Creating Small Area Income Deprivation Estimates For Northern Ireland: Spatial Microsimulation Modelling

This paper describes results from a preliminary investigation of the value of a spatial microsimulation technique in the estimation, for each SOA in Northern Ireland, of the incidence of income poverty as measured by the proportion of households whose income is below 60% of the UK median household income (%HBAI). In this paper we describe the spatial microsimulation approach and then present small area (SOA) estimates of median household income validated against the equivalent measure NIMDM 2005 income domain score, and the Experian 2005 median income estimates. We then turn to the %HBAI estimates and describe firstly results based on unequivalised gross income for 2004/5 using the FRS 2004/5 and the UK Census 2001. We then discuss results equivalised net household income before housing costs for 2003/5 using the UK Census 2001 and a pooled 2003/4 and 2004/5 FRS dataset. We discuss the results of validation against the source FRS and against the NIMDM income domain score. The paper concludes with a summary of the findings and recommendations for further work

Database Analysis to Support Nutrient Criteria Development (Phase I)

The intent of this publication of the Arkansas Water Resources Center is to provide a location whereby a final report on water research to a funding agency can be archived. The Texas Commission on Environmental Quality (TCEQ) contracted with University of Arkansas researchers for a multiple year project titled “Database Analysis to Support Nutrient Criteria Development”. This publication covers the first of three phases of that project and has maintained the original format of the report as submitted to TCEQ. This report can be cited either as an AWRC publication (see below) or directly as the final report to TCEQ

On The Inverse Geostatistical Problem of Inference on Missing Locations

The standard geostatistical problem is to predict the values of a spatially continuous phenomenon, $S(x)$ say, at locations $x$ using data $(y_i,x_i):i=1,..,n$ where $y_i$ is the realization at location $x_i$ of $S(x_i)$, or of a random variable $Y_i$ that is stochastically related to $S(x_i)$. In this paper we address the inverse problem of predicting the locations of observed measurements $y$. We discuss how knowledge of the sampling mechanism can and should inform a prior specification, $\pi(x)$ say, for the joint distribution of the measurement locations $X = \{x_i: i=1,...,n\}$, and propose an efficient Metropolis-Hastings algorithm for drawing samples from the resulting predictive distribution of the missing elements of $X$. An important feature in many applied settings is that this predictive distribution is multi-modal, which severely limits the usefulness of simple summary measures such as the mean or median. We present two simulated examples to demonstrate the importance of the specification for $\pi(x)$, and analyze rainfall data from Paran\'a State, Brazil to show how, under additional assumptions, an empirical of estimate of $\pi(x)$ can be used when no prior information on the sampling design is available.Comment: Under revie

Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

Measuring and explaining cross-country immigration policies

The intensified international migration pressures of the recent decades prompted many developed countries to revise their immigration regulations and increase border controls. However, the development of these reforms as well as their effectiveness in actually managing new immigration flows remains poorly understood. The main reason is that migration regulations are hard to quantify, which has prevented the construction of a universal measure of migration policy. To fill this gap in the literature, we construct an indicator of the restrictiveness of immigration entry policy across countries as well as a more comprehensive indicator of migration policy that also accounts for staying requirements and regulations to foster integration. These indexes are then used to disentangle the factors determining the toughness of migration regulations. Our empirical framework combines elements from the median voter and interest group approach and accounts for cross-country correlation in migration policies. We find strong evidence of spatial correlation in particular in entry restrictiveness, while the impact of economic determinants of migration policy remains much more modest

Latino Destinations and Environmental Inequality: Estimated Cancer Risk from Air Toxics in Latino Traditional and New Destinations

Since the 1990s, Latino migration patterns have shifted from traditional destinations to new destinations away from the Mexico border. Scholars note disparities between destinations in housing, crime, and health care, yet no study has examined environmental inequalities. In this article we employ theories of spatial assimilation and environmental inequality to evaluate health risks across Latino destinations by asking the question, is there a difference in estimated cancer risk from air toxics among established, new, and nondestination locations? Using county-level data with spatial lag regression analyses, we find that early new destinations (i.e., counties with significant Latino growth from 1990 to 2000) and recent new destinations (i.e., counties with significant Latino growth from 1990 to 2010) have higher estimated cancer risk from air toxics than established destinations (i.e., counties at or greater than the national average of Latinos in 1990) and nondestinations. The effect remains significant when controlling for various economic indicators

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

The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana

Schistosomiasis control in sub-Saharan Africa is enacted primarily through preventive chemotherapy. Predictive models can play an important role in filling knowledge gaps in the distribution of the disease and help guide the allocation of limited resources. Previous modeling approaches have used localized cross-sectional survey data and environmental data typically collected at a discrete point in time. In this analysis, 8 years (2008-2015) of monthly schistosomiasis cases reported into Ghana's national surveillance system were used to assess temporal and spatial relationships between disease rates and three remotely sensed environmental variables: land surface temperature (LST), normalized difference vegetation index (NDVI), and accumulated precipitation (AP). Furthermore, the analysis was stratified by three major and nine minor climate zones, defined using a new climate classification method. Results showed a downward trend in reported disease rates (~ 1% per month) for all climate zones. Seasonality was present in the north with two peaks (March and September), and in the middle of the country with a single peak (July). Lowest disease rates were observed in December/January across climate zones. Seasonal patterns in the environmental variables and their associations with reported schistosomiasis infection rates varied across climate zones. Precipitation consistently demonstrated a positive association with disease outcome, with a 1-cm increase in rainfall contributing a 0.3-1.6% increase in monthly reported schistosomiasis infection rates. Generally, surveillance of neglected tropical diseases (NTDs) in low-income countries continues to suffer from data quality issues. However, with systematic improvements, our approach demonstrates a way for health departments to use routine surveillance data in combination with publicly available remote sensing data to analyze disease patterns with wide geographic coverage and varying levels of spatial and temporal aggregation.Accepted manuscrip

Quantifying fisher responses to environmental and regulatory dynamics in marine systems

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Commercial fisheries are part of an inherently complicated cycle. As fishers have adopted new technologies and larger vessels to compete for resources, fisheries managers have adapted regulatory structures to sustain stocks and to mitigate unintended impacts of fishing (e.g., bycatch). Meanwhile, the ecosystems that are targeted by fishers are affected by a changing climate, which in turn forces fishers to further adapt, and subsequently, will require regulations to be updated. From the management side, one of the great limitations for understanding how changes in fishery environments or regulations impact fishers has been a lack of sufficient data for resolving their behaviors. In some fisheries, observer programs have provided sufficient data for monitoring the dynamics of fishing fleets, but these programs are expensive and often do not cover every trip or vessel. In the last two decades however, vessel monitoring systems (VMS) have begun to provide vessel location data at regular intervals such that fishing effort and behavioral decisions can be resolved across time and space for many fisheries. I demonstrate the utility of such data by examining the responses of two disparate fishing fleets to environmental and regulatory changes. This study was one of "big data" and required the development of nuanced approaches to process and model millions of records from multiple datasets. I thus present the work in three components: (1) How can we extract the information that we need? I present a detailed characterization of the types of data and an algorithm used to derive relevant behavioral aspects of fishing, like the duration and distances traveled during fishing trips; (2) How do fishers' spatial behaviors in the Bering Sea pollock fishery change in response to environmental variability; and (3) How were fisher behaviors and economic performances affected by a series of regulatory changes in the Gulf of Mexico grouper-tilefish longline fishery? I found a high degree of heterogeneity among vessel behaviors within the pollock fishery, underscoring the role that markets and processor-level decisions play in facilitating fisher responses to environmental change. In the Gulf of Mexico, my VMS-based approach estimated unobserved fishing effort with a high degree of accuracy and confirmed that the regulatory shift (e.g., the longline endorsement program and catch share program) yielded the intended impacts of reducing effort and improving both the economic performance and the overall harvest efficiency for the fleet. Overall, this work provides broadly applicable approaches for testing hypotheses regarding the dynamics of spatial behaviors in response to regulatory and environmental changes in a diversity of fisheries around the world.General introduction -- Chapter 1 Using vessel monitoring system data to identify and characterize trips made by fishing vessels in the United States North Pacific -- Chapter 2 Paths to resilience: Alaska pollock fleet uses multiple fishing strategies to buffer against environmental change in the Bering Sea -- Chapter 3 Vessel monitoring systems (VMS) reveal increased fishing efficiency following regulatory change in a bottom longline fishery -- General Conclusions