Reigniting GIS's Application in Ecotourism

2018 ◽  
pp. 98-115
Author(s):  
Sovik Mukherjee
Keyword(s):  
Principal Component Analysis ◽  
Spatial Data ◽  
Euclidean Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Secondary Source ◽  
Tourist Attractions ◽  
Potential Index ◽  
Travel Agencies ◽  
Euclidean Distance Function

The chapter brings out a brief note on the tourist attractions, hotels and lodges, NGOs/travel agencies operating in that region, railway/bus stations, land use profile, etc. in the Sundarban area of West Bengal in conjunction with exploring the potential of ecotourism using GIS and some secondary source data. Moving onto the analysis part, by making use of geo-spatial data, the attributes of ecotourism potential in the Sundarbans has been explored. The author makes use of the Euclidean distance mechanism and principal component analysis to rank the ecotourism sites in Sunderbans (i.e., based on the construction of ecotourism potential index [EPI]). The novelty of the chapter lies in comparing the ranks obtained by constructing the EPI following the principal component analysis and the Euclidean distance function. It needs to be mentioned here that these tourist spots have been selected based on the information collected on the inflow of both domestic and foreign tourists to these spots. The chapter concludes by discussing the future scope of research in this regard.

Reigniting GIS's Application in Ecotourism

2019 ◽  
pp. 1478-1492
Author(s):  
Sovik Mukherjee
Keyword(s):  
Principal Component Analysis ◽  
Spatial Data ◽  
Euclidean Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Secondary Source ◽  
Tourist Attractions ◽  
Potential Index ◽  
Travel Agencies ◽  
Euclidean Distance Function

The chapter brings out a brief note on the tourist attractions, hotels and lodges, NGOs/travel agencies operating in that region, railway/bus stations, land use profile, etc. in the Sundarban area of West Bengal in conjunction with exploring the potential of ecotourism using GIS and some secondary source data. Moving onto the analysis part, by making use of geo-spatial data, the attributes of ecotourism potential in the Sundarbans has been explored. The author makes use of the Euclidean distance mechanism and principal component analysis to rank the ecotourism sites in Sunderbans (i.e., based on the construction of ecotourism potential index [EPI]). The novelty of the chapter lies in comparing the ranks obtained by constructing the EPI following the principal component analysis and the Euclidean distance function. It needs to be mentioned here that these tourist spots have been selected based on the information collected on the inflow of both domestic and foreign tourists to these spots. The chapter concludes by discussing the future scope of research in this regard.

scholarly journals A Novel Enhanced-Oil-Recovery Screening Approach Based on Bayesian Clustering and Principal-Component Analysis

2016 ◽  
Vol 19 (03) ◽  
pp. 382-390 ◽  
Author(s):  
Martina Siena ◽  
Alberto Guadagnini ◽  
Ernesto Della Rossa ◽  
Andrea Lamberti ◽  
Franco Masserano ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Enhanced Oil Recovery ◽  
Parameter Space ◽  
Oil Recovery ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Bayesian Clustering ◽  
Global Features

Summary We present and test a new screening methodology to discriminate among alternative and competing enhanced-oil-recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques was successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests before fieldwide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Because similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria, we consider fluid (density and viscosity) and reservoir-formation (porosity, permeability, depth, and temperature) properties. Our approach is observation-driven and grounded on an exhaustive database that we compiled after considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through principal-component analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesian-clustering algorithm. Considering the cluster that includes the EOR field under evaluation, an intercluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components (PCs) and (b) the fraction of variance associated with each PC is taken as weight of the Euclidean distance that we determine. As a test bed, we apply our approach on three fields operated by Eni. These include light-, medium-, and heavy-oil reservoirs, where gas, chemical, and thermal EOR projects were, respectively, proposed. Our results are (a) conducive to the compilation of a broad and extensively usable database of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.

scholarly journals SVD-based principal component analysis of geochemical data

2005 ◽  
Vol 3 (4) ◽  
pp. 731-741 ◽  
Author(s):  
Petr Praus
Keyword(s):  
Principal Component Analysis ◽  
Euclidean Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Geochemical Data ◽  
Average Group ◽  
Testing Data ◽  
Value Decomposition ◽  
Geochemical Variables

AbstractPrincipal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2% of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration.The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.

scholarly journals Principal Component Analysis on Spatial Data: An Overview

2013 ◽  
Vol 103 (1) ◽  
pp. 106-128 ◽  
Author(s):  
Urška Demšar ◽  
Paul Harris ◽  
Chris Brunsdon ◽  
A. Stewart Fotheringham ◽  
Sean McLoone
Keyword(s):  
Principal Component Analysis ◽  
Spatial Data ◽  
Principal Component ◽  
Component Analysis

A Method for Spatial Data Registration Based on PCA-ICP Algorithm

2013 ◽  
Vol 718-720 ◽  
pp. 1033-1036 ◽  
Author(s):  
Shi Jun He ◽  
Shi Ting Zhao ◽  
Fan Bai ◽  
Jia Wei
Keyword(s):  
Principal Component Analysis ◽  
Basic Principle ◽  
Spatial Data ◽  
Laser Scanning ◽  
Principal Component ◽  
Component Analysis ◽  
3D Laser Scanning ◽  
Icp Algorithm ◽  
Data Registration ◽  
Registration Algorithm

The spatial data which acquired by 3D laser scanning is huge, aiming at the iteration time is long with classic ICP algorithm, a improved registration algorithm of spatial data ICP algorithm which based on principal component analysis (PCA) is proposed in this paper (PCA-ICP), the basic principle and steps of PCA-ICP algorithm are given. The experiment results show that this method is feasible and the iterative time of PCA-ICP algorithm is shorter than classical ICP algorithm.

scholarly journals METODE NILAI JARAK GUNA KESAMAAN ATAU KEMIRIPAN CIRI SUATU CITRA (KASUS DETEKSI AWAN CUMULONIMBUS MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS)

2017 ◽  
Vol 7 (2) ◽  
pp. 21 ◽  
Author(s):  
Dwi Nugraheny
Keyword(s):  
Principal Component Analysis ◽  
Euclidean Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Distance Measurement ◽  
Distance Method ◽  
City Block ◽  
Average Percentage ◽  
Data Elements ◽  
City Block Distance

One commonality or similarity matching phase characteristics of an image is by using the method of distance measurement. Distance is an important aspect in the development of methods of grouping and regression. Before the grouping of data or object to the detection process, first determined the size of the proximity distance between data elements. In this study, there will be a comparison of several methods including distance measurement using Euclidean distance, Manhattan/ City Block Distance, Mahalanobis which will be implemented in the case of cumulonimbus image clouds detection using Principal Component Analysis (PCA). The average percentage of accuracy of image similarity value Cumulonimbus clouds using the Euclidean distance method was 93 percent and the distance Manhattan/ City Block Distance is 90 percent, while the Mahalanobis distance method was 50 percent.

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