Introduction to Areal Interpolation and MGGGs maup Ruth Buck - - PowerPoint PPT Presentation

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Introduction to Areal Interpolation and MGGGs maup Ruth Buck - - PowerPoint PPT Presentation

Aug 07, 2023 147 likes •329 views

Introduction to Areal Interpolation and MGGGs maup Ruth Buck Framing the problem 10 10 10 10 ? ? 10 10 10 10 10 10 10 10 ? ? 10 10 10 10 We have data for the blue source units that we want on the orange target units.

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Introduction to Areal Interpolation and MGGG’s maup

Ruth Buck

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Framing the problem

We have data for the blue source units… …that we want on the orange target units. 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 ? ? ? ?

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Given data for a set of subareas, we want to find a function that best estimates the whole surface so that we may predict values for a different set of subareas (Lam 1983).

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10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10

Example 1: Centroid approach

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? = 20 ? = 50 ? = 30 ? = 60

Example 1: Centroid approach

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10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 ? = 20 ? = 50 ? = 30 ? = 60

Example 2: Overlay with simple aggregation

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10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10

Example 3: Overlay with areal weighting

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10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10

*

1 1 1 1 1 1 1 .99 .01 .16 .84 .58 .42 .45 .45 .55 .55 .23 .77 .23 .23 .77 .09 .68 .67 .25 .08

Example 3: Overlay with areal weighting

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? = 22.3 ? = 40.3 ? = 42.5 ? = 54.9 10 10 10 10 10 10 10 9.9 .1 1.6 8.4 5.8 4.2 5.5 5.5 4.5 4.5 .8 2.5 .9 6.8 2.3 7.7 7.7 2.3 2.3 6.7

Example 3: Overlay with areal weighting

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Which interpolation method is best?

It depends on…

  • The data you are interpolating
  • Availability of ancillary data
  • Computational power
  • Time

CIESEN 2018: Gridded Population of the World (~1 km)

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Which interpolation method is best?

NLCD 2011

It depends on…

  • The data you are interpolating
  • Availability of ancillary data
  • Computational power
  • Time
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Which interpolation method is best?

It depends on…

  • The data you are interpolating
  • Availability of ancillary data
  • Computational power
  • Time

Piotr Jaworski

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Which interpolation method is best?

It depends on…

  • The data you are interpolating
  • Availability of ancillary data
  • Computational power
  • Time

OpenClipart

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When is this relevant in redistricting?

  • We have population and other demographic data at the census block

level that we would like at the precinct level

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When is this relevant in redistricting?

  • We have election results at the precinct level that we would like to

disaggregate to census blocks

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When is this relevant in redistricting?

  • Absentee results are only available at the county level and our

analysis uses precincts as the unit of analysis

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When is this relevant in redistricting?

  • CVAP data is available at the census block group level and we need it

at the level of census blocks or precincts

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maup Package

assign intersections prorate Essentially a spatial join; maps each source unit to the target unit that contains the majority of its area Overlays the source and target units and

  • utputs the

common refinement Uses the common refinement to interpolate data from the sources to targets based on user-specified weights