Mobile Survey Research Articles

The demand potential of shared autonomous vehicles: a large-scale simulation using mobility survey data

Shared Autonomous Vehicles (SAV), or robotaxis, are expected to be commercially available within this decade. This new transport mode has the potential to revolutionize travel, offering a level of service comparable to traditional taxis with much lower prices. This may attract travelers currently using other modes, impacting the economic sustainability of public transport as well as car ownership levels. We investigate this potential demand using a scalable SAV simulation framework. We do not establish a future equilibrium considering the interaction between all users on a detailed road network, but establish the potential demand for a large metropolitan area. Travelers can choose between their current mode and the new SAV mode, with fare and waiting times which depend on real-time demand. For our input data we train a statistical model on a large transport survey from Germany for an urban region, allowing us to generate a large number of trips with realistic characteristics. We conduct a sensitivity analysis to study the effect of several key parameters on the modal shift. We find that SAVs can be attractive to many active mode and public transport users unless regulations are put in place. Our results also show that due to SAV fleet constraints, changes in incentives for travelers currently using cars may have significant consequences on the behavior of other travelers. We further calculate key economic indicators for the fleet, which can inform the discussion on the fleet size and fare level that operators are likely to choose when maximizing their own profit.

The Role of Personal Characteristics in Shaping Gender-Biased Job Losses during the COVID-19 Pandemic: The Case of South Africa

This paper contributes to the literature on the effects of adverse economic conditions on gender differences in the labour market by assessing how COVID-19, a global trigger for a critical adverse condition, impacted gender differences in the labour market in a country with pronounced discrimination and inequality in the labour market. In fact, the paper investigates how the personal characteristics of women and men affected their likelihood of losing jobs during and before COVID-19 in South Africa. Using the database of the National Income Dynamics Study-Coronavirus Rapid Mobile Survey (NIDS-CRAM) and NIDS wave 5 dataset and based on logit regression, the paper finds that personal characteristics such as tertiary education had a negative effect on job losses among females. Moreover, the results show that, compared to male workers, female workers were the most affected by the pandemic due to the lockdown regulation that affected many households’ behaviour.

"Did You Wash Your Hands?" The Socioeconomic Inequalities Preventing Youth From Adopting Protective Behaviors During COVID-19 in South Africa.

Background. Behavior change has been a critical factor in slowing the spread of COVID-19. In South Africa where infection rates are high, research is needed on the protective behaviors adopted by youth who have low infection rates but are carriers of the virus. Aims. The purpose of this study is to (1) identify the protective behaviors young people adopted during the pandemic and (2) to estimate the probability of positive behavior change by demographic and socioeconomic characteristics. Methods. The study uses data from the South African National Income Dynamics-Coronavirus Rapid Mobile Survey 2020. The sample includes 985 (n) youth aged 15-24 years. The outcome of interest is behavior change due to the Coronavirus. Cross-tabulations and an adjusted binary logistic regression model showing odds ratios, are fit to the data. Results. Not all youth adopted protective behaviors. The most prevalent behaviors adopted include washing hands (67.75%) and staying at home (54.02%). Youth in households with six or more members are more likely to change their behaviors (ORs = 1.67 and 1.64, both p-values < .05). However, youth who do not have access to water to wash hands (OR = 0.71), reside in households with food insecurity (OR = 0.94), and those living in nonformal housing (OR = 0.69) are less likely to adopt behavior change. Conclusion. Due to the socioeconomic inequalities associated with behavior change, there is need for more tailored approaches to address youth living in impoverished households in the country.

Gender Gaps in Improvements to Shared-Ride Services: Insights from a Shared Mobility Survey

This study analyzed data from a 2022 shared mobility survey to investigate the impact of gender on the potential improvements to on-demand transportation services, particularly shared-ride services such as ridesharing through transportation network companies (TNCs) and microtransit. Results revealed that male respondents were more inclined to opt for financial incentives such as pretax benefits, direct subsidies, and subsidizing certain trips. Employer-related programs such as parking cash-out programs and flexible working hours were also found to be more appealing to male respondents. In contrast, female respondents placed a greater emphasis on safety-related measures, indicating that safety concerns may be too significant for them to overlook, even when presented with financial incentives.

Impacts of commute mode on body mass index: A longitudinal analysis before and during the COVID-19 pandemic

IntroductionCOVID-19 has impacted millions of commuters by decreasing their mobility and transport patterns. While these changes in travel have been studied, less is known about how commute changes may have impacted individuals’ body mass index (BMI). The present longitudinal study explores the relationship between commute mode and BMI of employed individuals in Montréal, Canada. MethodsThis study uses panel data drawn from two waves of the Montréal Mobility Survey (MMS) conducted before and during the COVID-19 pandemic (n = 458). BMI was modeled separately for women and men as a function of commuting mode, WalkScore©, sociodemographic, and behavioral covariates using a multilevel regression modeling approach. ResultsFor women, BMI significantly increased during the COVID-19 pandemic, but telecommuting frequency, and more specifically telecommuting as a replacement of driving, led to a statistically significant decrease in BMI. For men, higher levels of residential local accessibility decreased BMI, while telecommuting did not have a statistically significant effect on BMI. ConclusionsThis study's findings confirm previously observed gendered differences in the relations between the built environment, transport behaviors, and BMI, while offering new insights regarding the impacts of the changes in commute patterns linked to the COVID-19 pandemic. Since some of the COVID-19 impacts on commute are expected to be lasting, findings from this research can be of use by health and transport practitioners as they work towards generating policies that improve population health.

Estimates of Predator Densities Using Mobile DIDSON Surveys: Implications for Survival of Central Valley Chinook Salmon

AbstractThe Sacramento–San Joaquin Delta (hereafter, “the Delta”) is one of the estuaries with the most invasive species in the world, and nonnative predators may be a major factor in the observed decline of Central Valley Chinook Salmon Oncorhynchus tshawytscha over recent decades. In order for managers to take actions that might reduce predation‐related mortality for these ecologically, culturally, and economically valuable fish, it is important to understand the factors influencing the distribution and abundance of piscivores in the Delta. In this study, we used a dual‐frequency identification sonar (i.e., DIDSON) to conduct mobile surveys to quantify the abundances of piscivores in the Delta. We then used these data to identify the habitat features that are correlated with the abundance of piscivores. Prior to conducting the surveys, we used DIDSON data from captured fish to develop an algorithm to distinguish piscivores from nonpiscivores with high confidence (98% accuracy). A generalized linear mixed‐effects model fit to these survey data indicated that predator abundances were most associated with areas of increased submerged aquatic vegetation patches, and channels that are straighter, with increased bathymetric complexity. When applied to the entire survey area, this model was successfully able to predict known areas of high predator densities. These results indicate that one approach to reduce predator densities in key locations throughout the Delta, and improve juvenile salmonid outmigration survival, is to reduce the extent of invasive submerged aquatic vegetation. Because experimental predator removals have been largely ineffective in the Delta, efforts to manipulate habitat to discourage nonnative predator recruitment and favor native species recruitment may provide a more effective solution to improve salmonid survival rates.

How has COVID-19 changed private car use in European urban areas? An analysis of the effect of socio-economic characteristics and mobility habits

The private car has been identified as the main winner among transport modes in urban areas during the COVID-19 pandemic. The fear of contagion when using public transport or the decrease in road congestion are likely to have induced changes in citizens’ travel habits with respect to cars. This work investigates the impact of the pandemic on individuals’ habits and preferences regarding their car ownership levels and car usage in the European urban context, with a special focus on the role played by individual socio-demographics and urban mobility patterns. For this purpose, a Path Analysis approach has been adopted to model car ownership and use before and after COVID-19. The main data source employed in this research is an EU-Wide Urban Mobility Survey that collects detailed information (individual and household socio-economic characteristics, built environment attributes and mobility habits) of 10,152 individuals from a total of 21 European urban areas of different sizes, geographical locations, and urban forms. The survey data has been complemented with city-level variables that account for differences across the cities that may explain changes in car-related behaviour. The results show that the pandemic has induced an increase in car use among socio-economic groups that are generally associated with low car-dependent behaviour, revealing that policy instruments that discourage the use of the private car in urban areas are needed to avoid reversing past trends in the reduction of urban transport emissions. High-income, well-educated teleworkers are observed to be the ones that have reduced their car use to a larger extent. On the contrary, low-income individuals are mostly maintaining similar levels of car mobility. Finally, frequent public transport users are more likely than occasional users to have substituted this mode by the private car.

Public Transport Usage and Perceived Service Quality in a Large Metropolitan Area: The Case of Porto

Using public transport as an alternative to private motorized vehicles (PMVs) is becoming increasingly popular in many cities worldwide. To understand what incentives and enhancements are necessary to attract and retain more passengers, a comprehensive understanding of the quality of public transport services is essential. This study aims to broaden the existing literature on the knowledge of public transport services in a large, heterogeneous metropolitan area. A cross-examination between a binary and an ordinal logit model is proposed, using data from a mobility survey in the Porto Metropolitan Area (PMA) in Portugal. The results show that households use PMVs mainly for speed (58.8%), comfort (49.3%), and lack of public transport to the destination (35.7%). Households using public transport cite not driving/owning a PMV (52.6%), lack of alternative transport modes (49.1%), and service cost (38.2%) as primary reasons. The perceived service quality (PSQ) within the PMA exhibits variance on multiple levels, depending on the characteristics of the household and the municipality’s location. This study provides policymakers of different cities in the PMA with insight into what incentives would most effectively increase the PSQ and, in turn, attract more passengers. This insight would be valuable in developing strategies to improve public transport usage and reduce PMV usage in the PMA. Adopting these strategies will contribute to reducing environmental impact and reducing traffic congestion.

Impact of skill development trainings on fish farmers’ knowledge and attitude: A case study from Bihar, India

Skill development training aims to impart new knowledge and skill as well as build up favourable attitude among trainees.Evaluating the farmers’ knowledge and attitude towards skill development trainings will help to assess effectivenessof training as well as adoption probability of the acquired knowledge/skill. This research paper evaluated the impactof skill development training on fish farmers’ knowledge and attitude, in different districts of Bihar. Primary data werecollected from randomly selected trainees (250 Nos.), using feedback proforma and mobile phonic survey methodology.The first three levels of training evaluation model given by Kirkpatrick and Kirkpatrick were followed for measuring theimmediate impact of skill development training. Data were subjected to descriptive and Z-test statistics. The findingsrevealed that skill development training had significant (p=0.000) positive impact on the knowledge level of trainees.They exhibited favourable attitude towards skill development trainings and would be potential adopters of the acquiredknowledge and skills. Keywords: Attitude, Fish farmers, Impact evaluation, Kirkpatrick model, Knowledge, Skill development training

Development of an item bank for measuring prosthetic mobility in people with lower limb amputation: The Prosthetic Limb Users Survey of Mobility (PLUS-M).

Achieving mobility with a prosthesis is a common post-amputation rehabilitation goal and primary outcome in prosthetic research studies. Patient-reported outcome measures (PROMs) available to measure prosthetic mobility have practical and psychometric limitations that inhibit their use in clinical care and research. To develop a brief, clinically meaningful, and psychometrically robust PROM to measure prosthetic mobility. A cross-sectional study was conducted to administer previously developed candidate items to a national sample of lower limb prosthesis users. Items were calibrated to an item response theory model and two fixed-length short forms were created. Instruments were assessed for readability, effective range of measurement, agreement with the full item bank, ceiling and floor effects, convergent validity, and known groups validity. Participants were recruited using flyers posted in hospitals and prosthetics clinics across the United States, magazine advertisements, notices posted to consumer websites, and direct mailings. Adult prosthesis users (N=1091) with unilateral lower limb amputation due to traumatic or dysvascular causes. Not applicable. Candidate items (N=105) were administered along with the Patient Reported Outcome Measurement Information System Brief Profile, Prosthesis Evaluation Questionnaire - Mobility Subscale, and Activities-Specific Balance Confidence Scale, and questions created to characterize respondents. A bank of 44 calibrated self-report items, termed the Prosthetic Limb Users Survey of Mobility (PLUS-M), was produced. Clinical and statistical criteria were used to select items for 7- and 12-item short forms. PLUS-M instruments had an 8th grade reading level, measured with precision across a wide range of respondents, exhibited little-to-no ceiling or floor effects, correlated expectedly with scores from existing PROMs, and differentiated between groups of respondents expected to have different levels of mobility. ThePLUS-M appearstobe well suited to measuring prosthetic mobility in people with lower limb amputation. PLUS-M instruments are recommended for use in clinical and research settings.

Estimation of University Education Effectiveness according to Individual Characteristics and Institutional Variables using Performance Indicators: Focusing on Student Satisfaction, GPA, and Job Search Efficacy

본 연구의 목적은 대학 교육 효과성의 의미를 살펴보고, 성과지표를 활용하여 개인 특성 및 기관 변인에 따른 대학 교육의 효과성을 추정하는 것이다. 선행연구를 통하여 대학 교육의 효과성 분석을 위한 주요 개인 특성 및 기관 변인을 선정한 후, 위계적 선형 모형(Hierarchical Linear Modeling, HLM)을 활용하여 분석을 수행하였다. 이를 위하여 한국고용정보원의 2018년도 대졸자 직업이동 경로 조사(Graduates Occupational Mobility Survey, 2018 GOMS) 자료를 활용하였다. 분석 결과는 다음과 같다. 첫째, 대학 수준 변인으로서 고려한 설립기준 및 소재지는 대학 교육 성과 변수 가운데 대학 만족도, 학과 만족도 및 학점에 대하여 대체로 유의미한 영향력을 가지고 있음을 알 수 있다. 둘째, 삶의 만족도는 모든 대학 교육 성과 변수에 대한 정적(+)인 영향력을 가지는 것은 대학 교육 효과성에 대한 주관적 인식에 개인의 삶의 태도가 그대로 반영되어 나타날 수 있음을 시사한다. 셋째, 대학 입학 시점을 포함한 교육 경험 변인은 더욱 다양하게 대학 교육의 성과에 영향을 주면서 효과성 추정의 근거로 활용될 가능성을 보여주고 있다. 넷째, 교육환경 변인은 대부분 대학 교육 성과 변수에 정적(+)인 영향력을 가지고 있음을 확인할 수 있다. 이러한 결과는 학령기 감소 및 4차 산업 시대의 도래를 맞이하여 새로운 교육환경에 대처하기 위한 고등교육의 역할을 모색하고 향후 발전 방향 수립을 위한 근거자료로 활용될 것으로 기대한다.This research tries to estimate the college education effectiveness by measuring the effect of individual characteristics and institutional components to the performance indicators of university based on Hierarchical Linear Model(HLM). This study used 2018 GOMS data for the research purpose. The main results show following results. First, institutional sector and location significantly affect to the college satisfaction, major satisfaction, and academic performance in general. Second, the life satisfaction gives positive impact on performance indicators of university and it could reflect the personal attitude of life to the effectiveness of university education. Third, variables of educational experience, including the time of college entrance, affect the performance of college education more diversely, showing the possibility of being used as a basis for estimating effectiveness. Fourth, it can be confirmed that educational environment variables have a positive influence on most college education performance indicators. Those results provide new institutional role and further direction for the postsecondary institutions dealing in shortage of student number and needs of 4th industry era.

The potential of Wi-Fi data to estimate bus passenger mobility

Last decades have been marked by deep socio-economic transformations, an uneven evolution of transport demand in main urban areas and the emergence of new and more sustainable modes of transportation (carpooling, self-services bicycles). These changes have strongly impacted the interaction between service supply and demand in the transport industry. In this context, passive data as Wi-Fi and Bluetooth become a key source of information to understand individual mobility behaviors and ensure the sustainable development of transport infrastructures. In this paper, we present a framework that uses disruptive technology to collect passive data in buses, continuously and at a lower cost than traditional mobility surveys. Previous research, conducted over a more limited spatial and temporal framework, uses filtering methods, which do not allow the results to be replicated. This study uses artificial intelligence to sort transmitted signals, get transit ridership and build Origin–Destination matrices. Its originality consists in providing a concrete, automatic and replicable method to transport operators. The comparison of the results with other data sources confirms the relevance of the presented algorithms in demand forecasting. Therefore, our findings provide interesting insights for data-driven decision making and service quality management in urban public transport.

Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys?—A Case Study Surveying Roadside City Trees

The continuous flow of autonomous vehicle-based data could revolutionize current map updating procedures and allow completely new types of mapping applications. Therefore, in this article, we demonstrate the feasibility of using perception data of autonomous vehicles to replace traditionally conducted mobile mapping surveys with a case study focusing on updating a register of roadside city trees. In our experiment, we drove along a 1.3-km-long road in Helsinki to collect laser scanner data using our autonomous car platform ARVO, which is based on a Ford Mondeo hybrid passenger vehicle equipped with a Velodyne VLS-128 Alpha Prime scanner and other high-grade sensors for autonomous perception. For comparison, laser scanner data from the same region were also collected with a specially-planned high-grade mobile mapping laser scanning system. Based on our results, the diameter at breast height, one of the key parameters of city tree registers, could be estimated with a lower root-mean-square error from the perception data of the autonomous car than from the specially-planned mobile laser scanning survey, provided that time-based filtering was included in the post-processing of the autonomous perception data to mitigate distortions in the obtained point cloud. Therefore, appropriately performed post-processing of the autonomous perception data can be regarded as a viable option for keeping maps updated in road environments. However, point cloud-processing algorithms may need to be adapted for the post-processing of autonomous perception data due to the differences in the sensors and their arrangements compared to designated mobile mapping systems. We also emphasize that time-based filtering may be required in the post-processing of autonomous perception data due to point cloud distortions around objects seen at multiple times. This highlights the importance of saving the time stamp for each data point in the autonomous perception data or saving the temporal order of the data points.

A study on improving turnover intention forecasting by solving imbalanced data problems: focusing on SMOTE and generative adversarial networks

This study aims to improve the accuracy of forecasting the turnover intention of new college graduates by solving the imbalance data problem. For this purpose, data from the Korea Employment Information Service's Job Mobility Survey (Graduates Occupations Mobility Survey: GOMS) for college graduates were used. This data includes various items such as turnover intention, personal characteristics, and job characteristics of new college graduates, and the class ratio of turnover intention is imbalanced. For solving the imbalance data problem, the synthetic minority over-sampling technique (SMOTE) and generative adversarial networks (GAN) were used to balance class variables to examine the improvement of turnover intention prediction accuracy. After deriving the factors affecting the turnover intention by referring to previous studies, a turnover intention prediction model was constructed, and the model's prediction accuracy was analyzed by reflecting each data. As a result of the analysis, the highest predictive accuracy was found in class balanced data through generative adversarial networks rather than class imbalanced original data and class balanced data through SMOTE. The academic implication of this study is that first, the diversity of data sampling methods was presented by expanding and applying GAN, which are widely used in unstructured data sampling fields such as images and images, to structured data in business administration fields such as this study. Second, two refining processes were performed on data generated using generative adversarial networks to suggest a method for refining only data corresponding to a more minority class. The practical implication of this study is that it suggested a plan to predict the turnover intention of new college graduates early through the establishment of a predictive model using public data and machine learning.

Do Internships Matter?: the Impact of Internship Participation on Employability

This study aims to analyze the effect of internship participation on university graduates’ overall employability and uses Korea’s Graduate Occupational Mobility Survey data in order to track university graduates’ employment prospects. Our findings reveal that participation in internships increases the employability of young adults, and that this impact is slightly greater for male college graduates than for female college graduates. Meanwhile, in order to supplement the reliability of our results, we conducted additional analytical tests, including a placebo test and an intensive sensitivity analysis test. It turns out that the estimated treatment effects on the placebo outcomes are of a small magnitude, with no statistical significance. In the analysis which includes simulated confounders, it appears that the risk of omitting relevant confounders does not threaten the baseline estimates.

Psychometric properties of the Patient Health Questionnaire-4 among Hong Kong young adults in 2021: Associations with meaning in life and suicidal ideation.

Young adults in Hong Kong are subject to elevated psychological distress given the societal stressors such as civil unrest and COVID-19 pandemic and suicide is a leading cause of death among them. The present study aimed to evaluate the psychometric properties and measurement invariance of the 4-item Patient Health Questionnaire-4 (PHQ-4) as a brief measure of psychological distress and its associations with meaning in life and suicidal ideation (SI) in young adults. A mobile survey recruited a large and random sample of 1,472 young adults (Mean age = 26.3 years, 51.8% males) in Hong Kong in 2021. The participants completed the PHQ-4 and Meaning in Life Questionnaire-short form (MLQ-SF) for presence of meaning in life (MIL), SI, COVID-19 impact, and exposure to suicide. Confirmatory factor analysis was conducted to examine the factorial validity, reliability, and measurement invariance of PHQ-4 and MLQ-SF across gender, age, and distress subgroups. Multigroup structural equation model evaluated and compared the direct and indirect effects of latent MIL factor on SI via latent PHQ-4 factor across distress groups. Both MIL and PHQ-4 supported a 1-factor model with good composite reliability (Ω = 0.80-0.86) and strong factor loadings (λ = 0.65-0.88). Both factors showed scalar invariance across gender, age, and distress groups. MIL showed significant and negative indirect effects (αβ = -0.196, 95% CI = -0.254 to -0.144) on SI via PHQ-4. PHQ-4 showed a stronger mediating role between MIL and SI in the distress group (Δ = -0.146, 95% CI = -0.252 to -0.049) than the non-distress group. Higher MIL predicted higher likelihoods of help-seeking (Odds ratios = 1.46, 95% CI = 1.14-1.88). The present results support adequate psychometric properties in terms of factorial validity, reliability, convergent validity, and measurement invariance for the PHQ-4 in young adults in Hong Kong. The PHQ-4 demonstrated a substantial mediating role in the relationship between meaning in life and SI in the distress group. These findings support clinical relevance for using the PHQ-4 as a brief and valid measure of psychological distress in the Chinese context.

MODAL SPLIT ANALYSIS BY BEST-WORST METHOD AND MULTINOMINAL LOGIT MODEL

The behavioral features of the population are addressed in transport models by different levels of territorial disaggregation and the creation of demand strata in a territory. The need for input data grows exponentially with the demand for a detailed zonal system of the territory. The basic source is the mobility survey. This article deals with the comparison of the calculation of the probability of choosing a transport mode for trips using the classic multinominal logit model and the best-worst method. We used data from a mobility survey in the Žilina region as a basic sample. The analysis covered 11 districts and their gravity areas. The individual transport relations are evaluated in detail in the analysis. The results confirm the high degree of accuracy of the best-worst method in the calculation of mode choice on a regional scale. Despite the promising results of the agreement in the confrontation with the mobility survey, it is necessary to verify the modeled data with a more detailed area with disaggregation on-demand strata.

Do referrals improve the representation of women in mobile phone surveys?

Random digit dial surveys with mobile phones risk under-representation of women. To address this, we compare the characteristics of women recruited directly with those of women recruited through referrals from male household members. The referral process improves representation of vulnerable groups, such as young women, the asset poor, and those living in areas with low connectivity. Among mobile phone users, we show a referral (rather than a direct dial) protocol includes more nationally representative proportions of women with these attributes. While seeking intra-household referrals may improve representation, we show that it does so at a higher cost.

Uptake of COVID-19 vaccines and associated factors among adults in Uganda: a cross-sectional survey

ObjectiveCOVID-19 pandemic remains one of the most significant public health challenges ever faced globally. Vaccines are key to ending the pandemic as well as minimise its consequences. This study determined...

수도권 외 대학졸업자의 취업 전 특성에 대한 잠재계층분석 및 일자리만족도와의 관련성 연구

The problem of youth employment related to the crisis of universities outside the metropolitan area and job satisfaction of university graduates outside the metropolitan area is emerging. In college, it is necessary to find ways to increase satisfaction with jobs after graduation as well as simple employment. Accordingly, this study derived the latent class according to the characteristics before employment and analyzed the difference in job satisfaction level by latent class. The analysis specifically examined the factors affecting the classification of latent classes. This study was conducted with 6,795 university students from outside the Seoul metropolitan area, using the Graduates Occupational Mobility Survey (GOMS) data of the Ministry of Employment and Labor and the Korea Employment Information Service in 2019. The results of the latent class analysis are as follows: First, the number of latent classes according to the characteristics before employment was 6. Second, as a result of examining whether there is a difference in demographic and occupational characteristics by each group according to the characteristics before employment, there was a significant difference in gender, current full-time job status, age, income, and job satisfaction. Third, as a result of examining factors affecting the latent class according to the characteristics before employment of college graduates, when the first group was the reference group, the job satisfaction was high in the order of 5 group, 2 group, 4 group, 3 group, 6 group and 1 group. This study is important in that it analyzed the factors affecting the job satisfaction and the characteristics of the college graduates outside the metropolitan area at the same time. In addition, it is meaningful in that it reflects the individual difference of pre-employment characteristics in the analysis methodologically.