Modeling Skewness in Spatial Data Analyis without Data Transformation

2005 ◽  
pp. 929-937
Author(s):  
Philippe Naveau ◽  
Denis Allard
Keyword(s):  
Spatial Data ◽  
Data Transformation

scholarly journals Gaussian Transformation Methods for Spatial Data

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 196
Author(s):  
Emmanouil A. Varouchakis
Keyword(s):  
Spatial Data ◽  
Random Number Generator ◽  
Data Transformation ◽  
Rainfall Data ◽  
Public Access ◽  
Negative Sign ◽  
Alternative Methods ◽  
Positive Sign ◽  
Observation Data ◽  
Transformation Methods

Data gaussianity is an important tool in spatial statistical modeling as well as in experimental data analysis. Usually field and experimental observation data deviate significantly from the normal distribution. This work presents alternative methods for data transformation and revisits the applicability of a modified version of the well-known Box-Cox technique. The recently proposed method has the significant advantage of transforming negative sign (fluctuations) data in advance to positive sign ones. Fluctuations derived from data detrending cannot be transformed using common methods. Therefore, the Modified Box-Cox technique provides a reliable solution. The method was tested in average rainfall data and detrended rainfall data (fluctuations), in groundwater level data, in Total Organic Carbon wt% residuals and using random number generator simulating potential experimental results. It was found that the Box-Cox technique competes successfully in data transformation. On the other hand, it improved significantly the normalization of negative sign data or fluctuations. The coding of the method is presented by means of a Graphical User Interface format in MATLAB environment for reproduction of the results and public access.

Review of Spatial and Non-spatial Data Transformation to 3D Geovisualization for Natural Disaster

2017 ◽  
pp. 340-351
Author(s):  
Muhammad Yudhi Rezaldi ◽  
Rabiah Abdul Kadir ◽  
Mohamad Taha Ijab ◽  
Azlina Ahmad
Keyword(s):  
Natural Disaster ◽  
Spatial Data ◽  
Data Transformation

A Two-Tier Spatial Index for Non-flat Spatial Data Broadcasting on Air

2014 ◽  
Vol E97.B (12) ◽  
pp. 2809-2818 ◽  
Author(s):  
SeokJin IM ◽  
HeeJoung HWANG
Keyword(s):  
Spatial Data ◽  
Spatial Index ◽  
Data Broadcasting

ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

2020 ◽  
Vol 5 (1) ◽  
pp. 414
Author(s):  
Amsar Yunan
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Flood Hazard ◽  
Parameter Analysis ◽  
Thematic Maps ◽  
Aceh Province ◽  
Object Area ◽  
Use Factors ◽  
Hazard Levels ◽  
Flood Prone Areas

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually.  Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.

An empirical analysis of town-forming enterprises' corporate social responsibility: Evidence from the Volga Federal District

2020 ◽  
Vol 18 (10) ◽  
pp. 1894-1909
Author(s):  
I.R. Badykova
Keyword(s):  
Regression Analysis ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Empirical Analysis ◽  
Spatial Data ◽  
Univariate Analysis ◽  
Federal District ◽  
Current Situation ◽  
Economic Situation ◽  
Corporate Social

Subject. This article explores the determinants of social responsibility of backbone enterprises. Objectives. The article aims to investigate the relationships between the socio-economic situation of the monotown where the backbone company operates, and corporate social responsibility (CSR). Methods. For the study, I used a regression analysis and univariate analysis of spatial data. The rating estimates calculated using an original methodology are used as a CSR proxy (dependent variable). Results. Presenting information about the current situation of backbone enterprises and monotowns in Russia, the article reveals the existence of relationships between the backbone enterprise's affiliation to a monotown with a certain socio-economic situation and the level of corporate social responsibility. Conclusions. The situation of the backbone companies is likely to deteriorate. Increasing the level of social responsibility during a crisis seems unlikely.

Modellgestützte Verfahren und „big (spatial) data“ in der regionalen Versorgungsforschung II

2018 ◽  
Vol 11 (03/18) ◽  
pp. 59-64
Author(s):  
Dr. rer. nat. Dipl.-Geogr. Thomas Wieland
Keyword(s):  
Spatial Data
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