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The country’s advancement is fueled by regional growth. It frequently has many detrimental effects in its application, including contamination. Climate, notably temperature, is negatively impacted by the ensuing pollution. This study uses the Multiple Correspondence Analysis (MCA) method to measure the pollution index, followed by the instrumental variable (IV) method to calculate the effect of development on pollution and temperature. Rural data from Podes 2018 is among the data used in this investigation. The findings of this study show that developed and developing areas are where the negative pollution index forms the most frequently. The construction and the resulting pollution index have a negative impact on temperature. The development process should pay attention to environmental aspects to anticipate worse temperature changes in the coming period.

Urban expansion has been significant and rapid over the last 30 years, with the outward growth of the Kolkata Metropolitan Area (KMA). Much of this growth has followed a lowdensity, disparate development pattern, commonly known as urban sprawl. This study aims to examine the spatial expansion pattern in the Kolkata Metropolitan Development Area (KMDA) between 1990 and 2020 through the application of advanced geoinformatics tools and spatial metrics. We analyzed Landsat Satellite images from 1990, 2000, 2010, and 2020 to evaluate urban areas, including their extent and trends. Patterns of directional expansion, assessed using standard deviation ellipses and wedge analysis, showed a clear north-to-south axis of growth in the study area. The expansion of urbanization by 2020 was therefore more concentrated in the south-western region. Urban growth rates were measured using the Annual Urban Expansion Rate (AUER), Urban Expansion Intensity Index (UEII), and Landscape Expansion Index (LEI). The urban land cover of the study area increased by 446.71 km2 during the study period. The highest growth rate was from 1990 to 2000 (5.42%), followed by a decline in subsequent decades. LEI analysis revealed edge expansion as the prevalent growth type, which is a typical feature of urban sprawl. A mixture of infilling and peripheral growth patterns points to the processes of urban diffusion and clustering. Results for the Department of Labrador were obtained using the Area-Weighted Mean Patch Fractal Dimension (AWMPFD), which classified the urban spatial patterns into four types: major core, secondary core, suburban fringe, and dispersed settlements. Central aggregation and peripheral fragmentation are related straightforwardly. Multiple correspondence analysis (MCA) further confirmed this spatial distribution pattern, which has valuable implications for both resource managers and urban planners.

This book examines the impact of the global justice movement, as seen from the southern hemisphere. Drawing upon a collective survey from the 2011 World Social Forum in Dakar, the essays explore a number of methodological issues pertaining to the study of transnational mobilizations.

Non-response in survey data, especially in multivariate categorical variables, is a common problem which often leads to invalid inferences and inefficient estimates. A regularized iterative multiple correspondence analysis (RIMCA) algorithm in single imputation (SI) has been suggested for the handling of missing categorical data in survey analysis. This paper proposes an adapted version of the SI algorithm for multiple imputation (MI). The SI and MI techniques are compared for both simulated and real questionnaire data. A comparison between RIMCA MI and Sequential Regression Multiple Imputation (SRMI) is shown to establish the success of the proposed MI procedure.

In light of future work challenges, actual human resource management (HRM) needs to be redesigned, including long-term development, regeneration, and renewal of human resources, passing from consuming to developing human resources by incorporating the concept of sustainability. Thus, sustainable HRM is seen as an extension of strategic human resources, presenting a new approach to human resource management. The labor market is constantly changing, atypical work acquiring a significant relevance, especially in these current times of coronavirus crisis restrictions. In Romania, promoting the law of teleworking transformed labor flexibility into a topic of interest, and became an increasingly vital requirement for employment and a motivating factor for Romanian employees. In such a context, this paper aims to investigate the link between employee development and worktime and workspace flexibility as relevant characteristics of sustainable HRM, job satisfaction and job performance among Romanian employees in order to identify how to redesign HRM in the face of “future work” challenges. Additionally, the paper aims to examine the impact of different types of flexibility—contractual, functional, working time, and workspace flexibility—in order to highlight the relevance of employee development and employee flexibility as important aspects of sustainable HRM in increasing the overall level of employee job satisfaction. In order to make this possible, an “employee flexibility composite indicator,” which takes into account different types of flexibility, has been developed using feedback from Romanian employees, which was gathered by a national representative survey using multiple correspondence analysis. Furthermore, the impact of both individual and employee flexibility on overall level of job satisfaction has been quantified using binary logistic regression models. Within the research, there is a particular focus on the impact of new types of workspaces (flex office, co-working, total home office, partial home office—FO, CW, HOT, HOP) on job performance, job satisfaction, organizational performance, professional growth and development, social and professional relationships, and personal professional performance as well as on the overall level of work motivation. The empirical results revealed that these new types of workspaces are highly appreciated by employees, generating a growing interest among them. Partial home working, the mix between working from home and working in a company’s office, has been considered an optimal solution in increasing organizational performance, social and professional relationships, learning and personal development, and the overall level of work motivation. The results of the multiple correspondence analysis highlighted a medium level of flexibility among those Romanian employees interviewed, with only one third of them exhibiting high levels of flexibility. The empirical analysis of logistic regression analysis pointed out the role of functional flexibility, working time, and workspace flexibility along with the flexibility composite indicator in increasing the level of job satisfaction in employees. Therefore, if the challenge is to redesign the actual human resource management in order to include the concept of sustainability, attention needs to be on a combination of employee development-flexible time and flexible places, leading to an increase in both employee job satisfaction and organizational performance as important outcomes of sustainable HRM.

To investigate the reproducibility and validity of latent class analysis (LCA) and hierarchical cluster analysis (HCA), multiple correspondence analysis followed by k-means (MCA-kmeans) and k-means (kmeans) for multimorbidity clustering. We first investigated clustering algorithms in simulated datasets with 26 diseases of varying prevalence in predetermined clusters, comparing the derived clusters to known clusters using the adjusted Rand Index (aRI). We then them investigated the medical records of male patients, aged 65 to 84 years from 50 UK general practices, with 49 long-term health conditions. We compared within cluster morbidity profiles using the Pearson correlation coefficient and assessed cluster stability using in 400 bootstrap samples. In the simulated datasets, the closest agreement (largest aRI) to known clusters was with LCA and then MCA-kmeans algorithms. In the medical records dataset, all four algorithms identified one cluster of 20–25% of the dataset with about 82% of the same patients across all four algorithms. LCA and MCA-kmeans both found a second cluster of 7% of the dataset. Other clusters were found by only one algorithm. LCA and MCA-kmeans clustering gave the most similar partitioning (aRI 0.54). LCA achieved higher aRI than other clustering algorithms.

Understanding the distribution of socioeconomic status (SES) and its temporal dynamics within a population is critical to ensure that policies and interventions adequately and equitably contribute to the well-being and life chances of all individuals. This study assesses the dynamics of SES in a typical rural South African setting over the period 2001–2013 using data on household assets from the Agincourt Health and Demographic Surveillance System. Three SES indices, an absolute index, principal component analysis index and multiple correspondence analysis index, are constructed from the household asset indicators. Relative distribution methods are then applied to the indices to assess changes over time in the distribution of SES with special focus on location and shape shifts. Results show that the proportion of households that own assets associated with greater modern wealth has substantially increased over time. In addition, relative distributions in all three indices show that the median SES index value has shifted up and the distribution has become less polarized and is converging towards the middle. However, the convergence is larger from the upper tail than from the lower tail, which suggests that the improvement in SES has been slower for poorer households. The results also show persistent ethnic differences in SES with households of former Mozambican refugees being at a disadvantage. From a methodological perspective, the study findings demonstrate the comparability of the easy-to-compute absolute index to other SES indices constructed using more advanced statistical techniques in assessing household SES.

The development and use of digital marketing strategies by entrepreneurs is a key element of success for innovative projects. Moreover, effective execution of marketing intervention in what is referred to as the digital customer journey is essential to achieving business success. Under this paradigm, the present study aims to identify the use of digital marketing activities by entrepreneurs in their projects at each phase of the customer journey. The research bridges a gap in in the existing literature, first by a systematic review of literature using the statistical approach known as Multiple Correspondence Analysis (MCA) under the homogeneity analysis of variance using alternating least squares (HOMALS) framework programmed in the R language. Based on the results of this analysis, 13 digital marketing techniques are identified along with their use across the five phases of the digital customer journey that are linked to technology transfer and adoption: awareness, engagement, conversion, loyalty, and advocacy. Furthermore, different applications of digital marketing techniques by entrepreneurs are discussed, and new applications for each phase are proposed. The results reveal that entrepreneurs lack knowledge about the customer journey, the use of the awareness phase, and the knowledge of Big Data tools to boost innovation. Finally, the main digital marketing strategies are appropriately classified for each phase of the customer journey, and 16 questions for future research in this research area are proposed.

As the world’s most populous country, China’s energy poverty reduction achievements directly impact the global energy poverty reduction process. Analyzing energy poverty in China is therefore critical to consolidating the results of poverty eradication, eliminating relative poverty, and improving the social welfare of residents. However, prior research neither considered the applicability of existing energy poverty indicators to the current Chinese reality, nor the spatiotemporal disparities of energy poverty using micro-level data. To study the dynamics of energy poverty in China at the household level, a new multidimensional energy poverty index is constructed with seven dimensions using multiple correspondence analysis methods. Furthermore, provincial disparities and characteristics of energy poverty are compared using a spatial autocorrelation analysis method. The findings show that energy poverty has improved in China from 2012 to 2018, but its incidence and intensity remain high. Moreover, significant regional differences in energy poverty exist between different regions of China. High levels of energy poverty are mainly concentrated in the western and northeastern regions (especially in rural areas), and the urban–rural gap shows a similar pattern. The results obtained from spatial autocorrelation analysis demonstrate that China's energy poverty exhibits significant spatial clustering characteristics. Further, the results of standard deviation ellipse show that during the study period, the center of gravity of energy poverty in China was in Henan province and gradually shifted to the northwest. These findings help policymakers to formulate specific energy poverty reduction policies for various groups affected by energy poverty.