The probability of spatial risk greater than 3 (Pr e i ξ >3) was calculated.
Earlier studies using geo-spatial analysis (Singh et al. 2011) found evidence of the importance of spatial risk factors in explaining differential IMRs in the region.
Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches.
These so-called exceedance probabilities (Clayton and Bernardinelli 1992) are the posterior probabilities for an area's spatial risk estimate exceeding some pre-set value.
The spatial risk term at the census tract level identified areas at increased risk of alcohol-related pedestrian/bicyclist injury throughout the 10-year period (Fig. 2).
Posterior probabilities for the 48 census tracts with a spatial risk estimate of alcohol-related injury exceeding 3 are presented in Fig. 3.
Spatial risk, controlling for or holding the covariates constant, was calculated as ζi = υ i + η i, and is interpreted as the residual spatial risk for each area (compared to all of New York City) after presence of alcohol outlets, social fragmentation, economics, average speed, and traffic density are taken into account.
We used presence-only Maximum Entropy (Maxent), a species distribution modeling approach, to model the spatial risk of PRRSv in swine populations.
Using an index for representing relative degree of crabbing pressure, we conducted a spatial risk analysis for each of the four refuges surveyed.
We computed spatial risk index by geographical analysis with Python scripts in ESRI® ArcGIS 10 on data projected in the reference system NAD 1927 UTM17.
This study quantifies the spatial risk of alcohol-related pedestrian injury in New York City at the census tract level over a recent 10-year period using a Bayesian hierarchical spatial regression model with Integrated Nested Laplace approximations.
