Classic and spatial shift-share analysis of state-level employment change in Brazil

This paper combines classic and spatial shift-share decompositions of 1981 to 2006 employment change across the 27 states of Brazil. The classic shift-share method shows higher employment growth rates for underdeveloped regions that are due to an advantageous industry-mix and also due to additional job creation, commonly referred to as the competitive effect. Alternative decompositions proposed in the literature do not change this broad conclusion. Further examination employing exploratory spatial data analysis (ESDA) shows spatial correlation of both the industry-mix and the competitive effects. Considering that until the 1960s economic activities were more concentrated in southern regions of Brazil than they are nowadays, these results support beta convergence theories but also find evidence of agglomeration effects. Additionally, a very simple spatial decomposition is proposed that accounts for the spatially-weighted growth of surrounding states. Favourable growth in northern and centre-western states is basically associated with those states’ strengths in potential spatial spillover effect and in spatial competitive effect

Geographic Coincidence of Increased Malaria Transmission Hazard and Vulnerability Occurring at the Periphery of two Tanzanian Villages.

The goal of malaria elimination necessitates an improved understanding of any fine-scale geographic variations in transmission risk so that complementary vector control tools can be integrated into current vector control programmes as supplementary measures that are spatially targeted to maximize impact upon residual transmission. This study examines the distribution of host-seeking malaria vectors at households within two villages in rural Tanzania. Host-seeking mosquitoes were sampled from 72 randomly selected households in two villages on a monthly basis throughout 2008 using CDC light-traps placed beside occupied nets. Spatial autocorrelation in the dataset was examined using the Moran's I statistic and the location of any clusters was identified using the Getis-Ord Gi* statistic. Statistical associations between the household characteristics and clusters of mosquitoes were assessed using a generalized linear model for each species. For both Anopheles gambiae sensu lato and Anopheles funestus, the density of host-seeking females was spatially autocorrelated, or clustered. For both species, houses with low densities were clustered in the semi-urban village centre while houses with high densities were clustered in the periphery of the villages. Clusters of houses with low or high densities of An. gambiae s.l. were influenced by the number of residents in nearby houses. The occurrence of high-density clusters of An. gambiae s.l. was associated with lower elevations while An. funestus was also associated with higher elevations. Distance from the village centre was also positively correlated with the number of household occupants and having houses constructed with open eaves. The results of the current study highlight that complementary vector control tools could be most effectively targeted to the periphery of villages where the households potentially have a higher hazard (mosquito densities) and vulnerability (open eaves and larger households) to malaria infection

On the Four Types of Weight Functions for Spatial Contiguity Matrix

This is a "spatial autocorrelation analysis" of spatial autocorrelation. I use the 1-dimension spatial autocorrelation function (ACF) and partial autocorrelation function (PACF) to analyze four kinds of weight function in common use for the 2-dimensional spatial autocorrelation model. The aim of this study is at how to select a proper weight function to construct a spatial contiguity matrix for spatial analysis. The scopes of application of different weight functions are defined in terms of the characters of their ACFs and PACFs.Comment: 8 pages, 5 figures, 2 table

Evaluation of Location-Specific Predictions by a Detailed Simulation Model of Aedes aegypti Populations

Skeeter Buster is a stochastic, spatially explicit simulation model of Aedes aegypti populations, designed to predict the outcome of vector population control methods. In this study, we apply the model to two specific locations, the cities of Iquitos, Peru, and Buenos Aires, Argentina. These two sites differ in the amount of field data that is available for location-specific customization. By comparing output from Skeeter Buster to field observations in these two cases we evaluate population dynamics predictions by Skeeter Buster with varying degrees of customization.Skeeter Buster was customized to the Iquitos location by simulating the layout of houses and the associated distribution of water-holding containers, based on extensive surveys of Ae. aegypti populations and larval habitats that have been conducted in Iquitos for over 10 years. The model is calibrated by adjusting the food input into various types of containers to match their observed pupal productivity in the field. We contrast the output of this customized model to the data collected from the natural population, comparing pupal numbers and spatial distribution of pupae in the population. Our results show that Skeeter Buster replicates specific population dynamics and spatial structure of Ae. aegypti in Iquitos. We then show how Skeeter Buster can be customized for Buenos Aires, where we only had Ae. aegypti abundance data that was averaged across all locations. In the Argentina case Skeeter Buster provides a satisfactory simulation of temporal population dynamics across seasons.This model can provide a faithful description of Ae. aegypti populations, through a process of location-specific customization that is contingent on the amount of data available from field collections. We discuss limitations presented by some specific components of the model such as the description of food dynamics and challenges that these limitations bring to model evaluation

Great bowerbirds create theaters with forced perspective when seen by their audience

Birds in the infraorder Corvida [1] (ravens, jays, bowerbirds) are renowned for their cognitive abilities [2–4], which include advanced problem solving with spatial inference [4–8], tool use and complex constructions [7–10], and bowerbird cognitive ability is associated with mating success [11]. Great bowerbird males construct bowers with a long avenue from within which females view the male displaying over his bower court [10]. This predictable audience viewpoint is a prerequisite for forced (altered) visual perspective [12–14]. Males make courts with gray and white objects that increase in size with distance from the avenue entrance. This gradient creates forced visual perspective for the audience; court object visual angles subtended on the female viewer’s eye are more uniform than if the objects were placed at random. Forced perspective can yield false perception of size and distance [12, 15]. After experimental reversal of their size-distance gradient, males recovered their gradients within 3 days, and there was little difference from the original after 2 wks. Variation among males in their forced-perspective quality as seen by their female audience indicates that visual perspective is available for use in mate choice, perhaps as an indicator of cognitive ability. Regardless of function, the creation and maintenance of forced visual perspective is clearly important to great bowerbirds and suggests the possibility of a previously unknown dimension of bird cognition

Understanding spatial and temporal processes of urban growth: cellular automata modelling

An understanding of the dynamic process of urban growth is a prerequisite to the prediction of land-cover change and the support of urban development planning and sustainable growth management. The spatial and temporal complexity inherent in urban growth requires the development of a new simulation approach, which should be process-oriented and have a strong interpretive element. In this paper the authors present an innovative methodology for understanding spatial processes and their temporal dynamics on two interrelated scales -- the municipality and project scale -- by means of a multistage framework and a dynamic weighting concept. The multistage framework is aimed at modelling local spatial processes and global temporal dynamics by the incorporation of explicit decisionmaking processes. It is divided into four stages: project planning, site selection, local growth, and temporal control. These four stages represent the interactions between top-down and bottom-up decisionmaking involved in land development in large-scale projects. Project-based cellular automata modelling is developed for interpreting the spatial and temporal logic between various projects that form the whole of urban growth. Use of dynamic weighting is an attempt to model local temporal dynamics at the project level as an extension of the local growth stage. As nonlinear function of temporal land development, dynamic weighting can link spatial processes and temporal patterns. The methodology is tested with reference to the urban growth of a fast growing city -- Wuhan, in the People's Republic of China -- from 1993 to 2000. The findings from this research suggest that this methodology can be used to interpret and visualise the dynamic process of urban growth temporally and transparently, globally and locally

The Prelude to the Deep Minimum between Solar Cycles 23 and 24: Interplanetary Scintillation Signatures in the Inner Heliosphere

Extensive interplanetary scintillation (IPS) observations at 327 MHz obtained between 1983 and 2009 clearly show a steady and significant drop in the turbulence levels in the entire inner heliosphere starting from around ~1995. We believe that this large-scale IPS signature, in the inner heliosphere, coupled with the fact that solar polar fields have also been declining since ~1995, provide a consistent result showing that the buildup to the deepest minimum in 100 years actually began more than a decade earlier.Comment: 9 pages, 4 figures, accepted for publication in Geophysical Research Letters on 28 September 201

Understanding Uncertainties in Model-Based Predictions of Aedes aegypti Population Dynamics

Dengue is one of the most important insect-vectored human viral diseases. The principal vector is Aedes aegypti, a mosquito that lives in close association with humans. Currently, there is no effective vaccine available and the only means for limiting dengue outbreaks is vector control. To help design vector control strategies, spatial models of Ae. aegypti population dynamics have been developed. However, the usefulness of such models depends on the reliability of their predictions, which can be affected by different sources of uncertainty including uncertainty in the model parameter estimation, uncertainty in the model structure, measurement errors in the data fed into the model, individual variability, and stochasticity in the environment. This study quantifies uncertainties in the mosquito population dynamics predicted by Skeeter Buster, a spatial model of Ae. aegypti, for the city of Iquitos, Peru. The uncertainty quantification should enable us to better understand the reliability of model predictions, improve Skeeter Buster and other similar models by targeting those parameters with high uncertainty contributions for further empirical research, and thereby decrease uncertainty in model predictions

Exploratory spatial data analysis for the identification of risk factors to birth defects

BACKGROUND: Birth defects, which are the major cause of infant mortality and a leading cause of disability, refer to "Any anomaly, functional or structural, that presents in infancy or later in life and is caused by events preceding birth, whether inherited, or acquired (ICBDMS)". However, the risk factors associated with heredity and/or environment are very difficult to filter out accurately. This study selected an area with the highest ratio of neural-tube birth defect (NTBD) occurrences worldwide to identify the scale of environmental risk factors for birth defects using exploratory spatial data analysis methods. METHODS: By birth defect registers based on hospital records and investigation in villages, the number of birth defects cases within a four-year period was acquired and classified by organ system. The neural-tube birth defect ratio was calculated according to the number of births planned for each village in the study area, as the family planning policy is strictly adhered to in China. The Bayesian modeling method was used to estimate the ratio in order to remove the dependence of variance caused by different populations in each village. A recently developed statistical spatial method for detecting hotspots, Getis's [Image: see text] [7], was used to detect the high-risk regions for neural-tube birth defects in the study area. RESULTS: After the Bayesian modeling method was used to calculate the ratio of neural-tube birth defects occurrences, Getis's [Image: see text] statistics method was used in different distance scales. Two typical clustering phenomena were present in the study area. One was related to socioeconomic activities, and the other was related to soil type distributions. CONCLUSION: The fact that there were two typical hotspot clustering phenomena provides evidence that the risk for neural-tube birth defect exists on two different scales (a socioeconomic scale at 6.84 km and a soil type scale at 22.8 km) for the area studied. Although our study has limited spatial exploratory data for the analysis of the neural-tube birth defect occurrence ratio and for finding clues to risk factors, this result provides effective clues for further physical, chemical and even more molecular laboratory testing according to these two spatial scales