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Table 8 Predicting water presence compared to random locations using GLMM

From: Identifying malaria vector breeding habitats with remote sensing data and terrain-based landscape indices in Zambia

Model Data Source Variable Odds Ratio P > |z| 95% CI Lower 95% CI Upper
SRTM-LandSat SRTM slope 0.38 0.01 0.18 0.81
  SRTM aspect 1.01 0.02 1.00 1.01
  SRTM TPI500 0.65 <0.001 0.52 0.82
LandSat -ASTER ASTER TPI500 1.29 0.01 1.06 1.57
  ASTER slope 0.75 0.03 0.59 0.97
  ASTER TPI2000 0.81 0.002 0.70 0.92