Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland

2014 ◽  
Vol 38 (3) ◽  
pp. 354-377 ◽  
Author(s):  
Thierry Feuillet ◽  
Julien Coquin ◽  
Denis Mercier ◽  
Etienne Cossart ◽  
Armelle Decaulne ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Spatial Analysis ◽  
Spatial Data ◽  
Fixed Effects ◽  
Regional Scale ◽  
Mass Movement ◽  
Predisposing Factors ◽  
The Other ◽  
Data Set ◽  
Landslide Occurrence

Most studies focusing on landslide spatial analysis have considered the relationships between predictors and landslide occurrence as fixed effects. Yet spatially varying relationships, i.e. non-stationarity, often occur in any spatial data set and should be theoretically considered in statistical models for a better fit. In Skagafjörður, a landslide-rich north–south oriented area located in northern Iceland, we investigated whether spatial non-stationarity in the relationships between paraglacial variables (glacio-isostatic rebound and post-glacial debuttressing, both captured in this area by latitude) and landslide locations is detectable. To explore the non-stationarity of factors that predispose landslide occurrence, we performed two logistic regression models, one global (GLR) and the other enabling the regression parameters to vary locally (geographically weighted logistic regression, GWLR). Each model was computed with two types of outcome, one based on the entire masses of landslides and the other only on the scarps of landslides. GLR results reveal that increasing latitude is associated with increasing probability of landslide occurrence, confirming that post-glacial rebound is of prime importance at the regional scale. Nevertheless, GWLR indicates that this relationship is absent or reversed at some locations, meaning that the influence of paraglacial and other predisposing factors of landsliding (slope, valley depth and curvature) vary at the local scale. This result sheds light on the spatial clustering of three subzones where landsliding drivers are homogeneous. We conclude that a GWR-based approach provides some significant inputs for spatial analysis of mass movement processes, by identifying multi-scale process control zones and by highlighting local drivers, indecipherable in global models.

Study of Potential Climate for Sugarcane Production in the State of Rio Grande Do Sul, Brazil, By Means of Geoprocessing

2011 ◽  
pp. 96-106
Author(s):  
Fernando Soares ◽  
José Alba ◽  
Elódio Sebem ◽  
Marcos Wrege
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Regional Scale ◽  
Rio Grande ◽  
Local Scale ◽  
The State ◽  
Rio Grande Do Sul ◽  
Gis Technology ◽  
Physiological Maturity ◽  
Climate Study

A potential climate study for sugarcane of a sector of Rio Grande do Sul State, Brazil is presented here. GIS technology was applied for evaluation of the risk of frost and for integration of spatial data. The problem was focused in regional scale and in local scale (Municipality of Jaguari). Results showed that cultivation can be programmed in order to obtain physiological maturity before the period of risk of frost, thus avoiding low production. Spatial analysis of the information allows rapid perspective for productivity of sugarcane in a specific territory. The Municipality of Jaguari has large potential for cultivation of sugarcane because of the absence of the risk of frost. Its productivity allows for expansion into suitable neighboring areas. Also, geoprocessing combined with the study of climate and soil appears as a significant tool for interpreting the areas with aptitude for production of sugarcane or for the industry of sugar and alcohol.

scholarly journals KRILLBASE: a circumpolar database of Antarctic krill and salp numerical densities, 1926–2016

2017 ◽  
Vol 9 (1) ◽  
pp. 193-210 ◽  
Author(s):  
Angus Atkinson ◽  
Simeon L. Hill ◽  
Evgeny A. Pakhomov ◽  
Volker Siegel ◽  
Ricardo Anadon ◽  
...  
Keyword(s):  
Spatial Data ◽  
Antarctic Krill ◽  
Regional Scale ◽  
Published Data ◽  
Numerical Density ◽  
Data Set ◽  
Data Resource ◽  
Abundance Data ◽  
Krill Abundance ◽  
Contact Points

Abstract. Antarctic krill (Euphausia superba) and salps are major macroplankton contributors to Southern Ocean food webs and krill are also fished commercially. Managing this fishery sustainably, against a backdrop of rapid regional climate change, requires information on distribution and time trends. Many data on the abundance of both taxa have been obtained from net sampling surveys since 1926, but much of this is stored in national archives, sometimes only in notebooks. In order to make these important data accessible we have collated available abundance data (numerical density, no. m−2) of postlarval E. superba and salp individual (multiple species, and whether singly or in chains). These were combined into a central database, KRILLBASE, together with environmental information, standardisation and metadata. The aim is to provide a temporal-spatial data resource to support a variety of research such as biogeochemistry, autecology, higher predator foraging and food web modelling in addition to fisheries management and conservation. Previous versions of KRILLBASE have led to a series of papers since 2004 which illustrate some of the potential uses of this database. With increasing numbers of requests for these data we here provide an updated version of KRILLBASE that contains data from 15 194 net hauls, including 12 758 with krill abundance data and 9726 with salp abundance data. These data were collected by 10 nations and span 56 seasons in two epochs (1926–1939 and 1976–2016). Here, we illustrate the seasonal, inter-annual, regional and depth coverage of sampling, and provide both circumpolar- and regional-scale distribution maps. Krill abundance data have been standardised to accommodate variation in sampling methods, and we have presented these as well as the raw data. Information is provided on how to screen, interpret and use KRILLBASE to reduce artefacts in interpretation, with contact points for the main data providers. The DOI for the published data set is doi:10.5285/8b00a915-94e3-4a04-a903-dd4956346439.

Topic-marking prepositions in Swedish: A corpus-based analysis of adpositional synonymy

2014 ◽  
Vol 37 (2) ◽  
pp. 257-296 ◽  
Author(s):  
Anton Granvik ◽  
Susanna Taimitarha
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Multinomial Logistic Regression ◽  
Initial Position ◽  
The Other ◽  
Data Set ◽  
Multinomial Logistic Regression Analysis ◽  
Usage Patterns ◽  
Head Element ◽  
The Relationship

This study analyses the relationship between four near-synonymous Swedish prepositions, namely angående, beträffande, gällande and rörande, which are used to establish what we call a topic-marking relation, as in statens avtal angående finansieringen ‘the agreement of the state regarding the financing’. By focusing on a single, loosely defined genre consisting of the written texts included in the Swedish PAROLE corpus, we address the question of what differences there are among these four prepositions, which intuitively seem highly similar and mutually interchangeable. In order to find out which contextual and grammatical factors might influence the choice of one preposition over the others, two complementary analyses were performed. First, a so-called collostructional analysis (see Stefanowitsch & Gries 2003, Gries & Stefanowitsch 2004) was performed on 791 cases of these prepositions found in the PAROLE corpus. Secondly, the corpus examples were annotated according to ten syntactic and four semantic criteria and a multinomial logistic regression analysis was performed on the annotated data set. The results show some tendencies pointing to differing usage patterns of the four prepositions. Beträffande stands out as the most frequent of them all and is also preferably used when no explicit head element is present, typically in sentence-initial position. Angående prefers words of communication while rörande is used when another topic-marking preposition is also present. On the other hand, neither of the two analyses leads to a clear distinction among the four prepositions, thus pointing to the fact that these topic-marking prepositions indeed constitute a fairly good case of adpositional synonymy, with few distinguishing factors separating one from the other.

Creation and Application of Highway Network Spatial Database

2013 ◽  
Vol 361-363 ◽  
pp. 2236-2239
Author(s):  
Bi He
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Spatial Database ◽  
Information Service ◽  
The Other ◽  
Traffic Information ◽  
Foundation Design ◽  
Management Level ◽  
Property Data ◽  
Highway Network

Highway network is the most important transportation facility, informatization is the effictive method to improve the management level of highway network. With GIS, highway network spatial database was built, the process include set the spatial mathmetics foundation, design the structure of database, process the spatial data, build the topological relation and process the property data. And two example of the highway network spatial database application was provided, one is spatial analysis, and the other is traffic information service on internet.

scholarly journals Evaluating factors of profitability for Indian banking sector: a panel regression

2018 ◽  
Vol 3 (2) ◽  
pp. 236-254
Author(s):  
Rohit Bansal ◽  
Arun Singh ◽  
Sushil Kumar ◽  
Rajni Gupta
Keyword(s):  
Fixed Effects ◽  
Banking Sector ◽  
Stock Exchange ◽  
Capital Adequacy ◽  
The Other ◽  
Data Set ◽  
Content Type ◽  
Independent Variables ◽  
Private Banks ◽  
Public Banks

Purpose The purpose of this paper is to quantify several measures to examine the determinants of profitability for the listed Indian banks. The authors include both public sector (PSUs) and private sector’s banks in the study. The authors have taken all the banks that are registered on the Bombay stock exchange (BSE) in the sample. This paper also intends to identify the association between the net profit margin (PM) and return on assets (ROA) with the several other independent variables of the Indian banking sector including private banks and public banks over the past six years starting from April 1, 2012 to March 31, 2017. Therefore, a sample of 39 listed banking companies and total 195 balanced observations are selected for the analysis purpose. Design/methodology/approach The authors have used profitability as a dependent variable represented by net PM, ROA and several financial ratios as independent variables. Financial statement and income statement of all listed banks were obtained from BSE and particular company’s website. Panel data regression has been analyzed with both the descriptive research techniques, i.e., fixed effects and random effects. The authors also verified both panel techniques with Hausman’s specification test, which is a widely used procedure for selecting a panel effect. The authors applied PP – Fisher χ2, PP – Choi Z-statistics and Hadri to testing whether the data set is free from unit root problem and data set is a stationary series. Findings Results imply that interest expended interest earned (IEIE) and credit deposit ratio (CRDR) reduced the profitability of private banks in India. IEIE, CRDR and quick ratio (QR) reduced the profitability of public banks in India, while cash deposit ratio (CDR) and Advances to Loan Funds (ALF) increased the effectiveness of public banks. Under the total banks IEIE, CRDR reduced the profitability, on the other side, CDR, ALF and Total Debt to Owners Fund (TDOF) increased the profitability of total banks in India. Under the dependency of ROA, CRDR and TDOF reduced the return of private banks in India, while CDR, ALF and QR enhanced the profitability of private banks. Originality/value No variables found significant under public banks while taking ROA as a dependent variable. Under the overall banking data, CRDR reduced the profitability. On the other side, capital adequacy ratio and ALF increased the profitability of total banks in India. The findings of this study will support policy creators, financial executives and investors in constructing investment decisions.

scholarly journals Integração de dados espaciais em SIG para avaliação da susceptibilidade de ocorrência de deslizamentos

Finisterra ◽  
2012 ◽  
Vol 38 (76) ◽  
Author(s):  
Eusébio Reis ◽  
José Luís Zêzere ◽  
Gonçalo Teles Vieira ◽  
Maria Luísa Rodrigues
Keyword(s):  
Land Use ◽  
Data Integration ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Slope Aspect ◽  
Probabilistic Algorithms ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Spatial Data Integration

SPATIAL DATA INTEGRATION IN GIS FOR LANDSLIDE SUSCEPTIBILITY PREDICTION. Improper land use is an important factor for the occurrence and intensification of natural hazards. Therefore, a correct hazard zonation is of extreme significance for the adequate planning of human activities. The main objective of this study, applied to a test site in the North of Lisbon (Fanhões-Trancão), is the application of a comprehensive methodology for landslide susceptibility evaluation at a regional scale. The methodological framework is supported by a relational database for GIS processing that includes maps of landslides and of conditioning factors (i.e. slope, aspect/geological structure, lithology, superficial deposits and land use). These maps were produced from detailed field surveys or derived from existing documents. Since the occurrence of distinct types of landslides is influenced differently by the conditioning factors, shallow translational landslides were chosen as an example for the application of the methodology. The susceptibility model is based on statistical and probabilistic algorithms that establish the relationships between the landslides and the conditioning factors (favourability functions), and allow the evaluation and validation of the map layers (independent variables) to be incorporated in the model. The spatial data integration in GIS environment is based in the bayesian interpretation of the favourability function, a technique that ranks the susceptibility values of landslide occurrence from 0 to 1. For model evaluation, following a cross-validation procedure, a random sample of the shallow translational landslides was used.

SOCIAL WELFARE, PHANTOM VOTERS, AND INTERNAL MIGRATION IN TAIWAN — EVIDENCE FROM PANEL SPATIAL ANALYSIS

2018 ◽  
pp. 1850012
Author(s):  
JR-TSUNG HUANG ◽  
MING-LEI CHANG
Keyword(s):  
Spatial Analysis ◽  
Panel Data ◽  
Social Welfare ◽  
Internal Migration ◽  
Spatial Dependence ◽  
Fixed Effects ◽  
The Other ◽  
Welfare Benefits ◽  
Magnetic Effects

This paper aims to investigate the following two issues related to internal migration in Taiwan: one is the widely discussed issue of the existence of magnetic effects induced by welfare benefits and the other is a rarely discussed issue of the existence of phantom voters. Using panel data for 23 counties and cities from 1995 to 2010 and estimating three fixed-effects spatial Durbin models, the primary findings of this study are that, by keeping other factors constant and considering the spatial dependence of migration, welfare migration is found to exist, particularly for females, and the number of phantom voters in an election year can significantly affect internal migration in Taiwan.

The Determinants of Capital Adequacy Ratio: The Case of the Vietnamese Banking System in the Period 2011-2015

2017 ◽  
Vol 33 (2) ◽  
Author(s):  
Ngoc Anh Nguyen
Keyword(s):  
Banking System ◽  
Precious Metals ◽  
Capital Adequacy ◽  
The Other ◽  
Total Asset ◽  
Data Set ◽  
Hand Size ◽  
Net Interest Margin ◽  
Capital Adequacy Ratio ◽  
Positive Effect

The analysis of a data set of observation for Vietnamese banks in period from 2011 - 2015 shows how Capital Adequacy Ratio (CAR) is influenced by selected factors: asset of the bank SIZE, loans in total asset LOA, leverage LEV, net interest margin NIM, loans lost reserve LLR, Cash and Precious Metals in total asset LIQ. Results indicate based on data that NIM, LIQ have significant effect on CAR. On the other hand, SIZE and LEV do not appear to have significant effect on CAR. Variables NIM, LIQ have positive effect on CAR, while variables LLR and LOA are negatively related with CAR.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

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