Results 21 to 30 of about 6,324 (226)
Assessing the role of spatial externalities in the survival of Italian innovative startups
Regional Science Policy &Practice, EarlyView., 2023 Abstract The paper provides novel empirical evidence about the effects of spatial externalities on the survival of innovative startups in Italy. Using geocoded firm‐level data, we build micro‐geographic measures of specialization and diversity that are robust to the modifiable areal unit problem.
Diego Giuliani +4 more
wiley +1 more source
On Robustness for Spatio-Temporal Data
Mathematics, 2022 The spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept.
Alfonso García-Pérez
doaj +1 more source
Application of Dueling DQN and DECGA for Parameter Estimation in Variogram Models
IEEE Access, 2020 Parameter estimation of variogram models is an important problem in geostatistics and environmental engineering. Most of existing works aim to estimate parameters of variogram single models, while neglecting the parameter estimation of variogram nested ...
Yu Liu, Cong Zhang
doaj +1 more source
Empirical variogram for achieving the best valid variogram
Communications for Statistical Applications and Methods, 2020 Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by
Esam Mahdi +2 more
openaire +2 more sources
A Diagnostic to Assess the Fit of a Variogram Model to Spatial Data
Journal of Statistical Software, 1996 The fit of a variogram model to spatially-distributed data is often difficult to assess. A graphical diagnostic written in S-plus is introduced that allows the user to determine both the general quality of the fit of a variogram model, and to find ...
Ronald Barry
doaj +1 more source
Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium [PDF]
Hydrology and Earth System Sciences, 2011 Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces.
S. Ly, C. Charles, A. Degré
doaj +1 more source
Verification with Variograms [PDF]
Weather and Forecasting, 2009 Abstract The verification of a gridded forecast field, for example, one produced by numerical weather prediction (NWP) models, cannot be performed on a gridpoint-by-gridpoint basis; that type of approach would ignore the spatial structures present in both forecast and observation fields, leading to misinformative or noninformative ...
Caren Marzban, Scott Sandgathe
openaire +1 more source
Remote Sensing, 2023 The analysis of spatiotemporal changes of landscape patterns is of great significance for forest protection. However, the selection of landscape metrics is often subjective, and existing composite landscape metrics rarely consider the effects of spatial ...
Ying Zhang +3 more
doaj +1 more source
Zoning and uncertainty analysis of heavy metal pollution risk in surface water resources of copper mine by Bayesian analysis and sequential Gaussian simulation [PDF]
علوم محیطی, 2020 Introduction: Industrial and agricultural activities resulting in the production of toxic heavy metals may endanger water quality, public health, and the environment.
Akram Seifi, Hossien Riahi
doaj +1 more source
Optimal interpolation and isarithmic mapping of soil properties .1. The semi-variogram and punctual kriging [PDF]
, 2019 Kriging is a means of spatial prediction that can be used for soil properties. It is a form of weighted local averaging. It is optimal in the sense that it provides estimates of values at unrecorded places without bias and with minimum and known variance.
Webster, R. +3 more
core +1 more source