Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Geographic Information Sciences
Title: A new insight into land use classification based on aggregated mobile phone data
Abstract: Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.
Extracted statement — Data: Normalized hourly call volume
Research field: Geographic Information Sciences
Title: A new insight into land use classification based on aggregated mobile phone data
Abstract: Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.
Extracted statement — Data: Total call volume
Research field: Geographic Information Sciences
Title: Evaluating the effect of visually represented geodata uncertainty on decision-making: systematic review, lessons learned, and recommendations
Abstract: ABSTRACT For many years, uncertainty visualization has been a topic of research in several disparate fields, particularly in geographical visualization (geovisualization), information visualization, and scientific visualization. Multiple techniques have been proposed and implemented to visually depict uncertainty, but their evaluation has received less attention by the research community. In order to understand how uncertainty visualization influences reasoning and decision-making using spatial information in visual displays, this paper presents a comprehensive review of uncertainty visualization assessments from geovisualization and related fields. We systematically analyze characteristics of the studies under review, i.e., number of participants, tasks, evaluation metrics, etc. An extensive summary of findings with respect to the effects measured or the impact of different visualization techniques helps to identify commonalities and differences in the outcome. Based on this summary, we derive “lessons learned” and provide recommendations for carrying out evaluation of uncertainty visualizations. As a basis for systematic evaluation, we present a categorization of research foci related to evaluating the effects of uncertainty visualization on decision-making. By assigning the studies to categories, we identify gaps in the literature and suggest key research questions for the future. This paper is the second of two reviews on uncertainty visualization. It follows the first that covers the communication of uncertainty, to investigate the effects of uncertainty visualization on reasoning and decision-making.
Extracted statement — Reviews: Visualization Techniques
Research field: Geology
Title: Mapping mineralogical alteration using principal-component analysis and matched filter processing in the Takab area, north-west Iran, from ASTER data
Abstract: The Takab area, located in north‐west Iran, is an important gold mineralized region with a long history of gold mining. The gold is associated with toxic metals/metalloids. In this study, Advanced Space Borne Thermal Emission and Reflection Radiometer data are evaluated for mapping gold and base‐metal mineralization through alteration mapping. Two different methods are used for argillic and silicic alteration mapping: selective principal‐component analysis and matched filter processing (MF). Running a selective principal‐component analysis using the main spectral characteristics of key alteration minerals enhanced the altered areas in PC2. MF using spectral library and laboratory spectra of the study area samples gave similar results. However, MF, using the image reference spectra from principal component (PC) images, produced the best results and indicated the advantage of using image spectra rather than library spectra in spectral mapping techniques. It seems that argillic alteration is more effective than silicic alteration for exploration purposes. It is suggested that alteration mapping can also be used to delineate areas contaminated by potentially toxic metals.
Extracted statement — Minerals Mapped/ Identified: Argillic alteration
Research field: Geology
Title: Mapping of Alteration Zones in Mineral Rich Belt of South-East Rajasthan Using Remote Sensing Techniques
Abstract: Remote sensing techniques have emerged as an asset for various geological studies. Satellite images obtained by different sensors contain plenty of information related to the terrain. Digital image processing further helps in customized ways for the prospecting of minerals. In this study, an attempt has been made to map the hydrothermally altered zones using multispectral and hyperspectral datasets of South East Rajasthan. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion (Level1R) dataset have been processed to generate different Band Ratio Composites (BRCs). For this study, ASTER derived BRCs were generated to delineate the alteration zones, gossans, abundant clays and host rocks. ASTER and Hyperion images were further processed to extract mineral end members and classified mineral maps have been produced using Spectral Angle Mapper (SAM) method. Results were validated with the geological map of the area which shows positive agreement with the image processing outputs. Thus, this study concludes that the band ratios and image processing in combination play significant role in demarcation of alteration zones which may provide pathfinders for mineral prospecting studies. Keywords—Advanced space-borne thermal emission and reflection radiometer, ASTER, Hyperion, Band ratios, Alteration zones, spectral angle mapper.
Extracted statement — Band Parameters: Band Ratio
Research field: Geology
Title: Principal Component Analysis for Alteration Mapping
Abstract: Reducing the number of image bands input for principal component analysis (PCA) ensures that certain materials will not be mapped and increases the likelihood that others will be unequivocally mapped into only one of the principal component images. In arid terrain, PCA of four TM bands will avoid iron-oxide and thus more reliably detect hydroxyl-bearing minerals if only one input band is from the visible spectrum. PCA for iron-oxide mapping will avoid hydroxyls if only one of the SWIR bands is used. A simple principal component color composite image can then be created in which anomalous concentrations of hydroxyl, hydroxyl plus iron-oxide, and iron-oxide are displayed brightly in red-green-blue (RGB) color space. This composite allows qualitative inferences on alteration type and intensity to be made which can be widely applied.
Extracted statement — Data Dimensionality Reduction Methods: Principal Component Analysis (PCA)
Research field: Geology
Title: Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis
Abstract: Principal component analysis (PCA) is an image processing technique that has been commonly applied to Landsat Thematic Mapper (TM) data to locate hydrothermal alteration zones related to metallic deposits. With the advent of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), a 14-band multispectral sensor operating onboard the Earth Observation System (EOS)-Terra satellite, the availability of spectral information in the shortwave infrared (SWIR) portion of the electromagnetic spectrum has been greatly increased. This allows detailed spectral characterization of surface targets, particularly of those belonging to the groups of minerals with diagnostic spectral features in this wavelength range, including phyllosilicates (‘clay’ minerals), sulphates and carbonates, among others. In this study, PCA was applied to ASTER bands covering the SWIR with the objective of mapping the occurrence of mineral endmembers related to an epithermal gold prospect in Patagonia, Argentina. The results illustrate ASTER's ability to provide information on alteration minerals which are valuable for mineral exploration activities and support the role of PCA as a very effective and robust image processing technique for that purpose.
Extracted statement — Data Dimensionality Reduction Methods: Principal Component Analysis (PCA)
Research field: Geology
Title: Mapping of Alteration Zones in Mineral Rich Belt of South-East Rajasthan Using Remote Sensing Techniques
Abstract: Remote sensing techniques have emerged as an asset for various geological studies. Satellite images obtained by different sensors contain plenty of information related to the terrain. Digital image processing further helps in customized ways for the prospecting of minerals. In this study, an attempt has been made to map the hydrothermally altered zones using multispectral and hyperspectral datasets of South East Rajasthan. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion (Level1R) dataset have been processed to generate different Band Ratio Composites (BRCs). For this study, ASTER derived BRCs were generated to delineate the alteration zones, gossans, abundant clays and host rocks. ASTER and Hyperion images were further processed to extract mineral end members and classified mineral maps have been produced using Spectral Angle Mapper (SAM) method. Results were validated with the geological map of the area which shows positive agreement with the image processing outputs. Thus, this study concludes that the band ratios and image processing in combination play significant role in demarcation of alteration zones which may provide pathfinders for mineral prospecting studies. Keywords—Advanced space-borne thermal emission and reflection radiometer, ASTER, Hyperion, Band ratios, Alteration zones, spectral angle mapper.
Extracted statement — Spectral Mapping Technique: Spectral Angle Mapper (SAM)
Research field: Geophysics and Seismology
Title: Application of support vector regression analysis to estimate total organic carbon content of Cambay shale in Cambay basin, India – a case study
Abstract: Abstract The objective of the present study is to estimate total organic carbon (TOC) content over the entire thickness of Cambay Shale, in the boreholes of Jambusar–Broach block of Cambay Basin, India. To achieve this objective, support vector regression (SVR), a supervised data mining technique, has been utilized using five basic wireline logs as input variables. Suitable SVR model has been developed by selecting epsilon-SVR algorithm and varying three different kernel functions and parameters like gamma and cost on a sample dataset. The best result is obtained when the radial-basis kernel function with gamma = 1 and cost = 1, are used. Finally, the performance of developed SVR model is compared with the ΔlogR method. The TOC computed by SVR method is found to be more precise than the ΔlogR method, as it has better agreement with the core-TOC. Thus, in the present study area, the SVR method is found to be a powerful tool for estimating TOC of Cambay Shale in a continuous and rapid manner.
Extracted statement — Area of study: Cambay Basin, India
Research field: Geophysics and Seismology
Title: Application of support vector regression analysis to estimate total organic carbon content of Cambay shale in Cambay basin, India – a case study
Abstract: Abstract The objective of the present study is to estimate total organic carbon (TOC) content over the entire thickness of Cambay Shale, in the boreholes of Jambusar–Broach block of Cambay Basin, India. To achieve this objective, support vector regression (SVR), a supervised data mining technique, has been utilized using five basic wireline logs as input variables. Suitable SVR model has been developed by selecting epsilon-SVR algorithm and varying three different kernel functions and parameters like gamma and cost on a sample dataset. The best result is obtained when the radial-basis kernel function with gamma = 1 and cost = 1, are used. Finally, the performance of developed SVR model is compared with the ΔlogR method. The TOC computed by SVR method is found to be more precise than the ΔlogR method, as it has better agreement with the core-TOC. Thus, in the present study area, the SVR method is found to be a powerful tool for estimating TOC of Cambay Shale in a continuous and rapid manner.
Extracted statement — Machine learning algorithms: support vector regression
Research field: Graphics
Title: Rich Representations of Visual Content for Screen Reader Users
Abstract: Alt text (short for "alternative text") is descriptive text associated with an image in HTML and other document formats. Screen reader technologies speak the alt text aloud to people who are visually impaired. Introduced with HTML 2.0 in 1995, the alt attribute has not evolved despite significant changes in technology over the past two decades. In light of the expanding volume, purpose, and importance of digital imagery, we reflect on how alt text could be supplemented to offer a richer experience of visual content to screen reader users. Our contributions include articulating the design space of representations of visual content for screen reader users, prototypes illustrating several points within this design space, and evaluations of several of these new image representations with people who are blind. We close by discussing the implications of our taxonomy, prototypes, and user study findings.
Extracted statement — User recommendation: Design space
Research field: Graphics
Title: Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality
Abstract: Ranking and recommendation of multimedia content such as videos is usually realized with respect to the relevance to a user query. However, for lecture videos and MOOCs (Massive Open Online Courses) it is not only required to retrieve relevant videos, but particularly to find lecture videos of high quality that facilitate learning, for instance, independent of the video's or speaker's popularity. Thus, metadata about a lecture video's quality are crucial features for learning contexts, e.g., lecture video recommendation in search as learning scenarios. In this paper, we investigate whether automatically extracted features are correlated to quality aspects of a video. A set of scholarly videos from a Mass Open Online Course (MOOC) is analyzed regarding audio, linguistic, and visual features. Furthermore, a set of cross-modal features is proposed which are derived by combining transcripts, audio, video, and slide content. A user study is conducted to investigate the correlations between the automatically collected features and human ratings of quality aspects of a lecture video. Finally, the impact of our features on the knowledge gain of the participants is discussed.
Extracted statement — Has evaluation: User Study
Research field: Graphics
Title: Using Semantics for Interactive Visual Analysis of Linked Open Data
Abstract: Providing easy to use methods for visual analysis of Linked Data is often hindered by the complexity of semantic technologies. On the other hand, semantic information inherent to Linked Data provides opportunities to support the user in interactively analysing the data. This paper provides a demonstration of an interactive, Web-based visualisation tool, the "Vis Wizard", which makes use of semantics to simplify the process of setting up visualisations, transforming the data and, most importantly, interactively analysing multiple datasets using brushing and linking methods.
Extracted statement — implementation: Vis Wizard
Research field: Human and Clinical Nutrition
Title: Understanding the Linkages between Crop Diversity and Household Dietary Diversity in the Semi-Arid Regions of India
Abstract: Agriculture is fundamental to achieving nutrition goals; it provides the food, energy, and nutrients essential for human health and well-being. This paper has examined crop diversity and dietary diversity in six villages using the ICRISAT Village Level Studies (VLS) data from the Telangana and Maharashtra states of India. The study has used the data of cultivating households for constructing the crop diversity index while dietary diversity data is from the special purpose nutritional surveys conducted by ICRISAT in the six villages. The study has revealed that the cropping pattern is not uniform across the six study villages with dominance of mono cropping in Telangana villages and of mixed cropping in Maharashtra villages. The analysis has indicated a positive and significant correlation between crop diversity and household dietary diversity at the bivariate level. In multiple linear regression model, controlling for the other covariates, crop diversity has not shown a significant association with household dietary diversity. However, other covariates have shown strong association with dietary diversity. The regression results have revealed that households which cultivated minimum one food crop in a single cropping year have a significant and positive relationship with dietary diversity. From the study it can be inferred that crop diversity alone does not affect the household dietary diversity in the semi-arid tropics. Enhancing the evidence base and future research, especially in the fragile environment of semi-arid tropics, is highly recommended.
Extracted statement — statistical_methods: Multiple linear regression model
Research field: Human and Clinical Nutrition
Title: Market Access, Production Diversity, and Diet Diversity: Evidence From India
Abstract: <jats:sec><jats:title>Background:</jats:title><jats:p> Recent literature, largely from Africa, shows mixed effects of own-production on diet diversity. However, the role of own-production, relative to markets, in influencing food consumption becomes more pronounced as market integration increases. </jats:p></jats:sec><jats:sec><jats:title>Objective:</jats:title><jats:p> This paper investigates the relative importance of two factors - production diversity and household market integration - for the intake of a nutritious diet by women and households in rural India. </jats:p></jats:sec><jats:sec><jats:title>Methods:</jats:title><jats:p> Data analysis is based on primary data from an extensive agriculture-nutrition survey of 3600 Indian households that was collected in 2017. Dietary diversity scores are constructed for women and households is based on 24-hour and 7-day recall periods. Household market integration is measured as monthly household expenditure on key non-staple food groups. We measure production diversity in two ways - field-level and on-farm production diversity - in order to account for the cereal centric rice-wheat cropping system found in our study locations. The analysis is based on Ordinary Least Squares regressions where we control for a variety of village, household, and individual level covariates that affect food consumption, and village fixed effects. Robustness checks are done by way of using a Poisson regression specifications and 7-day recall period. </jats:p></jats:sec><jats:sec><jats:title>Results:</jats:title><jats:p> Conventional measures of field-level production diversity, like the number of crops or food groups grown, have no significant association with diet diversity. In contrast, it is on-farm production diversity (the field-level cultivation of pulses and on-farm livestock management, and kitchen gardens in the longer run) that is significantly associated with improved dietary diversity scores, thus suggesting the importance of non-staples in improving both individual and household dietary diversity. Furthermore, market purchases of non-staples like pulses and dairy products are associated with a significantly higher dietary diversity. Other significant determinants of dietary diversity include women’s literacy and awareness of nutrition. These results mostly remain robust to changes in the recall period of the diet diversity measure and the nature of the empirical specification. </jats:p></jats:sec><jats:sec><jats:title>Conclusions:</jats:title><jats:p> This study contributes to the scarce empirical evidence related to diets in India. Additionally, our results indicate some key intervention areas - promoting livestock rearing, strengthening households’ market integration (for purchase of non-staples) and increasing women’s awareness about nutrition. These are more impactful than raising production diversity. </jats:p></jats:sec>
Extracted statement — statistical_methods: Ordinary least squares regression