19 th Advanced Summer School in Regional Science

1. 19th Advanced Summer School in Regional Science Overview of advanced techniques in ArcGIS data manipulation

2. Merging raster data with vector • Zonal statistics • Consider reading elevation into Dutch Municipalities • Now we can identify the Dutch cities most at risk from rising sea levels due to global warming • Join zonal statistics, select by attributes

3. Cutting the raster data down to size • Map of Dutch municipalities would be more attractive if elevation raster were smaller • Use Toolbox – Clip to trim raster • Loads more quickly as well

4. Raster Data • Creating rasters through interpolation • Interpolating from Points • Inverse distance weighted • Spline • Kriging • Interpolation from polygons is also possible – see this later in the program • Consider an example using the Netherlands zipcode data • Join poly data to point data by attributes • Interpolate manufacturing share • Join point data to poly spatially • Compare interpolations

5. Raster Interpolation • Given data at selected points • Most natural if these are samples from some process that is continuously distributed • Economic activity • Pollution levels • Construct a raster surface to approximate using these data • Value at each location should depend on the values of nearby points • Closer points should matter more • Simplest – average weighted by inverse distance

6. Raster Interpolation • Spatial Analyst can be used to construct an IDW raster approximation • Several paramters to set • Exponent to specify distance decay • Search radius (fixed distance, variable points) • Search radius (variable distance, fixed points)

7. Raster Interpolation: Kriging • Kriging provides a more sophisticated model of spatial dependence for interpolation • All interpolation approaches use some form of the relation: • location where an approximate value is to be calculated • locations with known values • Weights • IDW weights depend only on a power of distance • Kriging weights depend on the structure of spatial covariance

8. Raster Interpolation: Kriging • Kriging takes points with known values and estimates the “semi-variogram” as a function of distance • This is a scaled spatial covariance: • Kriging makes some assumptions about how this covariance depends on distance

9. Raster interpolations • How do these interpolation techniques compare? • IDW and Kriging capture some of the structure • The surface can be averaged over a region to provide an alternative measure • Zonal statistics again!

10. Rasters to measure distance • Raster data can be employed to measure distance and cost of travel • We started this process yesterday • Continue the analysis of distance • Spatial Analyst has several distance tools • Straight line • Cost weighted • Min distance

11. Rasters to measure distance • First step is to generate raster to represent the cost of traversing a pixel • Several possibilities • Use elevation – implies that traveler tries to remain at lowest elevation (like water!) • Use slope – implies that traveler tries to minimize the amount of climbing and descending • Use a transport network – cheapter to travel along major roads • Use a combination of these • Raster calculator can be used to combine different sources of cost

12. Rasters to measure distance • Analysis of minimum distance path • Identifies roadway sections that might carry less traffic • Generate a contour map of costs

13. Analysis of remotely sensed data • Modeling changes in urban land use • Theory suggests these changes should depend on several key variables • Population • Income • Transportation costs • Opportunity cost of urban land use (agricultural productivity) • Policy variables • Income measurement is a big problem for some countries • Strategy: use remotely sensed data to estimate

14. Night Lights Data • DMSP/OLS • Began in 1978 • Approx 2.5 KM resolution • Problems • Diffusion or “bloom” • Lighting technology • Sensitivity to density • Instrument saturation

15. Night light data • Night light levels might reasonably be related to several variables • Income • Per capita income • Latitude • Density of population • Global connectedness • Capital intensity of production • Explore to see what we can learn

16. Night light data • First – download data from NOAA • Second – clip to European region • Third – form composite image • Load different years into different colors

17. Night Light Data • Next – analyze • Dissolve NUTS3 data set as desired • Calculate GDP and Population for areas • Calculate log GDP • Use zonal statistics to calculate total light levels • Calculate log light level • Use graph to plot scatterplot

18. Now you can try this for smaller areas • Try this for NUTS 1 (or NUTS 2) areas • Try this for a particular country • Suggest some hypotheses about this relationship