Measuring urban vibrancy of neighborhood performance using social media data in Oslo, Norway

Lang, Wei; Lv, Hao; Hui, Eddie C.M.; Chen, Tingting; Wu, Jiemin; Jahre, Marianne

doi:10.1016/j.cities.2022.103908

Cited by 16 publications

(6 citation statements)

References 58 publications

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“…Furthermore, the physical characteristics of a neighborhood are closely linked to its vibrancy, defined as the extent to which urban form supports the essential functions, biological requirements, and capabilities of its inhabitants (Lynch, 1984). A study conducted in Oslo, Norway, indicated that the location and distribution of commercial, public, and residential areas and the total population are key drivers of neighborhood vibrancy (Lang et al, 2022). Vibrant neighborhoods typically manifest in high-density, central urban areas with a notable concentration of diverse commercial and public buildings along main thoroughfares.…”

Section: Neighborhood Performancementioning

confidence: 99%

Strategies for the Design of New and Existing High Energy Performance Solar Neighborhoods

Hachem-Vermette,

Singh Grewal,

Desthieux

et al. 2024

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This report provides a comprehensive overview of solar neighborhoods, including their definition, applications, standards, and regulations. It also outlines the methodology and tools used to develop archetype designs and analyses solar strategies at both building and neighborhood levels. The report presents selected archetypes from Canada, France, Italy, Norway, Sweden, and Switzerland and provides a decision-making tool for solar strategies. The target audience for this report includes architects, urban planners, policymakers, and anyone interested in sustainable and energy-oriented developments.

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Section: Neighborhood Performancementioning

confidence: 99%

Strategies for the Design of New and Existing High Energy Performance Solar Neighborhoods

Hachem-Vermette,

Singh Grewal,

Desthieux

et al. 2024

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“…For the exploration of land use functions, scholars in China and abroad have contributed a number of fruitful investigations based on different theoretical perspectives such as Western political economy, urban economics, and sociology, including the recognition of production-living-ecological functions at different spatial scales [26], the spatiotemporal pattern evolution of urban land use structure [27], spatial differentiation in the efficiency of urban land use structures [28], the trends in and regulation of urban construction land [29], and how human-related drivers such as policy and planning contribute to land use evolution in cross-boundary areas [30]. Traditional methods mostly rely on remote sensing images, land use data, and expert surveys or evaluations [31]. However, due to the characteristics of mixed and overlapping functional areas, diverse attributes, and dynamic changes in urban land use, such classification methods have the following shortcomings:…”

Section: Literature Reviewmentioning

confidence: 99%

Deciphering Urban Land Use Patterns in the Shenzhen–Dongguan Cross-Boundary Region Based on Multisource Data

Wu,

Lang,

Chen

2024

Land

Self Cite

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Against the backdrop of coordinated development and regional integration in the Guangdong–Hong Kong–Macao Greater Bay Area, China, the integration of Shenzhen and Dongguan has received increasing attention from both academia and the government. The cross-boundary area of the two cities has also shifted from being categorized as the development edge to becoming a key area, presenting more complex spatial and functional characteristics. This paper applies multi-source data to analyze the spatial characteristics and factors of influence in the Shenzhen–Dongguan cross-boundary region from the perspective of urban land use pattern evolution. The results showed that from 2008 to 2022, (1) the spatial structure presented a development trend from multi-core to axial, then to network, gradually breaking down the development barriers in the central region; (2) the function of regional land was transformed from a single type to a composite and mixed type, and the layout of commercial, public, green, and transportation land became more dispersed and balanced; (3) the cross-administrative boundaries of commercial land were exaggerated, and industrial land was widely transformed into composite industrial and commercial land, forming an integrated industrial belt; and (4) the overall increase in the mixing degree of land use functions indicated that the two regions were interconnected and integrated with each other, and the functional structure of the cross-boundary region was relatively more complete.

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“…References: Alcamo (2019); Avrampou et al (2019); Booth (2019); Camilleri (2019); Cramer et al (2018); Dawes (2020); Galford et al (2020); Gracia‐de‐Rentería et al (2022); Honegger et al (2021); Huan and Zhu (2023); Kempf and Dutta (2021); Lang et al (2022); Lee (2020); Lyytimäki et al (2020); Meschede and Henkel (2019); Nayak and Jespersen (2022); Nepal and Paija (2019); Puchol‐Salort et al (2021); Royston et al (2020); Schroeder et al (2019); Singh (2022); Stead (2018); Tan et al (2021); Tselios and Tompkins (2019); Weststrate et al (2019); Yao et al (2021); Zhou et al (2022).…”

Section: Literature Reviewmentioning

confidence: 99%

The coherence between resource investment and performance for sustainable development goals in Taiwan cities: A hybrid influence analysis

Chan

et al. 2023

Sustainable Development

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This study contributes to visualizing the complicated interrelationships among the sustainable development goals (SDGs) in Taiwanese cities' resource investment and performance under uncertainties. Taiwan faces bottlenecks and uncertainties in pursuing the SDGs due to the complex interrelationships among the SDGs. In particular, the lack of a comprehensive approach causes problems for governments in identifying whether resource investment and performance reach a coherent situation. To address this issue, this study proposes to use secondary data normalization, factor analysis and reliability testing for the validity and reliability of measures, performance‐resource analysis and the fuzzy synthetic method to translate qualitative information, as well as the decision‐making trial and evaluation laboratory to visualize the relationships. The study (1) establishes validation measures for resource investment and performance; (2) combines qualitative information and secondary data to understand the causal effects of the SDGs; and (3) provides directions for the government to improve the coherence of resource investment and performance. The findings show that SDG 3 has a strong effect on SDGs 11 and 9 and a moderate effect on SDG 8 to achieve the agenda. Guidelines are provided for resource‐poor counties/regions that could invest resources to achieve SDG 3. Many cities and counties are underperforming.

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Measuring urban vibrancy of neighborhood performance using social media data in Oslo, Norway

Cited by 16 publications

References 58 publications

Strategies for the Design of New and Existing High Energy Performance Solar Neighborhoods

Strategies for the Design of New and Existing High Energy Performance Solar Neighborhoods

Deciphering Urban Land Use Patterns in the Shenzhen–Dongguan Cross-Boundary Region Based on Multisource Data

The coherence between resource investment and performance for sustainable development goals in Taiwan cities: A hybrid influence analysis

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