Dataset Information

Identification of urban land use efficiency by indicator-SDG 11.3.1.

ABSTRACT: Inefficiency in urban land use is one of the problems caused by rapid urbanization. The UN Sustainable Development Goals (SDGs) indicator 11.3.1 is designed to test urban land use efficiency. This study employed geospatial and statistical data to compute land use efficiencies from 1990 to 2015 with five 5-year and ten 15-year intervals in Wukang, center of Deqing County, China. A flowchart was designed to extract the built-up lands from multiple data sources. The produced built-up lands were demonstrated to provide good accuracy by constructing an error matrix between the extracted and manually interpreted built-up lands as classified and reference images, respectively. By using the model provided by UN metadata to calculate SDG 11.3.1, the land use efficiencies from 1990 to 2015 were identified in Wukang. Our results indicate that the land use efficiency in Deqing County center is lower than the average of cities around the world, primarily because our in-situ study focused on a county center with larger rural regions than urban areas. Over the long term, urban land use becomes denser as the population grows, which will have a positive impact on the sustainability of urban development. This work is helpful for the local government to balance urban land consumption and population growth.

SUBMITTER: Cai G

PROVIDER: S-EPMC7769485 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of urban land use efficiency by indicator-SDG 11.3.1.

Cai Guoyin G Zhang Jinxi J Du Mingyi M Li Chaopeng C Peng Shu S

PloS one 20201228 12

Inefficiency in urban land use is one of the problems caused by rapid urbanization. The UN Sustainable Development Goals (SDGs) indicator 11.3.1 is designed to test urban land use efficiency. This study employed geospatial and statistical data to compute land use efficiencies from 1990 to 2015 with five 5-year and ten 15-year intervals in Wukang, center of Deqing County, China. A flowchart was designed to extract the built-up lands from multiple data sources. The produced built-up lands were dem ...[more]

PMID: 33370312

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Project description:Children's respiratory health has been linked to many factors, including air pollution. The impacts of urban land-use on health are not fully understood, although these relationships are of key importance given the growing populations living in urban environments.We investigated whether the degree of urban land-use near a family's residence is associated with severity of respiratory symptoms like wheeze among infants.Wheeze occurrence was recorded for the first year of life for 680 infants in Connecticut for 1996-1998 from a cohort at risk for asthma development. Land-use categories were obtained from the National Land Cover Database. The fraction of urban land-use near each subject's home was related to severity of wheeze symptoms using ordered logistic regression, adjusting for individual-level data including smoking in the household, race, gender, and socio-economic status. Nitrogen dioxide (NO(2)) exposure was estimated using integrated traffic exposure modeling. Different levels of urban land-use intensity were included in separate models to explore intensity-response relationships. A buffer distance was selected based on the log-likelihood value of models with buffers of 100-2000 m by 10 m increments.A 10% increase in urban land-use within the selected 1540 m buffer of each infant's residence was associated with 1.09-fold increased risk of wheeze severity (95% confidence interval, 1.02-1.16). Results were robust to alternate buffer sizes. When NO(2), representing traffic pollution, was added to the model, results for urban land-use were no longer statistically significant, but had similar central estimates. Higher urban intensity showed higher risk of prevalence and severity of wheeze symptoms.Urban land-use was associated with severity of wheeze symptoms in infants. Findings indicate that health effect estimates for urbanicity incorporate some effects of traffic-related emissions, but also involve other factors. These may include differences in housing characteristics or baseline healthcare status.

Dataset Information

Identification of urban land use efficiency by indicator-SDG 11.3.1.

Publications

Identification of urban land use efficiency by indicator-SDG 11.3.1.

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