Demonstration and validation of Kernel Density Estimation for spatial meta-analyses in cognitive neuroscience using simulated data

The data presented in this article are related to the research article entitled "Convergence of semantics and emotional expression within the IFG pars orbitalis" (Belyk et al., 2017) [1]. The research article reports a spatial meta-analysis of brain imaging experiments on the perception of semantic compared to emotional communicative signals in humans. This Data in Brief article demonstrates and validates the use of Kernel Density Estimation (KDE) as a novel statistical approach to neuroimaging data. First, we performed a side-by-side comparison of KDE with a previously published meta-analysis that applied activation likelihood estimation, which is the predominant approach to meta-analyses in cognitive neuroscience. Second, we analyzed data simulated with known spatial properties to test the sensitivity of KDE to varying degrees of spatial separation. KDE successfully detected true spatial differences in simulated data and displayed few false positives when no true differences were present. R code to simulate and analyze these data is made publicly available to facilitate the further evaluation of KDE for neuroimaging data and its dissemination to cognitive neuroscientists

PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis

Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society

Mapping the species richness and composition of tropical forests from remotely sensed data with neural networks

The understanding and management of biodiversity is often limited by a lack of data. Remote sensing has considerable potential as a source of data on biodiversity at spatial and temporal scales appropriate for biodiversity management. To-date, most remote sensing studies have focused on only one aspect of biodiversity, species richness, and have generally used conventional image analysis techniques that may not fully exploit the data's information content. Here, we report on a study that aimed to estimate biodiversity more fully from remotely sensed data with the aid of neural networks. Two neural network models, feedforward networks to estimate basic indices of biodiversity and Kohonen networks to provide information on species composition, were used. Biodiversity indices of species richness and evenness derived from the remotely sensed data were strongly correlated with those derived from field survey. For example, the predicted tree species richness was significantly correlated with that observed in the field (r=0.69, significant at the 95% level of confidence). In addition, there was a high degree of correspondence (?83%) between the partitioning of the outputs from Kohonen networks applied to tree species and remotely sensed data sets that indicated the potential to map species composition. Combining the outputs of the two sets of neural network based analyses enabled a map of biodiversity to be produce

Valid auto-models for spatially autocorrelated occupancy and abundance data

Auto-logistic and related auto-models, implemented approximately as autocovariate regression, provide simple and direct modelling of spatial dependence. The autologistic model has been widely applied in ecology since Augustin, Mugglestone and Buckland (J. Appl. Ecol., 1996, 33, 339) analysed red deer census data using a hybrid estimation approach, combining maximum pseudo-likelihood estimation with Gibbs sampling of missing data. However Dormann (Ecol. Model., 2007, 207, 234) questioned the validity of auto-logistic regression, giving examples of apparent underestimation of covariate parameters in analysis of simulated "snouter" data. Dormann et al. (Ecography, 2007, 30, 609) extended this analysis to auto-Poisson and auto-normal models, reporting similar anomalies. All the above studies employ neighbourhood weighting schemes inconsistent with conditions (Besag, J. R. Stat. Soc., Ser. B, 1974, 36, 192) required for auto-model validity; furthermore the auto-Poisson analysis fails to exclude cooperative interactions. We show that all "snouter" anomalies are resolved by correct auto-model implementation. Re-analysis of the red deer data shows that invalid neighbourhood weightings generate only small estimation errors for the full dataset, but larger errors occur on geographic subsamples. A substantial fraction of papers applying auto-logistic regression to ecological data use these invalid weightings, which are default options in the widely used "spdep" spatial dependence package for R. Auto-logistic analyses using invalid neighbourhood weightings will be erroneous to an extent that can vary widely. These analyses can easily be corrected by using valid neighbourhood weightings available in "spdep". The hybrid estimation approach for missing data is readily adapted for valid neighbourhood weighting schemes and is implemented here in R for application to sparse presence-absence data.Comment: Typos corrected in Table 1. Note that defaults in R package 'spdep' have changed in response to this paper; some results using defaults are therefore now version-dependen

IDENTIFICATION OF TECHNOLOGICAL DISTRICTS: THE CASE OF SPAIN

In recent years, several contributions have been focused on a new sort of productive systems that share some characteristics with Marshallian industrial districts. These contributions have analysed the competitiveness of these new areas and how have been promoted by policy makers. In this line, the Marshallian concept of industrial district has been increasingly related to high technology and innovation in order to analysis technological districts or clusters. The aim of this research is to show how these new areas have characteristics are not similar to those shown by traditional industrial districts. Therefore, framework and techniques for analysis that have been traditionally used for industrial districts must be adapted for identifying technological districts. Specifically, some reflections about the framework analysis of sector and spatial units are introduced in the first part of this research as well as those techniques that can be useful to identify and analyse technological districts. Next, the analysis is focused on the identification of technological districts in Spain. A multivariate analysis will be applied to calculate a synthetic index that will be used to identify those areas with a high degree of specialization in high and medium technology activities. This synthetic index will collect data about those technological activities that are involved not only in manufacturing but also in activities of innovation and R&D. Until now, there have been not many attempts to identify technological clusters through the application of quantitative methodologies; therefore, the purpose of this research is to contribute to the enhancement of knowledge about these areas in Spain. Keywords: technological districts, clusters, location, spatial agglomerations.

Cartography as a tool for interpreting the results of spatial decomposition: new proposals with application to the analysis of employment in Friuli Venezia Giulia

Quando si considera l’analisi di fenomeni quantitativi spazialmente distribuiti, come quelli di carattere economico, è necessario adottare strumenti specifici in grado di trattare il problema autocorrelazione spaziale. Negli ultimi dieci anni, grazie alla diffusione di software per l’analisi di dati spaziali, e di strumenti per la visualizzazione grafica dei dati, il numero di studi economici territoriali sta progressivamente crescendo. Nella maggior parte dei casi gli strumenti disponibili consentono una rappresentazione efficiente sia dei dati “grezzi” sia dei risultati finali delle analisi. Questo lavoro mostra invece come la cartografia possa fungere da risultato intermedio, ma fondamentale, nell’analisi shift-share di tipo spaziale. Un’osservazione grafica preliminare del vicinato, condotta considerando un algoritmo basato sulla autocorrelazione spaziale, può essere utile per ottenere non solo risultati significativi, ma anche più facilmente interpretabili. Nel presente articolo saranno dapprima sviluppati alcuni risultati teorici riguardanti la modifica del ben noto metodo di ricerca del vicinato AMOEBA. Successivamente, l’analisi spaziale sarà applicata ai dati sull’occupazione del Friuli Venezia Giulia raccolti nell’Archivio delle Imprese Attive (ASIA) gestito dall’Istituto Nazionale di Statistica (ISTAT). Sia la cartografia intermedia sia gli algoritmi di scomposizione sono stati sviluppati in R integrando le librerie già disponibili per la visualizzazione dei dati spaziali con uno script sviluppato per l’occasione.In the framework of the analysis of spatially distributed quantitative phenomena, as for instance the economic ones, it is necessary to adopt specific tools able to deal with the spatial autocorrelation issue. In the last decade, thanks to the deployment of software for spatial data analysis and visualization, the number of spatial economic studies progressively increased. In the most of cases the available software allow for efficient data representation. The present work aims at introducing cartography as an intermediate but crucial result in the Spatial Shift Share Analysis. A preliminary graphical analysis of the neighborhood, conducted by considering an algorithm based on the spatial autocorrelation, can be fundamental in order to obtain meaningful and interpretable results. Theoretical results regarding the modification of the well-known AMOEBA neighboring method are developed here and the spatial analysis is applied to the occupation data observed in Friuli Venezia Giulia. Data are collected in the Statistical Business Register, socalled ASIA, administered by the Italian National Statistical Institute (ISTAT). Both the intermediate cartography and the spatial decomposition algorithms are developed in R integrating the available spatial libraries with an ad-hoc script