Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.

A bayesian spatial individual effects probit model of the 2010 UK general election

The Conservative Party emerged from the 2010 United Kingdom General Election as the largest single party, but their support was not geographically uniform. In this paper, we estimate a hierarchical Bayesian spatial probit model that tests for the presence of regional voting effects. This model allows for the estimation of individual region-specic effects on the probability of Conservative Party success, incorporating information on the spatial relationships between the regions of the mainland United Kingdom. After controlling for a range of important covariates, we find that these spatial relationships are significant and that our individual region-specic effects estimates provide additional evidence of North-South variations in Conservative Party support.

Personal Indebtedness, Spatial Effects and Crime

There is a long and detailed history of attempts to understand what causes crime. One of the most prominent strands of this literature has sought to better understand the relationship between economic conditions and crime. Following Becker (1968), the economic argument is that in an attempt to maintain consumption in the face of unemployment, people may resort to sources of illicit income. In a similar manner, we might expect ex–ante, that increases in the level of personal indebtedness would be likely to provide similar incentives to engage in criminality. In this paper we seek to understand the spatial pattern of property and theft crimes using a range of socioeconomic variables, including data on the level of personal indebtedness.

Personal Indebtedness, Spatial Effects and Crime

There is a long and detailed history of attempts to understand what causes crime. One of the most prominent strands of this literature has sought to better understand the relationship between economic conditions and crime. Following Becker (1968), the economic argument is that in an attempt to maintain consumption in the face of unemployment, people may resort to sources of illicit income. In a similar manner, we might expect ex–ante, that increases in the level of personal indebtedness would be likely to provide similar incentives to engage in criminality. In this paper we seek to understand the spatial pattern of property and theft crimes using a range of socioeconomic variables, including data on the level of personal indebtedness.

Spatial Takeoff in the First Industrial Revolution

Using the framework of Desmet and Rossi-Hansberg (forthcoming), we present a model of spatial takeoff that is calibrated using spatially-disaggregated occupational data for England in c.1710. The model predicts changes in the spatial distribution of agricultural and manufacturing employment which match data for c.1817 and 1861. The model also matches a number of aggregate changes that characterise the first industrial revolution. Using counterfactual geographical distributions, we show that the initial concentration of productivity can matter for whether and when an industrial takeoff occurs. Subsidies to innovation in either sector can bring forward the date of takeoff while subsidies to the use of land by manufacturing firms can significantly delay a takeoff because it decreases spatial concentration of activity.

Spatial Takeoff in the First Industrial Revolution

Using the framework of Desmet and Rossi-Hansberg (forthcoming), we present a model of spatial takeoff that is calibrated using spatially-disaggregated occupational data for England in c.1710. The model predicts changes in the spatial distribution of agricultural and manufacturing employment which match data for c.1817 and 1861. The model also matches a number of aggregate changes that characterise the first industrial revolution. Using counterfactual geographical distributions, we show that the initial concentration of productivity can matter for whether and when an industrial takeoff occurs. Subsidies to innovation in either sector can bring forward the date of takeoff while subsidies to the use of land by manufacturing firms can significantly delay a takeoff because it decreases spatial concentration of activity.

Multilayer perceptron with local constraint as an emerging method in spatial data analysis

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

(Optimal) Spatial Aggregation in the Determinants of Industrial Location

Empirical studies on the determinants of industrial location typically use variables measured at the available administrative level (municipalities, counties, etc.). However, this amounts to assuming that the effects these determinants may have on the location process do not extent beyond the geographical limits of the selected site. We address the validity of this assumption by comparing results from standard count data models with those obtained by calculating the geographical scope of the spatially varying explanatory variables using a wide range of distances and alternative spatial autocorrelation measures. Our results reject the usual practice of using administrative records as covariates without making some kind of spatial correction. Keywords: industrial location, count data models, spatial statistics JEL classification: C25, C52, R11, R30

Spatial and seasonal trends of a natural population of Biomphalaria occidentalis in northeastern Argentina

This study aims to analyze the age of a population of Biomphalaria occidentalis on a pound of Riachuelo river basin, wich is one of the three most important Middle Paraná river affluents in Corrientes province. Samples were drawn from three stations, were spatial and temporal numerical variations of the snail, as well as its relation with different environmental parameters, mainly temperature, rainfall, pH and conductivity, were analyzed. Snail abundance is given in number of individuals/hour. The differences between the three sampling stations, estimated by nonparametric tests, was nonsignificant. A relative scale to the greatest shell diameter was employed to build the age pyramids. Temporal fluctuations of snail abundance correlated negatively with the highest monthly accumulated temperatures (P < 0.05). Although different floristic compositions were observed at the three stations, no significant numerical variations were detected in B. occidentalis spatial distribution. Reproductive activity took place between March-April and November with overlapping cohort system. During summer (December-Febuary) mortality increased along with temperature and reproductive activity was not evident.

The Accessibility City. When Transport Infrastructure Matters in Urban Spatial Structure

Suburbanization is changing the urban spatial structure and less monocentric metropolitan regions are becoming the new urban reality. Focused only on centers, most works have studied these spatial changes neglecting the role of transport infrastructure and its related location model, the “accessibility city”, in which employment and population concentrate in low-density settlements and close to transport infrastructure. For the case of Barcelona, we consider this location model and study the population spatial structure between 1991 and 2006. The results reveal a mix between polycentricity and the accessibility city, with movements away from the main centers, but close to the transport infrastructure.

Impaired early visual response modulations to spatial information in chronic schizophrenia.

Early visual processing stages have been demonstrated to be impaired in schizophrenia patients and their first-degree relatives. The amplitude and topography of the P1 component of the visual evoked potential (VEP) are both affected; the latter of which indicates alterations in active brain networks between populations. At least two issues remain unresolved. First, the specificity of this deficit (and suitability as an endophenotype) has yet to be established, with evidence for impaired P1 responses in other clinical populations. Second, it remains unknown whether schizophrenia patients exhibit intact functional modulation of the P1 VEP component; an aspect that may assist in distinguishing effects specific to schizophrenia. We applied electrical neuroimaging analyses to VEPs from chronic schizophrenia patients and healthy controls in response to variation in the parafoveal spatial extent of stimuli. Healthy controls demonstrated robust modulation of the VEP strength and topography as a function of the spatial extent of stimuli during the P1 component. By contrast, no such modulations were evident at early latencies in the responses from patients with schizophrenia. Source estimations localized these deficits to the left precuneus and medial inferior parietal cortex. These findings provide insights on potential underlying low-level impairments in schizophrenia.

Iceberg transport technologies in spatial competition. Hotelling reborn

Transport costs in address models of differentiation are usually modeled as separable of the consumption commodity and with a parametric price. However, there are many sectors in an economy where such modeling is not satisfactory either because transportation is supplied under oligopolistic conditions or because there is a difference (loss) between the amount delivered at the point of production and the amount received at the point of consumption. This paper is a first attempt to tackle these issues proposing to study competition in spatial models using an iceberg-like transport cost technology allowing for concave and convex melting functions.

Spatial and temporal distribution of anopheline larvae in two malarious areas in Sucre State, Venezuela

The spatial and temporal distribution of anopheline larvae was studied in two coastal malarious areas of Sucre, State, Venezuela. Seven habitat types were sampled in the village of Guayana and eight species of Anopheles were collected. Anopheles aquasalis was the predominant species collected and was most abundant in the brackish marsh habitat (71 larvae per 100 samples). It was most abundant during the rainy season. At the second location, Santa F e, six habitat types were sampled and four anopheline species were collected. Habitats where An. aquasalis was most abundant were temporary freshwater ponds (34 larvae per 100 samples) and mangroves (10.5 larvae per 100 samples). At this location it was also most abundant in the rainy season. During the dry season it was collected in small numbers in river pools (1.3 larvae per 100 samples) along with large numbers of An. pseudopunctipennis (479 larvae per 100 samples). Larval control could be an important component of the malaria control program because major habitats could be defined and presence and abundance of larvae was limited to specific times of year.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.