Publications

@article{Berkel2018,
title = {Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR},
author = {Derek Van Berkel and Payam Tabrizian and Monica A. Dorning and Lindsey S. Smart and Doug Newcomb and Megan Mehaffey and Anne Neale and Ross K. Meentemeyer },
url = {https://doi.org/10.1016/j.ecoser.2018.03.022},
doi = {10.1016/j.ecoser.2018.03.022},
year = {2018},
date = {2018-06-01},
journal = {Ecosystem Services},
volume = {31},
pages = {326-335},
abstract = {Landscapes are increasingly recognized for providing valuable cultural ecosystem services with numerous non-material benefits by serving as places of rest, relaxation, and inspiration that ultimately improve overall mental health and physical well-being. Maintaining and enhancing these valuable benefits through targeted management and conservation measures requires understanding the spatial and temporal determinants of perceived landscape values. Content contributed through mobile technologies and the web are emerging globally, providing a promising data source for localizing and assessing these landscape benefits. These georeferenced data offer rich in situ qualitative information through photos and comments that capture valued and special locations across large geographic areas. We present a novel method for mapping and modeling landscape values and perceptions that leverages viewshed analysis of georeferenced social media data. Using a high resolution LiDAR (Light Detection and Ranging) derived digital surface model, we are able to evaluate landscape characteristics associated with the visual-sensory qualities of outdoor recreationalists. Our results show the importance of historical monuments and attractions in addition to specific environmental features which are appreciated by the public. Evaluation of photo-image content highlights the opportunity of including temporally and spatially variable visual-sensory qualities in cultural ecosystem services (CES) evaluation like the sights, sounds and smells of wildlife and weather phenomena.},
keywords = {coastal scenery, cultural ecosystem services, social media, spatial analysis},
pubstate = {published},
tppubtype = {article}
}

@article{King2015,
title = {Geographic clustering of elevated blood heavy metal levels in pregnant women},
author = {Katherine E. King and Thomas H. Darrah and Eric S. Money and Ross K. Meentemeyer and Rachel L. Maguire and Monica D. Nye and Lloyd Michener and Amy P. Murtha and Randy Jirtle and Susan K. Murphy and Michelle A. Mendez and Wayne Robarge and Avner Vengosh and Cathrine Hoyo},
url = {http://www.biomedcentral.com/1471-2458/15/1035},
doi = {10.1186/s12889-015-2379-9},
year = {2015},
date = {2015-10-09},
journal = {BMC Public Health},
volume = {15},
pages = {1035},
abstract = {Background
Cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) exposure is ubiquitous and has been associated with higher risk of growth restriction and cardiometabolic and neurodevelopmental disorders. However, cost-efficient strategies to identify at-risk populations and potential sources of exposure to inform mitigation efforts are limited. The objective of this study was to describe the spatial distribution and identify factors associated with Cd, Pb, Hg, and As concentrations in peripheral blood of pregnant women.

Methods
Heavy metals were measured in whole peripheral blood of 310 pregnant women obtained at gestational age ~12 weeks. Prenatal residential addresses were geocoded and geospatial analysis (Getis-Ord G i * statistics) was used to determine if elevated blood concentrations were geographically clustered. Logistic regression models were used to identify factors associated with elevated blood metal levels and cluster membership.

Results
Geospatial clusters for Cd and Pb were identified with high confidence (p-value for G i * statistic <0.01). The Cd and Pb clusters comprised 10.5 and 9.2 % of Durham County residents, respectively. Medians and interquartile ranges of blood concentrations (μg/dL) for all participants were Cd 0.02 (0.01–0.04), Hg 0.03 (0.01–0.07), Pb 0.34 (0.16–0.83), and As 0.04 (0.04–0.05). In the Cd cluster, medians and interquartile ranges of blood concentrations (μg/dL) were Cd 0.06 (0.02–0.16), Hg 0.02 (0.00–0.05), Pb 0.54 (0.23–1.23), and As 0.05 (0.04–0.05). In the Pb cluster, medians and interquartile ranges of blood concentrations (μg/dL) were Cd 0.03 (0.02–0.15), Hg 0.01 (0.01–0.05), Pb 0.39 (0.24–0.74), and As 0.04 (0.04–0.05). Co-exposure with Pb and Cd was also clustered, the p-values for the G i * statistic for Pb and Cd was <0.01. Cluster membership was associated with lower education levels and higher pre-pregnancy BMI.

Conclusions
Our data support that elevated blood concentrations of Cd and Pb are spatially clustered in this urban environment compared to the surrounding areas. Spatial analysis of metals concentrations in peripheral blood or urine obtained routinely during prenatal care can be useful in surveillance of heavy metal exposure.},
keywords = {arsenic, cadmium, lead, mercury, spatial analysis},
pubstate = {published},
tppubtype = {article}
}