Local Spatial Dependence Research Articles

Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic

Land-cover characterization of large heterogeneous landscapes is challenging because of the confusion caused by high intra-class variability and heterogeneous landscape artefacts. Neighbourhood context can be used to supplement spectral information, and a novel way of incorporating spatial dependence in a heterogeneous region is tested here using an ensemble learning technique called random forests and a measure of local spatial dependence called the Getis statistic. The overall Kappa accuracy of the random forest classifier that used a combination of spectral and local spatial (Getis) variables at three different neighbourhood sizes (3 × 3, 7 × 7, and 11 × 11) ranged from 0.85 to 0.92. This accuracy was higher than that of a non-spatial random forest classifier having an overall Kappa accuracy of 0.78, which was run using the spectral variables only. This study demonstrated that the use of the Getis statistic with different neighbourhood sizes leads to substantial increase in per class classification accuracy of heterogeneous land-cover categories.

Spatio-temporal context for codebook-based dynamic background subtraction

In background subtraction, it is challenging to detect foreground objects in the presence of dynamic background motions. The paper proposes two new algorithms to this problem by improving the codebook model with the incorporation of the spatial and temporal context of each pixel. The spatial context involves the local spatial dependency between neighboring pixels, and the temporal context involves the preceding detection result. Only the spatial context is incorporated into the first algorithm which makes the background representation more compact than the standard codebook. The second algorithm explicitly models the spatio-temporal context with a Markov random field model, thus achieving more accurate foreground detection. Extensive experiments on several dynamic scenes are conducted to compare the two proposed algorithms with each other and with the standard codebook algorithm.

Informing surveillance programmes by investigating spatial dependency of subclinicalSalmonellainfection

The aim of this paper is to investigate local spatial dependency with regard to Salmonella seropositivity in data from the Danish swine salmonellosis control programme and its application in informing surveillance strategies. We applied inhomogeneous and observed-difference K-function estimation, and geo-statistical modelling to data from the Danish swine salmonellosis control programme. Slaughter-pig farm density showed large variation at both the country-wide and local level in Denmark (median 0.23, range 0.02-0.47 farms/km(2)). The spatial distribution of pig farms followed a random inhomogeneous Poisson process but was not aggregated. We found evidence for aggregation of Salmonella case farms over that of all farms at distances of up to 6 km and semivariogram analyses of Salmonella seropositivity revealed spatial dependency between pairs of farms up to 4 km apart. The strength of the spatial dependency was positively associated with slaughter-pig farm density. We proposed sampling more intensively those farms within a 4 km radius of farms that were identified with a high Salmonella status, and reduced sampling of farms that are within this radius of 'Salmonella-free' farms. Our approach has the potential to optimize sampling strategies while maintaining consumer confidence in food safety and also has potential to be used for other zoonotic disease surveillance systems.

Power calculations for global and local Moran’s [formula omitted

As in any statistical test, a power analysis can help in assessing the outcomes of whether global or local spatial dependencies exist. This point was briefly addressed with respect to global Moran’s I , but it has not been widely used. One reason may be that the most commonly used spatial analysis and GIS software packages do not support power analysis. Thus, apart from using the code for saddle-point approximation, applications have been restricted to employing normal approximations. An implementation of the exact distributions for global and local Moran’s I , which are integrated into the R-package spdep, is presented. Furthermore, assuming a simultaneous autoregressive spatial data generating scheme, substantial cases are provided, demonstrating the drawbacks and potential flaws of using the normal approximation in power calculations. The results confirm that, particularly for local Moran’s I , due to the smallness of sets of neighborhoods, this practice may potentially lead to errors of inference. An example concerned with Upper-Austrian migration, where using the exact distribution leads to different conclusions, is presented as well.

Harnessing defocus blur to recover high-resolution information in shape-from-focus technique

Traditional shape-from-focus (SFF) uses focus as the singular cue to derive the shape profile of a 3D object from a sequence of images. However, the stack of low-resolution (LR) observations is space-variantly blurred because of the finite depth of field of the camera. The authors propose to exploit the defocus information in the stack of LR images to obtain a super-resolved image as well as a high-resolution (HR) depth map of the underlying 3D object. Appropriate observation models are used to describe the image formation process in SFF. Local spatial dependencies of the intensities of pixels and their depth values are accounted for by modelling the HR image and the HR structure as independent Markov random fields. Taking as input the LR images from the stack and the LR depth map, the authors first obtain the super-resolved image of the 3D specimen and use it subsequently to reconstruct a HR depth profile of the object.

A comparison of Bayesian spatial models for disease mapping

With the advent of routine health data indexed at a fine geographical resolution, small area disease mapping studies have become an established technique in geographical epidemiology. The specific issues posed by the sparseness of the data and possibility for local spatial dependence belong to a generic class of statistical problems involving an underlying (latent) spatial process of interest corrupted by observational noise. These are naturally formulated within the framework of hierarchical models, and over the past decade, a variety of spatial models have been proposed for the latent level(s) of the hierarchy. In this article, we provide a comprehensive review of the main classes of such models that have been used for disease mapping within a Bayesian estimation paradigm, and report a performance comparison between representative models in these classes, using a set of simulated data to help illustrate their respective properties. We also consider recent extensions to model the joint spatial distribution of multiple disease or health indicators. The aim is to help the reader choose an appropriate structural prior for the second level of the hierarchical model and to discuss issues of sensitivity to this choice.

Local Productive Structures, Spatial Dependence and Employment Dynamics

This paper is concerned with employment growth at sectoral and local level in Italy. With respect to previous literature, we introduce across regions spatial spillovers and an explicit econometric dynamic set-up to analyse the behaviour of the model through time. We find that a province’s employment growth rate is closely related to that of nearby provinces. Spatial spillovers, however, are weaker in relatively large geographical units. A diversified mix of productive activities has a positive effect on employment growth rates at local level, while employment density and specialisation have a negative impact. We also find that these negative effects extend across different cyclical phases of the Italian economy, hence they are not restricted to a time period of decreasing employment levels.

Equilibrium and nonequilibrium molecular dynamics studies of diffusion in model one-dimensional micropores

Molecular dynamics simulation results are reported for self-diffusion of a hard-sphere fluid in a one-dimensional interconnected spherical cavity-cylindrical pore model of zeolites. The mode of transport within the cylindrical sections of the structure is by single-file diffusion and a scaling relationship between the self-diffusivity and the length of the particle files proposed in recent work is verified using two different nonequilibrium simulation techniques. It is also shown that strong nonlocal spatial correlations can exist between the fluid particles in pore systems containing single-file channels and these results in conjunction with the local spatial dependence of the transport and equilibrium properties indicate that care needs to be exercised when interpreting experimental measurements of diffusivities within zeolitic structures.

Wind Energy Directional Spatial Correlation Functions and Application for Prediction

In planning, design, operation and maintenance of wind farms, the spatial variations in wind velocity become significant. Although there are many wind energy studies at a single location, spatial assessment evaluation methods are scanty. Wind velocity maps may provide a common basis for regional assessment and interpretations, but they do not yield means of regional prediction. Hence, the main purpose of this paper is to propose some new concepts and methods that are useful prior to regional wind energy potential calculations. Among these are Directional Point Cumulative Semivariogram (DPCSV) concepts, which provide information concerning local spatial wind velocity and/or elevation changes. It is possible to obtain the radius of wind velocity influence around each station, along desired directions, based on the DPCSV concept. The implementation of the proposed methodology is presented for some wind velocity measurement stations in Turkey. For the application, the sample DPCSVs are first obtained from the available data, and then converted to Local Spatial Dependence Functions (LSDF). These functions are the basic ingredients for the regional wind velocity estimations. The reliability of the methodology is measured through the cross validation procedure and it is observed that the procedure is valid with less than 20% error.

Sparse spatial autoregressions

Given local spatial error dependence, one can construct sparse spatial weight matrices. As an illustration of the power of such sparse structures, we computed a simultaneous autoregression using 20 640 observations in under 19 min despite needing to compute a 20 640 by 20 640 determinant 10 times.

Textured image segmentation by context enhanced clustering

An unsupervised textured image segmentation technique based on multidimensional feature vector clustering is described, where the features are the parameters of an autoregressive model, The benefits of incorporating spatial contextual information are demonstrated on both true cluster number estimation and actual image segmentation. A simple within-cluster distance is used for cluster validity analysis, where feature vectors are modified through local spatial dependency. This greatly reduces the dispersion in the raw feature data fed to the clustering process, and improves the true cluster number estimation. At the segmentation stage, three schemes incorporating contextual information at feature vector and label levels are proposed to enhance the segmentation accuracy. One is a development of a technique due to Mardia and Hainsworth (1988). The proposed approaches are tested on a four-class textured image.