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Selected Data About Geographic Locations

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...

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Related Experiment Video

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The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

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Published on: February 19, 2018

Performance of Information Criteria for Spatial Models.

Hyeyoung Lee¹, Sujit K Ghosh

¹Korea Institute of Patent Information.

Journal of Statistical Computation and Simulation

|September 12, 2009

Summary

Selecting the best spatial data model is crucial. This study evaluates Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Corrected AIC (AICc) performance in spatial model selection via simulations.

Area of Science:

Statistics
Spatial Analysis
Geostatistics

Background:

Model selection is vital for accurate statistical inference.
Traditional information criteria assume independent observations, which is often violated in spatial data.
Spatial data analysis requires specialized model selection techniques.

Purpose of the Study:

To evaluate the performance of popular model selection criteria for spatial data.
To compare Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Corrected AIC (AICc) in identifying the true spatial model.
To assess criterion performance under various spatial covariance models, including non-stationary ones.

Main Methods:

Monte Carlo simulation experiments were conducted.
Performance was evaluated using small to moderate sample sizes.

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Comparison involved various spatial covariance models, from stationary isotropic to non-stationary.

Main Results:

The study quantifies the ability of AIC, BIC, and AICc to select the correct spatial model across different scenarios.
Simulation results provide insights into the practical performance of these criteria with spatial data.
The effectiveness of criteria varied depending on the specific spatial covariance structure.

Conclusions:

The performance of traditional information criteria in spatial model selection needs careful consideration.
AIC, BIC, and AICc show varying degrees of success in selecting true spatial models.
Further research may be needed to develop or adapt criteria for complex spatial dependencies.