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SMART-SLEUTH: Augmenting the SLEUTH urban growth model with new smart-growth scenario-building capabilities

Dates

Creation
2017-07-06 17:06:35
Last Update
2017-09-08 13:59:03
Start Date
2012-11-01
End Date
2015-02-28
Start Date
2012-11-01 04:00:00
End Date
2015-02-28 05:00:00

Citation

Ross Meentemeyer(Principal Investigator), Wenwu Tang(Cooperator/Partner), John B. Vogler(Cooperator/Partner), Douglas A. Shoemaker(Cooperator/Partner), Rua Mordecai(Cooperator/Partner), South Atlantic Landscape Conservation Cooperative(administrator), 2017-07-06(creation), 2017-09-08(lastUpdate), 2012-11-01(Start), 2015-02-28(End), SMART-SLEUTH: Augmenting the SLEUTH urban growth model with new smart-growth scenario-building capabilities

Summary

We will develop SMART-SLEUTH, an advanced spatially explicit modeling framework designed to augment the current SLEUTH model with sophisticated smart-growth capabilities. Based on the latest version of SLEUTH, we will create an open-source GIS-enabled software package that will implement SMART-SLEUTH with advanced modules and tools for evaluating, predicting, and visualizing smart growth scenarios and outcomes. In this software package, a more user friendly Graphic User Interface (GUI), a multi-level automatic calibration approach built on machine learning algorithms, and new spatial landscape metrics for quantifying land change patterns will provide enhanced support for complex model configuration, calibration, and validation. We [...]

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Project Extension

parts
typeShort Project Description
valueWe will develop SMART-SLEUTH, an advanced spatially explicit modeling framework designed to augment the current SLEUTH model with sophisticated smart-growth capabilities. Based on the latest version of SLEUTH, we will create an open-source GIS-enabled software package that will implement SMART-SLEUTH with advanced modules and tools for evaluating, predicting, and visualizing smart growth scenarios and outcomes. In this software package, a more user friendly Graphic User Interface (GUI), a multi-level automatic calibration approach built on machine learning algorithms, and new spatial landscape metrics for quantifying land change patterns will provide enhanced support for complex model configuration, calibration, and validation. We [...]
projectStatusCompleted

Budget Extension

annualBudgets
year2013
fundingSources
amount147748.0
recipientUniversity of North Carolina Charlotte
sourceU.S. Fish and Wildlife Service
amount146406.0
recipientNorth Carolina State Univerisity
sourceU.S. Fish and Wildlife Service
totalFunds294154.0
totalFunds294154.0

Communities

  • LC MAP - Landscape Conservation Management and Analysis Portal
  • South Atlantic Landscape Conservation Cooperative

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