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1. Dynamics of data availability in disease modeling: An example evaluating the trade-offs of ultra-fine-scale factors applied to human West Nile virus disease models in the Chicago area, USA

   https://doi.org/10.1371/journal.pone.0251517  

   Uelmen, J. A. ; Irwin, P. ; Brown, W. M. ; Karki, S. ; Ruiz, M. O. ; Li, B. ; Smith, R. L. ( May 2021 , PLOS ONE)

   Wen, Feng (Ed.)

   Background Since 1999, West Nile virus (WNV) has moved rapidly across the United States, resulting in tens of thousands of human cases. Both the number of human cases and the minimum infection rate (MIR) in vector mosquitoes vary across time and space and are driven by numerous abiotic and biotic forces, ranging from differences in microclimates to socio-demographic factors. Because the interactions among these multiple factors affect the locally variable risk of WNV illness, it has been especially difficult to model human disease risk across varying spatial and temporal scales. Cook and DuPage Counties, comprising the city of Chicago and surrounding suburbs, experience some of the highest numbers of human neuroinvasive cases of WNV in the United States. Despite active mosquito control efforts, there is consistent annual WNV presence, resulting in more than 285 confirmed WNV human cases and 20 deaths from the years 2014–2018 in Cook County alone. Methods A previous Chicago-area WNV model identified the fifty-five most high and low risk locations in the Northwest Mosquito Abatement District (NWMAD), an enclave ¼ the size of the combined Cook and DuPage county area. In these locations, human WNV risk was stratified by model performance, as indicated by differences in studentized residuals. Within these areas, an additional two-years of field collections and data processing was added to a 12-year WNV dataset that includes human cases, MIR, vector abundance, and land-use, historical climate, and socio-economic and demographic variables, and was assessed by an ultra-fine-scale (1 km spatial x 1 week temporal resolution) multivariate logistic regression model. Results Multivariate statistical methods applied to the ultra-fine-scale model identified fewer explanatory variables while improving upon the fit of the previous model. Beyond MIR and climatic factors, efforts to acquire additional covariates only slightly improved model predictive performance. Conclusions These results suggest human WNV illness in the Chicago area may be associated with fewer, but increasingly critical, key variables at finer scales. Given limited resources, these findings suggest large variations in model performance occur, depending on covariate availability, and provide guidance in variable selection for optimal WNV human illness modeling. 

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2. Urban ambient air temperature estimation using hyperlocal data from smart vehicle-borne sensors

   Yin, Y ; Hashemi, N ; Grundstein, A ; Mishra, D. R ; Ramaswamy, L ; Dowd, J ( July 2020 , Computers environment and urban systems)

   High-quality temperature data at a finer spatio-temporal scale is critical for analyzing the risk of heat exposure and hazards in urban environments. The variability of urban landscapes makes cities a challenging environment for quantifying heat exposure. Most of the existing heat hazard studies have inherent limitations on two fronts; first, the spatio-temporal granularities are too coarse, and second, the inability to track the ambient air temperature (AAT) instead of land surface temperature (LST). Overcoming these limitations requires developing models for mapping the variability in heat exposure in urban environments. We investigated an integrated approach for mapping urban heat hazards by harnessing a diverse set of high-resolution measurements, including both ground-based and satellite-based temperature data. We mounted vehicle-borne mobile sensors on city buses to collect high-frequency temperature data throughout 2018 and 2019. Our research also incorporated key biophysical parameters and Landsat 8 LST data into Random Forest regression modeling to map the hyperlocal variability of heat hazard over areas not covered by the buses. The vehicle-borne temperature sensor data showed large temperature differences within the city, with the largest variations of up to 10 °C and morning-afternoon diurnal changes at a magnitude around 20 °C. Random Forest modeling on noontime (11:30 am – 12:30 pm) data to predict AAT produced accurate results with a mean absolute error of 0.29 °C and successfully showcased the enhanced granularity in urban heat hazard mapping. These maps revealed well-defined hyperlocal variabilities in AAT, which were not evident with other research approaches. Urban core and dense residential areas revealed larger than 5 °C AAT differences from their nearby green spaces. The sensing framework developed in this study can be easily implemented in other urban areas, and findings from this study will be beneficial in understanding the heat vulnerabilities of individual communities. It can be used by the local government to devise targeted hazard mitigation efforts such as increasing green space, developing better heatsafety policies, and exposure warning for workers. 

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3. The Dynamic Relationship between Air and Land Surface Temperature within the Madison, Wisconsin Urban Heat Island

   https://doi.org/10.3390/rs14010165  

   Berg, Elizabeth ; Kucharik, Christopher ( January 2022 , Remote Sensing)

   The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (Tair) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both Tair measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012–2019). Overall, Tair and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R2 = 0.76, p = 0.0002) that peaked in late July. The seasonal evolution in the relationship between Tair and LST, along with particularly high variability in LST across agricultural land cover suggest that plant phenology contributes to a seasonally varying relationship between Tair and LST measurements of the UHI. 

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4. A global seamless 1 km resolution daily land surface temperature dataset (2003–2020)

   https://doi.org/10.5194/essd-14-651-2022  

   Zhang, Tao ; Zhou, Yuyu ; Zhu, Zhengyuan ; Li, Xiaoma ; Asrar, Ghassem R. ( January 2022 , Earth System Science Data)

   Abstract. Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate ResolutionImaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution withglobal coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination,indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporalgap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the datapre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In thespatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in eachpixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for eachday. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values inthe original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by largeareas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation withdifferent missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error(RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime andmid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling,and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhanget al., 2021). 

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5. Residential Location and Psychological Distance in Americans’ Risk Views and Behavioral Intentions Regarding Zika Virus

   https://doi.org/10.1111/risa.13184  

   Johnson, Branden B. ( September 2018 , Risk Analysis)

   Two 2017 experiments with a U.S. national opportunity sample tested effects of location, psychological distance (PD), and exposure to location‐related information on Americans’ Zika risk views and behavioral intentions. Location—distance from mosquito transmission of the virus in Florida and Texas; residence within states with 100+ Zika infections; residence within potential mosquito vector ranges—had small, inconsistent effects. Hazard proximity weakly enhanced personal risk judgments and concern about Zika transmission locally. It also increased psychological proximity, and intentions of mosquito control, avoiding travel to Zika‐infected areas, and practicing safe sex. PD—particularly social and geographical distance, followed by temporal distance, with few effects for uncertainty—modestly and inconsistently decreased risk views and intentions. Exposure to location‐related information from the U.S. Centers for Disease Control and Prevention website—naming states with 100+ Zika cases; maps of potential mosquito vector habitat—increased risk views and psychological closeness, but not intentions; maps had slightly stronger if inconsistent effects versus prevalence information. Structural equation modeling (SEM) of a location > PD > risk views > intention path explained modest variance in intentions. This varied in degree and kind (e.g., which location measures were significant) across behaviors, and between pre‐ and postinformation exposure analyses. These results suggest need for both theoretical and measurement advances regarding effects of location and PD on risk views and behavior. PD mediates location effects on risk views. Online background information, like that used here, will not enhance protective behavior without explicitly focused communication and perhaps higher objective risk.

    

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