19 th advanced summer school in regional science combining vectors and rasters in arcgis
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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
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