Geographic Data Research Articles

Exploring the Mental Health Benefits of Urban Green Spaces Through Social Media Big Data: A Case Study of the Changsha–Zhuzhou–Xiangtan Urban Agglomeration

Urban green spaces (UGSs) provide recreational and cultural services to urban residents and play an important role in mental health. This study uses big data mining techniques to analyze 62 urban parks in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA) based on data such as points of interest (POIs), areas of interest (AOIs), and user comments from the popular social media platform Dianping. In addition, the authors apply sentiment analysis using perceptual dictionaries combined with geographic information data to identify text emotions. A structural equation model (SEM) was constructed in IBM SPSS AMOS 24.0 software to investigate the relationship between five external features, five types of cultural services, nine landscape elements, four environmental factors, and tourist emotions. The results show that UGS external features, cultural services, landscape elements, and environmental factors all have positive effects on residents’ emotions, with landscape elements having the greatest impact. The other factors show similar effects on residents’ moods. In various UGSs, natural elements such as vegetation and water tend to evoke positive emotions in residents, while artificial elements such as roads, squares, and buildings elicit more varied emotional responses. This research provides science-based support for the design and management of urban parks.

Spatial network analysis

The impact of current changes in technology, society, and the environment demand innovative research approaches that can accurately capture the increasing complexity of modern geographic data. To this end, this article provides an introduction to the use and implementation of spatial network analysis for the study of systems where geography matters. Presented as a computational notebook, the article offers practical, hands-on learning resources in R, assuming no prior knowledge from readers. Using a network of road links between African cities as case study, readers will be guided through key concepts and applications of spatial network analysis. The article highlights the relevance of network thinking in addressing contemporary geographic challenges.

Association of Microbial Networks with the Coastal Seafloor Macrofauna Ecological State.

Recent evidence suggests that there is a major switch in coastal seafloor microbial ecology already at a mildly deteriorated macrofaunal state. This knowledge is of critical value in the management and conservation of the coastal seafloor. We therefore aimed to determine the relationships between seafloor microbiota and macrofauna on a regional scale. We compared prokaryote, macrofauna, chemical, and geographical data from 1546 seafloor samples, which varied in their exposure to aquaculture activities along the Norwegian and Icelandic coasts. We found that the seafloor samples contained either a network centralized by a sulfur oxidizer (42.4% of samples, n = 656) or a network centralized by an archaeal ammonium oxidizer (44.0% of samples, n = 681). Very few samples contained neither network (9.8% of samples, n = 151) or both (3.8% of samples, n = 58). Samples with a sulfur oxidizer network had a 10-fold higher risk of macrofauna loss (odds ratios, 95% CI: 9.5 to 15.6), while those with an ammonium oxidizer network had a 10-fold lower risk (95% CI: 0.068 to 0.11). The sulfur oxidizer network was negatively correlated to distance from Norwegian aquaculture sites (Spearman rho = -0.42, p < 0.01) and was present in all Icelandic samples (n = 274). The ammonium oxidizer network was absent from Icelandic samples and positively correlated to distance from Norwegian aquaculture sites (Spearman rho = 0.67, p < 0.01). Based on 356 high-quality metagenome-assembled genomes (MAGs), we found that bicarbonate-dependent carbon fixation and low-affinity oxygen respiration were associated with the ammonium oxidizer network, while the sulfur oxidizer network was associated with ammonium retention, sulfur metabolism, and high-affinity oxygen respiration. In conclusion, our findings highlight the critical roles of microbial networks centralized by sulfur and ammonium oxidizers in mild macrofauna deterioration, which should be included as an essential part of seafloor surveillance.

Mobility Data Representations for Spatiotemporal Tasks

Mobility data from smartphones, connected cars, and GPS devices are widely used for tasks such as transportation mode classification and suspicious movement detection. Time series research, a closely related field, focuses more on classification methods. Yet, Mobility Data analysis faces unique challenges like geographic transferability and limited public data due to privacy issues. My PhD work focuses on developing reusable, interpretable MD representations. I created Trajectory Interval Forest and later Geolet, a shapelet-based transformation to improve MD classification across geographic regions. Ongoing research explores improving geographic transferability and event-based trajectory clustering.

Next Point of Interest (POI) Recommendation System Driven by User Probabilistic Preferences and Temporal Regularities

The Point of Interest (POI) recommendation system is a critical tool for enhancing user experience by analyzing historical behaviors, social network data, and real-time location information with the increasing demand for personalized and intelligent services. However, existing POI recommendation systems face three major challenges: (1) oversimplification of user preference modeling, limiting adaptability to dynamic user needs, (2) lack of explicit arrival time modeling, leading to reduced accuracy in time-sensitive scenarios, and (3) complexity in trajectory representation and spatiotemporal mining, posing difficulties in handling large-scale geographic data. This paper proposes NextMove, a novel POI recommendation model that integrates four key modules to address these issues. Specifically, the Probabilistic User Preference Generation Module first employs Latent Dirichlet Allocation (LDA) and a user preference network to model user personalized interests dynamically by capturing latent geographical topics. Secondly, the Self-Attention-based Arrival Time Prediction Module utilizes a Multi-Head Attention Mechanism to extract time-varying features, improving the precision of arrival time estimation. Thirdly, the Transformer-based Trajectory Representation Module encodes sequential dependencies in user behavior, effectively capturing contextual relationships and long-range dependencies for accurate future location forecasting. Finally, the Next Location Feature-Aggregation Module integrates the extracted representation features through an FC-based nonlinear fusion mechanism to generate the final POI recommendation. Extensive experiments conducted on real-world datasets demonstrate the superiority of the proposed NextMove over state-of-the-art methods. These results validate the effectiveness of NextMove in modeling dynamic user preferences, enhancing arrival time prediction, and improving POI recommendation accuracy.

Study on the Evolution of Estuarine Sedimentary Geomorphology Based on Remote Sensing Image Technology

The dynamic geomorphology and sediment transport processes of estuaries play a crucial role in understanding the interactions between terrestrial and marine systems. This study investigates the spatial and temporal differences in sediment dynamics, including grain size characteristics, sediment transport trends, and sedimentary environments, in the Yellow River estuary. It also examines the estuarine responses to changes in incoming water and sediment under varying hydrodynamic conditions. Using high-resolution remote sensing data and advanced image analysis techniques, including the ESP tool in eCognition, we optimized image segmentation scales and quantified interannual and interdecadal geomorphic siltation changes. The LIBSVM toolbox was applied for classification, achieving efficient, and accurate results for landform feature detection. Furthermore, the study integrates sediment dynamics modeling, remote sensing technology, and geographic big data to explore estuarine water and sediment transport processes. The findings provide a scientific foundation for the sustainable management of estuarine systems and contribute to improving geomorphic evolution modeling and image classification methodologies.

Moran's I and Geary's C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases.

The COVID-19 outbreak has precipitated severe occurrences on a global scale. Hence, spatial analysis is crucial in determining the relationships and patterns of geospatial data. Moran's I and Geary's C are prominent methodologies used to measure the spatial autocorrelation of geographical data. Both measure the degree of similarity or dissimilarity between nearby locations based on attribute values in such a way that the selection of distance techniques and weight matrices significantly impact the spatial autocorrelation results. This paper aimed at carrying out the spatial epidemiological characteristics analysis of the pandemic comparing the results of Moran's I and Geary's C with different parameters to gain a comprehensive understanding of the spatial relationship of COVID-19 cases. We employed distance-based techniques, K-nearest neighbour, and Queen contiguity techniques to assess the sensitivity of the different parameter configurations for both Moran's I and Geary's C. The findings revealed that former provided more reliable and robust results compared to the latter, with consistent results of spatial autocorrelation (positive spatial autocorrelation). The distance weight of 0.05 using the Manhattan method of Moran's I is the recommended distance weight, as it outperformed other weight matrices (Moran's I = 0.0152, Z-value= 110.8844 and p-value=0.001).

Evaluation of Land Suitability for Construction in the Turpan–Hami Region Based on the Integration of the MaxEnt Model and Regional Planning

Land resources are fundamental to regional economic development and ecological protection. As a critical tool for the scientific allocation of land resources, land suitability evaluation plays a pivotal role in achieving sustainable development goals. This study integrates the MaxEnt model with regional planning to conduct a multi-period evaluation of the construction land suitability in the Turpan–Hami region, aiming to elucidate the distribution patterns of suitability and their driving mechanisms across different historical periods. By synthesizing natural geographic and socioeconomic data, a comprehensive suitability evaluation framework was developed, enabling a multi-temporal analysis of construction land suitability from 2000 to 2023. The results revealed a clear trend of optimization in construction land suitability within the Turpan–Hami region, characterized by the continuous expansion of highly suitable areas and a significant reduction in unsuitable areas, with the regional suitability distribution becoming increasingly balanced over time. The population density, GDP, and road density were identified as the primary drivers of suitability distribution, with the population density exerting the most substantial influence. Among the natural environmental factors, the Normalized Difference Vegetation Index (NDVI) imposed significant constraints on the land suitability, particularly in ecologically sensitive areas. This study innovatively applied the MaxEnt model to the evaluation of construction land suitability, integrating it with regional planning to comprehensively assess the spatial distribution and dynamic changes in land suitability in the Turpan–Hami region. Furthermore, this research aligns closely with policy frameworks, fully considering the impacts of ecological and agricultural protection constraints within regional planning policies on the suitability distribution, and it explores optimized land use strategies under policy guidance. The findings provide a robust scientific foundation for the efficient allocation of land resources and the enhancement of ecological protection in the Turpan–Hami region.

Fine-scale biogeographical ancestry inference in Southeast and East Asians via high-efficiency markers and machine learning approaches

Biogeographical ancestry inference offers valuable clues for forensic cold cases, but limited information is typically obtained from substructured populations within continental East Asian and Southeast groups. This study presents an integrative genomic dataset of 3,461 individuals from East Asia and Southeast Asia to elucidate the fine-scale population substructure and its role in precision forensic medicine. Six nested panels were developed with increasing ancestry-informative marker (AIM) density (ranging from 50 to 2,000 SNPs) to distinguish fine genetic differences between the six language groups and populations within the Sino-Tibetan language family. We found that the 2000 AIM panel exhibited differentiation efficiency in PCA comparable to that of all loci. Additionally, we constructed a classification machine learning model with an average prediction accuracy of 84%, highlighting the critical role of geographical information in improving model accuracy. Furthermore, we validated the accuracy of the deep learning method Locator in predicting geographical coordinates solely based on genetic information. This work highlights the power of integrating genetic and geographic data with artificial intelligence to refine fine-scale biogeographical ancestry inference, offering more profound insights into population structure in East Asia and Southeast Asia, with significant implications for forensic applications.

DEVELOPMENT OF A DEEP LEARNING MODEL FOR FORECASTING AND OPTIMIZING RIDE-SHARING ROUTES

The study investigates the potential use of machine learning (ML) technologies, including Recurrent Neural Networks (RNNs), in ride-sharing and urban mobility optimization. Advanced deep learning (DL) models can solve growing challenges in urban areas, such as road safety, environmental pollution, and traffic congestion. Three different RNN architectures (SimpleRNN, LSTM, GRU) are compared to predict trips with their pickup and drop-off points. According to the assessment metrics, GRU shows better results in terms of Mean Haversine Distance (6.450 km) than SimpleRNN (7.156 km) and LSTM (6.569 km). Moreover, the GRU model surpasses other models in other indicators, such as MSE (0.0010) and MAE (0.0211). In addition, OSRM API is used to build routes between predicted pickup and drop-off points, as well as to optimize ride-sharing routes using real-time geographic data. The study highlights that ML approaches, in particular DL, can be used to solve problems related to urban mobility by improving transport efficiency and reducing traffic. The study results provide recommendations for developing urban transport systems using data-driven approaches to enhance ride-sharing opportunities.

The structure of the end-Cretaceous dinosaur fossil record in North America.

Whether non-avian dinosaurs were in decline prior to their extinction 66 million years ago remains a contentious topic. This uncertainty arises from spatiotemporal sampling inconsistency and data absence, which cause challenges in distinguishing between genuine biological trends and sampling artifacts. Consequently, there is an inherent interest in better quantifying the quality of the data and concomitant biases of the dinosaur fossil record. To elucidate the structure of this record and the nature of the biases impacting it, we integrate paleoclimatic, geographic, and fossil data within a Bayesian occupancy modeling framework to simultaneously estimate the probability of dinosaurs occupying and being detected in sites across North America throughout the latest Cretaceous for the first time. We find that apparent declines in occupancy generated from the raw fossil record do not match modeled occupancy probability, which generally remained stable throughout the latest Cretaceous. Instead, they coincide with decreased probability of detecting dinosaur occurrences, despite high overall sampling during this interval. By incorporating model covariates, we additionally reveal that detection probability is directly and significantly influenced by the available area of geological outcrop and modern land cover. Our findings offer evidence that traditional comparisons of diversity estimates between time intervals are likely inaccurate due to underlying structural issues in the geological record operating at both local and regional scales. This study underscores the utility of occupancy modeling as a novel approach in paleobiology for quantifying the impact of heterogeneous sampling on the available fossil record.

An Improved Grid Clustering Algorithm for Geographic Data Mining

ABSTRACTGrid clustering is a classical clustering algorithm with the advantage of lower time complexity, which is suitable for the analysis of large geographic data. However, it is sensitive to the grid division parameter M and density threshold R, and the clustering accuracy is poor. The article proposes a hybrid clustering algorithm HCA‐BGP based on grid and division. the algorithm first uses grid clustering to obtain the core part of the class family, and then uses the division‐based method to obtain the edge part of the class family. Through experiments on simulated datasets and real geographic datasets, it is proved to have better results than the existing grid clustering as well as some other classical algorithms. In terms of clustering accuracy, compared with the classical grid clustering algorithm Clique, the clustering F‐value of this paper's algorithm is improved by 20.3% on dataset S1, 81.8% on dataset R15, and 7.6% on average on the eight geographic datasets. In terms of the sensitivity of parameters M and R, compared with Clique, the variance of the clustered F‐value of this paper's algorithm is reduced by 89.3% on dataset S1; the variance of the clustered ARI is reduced by 99.9% on the real geographic dataset Data8. Compared to another grid‐based clustering algorithm, GDB, HCA‐BGP also demonstrates significant advantages.

A FAIR and open geographic data collection for the Massaciuccoli Lake basin wetland in Italy.

The creation of a catalogue of geodata harmonised over time and space is essential for describing the status of ecosystem services in wetlands. In the present work, a specific methodology has been developed for the collection and generation of spatially and temporally harmonized geographic data to describe essential ecological and socio-economic charactesristics of the Massaciuccoli Lake basin (Tuscany, Italy), while providing a re-usable methodology for other areas. We developed a methodology, which we called 'Geodata Layers Harmonization Methodology' (GLHM), divided into four main phases: Geodata Census (GC), Geodata Selection (GS), Geodata Alignment (GA), and Geodata Publication (GP). The first phase, GC, involved a census of geodata made available online by public institutions, prioritizing those most relevant for describing ecosystem services, such as climatic, agro-environmental, pedo-geological, and biodiversity variables, with a preference for detailed data at the local level. The metadata of the collected geodata were organized into a structured tabular format. In the GS phase, geodata were selected based on a spatial resolution compatible with regional-scale ecological models (maximum 0.0005° ≈ 50 m), and a temporal coverage that could represent from remote past to far future scenarios. Geodata with partial spatial coverage or unsuitable for ecological models were excluded. Additionally, we evaluated the compliance of the geodata published on the websites of public institutions with the Findable-Accessible-Interoperable-Reusable (FAIR) principles through a newly developed scoring system. Based on this score, we selected only the data that exceeded a minimum FAIRness threshold. In the GA phase, the selected geodata were aligned semantically (i.e., by variable meaning), temporally, and spatially. Each geodata was georeferenced using the WGS84/EPSG:4326 reference system and clipped to the boundaries of the Massaciuccoli Lake basin. Raster data were resampled to achieve a uniform spatial resolution of 0.0005°. In the last phase, GP, the aligned geodata were published on public access repositories and services: The entire collection was organized as a QGIS project with legends and a metadata table associated. An Atlas was also produced, in PDF format, which visually represented the data and metadata. The geodata and their corresponding legends were exposed through Web Map Service (WMS) and Web feature Service (WFS) standards on a GeoServer instance and catalogued in a GeoNetwork instance, compliant with the ISO19139 standard and the INSPIRE European Directive. The collection contains 148 geo-datasets, representing 75 climatic, agro-environmental, pedo-geological, morphological, ecological, biological, and socio-economic information distributed across five temporal reference time frames: a remote past (1950-1980), a near past (1981-2015), the present (2016-2024), a near future (2025-2050), and a far future (2051-2100). Future projections are available under the Representative Concentration Pathways (RPC) 2.6, 4.5, and 8.5 to simulate low, medium, and high greenhouse gas concentration scenarios respectively. The present geodata collection is particularly useful for wetland monitoring, management and planning. It can easily be integrated with ecological models and predictive studies to analyse the effects of climate change and anthropogenic pressures on wetlands. The GLHM methodology is applicable to other ecological contexts to create standardised structured frameworks for evaluating the status of the biodiversity and the ecosystem services and the interplay between anthropic pressures and the ecosystem response.

Prevalence of toxoplasmosis in natural ungulates as human zoonotic meat‐borne pathogens: A systematic review and meta‐analysis

AbstractThe increasing consumer interest in game meat, along with the potential transmission of Toxoplasma gondii from game meat to humans, poses significant challenges in assessing the related public health risks. The diverse transmission pathways of T. gondii and its ability to infiltrate host organisms offer an excellent opportunity for enhancing our comprehension of this pathogen's transmission dynamics. Toxoplasma has traditionally been a valuable model for studying host–pathogen interactions and investigating population, community, and environmental or ecological aspects. Therefore, a meta‐analysis was conducted to estimate the worldwide prevalence and related ecological factors of parasites in wild ruminants. For this purpose, six data base were searched for studies published up to November, 2024. Apparent pooled prevalence was analyzed using a random‐effect model with a 95% confidence interval. Furthermore, a univariate and multivariate meta‐regression analyses were conducted to investigate associations of species, socio‐demographic, geographical, and meteorological data with the prevalence of parasites. The apparent prevalence of T. gondii in wild ruminants was estimated at 12% across four continents. The pooled prevalence estimate across the three species was relatively consistent, with wild sheep showing a rate of about 12.80%, while wild goats (10.30%) and cattle (14.40%) exhibited a slightly higher prevalence. Meta‐regression analyses showed a significant relationship among the variables of wild sheep, goat, and cattle species and subspecies, age, gender, samples, methods, continent, temperature, rainfall, humidity, and income level with the prevalence of parasites. The findings indicate the circulation of T. gondii but not a homogeneous exposure in wild ungulates, which could have significant implications for animal health, conservation, and public health.

Genomic and spatial epidemiology: lessons learned from SARS-CoV-2 pandemic.

The SARS-CoV-2 pandemic presented unprecedented challenges, particularly in understanding its complex spatial transmission patterns. The high transmissibility of the virus led to frequent super-spreading events. These events demonstrated clear spatial clustering patterns, often tied to specific events that facilitated transmission. The uneven geographic distribution of medical resources and varying access to care amplified the impact of SARS-CoV-2. Asymptomatic cases further complicated the situation, as infected individuals could silently spread the virus before being identified.Thus, this review examines how genomic and spatial epidemiology approaches can be integrated to answer some of the above-mentioned challenges. We first describe the methodological foundations of genomics and spatial epidemiology, detailing opportunities of their applications during the SARS-CoV-2 pandemic. We then present a novel interdisciplinary framework that combines these approaches to better guide public health interventions. During the pandemic, the genomic and spatial approaches were used to address key questions, including "how does the pathogen evolve and diversify?" and "how does the pathogen spread geographically?". Genomic epidemiology allows researchers to identify viral lineages and new variants. Conversely, spatial epidemiology focused on geographic distribution of infections, analyzing how the virus spread. However, despite their complementary nature, these approaches were largely applied independently during the pandemic. This separation limited our collective ability to fully understand the complex relationships between viral evolution and geographic spread. While phylogeography has traditionally combined phylogenetic and geographic data to understand long-term evolutionary patterns across large areas, events such as the recent SARS-CoV-2 pandemic demand frameworks that can inform public health interventions through joint analysis of genomic and local-scale spatial data.

Soil total nitrogen content and pH value estimation method considering spatial heterogeneity: Based on GNNW-XGBoost model.

Soil total nitrogen content and pH value estimation method considering spatial heterogeneity: Based on GNNW-XGBoost model.

Enhancing geodatabases operability: advanced human-computer interaction through rag and multi-agent systems

ABSTRACT The increasing demand for efficient geographic data querying has underscored the need to improve both the speed and accuracy of such operations. This study presents a novel approach that combines Retrieval-Augmented Generation (RAG) with a Large Language Model (LLM)-based Multi-Agent System (MAS) to address this challenge. By leveraging Geographic Information System (GIS) metadata, the method transforms natural language queries into precise SQL queries, enhancing the functionality of LLMs in geospatial database management. RAG enriches the model’s responses with relevant external knowledge, while the MAS framework decomposes complex queries into manageable subtasks, each handled by a dedicated agent. This ensures the generation of syntactically accurate and contextually relevant SQL queries, improving query precision. Rigorous experimentation shows that the approach achieves over 80% SQL generation accuracy, surpassing traditional LLM-based techniques, even with large-scale geospatial datasets and complex queries. Additionally, the average query execution time is only 11.74 seconds, demonstrating the method’s efficiency. The practical applicability of our method has been validated through its implementation and user testing in an urban planning platform. This has made geospatial database access more accessible and user-friendly, regardless of the user’s expertise in database management or SQL, thus facilitating more efficient geospatial data interaction.

Mobile Application for Market Data Collection of Property Tax Object

This research focuses on designing a mobile application for collecting property market data to support Land Value Zone (ZNT) analysis for local governments. Currently, market data collection processes are often conducted manually, which is time-consuming, resource-intensive, and prone to subjectivity. The proposed application leverages mobile technology to enhance the efficiency and effectiveness of field data collection with features such as user authorization, geographic data storage, and multimedia data storage, including images. The research adopts the prototyping method, involving direct interaction between developers and users. Testing results indicate that the application performs its functions effectively and facilitates data collection based on Geospatial Information Systems (GIS). This study suggests further development for other mobile platforms and modules for price analysis based on ZNT.

Mapping Demersal Fish Potential Fishing Areas with Training Vessels FV. Coelacanth in Bitung Waters, North Sulawesi

The waters of Bitung, located in North Sulawesi Province, have long been known for their rich underwater biodiversity, specifically unique and exotic creatures. They are a potential demersal fishing ground for coastal fishermen in Bitung City. However, information on demersal fishing grounds is limited, so it is necessary to conduct research and map demersal fishing grounds in Bitung waters. This study aims to obtain data on the geographical position of demersal fishing areas in Bitung waters to be mapped into a map of demersal fishing areas in Bitung waters and determine the types of catches from bottom fishing rods. Data was collected directly by following demersal fishing operations on the training ship FV. Coelacanth. The data were analyzed using survey methods and direct observation, which is an effort to collect information directly from a part of the population that is considered a specified population and has the nature of authentication or checking of existing theories. The study's results obtained geographical data on demersal fishing areas in Bitung waters at (1) 10°23.447' N, 125°06.934' E (Suar Batu Kapal area),, (2) 10°23.483' N, 125°09.567' E (Karang Tanjung Merah area) (3) 10°23.283' N, 125°10.852' E (Batu Angus Area), (4) 10°30.393' N, 125°15.392' E (Menara Lampu Hijau Area), and (5) 10°32.706' N, 125°18.105' E (Dua Pulau area). The primary catches of bottom fishing consist of fish species: grouper, snapper, trevally fish, Gora fish, emperor fish and parrot fish.

Mapping the Past: Unlocking Historical Explorer Narratives with AI and Geospatial Tools

This study explores the use of artificial intelligence and geospatial tools to analyze historical explorers’ narratives. Explorers’ accounts provide valuable insights into the cultural, environmental, and logistical dynamics of exploration journeys. However, traditional methods of analyzing these narratives are often subjective and difficult to reproduce on a large scale. The main objective is to overcome the limitations of traditional methods by using AI techniques to systematically extract and structure information from explorers’ narratives. This study employs Python scripts to extract factual data from narratives available on Project Gutenberg, followed by structuring the data in JSON format. Geographic data are enriched through geocoding using libraries such as Geopy and OpenCage. An interactive web interface based on Leaflet allows for the visualization and validation of explorers’ routes. The results show a concentration of visits in North and West Africa, with traditional modes of transport like caravans and traveling on foot being dominant. The main challenges faced were related to transportation, climatic conditions, and natural obstacles. Principal component analysis (PCA) and correspondence analysis reveal latent structures in the data, while clustering analysis segments the journeys based on similarity criteria. This research demonstrates the value of AI and geospatial tools for a more objective and detailed analysis of explorers’ narratives, opening new perspectives for historical and geographical studies.