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Spatial and temporal assessment of drought hazard over the Krishna River basin of India has been performed using long-term (January 1901–December 2002) precipitation and temperature data. Various meteorological drought indices such as the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Effective Precipitation Evapotranspiration Index and Reconnaissance Drought Index (RDI) have been evaluated on a 12-month timescale for assessment of the drought hazard. Various physical drought characteristics such as the maximum drought magnitude, maximum drought duration and probability of occurrence of droughts, i.e., drought frequency, are also analyzed for the Krishna River basin. Analysis led to identification of major dry periods from the evaluated drought characteristics and generation of spatial maps of magnitude, duration and intensity for each index for each of the dry periods. The socioeconomic aspects of drought have also been explored by analyzing drought hazard, vulnerability and risk which are mapped spatially to evaluate drought susceptibility of various regions in the basin. The study revealed a positive correlation between the maximum drought magnitude, drought duration and drought risk, and an indirect proportionality between drought intensities and drought frequency. The analysis of physical drought characteristics revealed that the RDI deviated significantly from the remainder indices. The importance of socioeconomic variables is also highlighted as districts having normal meteorological conditions became the hotspots of drought risk because of sensitive demographics in the study basin.

Dengue fever, which is spread by Aedes mosquitoes, has claimed many lives in Kerala, with the Thiruvananthapuram district bearing the brunt of the toll. This study aims to demarcate the dengue risk zones in Thiruvananthapuram district using the analytical hierarchy process (AHP) and the fuzzy-AHP (F-AHP) methods. For the risk modelling, geo-environmental factors (normalized difference vegetation index, land surface temperature, topographic wetness index, land use/land cover types, elevation, normalized difference built-up index) and demographic factors (household density, population density) have been utilized. The ArcGIS 10.8 and ERDAS Imagine 8.4 software tools have been used to derive the risk zone maps. The area of the risk maps is classified into five zones. The dengue risk zone maps were validated using dengue case data collected from the Integrated Disease Surveillance Programme portal. From the receiver operating characteristic (ROC) curve analysis and the area under the ROC curve (AUC) values, it is proved that the F-AHP method (AUC value of 0.971) has comparatively more prediction capability than the AHP method (AUC value of 0.954) in demarcating the dengue risk zones. Also, based on the comparison of the risk zone map with actual case data, it was confirmed that around 82.87% of the dengue cases occurred in the very high and high-risk zones, thus proving the efficacy of the model. According to the dengue risk map prepared using the F-AHP model, 9.09% of the area of Thiruvananthapuram district is categorized as very high risk. The prepared dengue risk maps will be helpful for decision-makers, staff with the health, and disaster management departments in adopting effective measures to prevent the risks of dengue spread and thereby minimize loss of life.

PurposeIndia has the second highest percentage of mobile wallet adoption driven by availability of affordable smartphones and Internet. Despite a general interest, studies on its adoption have been scarce. This research assumes that user segments exist, each with their own level of maturity, and addresses the question “Are there segments which can be profiled?” Thus, the objectives of the study are to propose a model that explains the attitude of user segments towards its adoption; identify probable user segments and profile them; examine the importance and performance of constructs which influence attitude within each cluster and recommend ways to improve performance.Design/methodology/approachThis paper employs the constructs from two popular theories on technology adoption, i.e. technology acceptance model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT). A synthesis of review of literature on these models, besides two focus group discussions (FGDs), was used to design a pilot instrument. A nationwide survey was conducted, and 744 responses were obtained. Convenience sampling was used to select the respondents. The average scores of various constructs were computed and subjected to hierarchical clustering. Further, k-means clustering was carried out. The demographic profiling of each cluster was done through cross-tabulation and differences related to attitude and intention between clusters were tracked by one-way Analysis of Variance (ANOVA). To determine the relative importance and performance of constructs within each cluster, Importance-Performance Map Analysis (IPMA) using Smart Partial Least Squares (PLS) was carried out.FindingsThe hierarchical clustering resulted in three clusters. The result of k-means clustering was used to label the clusters as Technology Enthusiasts (TE), Technology Sceptics (TS) and Technology Pragmatists (TP). The obtained clusters were found to differ in terms of perception, attitude, intention, behavior, marital status, education, occupation and income levels. With respect to each cluster, it was seen that the top three important constructs are Perceived Usefulness (PU), Security (SEC) and Lifestyle Compatibility (LC) as indicated by the IPMA. The findings indicate that mobile wallet providers should focus on all six constructs, with special focus on PU, SEC and LC. The findings of this study will help mobile wallet providers in customizing their offerings to enhance adoption attitude in all three clusters.Research limitations/implicationsThis study examines the perception of students and working professional towards mobile wallet adoption and uses this data for segmentation. However, there could be underlying differences between these two groups, as the motive behind adopting a technology may be different. Thus, treating them as homogenous user segments could be a limitation. Therefore, exploring segments and profiles for each type of user may be an area for future research. Mobile wallet providers should also give utmost importance to perceived usefulness, security and lifestyle compatibility while designing their services. This will not only enhance user trust and compatibility with mobile wallet but also improve the outcomes associated with its usage.Practical implicationsThis study will help mobile wallet providers understand the user segments and customize their service offerings.Originality/valueThis study provides a comparison of the respondent profiles of three obtained segments of mobile wallet users. While prior studies have identified segments associated with adoption of technologies like ATM banking, SMS banking, online banking, Internet banking, mobile banking etc., not much has been reported on mobile wallet adoption. To the best of the authors' knowledge, this is a novel study in India, aimed at identifying user clusters among adopters of mobile wallets and developing cluster profiles based on demographic, attitude and intention.

Coastal floods are the most prominent natural disaster causing severe damages to the local communities regarding food security, economy and shelter. Risks can be defined by physiographical sensitivity and vulnerability associated with socio-economic, demographic and infrastructure aspects of the region. Population with poor socio-economic status and high dependence on natural resources for livelihood in coastal dwellings of rural India are extremely vulnerable to flood hazards. Policy formulation to reduce coastal flood risks necessitates quantifying hazard vulnerability at an administrative scale. In this context, we propose a method for evaluating the coastal flood risk of an island located in the habited part of Sundarbans, West Bengal. Extending up to 282 sq. km, Sagar Island has been a keystone in harbouring and supporting both local and migrant population since the 1880s. Land-use classification of the island indicates an increase of 1.7% to 3.6% in the built-up class, almost double in the past eight years (2012–2020). A considerable rise in area under the water bodies is also seen from 6.6 to 8.6%, signifying fair evidence of a coastal breach. Flood risk assessment of Sagar Island was carried out using high spatial resolution data from Indian remote sensing satellites and census data. This assessment was performed by modifying the established MCDA technique considering the data limitations and accounting accessibility to infrastructure as a novel variable to a multi-dimensional framework. The framework maps spatial vulnerability of the region using sub-factors such as socio-demographic, economic, infrastructure and accessibility. The exposure profile of the area is drawn with the help of topographic factors and classified land-use results. Literature evidence was used to develop classification rules for data standardization from very high to very low based on their flood sensitivity. Further, the factors and sub-factors were ranked using AHP by a panel of experts belonging to diverse fields such as disaster management, regional planning, environment, hydrology and social science. The weighted sum technique was used to quantify total vulnerability and exposure parameters, respectively. The total risk map generated is the product of the hazard and vulnerability map of the region. The findings reveal the dominance of economic and accessibility parameters in defining the vulnerability of the regional population towards coastal flood risks. Proximity to coastline and tidal creeks enhances disaster sensitivity due to frequent inundation, erosion, saltwater intrusion and complete submergence of land area. Water bodies engulfing the coastline emerge as a serious threat to sustenance given the present rate of submergence of about 6 m/year. The research highlights the pressing need for grassroots development through social and economic upliftment. It also advocates the undeniable need for proactive adaptation such as flood resilient housing and coastline protection by stabilizing sandbars and planting/nurturing/maintaining native species (mangroves).

Fifty-three percent of Indian under-5 deaths occur during the neonatal age group. Recognizing that there is a lack of illustrated district-level data on neonatal mortality in India, we mapped this to visually highlight districts where neonatal health issues require the most attention. District-level estimates of 596 Indian districts were used to generate maps and to illustrate neonatal mortality rates (NMRs), absolute numbers of neonatal deaths; the best and worst performing districts (positive and negative deviants) in each Indian state; the neonatal female/male death ratio; and district lag in NMR reductions. The NMR ranged from 4.3 (Kannur, Kerala) to 65.1 (Datia, Madhya Pradesh), with the mean NMR being 29.8. Almost two-thirds of the districts ( = 380, 63.7%) had NMRs between 20 and 40. The top third of neonatal deaths could be accounted for by just 71 districts of a total of 596. There is an urgent need for up-to-date data on district-level neonatal mortality in India.

Purpose>The purpose of this paper is to convert real-world raster data into vector format and evaluate loss of accuracy in the conversion process. Open-source Geographic Information System (GIS) is used in this process and system resource utilizations were measured for conversion and accuracy analysis methods. Shape complexity attributes were analyzed in co-relation to the observed conversion errors.Design/methodology/approach>The paper empirically evaluated the challenges and overheads involved in the format conversion algorithms available in open-source GIS with real-world land use and land cover (LULC) map data of India. Across the different LULC categories, geometric errors of varying density were observed in Quantum GIS (QGIS) algorithm. Area extents of original raster data were compared to the vector forms and the shape attributes such as average number of vertices and shape irregularity were evaluated to explore the possible correlation.Findings>The results indicate that Geographic Resources Analysis Support System provides near error-free conversion algorithm. At the same time, the overall time taken for the conversion and the system resource utilizations were optimum as compared to the QGIS algorithm. Higher vector file sizes were generalized and accuracy loss was tested.Research limitations/implications>Complete shape complexity analysis could not be achieved, as the weight factor for the irregularity of the shapes is to be varied based on the demography as well as on the LULC category.Practical implications>Because of the higher system resource requirements of topological checker tool, positional accuracy checks for the converted objects could not be completed.Originality/value>This paper addresses the need of accuracy analysis of real-world spatial data conversions from raster to vector format along with experimental setups challenges and impact of shape complexity.

This study explored the land use and land cover changes (LULCC) during 1981–2006 over central India and their impact on the surface temperature over this region. The land use maps were prepared from the Advanced Very High Resolution Radiometer (AVHRR) datasets to investigate the LULCC during 1981–2006 and the impact of LULCC was investigated from the Observation Minus Reanalysis method. The overall analysis indicated a decrease in the small vegetation lands and open forests during 1981–2006 and an increase in the dry lands, agricultural lands and dense forests during this period. As a probable consequence, the temperature trend increased by 0.076 °C per decade due to the LULCC over central India.

Understanding the genetic origins and demographic history of Indian populations is important both for questions concerning the early settlement of Eurasia and more recent events, including the appearance of Indo-Aryan languages and settled agriculture in the subcontinent. Although there is general agreement that Indian caste and tribal populations share a common late Pleistocene maternal ancestry in India, some studies of the Y-chromosome markers have suggested a recent, substantial incursion from Central or West Eurasia. To investigate the origin of paternal lineages of Indian populations, 936 Y chromosomes, representing 32 tribal and 45 caste groups from all four major linguistic groups of India, were analyzed for 38 single-nucleotide polymorphic markers. Phylogeography of the major Y-chromosomal haplogroups in India, genetic distance, and admixture analyses all indicate that the recent external contribution to Dravidian- and Hindi-speaking caste groups has been low. The sharing of some Y-chromosomal haplogroups between Indian and Central Asian populations is most parsimoniously explained by a deep, common ancestry between the two regions, with diffusion of some Indian-specific lineages northward. The Y-chromosomal data consistently suggest a largely South Asian origin for Indian caste communities and therefore argue against any major influx, from regions north and west of India, of people associated either with the development of agriculture or the spread of the Indo-Aryan language family. The dyadic Y-chromosome composition of Tibeto-Burman speakers of India, however, can be attributed to a recent demographic process, which appears to have absorbed and overlain populations who previously spoke Austro-Asiatic languages.

Rivers are crucial components of human civilization, as they provide water for domestic, agricultural, and industrial use. Additionally, they transport domestic and industrial waste to the sea. The Ganga River is a major river in India, originating from Gangotri in the north, flowing through five provinces, and discharging into the Bay of Bengal. This study examined the impact of land use and land cover changes (LULC) on water quality along the River Hooghly in India. The research involved collecting water samples from different locations and analyzing them in the laboratory to estimate various parameters. The findings indicate that the expansion of built-up and agricultural lands is causing a reduction in tree cover and water bodies, leading to deteriorating water quality. The study highlights the need for sustainable land use practices and improved water management to preserve the river’s ecosystem and maintain water quality. Specifically, the study identified localities in the vicinity of Dakshineshwar, Shibpur, and Garden Reach as particularly vulnerable to water quality deterioration due to LULC changes and population growth. The study’s results provide valuable insights for policymakers and stakeholders in implementing strategies to address the challenges posed by land use changes and population growth.