Exploratory Data Analysis Techniques Research Articles

The spatial patterns of Airbnb offers, hotels and attractions: are professional hosts taking over cities?

ABSTRACT Airbnb has created a significant incentive to invest in real-estate with the purpose of short-term accommodation provision in touristic cities. The robust growth of Airbnb supply has been accompanied with an increasing professionalization of the platform, contributing to such challenges as overtourism and gentrification. The main aim of this analysis is to examine the spatial characteristics of Airbnb supply in Barcelona, Berlin and London, focussing on the differences between offers that belong to non-professional and professional hosts. Based on Exploratory Spatial Data Analysis techniques the study demonstrates that listings that belong to professional hosts are more concentrated in city centres, and are more influenced by the location of tourist attractions and hotels than non-professional offers. On the other hand, non-professional listings facilitate a spatially more diverse accommodation supply. The results suggest that professional offers may contribute more to the negative effects of Airbnb on local residents.

Digitalization-based process improvement and decision-making in offsite construction

The evaluation of process improvements measures in offsite construction shop floors often relies on experts' opinion, with limited use of empirical data gathered by sensors in real-time. To address this issue, there is a need for methods that integrate expert's tacit knowledge with robust data analysis techniques. This paper describes the application of exploratory data analysis techniques to evaluate improvement suggestions proposed by expert's, supported by data collected by sensors on the shop floor and building information models. The presented method involves a quantitative and qualitative digitalization-based approach where improvement suggestions are modelled and validated though machine learning algorithms and hypothesis testing. The contribution of this study is a method that combines real-time data, building information models, and knowledge modeling from experts to evaluate process improvement on offsite construction shop floors.

Exploratory Data Analysis for Building Energy Meters Using Machine Learning

The purpose of this research was to apply exploratory data analysis techniques to building energy meters, such as electricity, cold, heat, and steam meters. A thorough understanding of energy usage patterns becomes increasingly vital in an era of growing awareness of energy management and sustainability. Trends, patterns, and anomalies can be identified in building energy meter data using meticulous data exploration approaches, which can give significant insights for increasing energy efficiency. Exploratory data analysis combined with machine learning approaches may be was used to reveal hidden patterns of energy usage and examine the links between relevant factors. The findings of this exploratory data analysis gave vital insights into building energy use trends. Some significant and hidden information that was crucial for understanding energy usage within a certain time frame in each building was discovered via the investigation of the data used in this study. Steam had the highest use, whereas electricity had the lowest. Utilities were more popular before 5 a.m., followed by healthcare, with daytime use hours beginning around 10 a.m., depending on the area. During the working day, the industry needs more energy. Places of worships use more energy on weekends. There was a significant relation between the number of floors and spaces per level of a building and the height meter reading between May and October. There is a significant association between the kind of buildings used for schools, workplaces and high energy use. This study significantly contributed to the management of the energy and sustainability domains. Using exploratory data analysis and machine learning approaches to building energy meters could optimize energy usage, minimize running costs, and enhance overall energy efficiency. This research is still very open to be continued using other methods, to obtain other hidden information.

Surface analysis insight note: Initial, statistical evaluation of X‐ray photoelectron spectroscopy images

With advances in instrumentation and image processing, imaging X‐ray photoelectron spectroscopy (XPS) is becoming increasingly important in surface analysis. In this Insight Note, we suggest the following three steps for the initial evaluation of XPS images: (i) Plot all the data. Possible plots here include an overlay plot, a plot of the average and standard deviation of the spectra, a waterfall plot, and a contour plot. (ii) Examine slices through the data cube at different binding energies, identifying those that show significant contrast. (iii) Examine the spectra, including their average and standard deviation, in defined regions of slices through the data that show contrast. These steps allow XPS images to be probed without the use of more sophisticated exploratory data analysis (EDA) techniques. Indeed, these steps may set the stage for additional EDA analysis of XPS images. Furthermore, they provide an initial understanding of an XPS image that stays close to the original data. Our three‐step protocol was applied to an XPS image of a region of a silicon wafer with different oxide thicknesses. This image primarily contained two types of pixels/regions: one of silicon with a thin oxide layer and the other of silicon with a thicker oxide layer. We emphasize the importance of contour plots of all the data and examining the average and standard deviation of regions of contrast in slices through the data cube.

선박 추진용 2행정 저속엔진의 고장모드 데이터 개발 및 LSTM 알고리즘을 활용한 특성인자 신뢰성 검증연구

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

Evaluasi sifat mekanik baja paduan rendah bedasarkan komposisi kimia dan suhu perlakuan panas menggunakan teknik exploratory data analysis (EDA)

This research aims to evaluate the relationship between the chemical composition of low alloy steel, temperature, and the mechanical properties of low alloy steel using Exploratory Data Analysis (EDA) techniques. The low alloy steel dataset is visualized using a correlation heat map, which shows a relationship between the mechanical properties of low alloy steel and its chemical composition and heat treatment temperature. Based on the results of the correlation heat map, an evaluation is carried out using scatterplots. The visualization results with scatterplots show a trend line indicating a linear relationship between YS and the elements V, Ni, and Mn, as well as a positive relationship between TS and V. In addition, there are determination coefficients ( R²) that show how well the trend line can explain the variation of the data. The R² values obtained for V are 0.405, Ni is 0.226, Mn is 0.159, and Mo is 0.130, while El and RA have a positive correlation with temperature with R²values of 0.166 and 0.320, respectively. It can be concluded that the evaluation results using scatterplots and R² show that variations in chemical composition and heat treatment temperature affect the mechanical properties of low alloy steel. The correlations that occur between these variables can help in determining the pattern of the relationship and evaluating how well the trend line can explain the variation of the data. The use of correlation heat maps and scatter plots can help in decision-making and developing low-alloy steel materials that meet specific needs.

Exploratory data fusion of untargeted multimodal LC-HRMS with annotation by LCMS-TOF-ion mobility: White wine case study.

Applied sciences have increased focus on omics studies which merge data science with analytical tools. These studies often result in large amounts of data produced and the objective is to generate meaningful interpretations from them. This can sometimes mean combining and integrating different datasets through data fusion techniques. The most strategic course of action when dealing with products of unknown profile is to use exploratory approaches. For omics, this means using untargeted analytical methods and exploratory data analysis techniques. The current study aimed to perform data fusion on untargeted multimodal (negative and positive mode) liquid chromatography-high-resolution mass spectrometry data using multiple factor analysis. The data fusion results were interpreted using agglomerative hierarchical clustering on biplot projections. The study reduced the thousands of spectral signals processed to less than a hundred features (a primary parameter combination of retention time and mass-to-charge ratios, RT_m/z). The correlations between cluster members (samples and features from) were calculated and the top 10% highly correlated features were identified for each cluster. These features were then tentatively identified using secondary parameters (drift time, ion mobility constant and collision cross-section values) from the ion mobility spectra. These ion mobility (secondary) parameters can be used for future studies in wine chemical analysis and added to the growing list of annotated chemical signals in applied sciences.

SUICIDE AND COVID-19: A TEMPORAL-SPATIAL ANALYSIS IN THE STATE OF PARANÁ AND THE 15th HEALTH REGIONAL FROM 2010 TO 2020

IntroductionSuicide occurs throughout all the life course, but in 2019 it was the fourth leading cause of death among 15-29 years old worldwide.In Brazil, between 2010 and 2019, 112,230 deaths from suicide were recorded, with a 43% increase in the annual number of deaths from the beginning to the end of that period in all age groups. (SAÚDE, 2021).ObjectivesTo accomplish a space-time analysis of the rate of suicide deaths in the state of Paraná and in its 15th Health Regional, from 2010 to 2020, in the population aged 20 to 69 years, contrasting the pre-pandemic periods (2010 to 2019) and during the COVID-19 pandemic (2020).MethodsThis is an ecological, observational, cross-sectional, retrospective study, using spatial and temporal analysis tools, based on secondary data obtained from the Department of Informatics of the Unified Health System (DATASUS), in the period 2010 and 2020, referring to the 5568 Brazilian cities, with emphasis on the 399 of the state of Paraná and the 30 cities of its 15th Health Regional (HR).Exploratory Spatial Data Analysis (AEDE) techniques were used through GeoDA (version 1.20.0.8) and QGis (version 3.22.4) software to determine the existence of Spatial Autocorrelation (Moran’s Global Index or Moran I) and to calculation and visualization of the Local Spatial Association Indicator (LISA).Global (Moran I) and local (LISA) spatial autocorrelation coefficients were considered significant when p < 0.05 at a 95% confidence level.The Space Time Cube is a simultaneous spatial and temporal analysis methodology of ArcGIS Pro Software (ESRI, 2011) that evaluates the behavior of a value in space over a defined time interval.ResultsThe arithmetic mean of the age at death by suicide, from 2010 to 2020, was 41.1 years, with a standard deviation of 13.3 years and a median of 40 years. Most deaths in the pre-pandemic period occurred among men aged 20 to 29, followed by women aged 30 to 39 years and men aged 40 to 49 years.Regarding the most common causes and modes of suicide in the State, there was no significant difference between the periods from 2010 to 2019, pre-pandemic reference, and 2020 (pandemic) in a global perspective. The three main causes of death were: 1) Hanging, strangulation or suffocation (ICD-10 X70, T71) were the majority and accounted for more than half of all causes; 2) Self-harm by gunshot (ICD- 10 X74 and X72); 3) Self-poisoning by pesticides and drugs, respectively (ICD-10 X68 and X61).ConclusionsThis study showed an increase in suicide deaths in the state of Paraná, when analyzing the periods before and during the COVID-19 pandemic. The most affected population was men, aged 20 to 29 years.Disclosure of InterestNone Declared

A preliminary empirical study of react library related questions shared on stack overflow

React is a JavaScript library to develop user interfaces for single-page applications. Developers utilize react to build large web apps that allow users to update data without refreshing the page. Despite its benefits, many developers face react-related issues in the implementation. To find a solution, developers commonly shared and discussed their issues on stack overflow (SO). Although recent studies have demonstrated the benefits of utilizing react in web development, the trends of the users’ attentions remain unknown. In this study, we conducted a preliminary empirical study of react library-related questions shared on SO. We applied an exploratory data analysis technique to investigate the distribution of problems shared by the developers. The findings reveal that although the quantity of react-related topics on SO has risen over time, community interest is beginning to decrease. This is shown by the increase of the unsolved questions and the decrease of the number of views per year. Regarding the react users’ activity, most of them are more active in providing answers rather than commenting and providing scores. The findings of this study might point to future research that recommends approaches to assist the react community in overcoming issues while using react in the early phases.

A preliminary empirical study of react library related questions shared on stack overflow

React is a JavaScript library to develop user interfaces for single-page applications. Developers utilize react to build large web apps that allow users to update data without refreshing the page. Despite its benefits, many developers face react-related issues in the implementation. To find a solution, developers commonly shared and discussed their issues on stack overflow (SO). Although recent studies have demonstrated the benefits of utilizing react in web development, the trends of the users’ attentions remain unknown. In this study, we conducted a preliminary empirical study of react library-related questions shared on SO. We applied an exploratory data analysis technique to investigate the distribution of problems shared by the developers. The findings reveal that although the quantity of react-related topics on SO has risen over time, community interest is beginning to decrease. This is shown by the increase of the unsolved questions and the decrease of the number of views per year. Regarding the react users’ activity, most of them are more active in providing answers rather than commenting and providing scores. The findings of this study might point to future research that recommends approaches to assist the react community in overcoming issues while using react in the early phases.

Exploring the expressiveness of abstract metabolic networks.

Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified representation of metabolism, which we call an abstract metabolic network, is a graph in which metabolic pathways are nodes and there is an edge between two nodes if their corresponding pathways share one or more compounds. The abstract metabolic network of a given organism results in a small network that requires low computational power to be analysed and makes it a suitable model to perform a large-scale comparison of organisms' metabolism. To explore the potentials and limits of such a basic representation, we considered a comprehensive set of KEGG organisms, represented through their abstract metabolic network. We performed pairwise comparisons using graph kernel methods and analyse the results through exploratory data analysis and machine learning techniques. The results show that abstract metabolic networks discriminate macro evolutionary events, indicating that they are expressive enough to capture key steps in metabolism evolution.

Enhancing Predictive Modeling in Kuala Lumpur Real Estate: A Comprehensive Data Preprocessing and Feature Engineering Approach

The real estate market in Kuala Lumpur exhibits complex dynamics influenced by various factors. This paper presents a comprehensive approach to enhance the prediction of real estate prices in Kuala Lumpur utilizing a dataset of 49,416 records with advanced data preprocessing techniques, exploratory data analysis (EDA), and predictive modeling. Initially, the dataset undergoes rigorous preprocessing including handling missing values through property type-specific mean imputation, label encoding categorical variables, and standardization to ensure uniformity and compatibility for analysis. Subsequently, EDA techniques are employed to gain insights into the dataset's characteristics and relationships among variables. To improve model performance and interpretability, feature selection is performed based on Mutual Information (MI) score and correlation metrics. This aids in identifying the most relevant features for predicting real estate prices in Kuala Lumpur. Additionally, feature engineering techniques are applied to create two new features that capture nuanced aspects of the real estate market. The predictive modeling phase employs a Linear Regression algorithm to forecast real estate prices. Leveraging the preprocessed data and optimized feature set, the model aims to accurately predict property prices based on available features. The linear regression model offers interpretability, enabling stakeholders to understand the driving factors behind price variations. Through a rigorous methodology encompassing data preprocessing, exploratory analysis, feature selection, engineering, and predictive modeling, this study contributes to enhancing the accuracy and interpretability of real estate price prediction in Kuala Lumpurthat applies advanced machine learning methods. The findings offer valuable insights for real estate investors, policymakers, and stakeholders to make informed decisions in this dynamic market landscape.

Olympic Data Analysis using Data Science

Abstract: The Olympic games are international sports events with more than 200 nations participating in various competitions. The Sportspersons from various countries participate in competitions and make their countries proud of their excellence in sports. The primary objective of this paper is to analyze the Olympic dataset using python to compare overall performance of countries and to evaluate the contribution of each country in the Olympics. These analyses will give deeper insight into the performance of countries in Olympics over the years and helps sportspersons to quickly analyze their own and the competitor’s performance. In this paper, the exploratory data analysis techniques are used to provide comparison between performance of various countries and the contribution of each country in the Olympics. Visualization of Olympics dataset in many aspects provides the status of countries in Olympics and helps countries with poor performance to produce quality players and improve nation’s performance in Olympics. Despite a lot of hard work, many countries or players are unable to perform well during the events and grab medals whereas there are many countries that perform very well in the event and secure many medals. An analysis needs to be done by each country to evaluate the previous statistics which will detect the mistakes which they have done previously and will also help them in future development. Visualization of the data over various factors will provide us with the statistical view of the various factors which lead to the evolution of the Olympic Games and Improvement in the performance of various Countries/Players over time. The primary objective of this Research paper is to analyze the large Olympic dataset using Exploratory Data Analysis to evaluate the evolution of the Olympic Games over the years.

Spatial-Temporal Changes in Water Supply and Demand in the Citarum Watershed, West Java, Indonesia Using a Geospatial Approach

Balancing water supply demand is vital for sustaining livelihoods. Spatial mapping and calculating water yield dynamics due to land use changes over decades are needed to manage land resources and formulate ecological protection policies. This study mapped the supply, demand, and matching status of water product service using the Integrated Valuation of Ecosystem Service and Tradeoff (InVEST) biophysical models in the Citarum Watershed (CW) in 2000, 2010, and 2020. Moreover, this study used Exploratory Spatial Data Analysis (ESDA) and Geographic Information System (GIS) techniques to study the agglomeration characteristics and evolutionary trajectories of supply–demand over two decades. The results showed that between 2000–2010 and 2010–2020, the water supply decreased by 19.01 × 108 m3 (18.28%) and 12.97 × 108 m3 (15.27%), respectively. However, the water demand in the same period increased by 6.17 × 108 m3 (23%) and 15.74 × 108 m3 (47%), respectively. Over the decades, the contribution of land use land cover (LULC) changes to variations in water supply has yielded values ranging from 2.87% to 6.37%. The analysis of the water supply–demand imbalance indicated that the entire CW experienced water shortage, and the type of spatial matching for supply and demand is dominated by a high supply and high demand class (16.09% of the total area). Based on the level of water deficit calculation, the upstream and downstream areas were identified as zones that require ecological conservation, while the middle CW area requires ecological restoration or ecological improvement.

Homicides in Alagoas - Brazil: A spatial analysis

This paper aimed to identify the main factors that influence homicides in the state of Alagoas, Brazil. Therefore, we employed the Exploratory Spatial Data Analysis (ESDA) technique and estimated spatial models in order to fix spatial autocorrelation. Although several articles have explored this theme for other states in Brazil, we chose Alagoas, a small state in northeastern Brazil, since it lead the homicide ranking per 100 thousand inhabitants in 2010, at the time of the national census. The main results show that the higher the income concentration, the greater the number of poor and the lower the educational level, the higher the homicide rate.

Exploratory Analysis of Regional Disparities in Human Development in Cameroon

The objective of this paper is to analyze the link between geographic location and the level of human development in Cameroon. To achieve that objective, we use Exploratory Spatial Data Analysis (ESDA) techniques on regional human development indices (HDI) in Cameroon from 2001 to 2014. The Moran test confirms the presence of a positive and significant global spatial autocorrelation of the regional human development indices. Thus, the values of the regional human development index in Cameroon are not randomly distributed. There is thus spatial clustering of regions with similar HDI levels. The spatial dependence of the regional HDI in Cameroon suggests cooperation and synergy of action by the regions within the framework of their competences resulting from decentralization, in order to take advantage of the spatial diffusion effects of human development. The Cameroonian government could pursue geo-targeting policies in the implementation of development programs, focusing on regions where spatial externalities would have a greater impact.

Evaluation of genetic markers for the analysis of THC levels of Cannabis sativa samples using principal component analysis – A preliminary study

Cannabis sativa is a worldwide commercial plant used for medicinal purposes, food and fiber production, and also as a recreational drug. Therefore, the identification and differentiation between legal and illegal C. sativa is of great importance for forensic investigations. In this study, principal component analysis (PCA), an exploratory data analysis technique, was tested to correlate the specific genotype with the concentration of tetrahydrocannabinol (THC) in the samples. C. sativa samples were obtained from legal growers in Piedmont, Italy, and from illegal drug seizures in the Turin region. DNA was extracted, quantified, amplified with a 13-loci multiplex STR and finally analyzed with an automated sequencer. The results showed a trend in the analyzed samples as they differed by their THC content and allele profiles. PCA yielded two clusters of samples that differed by specific allele profiles and THC concentrations. Further validation studies are needed, but this study could provide a new approach to forensic investigation and be a valuable aid to law enforcement in significant marijuana seizures or in tracing illicit drug trafficking routes.

An Exploratory Data Analysis of Breast Cancer Features in South of Libya

Exploratory data analysis is a data visualization approach used to extract knowledge from raw data. This approach can be applied to medical data to improve healthcare providers services. In recent years, breast cancer has become more common in women and requires effective procedures to detect it in the early stage. In this context, breast cancer patients' data were collected from the Sebha oncology center through their routine blood tests. The exploratory data analysis technique is used in this study to better analyze the patients' markers. The analysis aims to discover prominent bio and tumor markers that can assist in determining whether a tumor is benign or malignant. Several statistical and visualizations methods are used. The results show that the most effective markers that may be used as cancer predictors are: Cancer Antigen-15.3, Carcinoma Embryonic Antigen, White Blood Cells, Blood platelets, and Albumin. These findings are consistent with the findings of Sebha oncology center specialists. which may eventually aid in their cancer diagnosis.

A Review and Analysis of the Impact of Homicide Measurement on Cross-National Research

The number of cross-national homicide studies is increasing rapidly. Many scholars, however, do not consider the details of how individual nations and the four main centralized homicide data sources—raw estimates from the World Health Organization (WHO) Mortality Database, adjusted estimates from the WHO Global Health Observatory, United Nations Office on Drugs and Crime, and World Bank World Development Indicators—generate national homicide rates and the impact this may have on results and the scientific record. We tested whether homicide trends, levels, and structural covariates are dependent on data source. We used 1990–2018 data in 5-year groupings and pooled them over time and nation. We utilized exploratory data analysis techniques to look for differences in homicide rates and trends. Then we employed seemingly unrelated regression (SUR) to determine whether associations with homicide of typical structural covariates were dependent on homicide data source. Finally, we examined Wald Tests to determine whether differences in the sizes of the SUR coefficients from each data source were significantly different from zero. We found differences in homicide trends and rates by data source and that associations with homicide rates of structural covariates varied in significance, magnitude, and even direction depending on homicide data source. Cross-national homicideresearch has a promising future for understanding short- and long-term global and regional trends and population-level covariates and constructing theoretical explanations for geographical and temporal variation. However, researchers must better understand how national homicide data are generated by nations and these four data sources. All four systems possess limitations, but homicide data from the WHO Mortality Database present the most attractive option.

Feasibility and acceptability of continuous at-home glucose monitoring during pregnancy: a mixed-methods pilot study

Background: Continuous monitoring of glucose (CGM) via subcutaneous patch is an accurate self-monitoring tool of blood glucose, but also introduces a range of additional benefits such as real-time feedback. While its value among pregnant women with gestational diabetes mellitus (GDM) is established in high-income countries, little is known about the feasibility and acceptability among pregnant women without GDM in low-resource settings in low- and middle-income countries. Objectives: This study aims to assess the feasibility and acceptability of CGM with pregnant mothers in South Africa and to explore the value of a collected data set for GDM prevention. Methods: Ten women between 12 and 18 weeks pregnant were recruited from the antenatal clinic at Chris Hani Baragwanath Academic Hospital into a prospective mixed-methods pilot study. Demographic and anthropometric data, HbA1c and a lipid panel were collected. Women then wore two consecutive Freestyle Libre 2 patches for a total of 28 days. In-depth interviews were undertaken with all 10 women on study exit to explore themes of acceptability and the use of technology during pregnancy. Thematic analysis was performed on the qualitative data while exploratory data-analysis techniques were applied to the CGM data. Results: Pregnant women (n = 10) had a mean (SD) age of 29.81 years (4.39), with most being unemployed (8), unmarried (8) and without a tertiary degree (7). Analysis suggests that fear of use was greater than the actual discomfort experienced during use of the CGM patch. The main barrier to use was the patch falling off and women being uncomfortable to reapply it. This was borne out by all 10 women wearing the first patch for at least 12 of the 14 days, but only 4 managing the same with patch two – primarily applied by themselves at home. Women expressed support for the use of technology during pregnancy, especially as it related to feeling that their pregnancy was being monitored and that they were being supported. Conclusion: In this pilot study, women overwhelmingly found the wearing of a CGM patch during pregnancy to be acceptable. Feasibility was reasonable with most data being successfully retrieved from the devices over a two-week period. Longer use was found to have additional challenges. The use of CGM patches appear to be a possible candidate for inclusion in GDM prevention or behavioural interventions during pregnancy in South Africa.