Population Density Index Research Articles

Microbial communities and high trophic level nematodes in protected argan soil show strong suppressive effect against Meloidogyne spp.

In many agroecosystems, soil suppressiveness to root knot nematodes (RKN) is of great interest in defining the biological agents controlling population density, especially with growing concerns about the environmental and human health impacts of chemicals. In this study, we evaluated the suppressiveness to Meloidogyne spp. in two land use soils: a protected argan soil from the Souss Massa National Park (SMNP), Morocco, and a conventional soil from an adjacent non-protected area. Using next-generation sequencing (NGS) technology, we characterized the fungal and bacterial communities in these soils for the first time. Nematodes belonging to different trophic guilds were also analyzed to further understand ecological factors that enable suppressive organisms to function and persist in the soil. The experiment was conducted in a greenhouse with tomato plants grown in untreated and autoclaved soils, each inoculated with 800 Meloidogyne infective juveniles (J2). We found that omnivore-predator nematode abundance, structure, and maturity indices were higher in the protected soil indicating the presence of a well-structured soil food web and a stable ecosystem. After sixty-seven days, RKN population density and gall index were reduced by 79.6 % and 81.5 % in the protected soil, respectively. 50 % autoclaved protected soil was also suppressive, but not 75 % and totally autoclaved soil. In contrast, conventional soil amplified the RKN population by 1319 %. More than 6770 bacterial and 558 fungal taxa were detected in this study, with Firmicutes, Actinobacteria, Mortierellales, Orbiliales, Agaricales, Diversisporales, and Pleosporales being consistently associated with RKN suppression. We found that fungal diversity was higher in SMNP-protected soil. In conclusion, soil protection enhances soil ecosystem resilience and that protected argan soils can serve as a valuable source of biological agents for the sustainable management of phytoparasitic nematodes.

Analysing Population Geographical Distribution and their Density in Irbid Governorate by Using Geographical Information Systms

This study aims to determine the patterns of population distribution in Irbid Governorate, in the light of the results of the general population censuses for the years (2004,2015), and to indicate the extent of concentration and regularity in the population distribution at the level of the governorate and its administrative districts. The study relied on a number of statistical methods to measure population concentrations in Irbid governorate, and to identify patterns of population distribution, to know the extent of concentration of the spatial phenomenon and the geographical factors influencing it. and the geographical factors affecting it, where population density indicators, the Lorenz gauge, and the concentration index were used to reach the objectives of the study. The research found that the districts of Irbid Governorate are characterized by low population density, with the exception of Al-Qasbah District and Bani Ubaid District, which are characterized by a high general population density. It was also found that the population distribution in all districts, except for Al-Qasbah District and Bani Ubaid District was closer to equality than to concentration. In addition, the natural environment and the multiplicity of natural regions play a prominent role in the distribution of population in the districts of Irbid Governorate. The study recommended the need to take population distribution into account when setting population plans and policies, and the various development programs for Irbid Governorate.

Potential distribution of three invasive agricultural pests in China under climate change

Invasive pests reduce biodiversity and ecosystem service functions, thereby leading to economic and also agricultural losses. Banana skipper (Erionota torus Evans), red palm weevil (Rhynchophorus ferrugineus), and coconut caterpillar (Opisina arenosella Walker) are invasive insect pests in the palm-growing regions and they have had serious consequences for the planting of bananas (Musa nana), palms (Trachycarpus fortune) and coconut (Cocos nucifera). Based on screened occurrence data, the present research utilized Maximum Entropy model (Maxent) to simulate the distribution dynamics of these three invasive insects in China, under current and future climate (2050s, 2070s, 2090s) in two shared socio-economic pathways (SSPs: 126 and 585) of the newly released coupled model intercomparison project phase6 (CMIP6). The results show that: (1) Under current and future climate conditions, all model groups exhibited an AUC value exceeding 0.92, which shows that the model prediction results are very good;(2) The suitable habitat area of E. torus Evans remains relatively stable with some expansion in the SSP126 of 2090s and some contraction in the SSP585 of 2090s. The suitable habitat area of R. ferrugineus showed an overall contraction, with substantial contraction in the SSP585 of 2090s.The suitable habitat area of O. arenosella has an overall expansion, with the most pronounced expansion in the SSP585 of 2070s; (3) The current centroid of suitable habitats for R. ferrugineus and E. torus Evans is located in Guangxi Province and wholely shift toward the south direction under future climate. The centroid of suitable habitats for O. arenosella is currently located in the northeastern maritime area of Hainan Province and will shift toward the north direction under future climate; (4) Temperature, precipitation and Human disturbance factors (Population density and Human influence index) were crucial variables for describing the distribution of the three species. For E. torus Evans in particular, percentage contributions of Population density was up to 31.4, which is only 0.1 different from ranked first Bio19 (Precipitation of the coldest quarter). The dynamics of habitats of these three species and the correlating driver factors proposed in this work provide essential insights into future spatial management of the three invasive insects in China. Our work is necessary and timely in identifying newly areas at high risk of expansion of the three invasive insects in the future, then suggesting strategic control measures to prevent their spread, and finally providing scientific evidence for the early prevention and rapid response to the three invasive insects.

Spatiotemporal Pattern and Drivers of Ecological Quality in Inner Mongolia

With the escalating global climate change and frequent human activities, Inner Mongolia, as a crucial ecological barrier in the Beijing-Tianjin-Hebei region, Bohai Economic Rim, and even the whole country, confronts many ecosystem issues. To reveal the intricate interplay between natural processes, human activities, and policy interventions shaping the region’s ecological landscape, we provide a comprehensive analysis of ecological quality in Inner Mongolia from 2002 to 2021. Key findings include: (1) The research uncovers an overall improvement in ecological quality across Inner Mongolia, albeit with certain regions exhibiting a pattern of deterioration followed by improvement, particularly notable in the central part of the province. (2) Cropland, shrub, grassland, barren, wetland, and water are decreasing, while impervious, and forestland are increasing. (3) Precipitation, temperature, and elevation are significant drivers affecting the quality of the ecological environment. Interestingly, while human production and activities have altered ecological conditions, factors like GDP, population density, and night lighting index do not directly impact environmental quality significantly. This underscores the intricate interplay among various factors within the socioeconomic system, highlighting its complexity. (4) Based on these findings, our policy recommendations include strengthening ecological monitoring and management, promoting sustainable urban development, conducting public education and awareness campaigns, and fostering stakeholder collaboration for data sharing and collective understanding. Adopting sustainable management strategies is essential for promoting ecological resilience and mitigating environmental degradation in Inner Mongolia, not only benefiting the local ecosystem but also contributing to national and global environmental well-being.

Assessment of Eco-Environmental Vulnerability Using Remote Sensing and GIS Tools in Maharashtra Region, India

Maharashtra region is prone to various disasters such as drought, floods, cyclones and earthquake and has been exposed to extreme weather events like dry spells. Communities within these dry lands are poor and face extreme conditions of water stress. This study has been carried out to analyze and quantify climatic and anthropogenic effect on eco-environmental vulnerability dynamic change. To achieve that a numerical model is set up, consisting of eight factors that are elevation, land use, drought, slope, NDVI, soil-type, soil erosion (water), and population density index & has been evaluated using the method of spatial principle component analysis (SPCA) on Remote Sensing and GIS platform. The integrated eco-environmental vulnerability index (EVI) of study area is estimated to analyse spatial-temporal dynamic vulnerability changes in the 11 years gap from 2000 and 2011. The results show that the study area has become eco-environmental vulnerable slightly (about 80% of the region) with an increased eco-environmental vulnerability integrated index (EVSI) value by more than 50% (i.e., about 74%) and the driving force of dynamic change is mainly caused by socio-economic activities. In addition the estimation has been regionalized into thirty-four districts to serve as a base for decision-making for eco-environmental recovering and rebuilding. It is found that the most vulnerable district in 2011 is Ratnagiri and the least one is Sangli. There are nine districts which shows more than 100% increase in EVSI value, with the highest increase in Hingoli(100.65%), indicating that the districts have become most environmental vulnerable in the study-period. The research concludes that the method, supported by G.I.S using SPCA can’t only represent distinctly the input spatial distribution of plain-mountain-belt feature, but also respect the whole river-valley as a single unit.

Disproportionate exposure to urban heat island intensity – The case study of Győr, Hungary

Extensive research has shown that urbanisation has a profound effect on the local climate system, leading to the formation of urban heat island. Exposure to urban heat islands poses a major health risk, and there is a growing body of literature recognising that urban population groups with particular demographic characteristics living in specific types of residential environments are disproportionately affected. By combining surface urban heat island data from the Global Surface Urban Heat Island Explorer with neighbourhood-level data on demographics and the type of housing, this study assesses disproportionate exposure to surface urban heat island intensity in the city of Győr, Hungary. Results of the study highlight the importance of targeted interventions for environmental justice, especially in areas characterised by housing estates, high population density and high ageing index.

The sustainability of development pathways and climate change vulnerability in the Americas

Sustainability transformation requires societies to shift their development pathways to maintain ecological integrity and supply basic human needs over generations. We examine the sustainability of the development pathways of 351 national and sub-national geopolitical units in the Americas between 2001 and 2020. Then, we test the hypothesis that such pathways are influenced by land area, population size and topographic heterogeneity. Finally, we combine information about the sustainability of development pathways with population density and future climate change index to identify the Americas' socio-climatic hotspots. The Americas is a mosaic of geopolitical units following distinct development pathways. Most (74.3%) of the geopolitical units have, to date, followed unsustainable development pathways. Altogether, they represent 37.6% of the Americas' land area and are home to 83.3% of the Americas' population. Land area and topographic heterogeneity facilitate sustainable pathways, while population size constrains them. Climate change will affect all geopolitical units, but those with unsustainable development pathways, a high population density and high climate change index are expected to be particularly vulnerable and should be considered priorities for climate-resilient development programmes. There is a need for integrated place-based policies that strengthen ecological and socio-economic infrastructures to advance sustainable development pathways under changing climate conditions.

Prediction of the size and spatial distribution of free-roaming dog populations in urban areas of Nepal

A factor constraining the elimination of dog-mediated human rabies is limited information on the size and spatial distribution of free-roaming dog populations (FRDPs). The aim of this study was to develop a statistical model to predict the size of free-roaming dog populations and the spatial distribution of free-roaming dogs in urban areas of Nepal, based on real-world dog census data from the Himalayan Animal Rescue Trust (HART) and Animal Nepal. Candidate explanatory variables included proximity to roads, building density, specific building types, human population density and normalised difference vegetation index (NDVI). A multivariable Poisson point process model was developed to estimate dog population size in four study locations in urban Nepal, with building density and distance from nearest retail food establishment or lodgings as explanatory variables. The proposed model accurately predicted, within a 95 % confidence interval, the surveyed FRDP size and spatial distribution for all four study locations. This model is proposed for further testing and refinement in other locations as a decision-support tool alongside observational dog population size estimates, to inform dog health and public health initiatives including rabies elimination efforts to support the ‘zero by 30′ global mission.

Contribution of infectious diseases to the selection of ADH1B and ALDH2 gene variants in Asian populations.

The gene variants ADH1B*2 (Arg48His, rs1229984) and ALDH2*2 (Glu504Lys, rs671) are common in East Asian populations but rare in other populations. We propose that selective pressures from pathogen exposure and dietary changes during the neolithic transition favored these variants. Thus, their current association with differences in alcohol sensitivity likely results from phenotypic plasticity rather than direct natural selection. Samples sourced from the Allele Frequency Database (ALFRED) were utilized to compute the average frequency of ADH1B*2 and ALDH2*2 across 88 and 61 countries, respectively. Following computation of the average national allele frequencies, we tested the significance of their correlations with ecological variables. Subsequently, we subjected them to Principal Component Analysis (PCA) and Elastic Net regularization. For comprehensive evaluation, we collected individual-level phenotypic associations, compiling a Phenome-Wide Association Study (PheWAS) spanning multiple ethnicities. Following multiple testing correction, ADH1B*2 displayed significant correlations with Neolithic transition timing (r = 0.405, p.adj = 2.013e-03, n = 57) and historical trypanosome burden (r = -0.418, p.adj = 0.013, n = 57). The first two components of PCA explained 47.7% of the total variability across countries, with the top three contributors being the historical indices of population density and trypanosome and leprosy burdens. Historical burdens of the Mycobacteria tuberculosis and leprosy were the sole predictive variables with positive coefficients that survived Elastic Net regularization. Our analyses suggest that Mycobacteria may have played a role in the joint selection of ADH1B*2 and ALDH2*2, expanding the "toxic aldehyde hypothesis" to include Mycobacterium leprae. Additionally, our hypothesis, linked to dietary shifts from rice domestication, emphasizes nutritional deficiencies as a key element in the selective pressure exerted by Mycobacteria. This offers a plausible explanation for the high frequency of ADH1B*2 and ALDH2*2 in Asian populations.

An environmental perspective of energy consumption, overpopulation, and human capital barriers in South Asia

Prior literature is substantive in highlighting the nexus between pollutant and socio-economic predictors; however, the role of human interaction has not been sufficiently explored. Thus, the present study examines the validity of the environmental Kuznets Curve (EKC) hypothesis in the presence of energy consumption, overpopulation, and human capital index in five South Asian countries. It employs fixed effects, random effects, and dynamic panel causality techniques with a set of panel data from 1972 to 2021. The baseline results validate the existence of the EKC hypothesis in the recipient panel. Nevertheless, the findings reveal that energy consumption and population density have positive effects, while human capital has negative impacts on CO2 emissions. Furthermore, the study observes that energy consumption and per capita GDP have a significant causal link with CO2 emissions, whereas CO2 emissions are evident to have causality with population density and human capital index. The results are robust and suggest that the consolidation of an effective regulatory framework and technological improvements are substantial measures to improve environmental quality in South Asia. Moreover, allocating sufficient resources to uplift contemporary educational and health status would be imperative to improving environmental quality as aspired to by the Paris Agreement.

ENVIRONMENTAL FACTORS’ INFLUENCE ON THE DIAMONDBACK MOTH (PLUTELLA XYLOSTELLA L.) POPULATION DYNAMICS ON CRUCIFEROUS CROPS

The diamondback moth (Plutella xylostella L.) (Lepidoptera: Plutellidae) is one of the most severe pests for cruciferous crops. Its widespread distribution and the ability to submissive migration led to an unexpected enhancement in its population, thereby obscuring the control measures aimed at this phytophagous. The presented study sought to determine the expansion of diamondback moth in cruciferous crops (rapeseed, mustard) in two agroclimatic zones of Northern Kazakhstan (Zone I: moderately humid and warm; Zone II: slightly humid and moderately warm). Analysis of population dynamics ran for the long-term during 2012–2022 based on bioecological patterns of the diamondback moth and factors affecting them. Numerical variables of population density and abundance indices served as diagnostic predictors for characterizing the phase state of the pest in a given period and, therefore, recognizing the population dynamics pattern of the diamondback moth. Based on the results, the biological regularity of the stage inception of the phytophagous abundance dynamics does not always persevere, and variations occur with the sway of environmental factors. Hence, the abundance dynamics of diamondback moth depend upon the weather conditions of the previous and current year, as well as, on the accomplishment of the extent of chemical treatment and its regulations. The obtained data can be criteria for predicting the phase state of diamondback moth populations in the agroclimatic zones of northern Kazakhstan to justify and plan protective measures, as well as, to improve phytosanitary control.

The relationship between graduation school background and geographical distribution of dentists in Taiwan

Background/purposeTaiwan’s eight dental schools are all located in the western region. This study attempted to analyze the relationship between the geographical distribution of dentists in Taiwan and their graduation school background and to develop several statistical indicators to analyze the geographical distribution of dentists. Materials and methodsThe method of the secondary data analysis was adopted to collect the open information related to the number of the practicing dentists based on their graduation schools and practice locations by cities and counties in Taiwan in January 2023. The data were used to develop several statistical indicators to analyze the geographical distribution of dentists. ResultsThere were 16,533 practicing dentists in Taiwan in January 2023. Of the 16,533 dentists, 15,687 (94.88 %) graduated from domestic dental schools and 846 (5.12 %) graduated from foreign dental schools. For the school location, the coefficient of variation, Gini coefficient, population proportion index, and population density index were the largest (168.57 %, 0.47, 10.76 %, and 3927.01 people/square kilometer) in the dentists graduating from the northern dental schools. In overall, the dentists were concentrated in municipalities, cities, and the northern region of Taiwan, especially those graduating from the northern dental schools. ConclusionIn Taiwan, the geographical distribution of dentists is highly related to their graduation school background. Promoting a balanced distribution of dental enrollment quotas and dental student sources from different geographical locations and inducing the movement of dentists to the dentist-shortage areas may be the more feasible ways to solve the uneven geographical distribution of dentists in Taiwan.

The recent burden of dengue infection in Bangladesh: A serious public health issue

Dengue is a rapidly increasing vector-borne disease with a high burden in South and Southeast Asia. This article presents an overview of the current dengue situation in Bangladesh, highlighting the critical public health challenges caused by this infectious disease. Between January and September 2023, a total of 203406 people were infected, and 989 people died, with a case fatality rate of 0.49%. Of these, 96.1% of infections and 95.2% of deaths occurred between July and September. Both infection and mortality rates showed a significant and positive correlation with population density and air quality index. Other environmental and socioeconomic factors may influence the burden of dengue infection. These include temperature, rainfall, humidity, unplanned urbanization, and water storage and waste management practices. To reduce transmission and mortality rates, it is urgent to prioritize early treatment of dengue cases and take measures to address the risk factors associated with dengue infection in the country.

Towards safer streets: A framework for unveiling pedestrians’ perceived road safety using street view imagery

Road safety has become a global concern but its impact in low- and middle-income countries is widespread mainly due to lack of appropriate crash database system and under-reporting. In this context, the primary objective of this paper is to provide a scalable framework for unveiling pedestrians’ perceived road safety that can also be applied in regions where accessible crash data are limited or near-crashes are left unreported. In the first step of our methodology, a deep learning architecture-based semantic segmentation model (HRNet+OCR) is trained using labeled Google Street View (GSV) images from specific study areas in Dhaka, Bangladesh, which facilitates the identification of both man-made components (such as roads, sidewalks, buildings, and vehicles) and natural elements (including trees and sky). The developed model showed excellent performance in identifying different features in an image by achieving high precision (0.95), recall (0.97), F1-score (0.96), and intersection over union (IoU) (91.86). Secondly, a group of trained raters scored the perceived road safety on an ordinal scale from 0 to 10 (extremely unsafe to extremely safe to walk in terms of road crashes) by assessing the GSV images. Then, several regression models have been used on features extracted from GSV images, and socio-demographic factors (i.e., population density, and relative wealth index) to estimate the perceived road safety, and random forest regression model was found to perform the best. Further, Shapley Additive Explanations (SHAP), a model-agnostic technique has been used for examining feature importance by computing the contribution of each feature to the random forest regression model output. The results show that sidewalk, road, population density, wall, and relative wealth index have higher impact on determining the perceived road safety rating. Additionally, the results of t-tests between the average perceived road safety scores for crash-prone and non crash-prone areas revealed the existence of significant differences. This study also provides perceived road safety rating map on a neighborhood scale, which can be a useful visualization tool for policy-makers and practitioners to identify the road safety deficiencies at specific locations, and formulate appropriate and strategic countermeasures to improve pedestrians’ road safety.

Borders, cross-border cooperation and depopulation: the case of the Spanish-Portuguese North-Central Border.

Cross-border cooperation has been (and still is) one of the main assets of the acquis communitiare in everything that refers to cross-border development. In fact, development initiatives aimed at addressing the problems of intra-European border areas could have better results if they were concerted (horizontally coordinated) and executed jointly by the governments, national, and local actors from both border sides, to improve their populations' quality of life. The study goal is to provide new contributions that enrich critical reflection and debate on the issue of depopulation, in order to propose economic policy recommendations within a framework of cross-border institutional cooperation and inter- and intra-governmental coordination of political actors involved. We prepared a descriptive analysis of its evolution during the period 1960-2020, with three essential variables: a) population evolution border based on the losses or gains verified with respect to 1960; b) critical population mass existing in 2020 based on the indicator of population density (inhabitants per square kilometer), and c) level of population aging based on the indicator percentage of population over 65 years of age over total population. Our descriptive analysis exclusively covers the border municipalities located along the both sides of the Spanish-Portuguese north-central border line, a total of 49 border municipalities (nine Portuguese and 40 spanish). The results show the existence of this recessive demographic continuum, which extends to both sides of the border, causes the need to face the depopulating phenomenon from a transnational and European perspective, in the sense that 'national' solutions to the phenomenon will not be able to offer answers common and consistent to a phenomenon as complex as depopulation. It is necessary to reinforce the existing cross-border cooperation, providing it with greater operability at the level of institutional and administrative coordination, deepening the multilevel governance of the territory.

Spatiotemporal variation and driving factor of vegetation coverage from 2000 to 2020 in southern Jiangxi Province, China.

Vegetation plays a critical role in the water and carbon cycling and energy flow, serving as an indicator for regulating land carbon balance and reflecting climate change and human activities. We analyzed the spatiotemporal variations of normalized difference vegetation index (NDVI) during the growing season in southern Jiangxi from 2000 to 2020, using statistical methods, including the Mann-Kendall test, Theil-Sen Median analysis, Hurst index, and coefficient of variation. We employed the geodetector model to comprehensively assess the impacts of climate, topography, soil and human factors on spatial differentiation of vegetation NDVI. The results showed NDVI exhibited an upward fluctuating trend with a rate of 0.003 per year from 2000 to 2020. The proportion of high-grade and medium-high-grade NDVI areas were 55.8% and 41.9%, respectively, while the areas with low and relatively low fluctuations accounted for 92.3%. The proportions of areas showing extremely significant improvement and significant improvement were 40.4% and 19.4%, respectively. In contrast, the combined proportion of areas displaying extremely significant degradation and significant degradation was only 2.2%. The proportions of areas demonstrating continuous improvement and future improvement were 28.0% and 60.2%, respectively. Elevation, precipitation, relative humidity, temperature, landform type, land use type, population density, and nighttime light were identified as the major factors for the vairations of NDVI in the study area, followed by slope, soil type, and GDP, while slope aspect and vegetation type had indirect influence. Throughout the study period, NDVI in southern Jiangxi was overall stable, with future changes primarily indicating improvement. Notably, human factors such as land use type, population density, and nighttime light index exhibited an upward trend in their impacts on NDVI.

A tale of 141 municipalities: the spatial distribution of dengue in Mato Grosso, Brazil.

In recent years, the state of Mato Grosso has presented one of the highest dengue incidence rates in Brazil. The meeting of the Amazon, Cerrado and Pantanal biomes results in a large variation of rainfall and temperature across different regions of the state. In addition, Mato Grosso has been undergoing intense urban growth since the 1970s, mainly due to the colonization of the Mid-North and North regions. We analyzed factors involved in dengue incidence in Mato Grosso from 2008 to 2019. The Moran Global Index was used to assess spatial autocorrelation of dengue incidence using explanatory variables such as temperature, precipitation, deforestation, population density and municipal development index. Areas at risk of dengue were grouped by the Local Moran Indicator. We noticed that areas at risk of dengue expanded from the Mid-North region to the North; the same pattern occurred from the Southeast to the Northeast; the South region remained at low-risk levels. The increase in incidence was influenced by precipitation, deforestation and the municipal development index. The identification of risk areas for dengue in space and time enables public health authorities to focus their control and prevention efforts, reducing infestation and the potential impact of dengue in the human population.

Distribution, Population Density, and Behavior of Dwarf Galagos (Paragalago sp.) in Taita Hills, Kenya

AbstractA good understanding of taxonomy, distribution, and population density is needed for conservation. Scientific knowledge of dwarf galagos (Paragalago sp.) remains limited. Two populations of Taita dwarf galagos survive in the largest remnants of moist montane forest in Taita Hills, Kenya. We obtained acoustic data on the two remaining populations using AudioMoths—small, passive recorders—more than 231 h in January and February 2021. We measured forest structure by using airborne LiDAR and ground-level observations and tested the influence of forest size and height, canopy coverage, and density on calls per hour as an indicator of population density. Forest size significantly affected population size. Taita dwarf galagos prefer dense canopy coverage, and they prefer the lower 20–30-m tall forest. Mbololo forest (185 ha) has steady population, whereas dwarf galagos are nearly extinct in Ngangao forest (120 ha). The calls of Taita dwarf galagos resemble those of Kenya coast dwarf galagos (Paragalago cocos). DNA studies are urgently needed to resolve the taxonomic status of both surviving populations of dwarf galagos in the Taita Hills. This simple study design should be conducted across African forests to obtain crucial knowledge for galago conservation.

Precipitation-based climate change hotspots across India through a Multi-model assessment from CMIP6

This study presents an analysis to identify precipitation-based climate change hotspots and key-vulnerable cities using multi-model, multi-scenario, high-resolution (0.25°×0.25°), bias-corrected precipitation dataset across India from 14 state-of-the-art General Circulation Models (GCMs), participating in Coupled Model Intercomparison Project version 6 (CMIP6). Preliminary analysis indicates an overall wetter future across India with 290 ± 150 mm to 530 ± 260 mm increase in mean annual precipitation towards end of this century under various climate change scenarios. Apart from the mean precipitation, the extremes are also found to be increasing by alarmingly higher rates. However, the spatio-temporal variations of such increments are notably diverse over different seasons in a year and across different Homogeneous Precipitation Zones (HPZs) in the country. Therefore, a new and more inclusive index, named as Precipitation-based Hotspot Index (PHI), is developed to identify the ‘precipitation-based hotspots’, i.e. the places with most pronounced changes in precipitation characteristics. The PHI considers the changes in various aspects of precipitation, such as mean, variability, and characteristics of extremes (magnitude, frequency and intensity). Based on the PHI values in the far-future period (2061–2100) and for the worst climate change scenario, the hotspot regions are identified mostly in the northwest, west-central, west coast, and northeast parts of India. Further, considering two important socio-economic vulnerability factors (population density and human development index) along with PHI, the key-vulnerable tier-I cities are identified across India. The analysis reveals four out of ten tier-I cities will be highly vulnerable towards end of this century. The findings of this analysis (hotspot maps) and the data products (high-resolution, bias-corrected precipitation dataset) are expected to be highly beneficial for impact assessments, hydrologic modelling, and formulating suitable adaptation and mitigation strategies for India over future.

Analysis of The Effect of Population Density, Unemployment, Minimum Wage, and Human Development Index (IPM) on Criminality in North Sumatra

The study aims to analyze the effect of population density, unemployment, minimum wage, inequality and human development index on crime in North Sumatra province. The type of research used in this study was descriptive research using quantitative research methods. The type of data used in this research is secondary data. In this study, Microsoft Excel Home and Student 2021 and Eviews 12 Student were used for data processing and hypothesis testing. The result of this study is that population density and Human Development Index has a positive and significant effect on crime. Unemployment has a positive and insignificant effect on crime. The Minimum Wage have a negative and insignificant effect on crime, and then Population Density, Unemployment, Minimum Wage, and Human Development Index together have an effect on crime in North Sumatra Province.