A spatial data structure integrating GIS and simulation in a marine environment

1995 ◽  
Vol 18 (3) ◽  
pp. 213-228 ◽  
Author(s):  
Christopher M. Gold ◽  
Alfonso R. Condal
Keyword(s):  
Data Structure ◽  
Marine Environment ◽  
Spatial Data ◽  
Spatial Data Structure

An innovative linear unsupervised space adjustment by keeping low-level spatial data structure

2018 ◽  
Vol 59 (2) ◽  
pp. 437-464 ◽  
Author(s):  
Samad Nejatian ◽  
Vahideh Rezaie ◽  
Hamid Parvin ◽  
Mohamadamin Pirbonyeh ◽  
Karamolah Bagherifard ◽  
...  
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
Low Level ◽  
Spatial Data Structure

scholarly journals Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

2020 ◽  
Author(s):  
Andrea Araujo Navas ◽  
Frank Osei ◽  
Ricardo J. Soares Magalhães ◽  
Lydia R. Leonardo ◽  
Alfred Stein
Keyword(s):  
Risk Factors ◽  
Data Structure ◽  
Schistosoma Japonicum ◽  
Spatial Data ◽  
Environmental Data ◽  
Environmental Drivers ◽  
Parameter Estimates ◽  
Reliable Parameter ◽  
Spatial Data Structure ◽  
Spatial Support

Abstract Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on Schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m, and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models.Methods: We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics. Results: Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease. Conclusions: Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programs by providing reliable parameter estimates at the same spatial support, and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.

A fault tolerant peer-to-peer spatial data structure

2016 ◽  
Vol 10 (4) ◽  
pp. 874-886
Author(s):  
Pouya Bisadi ◽  
Zahra Mirikharaji ◽  
Bradford G. Nickerson
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
Fault Tolerant ◽  
Peer To Peer ◽  
Spatial Data Structure

A robust and efficient spatial data structure

1992 ◽  
Vol 29 (4) ◽  
pp. 335-373 ◽  
Author(s):  
M. Aris Ouksel ◽  
Otto Mayer
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
Spatial Data Structure
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