TABLE 5.

Multivariate Poisson regression models of census tract level counts of calls to municipal flooding hotline, accounting for overdispersion in the outcome.

Dependent variable: Number of flooded homes in census tract
	Poisson regression models
	Non-spatial model	Spatial model
Poverty	1.210*** (1.087, 1.333)	1.135** (1.017, 1.254)
Black	1.376*** (1.297, 1.454)	1.408*** (1.325, 1.491)
Older residents	1.144*** (1.046, 1.242)	1.018 (0.922, 1.115)
Ethnic immigrants	1.254*** (1.110, 1.398)	1.466*** (1.319, 1.613)
Old units	1.016 (0.961, 1.071)	1.009 (0.955, 1.064)
Rental units	0.841*** (0.768, 0.914)	0.841*** (0.769, 0.912)
Disadvantage	0.790 (0.542, 1.038)	0.882 (0.636, 1.129)
Affluence	0.789*** (0.651, 0.927)	0.782*** (0.644, 0.919)
Distance to interceptor	1.127*** (1.085, 1.169)	1.117*** (1.076, 1.159)
Elevation	0.905*** (0.890, 0.919)	0.886*** (0.870, 0.901)
Observations	345	345

Note: A non-spatial model is presented along with the same model but includes terms accounting for spatial autocorrelation between census tracts. Estimates represent the mean change in expected counts of flooded households given (1) a 10% increase in the percentage of all homes within each census tract for poverty, percentage Black, older residents, ethnic immigrants, old units, and rental units; (2) a 0.1 increase in the disadvantage and affluence metrics (on a 0–1 scale); and (3) and 1 km increase in distance to the nearest interceptor and a 1 m increase in elevation.

^*p < 0.1;

^**p < 0.05;

^***p < 0.01.