Cartography Research Articles

AI and data-driven urbanism: The Singapore experience

This paper presents a deep and critical analysis of Singapore's new wave of state-built digital tools and services and how it connects to its larger smart urbanism project, also known as Smart Nation. The COVID-19 pandemic, and particularly Singapore's response, served as a real-world testing ground for smart urbanist strategies. In particular, we analysed the logic that emanates from these novel digital interventions, how they operate on the complex urban built environment and the population, and their effects on urban and citizenry morphologies. Next, we examined a series of state-led technological implementations that have emerged since the Covid-19 pandemic, providing digital solutions that assist citizens with the changing rhythms of everyday living, data-capturing sensors and gantries to aid authorities in contract tracing efforts and enforce vaccination differentiation measures, geospatial digital mapping of demographic data, in withal robotics for automated policing and cleaning activities; and the use of AI and automated data-driven tools in public health to improve service delivery and care to patients. While we are unable to exhaust every piece of technology for the purpose of this paper, these developments, along with their design thinking and operations, we argue, are helpful in revealing the contemporary conjectures of Singaporean digital urban idealism and the governing strategies of the state. By examining Singapore's response, this study aims to contribute to the ongoing discourse on smart urbanism, offering insights into how cities can leverage technology effectively while balancing technological innovation with privacy and public trust.

Using spatial video and deep learning for automated mapping of ground-level context in relief camps

BackgroundThe creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV).MethodsWe trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math.ResultsThe initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time.ConclusionsThe results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.

Nomadic Culture’s Place Names Named After the Cult of Domestic Livestock

The given research considers toponyms of the Republic of Kazakhstan related to the names of domestic livestock, their species, and products as nomadic culture’s place names named after the cult of domestic livestock. The total sum of 341 toponyms named after the cult of domestic livestock from all 14 regions of the Republic of Kazakhstan were taken as the data of the study. They were assembled from different geographical maps of the regions of the Republic of Kazakhstan, and they were divided into five main groups that were named after the five main species of domestic livestock. They are: 1. Nomadic culture’s place names named after the cult of horses; 2. Nomadic culture’s place names named after the cult of oxen; 3. Nomadic culture’s place names named after the cult of camels; 4. Nomadic culture’s place names named after the cult of sheep; 5. Nomadic culture’s place names named after the cult of goats. The complex analysis of research data revealed the percentage of each of the five groups mentioned above; it also identified the 15 most common toponyms named after the nomadic cult of domestic livestock and assisted in classifying the mechanisms of naming nomadic culture’s toponyms named after the cult of domestic livestock. As a result, this research proposes a pioneering classification of mechanisms for naming nomadic culture’s place names, which can manifest some peculiarities of nomadic culture. It also revealed that 56 percent of the 341 toponyms, which were used as data for the study, are associated with the cult of horses. Words associated with the cult of horses in Kazakh culture are widely used in toponyms and serve various functions. These include providing information about incidents that occurred at specific locations, as well as describing the features of geographical objects using characteristics associated with horses and their breeds. The second group of place names in nomadic cultures, named after the cult of domestic livestock, is dedicated to the cult of camels. These constitute approximately 16 percent of all nomadic place names related to the cult of domestic livestock. Camels have played a role in the lives of Central Asian nomads similar to that of horses. Similarly, place names in nomadic cultures associated with the cult of camels serve functions akin to those related to the cult of horses. The Kazakh people even name their children using names that contain elements associated with the cult of camels. The third significant group of place names within the nomadic culture, which is attributed to the worship of domestic livestock, pertains to the cult of sheep. This category constitutes ten percent of all place names related to the nomadic culture's veneration of domestic animals, marginally surpassing the last two groups of such toponyms by only one percent. In Central Asia, nomadic peoples primarily raise sheep for their meat and wool. The Kazakh people have numerous traditions linked to the sheep cult. Even the bones of sheep have been and still are used as toys during national Kazakh games such as “asyk oinau.” The etymological aspects and word structure of toponyms of a given group were studied, and the results of the research contain new details about the etymology of some nomadic culture’s place names that were considered as the data of the study. The whole research process was conducted in three main phases: the phase of reviewing literature and collecting geographical names; the phase of studying etymology and word structure of research data and identifying the 15 most frequent nomadic culture’s toponyms; and the phase of identifying and classifying the main mechanisms of naming nomadic culture’s place names named after the cult of domestic livestock. Received: 14 July 2024 / Accepted: 20 October 2024 / Published: 05 November 2024

Construction and Characterization of Traditional Village Landscape Cultural Genome Atlases: A Case Study in Xupu County, Hunan, China

The Yuan River Basin in Hunan Province, known for its rich history and cultural heritage, is home to traditional village landscapes characterized by distinct cultural genes and regional features. This study focuses on 21 traditional villages in Xupu County, Huaihua, and develops four types of traditional village landscape cultural genome atlases at three hierarchical levels: individual villages, multiple villages, and a specific research area. These atlases include genetic linkage maps, spatial sequence maps, distribution pattern maps, and geographical pattern maps. Using Yangquepo in Zhumucun Village as a case study, we constructed a genetic linkage map for the landscape cultural genome of a single traditional village, based on the arrangement structure of courtyard layouts. Spatial sequence maps were derived from the overall spatial configuration, while distribution pattern maps and geographical pattern maps for the 21 villages were developed using kernel density analysis in ArcGIS 10.8 software. The results reveal that traditional village landscapes in the region possess a diverse cultural genome, marked by distinct regional characteristics and spatial organization patterns.

Spatiotemporal variations of surface albedo in Central Asia and its influencing factors and confirmatory path analysis during the 21st century

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.

Geographic variation in kidney failure and transplantation in Aotearoa New Zealand: A population-based data linkage study.

This study aimed to describe the epidemiology of kidney replacement therapy (KRT) in Aotearoa New Zealand and assess the impact of residential location on access to kidney transplantation. AcceSS and Equity in Transplantation (ASSET), a health-linked data platform, was used to identify people commencing KRT in New Zealand from 2006 to 2019 and analyse regional epidemiology. Health services were classified as 'transplanting', 'intermediate' or 'remote' depending on their degree of separation from a transplant centre. Multiple logistic regression modelling was used to assess the predictors of deceased donor waitlisting or living donor transplantation within 6 months after starting KRT. Web-based mapping software was used to develop interactive geospatial maps. The cohort was 7704 people newly starting KRT. Living in an intermediate [odds ratio (OR): 0.73 (95% confidence interval (CI): 0.61-0.88)] or remote [OR: 0.38 (95% CI: 0.27-0.54)) region and Māori (OR: 0.35 (95% CI: 0.28-0.44)], Pacific [OR: 0.32 (95% CI: 0.24-0.42)) and Asian (OR: 0.66 (95% CI: 0.50-0.87)] ethnicity were associated with a decreased likelihood of timely waitlisting or transplantation. Regional maps can be explored here. There is marked geospatial and ethnic variation in the epidemiology of kidney failure and access to kidney transplantation across New Zealand. Geospatial mapping of kidney failure epidemiology and transplantation outcomes can provide opportunities to direct resources towards populations at greatest need.

Inequalities in healthy life expectancy and risk factors for poor health in small geographic areas

Abstract Background Reducing inequality in healthy life expectancy (HLE) is a UK government ambition and requires more detailed knowledge of interrelated morbidity and mortality risks at smaller areas than is routinely available. We assessed HLE and risk factors in small areas. Methods We used publicly available data to estimate HLE in 2011 and 2021 at birth and age 65 for males and females for 128 Middle layer Super Output Areas (MSOA) in Norfolk and Waveney, described the distribution using geospatial mapping, and analysed associations with risk factors using linear regression. Results Mean HLE at birth was 66.5 years (standard deviation 4.3; range 52.0 to 73.0) for men and 67.5 (3.9; 56.0 to 74.0) for women, and at age 65 was 10.9 years (1.8; 6.0 to 16.0) and 12.2 (1.9; 5.0 to 15.0) respectively in 2021. The range in HLE increased between 2011 and 2021 by 2.8 years for men and 1.6 years for women. HLE was usually lower and risks higher in urban and coastal areas. Lower HLE at area level was associated with lower weekly income, physical inactivity, air pollution, alcohol admissions, living alone as an older person and diet not meeting ‘5 a Day’. HLE at age 65 in 2021 increased by: 0.2 (95% CI 0.1,0.2) years for men and 0.2 (0.2,0.3) for women for each £10 rise in weekly net income after housing expenditure; 0.1 (0.1,0.2) years for men for each 10 fewer alcohol attributable hospital admissions per 100,000 admissions; 0.66 (0.31,1.00) years for men and 0.53 (0.15,0.91) years for women per 1 μg/m3 less 2.5 mm particulate matter concentration in air pollution; 0.6 (0.2,1.0) years for men and 0.6 (0.1,1.0) years for women for each 10% fewer people being physically inactive (multiple regression). Conclusions This innovative approach can be used to monitor risks and inform targeted public health interventions at a local level more relevant to local policy makers. Stronger public health surveillance systems are needed to accurately monitor a wider variety of local data on risks. Key messages • Healthy life expectancy at age 65 varies by over 10 years for men and women between different small geographic areas in an English county, alongside local variation in risk factors for poor health. • Lower HLE at area level was associated with lower weekly income, physical inactivity, air pollution, alcohol admissions, living alone as an older person and diet not meeting ‘5 a Day’.

Interrogating the environmental and social correlates of health risk behaviours in West Africa

Abstract Background Despite the high incidences of health risk behaviors (HRBs) in sub-Saharan Africa, studies on the contextual determinants of HRBs across countries are generally lacking, concentrated on isolated countries and often focus solely on specific health behaviors. Thus, this study investigated the environmental and social correlates of HRBs in West Africa. Methods Data was from post-2015 Demographic and Health Surveys in three West African countries of Benin, Mali and Nigeria. A composite outcome variable of health risk behaviors (HRBs) was derived from variables of tobacco smoking, intimate partner violence, overweight/obesity and risky sexual behaviors. The contextual variables were normalized difference vegetation index, precipitation, maximum and minimum temperatures, region of residence and population Density. Data analysis involved descriptive statistics, chi square test, geographical mapping, and multilevel logistic regression. Results The engagement in HRBs varied from 42.65% in Nigeria to 42.88% in Benin and 51.06% in Mali as there are spatial variations in the engagement in HRBs within different regions of these countries. There also existed significant variations in HRBs regarding age, education, employment status, wealth index, household size, place of residence, and region of residence in the three countries. In the contextual models of environmental and social factors, population density significantly increased odds of HRBs in the three countries, maximum temperature significantly increased odds of HRBs in Benin and Mali, while precipitation significantly increased odds of HRBs in Nigeria and Mali. Other factors which significantly increased odds of HRBs across the three countries were increase in age, wealth index, households with 9+ persons, and regions of residence. Conclusions These findings suggest that among other things, changes in lifestyles regarding the different HRBs should be emphasized in specific contextual situations Key messages • There are variations in engagement in health risk behaviours across the countries. • Cliamte variables influence engagement in health risk behaviours.

Climate change and health vulnerability assessments to increase regional climate resilience

Abstract Background The impacts of climate change on health and healthcare systems exhibit complex interrelationships, varying across time and space. Population groups already disadvantaged due to their demographics, health, socioeconomic status, or geographical location tend to be particularly vulnerable to climate-related health risks. Our work aims to develop a method for a systematic quantitative vulnerability assessment to make regional vulnerabilities comparatively measurable. Methods Building on the WHO framework on Climate Change and Health Vulnerability and Adaptation Assessment (2021) a data mapping process was carried out to identify and validate vulnerability factors and quantitative indicators on a small-scale spatial resolution for the five dimensions: demographic, socioeconomic, biological/health, sociopolitical, and geographic/climate. Regional vulnerability scores for each indicator were calculated and visualized as geographic maps and circular barplots to allow for easily understandable interpretation. Results A set of 25 quantitative vulnerability indicators from a multitude of data sources - combining register data, survey data, and geospatial data - was compiled. Main challenges were the identification of sociopolitical vulnerability factors suitable for Austria and data availability on a small-scale level. The indicator set was used to identify regions that are particularly vulnerable due to their population composition, disease burden, and geographical location (e.g. regions in Eastern Austria with high shares of persons < 65 years of age, unemployment, and heat days). Conclusions Systematically assessing vulnerabilities in key dimensions is central to identifying regionally specific pressure points, allowing for a quantitative baseline to derive adequate and tailored climate change adaptation measures to sustain future health. Systematic assessment provides essential insights for policymakers, public health officials, and disaster management agencies.

Derbent’s underwater cultural heritage as a tourist resource

<p>The article reflects the results of expeditionary works on revealing and studying historical artifacts on the bottom of the Caspian Sea near Derbent—the southernmost and most ancient city of Russia. The approaches to geographical study and mapping of the remains of ancient fortification architecture under water and other numerous finds of scuba divers in the area under consideration are described. The authors analyzed the specifics and results of underwater archaeological research from the point of view of the prospects for the development of diving tourism in the Derbent water area. The article also concludes that it is advisable to strengthen the integration of Dagestan historical and geographical science, which will allow researchers to reconstruct little-known pages of history with greater depth and accuracy. The constructive section of the article is devoted to the development of recreational diving and underwater museology in Dagestan, as well as to the general outlines of the underwater park project on the very southern coast of Russia.</p>

All-cause Years of life lost due to death at municipality level in Norway. Association to educational attainment and median income

Abstract Background Norway, with 5.5 million people spread across 356 municipalities, has pursued an active policy to reduce social and geographical health inequalities. Despite, studies find significant health disparities linked to socioeconomic factors at the individual-level, although evidence at larger geographical-levels has been mixed. We used a key burden of disease (BoD) metric, Years of Life Lost (YLL), to examine spatial health inequities among Norwegian municipalities and their association with socioeconomic factors. Methods We analysed data from all residents in Norwegian municipalities with over 1000 inhabitants as of January 1, 2019. Data was sourced from the National Population Register and Statistics Norway. All-cause YLL were calculated based on age-specific aspirational life expectancy obtained from GBD 2019. Deprivation was measured using educational attainment and median income, categorized into quartiles. Geographical maps illustrating municipality-level all-cause age-standardised YLL rates for males and females were generated. Negative binomial regression models assessed associations between socioeconomic factors and YLL rates, adjusting for age, sex, and an index of centralisation. Results High-deprivation municipalities exhibited a 9% higher YLL rate in terms of education (Incidence Rate Ratio [IRR] = 1.09, 95% Confidence Interval [CI] = 1.01-1.17) and a 10% higher YLL rate in terms of income (IRR = 1.10, 95% CI = 1.02-1.08) compared to the least deprived, translating to absolute differences of 1391 and 1466 YLL per 100,000 population, respectively. Additionally, we discuss the potential expansion of this study to incorporate cause-specific outcomes and other BoD metrics based on previous county-level analyses. Conclusions Our study demonstrates the feasibility of using BoD methods to assess geographical deprivation and premature mortality at the municipality level, providing valuable insights for public health policy targeted at small administrative areas.

Spatial heterogeneity and interacting intensity of drivers for trade-offs and synergies between carbon sequestration and biodiversity

Global crises of biodiversity loss and climate change highlighted the urgent need to understand the trade-off and synergy between carbon sequestration and biodiversity and the driving mechanisms behind them. However, existing research on driving mechanisms primarily focused on the influence of independent variables on dependent variables, neglecting the interaction between independent variables. The InVEST and PLUS models were utilized in this study to evaluate the dynamics of carbon sequestration and biodiversity in the Chishui River Basin, Southwest of China. By integrating the factor detection of the Geographic Detector and the Multi-scale Geographically Weighted Regression (MGWR) model, this study identified the key drivers affecting trade-off and synergy and revealed their spatial heterogeneity. Furthermore, utilizing the interaction detection of the Geographic Detector and the Geographic Cross-Convergence Mapping (GCCM) model, this study analyzed the interaction direction and interaction intensity among key drivers. The results showed that: (1) From 2012–2035, the total values of carbon sequestration and biodiversity exhibited an overall increasing trend, with 58.49 % of the area showing trade-off effects, 23.86 % showing positive synergy, and 17.65 % showing negative synergy. (2) Elevation, slope, topographic relief, temperature, and land use intensity were identified as key factors influencing trade-off and synergy, and these factors displayed significant spatial heterogeneity. (3) When multiple drivers interacted, their explanatory power for trade-off and synergy increased significantly. The interaction patterns among drivers presented a “topography➙climate↔human activity↔distance➙soil” structure, where human activity interacted bidirectionally with other factors, with the strongest interaction occurring between climate factors and human activity. This study advanced the understanding of the driving mechanisms behind trade-off and synergy through MWGR and GCCM model. Based on the supply-demand theory, a synergistic management framework was proposed to emphasize the “pattern-process-service-feedback” nexus. The hotspot areas identified through scenario simulation could be used for synergistic enhancement of carbon sequestration and biodiversity.

A review of the genus Lagynochthonius Beier, 1951 (Pseudoscorpiones, Chthoniidae) from China

The genus of Lagynochthonius from China was reviewed. Nine new hypogean species were described: Lagynochthonius apexus sp. nov. (♂♀), L. infirmus sp. nov. (♂♀), L. longquanensis sp. nov. (♀) and L. zhakouensis sp. nov. (♂♀) from Sichuan, L. baiguensis sp. nov. (♂♀), L. guangyangensis sp. nov. (♂♀), L. jingshengensis sp. nov. (♂♀) and L. zhaoae sp. nov. (♀) from Guangxi, L. galeatus sp. nov. (♀) from Guizhou. The male L. guanniuensis Hou, Feng & Zhang, 2023, L. maanensis Hou, Feng & Zhang, 2023 and L. xiaoensis Hou, Feng & Zhang, 2023 from Guizhou are reported for the first time. The species key and a geographical distribution map of Lagynochthonius species from China are provided.

Taxonomic Data Objects for Communicating the Meaning of Species Names

A central problem in biodiversity data science remains the inability to precisely aggregate observations that originate from the same species. Current approaches still rely heavily on the ‘Genus species’ pair of Linnaean taxonomy to label and group data. However, such binomial species names do not alone contain enough information to distinguish variation in a name’s conceptual usage (i.e., meaning) across time, place, or investigator. This “species name-to-meaning” problem is well known, but no robust and scalable solutions yet exist (Berendsohn 1995Bisby 2000, Sutherland et al. 2000, Page 2007, Franz and Sterner 2018, Upham et al. 2021, Sterner et al. 2023, Sterner et al. 2020, Beach et al. 1993). The problem is widespread especially for well-studied groups like mammals, for which 45% more species are now recognized than 30 years ago, including many species splits and rearrangements (Burgin et al. 2018, Mammal Diversity Database 2024). To address this problem, we here propose to digitally package and sign the relevant taxonomic data associated with a given species name usage to form a ‘Taxonomic Data Object’ (TDO). These TDOs are conceptually similar to 'secundus' references (i.e., species name sec. author, Berendsohn 1995) but differ in being machine readable and thus operational from the perspective of high-throughput data science. TDOs aim to move from analog secundus references, which require a human to track meaning by reading the cited article, to a digital and machine-actionable taxonomic reference. Versioned TDOs that are digitally signed using hash algorithms (e.g., md5 or sha256, see Elliott et al. 2023) will allow for precisely communicating, with verified data provenance from sender to receiver, certain aspects of what a species name means according to a given authority at a given time. Example TDO contents include DarwinCore terms (e.g., scientificName, namePublishedIn, nameAccordingTo, nomenclaturalStatus (Darwin Core Maintenance Group 2021)) as well as meaning-rich digital assets like geographic range maps (e.g., GeoJSON format), exemplar DNA sequences (e.g., FASTA format or National Center for Biotechnology Information (NCBI) accession number), holotype specimen information (e.g., catalog number, type locality coordinates), and taxonomic treatment texts (including material citations, e.g., as digitized and extracted by Plazi TreatmentBank, Agosti and Egloff 2009; see Fig. 1). We discuss pilot examples comparing global bat species according to the v1.2 vs. v1.11 taxonomies of the Mammal Diversity Database (Sep 2020 vs. Apr 2023), showing how TDOs enable the reliable tracking of taxonomic meaning for the 63 bat species affected by splits during this period. We posit that widespread use of TDOs will enable the traceable exchange of taxonomic information across a variety of existing platforms, providing a path for accurate species-level data aggregation at global scales, at least for well-studied taxa.

Revisiting <i>Loxanthocereus riomajensis</i>, lectotypification, biogeography and conservation status of an endemic species from Arequipa, Peru

This study aims to clarify the taxonomy of Loxanthocereus riomajensis, as well as to understand its actual and potential geographic distribution, and to present its conservation status. Specialized literature was reviewed, and field visits were conducted. The collected data, including herbarium samples, presence records, and additional information, were used to describe the species in detail, create a geographical distribution map, generate potential distribution models using the MaxEnt algorithm, and assess its conservation status using IUCN considerations. The results demonstrate that morphologically, L. riomajensis is an independent species endemic to the departament of Arequipa, found in the provinces of Condesuyos and Caylloma, spanning an altitudinal range from 1600 to 2100 m asl. The potential distribution covers an area of 1135.3 km2, in the provinces of Condesuyos, Castilla, La Unión, Caravelí, Caylloma, and Arequipa. Regarding its conservation status, it is classified as endangered (EN). Finally, a lectotype is designated.

Exploratory analysis of the potential impact of violence on HIV among female sex workers in Mombasa, Kenya: a mathematical modelling study

BackgroundUnderstanding the frequency of violence experienced by female sex workers (FSWs) and how violence contributes to HIV transmission can help improve HIV programs.MethodsUsing recent recommendations for modelling structural factors and associated causal pathways, we developed a HIV transmission dynamic model for FSWs and their clients in Mombasa, Kenya, mechanistically representing three types of violence (sexual violence, SV; physical violence, PV; police assault and arrest, PAA). Each type of violence affects HIV transmission through key mediators (condom non-use, HIV testing). We parameterized the model using data from a cross-sectional study of FSWs aged 15–24 recruited from a systematic geographical mapping sampling frame in Mombasa, Kenya (Cheuk E et al., Frontiers in Reproductive Health 2(7), 2020). Using this model, calibrated (and cross-validated) to HIV epidemiological and violence outcomes, we estimated the incidence of violence episodes, the contribution of violence to the HIV epidemic measured by the transmission population-attributable fraction, and the potential impact of possible violence interventions.ResultsThe median estimated incidence of PAA in 2023 among FSWs who had not previously experienced that type of violence was 0.20 (95% credible interval: 0.17–0.22) per person-year (ppy), about double the incidence of SV and PV (0.10 (0.09–0.11), 0.11 (0.09–0.12), respectively). The incidence of violence was higher among FSWs who had previously experienced violence: the incidence of recurrent PV was 2.65 (1.82–3.37) ppy, while the incidence of recurrent SV and PAA were 1.26 (0.80–1.67) and 1.37 (0.94–1.74 ppy, respectively. In this setting, we estimated that a median of 35.3% (3.4–55.8%) infections in FSWs and clients combined over the next 10 years may be due to all types of violence (and mediators), mainly through reduced condom use in FSWs who have ever experienced SV (34.6% (2.4–55.5%)). Interventions that prevent future violence without mitigating the effects of past violence may only prevent 8.8% (0.8–14.0%) infections over 10 years.ConclusionsFSWs in Mombasa experience violence frequently. In this population, we find that addressing sexual violence, including mitigating the effects of past violence, is potentially important in reducing HIV transmission in this population. However, the wide uncertainty range shows longitudinal studies are needed to strengthen the evidence of the influence of violence on HIV risk behavior. We find that the recommendations for modelling structural factors provide a useful framework for describing the model.

Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis.

Flooding is a global threat causing significant economic and environmental damage, necessitating a policy response and collaborative strategy. This study assessed global research trends and advances in geospatial and meteorological flood risk assessment (G_MFRA), considering the ongoing debate on flood risk management and adaptation strategies. A total of 1872 original articles were downloaded in BibTex format using the Web of Science (WOS) and Scopus databases to retrieve G_MFRA studies published from 1985 to 2023. The annual growth rate of 15.48% implies that the field of G_MFRA has been increasing over time during the study period. The analysis of global trends in flood risk research and practice highlights the key themes, methodologies, and emerging directions. There exists a notable gap in data and methodologies for flood risk assessment studies between developed and developing countries, particularly in Africa and South America, highlighting the urgency of coordinated research efforts and cohesive policy actions. The challenges identified in the body of extant literature include technical expertise, complex communication networks, and resource constraints associated with the application gaps of the study methodologies. This study advocates for a holistic research approach to flood disaster management through ecosystem-based adaptation that underpins the Sustainable Development Goals to develop innovative flood techniques and models with the potential to influence global decision-making in the G_MFRA domain. Addressing these global challenges requires a networked partnership between the research community, institutions, and countries.

Hermeneutic Cartography for the Restitution of a Lost Antique Seaport

This article deals with the reconstruction of an ancient seaport in the town of Bejaia, known as Muslubium Horrea. This settlement, ignored by the official historiography, would nevertheless have played an important role in wheat exports and trade with the Roman port of Ostia. Moreover, of all the ancient ports known to date, the Algerian coast has only two port warehouses, and Muslubium Horrea is the only granary belonging to the Roman province of Mauretania Sitifensis (400AD). The methodology adopted to locate and reconstruct the topography and the settlement’s structures is based on the concept of cartographic hermeneutics. The compilation of existing historical accounts and the interpretation derived from the superimposition of ancient geographical maps and 19th century archaeological and geological maps identified all the natural resources necessary for its construction, in particular the abundance of water sources and deposits of geomaterials for construction (clay, stone, gypse, marble, etc.). These data were confirmed by field and photogrammetric surveys, resulting in a map showing the archaeological context of the Muslubium Horrea port and the remains of structures still visible today. The approach adopted allowed to delineate the size of the port site and scale the structures that make it up, as well as identify the water supply source and its primitive topography prior to its establishment.

Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms.

Visual impairment (VI) is a prevalent global health issue, affecting over 2.2 billion people worldwide, with nearly half of the Chinese population aged 60 years and older being affected. Early detection of high-risk VI is essential for preventing irreversible vision loss among Chinese middle-aged and older adults. While machine learning (ML) algorithms exhibit significant predictive advantages, their application in predicting VI risk among the general middle-aged and older adult population in China remains limited. This study aimed to predict VI and identify its determinants using ML algorithms. We used 19,047 participants from 4 waves of the China Health and Retirement Longitudinal Study (CHARLS) that were conducted between 2011 and 2018. To envisage the prevalence of VI, we generated a geographical distribution map. Additionally, we constructed a model using indicators of a self-reported questionnaire, a physical examination, and blood biomarkers as predictors. Multiple ML algorithms, including gradient boosting machine, distributed random forest, the generalized linear model, deep learning, and stacked ensemble, were used for prediction. We plotted receiver operating characteristic and calibration curves to assess the predictive performance. Variable importance analysis was used to identify key predictors. Among all participants, 33.9% (6449/19,047) had VI. Qinghai, Chongqing, Anhui, and Sichuan showed the highest VI rates, while Beijing and Xinjiang had the lowest. The generalized linear model, gradient boosting machine, and stacked ensemble achieved acceptable area under curve values of 0.706, 0.710, and 0.715, respectively, with the stacked ensemble performing best. Key predictors included hearing impairment, self-expectation of health status, pain, age, hand grip strength, depression, night sleep duration, high-density lipoprotein cholesterol, and arthritis or rheumatism. Nearly one-third of middle-aged and older adults in China had VI. The prevalence of VI shows regional variations, but there are no distinct east-west or north-south distribution differences. ML algorithms demonstrate accurate predictive capabilities for VI. The combination of prediction models and variable importance analysis provides valuable insights for the early identification and intervention of VI among Chinese middle-aged and older adults.

Appraisal of contamination, hydrogeochemistry, and Monte Carlo simulation of health risks of groundwater in a lithium-rich ore area.

This study incorporated hydrogeochemical facies, the entropy-weighted water quality index (EWQI), multivariate statistics, and probabilistic human exposure assessment to investigate hydrogeochemistry, analyze groundwater quality, and estimate potential risks to human health in a lithium-rich ore area (Jadar River basin, Serbia). The findings designated the Ca·Mg-HCO3 hydrogeochemical type as the predominant type of groundwater, in which rock weathering and evaporation control the major ion chemistry. Due to the weathering of a lithium-rich mineral (Jadarite), the lithium content in the groundwater was very high, up to 567mg/L, with a median value of 4.3mg/L. According to the calculated EWQI, 86.4% of the samples belong to poor and extremely poor quality water for drinking. Geospatial mapping of the studied area uncovered several hotspots of severely contaminated groundwater. The risk assessment results show that groundwater contaminants pose significant non-carcinogenic and carcinogenic human health risks to residents, with most samples exceeding the allowable limits for the hazard index (HI) and the incremental lifetime cancer risk (ILCR). The ingestion exposure pathway has been identified as a critical contaminant route. Monte Carlo risk simulation made apparent that the likelihood of developing cancerous diseases is very high for both age groups. Sensitivity analysis highlighted ingestion rate and human body weight as the two most influential exposure factors on the variability of health risk assessment outcomes.