Moran's Autocorrelation Research Articles

Rabies in the Dutch East Indies a century ago – A spatio-temporal case study in disease emergence

Rabies continues to spread through the Indonesian archipelago. During the past 20 years, several islands – including Flores, Ambon and Bali – that had historically been free of rabies have become infected. However, the Dutch East Indies (a Dutch colony that became modern Indonesia following World War II) had been infected since the 1880s. The spread of rabies is a lesson in the emergence of an infectious disease.Reports of human cases treated for rabies and livestock rabies cases from the 1880s to 1917 were compiled. The spatial and temporal distribution of these cases was analyzed using maps, spatial statistics and time-series techniques. The first confirmed case of rabies was reported in 1889 from the Batavia [Jakarta] district (although disease suspicion was reported as early as 1884). During the 1890s rabies was already commonly reported from Java and the east coast of Sumatra, and by the late 1890s, from Celebes [Sulawesi]. Between 1900 and 1916, cases were reported from other parts of Java, Sumatra and Sulawesi, and from Borneo, the Moluccas and other outlying islands. Between 1897 and 1916, a total of 8826 human cases treated for rabies were reported and between 1908 and 1917, 1033 livestock cases were reported. Most (97.5%) human cases treated were attributed to rabid dogs. Increasing numbers of reports were observed during the period. Between 1908 and 1916 the correlation between human and livestock case reports was 64.2%, and at the district level it was 75.9%. Moderate correlations (>40%) were found between human cases and livestock cases reported up to six months previously. Based on year of first report from each district, human cases were strongly clustered (Moran's autocorrelation 0.47, P=0.005). The most likely spatio-temporal cluster of reported cases of humans treated for rabies originated from the west coast of Sumatra between 1899 and 1905, and other clusters were identified in west Java (1898–1899), the district of Batavia and in east Java (1910–1911), Nusa Tengarra Barat (1912), Borneo (1914) and the east coast of Sumatra (1903–1906).Rabies was probably first introduced to the colonial capital of the Dutch Indies, Batavia [Jakarta] in the 1880s. It then spread rapidly throughout most of the archipelago during the next two to three decades because of the movement of dogs via the military forces, for trade and as pets, despite government regulations designed to control the epidemic. Such a history suggests that further emergence and reemergence of rabies in rabies-free islands will occur based on an island's location and position within the complex social, trade and transport network that represents the Indonesian archipelago. Targeted surveillance and enforcement of quarantine regulations remain critical, to prevent history repeating itself.

Propy: a tool to generate various modes of Chou’s PseAAC

Sequence-derived structural and physiochemical features have been frequently used for analysing and predicting structural, functional, expression and interaction profiles of proteins and peptides. To facilitate extensive studies of proteins and peptides, we developed a freely available, open source python package called protein in python (propy) for calculating the widely used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes five feature groups composed of 13 features, including amino acid composition, dipeptide composition, tripeptide composition, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors, composition, transition and distribution of various structural and physicochemical properties and two types of pseudo amino acid composition (PseAAC) descriptors. These features could be generally regarded as different Chou's PseAAC modes. In addition, it can also easily compute the previous descriptors based on user-defined properties, which are automatically available from the AAindex database. The python package, propy, is freely available via http://code.google.com/p/protpy/downloads/list, and it runs on Linux and MS-Windows. Supplementary data are available at Bioinformatics online.

Molecular Descriptors in Modelling the Tumour Necrosis Factor-α Converting Enzyme Inhibition Activity of Novel Tartrate-Based Analogues

The tumour necrosis factor-α converting enzyme inhibition activity of a series comprising of novel tartrate-based analogues has been quantitatively analysed in terms of molecular descriptors. The statistically validated quantitative structure-activity relationship models provided rationales to explain the inhibition activity of these congeners. The descriptors identified through combinatorial protocol in multiple linear regression analysis have highlighted the role of Moran autocorrelation of lag 7, weighted by atomic van der Waals volume, presence of both prime and nonprime amide carbonyl oxygen in the tartrate moiety and occurrence of five membered ring bearing substituents at varying sites. A few potential novel tartrate-based analogues have been suggested for further investigation.

Two Phase Temporal-Spatial Autocorrelation of Urban Patterns: Revealing Focal Areas of Re-Urbanization in Tel Aviv-Yafo

A new two-phase temporal-spatial analysis technique is presented for exploring core areas of re-urbanization in complex patterns of urban change. The Local Autocorrelation of Pearson (LAoP) technique first computes temporal Pearson correlations between time series of data for each sub-area and the city’s average time series and then computes the local Moran autocorrelation (LISA) of these temporal correlations. This technique was implemented on Construction Initiation (CI) data for the city Tel Aviv-Yafo since the mid 1970s, where complex patterns evolved through spontaneous socio-economic processes, individual and corporate behavior and local and national planning policies. Comparing the results from the temporal-spatial autocorrelation analysis on CI data, with those of the accumulated built-up areas enhances the understanding of the patterns of re-urbanization in Tel Aviv-Yafo and the detection of new core areas of development.

A rationale for the activity profile of benzenesulfonamide derivatives as cyclooxygenase (COX) inhibitors

The COX inhibition actions of benzenesulfonamides have been analyzed in terms of 0D-, 1D- and 2D-DRAGON descriptors using combinatorial protocol in multiple linear regression (CP-MLR). The derived QSAR models revealed that higher values of BEHm2 (the highest eigenvalue n.2 of Burden matrix) and C-009 (fragment CHRX2) and lower value of MATS2m (atomic masses weighted Moran autocorrelation of lag 2) are advantageous to the COX-2 inhibition activity. For a compound to be a potent COX-1 inhibitor, higher values of MATS6v (atomic van der Waals volumes weighted Moran autocorrelation of lag 6), GATS6p (atomic polarizabilities weighted Geary autocorrelation of lag 6), Hy (hydrophilic factor) and lower value of MATS5m (atomic masses weighted Moran autocorrelation of lag 5) are desirable.

Sequence-Based Prediction of Protein-Protein Interactions by Means of Rotation Forest and Autocorrelation Descriptor

We propose a sequence-based multiple classifier system, i.e., rotation forest, to infer protein-protein interactions (PPIs). Moreover, Moran autocorrelation descriptor is used to code an interaction protein pair. Experimental results on Saccharomyces cerevisiae and Helicobacter pylori datasets show that our approach outperforms those previously published in literature, which demonstrates the effectiveness of the proposed method.

CP-MLR directed QSAR study of carbonic anhydrase inhibitors: sulfonamide and sulfamate inhibitors

Abstract The inhibition activities of sulfonamide and sulfamate derivatives for human carbonic anhydrases have been quantitatively analyzed using DRAGON descriptors. QSAR models have been obtained through combinatorial protocol-multiple linear regression (CP-MLR) computational procedure. For the hCA I inhibition activity, a higher value of information content index of the 1-order neighborhood symmetry (IC1) and a lower value of the Moran autocorrelations, MATS2v and MATS1p, along with a lower number of sulfur atoms in a molecular structure (nRSR) is beneficial to the activity. A higher number of 5-membered rings (nR05), a bigger distance between nitrogen and sulfur T(N..S), and a higher value of van der Waals volume weighted descriptor (GATS6v), are helpful to improve the hCA II inhibition activity. For the inhibition of hpCA, a lower value of the descriptors Jhetv and PW5, and higher values of the eigenvalue sum from Z weighted distance matrix, SEigZ, the Moran autocorrelation of lag 8 weighted by atomic van der Waals volumes, MATS8v and the Moran autocorrelation of lag 4 weighted by atomic Sanderson electronegativities, MATS4e are favorable. The derived significant models in such descriptors may further be used to synthesize new potential compounds and to decipher the mode of their actions at molecular level.

Quantitative Structure–Properties Relationship Study of the 29Si-NMR Chemical Shifts of Some Silicate Species

ABSTRACT In this work, the quantitative structure–properties relationship (QSPR) was applied to modeling and predicting the 29Si-NMR chemical shifts of a series of silicate species (on Q2 sites). The descriptors that were selected by stepwise multiple linear regression technique were square of alpha polarizability, Moran autocorrelation–lag3/unweighted by atomic Sanderson electronegativities, square of asphericity, and topological path/walk 2-Randic shape index. These descriptors could encode electronic, geometric, and topological characteristics that affect the chemical shifts of the molecules of interest. The results obtained using the multiple linear regression (MLR) model were comparable with the experimental values. The cross-validation test was also performed to evaluate the prediction power of the MLR model obtained. The q2 and PRESS of this model are 0.976 and 0.44761, respectively, revealing the credibility of the model.

The detection of spatial structure in populations and communities: An empirical case study

The presence of spatial autocorrelation in abundance and richness patterns has been recognised for some time. Evaluation of the tools to quantify patterning often uses simulated data that may be unrealistic or empirical field data where the presence and cause of structuring are unknown. We examine the efficacy of spatial pattern analysis for detecting pattern in empirical data at a fine scale using a field-based mesocosm experiment of a Drosophilidae community associated with decaying fruit. The mesocosm comprised 2 microclimate treatments that generated a particular expected spatial pattern in abundance and species richness. The magnitude of Moran's autocorrelation coefficients (I) was < 0.3 (i.e., low). However, the detected pattern was unaffected. Low Moran's I values did not result from low sample sizes, neither was the significance of Moran's I falsely inflated by large sample sizes. Examination of published I values revealed that autocorrelation values between 0.1 and 0.3 are common in empirical data, particularly at fine (lag distance ≤ 1 m) spatial scales. Pooling temporal samples strengthened the detected output without affecting the form of spatial pattern. We conclude that “weak” responses provide a valuable basis for mechanistic hypothesis generation, especially at fine spatial scales, and that the strength of spatial structure is likely to be determined by the spatial scale of the study.

Quantitative Analysis of Scale of Aeromagnetic Data Raises Questions About Geologic-Map Scale

A recently published study has shown that small-scale geologic map data can reproduce mineral assessments made with considerably larger scale data. This result contradicts conventional wisdom about the importance of scale in mineral exploration, at least for regional studies. In order to formally investigate aspects of scale, a weights-of-evidence analysis using known gold occurrences and deposits in the Central Lapland Greenstone Belt of Finland as training sites provided a test of the predictive power of the aeromagnetic data. These orogenic-mesothermal-type gold occurrences and deposits have strong lithologic and structural controls associated with long (up to several kilometers), narrow (up to hundreds of meters) hydrothermal alteration zones with associated magnetic lows. The aeromagnetic data were processed using conventional geophysical methods of successive upward continuation simulating terrane clearance or ‘flight height’ from the original 30 m to an artificial 2000 m. The analyses show, as expected, that the predictive power of aeromagnetic data, as measured by the weights-of-evidence contrast, decreases with increasing flight height. Interestingly, the Moran autocorrelation of aeromagnetic data representing differing flight height, that is spatial scales, decreases with decreasing resolution of source data. The Moran autocorrelation coefficient scems to be another measure of the quality of the aeromagnetic data for predicting exploration targets.

PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence.

Sequence-derived structural and physicochemical features have frequently been used in the development of statistical learning models for predicting proteins and peptides of different structural, functional and interaction profiles. PROFEAT (Protein Features) is a web server for computing commonly-used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes six feature groups composed of ten features that include 51 descriptors and 1447 descriptor values. The computed features include amino acid composition, dipeptide composition, normalized Moreau–Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors and the composition, transition and distribution of various structural and physicochemical properties. In addition, it can also compute previous autocorrelations descriptors based on user-defined properties. Our computational algorithms were extensively tested and the computed protein features have been used in a number of published works for predicting proteins of functional classes, protein–protein interactions and MHC-binding peptides. PROFEAT is accessible at

Sobre la espacialidad de los procesos electorales urbanos y una comparación entre las técnicas de regresión OLS y SAM / On the Spatial Distribution of Urban Electoral Processes and a Comparison between OLS and SAM Regression Techniques

El propósito de este trabajo es demostrar que debido a que algunos fenómenos sociales presentan variación espacial, la modelación de los mismos tiene implicaciones metodológicas importantes, y por lo tanto es recomendable utilizar una técnica de regresión que incorpore la espacialidad de los datos en su diseño. Se siguen dos procedimientos generales: primero se calcula el coeficiente de autocorrelación I de Moran para demostrar la espacialidad del comportamiento electoral en el México urbano entre 1994 y 2000; después se comparan dos técnicas de regresión, OLS y SAM. De la comparación se concluye que la técnica SAM es más robusta que la OLS con base en que la primera obtiene coeficientes de regresión más precisos y detecta efectos regionales estadísticamente significativos aun en la presencia de modelos con problemas de heterodasticidad. AbstractThe aim of this work is to demonstrate two things: 1) That due to the fact that some social phenomena presents spatial variation, its modeling also presents important methodological implications, and therefore 2) it is advisable to use a regression technique that incorporates data spatiality in its design. A two-step process is followed: First, the Moran autocorrelation coefficient is calculated to demonstrate the spatial concentration of electoral behavior in urban Mexico between 1994 and 2000. Secondly, two regression techniques are compared, OLS and SAM. From the comparison it is concluded that the SAM technique is a more robust technique than the OLS, based on that the former is able to compute more accurate regression coefficients and detects significant regional effects even in the presence of models dealing with the heteroskedasticity problem.

Structuring the genetic heterogeneity of the Basque population: a view from classical polymorphisms.

In this study we analyze 18 classical polymorphisms (ABO, Rh, MNSs, Lewis, P, Duffy, Kell, ADA, ESD, PGM1, PGD, AK1, ACP1, GLO1, HP, GC, TF, and PI) in over 2000 autochthonous individuals from 14 natural districts in three provinces of the Basque Country (Alava, Guipuzcoa, and Biscay). Heterogeneity analysis via the chi2 test and a calculation of F(ST) indicate that there is significant genetic heterogeneity between the Basque districts. The R matrix informs us that this heterogeneity is not significantly concentrated in a single district or in the districts of a single province, but is rather distributed among several districts belonging to the three provinces analyzed. We undertake to assess the influence of various historical, geographical, and cultural factors on the genetic structure of the Basque population. Analysis suggests that allele distribution is geographically patterned in the Basque Country. The gradient distributions observed in the case of some alleles (ABO*O, RH*cDE, RH*cde, MNS*MS, and ACP1*C) on the basis of Moran's autocorrelation coefficient I, along with the influence of the two main travel routes through the Basque Country (western route through Bilbao and eastern route through Vitoria), suggest that the gene flow tends toward the coast. As regards other factors considered (administrative division, repopulation processes, linguistic heterogeneity, and north vs. south cultural heterogeneity), we detected only a certain influence exerted by an old tribal differentiation (2000 B.P.), which would diminish with the passage of time.

Local Spatial Autocorrelation Statistics: Distributional Issues and an Application

The statistics Gi(d) and Gi*(d), introduced in Getis and Ord (1992) for the study of local pattern in spatial data, are extended and their properties further explored. In particular, nonbinary weights are allowed and the statistics are related to Moran's autocorrelation statistic, I. The correlations between nearby values of the statistics are derived and verified by simulation. A Bonferroni criterion is used to approximate significance levels when testing extreme values from the set of statistics. An example of the use of the statistics is given using spatial‐temporal data on the AIDS epidemic centering on San Francisco. Results indicate that in recent years the disease is intensifying in the counties surrounding the city.

Comparison of techniques for measuring cloud texture in remotely sensed satellite meteorological image data

This investigation has attempted to discover appropriate texture descriptors and to reveal more clearly the importance of texture analysis techniques for multispectral cloud classification. The textural features considered in this study include both spatial and frequency features. The spatial features were mainly those based on spatial grey-level difference statistics and circular Moran autocorrelation measures. The frequency features were those based on summed energies of polar co-ordinate Fourier power spectra and entropy-based measures of the spatial distribution of frequency entries in the polar spectra. Some other textural features, such as the Roberts gradient measure, were also investigated. The work was performed with TIROS-N AVHRR image data acquired in the late spring of 1979 over areas near the British Isles. The results of the evaluation and corresponding conclusions show which individual, and group of textural features, appear the most appropriate for aiding multispectral cloud classification.

Autocorrelation coefficients from irregularly spaced areal data

A FORTRAN subroutine is presented for computation of the generalized Moran autocorrelation coefficient and Geary coefficient from irregularly spaced data points. The algorithm used assumes isotropy of the data, so no directional information is obtained.