Dataset Information

Spatializing household energy consumption in the Netherlands: Socioeconomic, urban morphology, microclimate, land surface temperature and vegetation data.

ABSTRACT: Household energy consumption (HEC) is affected by a variety of determinants. In addition to the level of HEC in 2612 residential zones in the Netherlands (the so-called wijk) in 2014, this dataset provides a geographically-referenced data of 11 determinants of HEC on: (1) socioeconomic characteristics - namely income per capita, household size, population density; (2) urban morphology -namely buildings' surface to volume ratio, building age; (3) microclimate factors -namely number of summer days, number of frost days, humidity, wind speed at 10 m height; (4) land surface temperature; (5) normalized difference vegetation index (NDVI). The dataset is initially prepared for an analysis titled as "Land surface temperature and households' energy consumption: who is affected and where?" [1].

SUBMITTER: Mashhoodi B

PROVIDER: S-EPMC6970170 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Spatializing household energy consumption in the Netherlands: Socioeconomic, urban morphology, microclimate, land surface temperature and vegetation data.

Mashhoodi Bardia B Stead Dominic D van Timmeren Arjan A

Data in brief 20200110

Household energy consumption (HEC) is affected by a variety of determinants. In addition to the level of HEC in 2612 residential zones in the Netherlands (the so-called wijk) in 2014, this dataset provides a geographically-referenced data of 11 determinants of HEC on: (1) socioeconomic characteristics - namely income per capita, household size, population density; (2) urban morphology -namely buildings' surface to volume ratio, building age; (3) microclimate factors -namely number of summer days ...[more]

PMID: 31989011

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Project description:The emergence of mosquito-transmitted viruses poses a global threat to human health. Combining mechanistic epidemiological models based on temperature-trait relationships with climatological data is a powerful technique for environmental risk assessment. However, a limitation of this approach is that the local microclimates experienced by mosquitoes can differ substantially from macroclimate measurements, particularly in heterogeneous urban environments. To address this scaling mismatch, we modeled spatial variation in microclimate temperatures and the thermal potential for dengue transmission by Aedes albopictus across an urban-to-rural gradient in Athens-Clarke County GA. Microclimate data were collected across gradients of tree cover and impervious surface cover. We developed statistical models to predict daily minimum and maximum microclimate temperatures using coarse-resolution gridded macroclimate data (4000 m) and high-resolution land cover data (30 m). The resulting high-resolution microclimate maps were integrated with temperature-dependent mosquito abundance and vectorial capacity models to generate monthly predictions for the summer and early fall of 2018. The highest vectorial capacities were predicted for patches of trees in urban areas with high cover of impervious surfaces. Vectorial capacity was most sensitive to tree cover during the summer and became more sensitive to impervious surfaces in the early fall. Predictions from the same models using temperature data from a local meteorological station consistently over-predicted vectorial capacity compared to the microclimate-based estimates. This work demonstrates that it is feasible to model variation in mosquito microenvironments across an urban-to-rural gradient using satellite Earth observations. Epidemiological models applied to the microclimate maps revealed localized patterns of temperature suitability for disease transmission that would not be detectable using macroclimate data. Incorporating microclimate data into disease transmission models has the potential to yield more spatially precise and ecologically interpretable metrics of mosquito-borne disease transmission risk in urban landscapes.

Dataset Information

Spatializing household energy consumption in the Netherlands: Socioeconomic, urban morphology, microclimate, land surface temperature and vegetation data.

Publications

Spatializing household energy consumption in the Netherlands: Socioeconomic, urban morphology, microclimate, land surface temperature and vegetation data.

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