Population Density Map Research Articles

Unravelling flood risk in the Rel River watershed, Gujarat using coupled earth observations, multi criteria decision making and Google Earth Engine

Socioeconomic developments, ineffective drainage systems, and insufficient river control, all contribute to significant loss of property and life due to the constant threat of floods. Therefore, controlling flood threats across Rel River, Dhanera, Gujarat has become even more crucial due to floods causing strain throughout the area during monsoon season. The different 52 micro-watersheds were formed across the study region using earth observations for the estimation of flood hazards, vulnerability, and risk. The AHP-MCDM was employed to assign priority rank, weightage, and risk category for each micro watershed. The flood hazard zone was mapped and its vulnerability was characterized in different categories varying from very low to very high. The normalized weights of each factor i.e, hazard indicator (soil, elevation, slope, flow accumulation, rainfall) and vulnerability indicator (LULC, distance from the hospital, population density map) were estimated employing the AHP-MCDM technique whereas LULC along with most of other factors were derived from GEE. The integration of vulnerability and hazard indicators, provides insights into understanding the flood sensitivity, facilitating the preparation of the flood risk map. 20 micro-watersheds were susceptible to high to very high risk and covered an area of 213.15 km2 whereas 32 micro-watersheds were in the range of very low to moderate category which covers the area of 228.41 km2. Therefore, the integration of GEE and spectral indices for obtaining various hazard & vulnerability indicators, which were prioritized and ranked using AHP is a unique methodology, facilitating a robust evaluation of flood risk mapping.

Estimation of spatial demographic maps from polymorphism data using a neural network.

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity-by-descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN.

Estimation of spatial demographic maps from polymorphism data using a neural network.

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN .

Population health implications of exposure to pervasive military aircraft noise pollution.

While the adverse health effects of civil aircraft noise are relatively well studied, impacts associated with more intense and intermittent noise from military aviation have been rarely assessed. In recent years, increased training at Naval Air Station Whidbey Island, USA has raised concerns regarding the public health and well-being implications of noise from military aviation. This study assessed the public health risks of military aircraft noise by developing a systematic workflow that uses acoustic and aircraft operations data to map noise exposure and predict health outcomes at the population scale. Acoustic data encompassing seven years of monitoring efforts were integrated with flight operations data for 2020-2021 and a Department of Defense noise simulation model to characterize the noise regime. The model produced contours for day-night, nighttime, and 24-h average levels, which were validated by field monitoring and mapped to yield the estimated noise burden. Established thresholds and exposure-response relationships were used to predict the population subject to potential noise-related health effects, including annoyance, sleep disturbance, hearing impairment, and delays in childhood learning. Over 74,000 people within the area of aircraft noise exposure were at risk of adverse health effects. Of those exposed, substantial numbers were estimated to be highly annoyed and highly sleep disturbed, and several schools were exposed to levels that place them at risk of delay in childhood learning. Noise in some areas exceeded thresholds established by federal regulations for public health, residential land use and noise mitigation action, as well as the ranges of established exposure-response relationships. This study quantified the extensive spatial scale and population health burden of noise from military aviation.We employed a novel GIS-based workflow for relating mapped distributions of aircraft noise exposure to a suite of public health outcomes by integrating acoustic monitoring and simulation data with a dasymetric population density map. This approach enables the evaluation of population health impacts due to past, current, and future proposed military operations. Moreover, itcanbemodified for application to other environmental noise sources and offers an improved open-sourcetool to assess the population health implications of environmental noise exposure, inform at-risk communities, and guide efforts in noise mitigation and policy governing noise legislation, urban planning, and land use.

Prediction of viral spillover risk based on the mass action principle

Infectious zoonotic disease emergence, through spillover events, is of global concern and has the potential to cause significant harm to society, as recently demonstrated by COVID-19. More than 70% of the 400 infectious diseases that emerged in the past five decades have a zoonotic origin, including all recent pandemics. There have been several approaches used to predict the risk of spillover through some of the known or suspected infectious disease emergence drivers, largely using correlative approaches. Here, we predict the spatial distribution of spillover risk by approximating general transmission through animal and human interactions. These mass action interactions are approximated through the multiplication of the spatial distribution of zoonotic virus diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both virus diversity and human population density are high, it should be noted that this is primarily a conceptual exercise. We compared our spillover risk map to key factors, including the model inputs of zoonotic virus diversity estimate map, human population density map, and the spatial distribution of species richness. Despite the limitations of this approach, this viral spillover map is a step towards developing a more comprehensive spillover risk prediction system to inform global monitoring.

Cost estimation for the monitoring instrumentation of landslide early warning systems

Abstract. Landslides are socio-natural hazards. In Colombia, for example, these are the most frequent hazards. The interplay of climate change and the mostly informal growth of cities in landslide-prone areas increases the associated risks. Landslide early warning systems (LEWSs) are essential for disaster risk reduction, but the monitoring component is often based on expensive sensor systems. This study presents a data-driven approach to localize landslide-prone areas suitable for low-cost and easy-to-use LEWS instrumentation, as well as to estimate the associated costs. The approach is exemplified in the landslide-prone city of Medellín, Colombia. A workflow that enables decision-makers to balance financial costs and the potential to protect exposed populations is introduced. To achieve this, city-level landslide susceptibility is mapped using data on hazard levels, landslide inventories, geological and topographic factors, and a random forest model. Then, the landslide susceptibility map is combined with a population density map to identify highly exposed areas. Subsequently, a cost function is defined to estimate the cost of LEWS monitoring sensors at the selected sites, using lessons learned from a pilot LEWS in Bello Oriente, a neighbourhood in Medellín. This study estimates that LEWS monitoring sensors could be installed in several landslide-prone areas with a budget ranging from EUR 5 to EUR 41 per person (roughly COP 23 000 to 209 000), improving the resilience of over 190 000 exposed individuals, 81 % of whom are located in precarious neighbourhoods; thus, the systems would particularly reduce the risks of a social group of very high vulnerability. The synopsis of all information allows us to provide recommendations for stakeholders on where to proceed with LEWS instrumentation. These are based on five different cost-effectiveness scenarios. This approach enables decision-makers to prioritize LEWS deployment to protect exposed populations while balancing the financial costs, particularly for those in precarious neighbourhoods. Finally, the limitations, challenges, and opportunities for the successful implementation of a LEWS are discussed.

Дифференциация плотности населения в пределах крупного города для выявления уровня антропогенной нагрузки (на примере Белгорода)

Residential areas occupy a special place in the planning structure of the city. The building density and the number of floors of houses have an impact on the architectural appearance of the city and the concentration of the population. However, the high population density indicates a significant pressure on the natural framework. The main objective of this article is to show the differences and heterogeneity of population density within the residential area of the city of Belgorod. To accomplish this task, a methodology for determining the population density in the private sector and blocks of high-rise buildings was developed. Using statistical data, the population of all apartment buildings is plotted on the map of the city, then, the city is divided into polygons that make up the residential area. The article proposes a methodological approach that allows taking into account the heterogeneity of the residential area – the private sector and high-rise buildings. The availability of statistical information on the number of registered residents of apartment buildings and the average size of a household made it possible to complete the task with a high percentage of accuracy. The results of this study do not take into account the migration of temporarily registered residents,students from othercities, etc. The population ofthecity, determined duringthestudy,was 333,692 people,which is lessthantheresult of the 2020 census (339,978 people); the accuracy of the study is 98.15%. More likely, this inaccuracy is due to the lack (or untimely updating) of the statistical information on new buildings. As a result, maps of population density and population distribution were constructed. The city is divided into areas that stand out due to the heterogeneity of the urban environment and the dominant mode of settlement. Through the analysis of the cartographic material, the features of the concentration and resettlement of the population were revealed. The approximate number of the population of the private sector and the main areas of settlement has been calculated.

Geographical Distribution of Neurosurgeons and Emergency Neurosurgical Services in Pakistan

According to the World Federation of Neurosurgical Societies (WFNS), a minimum neurosurgery workforce density should be 1 per 200,000 population for optimum access to neurosurgical care. Pakistan lags behind in the number of neurosurgeons, and disproportionate geographical distribution further increases disparity. Our objective was to geographically map the density of neurosurgeons and emergency neurosurgical services (ENS) in Pakistan. This survey was circulated among 307 neurosurgeons. Data were analyzed using SPSS v21. The number of neurosurgeons and ENS were plotted on the population density map using ArcGIS Pro 3.0.0 software. Three hundred and seven neurosurgeons working at 74 centers responded to our survey (93.3% coverage). The current density of neurosurgeons in Pakistan is 0.14/100,000. The 2 more populous provinces, Punjab and Sindh, have 42.3% (130) and 35.8% (110) neurosurgeons, respectively. They also housed nearly 3 quarters of all the neurosurgery centers in urban districts. Karachi and Lahore accommodate 135 (44%) of all the country's neurosurgeons, having 0.29 and 0.51 neurosurgeons/100,000 respectively. Management of traumatic brain injury is offered at 65 centers (87.8%). Nearly all centers are equipped with computed tomography (CT) scan machine (74; 97%), but magnetic resonance imaging (MRI) facility is available at 55 (72%) centers and 37 (49%) centers have angiography suites. Sixty nine centers (93.2%) have C-arm fluoroscopes available. The geographical mapping of neurosurgeons and neurosurgical facilities is highly skewed towards urban centers, increasing disparity in access to timely neurosurgical emergency services. Four times more neurosurgeons are required in Pakistan to bridge the gap in neurosurgical workforce.

On Farmland and Floodplains—Modeling Urban Growth Impacts Based on Global Population Scenarios in Pune, India

Emerging megacities in the global south face unprecedented transformation dynamics, manifested in rapid demographic, economic, and physical growth. Anticipating the associated sustainability and resilience challenges requires an understanding of future trajectories. Global change models provide consistent high-level urbanization scenarios. City-scale urban growth models accurately simulate complex physical growth. Modeling approaches linking the global and the local scale, however, are underdeveloped. This work introduces a novel approach to inform a local urban growth model by global Shared Socioeconomic Pathways to produce consistent maps of future urban expansion and population density via cellular automaton and dasymetric mapping. We demonstrate the approach for the case of Pune, India. Three scenarios are explored until 2050: business as usual (BAU), high, and low urbanization. After calibration and validation, the BAU scenario yields a 55% growth in Pune’s population and 90% in built-up extent, entailing significant impacts: Pune’s core city densifies further with up to 60,000 persons/km2, adding pressure to its strained infrastructure. In addition, 66–70% more residents are exposed to flood risk. Half of the urban expansion replaces agriculture, converting 167 km2 of land. The high-urbanization scenario intensifies these impacts. These results illustrate how spatially explicit scenario projections help identify impacts of urbanization and inform long-term planning.

Temporal-spatial monitoring of population growth with RS-GIS technology at Wasit province of Iraq

In this study, the growth rate of the population in a spatial-temporal manner has been modeled and explored by relying on the data from remote sensing and using the analytical functions of the geographic information system in Wasit province of Iraq during the period from 2000 to 2020. WorldPop layers were used to prepare population maps and forecast it in 2025 with the help of artificial neural networks. The predictive model was highly accurate (R 2 = 0.98). The next step was to measure the relationship between the rate of population growth and the changes in water areas and the primary production of vegetation with the help of time series of satellite data, which showed the distribution of the population. It is denser in the central, northwestern and southeastern areas of Wasit province. Low density populations have higher frequency. While from 2015 onwards, the frequency of some population classes with higher density increased compared to lower densities. In general, the trend of changes in the average population in the study area has been completely linear. But the maximum annual population density changes were upward and nonlinear. Changes in the area of ​​deciles representing denser areas have increased and the area of ​​deciles representing less densely populated areas has faced a decrease. The results and findings of this study showed that NPP changes were less predictable compared to population density changes because it is a complex function of climatic and human factors. In the estimated population density map in 2025, dense population cores have been formed in the northeastern areas of Wasit province in the vicinity of the border areas with Iran.

Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology

BackgroundAlthough malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts.MethodsA workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m.ResultsThe identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation.ConclusionsThis study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.

Urban development modeling in Al Diwaniyah province of Iraq using RS-GIS

The rapid growth of the population and the consequent increase in migration to the cities during the past decades have caused the expansion of many urban areas. In this study, the growth and development of the urban areas of Al-Diwaniyeh during the recent decades using three categories: 1. urban limits layer extracted from land use maps during the period, 2. population density layers and 3. Degree of urbanization in the period 1975 to 2015 It was investigated, monitored and modeled. Modeling and forecasting for the future was done using layers of population density and degree of urbanization. The population density map for 2025 was implemented based on population changes in the last two decades using the multilayer artificial neural network method. While the prediction of the degree of urbanization was operationalized by creating and applying a multi-layer neural network model for 2030. The results of the study showed that the general trend of the expansion of Al-Diwaniye city limits during the period of 1975 to 205 was increasing. The degree of urbanization in Al Diwaniyah has been increasing since 1975. After 1975, the initial cores of the dense areas were formed, and gradually, in the following periods, the expansion of the urban areas of Al-Diwaniyeh was observed around the cores. The class of rural areas has shown an upward trend with a more uniform slope compared to other classes of the degree of urbanization. While after 1990, high density and low density classes with close areas have also gone through an upward trend. The rate of population density and, accordingly, the rate of population in Al-Diwaniye has been increasing between 1975 and 2015. The trend of maximum population changes has been downward; therefore, it can be claimed that the initial dense cores of the urban population of Al-Diwaniyeh have moved to the surrounding areas. In the estimated degree of urbanization in 2030, the class with high density has faced a decrease in area compared to the previous period. In general, the area of ​​built-up areas in 2030 will increase compared to previous periods, but this will be achieved with a slower acceleration and slope compared to previous periods.

COVID-19 causes and influences: rethinking neighborhood design in Jordan

ABSTRACT This research aims to propose recommendations to improve neighborhood urban design in Amman, the capital of Jordan; decreasing the risk of spreading pandemics such as COVID-19. The research methodology follows a mixed-methods comparative approach, comparing three neighborhoods in Amman with different COVID-19 infection rates; Ar-Rawabi, Ash-Shmeisani, and Al-Qwesmeh. These more sensitive locationsare selected using a multi-stage cluster-sampling technique that overlaps COVID-19 infection heat maps, population density maps, and neighborhood areas. The study analyzes these neighborhoods’ land-use patterns, and distributes a survey to the neighborhoods’ residents to study their demographic information, compliance with COVID-19 safety measures, daily activities during the COVID-19 pandemic’s peak in Amman, satisfaction level with their neighborhood’s spatial characteristics, and the emotional impact of social distancing on them. The conclusion contributes a list of recommendations for post-pandemic neighborhood design to help mitigate the spread of highly infectious diseases and improve residents’ quality of life, creating pandemic-resilient, healthy, and walkable neighborhoods. These recommendations include planning for commercial land-use to be spread throughout a neighborhood, allocating a higher percentage of land-use for public green spaces, and planning for smaller-scale residential buildings. These recommendations can be applied to neighborhood design internationally to help reduce the spread of pandemics worldwide.

Spatiotemporal Ground Risk Mapping for Uncrewed Aircraft Systems Operations

In this paper we propose the use of spatiotemporal population density data in the analysis of ground risk posed by uncrewed aircraft system (UAS) operations. The spatiotemporal population density maps are generated through the combination of authoritative data sources, open source geospatial databases, and past works to dynamically classify proportions of a population to their expected daily activities based upon a given time. This adds a further dimension to analysis allowing evaluation and optimization of ground risk, both spatially and temporally. This approach is used to analyze the ground risk posed under ballistic and gliding descents of a parameterized UAS along a case study path. An open source tool is implemented as part of this work to aid the decision making of operators and promote safer UAS operations.

Monitoring and Analysis of Population Distribution in China from 2000 to 2020 Based on Remote Sensing Data

Accurately and precisely grasping the spatial distribution and changing trends of China’s regional population is of great significance in new urbanization, economic development, public health, disaster assessment, and ecological environmental protection. To monitor and evaluate the long-term spatiotemporal characteristics of the population distribution in China, a population monitoring estimation model was proposed. Based on remote sensing data such as nighttime light (NTL) images, land use data, and data from the fifth, sixth, and seventh censuses of China, the population spatiotemporal distribution in China from 2000 to 2020 was analyzed with a random forest algorithm. This study obtained spatial distribution maps of population density at a 1 km x 1 km resolution in 2000, 2010, and 2020. The results revealed the trend of the spatiotemporal pattern of population change from 2000 to 2020. It shows that: the accuracy assessment using the 2020 census population of townships/streets as a reference shows an R2 of 0.67 and a mean relative error (MRE) of 0.44. The spatial pattern of the population in 2000 and 2010 is generally unchanged. In 2020, population agglomeration is evident in the east, with a slight increase in the proportion of the population in the west. The patterns of population agglomeration and urbanization also change over time. The population spatiotemporal distribution obtained in this study can provide a scientific reference for urban sustainable development and promote the rational allocation of urban resources.

Conservation planning for threatened marine megafauna: Moving forward with a better approach

Abstract For threatened marine megafauna, such as the Indo‐Pacific humpback dolphin (Sousa chinensis), sound conservation planning should aim to ensure demographic and ecological persistence of populations. One method to address this challenge is through ecosystem‐based conservation planning (ECP) based on threatened marine megafauna distribution, biodiversity richness, and ecosystem functionality. ECP exercises for the Indo‐Pacific humpback dolphin in Chinese and adjacent northern Vietnamese waters were conducted based on the Indo‐Pacific humpback dolphin distribution, Hill‐2 biodiversity index, and marine net primary productivity. Habitat protection priorities were scored using the program MARXAN and were used to identify special areas for conservation (SACs) for the Indo‐Pacific humpback dolphin. To ensure the minimal risk of local extinction, the SACs enclosed a total of 26,858 km2 of waters, accounting for 49.9% of core habitats of the Indo‐Pacific humpback dolphin and 24.3% of the IUCN Red List species in the study region. To enclose 30% of biodiversity richness and ecosystem functionality, a total of 40,179 km2 SACs was required. In the context of threatened marine megafauna conservation, megafauna distribution can be used to highlight focal areas, while information on biodiversity richness and ecosystem functionality should be factored in. Using surrogates with different ecological niches would be a better tactic to ensure sufficient protection coverage by minimizing the omission bias. The conservation of threatened marine megafauna in coastal and estuarine waters shares similar objectives with global marine biodiversity conservation. The challenges to conduct ECP exercises come from data scarcity and poor data quality in representing distributions of biodiversity features. A certified open‐access database that shares survey effort, occurrence, population density, and habitat mapping at national and local scales is recommended. Such a result requires national planning, investment, and policing.

Temporal and Spatial Variation of Geographical Population Distribution in Jordan for the Period (1994 – 2015) Using GIS

The study aimed at analysing temporal and spatial patterns of population distribution in Jordan at governorate level. It also aimed to define the influencing geographical factors that affected the population distribution and caused the changes over the last decades based on census data of 1994, 2004, and 2015 years. Several approaches and statistical methods were used in this study to achieve study objectives, including analytical and descriptive, evolutionary, and spatial analysis approaches along with some quantitative methods. The choropleth maps and population density maps were created for mapping the population distribution in Jordan. In addition, the researchers has defined and the future population projections at governorate level in Jordan. The study revealed that there is a population concentration during the study period in three main Jordanian governorates: Amman, Irbid and Zarqa. Population numbers are also predicted to increase in some governorates in the future. The study concluded that both natural and human factors have influenced population distribution in Jordan, and the impact of the latter factors are more significant. Accordingly, the study showed that there are three main factors affecting the geographical distribution of population in Jordan: population growth, political and economic conditions in neighbouring Arab countries, and the reclassifying bases of urban areas in Jordan. The study recommends the need for population strategies and policies to be applied to face the changes that occur to population distribution in Jordan. Population densities among governorates and its influencing factors and consequences should be taken into consideration when planning for regional development.

Less is more: Developing an approach for assessing clustering at the lower administrative boundaries that increases the yield of active screening for leprosy in Bihar, India.

In India, leprosy clusters at hamlet level but detailed information is lacking. We aim to identify high-incidence hamlets to be targeted for active screening and post-exposure prophylaxis. We paid home visits to a cohort of leprosy patients registered between April 1st, 2020, and March 31st, 2022. Patients were interviewed and household members were screened for leprosy. We used an open-source app(ODK) to collect data on patients' mobility, screening results of household members, and geographic coordinates of their households. Clustering was analysed with Kulldorff's spatial scan statistic(SaTScan). Outlines of hamlets and population estimates were obtained through an open-source high-resolution population density map(https://data.humdata.org), using kernel density estimation in QGIS, an open-source software. We enrolled 169 patients and screened 1,044 household contacts in Bisfi and Benipatti blocks of Bihar. Median number of years of residing in the village was 17, interquartile range(IQR)12-30. There were 11 new leprosy cases among 658 household contacts examined(167 per 10,000), of which seven had paucibacillary leprosy, one was a child under 14 years, and none had visible disabilities. We identified 739 hamlets with a total population of 802,788(median 163, IQR 65-774). There were five high incidence clusters including 12% of the population and 46%(78/169) of the leprosy cases. One highly significant cluster with a relative risk (RR) of 4.7(p<0.0001) included 32 hamlets and 27 cases in 33,609 population. A second highly significant cluster included 32 hamlets and 24 cases in 33,809 population with a RR of 4.1(p<0.001). The third highly significant cluster included 16 hamlets and 17 cases in 19,659 population with a RR of 4.8(p<0.001). High-risk clusters still need to be screened door-to-door. We found a high yield of active household contact screening. Our tools for identifying high-incidence hamlets appear effective. Focusing labour-intensive interventions such as door-to-door screening on such hamlets could increase efficiency.

SAR-based Subsidence Monitoring and Assessment of the Factors Involved in the Occurrence of Subsidence, Lahore City

The judicious use and management of natural resources is vital to achieve sustainable development. Land and water are prime natural resources, and their depletion and degradation can lead to serious threats like land subsidence. Land subsidence is a phenomenon of the alteration of elevation at a point on the earth through the sinking of the surface. It occurs when the earth's surface loses its support. The major causes of land subsidence include groundwater extraction, mining, construction overload, and other similar factors that increase pressure on the surface and eventually subsidize the land. Urban centers with excessive groundwater extraction and infrastructure development are at a high risk of subsidence. Lahore, the second-largest city in Pakistan, is undergoing an enormous increase in population density, uncontrolled urbanization with very large-scale construction projects, and intensive groundwater extraction which are responsible for subsidence directly or indirectly. Therefore, studies on groundwater status and unplanned urban appraisals have seriously urged monitoring of the subsidence in Lahore. Herein, we used freely available Sentinel-1 data for one year (from August 2018 to August 2019), with a high spatial and temporal resolution, to monitor subsidence in Lahore. The data were processed using the SNAP/StaMPS approach for Persistent Scatterer Interferometric Synthetic Aperture Radar (PSI) analysis, which is an advanced InSAR technique. The displacement velocity map from InSAR processing shows a significant land deformation in the area with values ranging from –114 to 15 mm yr–1. Along with the Sentinel-1 data, we also used supplementary data obtained from various government agencies of Pakistan to study the land cover map, transportation network and waterways of Lahore, soil types, population density, and field points for assessing the results and understanding the roles of various factors in the occurrence of uplift or subsidence. A strong correlation was established between subsidence and various parameters such as groundwater extraction and lowering of the water table, soil type variations, land cover changes, surface water channels, and population density. The deformation map confirms the greatest subsidence in the central part of Lahore, while the uplift is observed in the less populated and rural areas situated near Ravi River. The land subsidence and uplift could be attributed to groundwater extraction and recharge through the canal system and the river, respectively.

High-Precision Population Spatialization in Metropolises Based on Ensemble Learning: A Case Study of Beijing, China

Accurate spatial population distribution information, especially for metropolises, is of significant value and is fundamental to many application areas such as public health, urban development planning and disaster assessment management. Random forest is the most widely used model in population spatialization studies. However, a reliable model for accurately mapping the spatial distribution of metropolitan populations is still lacking due to the inherent limitations of the random forest model and the complexity of the population spatialization problem. In this study, we integrate gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and support vector regression (SVR) through ensemble learning algorithm stacking to construct a novel population spatialization model we name GXLS-Stacking. We integrate socioeconomic data that enhance the characterization of the population’s spatial distribution (e.g., point-of-interest data, building outline data with height, artificial impervious surface data, etc.) and natural environmental data with a combination of census data to train the model to generate a high-precision gridded population density map with a 100 m spatial resolution for Beijing in 2020. Finally, the generated gridded population density map is validated at the pixel level using the highest resolution validation data (i.e., community household registration data) in the current study. The results show that the GXLS-Stacking model can predict the population’s spatial distribution with high precision (R2 = 0.8004, MAE = 34.67 persons/hectare, RMSE = 54.92 persons/hectare), and its overall performance is not only better than the four individual models but also better than the random forest model. Compared to the natural environmental features, a city’s socioeconomic features are more capable in characterizing the spatial distribution of the population and the intensity of human activities. In addition, the gridded population density map obtained by the GXLS-Stacking model can provide highly accurate information on the population’s spatial distribution and can be used to analyze the spatial patterns of metropolitan population density. Moreover, the GXLS-Stacking model has the ability to be generalized to metropolises with comprehensive and high-quality data, whether in China or in other countries. Furthermore, for small and medium-sized cities, our modeling process can still provide an effective reference for their population spatialization methods.