Search Results (61)

The objective of this study is to explore the applicability of irregular area cartograms for the visualization of sustainable development indicator components, utilizing earth observation (EO) data. The analysis focuses on selected Sustainable Development Goals (SDG 11 ‘Make cities and human settlements inclusive, safe, resilient and sustainable’ and SDG 13 ‘Take urgent action to combat climate change and its impacts’) and specific targets and indicators related to green urban areas and air quality (targets: 13.2, 11.6, and 11.7; indicators: 11.6.2., 11.7.1., 13.2.2.). A comprehensive review of the relevant literature indicates that irregular area cartograms are employed only sporadically in the context of SDG monitoring, particularly at lower levels of territorial division (i.e., communes and counties). To address this gap, a series of thematic maps, including choropleth maps and irregular area cartograms, was developed. These visualizations are based on EO-derived datasets and supplemented with statistical information obtained from the Local Data Bank of the Statistics Poland. The analysis demonstrates that irregular area cartograms provide an effective means of visualizing spatial disparities in variables such as urban green space availability and air pollution at the commune and county levels. These visualizations enhance the interpretability of complex indicator structures and support more nuanced assessments of progress toward selected Sustainable Development Goals, especially in spatially detailed analytical frameworks. Preliminary usability testing among potential users revealed that irregular area cartograms are perceived as an interesting visualization technique that enhances data interpretation. Full article

Background: Drug-resistant tuberculosis (DR-TB) constitutes a serious threat to global public health due to the increase in strains resistant to multiple drugs, especially isoniazid and rifampicin. This resistance increases mortality, estimated at 25.6% globally, and complicates treatments due to its high toxicity and cost. Materials and Methods: A quantitative ecological study was carried out with data on drug-resistant tuberculosis reported in Sivigila in the years (2020–2023) SIVIGILA database. 1694 cases were analyzed, considering sociodemographic variables such as age, sex, nationality and prioritized population groups. Departmental rates per 100,000 inhabitants were calculated with DANE projection, from these choropleth maps were developed. Applying a Kulldorff spatial scan under a Poisson model using the SMERC package of R (version 4.5.1), with windows centered on each department and Monte Carlo simulation contrast to identify high-risk clusters (RR > 1). Results: (DR-TB) Predominantly in men aged 30–44 years, with a progressive increase until 2023 (IRR = 2.11). Three high-risk clusters were detected in the southwest and center of the country. Discussion: Drug-resistant tuberculosis in Colombia showed a sustained increase in the years of study, with a cumulative increase of 110% compared to 2020, associated with economically active people more exposed due to occupational and social factors. The greatest burden was observed in the general population. Cases also increased in groups with social and health vulnerability conditions. Conclusions: The departments of Risaralda, Meta, and Valle del Cauca presented the highest drug resistance rates in Colombia. Full article

This study explores the potential of ChatGPT-4, an AI-powered large language model, to generate thematic maps and compare its outputs to the traditional method in which maps are produced manually by humans using GIS software. Prompt engineering is a crucial methodology of large language models that can enhance output quality. The main objective of this study is to assess the capability of AI-generated maps and to compare the quality with a traditional method. The study evaluates two prompt patterns: basic (zero-shot prompts) and advanced (Cognitive Verifier and Question Refinement). The performance of AI-generated maps is assessed based on attempts, errors, incorrect results, and map completeness. The final stage involved evaluating AI-generated maps against cartographic rules to assess their suitability. ChatGPT-4 performs well in generating suitable choropleth maps but faced challenges in understanding the prompts and potential errors in the generated code. Advanced prompts reduced errors and improved the quality of outputs, particularly for complex map elements. This paper enhances the understanding of AI’s role in cartography and further research in automated cartography. The study assesses cartographic aspects, offering insights into the strengths and limitations of AI in cartography, illustrating how large language models can process geospatial data and adhere to cartographic principles. The study also paves the way for future innovations in automated geovisualization. Full article

This research investigates higher education admission outcomes at Hungarian universities for ethnic Hungarian minority students residing in countries within the Carpathian Basin. The region is distinguished by a variety of national policies that impact minority education. By analyzing extensive data on the availability of mother tongue education, the status of minority rights, advanced level examination performance, and types of settlement using a wide range of statistical methods, our study reveals significant cross-national differences in the distribution of admission scores and central tendencies. Compared to lower and more varied scores for students from Ukraine and Romania, ethnic Hungarian students from Serbia and Slovakia achieved high average admission scores. Performance was notably more consistent among students from EU member states compared to non-EU regions, strongly linking outcomes to the more robust implementation of minority rights and better access to mother-tongue education within the EU framework. A critical finding is the strong positive correlation (Pearson r = 0.837) between admission scores and advanced level examination results, highlighting the pivotal role of these exams for the academic progression of these minority students. The Jonckheere-Terpstra test (p < 0.05) further confirmed significant performance differences between ranked country groups, with Serbian and Slovak students generally outperforming their Ukrainian and Romanian counterparts. Counterintuitively, settlement type (urban vs. rural) exhibited a negligible relationship with admission scores (r = 0.150), explaining only 2% of score variability. This challenges common assumptions and suggests other factors specific to the Hungarian minority context are more influential. This study provides crucial insights into the complex dynamics influencing Hungarian minority students’ access to higher education, underscoring cross-country educational inequalities, and informing the development of equitable minority rights and mother-tongue education policies in Central Europe for these often-marginalized communities. Full article

Road traffic accidents remain a critical public health concern worldwide, with Poland consistently experiencing high fatality rates—52 deaths per million inhabitants in 2023, compared to the EU average of 46. To investigate the underlying factors contributing to these accidents, we developed a multifactorial dataset integrating 250,000 accident records from 2015 to 2023 with spatially interpolated weather data and sociodemographic indicators. We employed Kriging interpolation to convert point-based weather station data into continuous surfaces, enabling the attribution of location-specific weather conditions to each accident. Following comprehensive preprocessing and spatial analysis, we generated visualizations—including heatmaps and choropleth maps—that revealed distinct regional patterns at the county level. Our preliminary findings suggest that accident occurrence and severity are driven by different underlying factors: while temperature and vehicle counts strongly correlate with total accident numbers, humidity, precipitation, and road infrastructure quality show stronger associations with fatal outcomes. This integrated dataset provides a robust foundation for Bayesian and time-series modeling, supporting the development of evidence-based road safety strategies. Full article

The transition of the energy sector to green energy is one of the priorities of sustainable development, serving as an important instrument for balancing economic growth and environmental protection. The purpose of this article is to analyze the relationship between the share of renewable energy in total electricity production and the Environmental Dimension of Sustainable Development in the voivodeships of Poland during the years 2005–2023. To avoid difficulties in interpreting the statistical model—arising from challenges in determining the precise nature of the relationship between individual explanatory variables and the dependent variable—a collinearity test (using the Variance Inflation Factor, VIF, in three stages) was conducted. The relationship was examined using various statistical methods, including Pearson’s linear correlation and linear regression. Additionally, to visualize the local variation in this relationship, a spatial variation study was carried out using Geographic Information System (GIS) tools, supported by a series of bivariate choropleth maps. The results may suggest a positive impact of an increase in the share of electricity production from renewable energy sources on the state of the environment; however, this finding requires further, more detailed research. Full article

This study examines the spatial and temporal evolution of obesity among adults in Italian regions. In Italy, regional administrative areas are responsible for providing health services. Moreover, Italian regions present different socioeconomic conditions and health and nutritional habits. As a result, a regional analysis of the spatiotemporal evolution of obesity allows the identification of key areas for prevention and control, enabling the design of more targeted and effective interventions. In this study, the geographic clustering of obesity in Italy was explored by analyzing the local spatial autocorrelation of regional-level prevalence rates of adulthood obesity between 2010 and 2022, updating and expanding the existing literature. Data from the Health For All repository are analyzed to determine distribution patterns and trends, employing choropleth maps, Moran’s Index and Welch’s t-test. Gender inequalities have been underlined both in the spatial and temporal distribution. Results show that obesity exhibits spatial clustering, with greater severity in the south. During the period under analysis, obesity prevalence rates in Italy show a tendency to grow, with a sharp increase during the COVID-19 lockdown. Full article

This paper presents a Generalized Sentiment Analytics Framework (GSAF) for understanding public sentiments on different key societal issues in real time. The framework uses natural language processing techniques for computing sentiments and displays them in different emotions leveraging publicly available social media data (i.e., X threads (formally Twitter)). As a case study of our developed framework, we have leveraged over 3 million tweets to map, analyze, and visualize public sentiment state-wise across the United States on different societal issues. With X as a key social media platform, this study harnesses its vast user base to provide real-time insights into emotional responses surrounding key societal and political events. Built using R and the Shiny web framework, the platform offers users interactive visualizations of emotion-specific sentiments, such as anger, joy, and trust, displayed on a U.S. state-level choropleth map. The platform allows keyword-based searches and employs advanced text-processing techniques to filter and clean tweet data for robust analysis. Furthermore, it implements efficient caching mechanisms to enhance performance, comparing various strategies like LRU and Size-Based Eviction. This research highlights the potential of sentiment analysis for policymaking, marketing, and public discourse, providing a valuable tool for understanding and predicting public sentiment trends. Full article

(1) Background: Lung cancer is the deadliest and second most prevalent cancer in Pennsylvania (PA), and African American patients are disproportionately affected. Lung cancer morbidity and mortality in Philadelphia County are among the highest in PA. Geographic information systems (GIS) are useful to explore geospatial variations in the cancer burden and risk factors. Therefore, we used GIS to analyze the lung cancer burden in Philadelphia to assess which areas of the city have the highest morbidity and mortality, identify potential clusters, and determine which census tract-level characteristics were associated with higher tract-level cancer burden. (2) Methods: Using secondary data from the Pennsylvania Cancer Registry, age-adjusted standardized incidence and mortality ratios (SIR and SMR) were calculated by census tract, and choropleth maps were created to visualize geographic variations in the disease burden. Two geostatistical methods were used to determine the presence of lung cancer clusters. Multivariable regression analyses were performed to identify which census-tract level characteristics correlated with a higher lung cancer burden. (3) Results: Three distinct geographical lung cancer clusters were identified. After controlling for demographics and other covariates, adult smoking prevalence, prevalence of chronic obstructive pulmonary disease, and percentage of residential addresses vacant were positively associated with higher lung cancer SIR and SMR. (4) Conclusions: Our findings may inform cancer control efforts within the region and guide future municipal-level GIS analyses of the lung cancer burden. Full article

Background: South Korea was one of the first countries to experience the Coronavirus disease (COVID-19) epidemic, and the regional-level trends and patterns in the incidence and case-fatality rates have been observed to evolve with time. This study established yearly spatiotemporal evolution patterns of COVID-19 by region and identified possible regional risk factors accounting for the observed spatial variations. Methods: COVID-19 data between 20 January 2020 and 31 August 2023 were collected from the Korean Centers for Disease Prevention and Control (KCDA). We generated epidemic curves and calculated the yearly incidence and case-fatality rates for each region. In addition, choropleth maps for the location quotient of cases and deaths to visualize yearly regional intensities were generated and the Moran’s I calculated. Associations between the incidence and case-fatality rates with regional risk factors were estimated using regression models. All analyses were performed in R version 4.4.2. Results: We noted a significant difference in the incidence rate by year, with 2022 recording the highest for all regions. A consistent and significant spatial autocorrelation for cases and deaths across all years was observed with Moran I values above 0.4 (p < 0.05). There was a positive association of COVID-19 incidence rates with the population density (RR = 0.02, CI: 0.01–0.04, p = 0.03), percentage aged 60 years and above (RR = 0.03, CI: 0.01–0.05, p = 0.01), smoking prevalence (women) (RR = 0.79, CI: 0.54–1.04, p = 0.01), and diabetes prevalence (women) (RR = 0.51, CI: 0.32–0.71, p = 0.04). Conclusions: The spatiotemporal evolution patterns of COVID-19 in Korea consisted of oscillating hot and cold spots across the pandemic period in each region. These findings provide a useful reference to the government as it continues with the routine surveillance of COVID-19 across the country. Full article

Timely, region-based geo-maps like choropleths are essential for smart city applications like traffic monitoring and urban planning because they can reveal statistical patterns in geotagged data. However, because data overloading is brought on by the quick inflow of massive geospatial data, creating these visualizations in real time presents serious difficulties. This paper introduces ApproxGeoMap, a novel system designed to efficiently generate approximate geo-maps from fast-arriving georeferenced data streams. ApproxGeoMap employs a stratified spatial sampling method, leveraging geohash tessellation and Earth Mover’s Distance (EMD) to maintain both accuracy and processing speed. We developed a prototype system and tested it on real-world smart city datasets, demonstrating that ApproxGeoMap meets time-based and accuracy-based quality of service (QoS) constraints. Results indicate that ApproxGeoMap significantly enhances efficiency in both running time and map accuracy, offering a reliable solution for high-speed data environments where traditional methods fall short. Full article

Background and Objectives: Suicide is a pressing public health issue globally, including in Brazil, where it ranks among the leading causes of mortality. This study aimed to analyze the spatial, temporal, and spatiotemporal distribution of suicide mortality in Brazil from 2000 to 2022. Materials and Methods: Using secondary data from the Mortality Information System of Brazil’s 5570 municipalities, an ecological study of time series was conducted. Segmented linear regression (Joinpoint 4.6 version) was used to calculate temporal trends, while Moran’s indices were employed to analyze spatial autocorrelations. Retrospective scanning was utilized to investigate spatiotemporal clusters, and choropleth maps were developed to visualize high-risk areas. Results: The analysis revealed the occurrence of 240,843 suicides in Brazil, with higher percentages in the southeast, south, and northeast regions. The south, central–west, and southeast regions exhibited the highest mortality rates, predominantly among white, single men, aged 20 to 59, with 1 to 11 years of schooling. Intentional self-harm by hanging, strangulation, and suffocation was the main cause. The general trend of mortality due to suicide in Brazil was increasing (AAPC: 2.9; CI 95%: 2.6 to 3.0), with emphasis on the age groups from 10 to 19 years (AAPC: 3.7; CI 95%: 2.9 to 4.5) and 20–39 years old (AAPC: 2.9; CI 95%: 2.3 to 3.5). The brutal and smoothed rates revealed areas of high mortality in the south, north, and central–west regions. Conclusions: The findings of this study highlight the need to direct resources and efforts to the south and midwest regions of Brazil, where suicide rates are the highest. Additionally, implementing targeted prevention programs for young men, who are the most affected, is essential to reduce suicide mortality in these areas. Full article

This study provides a comprehensive overview of Phase I of the deforestation dryland alert system. It focuses on its operation and outcomes from 2020 to 2022 in the Caatinga biome, a unique Brazilian dryland ecosystem. The primary objectives were to analyze deforestation dynamics, identify areas with highest deforestation rates, and determine regions that require prioritization for anti-deforestation efforts and conservation actions. The research methodology involved utilizing remote sensing data, including Landsat imagery, processed through the Google Earth Engine platform. The data were analyzed using spectral unmixing, adjusted Normalized Difference Fraction Index, and harmonic time series models to generate monthly deforestation alerts. The findings reveal a significant increase in deforestation alerts and deforested areas over the study period, with a 148% rise in alerts from 2020 to 2022. The Caatinga biome was identified as the second highest in detected deforestation alerts in Brazil in 2022, accounting for 18.4% of total alerts. Hexagonal assessments illustrate diverse vegetation cover and alert distribution, enabling targeted conservation efforts. The Bivariate Choropleth Map demonstrates the nuanced relationship between alert and vegetation cover, guiding prioritization for deforestation control and native vegetation restoration. The analysis also highlighted the spatial heterogeneity of deforestation, with most deforestation events occurring in small patches, averaging 10.9 ha. The study concludes that while the dryland alert system (SAD-Caatinga—Phase I) has effectively detected deforestation, ongoing challenges such as cloud cover, seasonality, and more frequent and precise monitoring persist. The implementation of DDAS plays a critical role in sustainable forestry by enabling the prompt detection of deforestation, which supports targeted interventions, helps contain the process, and provides decision makers with early insights to distinguish between legal and illegal practices. These capabilities inform decision-making processes and promote sustainable forest management in dryland ecosystems. Future improvements, including using higher-resolution imagery and artificial intelligence for validation, are essential to detect smaller deforestation alerts, reduce manual efforts, and support sustainable dryland management in the Caatinga biome. Full article

Despite consensus guidelines, most patients with early-stage triple-negative (TN) and HER2-positive (HER2+) breast cancer do not see a medical oncologist prior to surgery and do not receive neoadjuvant chemotherapy (NAC). To understand barriers to care, we aimed to characterize the relationship between geography (region of residence and cancer centre proximity) and receipt of a pre-treatment medical oncology consultation and NAC for patients with TN and HER2+ breast cancer. Using linked administrative datasets in Ontario, Canada, we performed a retrospective population-based analysis of women diagnosed with stage I–III TN or HER2+ breast cancer from 2012 to 2020. The outcomes were a pre-treatment medical oncology consultation and the initiation of NAC. We created choropleth maps to assess the distribution of the outcomes and cancer centres across census divisions. To assess the relationship between distance to the nearest cancer centre and outcomes, we performed multivariable regression analyses adjusted for relevant factors, including tumour extent and nodal status. Of 14,647 patients, 29.9% received a pre-treatment medical oncology consultation and 77.7% received NAC. Mapping demonstrated high interregional variability, ranging across census divisions from 12.5% to 64.3% for medical oncology consultation and from 8.8% to 64.3% for NAC. In the full cohort, compared to a distance of ≤5 km from the nearest cancer centre, only 10–25 km was significantly associated with lower odds of NAC (OR 0.83, 95% CI 0.70–0.99). Greater distances were not associated with pre-treatment medical oncology consultation. The interregional variability in medical oncology consultation and NAC for patients with TN and HER2+ breast cancer suggests that regional and/or provider practice patterns underlie discrepancies in the referral for and receipt of NAC. These findings can inform interventions to improve equitable access to NAC for eligible patients. Full article

The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data, CRS and datum are first presented along with a basic workflow to conduct reproducible geospatial research in R. Examples of important types of maps (scatter, bubble, choropleth, hexbin and faceted) created from open-source environmental data are illustrated and their practical implementation in R is discussed. Through these examples, essential manipulations on geospatial vector data are demonstrated (reading, transforming CRS, creating geometries from scratch, buffer zones around existing geometries and intersections between geometries). Full article