19th Advanced Summer School in Regional Science

1
19th Advanced Summer School in Regional Science

• Combining Vectors and Rasters in ArcGIS

2
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

3
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/Home
  Page.htm
• Shapefile conversion utilities available at
  esri.com

4
ArcMap Intermediate Merging Features

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

5
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

6
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

7
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

8
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

9
Raster Data

• Order of loading layers makes a difference
• Load municipal points then elevation
• Load elevation then municipal points
• Note the difference!

10
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

11
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

12
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

13
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

14
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

15
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)

16
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

17
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

18
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!

19
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

20
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

21
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

22
Rasters to measure distance

• Analysis of minimum distance path
• Identifies roadway sections that might carry less
  traffic
• Generate a contour map of costs

23
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