Multidimensional Index Research Articles

ANALISIS KEMISKINAN DENGAN PENDEKATAN MULTIDIMENSI DI JAWA BARAT

This paper attempts different approaches to analyzing poverty in West Java. The aim is to provide an alternative multidimensional measurement of poverty using Alkire-Foster methods based on 5 dimensions, namely health, education, standard of living, employment and wealth. We estimated multidimensional poverty index (MPI) and identified the poor using the SUSENAS 2020. We constructed the MPI using 19 indicators pertaining to 5 valuable dimensions of well-being. We find that 23% of populations fall below the poverty line. Another interesting result is multidimensional poverty is significantly higher in rural than in urban areas. In 2020, two-thirds of the multidimensional poor people live within rural areas. Indicators having the highest contribution on MPI respectively are years of schooling, informal job, and health insurance.

Moderate Multidimensional Poverty Index: Paving the Way Out of Poverty

Eradicating poverty in all its forms, everywhere, requires indicators that measure sustainable pathways out of poverty, and not only the absence of acute poverty. This paper introduces a trial Moderate Multidimensional Poverty Index (MMPI) that reflects moderate rather than acute levels of multidimensional poverty. The MMPI adjusts nine of the 10 indicators of the global Multidimensional Poverty Index (global MPI) to reflect moderate poverty and create a meaningful superset of the acutely poor population. Although data-constrained, the trial MMPI outlines a methodology and potential indicators for a measure that would: (i) be meaningful and comparable across populations at higher levels of development; (ii) align with higher standards defined in Agenda 2030; and (iii) provide insight into aspects of intrahousehold deprivation. The MMPI is illustrated empirically using nationally representative household surveys from Bangladesh, Guatemala, Iraq, Serbia, Tanzania and Thailand. The results confirm the added value of having three nested measures of destitution, acute, and moderate multidimensional poverty. The MMPI also complements monetary measures with informative differences in poverty levels observed. The results demonstrate that a Moderate MPI is a desirable global poverty index, which is likely to illuminate thus far hidden aspects in of multidimensional poverty, such as intrahousehold deprivations in education. Challenges remain regarding data availability, and further study across additional countries is required before the MMPI structure can be finalised.

LiteHST: A Tree Embedding based Method for Similarity Search

Similarity search is getting increasingly useful in real applications. This paper focuses on the in-memory similarity search, i.e., the range query and k nearest neighbor (kNN) query, under arbitrary metric spaces, where the only known information is the distance function to measure the similarity between two objects. Although lots of research has studied this problem, the query efficiency of existing solutions is still unsatisfactory. To further improve the query efficiency, we are inspired by the tree embeddings, which map each object into a unique leaf of a well-structured tree solely based on the distances. Unlike existing embedding techniques (e.g., Lipschitz embeddings and pivot mapping) for similarity search, where an extra multi-dimensional index is needed to index the embedding space (e.g., Lp metrics), we directly use this tree to answer similarity search. This seems to be promising, but it is challenging to tailor tree embeddings for efficient similarity search. Specifically, we present a novel index called LiteHST, which is based on the most popular tree embedding (HST) and heavily customized for similarity search in the node structure and storage scheme. We propose a new construction algorithm with lower time complexity than existing methods and prove the optimality of LiteHST in the distance bound. Based on this new index, we also design optimization techniques that heavily reduce the number of distance computations and hence save running time. Finally, extensive experiments demonstrate that our solution outperforms the state-of-the-art in the query efficiency by a large margin.

An Integrated Approach to the Conceptualisation and Measurement of Social Cohesion

The core sociological subject of ‘social cohesion’ (hereafter SC) has re-emerged as a key concept in the social sciences. On the one hand, SC is thought to be influenced by a society’s degree of inequalities and the quality of its welfare state. On the other hand, SC is thought to be instrumental in its own right to other factors such as economic growth, institutional quality, and individual well-being. In recent years, a few attempts have been made to measure SC empirically. Many current indices have not been sufficiently theoretically substantiated, and do not consider the importance of different ‘social levels’ when explaining and measuring SC as both cause and effect of other correlates. Very often, SC is simply defined as a ‘social quality’ or a quality of a collective. As a result, measures are often aggregate macro-indices leading to a loss of the information base of any social ‘units’ below the macro-societal-level. Contributing to this important methodological debate, this paper provides a conceptual reformulation of SC. Hence, when assessing SC based on a multi-dimensional index, it is insightful and feasible to evaluate both its internal variation as well as its holistic validity. In fact, it is proposed that these two aspects of measurement stand in direct relationship to one-another. The paper starts out with a discussion of SC as a ‘social fact’ in the Durkheimian sense. In addition, three bridging propositions on the measurement of SC are advanced: (a) SC as outcome or consequence at the level of individual attitudes and orientations (‘micro’); (b) SC as degree of dissimilarity and presence of latent conflict within a society at the level of salient social categories (‘meso’), and (c) SC as predictor, social determinant and hence antecedent at the societal-level (‘macro’). Using all rounds of the European Social Survey with a very large sample size, the advantages of this approach are illustrated by singling-out the important link between socio-economic inequalities, social cohesion and individual subjective well-being in a path of action.

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Research on the Mechanism and Effect of Digital Economy in Reducing Rural Relative Poverty

At present, China's poverty alleviation has achieved some results, but there is still relative poverty in some areas. To build digital villages, we will strengthen the "weak links" in poverty alleviation. Through the construction of multi-dimensional poverty index system, the digital economy has a positive poverty reduction effect, but there is some heterogeneity. In the central and western regions, areas with high level of digital economy development and areas with high level of poverty, the poverty reduction effect of digital economy is more obvious. According to the development of digital economy, we should put forward feasible poverty alleviation methods to improve residents' income level and increase employment opportunities, and strive to explore the path of poverty alleviation with Chinese characteristics.

Competition for popularity and interventions on a Chinese microblogging site.

Microblogging sites are important vehicles for the users to obtain information and shape public opinion thus they are arenas of continuous competition for popularity. Most popular topics are usually indicated on ranking lists. In this study, we investigate the public attention dynamics through the Hot Search List (HSL) of the Chinese microblog Sina Weibo, where trending hashtags are ranked based on a multi-dimensional search volume index. We characterize the rank dynamics by the time spent by hashtags on the list, the time of the day they appear there, the rank diversity, and by the ranking trajectories. We show how the circadian rhythm affects the popularity of hashtags, and observe categories of their rank trajectories by a machine learning clustering algorithm. By analyzing patterns of ranking dynamics using various measures, we identify anomalies that are likely to result from the platform provider's intervention into the ranking, including the anchoring of hashtags to certain ranks on the HSL. We propose a simple model of ranking that explains the mechanism of this anchoring effect. We found an over-representation of hashtags related to international politics at 3 out of 4 anchoring ranks on the HSL, indicating possible manipulations of public opinion.

Finite element method for radiative transfer in multi-dimensional graded index media based on second-order form formulation

Gradient distribution of refractive index appears commonly in gas of non-uniform temperature/pressure and water of non-uniform salinity. Efficient and accurate solution of radiative transfer in graded index media is essential for related applications. Finite element method (FEM) is a very effective tool to solve various problems governed with partial differential equations. However, FEM may suffer stability problem for solving radiative transfer in semitransparent media due to the convection-dominated property of the transfer equation. The existence of two angular derivative terms complicates the solution of radiative transfer in graded index media as compared to uniform index media. In this paper, a new stabilized FEM is proposed to solve radiative transfer in multi-dimensional graded index media, based on the second-order form transformation approach. Using the approach, a second order diffusion term is naturally introduced in the transfer equation, which ensures numerical stability, and avoids the false smearing effects as compared to the common schemes based on artificial diffusion. The performance of the presented FEM is verified with one- and two-dimensional test cases. It is demonstrated that the new FEM owns good numerical stability and accuracy, and could potentially be an effective tool to solve radiative transfer in graded index media.

A Study of Multidimensional and Persistent Poverty among Migrant Workers: Evidence from China’s CFPS 2014–2020

Poverty is a critical social problem in numerous countries. It is a result of many aspects and has been addressed worldwide for a long time. In this study, we construct the multidimensional poverty index (MPI) for migrant workers in China based on Amartya Sen’s capabilities approach. Using the Chinese Family Panel Studies (CFPS) data during 2014–2020 and the Alkire–Foster methodology, our study examines the multidimensional poverty of migrant workers using static to dynamic analyses. The results show the following: (1) The multidimensional poverty of migrant workers shows a general decreasing trend over time. (2) Over one third of migrant workers are in severe multidimensional poverty. (3) The in-work poverty of migrant workers is serious, which is reflected in the indicators of employment security, housing provident fund, labor contract, and labor union. (4) Approximately 30% of migrant workers’ multidimensional poverty is persistent. It is difficult for them to rid themselves of persistent poverty by their own effort. This study suggests that the government should pay more attention to poverty reduction and capability improvement for migrant workers.

Large-scale monitoring in the DRC’s Ituri forest with a locally informed multidimensional well-being index

To monitor quality of life in changing landscapes and assess impacts of interventions, development scholars and practitioners continue to seek sensitive, flexible, practical means of measuring well-being. An approach that has received relatively little attention from development scholars but that is gaining traction among NGOs is the use of a well-being index derived from a list of locally defined and democratically weighted basic necessities. The Wildlife Conservation Society has been piloting a tool called the Basic Necessities Survey (BNS) in and around protected areas in Central Africa and beyond for over a decade. Adapted from consensual relative poverty metrics developed in the UK and Sweden, BNS data can be used to calculate a Well-being Index (WBI) that is locally relevant and comparable. To demonstrate its applicability in a lower-income context, we present findings from the Ituri Region of the Democratic Republic of the Congo (DRC) where over 2,000 households were surveyed using the BNS tool in 2015, 2017, and 2019. WBI scores were lower among traditionally vulnerable and marginalized groups: Indigenous and female-headed households, those with young or elderly heads, and households that were smaller or had high ratios of dependents. WBI varied with livelihood and geography and was sensitive enough to detect group-specific changes over a short time; namely an economic shock concentrated in villages along the main local highway in 2017 when the DRC experienced a major currency devaluation. Scores can be calculated to either incorporate or isolate variability in subjective expectations about what constitutes well-being; we show that expectations differed for Indigenous households and expectations rose faster than assets in this period. Findings build confidence in the utility of this type of locally informed multidimensional well-being metric in low-income regions. Those seeking practical instruments to produce flexible and regionally comparable well-being measures may wish to consider this approach.

Virtual Group Music Therapy for Apathy in Parkinson's Disease: A Pilot Study.

To evaluate the effect of virtual group music therapy on apathy in people with Parkinson's disease (PD). Apathy affects 40% of people with PD, lacks effective therapies, and independently predicts poorer quality of life and greater caregiver burden. Music therapy is the clinical application of music to address a person's physical or emotional needs and is effective in treating apathy in dementia. People with idiopathic PD and apathy (Movement Disorders Society-Unified Parkinson's Disease Rating Scale, apathy item ≥ 2) and their caregivers participated in twelve, weekly virtual group music therapy sessions, with session attendance signifying adherence. Participants completed pre- and post-intervention assessments of apathy (Apathy Scale (AS)), quality of life (Parkinson's Disease Questionnaire-short form), functional ability (Schwab & England Activities of Daily Living Scale), depression (Beck Depression Inventory (BDI-II)), and cognition (Montreal Cognitive Assessment-Blind). Among secondary outcomes, we assessed caregiver burden (Zarit Burden Interview-short form) and strain (Multidimensional Caregiver Strain Index). Sixteen PD participants (93.8% men, mean age 68.3 ± 8.4years, median 6 years PD duration) and their caregivers (93.8% women, mean age 62.6 ± 11years) completed the study. All PD participants and 88% of caregivers were >70% adherent to the intervention. Apathy (AS, effect size = 0.767, P = 0.002) and depression (BDI-II, effect size = 0.542, P = 0.03) improved, with no change in caregiver measures. Group music therapy is an effective treatment for apathy in PD and may improve mood. The virtual format is a feasible alternative to in-person sessions with high adherence and satisfaction.

Anterior Lumbar Interbody Fusion in Elderly Patients: Peri- and Postoperative Complications and Clinical Outcome.

Anterior lumbar interbody fusion (ALIF) is an effective surgical technique for treating various lumbar pathologies, but its use in elderly patients is controversial. Data concerning complications and effectiveness are sparse. We investigated peri- and postoperative complications, radiographic parameters, and clinical outcome in elderly patients. Patients ≥65 years who underwent ALIF between January 2008 and August 2020 were included in the study. All surgeries were performed through a retroperitoneal approach. Clinical and surgical data as well as radiologic parameters were collected prospectively and analyzed retrospectively. A total of 39 patients were included; the mean age was 72.6 (±6.3) years (range: 65-90 years); and the mean American Society of Anesthesiologists (ASA) risk classification was 2.3 (±0.6). A laceration of the left common iliac vein was the only major complication recorded (2.6%). Minor complications occurred in 20.5% of patients. Fusion rate was 90.9%. Reoperation rate at the index level was 12.8 and 7.7% in adjacent segments. The multidimensional Core Outcome Measures Index (COMI) improved from 7.4 (±1.4) to 3.9 (±2.7) after 1 year and to 3.3 (±2.6) after 2 years. Oswestry disability index (ODI) improved from 41.2 (±13.7) to 20.9 (±14.9) after 1 year and to 21.5 (±18.8) after 2 years. Improvements of at least the minimal clinically important change score of 2.2 and 12.9 points in the ODI and COMI after 2 years were noted in 75 and 56.3% of the patients, respectively. With careful patient selection, ALIF is safe and effective in elderly patients.

Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models

The evaluation of land comprehensive carrying capacity (LCCC) is a popular topic in regional sustainable development and land science research. However, these evaluations are often not objective due to the complexity and nonlinear characteristics of the LCCC evaluation indicators. This study provides a new framework for using machine learning methods to objectively evaluate LCCC. The Driver-Pressure-State-Impact-Response (DPSIR) indicator conceptual framework was used for the spatial visualisation of indicators and was then combined with the random forest (RF) model to optimise the LCCC evaluation. The results showed the following: (1) The accuracy of the integrated RF and DPSIR model was better than that of traditional support vector machine (SVM) and principal component analysis (PCA) methods, indicating that RF model was more suitable for LCCC evaluations. (2) The contribution of the DPSIR indicators after RF optimisation was significantly different from that of the traditional analytic hierarchy process (AHP), the contribution of the influence subsystem (I) (48.7%) was enhanced, while the contribution of the drive subsystem (D) (6.8%) was weakened, demonstrating that after RF optimisation, the DPSIR was more conducive for handling complex nonlinear systems and objectively reflecting indicator contributions. (3) The objective attributes of the DPSIR indicators were optimised through the processing of spatial visualisation models, indicating that RF was suitable for processing evaluations with a multidimensional spatial heterogeneity index system. The LCCC evaluation model can be applied to other carrying capacity case studies and operational processes and provide a scientific method for handling the multidimensional nonlinear evaluation index system and objective evaluation of LCCC.

Multidimensional prognostic index predicts short- and long-term mortality and rehospitalizations in older patients with hip fracture.

Multidimensional Prognostic Index (MPI), calculated on cognitive, functional, nutritional, social, pharmacological and comorbidity domains, strongly correlates with mortality in older patients. Hip fractures are a major health problem and are associated with adverse outcomes in those affected by frailty. We aimed at evaluating whether MPI is a predictor of mortality and rehospitalization in hip fracture older patients. We investigated the associations of MPI with all-cause 3- and 6-month mortality and rehospitalization in 1259 older patients admitted for hip fracture surgical treatment and managed by an orthogeriatric team [age 85years (65-109); male gender: 22%]. Overall mortality was 11,4%, 17% and 23,5% at 3, 6 and 12months from surgery (rehospitalizations: 15, 24,5 and 35,7%). MPI was associated (p < 0.001) with 3-, 6- and 12- month mortality and readmissions; Kaplan-Meier estimate for rehospitalization and survival according to MPI risk classes confirmed these results. In multiple regression analyses these associations were independent (p < 0.05) of mortality and rehospitalization-associated factors not included in the MPI, such as gender, age and post-surgical complications. Similar MPI predictive value was observed in patients undergoing endoprosthesis or other surgeries. ROC analysis confirmed that MPI was a predictor (p < 0.001) of both 3- and 6- month mortality and rehospitalization. In hip fracture older patients, MPI is a strong predictor of 3-, 6- and 12- months mortality and rehospitalization, independently of surgical treatment and post-surgical complications. Therefore, MPI should be considered a valid pre-surgical tool to identify patients with higher clinical risk of adverse outcomes.

Multidimensional Energy Poverty in China: Measurement and Spatio-Temporal Disparities Characteristics.

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.

Predicting Multidimensional Poverty with Machine Learning Algorithms: An Open Data Source Approach Using Spatial Data

This paper presents a methodology to estimate the multidimensional poverty index using spatial data at the street block level. The data used in this study were obtained from Open Street Maps and ESA’s land use cover, which are freely available sources of spatial information. The study employs five machine-learning algorithms, including Catboost, Lightboost, and Random Forest, to estimate the multidimensional poverty index with spatial granularity. The results indicate that these models achieve promising performance in predicting poverty levels in Medellín, Colombia. The results showed that the Random Forest algorithm achieved the highest performance, with an MAE of 0.07504. Furthermore, the spatial distribution of the multidimensional poverty estimate was highly correlated with the true values of the distribution. This work contributes to predicting multidimensional poverty by demonstrating the potential of machine learning algorithms to utilize accessible spatial data. By providing evidence of the feasibility of estimating poverty levels at a granular spatial level, this methodology offers a powerful tool for policymakers to make poverty social interventions with low-cost evidence. Furthermore, this study has important implications for poverty eradication efforts in developing countries, where access to reliable data remains challenging.

A Statistical Dependence Framework Based on a Multivariate Normal Copula Function and Stochastic Differential Equations for Multivariate Data in Forestry

Stochastic differential equations and Copula theories are important topics that have many advantages for applications in almost every discipline. Many studies in forestry collect longitudinal, multi-dimensional, and discrete data for which the amount of measurement of individual variables does not match. For example, during sampling experiments, the diameters of all trees, the heights of approximately 10% of the trees, and the tree crown base height and crown width for a significantly smaller number of trees are measured. In this study, for estimating five-dimensional dependencies, we used a normal copula approach, where the dynamics of individual tree variables (diameter, potentially available area, height, crown base height, and crown width) are described by a stochastic differential equation with mixed-effect parameters. The approximate maximum likelihood method was used to obtain parameter estimates of the presented stochastic differential equations, and the normal copula dependence parameters were estimated using the pseudo-maximum likelihood method. This study introduced the normalized multi-dimensional interaction information index based on differential entropy to capture dependencies between state variables. Using conditional copula-type probability density functions, the exact form equations defining the links among the diameter, potentially available area, height, crown base height, and crown width were derived. All results were implemented in the symbolic algebra system MAPLE.

Attributable Perioperative Cost of Frailty after Major, Elective Noncardiac Surgery: A Population-based Cohort Study.

Patients with frailty consistently experience higher rates of perioperative morbidity and mortality; however, costs attributable to frailty remain poorly defined. This study sought to identify older patients with and without frailty using a validated, multidimensional frailty index and estimated the attributable costs in the year after major, elective noncardiac surgery. The authors conducted a retrospective population-based cohort study of all patients 66 yr or older having major, elective noncardiac surgery between April 1, 2012, and March 31, 2018, using linked health data obtained from an independent research institute (ICES) in Ontario, Canada. All data were collected using standard methods from the date of surgery to the end of 1-yr follow-up. The presence or absence of preoperative frailty was determined using a multidimensional frailty index. The primary outcome was total health system costs in the year after surgery using a validated patient-level costing method capturing direct and indirect costs. Secondary outcomes included costs to postoperative days 30 and 90 along with sensitivity analyses and evaluation of effect modifiers. Of 171,576 patients, 23,219 (13.5%) were identified with preoperative frailty. Unadjusted costs were higher among patients with frailty (ratio of means 1.79, 95% CI 1.76 to 1.83). After adjusting for confounders, an absolute cost increase of $11,828 Canadian dollar (ratio of means 1.53; 95% CI, 1.51 to 1.56) was attributable to frailty. This association was attenuated with additional control for comorbidities (ratio of means 1.24, 95% CI, 1.22 to 1.26). Among contributors to total costs, frailty was most strongly associated with increased postacute care costs. For patients with preoperative frailty having elective surgery, the authors estimate that attributable costs are increased 1.5-fold in the year after major, elective noncardiac surgery. These data inform resource allocation for patients with frailty.

Measuring Women Self-Help Groups and Their Enterprises Sustainability in Odisha

The SHG programme has successfully transformed the lives of rural and urban poor by providing financial and non-financial assistance. The effectiveness of the programme is evident from the gradually increasing enrolment. By targeting women as their major priority, the promoting agencies/government is trying to assure the recurrence of the programme. The SHG programme in India has widespread coverage with due effort from NABARD and specialised State agencies with the highest coverage in the southern region. Even the eastern region also holds a significant share with a major presence in West Bengal and Odisha. The present study attempts to examine the sustainability position of WSHGs of Odisha with the help of a multidimensional sustainability index. Two-way analysis was made to evaluate the actual status of SHGs and their enterprises. Initially, the evaluation was done at the SHG level and later enterprise-level analysis was made to conclude the overall sustainability status. Though many studies reported successful implementation and effectiveness of the programme, the actual scenario is quite different. Group-level analysis reveals that financial assistance alone will not break the multi -dimensional poverty. Therefore, the study suggests active participation of government and non-government agencies for developing the required skills.

A new type of urbanization policy and transition of low-carbon society: A "local- neighborhood" perspective

This study aims to develop a framework linking New-Type Urbanization (NTU) and carbon emissions, considering both direct and spillover effects. Firstly, the paper constructs a multi-dimensional index to measure NTU and documents the spatial patterns of NTU levels in China, showing remarkable inequality between the eastern and western regions. Secondly, the study evaluates the effectiveness of pilot policies aimed at achieving Smart City and NTU goals. Using the Difference-in-Difference model based on panel data collected from 108 cities in the Yangtze River Economic Belt from 2006 to 2019, the study provides empirical evidence that these policies can significantly reduce carbon emissions in terms of production and consumption. Moreover, the study finds that the policies have significant spatial spillover effects on neighboring cities based on the Spatial Panel Durbin Model. The findings suggest that such policies can effectively reduce carbon emissions in areas with significant human activity and have important policy implications for achieving sustainable development in China.