Cartography Research Articles

Trend, Pattern, and Prevalence of Adverse Pregnancy Outcomes among Women of Reproductive Age in India, 1992-2021.

Adverse pregnancy outcomes (APOs) are the most important vital statistics used to assess maternal health and child health statistics. They are an indicator of the quality of maternal and child health care services, i.e., antenatal care, intrapartum care, and medical services. The objective of the study is to analyze the trend, pattern, and prevalence of APOs among women of reproductive age group at the national level over successive NFHS rounds. The current study uses data from the National Family Health Survey (NFHS), conducted during 1992-2021. The study uses geo-spatial mapping techniques through QGIS software and report analysis to arrive at definitive conclusions. The study finds that the incidence of APOs among women of reproductive age (15-49 years) has increased over the years. Twenty states and union territories have APOs that are below the national average. On the other hand, States like Madhya Pradesh, Meghalaya, Sikkim, Goa, Maharashtra, Andhra Pradesh, Karnataka, and Kerala have witnessed their APOs worsening as per NFHS-5 vis-à-vis NFHS-4. The study also finds that apart from the Himalayan belt and the east coast of India, APOs are more prominent in the contiguous regions adjoining these areas. The findings of the study have thrown on very interesting facts. Despite rapid economic development during the intervening period between NFHS-4 and NFHS-5, rising APOs are a testament to the fact that the policymakers in the country need to be more target-oriented and get their acts together.

Geographical Mapping of Biological and Environmental Risk Factors of Colorectal Cancer in the United States

Geographical Mapping of Biological and Environmental Risk Factors of Colorectal Cancer in the United States

Naive Bayes Algorithm with Feature Selection Using Particle Swarm Optimization

The COVID-19 vaccine in Indonesia has led to the emergence of public opinion which is conveyed on social media such as Twitter. One of the analyses that can be done to produce various information from public opinion is sentiment analysis. Sentiment analysis is used to determine whether an opinion tends to be positive or negative. This study aims to classify the public opinion of the COVID-19 vaccine in Indonesia with sentiment analysis and to visualize the location of the sentiment of the COVID-19 vaccine tweet data in Indonesia. To achieve this aim, the Naïve Bayes algorithm with Particle Swarm Optimization (PSO) feature selection was used. This study uses opinions into positive and negative class sentiments towards 2,547 tweets related to the COVID-19 vaccine in Indonesia from January to June 2021. The results show that the distribution of positive and negative class sentiments is 2,328 and 219, respectively. In addition, the positive sentiment for the COVID-19 vaccine was dominated by people on the island of Java based on a random number matrix initialized by the PSO method. The classification of public opinion on Twitter media provides accurate and optimal performance results using a combination of the Naïve Bayes algorithm with PSO feature selection. The results of the combination of these methods have accuracy and F1 score values of 91.28% and 95.38%, respectively. The visualization of geo-spatial mapping showed that positive sentiments related to the COVID-19 vaccine exist in almost all regions in Indonesia but are dominated by the Jabodetabek area.

Assessment and Mapping of Nutrients in Soils of Babain and Ladwa Blocks of Kurukshetra using GIS

The study was conducted in 2021-22 to evaluate the soil fertility status and create geospatial thematic mapping of available macro and micronutrients using GIS, and their correlation with physico-chemical properties of the soils of the Babain and Ladwa blocks of Kurukshetra, India. A composite of 204 georeferenced soil samples was collected from different villages of both blocks and samples were analysed using standard procedures. The average pH of the soils of Babain and Ladwa had a mean value of 8.10 and 8.18. Among the total soil samples, 92 and 80 samples were found to be saline in Babain and Ladwa, respectively. The nitrogen, phosphorous, potassium and sulphur content in the soils of the Babain block ranged from 105-280, 6-32, 80-500 and 34-298 kg ha-1, while in the Ladwa block nitrogen, phosphorous, potassium and sulphur content in the soils ranged from 89-287, 7-27, 65-510 and 45-430 kg ha-1, respectively. The soil samples exhibited varying levels of zinc, iron, and copper, with some falling into the sufficient category and others into the deficient category. However, all samples had sufficient manganese and boron content. The observed deficiency of zinc, iron and copper can be replenished with the application of manures and fertilizers to improve soil fertility and crop productivity.

Efficient geospatial mapping of buildings, woodlands, water and roads from aerial imagery using deep learning.

As more aerial imagery becomes readily available, massive volumes of data are being gathered constantly. Several groups can benefit from the data provided by this geographical imagery. However, it is time-consuming to manually analyze each image to gain information on land cover. This research suggests using deep learning methods for precise and rapid pixel-by-pixel classification of aerial imagery for land cover analysis, which would be a significant step forward in resolving this issue. The suggested method has several steps, such as the augmentation and transformation of data, the selection of deep learning models, and the final prediction. The study uses the three most popular deep learning models (Vanilla-UNet, ResNet50 UNet, and DeepLabV3 ResNet50) for the experiments. According to the experimental results, the ResNet50 UNet model achieved an accuracy of 94.37%, the DeepLabV3 ResNet50 model achieved an accuracy of 94.77%, and the Vanilla-UNet model achieved an accuracy of 91.31%. The accuracy, precision, recall, and F1-score of DeepLabV3 and ResNet50 are higher than those of the other two models. The proposed approach is also compared to the existing UNet approach, and the proposed approaches have produced greater probability prediction scores than the conventional UNet model for all classes. Our approach outperforms model DeepLabV3 ResNet50 on aerial image datasets based on the performance.

Fostering students’ geographical synthetic thinking using geographic subject mind maps

The term “geographical synthetic thinking” is taken from the latest Chinese version of the “Geography Curriculum Standards” (General High School 2017 edition, Compulsory Education 2022 edition) and is one of the four core competencies prioritized in the teaching of geography at the secondary-school level. This study proposes that using geographic subject mind maps improves students’ geographical synthetic thinking and develops a set of geographic subject mind map (GSMM) drawing standards for teachers and matching standards and suggestions for teaching applications. Seven geography teachers and their students from eight class sections in seven provinces and cities in China were recruited to participate in the study. The teachers taught students how to use geographic subject mind maps designed to develop students’ geographical synthetic thinking in the geography classroom. After 4–5 experimental lessons, the geographical synthetic thinking level of the 267 valid student participants showed a significant improvement, with a statistically significant difference between pretest and posttest scores. The teachers’ level of application of geographic subject mind maps in teaching was significantly and positively correlated with the students’ improvements in geographical synthetic thinking, demonstrating that the standardized use of geographic subject mind maps in accordance with teaching application standards can effectively improve students’ geographical synthetic thinking levels.

Investigating the Readability of School Geographic Map Symbols Using Eye-Tracking Technology

Geography atlas is an educational material that is very important in geography education. For the eye tracking experiment, six mineral maps were selected from different geography atlases for high school students. The aim of this study was to analyze which design of conventional symbols is more understandable for high school students. We hope that the presented methodology and the case study will help mapmakers to create more effective school geographic maps.

The Correlation Between Asian Port Cities and Traditional Portuguese Urban Forms Based on Map and Machine Learning Analyses

In the 16th and 17th centuries, under the influence of the Portuguese Empire’s overseas expansion and cultural integration, the island city of Macau became an important international trading port in the Eastern Sea, with close ties to the Asian port cities governed by Portugal. This study introduces a new method for comparing urban morphological layouts using machine learning and investigates the potential benefits of combining urban morphological analysis with machine learning techniques. In addition, a combination of urban morphology theory and machine learning is used to excise samples of urban morphology from Portuguese urban geographical information maps. The morphological characteristics of port city areas are further extracted, and training labels for typical Portuguese urban textures are established. Using the YOLOv4 object detection algorithm, the results are compared with the urban textures of typical island and port cities of the Asian Silk Road—Goa in India, Malacca in Malaysia, Macau in China, and Dili in Timor-Leste—revealing the similarities and differences among the port cities in Asia influenced by traditional Portuguese urban practices. The results reveal the relationship between maritime trade and urban form.

The microgeo: an R package rapidly displays the biogeographic traits of soil microbial communities on maps.

Many R packages provide statistical approaches for elucidating the diversity of soil microbes, yet they still struggle to visualize microbial traits on a geographical map. This creates challenges in interpreting microbial biogeography on a regional scale, especially when the spatial scale is large or the distribution of sampling sites is uneven. Here, we developed a lightweight, flexible, and user-friendly R package called microgeo. This package integrates many functions involved in reading, manipulating, and visualizing geographical boundary data; downloading spatial datasets; and calculating microbial traits and rendering them onto a geographical map using grid-based visualization, spatial interpolation, or machine learning. Using this R package, users can visualize any trait calculated by microgeo or other tools on a map and can analyze microbiome data in conjunction with metadata derived from a geographical map. In contrast to other R packages that statistically analyze microbiome data, microgeo provides more-intuitive approaches in illustrating the biogeography of soil microbes on a large geographical scale, serving as an important supplement to statistically driven comparisons and facilitating the biogeographic analysis of publicly accessible microbiome data at a large spatial scale in a more convenient and efficient manner. The microgeo R package can be installed from the Gitee (https://gitee.com/bioape/microgeo) and GitHub (https://github.com/ChaonanLi/microgeo) repositories. Detailed tutorials for the microgeo R package are available at https://chaonanli.github.io/microgeo.

A novel voting ensemble model empowered by metaheuristic feature selection for accurate flash flood susceptibility mapping

This study addresses the challenges of flash flood susceptibility mapping in Yemen’s Qaa’Jahran Basin, characterized by complex terrain and limited hydro-meteorological data. To enhance predictive accuracy, we integrate metaheuristic feature selection with ensemble learning models. Initially, fifteen flash flood variables were retrieved using Geographic Information System (GIS) based remote sensing, setting the stage for a novel feature selection algorithm. Then, the Memo Search Algorithm (MSA), a metaheuristic approach is proposed to efficiently reduce feature space. Through comprehensive comparisons with established algorithms such as the Artificial Bee Colony (ABC) and Gray Wolf Optimizer (GWO), MSA refined the selection, identifying 'elevation’ and 'distance to streams’ as optimal factors. Statistical validations using the Friedman and Wilcoxon signed-rank tests confirmed the significant superiority of MSA over competing algorithms. Ensemble classifiers (bagging, boosting, stacking) were then applied to the reduced feature space. Comprehensive evaluation revealed the boosting ensemble with MSA outperformed traditional techniques reaching 98.75% accuracy, 0.9896 Area Under the Curve (AUC), and 98.95% the harmonic mean of the precision and recall (F1-score). Precision in identifying high-risk flash flood zones was underlined via spatial prediction, confirming the integrated framework’s ability to significantly improve forecast accuracy. The findings aid disaster management with powerful geographic mapping in data-poor regions. The proposed framework is adaptable globally for flash flood-prone areas with similar constraints. As climate change is expected to increase extreme rainfall events, communities globally will need robust data-driven methodologies for flash flood susceptibility mapping. The Key recommendations of the current study include investigating hybrid feature selection methods to better enhance predictive inputs and analyzing transferability across hydro-climatic zones.

Decomposing geographical judgments into spatial, temporal and linguistic components

When mentally exploring maps representing large-scale environments (e.g., countries or continents), humans are assumed to mainly rely on spatial information derived from direct perceptual experience (e.g., prior visual experience with the geographical map itself). In the present study, we rather tested whether also temporal and linguistic information could account for the way humans explore and ultimately represent this type of maps. We quantified temporal distance as the minimum time needed to travel by train across Italian cities, while linguistic distance was retrieved from natural language through cognitively plausible AI models based on non-spatial associative learning mechanisms (i.e., distributional semantic models). In a first experiment, we show that temporal and linguistic distances capture with high-confidence real geographical distances. Next, in a second behavioral experiment, we show that linguistic information can account for human performance over and above real spatial information (which plays the major role in explaining participants’ performance) in a task in which participants have to judge the distance between cities (while temporal information was found to be not relevant). These findings indicate that, when exploring maps representing large-scale environments, humans do take advantage of both perceptual and linguistic information, suggesting in turn that the formation of cognitive maps possibly relies on a strict interplay between spatial and non-spatial learning principles.

Spatio-temporal pattern and associate meteorological factors of airborne diseases in Bangladesh using geospatial mapping and spatial regression model.

Airborne diseases due to climate change pose significant public health challenges in Bangladesh. Little was known about the spatio-temporal pattern of airborne diseases at the district level in the country. Therefore, this study aimed to investigate the spatio-temporal pattern and associated meteorological factors of airborne diseases in Bangladesh using exploratory analysis and spatial regression models. This study used district-level reported cases of airborne diseases (meningococcal, measles, mumps, influenza, tuberculosis, and encephalitis) and meteorological data (temperature, relative humidity, wind speed, and precipitation) from 2017 to 2020. Geospatial mapping and spatial error regression models were utilized to analyze the data. From 2017 to 2020, a total of 315 meningococcal, 5159 measles, 1341 mumps, 346 influenza, 4664 tuberculosis, and 229 encephalitis cases were reported in Bangladesh. Among airborne diseases, measles demonstrated the highest prevalence, featuring a higher incidence rate in the coastal Bangladeshi districts of Lakshmipur, Patuakhali, and Cox's Bazar, as well as in Maulvibazar and Bandarban districts from 2017 to 2020. In contrast, tuberculosis (TB) emerged as the second most prevalent disease, with a higher incidence rate observed in districts such as Khagrachhari, Rajshahi, Tangail, Bogra, and Sherpur. The spatial error regression model revealed that among climate variables, mean (β = 9.56, standard error [SE]: 3.48) and maximum temperature (β = 1.19, SE: 0.40) were significant risk factors for airborne diseases in Bangladesh. Maximum temperature positively influenced measles (β = 2.74, SE: 1.39), whereas mean temperature positively influenced both meningococcal (β = 5.57, SE: 2.50) and mumps (β = 11.99, SE: 3.13) diseases. The findings from the study provide insights for planning early warning, prevention, and control strategies to combat airborne diseases in Bangladesh and similar endemic countries. Preventive measures and enhanced monitoring should be taken in some high-risk districts for airborne diseases in the country.

Mapping the Ecological Terrain of Stroke Prehospital Delay: A Nationwide Registry Study.

Delays in hospital presentation limit access to acute stroke treatments. While prior research has focused on patient-level factors, broader ecological and social determinants have not been well studied. We aimed to create a geospatial map of prehospital delay and examine the role of community-level social vulnerability. We studied patients with ischemic stroke who arrived by emergency medical services in 2015 to 2017 from the American Heart Association Get With The Guidelines-Stroke registry. The primary outcome was time to hospital arrival after stroke (in minutes), beginning at last known well in most cases. Using Geographic Information System mapping, we displayed the geography of delay. We then used Cox proportional hazard models to study the relationship between community-level factors and arrival time (adjusted hazard ratios [aHR] <1.0 indicate delay). The primary exposure was the social vulnerability index (SVI), a metric of social vulnerability for every ZIP Code Tabulation Area ranging from 0.0 to 1.0. Of 750 336 patients, 149 145 met inclusion criteria. The mean age was 73 years, and 51% were female. The median time to hospital arrival was 140 minutes (Q1: 60 minutes, Q3: 458 minutes). The geospatial map revealed that many zones of delay overlapped with socially vulnerable areas (https://harvard-cga.maps.arcgis.com/apps/webappviewer/index.html?id=08f6e885c71b457f83cefc71013bcaa7). Cox models (aHR, 95% CI) confirmed that higher SVI, including quartiles 3 (aHR, 0.96 [95% CI, 0.93-0.98]) and 4 (aHR, 0.93 [95% CI, 0.91-0.95]), was associated with delay. Patients from SVI quartile 4 neighborhoods arrived 15.6 minutes [15-16.2] slower than patients from SVI quartile 1. Specific SVI themes associated with delay were a community's socioeconomic status (aHR, 0.80 [95% CI, 0.74-0.85]) and housing type and transportation (aHR, 0.89 [95% CI, 0.84-0.94]). This map of acute stroke presentation times shows areas with a high incidence of delay. Increased social vulnerability characterizes these areas. Such places should be systematically targeted to improve population-level stroke presentation times.

Spatial Analysis of Forest Fire Potential in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi

This study aims to analyze the potential for forest fires in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi, by utilizing geographic information system data and mapping areas based on fire potential. The research method involved field surveys and the analysis of primary and secondary data. Forest fire hazards maps were obtained from the analysis of Landsat satellite images that were classified to provide information on land cover, slope, distance from roads, and categorized based on predetermined weighting categories. The overlay process was conducted using Geographic Information System software. The main findings indicate that the area has diverse land cover, with reed grassland savanna having the highest fire hazards covering an area of 1203.78 ha. Very steep slopes cover 339.94 ha, and the distance from roads and settlements to the highest hazards point is 140 meters. These variables also influence the level of hazards. Implications of this study include the placement of forest fire prevention patrols, the use of weather modification technology, and post-fire support to restore ecosystems.

Intelligent Transportation System Technologies, Challenges and Security

Intelligent Transportation Systems (ITS) first appeared in 1868 with traffic lights. With developing technology, the need to bring a smart approach to transportation applications within the scope of speed and environmental protection has emerged. Protecting ITS infrastructure against cyber attacks has become a matter of reputation for states. It is essential to provide the necessary technological infrastructure for the integrated operation of the systems used in ITS, especially geographical location, communication, and mapping. These technological developments bring cyber attacks, risks, and many dangers that should be avoided, especially on the systems used. This study examines ITS architecture, applications, communication technologies, and new trend technologies in detail. This study includes contributing to studies in the field of ITS and preventing attacks and incidents that may occur in terms of cyber security. The most important cyber attacks that may occur in ITS applications are included. In addition, the minimum security requirements that can be taken in ITS applications and infrastructures against these attacks are included.

Understanding the important characteristics of neighbourhoods to facilitate ageing in place and longevity.

This study investigates which local area characteristics, included in the Healthy Ageing/Vulnerable ENvironment (HAVEN) Index, are the strongest factors predicting transition into permanent residential aged care (PRAC) and mortality, and the geographic distribution of these factors. A retrospective cohort study of older individuals living in the community in Adelaide who received their first eligibility assessment for age care services between 2013 and 2015 (n = 16,939) was conducted. The study cohort, from the Registry of Senior Australians (ROSA), was linked by postcode to HAVEN Index items, selected following item response theory (IRT) analysis to determine the strongest local area factors associated with PRAC and mortality. Geospatial mapping of the factors determined the geographic distribution of these significant factors. Fourteen HAVEN Index items were associated with entry into PRAC and mortality. Three area-level items, lower educational attainment, financial housing stress and low levels of volunteering by older people, were risk factors for entry into PRAC and mortality while the remainder of the items identified were different for each outcome. The mapped local area risk factors for each outcome highlighted similar geographical areas of vulnerability. Local area characteristics are associated with entering PRAC and mortality. Our findings can inform area-level responses to make neighbourhoods more age-friendly, potentially allowing more people to age longer in place. Similar analyses, conducted for other areas, could provide evidence to support the widespread development of age-friendly neighbourhoods reducing area-level inequalities in ageing.

Somewhere downstairs: Re-animating a departmental geography collection

This paper discusses the re-animation of a geography departmental collection through a study of the archives and map collection of the School of Geography, University of Nottingham. The discussion is situated within parallel examples of work on geographical archives and map collections, and wider debates on engagement with archival sources. The paper considers how a previously dormant collection has been re-animated in recent years, conveys the range of source material involved, and discusses the possibilities of digitisation. The paper thereby raises questions which could be asked of the holdings of any academic department, from whatever time period.

DEVELOPMENT OF THE INFORMATION PORTAL “KAZGEOWARNING” FOR FORECASTING FIRES ON THE TERRITORY OF KAZAKHSTAN

This article presents the development of an information system tailored for predicting and managing forest fires in Kazakhstan. The system's primary objective is to establish an information portal dedicated to fire forecasting, enhancing the accuracy of predictions and timely warnings. To achieve this, the system focuses on tasks such as predicting the likelihood of forest and steppe fires, monitoring environmental changes in real-time, and visualising the current situation on maps. It also generates comprehensive reports detailing fire risk, available resources, and necessary measures. Users will receive alerts about potential fire threats, facilitating rapid response. The system's data analysis incorporates geographical and climatic factors for precise forecasting. The ultimate goal is to significantly reduce the environmental and economic impact of forest fires in the country, emphasising the creation of a versatile platform for swift emergency response, integrating modern technologies into practical use. The practical significance encompasses improved fire warnings, enhanced safety for both the population and nature, and a reduction in economic losses. The accompanying website serves as a dynamic platform to collect, analyse, and visualise real-time data on disasters and crises, providing users with geographic information, maps, and reports crucial for making informed decisions during emergency situations.

Calycosiphonia or Kupeantha (Coffeeae, Rubiaceae)? A morphological and molecular study of a new species from the Albertine Rift

Field work in the Kibira National Park (Burundi), located in the Kivu-Ruwenzori system of the Afromontane Region, revealed the existence of a new species clearly belonging to the Argocoffeopsis-Calycosiphonia clade (Coffeeae, Rubiaceae). The species shows striking heterophylly: the plagiotropous branches have several nodes bearing reduced or even scaly leaves. For the rest, it shares characters with Calycosiphonia and Kupeantha. Therefore, a morphological comparison with the clade is done, as well as molecular phylogenetic analyses. The morphology of the novelty is closer to Kupeantha than to Calycosiphonia, inter alia because the anthers have no transverse septa, in contrast to the multilocellate anthers of Calycosiphonia. However, the molecular data advocate for a position in Calycosiphonia – a result weakening the morphological distinction between Calycosiphonia and Kupeantha. The former genus is no longer restricted to species with transverse septa in the anthers and with placental outgrowths around the seed. The new species is formally described as Calycosiphonia albertina Ntore &amp; Robbr. sp. nov. Nomenclaturally, this placement is also the most conservative option. A taxonomic treatment, illustrations, a geographical distribution map, and a preliminary conservation assessment are provided. The previous inclusion of Calycosiphonia pentamera in Kupeantha based on morphology is here corroborated by molecular analyses.

A new insight for geophysical and geospatial mapping quaternary alluvium of Indo-Gangetic plains for exploration and assessment of groundwater in Haryana, India

A comprehensive study on groundwater exploration and assessment was conducted, integrating geophysical, geological, lithological, and resistivity logging techniques for a holistic evaluation of groundwater resources in Haryana, India. The primary research objective was to gain insight into the geological composition of alluvial sand and its implications for groundwater occurrence in Haryana's semi-arid region. The study showcases high-resolution electrical resistivity tomography results alongside conceptual cross-sectional models and 3D lithological models, revealing buried channels that serve as groundwater sources and saturated sand as well as recharge conduits within the alluvial region of Haryana. Moreover, borehole drilling at two sites and resistivity logging data demonstrate a strong correlation between various lithologies and resistivity logs. Overall, groundwater prospect zones are delineated at depths ranging from 18 m to over 100 m based on resistivity values of aquifer zones inferred from electrical resistivity tomography models, as well as mapped thick sand layers with significant hydrogeological importance in the study area. This emphasizes the stress on the aquifer system, where groundwater is being extracted from deeper depths. The conceptual litho-sectional model reveals a thick sand signature at intermediate depths, indicating the presence of buried paleochannels extending from Galedwa (Kurukshetra) to Jubbal (Yamunanagar). Additionally, the 3D lithological models, along with the electrical resistivity tomography results, facilitate strategic planning for shallow and deep boreholes for groundwater exploration and prospecting, aiming at sustainable groundwater development and management of resources in both the present and future.