10 datasets

NCL Data2026 · dataset

<p>Current approaches for modeling discrete-valued outcomes associated with spatially-dependent areal units incur computational and theoretical challenges, especially in the Bayesian setting when full posterior inference is desired. As an alternative, we propose a novel statistical modeling framework for this data setting, namely a mixture of directed graphical models (MDGMs). The components of th

figshare2026 · Astronomical catalogue

<p dir="ltr">Local autogressive geographically and temporally weighted regression (LARGTWR) models have been demonstrated to be an effective tool for simultaneously capturing the spatiotemporal heterogeneity of autocorrelation in the response variable and regression relationship. In this model, both the autoregressive and regression coefficients are assumed to be varying over both space and time.

figshare2026 · Astronomical catalogue

<p dir="ltr">This repository contains the data and code supporting the study “Construction and Spatial Uncertainty Assessment of Urban Morphology-Constrained Atomic-Level Population Analysis Units” The study proposes an urban spatial morphology-constrained atomic population analysis unit (APAU) framework for delineating fine-scale population analysis units that are both morphologically meaningful

figshare2026 · Astronomical catalogue

<p dir="ltr">The example of using Python package fuzzygd to analyze the spatial data by the Fuzzy Geographical Detector (FGD)</p><p dir="ltr">Cite:Chen, J., Cheng, S., Zhang, W., Wang, J., & Lu, F. (2026). Fuzzy geographical detector: enhancing spatial attribution analysis for continuous geographical variables. International Journal of Geographical Information Science, 1–30. DOI: https://doi.org/1

figshare2026 · Astronomical catalogue

<p dir="ltr">The research examines the impact of the Kriging method, variogram model, and grid configuration on the spatial interpolation outcomes for soil data in South Africa. Two independent spatial datasets containing measurements of cation exchange capacity (CEC) were analysed. Five Kriging techniques, Simple Kriging, Ordinary Kriging, Universal Kriging, Ordinary Co-Kriging, and Regression Kr

figshare2026 · Astronomical catalogue

<p dir="ltr">This is the supporting material containing statistical software files and code used in the analysis of the main manuscript.</p>

Teesside University Research Data Repository2026 · dataset

This dataset contains georeferenced locations of individual Amaranthus retroflexus L. plants mapped across two vegetable field trials at the North Willamette Research and Extension Center, Aurora, Oregon, in 2025, together with the plot boundaries and the trained instance-segmentation models used to generate them. The data were collected to address whether conventional quadrat sampling can adequat

BERD@NFDI Data Portal2025 · dataset

The "Africapolis" dataset is the only comprehensive, standardized geospatial database on cities and urbanization dynamics across the entire African continent, produced by the OECD Sahel and West Africa Club (OECD/SWAC). It maps over 11,000 urban agglomerations with 10,000 or more inhabitants in 54 countries, providing historical demographic data from 1950 to 2020 and detailed spatial boundaries fo

Borealis + Agri-environmental Research Data Dataverse2021 · dataset · unknown

Abstract Although genome scans have become a popular approach towards understanding the genetic basis of local adaptation, the field still does not have a firm grasp on how sampling design and demographic history affect the performance of genome scans on complex landscapes. To explore these issues, we compared 20 different sampling designs in equilibrium (i.e. island model and isolation by distanc

Harvard Dataverse2007 · dataset · unknown

Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for creating storm surge forecasts for coastal areas. These models are driven primarily by the surface wind