Summary: Topic 1 Spatial Analysis
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1.1 Spatial Analysis
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What are spatial statistical analysis?
Subsumes statistical methods to determine the likelihood of spatial processes and patterns
- evaluates how entities are spatially distributed
- analyze the relationships between patterns -
What are fundamental components of these methods?
Space and spatial arrangement -
1.2 Spatial concepts
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What are the two spatial concepts?
Spatial autocorrelation
Spatial hetrogeneity -
What is spatial autocorrelation (SAC)?
An obvious fact --> temperature in Utrecht and Amsterdam
Refelects a situation where values observed at one location depend on the values of neighboring oberservations
And functional relationships of objects of one variable changes with distance --> known as spatial spillovers -
What is the First Law of Geography?
All things are related, but nearby things are more related than distance things -
What is spatial hetrogeneity?
Characteristics of a population/sample depends on the absolute location
Patterns vary over space
No "average places" -
Why does space matter? Why is it important to know how a pattern is distributed?
- Data is not independent
- Redundancies reduce the data information content
- Diagnostic tool for statistical models
- Regression model: t-test, F-test are affected
- SAC has serious concequences for non-spatial statistical analysis --> might result in wrong conclusions
- Data is not independent
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What is row standardization?
Each weight is divided by its row sum, rows sum to 1, thus allows comparison between parameters -
1.3 Exploratory spatial data analysis
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What are methods for global statistics?
Join Count statistic: for nominal data
Moran’s I: Interval/ratio data
Geary’s C: Interval/ ratio data
Autoregressive models -
What is the workflow of local/global SAC analysis?
1. Define the spatial system
2. Select and compute a statistic
3. Test the significance
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