Dataset Information

Land cover matters to human well-being.

ABSTRACT: People migrate from rural to urban areas. In the meantime, the benefits of staying in greener areas are also known. People's preferences might be different by area that is composed of several land types. If so, the effect of particular land cover on human well-being is different spatially. The spatial analysis is required to formulate effective land-use policies. Here we show that urban land, water, and grassland are positively related to human well-being, whereas bare land is negatively associated in Japan. A 1 [Formula: see text] increase in the area of urban land per capita in a city is equivalent to an about 346 USD increase in the individual annual income of all the people in the city. Additionally, monetary values of areas of water, crops, and bare land per capita are 102, - 30, and - 268 [Formula: see text]. Furthermore, the spatial context matters to the relationship between land cover and human well-being. This paper investigates the monetary values of several land types and their spatial variability, which provides insights to make better usage for land cover.

SUBMITTER: Li C

PROVIDER: S-EPMC8342716 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Land cover matters to human well-being.

Li Chao C Managi Shunsuke S

Scientific reports 20210805 1

People migrate from rural to urban areas. In the meantime, the benefits of staying in greener areas are also known. People's preferences might be different by area that is composed of several land types. If so, the effect of particular land cover on human well-being is different spatially. The spatial analysis is required to formulate effective land-use policies. Here we show that urban land, water, and grassland are positively related to human well-being, whereas bare land is negatively associa ...[more]

PMID: 34354163

Similar Datasets

Project description:PurposeTo test the feasibility of introducing 'Free Time for Wellness' (FT4W) an intervention to increase healthy behaviours and reduce the risk of cancer.DesignFeasibility study; Setting: Washington Heights, New York, USA is a low socioeconomic status area.SubjectsMothers aged 18 and above with children under 12 years of age and living in Washington Heights were recruited.InterventionFT4W, a community-based intervention delivered through a neighbourhood-based app, offering weekly dance and yoga classes, food pantry visits and group playdates. Childcare professionals cared for participants' children during wellness activities.MeasuresA bespoke before and after survey was designed and tested for its ability to collect relevant data to assess the impact of FT4W. Outcomes included recruitment rates, participation, attrition, acceptability, and success of the community champion.AnalysisComparisons of proportions and means.ResultsTwenty-one mothers participated in the study of which 90% attended ≥ 1 FT4W activity; 65% ≥ 2; 52% ≥ 3. The survey was completed by a 100% of participants indicating it was easy to understand and not too burdensome. All measures detected change in constructs from baseline to follow-up. Availability of childcare was the most commonly (66%) reported reason participants were able to engage in the offered wellness activities.ConclusionConducting a larger-scale trial to assess the impact of FT4W is feasible considering 4 major lessons. (1) Recruitment, retention, and acceptability rates were high; however, moms need additional support to increase participation in wellness activities and improve tech literacy. (2) Research measures were sensitive enough to detect change, but the timing of assessments needs to be considered. (3) Participants greatly valued access to professional childcare. (4) The Community Champion is a necessary, but difficult role to fill that requires careful consideration by the Institutional Review Board (IRB).

Project description:Research on spatial non-stationarity of land-cover classification accuracy has been ongoing for over two decades with most of the work focusing on single date maps. We extend the understanding of thematic map accuracy spatial patterns by: (1) quantifying spatial patterns of map-reference agreement for class-specific land-cover change rather than class-specific land cover for both omission and commission expressions of map error; (2) reporting goodness-of-fit estimates for the empirical models, which have been lacking in previous assessments, and; (3) using the empirical model results to map the locations of the relative likelihoods of map-reference agreement for specific land-cover change classes. We evaluated 10 map-based explanatory variables in single and multivariable logistic regression models to predict the likelihood of agreement between map and reference land-cover change (2001-2011) labels using the National Land Cover Database (NLCD) 2011 land cover and accuracy data. Logistic models for omission error had better goodness-of-fit estimates than models for commission error. For the omission error models, the explanatory variable, density of the mapped class-specific change in the immediate neighbourhood surrounding the sample pixel, produced the best model fit results (Tjur coefficient of discrimination, D, ranged from 0.59 to 0.98) compared to multivariable models and all other single explanatory variable models. Maps of the predicted likelihood of map-reference agreement produced from the best fitting omission error models provide a spatially explicit description of spatial variation of classification uncertainty at both local and regional scales. Application of the models indicated higher likelihoods of agreement (>50%) comprised a greater proportion of the land-cover change class area than the proportion of the land-cover change class with lower likelihoods of agreement. NLCD users can apply reported equations to map land-cover change uncertainty.

Dataset Information

Land cover matters to human well-being.

Publications

Land cover matters to human well-being.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets