Dataset Information

Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region.

ABSTRACT: Because of the warming climate urban temperature patterns have been receiving increased attention. Temperature within urban areas can vary depending on land cover, meteorological and other factors. High resolution satellite data can be used to understand this intra-urban variability, although they have been primarily studied to characterize urban heat islands at a larger spatial scale.This study examined whether satellite-derived impervious surface and meteorological conditions from multiple sites can improve characterization of spatial variability of temperature within an urban area.Temperature was measured at 17 outdoor sites throughout the Detroit metropolitan area during the summer of 2008. Kriging and linear regression were applied to daily temperatures and secondary information, including impervious surface and distance-to-water. Performance of models in predicting measured temperatures was evaluated by cross-validation. Variograms derived from several scenarios were compared to determine whether high-resolution impervious surface information could capture fine-scale spatial structure of temperature in the study area.Temperatures measured at the sites were significantly different from each other, and all kriging techniques generally performed better than the two linear regression models. Impervious surface values and distance-to-water generally improved predictions slightly. Restricting models to days with lake breezes and with less cloud cover also somewhat improved the predictions. In addition, incorporating high-resolution impervious surface information into cokriging or universal kriging enhanced the ability to characterize fine-scale spatial structure of temperature.Meteorological and satellite-derived data can better characterize spatial variability in temperature across a metropolitan region. The data sources and methods we used can be applied in epidemiological studies and public health interventions to protect vulnerable populations from extreme heat events.

SUBMITTER: Zhang K

PROVIDER: S-EPMC4345124 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region.

Zhang Kai K Oswald Evan M EM Brown Daniel G DG Brines Shannon J SJ Gronlund Carina J CJ White-Newsome Jalonne L JL Rood Richard B RB O'Neill Marie S MS

Environmental research 20110914 8

<h4>Background</h4>Because of the warming climate urban temperature patterns have been receiving increased attention. Temperature within urban areas can vary depending on land cover, meteorological and other factors. High resolution satellite data can be used to understand this intra-urban variability, although they have been primarily studied to characterize urban heat islands at a larger spatial scale.<h4>Objective</h4>This study examined whether satellite-derived impervious surface and meteor ...[more]

PMID: 21924413

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Project description:BackgroundHomeless individuals suffer and die disproportionately from chronic diseases and disorders. We describe the epidemiology of cancer among homeless persons in metropolitan Detroit.MethodsA retrospective cohort study was performed using 1973-2014 data from the Metropolitan Detroit Cancer Surveillance System, a population-based cancer registry and member of the National Institutes of Health-National Cancer Institute's Surveillance, Epidemiology, and End Results program. Homeless adults were identified through address at diagnosis listed as a homeless shelter, hospital, or supplemental field indicating homelessness. Age-adjusted, sex-specific proportional incidence ratios (PIR) compared cancer incidence proportions by primary tumor site of homeless patients to the nonhomeless referent population. Kaplan-Meier curves depicted unadjusted survival differences in a propensity score matched sample. Differences in 10-year survival were assessed using the score test with a sandwich estimator accounting for matched cluster effects. Statistical tests were two-sided.ResultsA total of 388 individuals experienced homelessness at first primary invasive cancer diagnosis. Statistically significantly higher proportions of respiratory system (PIR = 1.51; 95% confidence interval = 1.28 to 1.79) and female genital system (PIR = 1.83; 95% confidence interval = 1.31 to 2.55) cancers were observed among homeless men and women, respectively. Homeless persons had poorer overall and cancer-reported survival compared with a propensity score matched referent population (median: overall survival, 20.0 vs 38.0 months, respectively, P < .001; cancer-reported survival, 38.0 vs 64.0 months, respectively, P < .001).ConclusionDisparities in disease burden exist between adults who are experiencing homelessness compared with the nonhomeless population at cancer diagnosis. These findings provide clinically relevant information to understand the cancer burden in this medically underserved population and suggest an urgent need to develop cancer prevention and intervention programs to reduce disparities and improve the health of homeless persons.

Project description:Geo-statistical models have been applied to assess fine-scale air pollution exposures in epidemiological studies. Many of the models were developed for criteria air pollutants rather than others that have not been regulated (e.g., ultrafine particles, black carbon, and benzene) which may also be harmful to human health. We aim to develop spatial models for regulated and non-regulated air pollutants using 6 algorithms and compare their prediction performances. A mobile platform with fast-response monitors was used to measure gaseous air pollutants (nitrogen dioxides, carbon monoxide, sulfur dioxides, ozone, benzene, toluene, methanol) and particulate matters (black carbon, surface area, count- and volume-concentrations of ultrafine particles) in Beijing, China for 30 days from July to October 2008. Mobile monitoring data for model building were spatially aggregated into 130 road segments of approximately 600-m interval on the sampling routes after temporal adjustment of background concentrations. The best models for the air pollutants were dominated by traffic variables, which explained more than 60% of the spatial variations (range: 0.61 for methanol to 0.88 for ozone) based on the highest cross-validation R2 and the lowest root mean square error among different algorithms. Amongst the 6 algorithms, the spatial models using partial least squares regression (PLS, a dimension reduction algorithm) and random forest (RF, a machine learning algorithm) algorithms outperformed the models with other algorithms. Exposure predictions from the best models varied substantially with distinct spatial patterns between the air pollutants. Predictions with multiple modeling algorithms were moderately correlated with each other for the same pollutant at the fine-scale grids across the city. Exposure models, especially based on PLS and RF algorithms, captured the spatial variation of short-term average concentrations, had adequate predictive validity, and could be applied to assess toxic air pollutant exposures in human health studies.

Project description:ObjectiveDisparities in treatment outcomes for traumatic injury are an important concern for care providers and policy makers. Factors that may influence these disparities include differences in risk exposure based on neighbourhood of residence and differences in quality of care between hospitals in different areas. This study examines geographical disparities within a single region: the Detroit metropolitan area.DesignData on all trauma admissions between 2006 and 2014 were obtained from the Michigan State Inpatient Database. Admissions were grouped by patient neighbourhood of residence and admitting hospital. Generalised linear mixed modelling procedures were used to determine the extent of shared variance based on these two levels of categorisation on three outcomes. Patients with trauma due to common mechanisms (falls, firearms and motor vehicle traffic) were examined as additional subgroups.Setting66 hospitals admitting patients for traumatic injury in the Detroit metropolitan area during the period from 2006 to 2014.Participants404 675 adult patients admitted for treatment of traumatic injury.Outcome measuresIn-hospital mortality, length of stay and hospital charges.ResultsIntraclass correlation coefficients indicated that there was substantial shared variance in outcomes based on hospital, but not based on neighbourhood of residence. Among all injury types, hospital-level differences accounted for 12.5% of variance in mortality risk, 28.5% of variance in length of stay and 32.2% of variance in hospital charges. Adjusting the results for patient age, injury severity, mechanism and comorbidities did not result in significant reduction in the estimated variance at the hospital level.ConclusionsBased on these data, geographical disparities in trauma treatment outcomes were more strongly attributable to differences in access to quality hospital care than to risk factors in the neighbourhood environment. Transfer of high-risk cases to hospitals with greater institutional experience in the relevant area may help address mortality disparities in particular.

Dataset Information

Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region.

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Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region.

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