Multilevel modeling and regression of community annoyance to transportation noise.

A multilevel (hierarchical) model is devised that separates noise tolerance into variations occurring at the levels of individual listeners and communities. This approach successfully describes the characteristics of real community transportation noise surveys, with the individual- and community-level variations producing distinct statistical signatures, both of which are evident in the surveys. Predictions are provided for quantities such as the probability of annoyance based on the observed noise level and statistical parameters characterizing the community tolerance. Regression analyses are performed using a multilevel, generalized linear model, which provides an appropriate generalization encompassing both no pooling (separate community-by-community analysis) and full pooling (all communities together) of survey data, and enables noise tolerances and their variations at the individual and community levels to be distinguished and quantified. Variations in individual tolerance and sound exposure within communities are found to be larger than variations in tolerance between communities; however, the variations between communities are still significant and observable. Analysis of multiple types of transportation noise with the multilevel model indicates that tolerance is highest for railway noise with low vibrations, followed by roadway noise, airport noise, and railway noise with high vibrations, as consistent with previous studies.

Correlation between normalized difference vegetation index and malaria in a subtropical rain forest undergoing rapid anthropogenic alteration.

Time-series of coarse-resolution greenness values derived through remote sensing have been used as a surrogate environmental variable to help monitor and predict occurrences of a number of vector-borne and zoonotic diseases, including malaria. Often, relationships between a remotely-sensed index of greenness, e.g. the normalized difference vegetation index (NDVI), and disease occurrence are established using temporal correlation analysis. However, the strength of these correlations can vary depending on type and change of land cover during the period of record as well as inter-annual variations in the climate drivers (precipitation, temperature) that control the NDVI values. In this paper, the correlation between a long (260 months) time-series of monthly disease case rates and NDVI values derived from the Global Inventory Modeling and Mapping Studies (GIMMS) data set were analysed for two departments (administrative units) located in the Atlantic Forest biome of eastern Paraguay. Each of these departments has undergone extensive deforestation during the period of record and our analysis considers the effect on correlation of active versus quiescent periods of case occurrence against a background of changing land cover. Our results show that timeseries data, smoothed using the Fourier Transform tool, showed the best correlation. A moving window analysis suggests that four years is the optimum time frame for correlating these values, and the strength of correlation depends on whether it is an active or a quiescent period. Finally, a spatial analysis of our data shows that areas where land cover has changed, particularly from forest to non-forest, are well correlated with malaria case rates.