Interaction Relationship Between Built-Up Land Expansion and Demographic-Social-Economic Urbanization in Shanghai-Hangzhou Bay Metropolitan Region of Eastern China

Xiao, Rui; Huang, Xin; Yu, Wei; Lin, Meng; Zhang, Zhonghao

doi:10.14358/pers.85.3.231

Cited by 17 publications

(9 citation statements)

References 47 publications

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“…According to the statistics, the population of the SHB area increased from 20.52 million to 47.81 million, and the area of built‐up land increased from 3,208.35 to 8,616.76 km 2 during 1994–2015 (Zhejiang Statistical Yearbook, 2016; Shanghai Statistical Yearbook, 2016). After the population explosion and economic growth, the demand for built‐up land increased rapidly (Xiao et al., 2019), which resulted in serious problems, such as biodiversity loss and land resources waste (Li, Zhang, et al, 2017). Xiao et al., 2018 indicates that majority of the cultivated land in the SHB area was decreasing from 1994 to 2015.…”

Section: Materials and Methodologymentioning

confidence: 99%

“…Many factors influence built‐up land expansion. In view of previous research (Ebeke & Etoundi, 2017; Wang, 2017; Wang et al., 2018; Xiao et al., 2019), four kinds of variables were considered, including natural, economic, social, and policy issues.…”

Section: Materials and Methodologymentioning

confidence: 99%

“…In addition, traffic conditions play a supporting and guiding role in the spatial expansion of large cities (Reitveld, 2003). As one of the most economically developed regions in China, the SHB area has experienced rapid economic growth each year, and possessed a well‐developed transportation network (Xiao et al., 2019). Therefore, economic development level, commercial center, train station point, rail transit, and road network can be seen as economic variables.…”

Section: Materials and Methodologymentioning

confidence: 99%

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Built‐up land expansion simulation with combination of naive Bayes and cellular automaton model—A case study of the Shanghai‐Hangzhou Bay agglomeration

Xiao

Zhang

et al. 2021

Growth and Change

Self Cite

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Simulating and predicting the urban land use change can provide deeper spatial insights into dynamics and sustainable developments of urban planning. This research takes the Shanghai‐Hangzhou Bay (SHB) agglomeration as a study area and selects natural, economic, social, and policy variables as restraint conditions. A cellular automaton (CA) model and the naive Bayes‐cellular automaton (NB‐CA) model are employed and compared to simulate the built‐up land in SHB. Results show that the NB‐CA model greatly improves the simulation accuracy of built‐up land compared to CA model. Specifically, the simulation accuracy of the NB‐CA model is 14.68%, 14.03%, 7.43%, 6.00%, 5.32%, and 2.65% higher than that of the traditional CA model in Shanghai, Hangzhou, Huzhou, Jiaxing, Ningbo, and Shaoxing, respectively. Among the four restraint conditions, the least influential variable is the natural variable and the most influential is the policy variable in Shanghai, Ningbo, and Shaoxing and the social variable in Hangzhou, Huzhou, and Jiaxing. It is the first usage of naive Bayes and CA to simulate built‐up expansion and this new combination method highlights the improvement of simulation accuracy. The naive Bayes technology implies that government policy is an unstable factor that can influence the simulation of built‐up land change. The methodology will be applicable to other regions experiencing rapid built‐up land expansion under government policy.

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Section: Materials and Methodologymentioning

confidence: 99%

Section: Materials and Methodologymentioning

confidence: 99%

Section: Materials and Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Built‐up land expansion simulation with combination of naive Bayes and cellular automaton model—A case study of the Shanghai‐Hangzhou Bay agglomeration

Xiao

Zhang

et al. 2021

Growth and Change

Self Cite

View full text Add to dashboard Cite

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“…Cui presented an integrated assessment of the city in terms of its socio-economic and water environment [39]. Xiao conducted an urban assessment between built-up, heavily used land and demographic socioeconomic urbanization [40]. In the assessment of a sustainable city, researchers combined population, land, and industry within the assessment model [41].…”

Section: The Concept Of a Sustainable City And Sustainable Developmentmentioning

confidence: 99%

An Integrated Assessment of the Competitiveness of a Sustainable City within the Context of the COVID-19 Impact

Činčikaitė

Meidutė‐Kavaliauskienė

2022

Sustainability

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It is crucial for a city to ensure economic stability and growth, along with social security and prosperity, not only for the present, but also for future generations. Increasingly, researchers are highlighting the need to apply sustainable urban growth principles to the field of urban development, arguing that this would reduce the negative impacts of urbanization processes (poverty, air pollution, unemployment, and crime). At the same time, cities are competing with one another to maintain their position in the urban hierarchy, not only in the short term, but also in the long term. The COVID-19 pandemic affected many areas of our everyday life: over 5.85 million deaths, increased unemployment, the introduction of restrictions, the closure of national borders, and various other circumstances have all undoubtedly affected to a certain degree those factors which serve to influence competitiveness. The aim of this article is to conduct an integrated competitiveness assessment of the Baltic capitals within the context of the impact of the COVID-19 pandemic for the period 2015–2020, according to an integrated assessment model for urban competitiveness (MDK), which is based upon the principles of sustainable development. A systematic and comparative analysis of the concepts published in the scientific literature has been performed, the concepts of sustainable city and sustainable urban competitiveness have been formed, research and methods of urban competitiveness evaluation have been carried out, and a comprehensive competitiveness assessment of the results of the study showed that the evaluation of integrated competitiveness of a sustainable city in the context of the impact of COVID-19 (using the Baltic capitals as an example in the period 2015–2020), in terms of the multi-criteria SAW and TOPSIS evaluation methods, is in the highest position in 2016–2019. Vilnius is in second place during the whole period 2016–2019. Riga takes third place in 2015–2019 (except in 2020, when it exchanges places with Vilnius and takes second place). Meanwhile, the results of the COPRAS multicriteria method differ from those discussed above. In 2016, 2019, and 2020, Tallinn is in the highest position, and in 2015, 2017, and 2018, it is surpassed by Vilnius. Riga remained in third place from 2015 to 2019. In 2020, Vilnius took over this position.

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“…Bare land was the surface with no vegetation that was similar to the definition in CORINE land-cover nomenclature [33]. We combined high-resolution images in Google Earth to collect samples [34,35] and adopted the Support Vector Machine (SVM) classifier for classification [33,36]. The overall accuracy (OA) of the classification results were greater than 90%, and the results passed the Kappa coefficient test.…”

Section: Remote Sensing Data Processing and Landscape Pattern Calculamentioning

confidence: 99%

Understanding the Correlation between Landscape Pattern and Vertical Urban Volume by Time-Series Remote Sensing Data: A Case Study of Melbourne

Fang

Gong

et al. 2021

IJGI

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Urbanization is changing the world’s surface pattern more and more drastically, which brings many social and ecological problems. Quantifying the changes in the landscape pattern and 3D structure of the city is important to understand these issues. This research study used Melbourne, a compact city, as a case study, and focused on landscape patterns and vertical urban volume (volume mean (VM), volume standard deviation (VSD)) and investigate the correlation between them from the scope of different scales and functions by Remote Sensing (RS) and Geographic Information System (GIS) techniques. We found: (1) From 2000 to 2012, the landscape pattern had a trend of decreasing fragmentation and increasing patch aggregation. The growth of VM and VSD was more severe than that of landscape metrics, and presented a “high–low” situation from the city center to the surroundings, maintaining the structure of “large east and small west”. (2) Landscape pattern was found closely associated with the urban volume. In the entire study area, landscape pattern patches with low fragmentation and high aggregation were directly proportional to VM with high value, which represented high urbanization, and patches with high connectivity and fragmentation had a positive relationship with high VSD, which represented strong spatial recognition. (3) The urban volumes of different urban functional areas were affected by different landscape patterns, and the analysis based on the local development situation can explain the internal mechanism of the interaction between the landscape pattern and the urban volume.

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Interaction Relationship Between Built-Up Land Expansion and Demographic-Social-Economic Urbanization in Shanghai-Hangzhou Bay Metropolitan Region of Eastern China

Cited by 17 publications

References 47 publications

Built‐up land expansion simulation with combination of naive Bayes and cellular automaton model—A case study of the Shanghai‐Hangzhou Bay agglomeration

Built‐up land expansion simulation with combination of naive Bayes and cellular automaton model—A case study of the Shanghai‐Hangzhou Bay agglomeration

An Integrated Assessment of the Competitiveness of a Sustainable City within the Context of the COVID-19 Impact

Understanding the Correlation between Landscape Pattern and Vertical Urban Volume by Time-Series Remote Sensing Data: A Case Study of Melbourne

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