19th Advanced Summer School in Regional Science

Combining Vectors and Rasters in ArcGIS

Outline First Day

– Introduction to GIS using ArcGIS– Training with ArcGIS– Overview and more advanced directions– Training with ArcGIS

Second Day– GIS topics with ArcGIS: Raster and other data– Training with ArcGIS– Overview and advanced data manipulation– Training with ArcGIS

Online Data and Presentation Sources of Data and Assistance

– http://www.esri.com – http://www.geographynetwork.com– CIESIN – GRUMP land use data– NOAA night light data

Data Presentation– Google Earth– http://www.williams.edu/Economics/UrbanGrowth/HomePage.htm – Shapefile conversion utilities available at

esri.com

ArcMap Intermediate: Merging Features Editing Data

– Yesterday we say modifying– Consider the problem of merging features– The Editor can be useful for small jobs

ArcMap Intermediate: Merging Features Merging features according to a variable?

– Arises when we have data at a fine geography and we want to merge to a coarser geography to match other data

– Could be done using editor– Faster to use Toolbox - Dissolve

Problems merging features Problems arise with small topographical errors

– “Slivers”– Gaps between adjacent features that should match up– Clean this up with Toolbox – Integrate

If small number of errors arise – clean up manually

Making your own shapefiles Some research relies on historical data or

data from developing countries with little GIS compatible data available

Paper maps can be scanned and registered Once scanned, the structures in the maps

can be traced– Manually – using the editor– Semi-automatically – using the ArcScan

extension

Raster Data Raster data (like vector) require projection ArcGIS can handle data more efficiently if

they are projected Consider the elevation data provided for the

second lab

Raster Data Order of loading layers makes a difference

– Load municipal points then elevation– Load elevation then municipal points– Note the difference!

Raster Data Values can be a problem Note elevation for many Dutch

municipalities– Elevation data are coded 99999 for below sea

level– Easily corrected through reclassification

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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i ii

Z s Z s

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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2 i jE Z s Z s

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 Use highway raster to find the shortest path

to Groningen Use zonal statistics to add cost of travel for

each city Use cost to scale city symbols

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Rasters to measure distance Analysis of minimum distance path

– Identifies roadway sections that might carry less traffic

– Generate a contour map of costs

Final topics Raster elevation data are particularly widely

used– For calculating slope

• Caution! – if cell size is not in the same units as vertical measurements

• Scale using Z factor

– For calculating aspect– For calculating viewshed