Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty

The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects

Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging

A computational framework to map species’ distributions (realized density) using occurrence-only data and environmental predictors is presented and illustrated using a textbook example and two case studies: distribution of root vole (Microtes oeconomus) in the Netherlands, and distribution of white-tailed eagle nests (Haliaeetus albicilla) in Croatia. The framework combines strengths of point pattern analysis (kernel smoothing), Ecological Niche Factor Analysis (ENFA) and geostatistics (logistic regression-kriging), as implemented in the spatstat, adehabitat and gstat packages of the R environment for statistical computing. A procedure to generate pseudo-absences is proposed. It uses Habitat Suitability Index (HSI, derived through ENFA) and distance from observations as weight maps to allocate pseudo-absence points. This design ensures that the simulated pseudo-absences fall further away from the occurrence points in both feature and geographical spaces. The simulated pseudo-absences can then be combined with occurrence locations and used to build regression-kriging prediction models. The output of prediction are either probabilitiesy of species’ occurrence or density measures. Addition of the pseudo-absence locations has proven effective — the adjusted R-square increased from 0.71 to 0.80 for root vole (562 records), and from 0.69 to 0.83 for white-tailed eagle (135 records) respectively; pseudo-absences improve spreading of the points in feature space and ensure consistent mapping over the whole area of interest. Results of cross validation (leave-one-out method) for these two species showed that the model explains 98% of the total variability in the density values for the root vole, and 94% of the total variability for the white-tailed eagle. The framework could be further extended to Generalized multivariate Linear Geostatistical Models and spatial prediction of multiple species. A copy of the R script and step-by-step instructions to run such analysis are available via contact author’s website

Residential Appraisal and the Lending Process: A Survey of Issues

This article surveys mainly academic literature for issues concerning the use of appraisals in the residential lending process. The development of appraisal methodologies is reviewed, and the strengths and weaknesses of various appraisal techniques are assessed. Issues relating to the use of neighborhood characteristics in appraisals for lending purposes are also explored. Finally, institutional incentives that give rise to biased and self-serving appraisals and possible solutions to these incentive problems are examined.

Farmland Allocation along the Rural-Urban Gradient: The Impacts of Urbanization and Urban Sprawl

In the vicinity of a city, farmers are confronted with increasing agricultural land prices and rents along the rural-urban gradient, but they concurrently enjoy the advantages associated with proximity to a larger and wealthier consumer base. We hypothesize that farmers transition from low-value, land-intensive \traditional" crops to high-value, labor-intensive \specialized" crops on parcels located closer to urban centers. Once returns to development of a parcel exceed the profits associated with farming, exurban farmers may sell their land for conversion to urban use. Urban pressure in the rural-urban fringe intensifies as cities expand. We differentiate between a gradual process of urban growth (or urbanization) and urban sprawl. Utilizing farmland fragmentation measures as indicators of sprawl, we hypothesize that urban sprawl burdens \traditional" farms to the extent that they accelerate the transition to specialized crops or convert farmland to urban use. We use crop-specific land cover data at the level of grid cells and a state-of-the-art system of spatially correlated simultaneous equations with data for the metropolitan area of Indianapolis, IN and its immediate hinterland. Our initial empirical results corroborate that accelerated urban development around Indianapolis in the 1990s is associated with land uses characterized by fewer field crops and more idle land.land use, urban sprawl, agriculture, specialized crops, spatial econometrics, Community/Rural/Urban Development, Land Economics/Use, C31, O13, Q15, R14,