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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

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International Journal of Health Geographics

ISSN: 1476-072X