Spatial and temporal dynamics of multidimensional well-being, livelihoods and ecosystem services in coastal Bangladesh.
Helen AdamsW Neil AdgerSate AhmadAli AhmedDilruba BegumAttila N LázárZoe MatthewsMohammed Mofizur RahmanPeter Kim StreatfieldPublished in: Scientific data (2016)
Populations in resource dependent economies gain well-being from the natural environment, in highly spatially and temporally variable patterns. To collect information on this, we designed and implemented a 1586-household quantitative survey in the southwest coastal zone of Bangladesh. Data were collected on material, subjective and health dimensions of well-being in the context of natural resource use, particularly agriculture, aquaculture, mangroves and fisheries. The questionnaire included questions on factors that mediate poverty outcomes: mobility and remittances; loans and micro-credit; environmental perceptions; shocks; and women's empowerment. The data are stratified by social-ecological system to take into account spatial dynamics and the survey was repeated with the same respondents three times within a year to incorporate seasonal dynamics. The dataset includes blood pressure measurements and height and weight of men, women and children. In addition, the household listing includes basic data on livelihoods and income for approximately 10,000 households. The dataset facilitates interdisciplinary research on spatial and temporal dynamics of well-being in the context of natural resource dependence in low income countries.
Keyphrases
- climate change
- human health
- healthcare
- mental health
- blood pressure
- electronic health record
- cross sectional
- polycystic ovary syndrome
- risk assessment
- big data
- body mass index
- primary care
- public health
- heavy metals
- physical activity
- young adults
- high resolution
- pregnancy outcomes
- health information
- adipose tissue
- weight loss
- machine learning
- pregnant women
- depressive symptoms
- cervical cancer screening
- insulin resistance
- mass spectrometry
- hypertensive patients
- blood glucose
- skeletal muscle
- artificial intelligence
- middle aged
- patient reported