19th Advanced Summer School in Regional Science

Overview of advanced techniques in ArcGIS data manipulation

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

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

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

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

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)

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

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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

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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!

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

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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

Rasters to measure distance Analysis of minimum distance path

– Identifies roadway sections that might carry less traffic

– Generate a contour map of costs

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

Night Lights Data DMSP/OLS

– Began in 1978– Approx 2.5 KM

resolution– Problems

• Diffusion or “bloom”

• Lighting technology

• Sensitivity to density

• Instrument saturation

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

Night light data First – download data from NOAA Second – clip to European region Third – form composite image

– Load different years into different colors

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 GDP as a function of Light

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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