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Investigaciones geográficas

versão On-line ISSN 2448-7279versão impressa ISSN 0188-4611


CASTRO MIGUEL, Rutilio  e  LEGORRETA PAULIN, Gabriel. Application of the Continuum Neighborhood Spatial Analysis and Logistic Regression in the Spatial Modeling of Probability of Occurrence of Landslides. Invest. Geog [online]. 2019, n.98, 00006. ISSN 2448-7279.

Spatial models of probability based on the Logistic Regression (RL) usually collect data for model calibration directly from the location of the sampling site. This data collection method involves the isolation of the site, leading to loss of information, as the neighborhood area is not considered; therefore, the LR model may be less representative of reality.

Aiming to construct spatial models of higher accuracy when using the RL statistical model, this work addresses the analysis and integration of data on independent variables for areas surrounding the sampling sites used for the calibration of the statistical model.

A few works have conducted a statistical evaluation of how the neighborhood areas to calibration sites may yield a higher relationship with the occurrence of landslides processes, leading to higher precision in the classification of areas based on the probability of occurrence, as compared to in-situ data collection at the sampling site. Hence the importance of considering the relationship between the sampling site and its neighborhood area when gathering information for calibrating the probability model.

This paper reports a comparative analysis of two statistical models of probability of occurrence of gravitational processes (PG) involving the application of RL and using terrain slope as the independent variable. A first model analyzed data collected in situ on the independent variable from sampling sites with landslides and in stable areas; the second analyzed information for these same sites using Spatial Analysis of Continuum Neighborhood (AEVC) to derive information about the terrain slope variable.

The implementation of AEVC for the elaboration of the statistical model provided information for a detailed assessment of how the area surrounding sampling sites is statistically related to the process studied. The neighborhood area was estimated by using a circular shape centered in the sampling point, the radius of which was increased gradually in 1-pixel increments to 20 pixels.

The data of the terrain slope variable were analyzed separately for the site location (in situ) and for each of the neighboring areas, from a distance of 1 to 20 pixels in radio. This approach was used for calibrating the RL statistical models for each distance analyzed, which were then evaluated in statistical terms aiming to identify the model(s) that yield the best classification level.

The precision of in-situ and AEVC models was evaluated using -2 Logarithm of Likelihood (-2LL) as a fit measure. This measure facilitates the comparison of two models, where the difference between the values obtained represents the shift in prediction level between models. A lower value of -2LL indicates better goodness of fit of the model; therefore, the size of the neighborhood area analyzed and the value of -2LL were both used for selecting the area for which the terrain slope contributed to better goodness of fit of the probability model.

Models were calibrated using an inventory of landslides, and the terrain slope variable was derived from the Continuo de Elevación Mexicano version 3.0 (CMS 3.0). The results show that using data for neighboring areas yields higher goodness of fit of the equation relative to the model developed using in-situ data.

The value of -2LL for the model was 264.3 using neighborhood data and 269.5 using in-situ data. The table on overall classification reported 58.5 % for the neighborhood model and 51.8 % for the in-situ analysis, showing a 6.7 % increase in the classification of the statistical model when the neighborhood analysis is used.

The information used for the selection of the optimal distance for AEVC and the calibration of the statistical model can be depicted spatially; therefore, the results from the LR model can be represented in a map of the distribution of probability of landslides in the study area.

The study area is the La Ciénega river basin located on the eastern slope of the Nevado de Toluca volcano, in the State of Mexico.

Palavras-chave : Neighborhood area; sampling site; probability; gravitational processes; spatial analysis.

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