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In today's academic environment, social media platforms, ResearchGate in particular, play an essential role in facilitating collaboration and communication among researchers. Even with the wide usage of this site, there remains a significant gap in the availability of structured datasets that focus on researchers and their academic outputs. This lack of accessible data obstructs comprehensive analysis and evaluation of research trends and impact. This study seeks to address this gap by employing web scraping techniques to construct a dataset derived from ResearchGate, a leading platform for academic professionals. The introduced dataset consists of eight key features, including metrics related to publications, citations, and areas of research specialization. The availability of such a dataset not only provides a valuable resource for future research but also enables scholars to analyze research performance, identify collaboration opportunities, and uncover trends in academic productivity. Beyond dataset construction, this paper also details the exploratory data analysis (EDA) conducted to derive insights from the collected data. The K-means clustering algorithm was applied to categorize researchers according to their publication and citation patterns, offering a clearer understanding of academic achievements. The main contribution of this work is the establishment of a researcher’s dataset that can be used in future studies to analyze and examine the efforts and trends of researchers in their scientific work.

This study aims to analyze pharmaceutical sales performance by leveraging big data and advanced information technology tools, providing insights for improved business strategies. Using the Exploratory Data Analysis (EDA) method, the study processes raw sales data through spreadsheet applications to identify key patterns and trends. The findings are then visualized with Looker Studio on an intelligent dashboard tailored to the needs of the marketing team. The dashboard enables quick, data-driven decision-making by displaying performance metrics and trends relevant to sales and marketing strategies. The results reveal critical insights into sales behavior, including high-performing products, regional sales variations, and temporal sales trends. The analysis equips the marketing team with actionable data to refine their strategies, prioritize product focus, and adjust marketing efforts in response to identified patterns. In conclusion, the use of EDA and data visualization provides a structured approach to big data interpretation, allowing the company to optimize business and marketing performance. The study underscores the potential of data-driven dashboards to enhance strategic planning, suggesting broader applications in various data-intensive business environments. [ABSTRACT FROM AUTHOR]

This study explores the complex relationship between economic growth and environmental sustainability through a multifaceted analytical approach. Utilizing a robust dataset that includes key economic and sustainability metrics from various countries, the research employs exploratory data analysis, regression analysis, time-series analysis, and clustering techniques to unveil these relationships. The findings of the research illustrate a significant correlation between gross domestic product and carbon dioxide emissions, supporting the Environmental Kuznets Curve hypothesis, which proposes that initial economic growth worsens environmental degradation until a certain level of wealth is reached. Furthermore, the research emphasizes that while renewable energy consumption and electricity access are crucial, their current impact is insufficient to offset the environmental costs of economic growth. Through clustering analysis, the study identifies diverse national profiles, highlighting the necessity of tailored strategies to effectively address sustainability challenges. These insights emphasize the importance of integrated policies that promote both economic and environmental goals, supporting energy efficiency, cleaner technologies, and stringent environmental regulations. This comprehensive study lays the groundwork for informed policymaking aimed at achieving sustainable development. [ABSTRACT FROM AUTHOR]

This study assesses and quantifies the economic and financial impacts of the COVID-19 pandemic during the period of business operation restrictions countrywide (lockdown measures). We examine the strategies adopted by small and medium-sized enterprises (SMEs) to reopen their business operations after lockdown measures had been relaxed or lifted. Data were collected in Rwanda from nearly 244 SMEs across the country, providing firsthand and reliable information on the effects of the pandemic on business performance, with a particular emphasis on wood-based enterprises. We used Exploratory Data Analysis (EDA) and multivariate linear regression methods to measure the pandemic's effects on employment, sales, and tax payments among SMEs. The findings reveal that firms downsized employment by 36%, with significant deviations within different SME sizes. Small businesses were particularly affected by reduced sales levels due to the pandemic. Although there was an overall reduction in tax payments during the crisis, medium-sized enterprises experienced a more significant decrease in taxes paid to the government by 74.6%. Additionally, regression findings affirm that the COVID-19 effects on SMEs were manifested in reduced sales across all categories of SMEs, reduced employment, and a reduced amount of taxes paid to the government, which further translate to reduced economic performance during COVID-19 period. Furthermore, SME owners utilised various coping mechanisms during the reopening phase, including a reliance on savings and selling assets. The analysis recommends establishing medium-term financing mechanisms and providing technical support for SMEs to ensure a steady and sustainable recovery from the pandemic's effects, as well as enhancing their resilience to future socio-economic shocks. [ABSTRACT FROM AUTHOR]

Abstract Purpose This paper explores how Educational Big Data Analytics can enhance student learning. It investigates the role of active learning in improving students’ data analysis skills and critical thinking. By actively engaging students in data analysis assessments, the aim is to equip them with the skills to navigate the data-rich educational landscape. Methods The study uses a teaching strategy that combines structured and unstructured data analysis using Python tools and ChatGPT APIs. It presents five assignments, each highlighting data analysis skills and encouraging critical thinking. Results The paper offers insights into how the teaching strategy effectively enhances students’ data analysis and critical thinking skills. It investigates the specific impact of active learning on students’ engagement with educational data. The study reveals that all students can complete a comprehensive project, integrating the skills they have learned in the five assignments related to educational big data while incorporating the educational implications from their respective disciplines. Conclusion The key lies in instructors being able to design individual assignments that link practical experiences, enabling each teaching session’s effectiveness to accumulate in students’ personal experiences and practical skills, ultimately empowering them with the abilities necessary to work effectively with Educational Big Data Analytics. The findings of this study make a valuable contribution to the ongoing conversation about enhancing the educational experience for students in this data-rich era.

Purpose: This paper explores how Educational Big Data Analytics can enhance student learning. It investigates the role of active learning in improving students' data analysis skills and critical thinking. By actively engaging students in data analysis assessments, the aim is to equip them with the skills to navigate the data-rich educational landscape. Methods: The study uses a teaching strategy that combines structured and unstructured data analysis using Python tools and ChatGPT APIs. It presents five assignments, each highlighting data analysis skills and encouraging critical thinking. Results: The paper offers insights into how the teaching strategy effectively enhances students' data analysis and critical thinking skills. It investigates the specific impact of active learning on students' engagement with educational data. The study reveals that all students can complete a comprehensive project, integrating the skills they have learned in the five assignments related to educational big data while incorporating the educational implications from their respective disciplines. Conclusion: The key lies in instructors being able to design individual assignments that link practical experiences, enabling each teaching session's effectiveness to accumulate in students' personal experiences and practical skills, ultimately empowering them with the abilities necessary to work effectively with Educational Big Data Analytics. The findings of this study make a valuable contribution to the ongoing conversation about enhancing the educational experience for students in this data-rich era. [ABSTRACT FROM AUTHOR]

Fine-grained soils present engineering challenges. Stabilization with marble powder has shown promise for improving engineering properties. Understanding the temporal evolution of Unconfined Compressive Strength (UCS) and geotechnical properties in stabilized soils could aid strength assessment. This study investigates the stabilization of fine-grained clayey soils using waste marble powder as an alternative binder. Laboratory experiments were conducted to evaluate the geotechnical properties of soil–marble powder mixtures, including Atterberg's limits, compaction characteristics, California Bearing Ratio (CBR), Indirect Tensile Strength (ITS), and Unconfined Compressive Strength (UCS). The effects of various factors, such as curing time, molding water content, and composition ratios, on UCS, were analyzed using Exploratory Data Analysis (EDA) techniques, including histograms, box plots, and statistical modeling. The results show that the CBR increased from 10.43 to 22.94% for unsoaked and 4.68 to 12.46% for soaked conditions with 60% marble powder, ITS rose from 100 to 208 kN/m2 with 60–75% marble powder, and UCS rose from 170 to 661 kN/m2 after 28 days of curing, molding water content (optimum at 22.5%), and composition ratios (optimum at 60% marble powder). Complex modeling yielded R2 (0.954) and RMSE (29.82 kN/m2) between predicted and experimental values. This study demonstrates the potential of utilizing waste marble powder as a sustainable and cost-effective binder for soil stabilization, transforming weak soils into viable construction materials. [ABSTRACT FROM AUTHOR]

This paper focuses on crime status prediction through an ensemble methodology applied to extensive datasets obtained from catalog.data.gov, specifically targeting Los Angeles crime incidents since 2020. The research methodology comprises meticulous data collection, rigorous preprocessing, exploratory data analysis, model selection, and comprehensive model evaluation. Initial challenges included data inaccuracies and privacy-preserving measures in location data, necessitating thorough cleaning and transformation processes. Exploratory data analysis revealed crucial insights, including the 'Status' attribute's limited correlation, crime code distributions, areawise crime counts, and temporal patterns. To address class imbalance within 'Status', the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. Model evaluation highlighted the superiority of random forest models employing 10 and 20 decision trees, alongside KNN, which demonstrated consistent high accuracy, balanced precision-recall trade-offs, and notable F1 scores in crime status prediction. [ABSTRACT FROM AUTHOR]

This study assesses and quantifies the economic and financial impacts of the COVID-19 pandemic during the period of business operation restrictions countrywide (lockdown measures). We examine the strategies adopted by small and medium-sized enterprises (SMEs) to reopen their business operations after lockdown measures had been relaxed or lifted. Data were collected in Rwanda from nearly 244 SMEs across the country, providing firsthand and reliable information on the effects of the pandemic on business performance, with a particular emphasis on wood-based enterprises. We used Exploratory Data Analysis (EDA) and multivariate linear regression methods to measure the pandemic’s effects on employment, sales, and tax payments among SMEs. The findings reveal that firms downsized employment by 36%, with significant deviations within different SME sizes. Small businesses were particularly affected by reduced sales levels due to the pandemic. Although there was an overall reduction in tax payments during the crisis, medium-sized enterprises experienced a more significant decrease in taxes paid to the government by 74.6%. Additionally, regression findings affirm that the COVID-19 effects on SMEs were manifested in reduced sales across all categories of SMEs, reduced employment, and a reduced amount of taxes paid to the government, which further translate to reduced economic performance during COVID-19 period. Furthermore, SME owners utilised various coping mechanisms during the reopening phase, including a reliance on savings and selling assets. The analysis recommends establishing medium-term financing mechanisms and providing technical support for SMEs to ensure a steady and sustainable recovery from the pandemic’s effects, as well as enhancing their resilience to future socio-economic shocks.

Abstract Background Quality control (QC) of cells, a critical first step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds applied to QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation. Results We first demonstrate that QC metrics vary with both tissue and cell types across technologies, study conditions, and species. We then propose data-driven QC (ddqc), an unsupervised adaptive QC framework to perform flexible and data-driven QC at the level of cell types while retaining critical biological insights and improved power for downstream analysis. ddqc applies an adaptive threshold based on the median absolute deviation on four QC metrics (gene and UMI complexity, fraction of reads mapping to mitochondrial and ribosomal genes). ddqc retains over a third more cells when compared to conventional data-agnostic QC filters. Finally, we show that ddqc recovers biologically meaningful trends in gradation of gene complexity among cell types that can help answer questions of biological interest such as which cell types express the least and most number of transcripts overall, and ribosomal transcripts specifically. Conclusions ddqc retains cell types such as metabolically active parenchymal cells and specialized cells such as neutrophils which are often lost by conventional QC. Taken together, our work proposes a revised paradigm to quality filtering best practices—iterative QC, providing a data-driven QC framework compatible with observed biological diversity.

Many small and rural places are shrinking. Interactive dashboards are the most common use cases for data visualization and context for exploratory data tools. In our paper, we will use Iowa data to explore the specific scope of how dashboards are used in small and rural area to empower novice analysts to make data-driven decisions. Our framework will suggest a number of research directions to better support small and rural places from shrinking using an interactive dashboard design, implementation and use for the every day analyst. [ABSTRACT FROM AUTHOR]

Social media has become a contemporary platform for vox populi, enriched with the facilities of almost unrestricted access and versatility in terms of time and location of the users. The behaviors of social media users are creating a plethora of data, and is a fertile ground for Exploratory Data Analysis (EDA), which enables users to discover the veiled story within the datasets often using visual methods. This study focuses on the tragic incident of George Floyd’s death that took place on May 25, 2020, in Minneapolis, Minnesota, US, in terms of the social media responses by analyzing the corpus of comments for a selected YouTube video. Python programming language has been used to implement the EDA process using the methods of Text statistics analysis, Sentiment analysis, Ngram exploration, Topic modeling, Parts of Speech (POS) tagging, Word cloud formation, Named Entity Recognition (NER) and Text complexity analysis. By exploring the video disclosure with relevant tools, the study provides insights on the netizens, their behavior and their influence on society. This endeavor will help in preventing the manipulation of public opinion. [ABSTRACT FROM AUTHOR]

An initial public offering (IPO) refers to a process by which private corporations offer their shares in a public stock market for investment by public investors. This listing of private corporations in the stock market leads to the easy generation and exchange of capital between private corporations and public investors. Investing in a company's shares is accompanied by careful consideration and study of the company's public image, financial policies, and position in the financial market. The stock market is highly volatile and susceptible to changes in the political and socioeconomic environment. Therefore, the prediction of a company's IPO performance in the stock market is an important study area for researchers. However, there are several challenges in this path, such as the fragile nature of the stock market, the irregularity of data, and the influence of external factors on the IPO performance. Researchers over the years have proposed various artificial intelligence (AI)-based solutions for predicting IPO performance. However, they have some lacunae in terms of the inadequate data size, data irregularity, and lower prediction accuracy. Motivated by the aforementioned issues, we proposed an analytical model for predicting IPO gains or losses by incorporating regression-based AI models. We also performed a detailed exploratory data analysis (EDA) on a standard IPO dataset to identify useful inferences and trends. The XGBoost Regressor showed the maximum prediction accuracy for the current IPO gains, i.e., 91.95%. [ABSTRACT FROM AUTHOR]

Whole genome sequencing has rapidly progressed in recent years, with sequencing the SARS-CoV-2 genomes, making it a more reliable clinical tool for public health surveillance. This development has resulted in the production of a large amount of genomic data used for various types of genomic exploration. However, without a proper standard protocol, the usage of genomic data for analyzing various biological phenomena, such as mutation and evolution, may result in a propagating risk of using an unvalidated data set. This process could lead to irregular data being generated along with a high risk of altered analysis. Thus, the current study lays out the foundation for a preprocess pipeline using data analysis to analyze the genomic data set for its accuracy. We have used the recent example of SARS-CoV-2 to demonstrate the process overflow that can be utilized for various kinds of biological exploration such as understanding mutational events, evolutionary divergence, and speciation. Our analysis reveals a significant amount of sequence divergence in the gamma variant as compared with the reference genome thereby making the variant less infective and deadly. Moreover, we found regions in the genomic sequence that is more prone to mutational localization thereby altering the structural integrity of the virus resulting in a more reliable molecular viral mechanism. We believe that the current work will help for an initial check of the genomic data followed by the biological assessment of the process overflow which will be beneficial for the variant analysis and mutational uprising. [ABSTRACT FROM AUTHOR]

The data concerning land and real estate transactions, which for a long time were stored in various complex and sparsely accessible databases, are now more easily available since the opening of the DVF database ("Demandes de Valeurs Foncières" in French, "Real Estate Transaction Datafiles" in English) in 2019. However, the use of these information resources for analytical purposes requires time for acculturation and significant preparation work that should not be underestimated. This article presents a workflow dedicated to the analysis of open data DVF that we formalized in the R environment and documented in order to ensure replicability. This workflow illustrates the potential of these data to understand the spatial dynamics of residential real estate markets at different scales. Through a case study of the Brittany region and the Rennes urban/metropolitan area, the objective is to propose and discuss some methodological avenues for the preparation, analysis and (carto)graphic representation of these "new data" as well as the challenges and issues associated with their use.

Abstract Background With the enactment of the Brazilian Law Arouca 11,794/2008 and Decree 6.899/2009, there has been an urgent need for changes in the processes related to animal experimentation in Brazil; in particular, there is a need for improvements in enhancements of the lab animal management. To improve the management capacity of the Lab animal facility of the Carlos Chagas Institute’s Laboratory Animals Science (LAS), BioterC software was developed and implemented in 2014 for tracking mouse laboratory colonies. Five years after the implementation of this software, we sought to analyze the information in the database originated from BioterC using the Exploratory Analysis Data methodology (EDA). This article aims to identify animal breeding patterns using a data mining tool (Data Science) with Python programming language. Results The results show that from September 2014 to June 2019, under the license IACUC number LW- 6/17, 15.106 animals were produced. The C57BL/6, BALB/c and Swiss strains were the most frequently produced strains. The distribution of births due to crosses between these strains showed a median of 6 to 10 animals, depending on the genetic homozygosis and heterozygosis of the animal. The median number of days of mating was 35 days. In the sexing period, the records reported a median of 19 days. A total of 393 requests for animals from internal and external laboratories were registered. It was noted that approximately half of the animals produced to meet the demand for orders were discarded. Of the 15,106 animals, 38% were requested for animal experimentation, 58% were discarded and 4% did not have an outcome recorded in the data. Conclusions This volume of data provides an initial view of the information retrieval capabilities contained in BioterC, allowing for unique breeding knowledge by installing laboratory animals.

This paper presents an analysis of cosmic ray intensity in Pakistan air space using spatial interpolation, comparing it with Chinese cosmic ray records from 1984 to 1993. The Exploratory Data Analytic (EDA) approach was applied to compare the cosmic ray fluctuations in both countries. The time series plot of the monthly cosmic rays showed relatively flatter counts in Pakistan than in China. The cosmic ray data for the years 1984 to 1993 fell within Solar Cycle 22, which lasted from 1986 to 1996, with its maximum phase in 1989 to 1991. The cosmic radiation varies between the atmospheric regions of Pakistan and China due to modulations in intensity that are accessible accordingly. It can be explained by purely astrophysical phenomena: (1) the source of emission of cosmic radiation may be different, (2) the rate at which emanation takes place depends on bursts of deep space dynamical objects from their sources that may be affected by solar wind and other solar radiations. Therefore, modulations in intensity are not only due to different geophysical locations. This study will help government organizations to predict and forecast cosmic rays values.

Political alignment identification is an author profiling task that aims at identifying political bias/orientation in people’ writings. As usual in any automatic text analysis, a critical aspect here is having available adequate data sets so that the data mining and machine learning approaches can obtain reliable and informative results. This article makes a contribution in this regard by presenting a new corpus for the study of political alignment in documents of Argentinian journalists. The study also includes several kinds of analysis of documents of pro-government and opposition journalists such as the relevance of terms in each journalist class, sentiment analysis, topic modelling and the analysis of psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count (LIWC) system. From the experimental results, interesting patterns could be observed such as the topics both types of journalists write about, how the sentiment polarities are distributed and how the writings of pro-government and opposition journalists differ in the distinct LIWC categories.

The present introductory course of lectures summarizes the principles and algorithms of several widely used multivariate statistical methods: cluster analysis, principal components analysis, principal components regression, N-way principal components analysis, partial least squares regression and self-organizing maps with respect to their possible application in intelligent analysis, classification, modelling and interpretation to environmental monitoring data. The target group of possible users is master program students (environmental chemistry, analytical chemistry, environmental modelling and risk assessment etc.). [ABSTRACT FROM AUTHOR]

This paper presents an analysis of cosmic ray intensity in Pakistan air space using spatial interpolation, comparing it with Chinese cosmic ray records from 1984 to 1993. The Exploratory Data Analytic (EDA) approach was applied to compare the cosmic ray fluctuations in both countries. The time series plot of the monthly cosmic rays showed relatively flatter counts in Pakistan than in China. The cosmic ray data for the years 1984 to 1993 fell within Solar Cycle 22, which lasted from 1986 to 1996, with its maximum phase in 1989 to 1991. The cosmic radiation varies between the atmospheric regions of Pakistan and China due to modulations in intensity that are accessible accordingly. It can be explained by purely astrophysical phenomena: (1) the source of emission of cosmic radiation may be different, (2) the rate at which emanation takes place depends on bursts of deep space dynamical objects from their sources that may be affected by solar wind and other solar radiations. Therefore, modulations in intensity are not only due to different geophysical locations. This study will help government organizations to predict and forecast cosmic rays values. [ABSTRACT FROM AUTHOR]

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Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data.

Big data is a field of investigation rich in promises but still complex in renewing spatial mobility analysis. In fact, thinking about mobility through the prism of digital footprints raises many questions regarding the data nature, the accessibility procedures and the methods and techniques of treatment. This paper aims to explore the potential of digital footprints in the analysis of mobility through the example of data from the Carpooling platform BlaBlaCar. By analyzing (temporal and spatial) trips to and from city of Rennes over the course of 5 months, the goal is both to draw a portrait of Carpooling in the capital of Britanny and to give an overview of advantages and limitations of these data in understanding this new form of mobility.

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. • A method to assess improvements based on experts input and real-time data. • Machine learning is applied to analyze data from RFID sensors and BIM models. • The automation in workstations is rated based on production balance and efficiency. • Strategies to increase production flexibility are rated using statistical analysis. [ABSTRACT FROM AUTHOR]

An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills--the use of satellite sensors for this task is poorly documented in scientific literature. A long-term RADARSAT dataset (2008-2012) is exploited to investigate oil slicks in Campeche Bay (Gulf of Mexico). Simple Classification Algorithms to distinguish the oil slick type are designed based on standard multivariate data analysis techniques. Various attributes of geometry, shape, and dimension that describe the oil slick Size Information are combined with SAR-derived backscatter coefficients--sigma-(σo), beta-(βo), and gamma-(γo) naught. The combination of several of these characteristics is capable of distinguishing the oil slick type with ~70% of overall accuracy, however, the sole and simple use of two specific oil slick's Size Information (i.e., area and perimeter) is equally capable of distinguishing seeps from spills. The data mining exercise of our EDA promotes a novel idea bridging petroleum pollution and remote sensing research, thus paving the way to further investigate the satellite synoptic view to express geophysical differences between seeped and spilled oil observed on the sea surface for systematic use. [ABSTRACT FROM AUTHOR]

The forms of socio-spatial differentiation found in Lima are for the most part in line with the model of Andean metropolises. There is, for example, an opposition between central areas occupied by the middle and upper classes and the more recent and lower-class areas in the peripheries. There is also a directional expansion of the wealthier areas, in this case, towards the east of the central areas. This model is still very pronounced but seems to have changed since the end of the last century with internal diversification of previously homogeneous units. Our objective is to understand the current spatial organization of social differentiation in Lima. On the basis of data of the last national census of population in 2007 and of a set of aerial photos and satellite images taken since the middle of the 20th century, we build on, at the level of the blocks, various cartographical and statistical indicators concerning the successive layers of the expansion of Lima and its current socio-spatial structure. We conclude from this first, that the urban area is organized according to a scale of social and urban integration which is registered spatially on a gradient, from the center towards the peripheries, in correlation with the time the blocks were built. In a second phase, a series of multidimensional statistical analyses which visually reintroduce each block allows us to moderate this conclusion by the means of the study of the variability inside each layer of expansion. It is thus possible to understand the current territorial dynamics and to formulate the hypothesis of a change in the structure contrasting the center and the peripheries.

This article attempts to improve the performance of classification algorithms used in the bank customer marketing response prediction of an unnamed Portuguese bank using the Random Forest ensemble. A thorough exploratory data analysis (EDA) was conducted on the data in order to ascertain the presence of anomalies such as outliers and extreme values. The EDA revealed that the bank data had 45, 211 instances and 17 features, with 11.7% positive responses. This was in addition to the detection of outliers and extreme values. Classification algorithms used for modelling the bank dataset include; Logistic Regression, Decision Tree, Naïve Bayes and the Random Forest ensemble. These algorithms were applied to both the balanced and original bank data using Orange 3.2 data mining application following the Cross Industry Standard for Data Mining (CRISP-DM), and the ten-fold cross-validation method. Results from the experimental methods revealed that the performance of the Random Forest ensemble improved when the data was balanced. Results also showed that the features duration, poutcome, contact, month and housing were the most important features that contribute to the success of the bank customer marketing campaign for deposit subscription. The study also revealed that the duration of call to clients, response to past promotions, and the use of cell phone contribute positively to the success of the campaign. While the months of September, November, March and April recorded higher subscription rates. Those in management cadre and technicians were found to have responded more positively to the campaign than those in other job categories. [ABSTRACT FROM AUTHOR]