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• Mas, J
• Soares Filho, B
• Pontius, RGilmore
• Farfán Gutiérrez, M
• Rodrigues, H
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• Mas, J
• Soares Filho, B
• Pontius, RGilmore
• Farfán Gutiérrez, M
• Rodrigues, H
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• Mas, J
• Soares Filho, B
• Pontius, RGilmore
• Farfán Gutiérrez, M
• Rodrigues, H

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ISPRS Int. J. Geo-Inf. 2013, 2(3), 869-887; doi:10.3390/ijgi2030869

Article

A Suite of Tools for ROC Analysis of Spatial Models

Jean-François Mas ^1,* , Britaldo Soares Filho ², Robert Gilmore Pontius ³, Michelle Farfán Gutiérrez ¹ and Hermann Rodrigues ²

¹ Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México, Antigua Carretera a Pátzcuaro 8701, Col. Ex-Hacienda de San José de La Huerta, Morelia C.P. 58190, MIC, Mexico ² Centro de Sensoriamento Remoto, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte-MG, 31270-901, Brazil ³ Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610-1477, USA

* Author to whom correspondence should be addressed.

Received: 25 July 2013; in revised form: 13 August 2013 / Accepted: 29 August 2013 / Published: 10 September 2013

Download PDF Full-Text [637 KB, Updated Version, uploaded 11 September 2013 15:57 CEST]

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Abstract: The Receiver Operating Characteristic (ROC) is widely used for assessing the performance of classification algorithms. In GIScience, ROC has been applied to assess models aimed at predicting events, such as land use/cover change (LUCC), species distribution and disease risk. However, GIS software packages offer few statistical tests and guidance tools for ROC analysis and interpretation. This paper presents a suite of GIS tools designed to facilitate ROC curve analysis for GIS users by applying proper statistical tests and analysis procedures. The tools are freely available as models and submodels of Dinamica EGO freeware. The tools give the ROC curve, the area under the curve (AUC), partial AUC, lower and upper AUCs, the confidence interval of AUC, the density of event in probability bins and tests to evaluate the difference between the AUCs of two models. We present first the procedures and statistical tests implemented in Dinamica EGO, then the application of the tools to assess LUCC and species distribution models. Finally, we interpret and discuss the ROC-related statistics resulting from various case studies.

Keywords: accuracy; AUC; Dinamica EGO; LUCC; prediction; ROC; species distribution modeling; uncertainty; validation

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