Sentence examples for spatially disaggregated from inspiring English sources

We examine studies across spatial scales: national scale studies using energy system models; regional scale studies optimising spatially disaggregated hydrogen infrastructure; and local scale studies optimising the siting of filling stations.

Seismicity data for the region of interest must be spatially disaggregated into discrete seismic sources.

Crucially, recent work has shown robust correlations between the relative intensity of spatially disaggregated nighttime luminosity and GDP at both national and subnational resolutions.

This paper describes a spatially disaggregated, economic agent-based model of urban land use, which is named for its innovative feature of coupled housing and land markets (CHALMS).

A multimodal social welfare maximisation model with spatially disaggregated demand is developed, in which users choose between travelling by bus, car or walking in a transport corridor.

Consequently, African states would be in the position to invest huge expenditures to redress environmental damages on the numerous and spatially disaggregated small fields if negatively impacted upon and the resulting public health consequences if adverse effects emerge [9].

A multivariate stochastic soil moisture (SM) estimation approach based on a Gaussian-mixture nonstationary hidden Markov model (GM-NHMM) is introduced in this study to spatially disaggregate the AMSR2 SM data for multiple locations in the Yongdam dam watershed in South Korea.

The aim of this study was to predict STUs by spatially disaggregating SMUs through a decision-tree approach that considered expert knowledge about soil-landscape relationships embedded in soil databases.

SASI is a recursive (i.e. equilibrium-seeking) model aimed at analysing the impacts of infrastructures and other major changes in the transport system on local economies, in which these impacts are modelled using regional production functions including spatially disaggregate accessibility indicators.

More recently, the increasing availability of geographical information systems (GIS) together with the proliferation of spatially disaggregate data has led to an improved analytical and evidence base with which to identify and target those groups and areas with poorer accessibility and physician shortage [ 19, 20].

However, these low spatial-temporal resolution datasets are often difficult to downscale spatially and disaggregate temporarily, and they may not be accurate for local watersheds (i.e., state level or smaller watersheds).