Suburban Research Articles

The effect of Islamic financial literacy on business performance with emphasis on the role of Islamic financial inclusion: case study in Indonesia

Purpose Recently, financial inclusion promoters have observed that financial literacy is a vital tool for financial inclusion growth, especially among the poor who are considered financially illiterate. The purpose of this paper is to look at the role of Islamic financial literacy (IFL) and inclusion in improving the performance of micro, small and medium enterprises (MSMEs) in Indonesia. Design/methodology/approach This study uses questionnaire data collected as many as 197 MSMEs. This study was analyzed using structural equation modeling approach to test the hypothesis. Findings The results show that IFL is an important variable to increase Islamic financial inclusion. The results also show that both have a significant influence in developing the MSME sector business. Research limitations/implications This research is not without limitations. This study only adopted a sample in one Province in Indonesia with focus on creative economy sector and some others sectors located in the province of Riau, therefore ignoring suburban and urban areas in Indonesia. Therefore, future studies using a cross-sectional research design are feasible. In addition, this study only uses quantitative data, so that qualitative research with key informant interviews can be considered for further research. Practical implications The findings of this study enlighten policymakers, managers of financial institutions and advocates of financial literacy and inclusion about the importance of improving the performance of MSMEs. As a policy recommendation, this study suggests that Islamic policy makers and financial institutions should play a proactive role in increasing service outreach and providing the regulatory environment needed for MSMEs given the important role MSMEs play in the Indonesian economy. Originality/value This study combines functional components (behaviors and attitudes) and nonfunctional measures (knowledge and skills) of financial literacy and financial inclusion in explaining the performance of MSMEs. Thus, this research is an initial effort to explain financial literacy and inclusion on the performance of MSMEs.

The Test and Protect Program: A Data-Driven, Community-Engaged Approach to COVID-19 Testing Site Localization.

As the COVID-19 pandemic progressed, reliable, accessible, and equitable community-based testing strategies were sought that did not flood already overburdened hospitals and emergency departments. In Hamilton County, Ohio, home to~800 000 people across urban, suburban, and rural areas, we sought to develop and optimize an accessible, equitable county-wide COVID-19 testing program. Using Coronavirus Aid, Relief, and Economic Security Act funding, multidisciplinary, multiorganization partners created the test and protect program to deliver safe, reliable testing in neighborhoods and organizations needing it most. Our approach involved: (1) use of geospatial analytics to identify testing locations positioned to optimize access; (2) community engagement to ensure sites were in trusted places; and (3) tracking of data over time to facilitate ongoing improvement. Between August 2020 and December 2021, more than 65 000 tests were completed for nearly 46 000 individuals at community-based testing sites. These methods could have application beyond COVID-19 and our region.

Spectral Properties of Turbulence in a Suburban Area of São Paulo Megacity

Spectral Properties of Turbulence in a Suburban Area of São Paulo Megacity

Rural residential choices: unravelling the nexus of accessibility and preferences

ABSTRACT As rural residents increasingly migrate to urban areas to improve living conditions, employment, and education, closely examining the relationship between residential choices and accessibility becomes crucial. This study employs a Stated Preference Survey (SP) with an orthogonal experimental design and uses the Multinomial Logit Model (MNL) to explore the preferences of rural residents for accessibility in the built environment when choosing new residences, including potential disparities between residents on the urban fringe and those in suburban areas. The findings reveal a strong inclination among residents for proximity to essential services such as education, transportation, healthcare, markets, recreation, and amenities. Preferred residential locations include within a 2-kilometre radius of schools, 300 metres from bus stops, 500 metres from medical facilities, 2 kilometres from markets, between 500 metres and 1 kilometre from leisure facilities, and within 1 kilometre of supermarkets. Additionally, commuting time preferences vary, with rural residents favoring a 15-minute commute, peri-urban residents preferring 5 minutes, and distant suburban residents showing no clear preference for commute times ranging from 5 to 15 minutes. These insights carry significant implications for urban and rural planning and development.

Zróżnicowanie warunków bioklimatycznych między centrum Lublina i obszarem podmiejskim w półroczu letnim

The aim of this study is to assess the differences in the occurrence of harsh bioclimatic conditions between the center of Lublin and non-urban areas in the warm half of the year (April-September) and to determine the rate of changes in these conditions in both analysed meteorological stations in the years 1973–2022. To assess the bioclimatic conditions, the biometeorological index Humidex was used. In the first part of the study period, the number of days with unfavourable bioclimatic conditions prevailed in the city center, while in the last two decades the differences have decreased, mainly as a result of the development of concentrated buildings in the suburban area. In both measurement points, there was a significant increase in the number of days with strong thermal and hygric loads on the human body. The study also presents the reasons for the occurrence of selected cases with significant differences in the index values in the Lublin region, as well as the synoptic conditions on the day with the highest recorded Humidex value during the study period.

Risk factors for Lyme disease among residents of rural, suburban, and urban areas in the United States: A Case-Control Study

Abstract lyme disease (LD) is the most common vector-borne disease in the United States, with 476,000 cases estimated each year. It is unclear how LD risk factors vary by residential setting. We conducted a case-control study on LD risk by rural, urban, and suburban residential settings. Individuals from 15 high-incidence states and the District of Columbia in the Optum Research Database were identified as cases (LD medical claim) or controls (no LD medical claim, matched by county of residence and census block group population density). Participants were surveyed about LD history, outdoor activities, and residential characteristics. The final analytic dataset had 750 LD cases and 965 controls. Residence in a rural setting had increased LD risk (OR 1.41, 95% CI 1.16, 1.72). In multivariable analyses, activities associated with LD were hiking/walking/running or having an occupation in forests, wooded areas, or areas of tall grass (all respondents), and spending time in a yard (rural and urban residents only). Public health interventions can help prevent LD in high-incidence jurisdictions by reinforcing the nearuniversal LD risk for rural residents, and highlighting activities that lead to increased LD risk for those in areas with less ubiquitous tick exposure like in urban and suburban settings.

The influence of residential location and public transit options on commuters’ intention to use travel apps for different travel purposes

While living in city centers is usually linked to higher accessibility levels, shorter travel times, and higher levels of public transit (PT) utilization, the opposite is true for residents of suburban areas. This assumption holds in metropolitan contexts, where central areas offer better accessibility and are associated with higher levels of PT use. In metropolitan peripheries, a large part of commuting is done on an interurban basis, so that the level of use of public transit can be linked to the supply and information available. This work aims to understand the conditions in terms of transit supply and land use, considering the most frequent trip, psychological variables, and the modal choice of commuters’ motorized modes, in the intention to use the new real-time multi-modal travel app, such as advanced traveler information systems (ATIS) for digital mobility-management assistance. A Structural Equation Model is developed to empirically test a sample of 768 respondents collected in two suburban corridors in the Lisbon Metropolitan Area (LMA). Finally, our findings indicate that residential location, mode choice, and trip complexity have a relevant influence on the intention to adopt travel apps. Male students belonging to Generation Y/Z are the most likely users of travel apps. Regardless of the reason, travel patterns associated with more complex (more transfers) and more frequent trips can reinforce the intention to use apps. Also, it is worth noting that students are frequent public transit users, and public transit is also related to trip complexity.

A Python toolkit for integrating geographic information system into regulatory dispersion models for refined pollution modeling

AERMOD is designated as U.S. Environmental Protection Agency (EPA)'s preferred air dispersion model for refined transportation project hot-spot analyses beginning in 2020. One of the key challenges in its modeling process is spatially encoding roadway geometry, especially when simulating highways with complex geometric designs. This research proposed an open-source Python package, GTA, which enables conversion of publicly available roadway Geographic Information System (GIS) layers into defined sources, and source-based emission rates from MOtor Vehicle Emissions Simulator (MOVES) output for AERMOD modeling. The research selected a suburban area in Atlanta, and conducted a comprehensive analysis in terms of annual PM2.5 concentration results and the speed of preparing AERMOD input files for highway network modeling both manually and using software developed based on the proposed methodology. The results prove that the proposed methodology significantly expedites the AERMOD input preparation process, and facilitates convenient testing of multiple modeling configurations for multi-scenario or sensitivity analysis.

What influences intention to use a first-mile/last-mile automated shuttle service in a suburban area? A case study in Toronto, Canada

ABSTRACT We surveyed the public in 2021 about a temporary first-mile/last-mile (FMLM) automated shuttle trial (planned for operation on public roads) in Toronto, Canada, before its deployment for public use. Our objectives were to investigate predictors of intention-to-use in a mixed traffic context in Canada and whether factors affecting the likelihood of trying the shuttle differed from those affecting the intended frequency of use. Our results showed that higher perceived usefulness, positive attitude towards the service, and higher trust in the shuttle capabilities significantly predicted both measures, but age was a significant (negative) predictor only for the intended frequency of use. This difference in demographic effects for the two examined measures suggests that future research should assess intention-to-use in more detail. Our results can also inform strategies to promote future automated shuttle trials. For example, informational campaigns to promote trust in the shuttle’s capabilities and highlight the benefits of the service may improve intention-to-use.

Challenges in rescuing snakes to protect human lives and promote snake conservation in Tamil Nadu, India.

Human-snake conflicts are common worldwide, often resulting in snakebites. Snakebite envenoming causes over 125,000 deaths and 400,000 permanent disabilities worldwide every year. India alone accounts for an average of ~58,000 annual snakebite-induced deaths. As human developments rapidly expand into suburban and rural areas, snakes are being displaced and incidences of residents finding snakes within their dwellings are increasing. Most people have an innate fear of snakes, compounded by centuries of negative influence from culture and mythology manifesting in people often attempting to kill snakes. Snake rescuers are volunteers who remove and relocate snakes to safe areas. This is a risky job that poses potentially fatal implications if bitten. These volunteers mostly receive no financial compensation for their time or transportation costs, but they choose to do it for their love of snakes, conservation, and for the altruistic nature of helping others. Snake rescuers often receive no formal training and are unfunded resulting in removing snakes improperly without adequate safety equipment or the required skill set to safely complete the task. Therefore, it is critical to determine their challenges and requirements to promote the safe rescue of snakes while protecting human lives. In this study, we developed an online questionnaire and interviewed 152 snake rescuers in Tamil Nadu, India following written informed consent to determine their challenges and needs for rescuing snakes safely. The results demonstrate that most rescuers are males, and they conduct snake rescues for varying lengths of time. They mostly receive no formal training and are bitten by snakes. They spend their own money on the purchase of snake-handling equipment and on treatments if bitten or injured during a rescue. The rescuers highlighted the urgent need for formal training, safety equipment and standard protocols for rescuing snakes in Tamil Nadu. Overall, this study demonstrates that snake rescuing should be appropriately regulated by the authorities, in particular the Wildlife Division of State Forest Departments in India, and formal training along with necessary equipment, medical insurance and appropriate recognition should be provided to them to safely remove snakes from human dwellings and manage the safety of both snakes and humans. They can also act as educators to disseminate information about the preventive and first aid measures for snakebites as well as the ecological importance of snakes.

Multi-scale matching and simulating flows of ecosystem service supply and demand in the Wuhan metropolitan area, China

Metropolitan areas face the challenge of balancing increasing social needs against limited natural resources. The multi-scale evaluation of ecosystem services (ES) supply, demand and flows is crucial but under explored. Four prominent ES, food provision (FP), carbon sequestration (CS), soil conservation (SC), and outdoor recreation (OR) in Wuhan metropolitan area (WMA) were selected as cases. ES supply and demand were assessed by InVEST model, geographic information system (GIS) spatial analysis, and statistical methods. A linear programming model was employed to reveal the characteristics of ES flows. Results showed the following: (1) the total ES supply capacity can meet the demand of the study area. At different scales, the supply patterns were fundamentally consistent, except for FP, which was significantly higher in Wuhan's suburban areas. In contrast, the other three ES exhibited high supply in the northeast and south WMA, encompassing the Dabie and Mufu Mountains. The high demand for all ES radiated from the city center, except for SC. (2) A significant scale effect of ES mismatch was found, and ecological deficit patches decreased with increasing research scale. The Mismatch was evident on finer scales but lessened at coarser ones. (3) ES flows, consisting of FP, CS, and OR, formed a flow pattern from the suburbs to the city center. Coarse-scale management integrating ecological flows may better maintain ES balance than fine-scale measures. These findings provide insights for multi-scale management strategies that not only contribute to localized ecological restoration, but also optimize strategies for ecological corridors and compensation mechanisms.

Assessing Drug Crime Concentrations Across Types of Rural and Urban Areas

Crime is highly concentrated in a small percentage of micro-places, such as street segments. Research indicates that drug crimes might show even higher spatial concentration levels than property and violent crimes. Similarly, studies have shown that hot spots policing interventions for drug crimes are more effective than for other crimes. However, what we know about drug crime concentrations in micro-places is based almost exclusively on urban areas. Differences in crime concentrations in micro-places between rural and urban areas might, in turn, impact the effectiveness and transferability of common intervention strategies. This study addresses these issues by assessing drug crime concentrations in micro-places across diverse geographic area types (i.e., small cities, suburban areas, small towns, touristic, and rural areas). Adapting recent methodological advancements that allow for accurately assessing spatial concentrations for rare events, the study shows that drug crimes are, overall, highly spatially concentrated and can show even higher levels of concentration in some types of less urbanized areas.

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

Physico-chemical Characteristics and Evolution of NR-PM1 in the Suburban Environment of Seoul

In South Korea, particulate matter (PM) is generated from various emission sources, including domestic air pollutants and long-range transport. Effective air quality policies require an understanding of the chemical characteristics of PM and differences between urban and non-urban areas (suburban and background areas). We analyzed the chemical characteristics of non-refractory submicron aerosols (NR-PM1) in Yongin City, a suburban area southeast of Seoul, using a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) in spring/summer. In spring/summer, photochemical reactions resulted in the highest proportion of Organic Aerosol (OA) among NR-PM1. Positive matrix factorization (PMF) revealed four OA sources: Primary OA and Secondary OA (Oxidized Primary OA (OPOA), Less Oxidized Oxygenated OA (LO-OOA), and More Oxidized Oxygenated OA (MO-OOA)). OPOA was formed from the oxidation of emissions from biomass burning and coal combustion; LO-OOA and MO-OOA were correlated with inorganic compounds and influenced by long-range transport. The majority of OA was SOA (82%). High temperatures and humidity accelerated the conversion of SO2 to SO42−, resulting in the proportion of SO42−, second only to OA. Despite favorable conditions for nitrate formation in ammonium-rich conditions, the proportion of NO3− was relatively low due to the decomposition of NH4NO3 into gaseous NH3 and HNO3 at high temperatures. This indicated that while ammonium-rich conditions are conducive to NH4NO3 production, elevated temperatures lead to its decomposition, resulting in lower NO3− concentrations in spring/summer. In the case study, for the case associated with long-range transport, the PM concentration increased due to inorganic compounds such as NO3− and SO42−. Conversely, in cases of domestic air stagnation, the concentration of PM increased primarily due to the presence of OA. These findings provide crucial insights for air quality management in suburban areas and can guide policies to reduce PM levels in spring and summer.

Exploring the relationship between the determinants and the ridership decrease of urban rail transit station during the COVID-19 pandemic incorporating spatial heterogeneity

The study explores the relationship between the determinants and the ridership decrease incorporating spatial heterogeneity. ARIMA model is utilized to estimate the normal ridership assumed absence of COVID-19. Geography weighted regression (GWR) with Gaussian kernel function is constructed for regression. The K-means algorithm is applied to cluster the stations based on coefficients. Stations of Tokyo case are clustered into 2 groups: city area and western ward which represents mainly suburban areas. City stations are mainly influenced by the number of transfer lines, distance to the CBD, number of jobs and residents. In the western ward, the level of importance that residents place on public health primarily influences the ridership decrease. The implementation of work-from-home policies makes number of jobs a positive impactor on the decrease in ridership, with a greater impact observed on urban stations compared to suburban stations. City residents tend to engage in more travel than suburban residents because of less spacious living environments, which partially offsets the decrease in ridership. The findings offer parameters for predicting ridership of both city and suburban stations during public health emergency events, such as COVID-19. They can assist URT operators in developing strategies for balancing passenger demand and operational costs.

An MCDM-GIS framework for assessing flooding resilience of urban metro systems

Extreme rainstorms pose significant challenges to the operation of urban metro systems. This study proposes a data-driven framework for evaluating the flood resilience of metro systems, considering the spatio-temporal differences in rainfall patterns. Furthermore, it offers recommendations to bolster resilience against flooding, ensuring the safety and continuity of metro systems in the face of increasing climatic challenges. The proposed framework adheres to the tenets of design science research, a methodological approach that synergizes theoretical and empirical aspects. This framework was examined by assessing flood resilience at 254 metro stations in Wuhan. The study reveals insights into the spatial distribution of flood resilience among metro stations, with those located in suburban areas being riskier than their counterparts in city centers. Additionally, the implementation of emergency flood response strategies and the execution of pre-disaster drills play a pivotal role in reducing the potential losses caused by flooding.

OS-PSO: A Modified Ratio of Exponentially Weighted Averages-Based Optical and SAR Image Registration.

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.

Alteration in butterfly community structure along urban–rural gradient: with insights to conservation management

BackgroundEcosystem services rendered by the butterflies are important for the sustenance of community interaction. Butterfly species have also coevolved with the host and nectaring plants. In the adult condition, they mostly rely on nectar, while in the larval condition, they feed on the leaves of their host plants. Butterfly species are sensitive to changes in environmental parameters and are considered excellent indicators of ecosystem health. The study of species diversity and richness indices aids in better ecosystem management. The present study's goal was to determine butterfly diversity in the urban–rural gradient of Purulia district, West Bengal, India, a part of the Chota Nagpur Plateau. We aim to complement crucial information on butterfly conservation management in Purulia, West Bengal, India, and other similar geographical areas with the findings of this study.ResultsIt was found that out of 3809 sampled butterflies, the individual contribution of the family Nymphalidae was the highest (51.24%), followed by Lycaenidae (18.40%), Pieridae (17.32%), Papilionidae (9.74%), and Hesperiidae (3.12%). A total of 54 butterfly species were observed in the urban–rural gradient, out of which the urban region contained 49 species, the suburban region had 32 species, and the rural region had 30 species. Significant differences were observed in butterfly abundance for the sites, seasons, and families during the study period. PERMANOVA and ANOSIM for species abundance and species presence-absence data show that all three sites are significantly different. Results Both PCoA and NMDS revealed clear differences among sites (groups) in terms of species abundance and presence-absence data. According to the findings of this study, the urban region has the highest species richness, followed by the suburban and rural regions. We discovered that urban areas have the highest butterfly abundance, followed by suburban and rural areas. Numerous butterfly species prefer the bushes dominated by Lantanacamara in the urban region with the highest species richness. Aside from this invasive weed, the site also contains Tridaxprocumbens, Catharanthusroseus, Synedrellanodiflora, and Ocimumamericanum, which are well known for being butterfly nectaring plants. In the case of the suburban region, members of the Lycaenidae family contributed the highest percentage after Nymphalidae, which was dominated by Tridaxprocumbens and Sphagneticolatrilobata, which was preferred by the members of the Lycaenidae family observed during the survey, this site also contained Ixoracoccinea, Catharanthusroseus, and Lantanacamara. This site, in terms of nectaring plants, remains homogeneous in a rural region.ConclusionsOut of 3809 butterfly individuals, the family Nymphalidae contributed the most, followed by Lycaenidae, Pieridae, Papilionidae, and Hesperiidae. Both species richness and butterfly abundance were highest in urban regions, followed by sub-urban and rural regions. The current study has shown that this particular geographic location can sustain a variety of butterfly species. However, it is important to note that conservation planning is necessary not only for the butterfly species but also for the nectaring plant species that contribute to the diversity of these insects. The conservation of butterfly species can also lead to the achievement of ecosystem services they provide.

Using heat map data to measure population development trends and urban disparities of large-scale affordable communities in China

Over the past decade, large-scale affordable communities have proliferated in the suburban areas of major Chinese cities. Most of the housing in these communities consists of affordable housing provided by the government and constructed by developers, targeted at middle to low-income individuals through restricted-price sales and rentals. With more than a decade of occupancy, it is crucial to examine the development status of these communities. This study employs regular collection of heat map data to observe the population density trends in communities from 2020 to 2024. By analyzing population density and its derived variables, the study examines changes in daytime and nighttime populations during weekdays, as well as the differences between the Spring Festival period and regular population data. Combining urban context, we use both quantitative and qualitative methods to analyze population quantity and structure trends and further explore the development disparities among cities. The study reveals a widespread increase in population across 35 communities in five cities from 2020 to 2021, peaking in 2021 and 2022, with differentiation among cities after 2023. Before 2022, there was a pattern in the development of community population quantity and structure: communities with faster population growth exhibited a simultaneous increase in commuter proportions. The impetus for community population changes is largely attributed to the varying attractiveness of cities to the floating population, with the five cities demonstrating distinct patterns. The method used in this study to collect population data, as opposed to traditional questionnaire surveys, presents advantages in terms of real-time, efficiency, convenience, and high sensitivity. This approach can offer feasible insights for establishing a real-time monitoring system for the development of large-scale affordable communities in China in the future.

The Impact of Information Quality, Trust, Anxiety, and Health Consciousness on Intention to Buy Health Supplements in The Post Pandemic Era

The purpose of this study is to analyze the factors affecting consumer’s intention to buy health supplements using the expanding theory of planned behavior model. The variables of perceived information quality, trust, anxiety, health consciousness are integrated in the model of this study. A total of 333 valid questionnaire responses were collected via an online survey, from respondents living in Jakarta and suburb (Jabodetabek) area, Indonesia. The findings indicate that perceived information quality influences trust, anxiety, and health consciousness. Moreover, both of trust and health consciousness influence customer’s attitude. Eventually, the customer’s attitude and subjective norms influence the intentions to buy health supplements. The authors used a sample based on Jabodetabek area that assume they have similar access to perceived information quality. The present study also has not considered educational and economic status of the respondents