Stata in Space: Stata in Space: An example for the An example for the econometric analysis of econometric analysis of spatially explicit raster spatially explicit raster data data --- Daniel Müller --- Institute of Agricultural Economics and Social Sciences Humboldt University Berlin Berlin -- August, 12 th , 2003
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Stata in Space: An example for the econometric analysis of spatially explicit raster data --- Daniel Müller --- Institute of Agricultural Economics and.
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Stata in Space:Stata in Space:An example for the econometric An example for the econometric
analysis of spatially explicit raster dataanalysis of spatially explicit raster data
--- Daniel Müller ---
Institute of Agricultural Economics and Social Sciences
Humboldt University Berlin
Berlin -- August, 12th, 2003
Outline
1. Introduction
2. Spatial data analysis
3. Data preparation
4. The empirical example
5. Econometric estimation
6. Export of results and geovisualization
Introduction- Socioeconomic data usually exist for (discrete)
social entities, rarely explicitly linked to location (georeferenced)
- ‘Natural’ data: often continuous (rainfall, slope, elevation) and georeferenced
- Integration of both data sources can provide additional insights
- Allows to understand spatial patterns & processes- Knowing the where can help us infer the why
Spatial data analysis- Spatial analysis is the analysis of data linked to
location (spatial data)
- Why analysis of spatial data ?- Variables of interest vary in space- Location matters!
- Spatial analysis can provide important insights:- geographical targeting of investments- diffusion of technologies- causes and consequences of land-use change
What’s special about spatial data ?
=> Location matters !!!
=> Tobler’s 1st law of geography (1979):
“Everything is related to everything else, but near things are more related than distant things.”
=> Spatial effects:- spatial autocorrelation
- spatial heterogeneity
Spatial data analysis
Spatial data analysisPeculiarities in space: Spatial effects
1. Spatial autocorrelation
- Coincidence of value similarity with locational similarity
- Second dimension adds mathematical complexity (multiple directions)
2. Spatial heterogeneity
- Each location is unique
- Units of observations not homogeneous across space
- Structural instability over space, e.g. heteroskedasticity
Spatial data analysisPeculiarities in space: spatial effects [2]
Spatial effects due to:- interactions among neighboring agents- data from different sources- different sample designs- varying aggregation rules
“Spatial relationships among observations can result in unreliable estimates and misguided statistical inference of the parameters.” (Anselin 1988).
=> Corrections necessary
Spatial data analysis
Geographic Information Systems (GIS):
- Compile, store, manipulate, analyse, visualize spatial data
- Consist of hardware, software, data and procedures
- Data models: vector & raster
Spatial data analysis
Raster data model:- Arrangement of regularly shaped, contiguous cells
- Continuous data layers; fit together edge-to-edge
- Typically consist of square cells
- Each cell represents a location in a raster GIS
- Cells are arranged in layers
- Values of a cell indicate characteristics of that location
- Data is composed of many layers covering the same geographical area
Spatial data analysis
Raster data model --- file structure:
1 1 2 2 2
3 1 3 2 1
2 3 3 4 2
3 5 5 6 6
6 6 4 4 6
Header: Contains spatial information!
Spatial data analysisRaster data model --- land use map:
Spatial data analysisFrom data layers to resulting map