Search Results (235)

Over the last decade, the scientific community has increasingly focused on forest-based practices for human well-being (FBPW), a term that includes all forest activities (e.g., forest bathing, forest therapy, social outdoor initiatives) important for improving people’s health and emotional status. This paper aims to develop a quantitative literature review on FBPW based on big data analysis (text mining on Scopus title and abstract) and PRISMA evaluation. The two techniques facilitate investigations across different geographic areas (major areas and geographical regions) and allow a focus on various topics. The results of text mining highlight the prominence of publications on FBPW for the improvement of human health in East Asia (e.g., Japan and South Korea). Furthermore, some specific themes developed by the literature for each geographical area emerge: urban green areas, cities, and parks in Africa; sustainable forest management and planning in the Americas; empirical studies on physiological and psychological effects of FBPW in Asia; and forest management and FBPW in Europe. PRISMA indicates a gap in studies focused on the reciprocal influences of forest variables and well-being responses. An investigation of the main physiological indicators applied in the scientific literature for the theme is also developed. The main strengths and weaknesses of the method are discussed, with suggestions for potential future lines of research. Full article

This paper presents a conceptual and methodological framework for using Geographical Information Systems (GIS) to “deep map” cultural heritage sites along Ireland’s Wild Atlantic Way, with a focus on the 1588 Spanish Armada wrecks in County Kerry and archaeological landscapes in County Sligo’s “Yeats Country.” Drawing on interdisciplinary dialogues from the humanities, social sciences, and geospatial sciences, it illustrates how digital spatial technologies can excavate, preserve, and sustain intangible cultural knowledge embedded within such palimpsestic landscapes. Using MAXQDA 24 software to mine and code historical, literary, folkloric, and environmental texts, the study constructed bespoke GIS attribute tables and visualizations integrated with elevation models and open-source archaeological data. The result is a richly layered cartographic method that reveals the spectral and affective dimensions of heritage landscapes through climate, memory, literature, and spatial storytelling. By engaging with “deep mapping” and theories such as “Spectral Geography,” the research offers new avenues for sustainable heritage conservation, cultural tourism, and public education that are sensitive to both ecological and cultural resilience in the West of Ireland. Full article

Text-to-SQL for spatial databases enables the translation of natural language questions into corresponding SQL queries, allowing non-experts to easily access spatial data, which has gained increasing attention from researchers. Previous research has primarily focused on rule-based methods. However, these methods have limitations when dealing with complicated or unknown natural language questions. While advanced machine learning models can be trained, they typically require large labeled training datasets, which are severely lacking for spatial databases. Recently, Generative Pre-Trained Transformer (GPT) models have emerged as a promising paradigm for Text-to-SQL tasks in relational databases, driven by carefully designed prompts. In response to the severe lack of datasets for spatial databases, we have created a publicly available dataset that supports both English and Chinese. Furthermore, we propose a GPT-based method to construct prompts for spatial databases, which incorporates geographic and spatial database knowledge into the prompts and requires only a small number of training samples, such as 1, 3, or 5 examples. Extensive experiments demonstrate that incorporating geographic and spatial database knowledge into prompts improves the accuracy of Text-to-SQL tasks for spatial databases. Our proposed method can help non-experts access spatial databases more easily and conveniently. Full article

In social networks like community Question-Answering (cQA) services, members interact with each other by asking and answering each other’s questions. This way they find counsel and solutions to very specific real-life situations. Thus, it is safe to say that community fellows log into this kind of social network with the goal of satisfying information needs that cannot be readily resolved via traditional web searches. And in order to expedite this process, these platforms also allow registered, and many times unregistered, internauts to browse their archives. As a means of encouraging fruitful interactions, these websites need to be efficient when displaying contextualized/personalized material and when connecting unresolved questions to people willing to help. Here, demographic factors (i.e., gender) together with frontier deep neural networks have proved to be instrumental in adequately overcoming these challenges. In fact, current approaches have demonstrated that it is perfectly plausible to achieve high gender classification rates by inspecting profile images or textual interactions. This work advances this body of knowledge by leveraging lexicalized dependency paths to control the level of abstraction across texts. Our qualitative results suggest that cost-efficient approaches exploit distilled frontier deep architectures (i.e., DistillRoBERTa) and coarse-grained semantic information embodied in the first three levels of the respective dependency tree. Our outcomes also indicate that relative/prepositional clauses conveying geographical locations, relationships, and finance yield a marginal contribution when they show up deep in dependency trees. Full article

Earth observation data serve as a fundamental resource in Earth system science. The rapid advancement of remote sensing and in situ measurement technologies has led to the generation of massive volumes of data, accompanied by a growing body of geographic textual information. Efficient and accurate classification and management of these geographic texts has become a critical challenge in the field. However, the effectiveness of traditional classification approaches is hindered by several issues, including data sparsity, class imbalance, semantic ambiguity, and the prevalence of domain-specific terminology. To address these limitations and enable the intelligent management of geographic information, this study proposes an efficient geographic text classification framework based on large language models (LLMs), tailored to the unique semantic and structural characteristics of geographic data. Specifically, LLM-based data augmentation strategies are employed to mitigate the scarcity of labeled data and class imbalance. A semantic vector database is utilized to filter the label space prior to inference, enhancing the model’s adaptability to diverse geographic terms. Furthermore, few-shot prompt learning guides LLMs in understanding domain-specific language, while an output alignment mechanism improves classification stability for complex descriptions. This approach offers a scalable solution for the automated semantic classification of geographic text for unlocking the potential of ever-expanding geospatial big data, thereby advancing intelligent information processing and knowledge discovery in the geospatial domain. Full article

As major countries around the world have successively proposed the construction goal of “Beautiful National Land Space”, how to effectively integrate urban design with spatial control in specific geographical environments and use urban design to achieve efficient spatial control has become a new research trend. The process of planning the national territory is constrained by the legal framework, involving multiple planning stages and multiple stakeholders. In an ideal state, these planning stages and stakeholders should coordinate with each other, but during the actual implementation of the plan, these factors are often not coordinated enough, making it difficult for the plan to play a role. In this study, Mile City in Yunnan Province, a representative city in the unique intermontane basin area of Southwestern China, was used as a case to explore how to use urban design methods in territorial spatial planning to achieve more efficient spatial control. This study provides scientific support for establishing an indicator control system for urban design methods by combining multiple data collection methods such as text analysis, image analysis, and interview methods. The distinctive features of Mile City have been further enhanced by optimizing its spatial layout through urban design, and it has been scientifically integrated into the territorial spatial planning system. The results indicate that the successful implementation of urban design highly relies on the reform willingness of local governments, clear control frameworks, and the coordinated integration of regional ecological resources and landscape features. This study proposed a set of urban design control methods suitable for intermontane basin-type cities and formed a comprehensive control framework including city, town, and landscape. In addition, it will provide methodological support and references for improving the scientific management of “Beautiful Land” in the special geographical environment of Southwest China. Full article

Understanding morphological descriptions of plants documented by ancient peoples over 1000 years ago and identifying the species they described are critical for reconstructing the natural geographic distribution of plant taxa, tracking taxonomic variations, and inferring historical climate dynamics. Analyzing shifts in plant communities and climatic conditions during this period is essential to unravel the interplay among floristic composition, climate fluctuations, and anthropogenic impacts. However, research in this field remains limited, with greater emphasis placed on plant taxa from hundreds of millions of years ago. Investigations into flora and climate during the last two millennia are sparse, and pre-millennial climatic conditions remain poorly characterized. In this study, a historical text written 1475 years ago was analyzed to compile plant names and morphological features, followed by taxonomic identification. The research identified three gymnosperm species (one in Pinaceae, two in Cupressaceae), 1 Tamaricaceae species (dicotyledon), and 19 dicotyledon species. However, three plant groups could only be identified at the genus level. Using textual analysis and woody plant coexistence methods, the climate of 1475 years ago in western Henan Province, located in the middle-lower Yellow River basin in East Asia, was reconstructed. Results indicate that the mean temperature of the coldest month (MTCM) was approximately 1.3 °C higher than modern values. In comparison, the mean temperature of the warmest month (MTWM) and mean annual temperature (MAT) were lower than present-day levels. This suggests slightly cooler overall conditions with milder seasonal extremes in ancient Luoyang—a finding supported by contemporaneous studies. Furthermore, annual precipitation (AP), precipitation of the warmest quarter (PWQ), and precipitation of the coldest quarter (PCQ) in the Luoyang region 1475 years ago exceeded modern measurements, despite the area’s monsoonal climate. This suggests significantly higher atmospheric moisture content in ancient air masses compared to today. This study provides floristic and climatic baseline data for advancing our understanding of global climate variability at millennial scales. Full article

Due to global warming, the impact of heat waves on the sentimental health of urban residents has significantly intensified. However, the associative mechanism between diverse urban functional layouts and residents’ emotions at the street scale remains underexplored. Taking the five core urban areas of Chengdu as an example, this study used natural language processing technology to quantify the sentiments in social media texts and combined traditional geographical information for spatial analysis and correlation analysis, to explore the spatial distribution pattern of sentiments during heat waves (SDHW), as well as the correlation between SDHW and the functional categories of streets (FCS). The findings are as follows: (1) There are significant differences in the spatial distribution pattern of residents’ sentiments in the five core urban areas, and positive emotions within the Second Ring Road exhibit a higher proportion than those of peripheral areas, while negative sentiments are more gathered in the eastern area. (2) The street categories of green space, park, and public show a significant promoting role on residents’ positive sentiments. (3) There is an association between the industrial and commercial categories and negative sentiments, and the impact of the traffic category on residents’ sentiments shows spatial differences. (4) The combination of the residential category and other functional categories has a strong correlation with sentiments, indicating that a reasonable functional combination within residential areas plays a crucial role in promoting residents’ positive sentiments. The current study revealed the influence mechanism of the functional categories of streets on residents’ sentiments during heat waves, providing a scientific basis from the sentimental dimension for the optimization of street functional categories, heat wave emergency management, and the construction of resilient cities. Full article

Climate change threatens health and social development gains in Kenya, necessitating health policy planning for risk reduction and mitigation. To understand the state of knowledge on climate-related health impacts in Kenya, a scoping review of 25 years of environmental health research was conducted. In compliance with a pre-registered protocol, nine bibliographic databases and grey literature sources were searched for articles published from 2000 to 2024. Of 19,234 articles screened, 816 full texts were reviewed in duplicate, and a final 348 articles underwent data extraction for topic categorisation, trend analysis, and narrative summary. Most of the studies (97%, n = 336) were journal articles, with 64% published after 2014 (n = 224). The health topics centred on vector-borne diseases (45%, n = 165), primarily vector abundance (n = 111) and malaria (n = 67), while mental health (n = 12) and heat exposure (n = 9) studies were less frequent. The research was geographically concentrated on the Lake Victoria Basin, Rift Valley, and Coastal regions, with fewer studies from the northern arid and semi-arid regions. The findings show a shift from a focus on infectious diseases towards broader non-communicable outcomes, as well as regional disparities in research coverage. This review highlights the development of baseline associations between environmental exposures and health outcomes in Kenya, providing a necessary foundation for evidence-informed climate change and health policy. However, challenges in data and study designs limit some of the evidentiary value. Full article

In an earlier essay, I drew on Sara Ahmed’s formulation of a “homing device” to describe U.S. Latine uses of biblical texts and traditions, as well as “scriptures” more broadly conceived. In this essay, I hope to complicate that idea a little further. I draw on ethnographic methods and share two stories of two people who came from the same generation and lived in geographic proximity in the suburbs of Los Angeles, California, but who represent important differences in Latine contexts. These two case studies, when read comparatively, demonstrate how the Bible serves as a homing device, as an object around which both people look for and make sense of ideas of “home”, but the understandings of home and the ways they relate to biblical texts and traditions remain quite distinct. Full article

Schistosomiasis, a neglected tropical disease caused by parasitic worms of the genus Schistosoma and transmitted through freshwater snails, affects over 200 million people worldwide. Climate change, through rising temperatures, altered rainfall patterns, and extreme weather events, is influencing the distribution and transmission dynamics of schistosomiasis. This scoping review examines the impact of climate change on schistosomiasis transmission and its implications for disease control. This review aims to synthesize current knowledge on the influence of climate variables (temperature, rainfall, water bodies) on snail populations, transmission dynamics, and the shifting geographic range of schistosomiasis. It also explores the potential effects of climate adaptation policies on disease control. The review follows the Arksey and O’Malley framework and PRISMA-ScR guidelines, including studies published from 2000 to 2024. Eligible studies were selected based on empirical data on climate change, schistosomiasis transmission, and snail dynamics. A two-stage study selection process was followed: title/abstract screening and full-text review. Data were extracted on environmental factors, snail population dynamics, transmission patterns, and climate adaptation strategies. Climate change is expected to increase schistosomiasis transmission in endemic regions like Sub-Saharan Africa, Southeast Asia, and South America, while some areas, such as parts of West Africa, may see reduced risk. Emerging hotspots were identified in regions not currently endemic. Climate adaptation policies, such as improved water management and early warning systems, were found effective in reducing transmission. Integrating climate adaptation strategies into schistosomiasis control programs is critical to mitigating the disease’s spread, particularly in emerging hotspots and shifting endemic areas. Full article

Ting, Tai, Lou, and Ge are types of ancient buildings that represent traditional Chinese architecture and culture. They are primarily constructed using mortise and tenon joints, complemented by brick and stone foundations, showcasing traditional architectural craftsmanship. However, research aimed at conserving, inheriting, and rejuvenating these buildings is limited, despite their status as Provincial Cultural Relic Protection Units of China. Therefore, the aim of this study was to reveal the spatial distribution of Ting, Tai, Lou, and Ge buildings across China, as well as the factors driving differences in their spatial distribution. Tourist experiences and building popularity were also explored. The spatial analysis method (e.g., Standard deviation ellipse and Geographic detector), Word cloud generation, and sentiment analysis, which uses Natural Language Processing techniques to identify subjective emotions in text, were applied to investigated the research issues. The key findings of this study are as follows. The ratio of Ting, Tai, Lou, and Ge buildings in Southeast China to that in Northwest China divided by the “Heihe–Tengchong” Line, an important demographic boundary in China with the ratio of permanent residents in the two areas remaining stable at 94:6, was 94.6:5.4. Geographic detector analysis revealed that six of the seven natural and socioeconomic factors (topography, waterways, roads, railways, population, and carbon dioxide emissions) had a significant influence on the spatial heterogeneity of these cultural heritage buildings in China, with socioeconomic factors, particularly population, having a greater influence on building spatial distributions. All seven factors (including the normalized difference vegetation index, an indicator used to assess vegetation health and coverage) were significant in Southeast China, whereas all factors were non-significant in Northwest China, which may be explained by the small number of buildings in the latter region. The average rating scores and heat scores for Ting, Tai, Lou, and Ge buildings were 4.35 (out of 5) and 3 (out of 10), respectively, reflecting an imbalance between service quality and popularity. According to the percentages of positive and negative reviews, Lou buildings have much better tourism services than other buildings, indicating a need to improve services to attract more tourists to Ting, Tai, and Ge buildings. Four main types of words were used with high frequency in the tourism reviews collected form Ctrip, a popular online travel platform in China: (1) historical stories; (2) tourism; (3) culture; and (4) cities/provinces. Ting and Tai buildings showed similar word clouds, as did Lou and Ge buildings, with only the former including historical stories. Conversely, landmark was a high-frequency word only in the reviews of Lou and Ge buildings. Specific suggestions were proposed based on the above findings to promote tourism and revive ancient Chinese architecture. Full article

Background/Objective: Mississippi has the highest breast cancer mortality rate in the nation, yet there remains a limited understanding of the factors currently contributing to breast cancer in the state. This systematic review aims to provide insights into breast cancer epidemiology, disparities, and outcomes in Mississippi by synthesizing existing research published over the past 25 years. Methods: A systematic search of PubMed and Google Scholar identified studies published between 2000 and 2024 that focused on breast cancer in Mississippi. Quality appraisal was performed using the Joanna Briggs Institute (JBI) checklist, and a narrative synthesis approach was used to analyze the findings. Results: The initial literature search yielded 33 articles. After removing duplicates, screening titles and abstracts, and conducting a full-text review, 15 studies met the inclusion criteria. The findings revealed significant racial and geographic disparities in breast cancer incidence, mortality, and access to care. Black women in Mississippi are more likely to be diagnosed in later stages and experience worse outcomes, primarily due to socioeconomic disadvantages, healthcare access barriers, and structural inequities. Geographic barriers, particularly in rural areas, further worsen these disparities. Conclusions: This review identified a scarcity of breast cancer research in Mississippi over the last 25 years, with only three studies in the past 5 years, leaving critical knowledge gaps in understanding current dynamics. This review highlights an urgent need for expanded, Mississippi-specific breast cancer research to better understand the factors driving disparities and to develop culturally tailored, evidence-based interventions to address breast cancer disparities. Full article

Cultural Heritage Sites (CHS) serve as tangible evidence of regional human–environment interactions and spatial representation of historical memory. The research developed a Xinjiang CHS database and integrated geographic information technology and historical geography research methods to examine the spatio-temporal distribution evolution characteristics and geographic influencing factors in the arid region. It utilized the nearest neighbor index, kernel density estimation, the center of gravity model, and standard deviation ellipse to explore the spatio-temporal evolution law. Furthermore, it employed spatial overlay and qualitative text to analyze the geographical influence mechanism of the CHS. The results showed the following: (1) The CHS spatial distribution showed a pattern of “multicore agglomeration-linear extension”, concentrated in 13 key cities and four major areas that extended along the Silk Road routes. (2) The CHS diachronic development fluctuated in a pattern of “three peaks and three valleys”. The spatial center of gravity has shifted from southern Xinjiang to northern Xinjiang, manifesting a concentrated-diffused characteristic along the northeast–southwest axis. (3) The spatial selection followed the rules of “preferring lower terrain” and “proximity to water”. The elevation distribution of CHS has shifted from mid-high elevations to low elevations. The proportion of CHS on low-slope terrain increased from 78.6% in the Pre–Qin period to 93.02% in Modern History. 93.02% of CHS in Modern History were distributed within the 10 km buffer zone of rivers. (4) Climate aridity and human activities formed a dynamic influence mechanism; natural factors constructed the base pattern of CHS distribution, and human activities drove the dynamic adjustment. The findings revealed the historical trajectory and driving logic of the evolution of CHS in Xinjiang and provided a scientific basis for cultural heritage protection and ecological governance. This study had limitations in terms of the limited research scope and the lack of comprehensive quantitative analysis of influencing factors. Full article

Typhoons are among the most destructive natural phenomena, posing significant threats to human society. Therefore, accurate damage assessment is crucial for effective disaster management and sustainable development. While social media texts have been widely used for disaster analysis, most current studies tend to neglect the geographic references and primarily focus on single-label classification, which limits the real-world utility. In this paper, we propose a multi-task learning method that synergizes the tasks of location extraction and damage identification. Using Bidirectional Encoder Representations from Transformers (BERT) with auxiliary classifiers as the backbone, the framework integrates a toponym entity recognition model and a multi-label classification model. Novel toponym-enhanced weights are designed as a bridge to generate augmented text representations for both tasks. Experimental results show high performance, with F1-scores of 0.891 for location extraction and 0.898 for damage identification, representing improvements of 4.3% and 2.5%, respectively, over single-task and deep learning baselines. A case study of three recent typhoons (In-fa, Chaba, and Doksuri) that hit China’s coastal regions reveals the spatial distribution and temporal pattern of typhoon damage, providing actionable insights for disaster management and resource allocation. This framework is also adaptable to other disaster scenarios, supporting urban resilience and sustainable development. Full article