Nature of Spatial Data, Accuracy, and Validation

2015 ◽  
pp. 164-175
Keyword(s):  
Spatial Data ◽  
Data Accuracy

Spatial Data Accuracy

2017 ◽  
pp. 305-328
Author(s):  
Earl F. Burkholder
Keyword(s):  
Spatial Data ◽  
Data Accuracy

scholarly journals Métodos de orlado para la evaluación de la exactitud altimétrica en modelos digitales de elevaciones del terreno

2021 ◽  
pp. 33-45
Author(s):  
Francisco Javier Ariza-López ◽  
Juan Francisco Reinoso-Gordo
Keyword(s):  
Spatial Data ◽  
Data Accuracy ◽  
National Standard

Tradicionalmente, la evaluación de la exactitud altimétrica de modelos digitales de elevaciones del terreno (MDE), se ha realizado aplicando estándares (p.ej. National Standard for Spatial Data Accuracy) basados en el muestreo de puntos en el modelo de referencia (S1) y en el modelo a evaluar (S2). Estos estándares plantean dos inconvenientes: 1) los puntos utilizados en las evaluaciones son escasos comparados con la superficie total de un MDE y, por tanto, dejan gran parte del terreno sin evaluar, 2) la evaluación de un elemento superficial se realiza por comparación de elementos puntuales, cuando parece más adecuado evaluar por comparación de superficies. Ambos inconvenientes pueden ser superados si se utilizan métodos de orlado sobre superficies. En este trabajo se presentan por primera vez, en el ámbito de la evaluación altimétrica de MDE, los métodos de orlado simple (MOS) y doble (MOD) sobre superficies. El estudio se ha realizado sobre datos sintéticos que permiten plantear una situación de estudio predeterminada. Se ha demostrado que ambos métodos permiten la detección de atípicos y sesgos al evaluar S2. Además, se pueden elaborar funciones de distribución observadas, que eliminen la necesidad considerar hipótesis de normalidad sobre las discrepancias.

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.

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