Using GIS to Model Patterns of UrbanRural Land Use Change

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Using GIS to Model Patterns of Urban-Rural Land
Use Change

• Elena Irwin,
• Assistant Professor
• Dept. of Agricultural, Environmental,
• and Development Economics
• Ohio State University

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Outline of Talk

• Land use trends at the rural-urban interface
• Modeling land use change
• GIS as a modeling tool

• Exploring patterns of land use change
• Generating spatial variables
• Predicting future land use changes

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What is the Rural-Urban Interface?

• Exurban Areas beyond the suburbs of an urban
  area, but within commuting distance.

Legend Urban High Density Urban Low
Density Suburban Exurban Rural
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Source Hart, 1995.
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Land Use Trends Ohio

• Increasing low density development
• 1992-97 Ohio ranked 8th in amount of land
  converted to urban uses (364,800 acres)
• 1990-2000 Ohio ranked 22nd in population growth
  (506,025 persons)
• 1982-1997 Amount of urban land consumed per
  person in Ohio increased 25 (from 0.36 to 0.45
  acres)
• Farmland Loss
• 1992-97 Ohio ranked 2nd in the nation in loss of
  prime agricultural land due to development
  (212,200 acres)

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Modeling Land Use Change

• Goal
• Understanding causes of land conversion to urban
  uses in order to predict how alternative policies
  will alter future land use change patterns.
• Model
• Statistical model that uses historical data on
  land use change to estimate the factors that are
  significant in causing conversion of land to
  urban use
• Approach
• Land use change is driven by individual decisions
  about location and land use
• Need a model of individual decisionmaking
• Change occurs at an individual parcel level
• Need parcel-level data

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Factors Affecting Individual Land Use Decisions

• Physical attributes of parcel
• Soil type
• Slope
• Land cover
• Location of parcel
• Proximity to urban areas
• Proximity to employment, retail, recreation
• Surrounding land uses
• Public services policies
• Public sewer/water
• School district quality
• Zoning

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Statistical Model

• Observed event A land parcel is either converted
  to an urban use or is not within a certain period
  of time.
• Statistical model explains this event as a result
  of the many factors (explanatory variables) that
  are hypothesized to influence land use conversion
  decisions.

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Data

• Parcel land use change
• Parcel characteristics
• Physical features
• Location
• Public services/policies

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Results of Statistical Model

• Estimates the significance and magnitude of each
  hypothesized factor in terms of its importance in
  causing land use change of parcel.
• Results can be used to predict future changes in
  land use pattern
• Baseline scenario
• No change in any factor
• Alternative scenarios
• How does a change in one of the factors that
  influences land use change alter the predicted
  land use pattern?

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Using GIS as a Modeling Tool

• Exploring patterns of land use change
• Generating spatial variables for a statistical
  model of land use change
• Predicting future patterns of land use change

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Exploring Patterns of Land Use Change

• Land use change maps to identify hot spots of
  development
• Using landscape metrics to measure the spatial
  pattern

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Baltimore-Washington Urban Growth Animation

• Source USGS Urban Dynamics Research Program
• http//landcover.usgs.gov/urban/data.html

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Landscape Pattern Metrics
Amount of fragmentation, dispersion, and other
spatial aspects of land use pattern can be
quantified using landscape pattern
measures. Aspects of pattern include number of
patches, mean patch size, total edge length, mean
nearest neighbor, mean perimeterarea ratio
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Carrion and Irwin (2002) Measuring Land Use
Pattern Change in Medina County, Ohio

• Landscape Pattern Metrics
• Four land uses residential, industrial,
  commercial and agricultural/forested use.
• Examples
• Number of patches, mean patch size, mean nearest
  neighbor
• Results
• Residential land use has become more fragmented
  and dispersed over time

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Number of Ag/Forest Patches Vs. Mean Patch Size
of Ag/Forest Land Uses
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Mean Nearest Neighbor
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Generating Spatial Variables

• Measuring distance via roads network to important
  destinations (cities, towns, recreational areas)
• Using overlays to calculate parcel features such
  as zoning, public sewer and water, school
  district
• Using buffers to calculate neighborhood features
  such as surrounding land uses

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Predicting Future Land Use Change

• Predicted Probabilities Map of predicted
  probability assigned to each parcel
• Baseline and alternative scenarios
• Predicted Change Map of predicted change in land
  use of each parcel
• Baseline and alternative scenarios

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Population Density Change in Ohios Landscape
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Conclusions

• Spatial pattern of land use change is critical
• Impacts of land use change depend on spatial
  pattern of land use change
• GIS is a necessary tool in analyzing spatial
  pattern