WIFI FIRE: A A Scalable e Da Data-Driven M Monitoring, D - - PowerPoint PPT Presentation

Data Fire Modeling Visualization Monitoring WIFI FIRE: A A Scalable e Da Data-Driven M Monitoring, D Dynamic Prediction an and R Res esil ilie ience C Cyb yberin infrastructure for Wild ildfir ires

CA F Fires 10/2017 t 2017 throug ugh 1 h 12/2017 2017 800K+ u unique visitors a and 8M+ h hits ts

http://firemap.sdsc.edu

Fire M e Model eling g in W WIFIRE RE

Real-time sensors Weather forecast Fire perimeter Landscape data Monitoring & fire mapping

Preexisting Diabetes

Preexisting Hypertension w/ Preclampsia

Infection

UCSF F PRETERM B BIRTH I INITIIA IATIVE Distri ribution o

• f p

preterm b birt rth by race & cl clinical r risk f fact ctors

White women Black women Hispanic women

aRR > 1.0 to <1.5 aRR 1.5 to <2.0 aRR 2.0 to <3.0 aRR 3.0 to <4.0 aRR 4.0 to <5.0 aRR 5.0 to < 6.0 aRR < 1.0 to >0.67 aRR 0.67 to >0.5

PRETERM BIRTH BY CLINICAL RISK FACTORS

Risk/protective factor Urban Suburban Rural

Born in Mexico

0.9 (0.8, 1.0)

Underweight BMI

1.3 (1.1, 1.5)

Overweight BMI

0.9 (0.8, 1.0)

Preexisting diabetes

1.7 (1.3, 2.1) 1.9 (1.4, 2.5) 1.8 (1.4, 2.4)

Gestational diabetes

1.3 (1.2, 1.5) 1.3 (1.2, 1.6) 1.4 (1.2, 1.6)

Preexisting hypertension without preeclampsia

1.6 (1.2, 2.1) 1.7 (1.2, 2.3) 1.7 (1.3, 2.3)

Preexisting hypertension with preeclampsia

4.7 (3.7, 6.0) 5.6 (4.3, 7.2) 5.7 (4.4, 7.5)

Gestational hypertension without preeclampsia

1.6 (1.2, 2.0) 1.5 (1.1, 1.9)

Gestational hypertension with preeclampsia

3.5 (3.1, 3.9) 4.1 (3.6, 4.7) 4.4 (3.9, 4.9)

Infection

1.4 (1.3, 1.5) 1.4 (1.2, 1.5) 1.4 (1.3, 1.6)

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#Geo4Dev @CEGA_UC | @GPS_UCSD

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