, structural factors, or physical barriers (Chen, Hui, Wu, Lang, & Li, 2019; Lee, 1991; Wang & Luo, 2005). Geographic accessibility is defined by factors that impede a patient from accessing a medical facility (Guagliardo, 2004).…”

Section: Introductionmentioning

confidence: 99%

Environment resilience and public health: Assessing healthcare's vulnerability to climate change in the San Francisco Bay Area

et al. 2020

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This study investigated the vulnerability of people's health to the impact of climate change on healthcare accessibility in the San Francisco Bay Area. The study developed spatial analysis models in ArcGIS with a high-resolution elevation data set (1 m raster base map) and summarized the scenario assessments of the associations between healthcare and the populations vulnerable to the effects of climate change. The results reveal that 34.3% of low-income households could reach hospitals in the baseline scenario. In the peak water level (PWL) (140) scenario, 33.3% of low-income households were within 30 min of the nearest hospital. In the baseline scenario, hospitals were accessible to 35.9% of the general population, whereas in the PWL (140) scenario the percentage was slightly lower, with 35.1% having access to hospitals. Healthcare reform increased the accessibility of hospitals to low-income households, and these populations had equal access in the PWL (140) scenario. However, access to healthcare is controlled by distance and other variables, including income, insurance, spatial distribution, and transportation mobility. The findings and implications of this study address the explicit recognition in strategic planning of the effects of climate change in adopting policies to benefit health in the future. 608 | CHEN Et al.

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Identifying urban spatial structure and urban vibrancy in highly dense cities using georeferenced social media data

Cited by 128 publications

References 62 publications

Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China

Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China

Identifying Urban Structure Based on Transit-Oriented Development

Environment resilience and public health: Assessing healthcare's vulnerability to climate change in the San Francisco Bay Area

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