Hasil untuk "Cartography"

Mark Mets, Peter Sheridan Dodds, Maximilian Schich

Aggression against Ukraine has drawn widespread international attention, particularly in the wake of the two Russian invasions into Ukrainian territory in 2014 and 2022. Although previous studies have examined social-media dynamics around these events, a comparative longitudinal data-driven view across languages is still missing. This article fills this gap by mapping added attention to "Ukraine" on Twitter in 28 languages from 2008 to 2023, using a deceptively simple DNA microarray-inspired cartography of log over-expression relative to each language's baseline frequency. This macro-scale visualization makes familiar events stand out while uncovering subtler patterns beyond the cognitive reach of any single-language audience. Most strikingly, two nearly non-overlapping language clusters emerge, one peaking around 2014 and the other around 2022 with distinct onset and decay profiles that mirror national readiness (or reluctance) to support Ukraine. By capturing attention at local, meso, and global scales, our approach offers a versatile tool for comparing relative bias across languages, user subgroups, platforms, or even historical print corpora. Ultimately, our cartographic approach reveals a troubling asymmetry: while publicly accessible data allows for an approximation of global attention patterns, the complete and unfiltered view remains largely hidden behind the closed, proprietary algorithms of major social media platforms, granting a far more comprehensive access to understanding global information flows.

H. Sinan Bank, Casey E. Eaton

Charting the intellectual evolution of a scientific discipline is crucial for identifying its core contributions, challenges, and future directions. The IISE Annual Conference proceedings offer a rich longitudinal archive of the Industrial and Systems Engineering (ISE) community's development, but the sheer volume of scholarship produced over two decades makes a holistic analysis difficult. Traditional reviews often fail to capture the full scale of thematic shifts and complex collaboration networks that define the community's growth. This paper presents a computational analysis of IISE proceedings from 2002 to 2025, drawing on an initial dataset of 9,350 titles from ProQuest for thematic analysis and 8,958 titles from Google Scholar for citation analysis, to deliver a cartography of the ISE field's intellectual history. Leveraging Large Language Models (LLMs) for domain-aware classification, Natural Language Processing, and Network Science, our study systematically maps thematic evolution to identify dominant, emerging, and receding research topics. We analyze citation data and co-authorship networks to uncover influential papers and authors, providing critical insights into knowledge diffusion and community structure. Through this comprehensive analysis, we establish a baseline for understanding the trajectory of ISE research and offer valuable insights for researchers, practitioners, and educators. The findings illuminate the field's intellectual assets and provide a data-informed map to guide the future of ISE. To foster reproducibility and further research, the curated dataset used in this study and the results will be made publicly available.

Qun Yan, Linfu Xue, Yongsheng Li et al.

Data-driven MPM often overlooks expert knowledge, leading to poor interpretability and overly broad predictions. We convert the semantic information of geological maps into a semantic knowledge graph(Geo-mapSKG). By embedding the Geo-mapSKG using the TransG model and integrating with geochemical data to enhance the knowledge constraints. Given the spatial variability of geological features, we use a window sampling method for data collection to ensure the completeness of geospatial structural features. To improve the model’s ability to learn the complex variations in geospatial features, we employ the Conformer deep learning model for gold prospectivity prediction. This approach combines the local geological feature extraction capability of CNN with the Transformer’s overall geological dependencies. To validate the method effectiveness, a gold prospective exercise was conducted at Ankang in Shaanxi Province (North China). Results show Geo-mapSKG embedding effectively constrains predicted area distribution, yielding a smaller predicted area, and that the geological semantic features of the predicted areas show strong consistency with the ore geological features of known deposits. Compared with the prediction results of the CNN and Transformer models, the accuracy of the Conformer model is 1.38% higher than the CNN model and 2.92% higher than the Transformer model.

Zeyin Chen, Siying Li, Tao Wu et al.

Abstract In rapidly urbanizing cities, historical neighborhoods often experience drastic spatial transformation, leading to the erosion of urban form, memory, and identity. This study examines the morphological transformation of the Siwenli Lilong neighborhood in central Shanghai, tracing its evolution from 1948 to 2021. Drawing on a 70-year fine-scale GIS dataset at the lane-block level which is a rare longitudinal resolution in related urban research, the study integrates historical cartography, urban morphology, and heritage interpretation to identify three key phases: wartime densification, socialist consolidation, and market-driven redevelopment. Each phase reflects distinct governance rationales, cumulatively producing a shift from spatial continuity to fragmentation. The research introduces the concept of “interface rupture” to capture the disjunction between old and new typologies, particularly in façade logic and public–private transitions. Rather than treating transformation as incidental, it proposes a conceptual model linking governance regimes, development logics, and spatial consequences. While symbolic heritage elements are selectively retained, most morphological memory is weakened or erased. By integrating urban morphology with the Historic Urban Landscape (HUL) framework, the study contributes to heritage-led urbanism by moving beyond site-specific diagnosis toward transferable explanatory mechanisms. It calls for adaptive conservation frameworks that recognize spatial memory as a planning asset, promoting continuity during inevitable change. The Siwenli case thus serves as both empirical evidence and a theoretical lens for understanding structural dynamics behind morphological rupture in East Asian cities.

Trong Phuong Tran, Duc Vien Tran, Van Khue Phan et al.

In recent years, the socioeconomic development of Ha Tinh province, particularly in the Can Loc district, has been significantly influenced by substantial agricultural growth, however, the grassroot factors such as economic condition, policy mechanism, employment, natural factors have not been considered. This study aims to investigate the impact of these factors on agricultural land transformation in Can Loc district, Ha Tinh province, Vietnam. The methodology utilizes a survey-based approach to collect data from 200 households and employs multivariate regression statistics to investigate the factors that drive changes in agricultural land use in response to climate change in the Can Loc district. The findings reveal a hierarchy of factors that influence agricultural land use change for climate adaptation in the district. Economic factors (X4) have the most substantial influence, accounting for 23.56% of the observed changes. Policies mechanisms (X1) rank second, contributing to 21.15% of the observed changes. Employment considerations (X5) rank third, with a contribution of 19.87%. Climate change considerations (X2) closely follow, accounting for 18.69%. Nature factors (X3) round up the list, with a 16.73% influence. Furthermore, the study proposes policies mechanisms and suggests implementing comprehensive mechanization processes to enhance the agricultural production capacity, enabling better adaptation to climate change.

Le Xu, Lei Cheng, Junting Chen et al.

Radio map estimation (RME), also known as spectrum cartography, aims to reconstruct the strength of radio interference across different domains (e.g., space and frequency) from sparsely sampled measurements. To tackle this typical inverse problem, state-of-the-art RME methods rely on handcrafted or data-driven structural information of radio maps. However, the former often struggles to model complex radio frequency (RF) environments and the latter requires excessive training -- making it hard to quickly adapt to in situ sensing tasks. This work presents a spatio-spectral RME approach based on plug-and-play (PnP) denoising, a technique from computational imaging. The idea is to leverage the observation that the denoising operations of signals like natural images and radio maps are similar -- despite the nontrivial differences of the signals themselves. Hence, sophisticated denoisers designed for or learned from natural images can be directly employed to assist RME, avoiding using radio map data for training. Unlike conventional PnP methods that operate directly in the data domain, the proposed method exploits the underlying physical structure of radio maps and proposes an ADMM algorithm that denoises in a latent domain. This design significantly improves computational efficiency and enhances noise robustness. Theoretical aspects, e.g., recoverability of the complete radio map and convergence of the ADMM algorithm are analyzed. Synthetic and real data experiments are conducted to demonstrate the effectiveness of our approach.

Arpit Narechania, Alex Endert, Clio Andris

When creating choropleth maps, mapmakers often bin (i.e., group, classify) quantitative data values into groups to help show that certain areas fall within a similar range of values. For instance, a mapmaker may divide counties into groups of high, middle, and low life expectancy (measured in years). It is well known that different binning methods (e.g., natural breaks, quantile) yield different groupings, meaning the same data can be presented differently depending on how it is divided into bins. To help guide a wide variety of users, we present a new, open source, web-based, geospatial visualization tool, Exploropleth, that lets users interact with a catalog of established data binning methods, and subsequently compare, customize, and export custom maps. This tool advances the state of the art by providing multiple binning methods in one view and supporting administrative unit reclassification on-the-fly. We interviewed 16 cartographers and geographic information systems (GIS) experts from 13 government organizations, non-government organizations (NGOs), and federal agencies who identified opportunities to integrate Exploropleth into their existing mapmaking workflow, and found that the tool has potential to educate students as well as mapmakers with varying levels of experience. Exploropleth is open-source and publicly available at https://exploropleth.github.io.

Yanbing Chen, Jonathan Nelson, Bing Zhou et al.

Academia is profoundly influenced by faculty hiring networks, which serve as critical conduits for knowledge dissemination and the formation of collaborative research initiatives. While extensive research in various disciplines has revealed the institutional hierarchies inherent in these networks, their impacts within GIScience remain underexplored. To fill this gap, this study analyzes the placement patterns of 946 GIScience faculty worldwide by mapping the connections between PhD-granting institutions and current faculty affiliations. Our dataset, which is compiled from volunteer-contributed information, is the most comprehensive collection available in this field. While there may be some limitations in its representativeness, its scope and depth provide a unique and valuable perspective on the global placement patterns of GIScience faculty. Our analysis reveals several influential programs in placing GIScience faculty, with hiring concentrated in the western countries. We examined the diversity index to assess the representation of regions and institutions within the global GIScience faculty network. We observe significant internal retention at both the continental and country levels, and a high level of non-self-hired ratio at the institutional level. Over time, research themes have also evolved, with growing research clusters emphasis on spatial data analytics, cartography and geovisualization, geocomputation, and environmental sciences, etc. These results illuminate the influence of hiring practices on global knowledge dissemination and contribute to promoting academic equity within GIScience and Geography.

Tingting Li, Haipeng Chen, Chao Ma

ABSTRACTChina’s massive urbanisation development will undoubtedly serve as a global reference. Due to the uncertainty of statistical data, nighttime light (NTL) data have emerged as alternative data for urbanisation evaluation. Here, the application of the Prolonged Artificial NTL Dataset of China (1984–2020) has been newly expanded. Total-value statistical, complex NTL index (CNLI), NTL concentration degree, rank-size rule, and Markov transfer matrix were used to systematically mine information about urban change from multiple perspectives. China’s urbanisation exhibited rapid growth and outwards expansion. The total percentage increase in NTL brightness and NTL area was 409.38% and 302.58%, respectively. The Yangtze River Delta urban agglomeration of East China had the fastest urbanisation (CNLI Trend = 0.0057/a, p < 0.01). Low NTL areas transitioned to medium NTL areas, and high and extremely-high NTL areas diffused to medium NTL areas. The cities in Eastern China and Southern China typically exhibited extremely-high type NTL areas, whereas other cities primarily exhibited medium NTL areas. The gaps between city sizes decreased over time (q Trend =  – 0.0200/a, p < 0.01). Lowest-ranked cities exhibited the highest stability (>95%) in city size type transition. The spatiotemporal changes in NTL obtained were of great significance for monitoring urban expansion patterns, making government decisions, and quantifying China’s sustainable development.

Eva Neuwirthová, Zuzana Lhotáková, Lucie Červená et al.

Leaf traits can be used to elucidate vegetation functional responses to global climate change. Pigments, water and leaf mass per area are the most used traits. However, detailed anatomical traits such as leaf thickness, the thickness of palisade and spongy parenchyma are often neglected, although they affect leaf physiological function and optical properties. Our aim was to produce partial least squares regression (PLSR) models for estimating leaf traits using biconical reflectance factor (BCRF). We established that estimation of leaf anatomical properties differs when using BCRF obtained from the upper and lower surface of the leaf. PLSR explained that 90% of the variability was based on total chlorophyll content (R2 = 0.95), spongy parenchyma to leaf thickness ratio (R2 = 0.94), equivalent water thickness (R2 = 0.93) and leaf mass per area (R2 = 0.91) or leaf thickness (R2 = 0.90). We conclude that internal asymmetry in leaf structure affects significantly leaf optical properties and should not be neglected in radiative transfer modelling at the leaf level and when upscaling leaf properties to the canopy.

Świerczyńska Ewa Joanna, Kurdek Damian, Jankowska Iwona

The “Kłodawa” salt mine, due to geological conditions and continuous salt extraction, is subject to a range of measurements documenting the speed of changes in the geometry of the chambers. Cyclic surveys are conducted under challenging conditions several hundred metres underground. Consequently, measurement methods used for determining the parameters of the ongoing clamping should be of high precision but also be resistant to dense dust (in fields of active mining) and strong gusts (near ventilation shafts).

Audrey Gardahaut, Hugues Lourme, Steve Mahaut et al.

Widely present in the primary circuit of Nuclear Power Plants (NPP), Dissimilar Metal Welds (DMW) are inspected using Ultrasonic nondestructive Testing (UT) techniques to ensure the integrity of the structure and detect defects such as Stress Corrosion Cracking (SCC).In a previous collaborative research, CRIEPI and CEA have worked on the understanding of the propagation of ultrasonic waves in complex materials. Indeed, the ultrasonic propagation can be disturbed due to the anisotropic and inhomogeneous properties of the medium and the interpretation of inspection results can then be difficult. An analytical model, based on a dynamic ray theory, developed by CEA-LIST and implemented in the CIVA software had been used to predict the ultrasonic propagation in a DMW. The model evaluates the ray trajectories, the travel-time and the computation of the amplitude along the ray tube in a medium described thanks to a continuously varying description of its physical properties. In this study, the weld had been described by an analytical law of the crystallographic orientation. The simulated results of the detection of calibrated notches located in the buttering and the weld had been compared with experimental data and had shown a good agreement.The new collaborative program presented in this paper aims at detecting a real SCC defect located close to the root of the DMW. Thus, simulations have been performed for a DMW described with an analytical law and a smooth cartography of the crystallographic orientation. Furthermore, advanced ultrasonic testing methods have been used to inspect the specimen and detect the real SCC defect. Experimental and simulated results of the mock-up inspection have been compared.

Joao Gouveia, Fernando Branco, Armanda Rodrigues et al.

Project Lx Conventos aims to study, in a systematic and integrated manner, the impact of the dissolution of religious orders in the dynamics of urban transformation in nineteenth century Lisbon. After the liberal revolution and the civil war, in the 19th century, the dissolution of religious orders led to the alienation, in Lisbon, of nearly 130 religious buildings which were then given profane occupations (mainly public services) or demolished and divided in plots, originating new urban realities. Project Lx Conventos thus aims to show that the extinction of the convents was decisive in the urban development of Lisbon, in the eighteen hundreds. The project stands on a large set of multimedia data which includes historic and contemporary cartography and geo-referenced photos, videos and 3D models, provided by the projects partners, Lisbon Municipality and the Portuguese National Archive, Torre do Tombo. Supported by these materials, the project's team is creating an online system that will implement a spatial and temporal navigation of these resources integrated in an interactive Map of Lisbon. Besides spatially locating and analyzing the data available for each of the religious buildings considered in the project, the tool integrates cutting edge interaction technology for: 1) Enabling a temporal voyage over the available traces of religious buildings; 2) Analyzing the evolution of religious buildings and their surroundings, through available data; 3) Using 3D representations of the buildings for accessing related data, through time. In this paper, the tools under development in the context of Lx Conventos are described, as well as the technologies supporting them. The current status of the system is presented and future developments are proposed.

Haoan Feng, Yunting Song, Leila De Floriani

The scale-space method is a well-established framework that constructs a hierarchical representation of an input signal and facilitates coarse-to-fine visual reasoning. Considering the terrain elevation function as the input signal, the scale-space method can identify and track significant topographic features across different scales. The number of scales a feature persists, called its life span, indicates the importance of that feature. In this way, important topographic features of a landscape can be selected, which are useful for many applications, including cartography, nautical charting, and land-use planning. The scale-space methods developed for terrain data use gridded Digital Elevation Models (DEMs) to represent the terrain. However, gridded DEMs lack the flexibility to adapt to the irregular distribution of input data and the varied topological complexity of different regions. Instead, Triangulated Irregular Networks (TINs) can be directly generated from irregularly distributed point clouds and accurately preserve important features. In this work, we introduce a novel scale-space analysis pipeline for TINs, addressing the multiple challenges in extending grid-based scale-space methods to TINs. Our pipeline can efficiently identify and track topologically important features on TINs. Moreover, it is capable of analyzing terrains with irregular boundaries, which poses challenges for grid-based methods. Comprehensive experiments show that, compared to grid-based methods, our TIN-based pipeline is more efficient, accurate, and has better resolution robustness.

Sagar Bharadwaj, Ziyong Ma, Ivan Liang et al.

Spatial applications, i.e., applications that tie digital information with the physical world, have improved many of our daily activities, such as navigation and ride-sharing. This class of applications also holds significant promise of enabling new industries such as augmented reality and robotics. The development of these applications is enabled by a system that can resolve real-world locations to names, or a spatial naming system. Today, mapping platforms provided by organizations like Google and Apple serve as spatial naming systems. These maps are centralized and primarily cover outdoor spaces. We envision that future spatial applications, such as persistent world-scale augmented reality, would require detailed and precise spatial data across indoor and outdoor spaces. The scale of cartography efforts required to survey indoor spaces and their privacy needs inhibit existing centralized maps from incorporating such spaces into their platform. In this paper, we present the design and implementation of OpenFLAME stands for Open Federated Localization and Mapping Engine, a federated spatial naming system, or in other words, a federated mapping infrastructure. It enables independent parties to manage and serve their own maps of physical regions. This unlocks scalability of map management, isolation, and privacy of maps. The discovery system that identifies maps hosted at a given location is a primary component of our system. We implement OpenFLAME on top of the existing Domain Name System (DNS), which enables us to leverage its existing infrastructure. We implement map services such as address-to-location mapping, routing, and localization on top of our federated mapping infrastructure.

Erico Kutchartt, José Ramón González-Olabarria, Núria Aquilué et al.

Canopy fuels and surface fuel models, topographic features and other canopy attributes such as stand height and canopy cover, provide the necessary spatial datasets required by various fire behaviour modelling simulators. This is a technical note reporting on a pan-European fuel map server, highlighting the methods for the production and validation of canopy features, more specifically canopy fuels, and surface fuel models created for the European Union Horizon 2020 FIRE-RES project, as well as other related data derived from earth observation. The aim was to deliver a fuel cartography in a findable, accessible, interoperable and replicable manner as per F.A.I.R. guiding principles for research data stewardship. We discuss the technology behind sharing large raster datasets via web-GIS technologies and highlight advances and novelty of the shared data. Uncertainty maps related to the canopy fuel variables are also available to give users the expected reliability of the data. Users can view, query and download single layers of interest, or download the whole pan-European dataset. All layers are in raster format and co-registered in the same reference system, extent and spatial resolution (100 m). Viewing and downloading is available at all NUTS scales, ranging from country level (NUTS0) to province level (NUTS3), thus facilitating data management and access. The system was implemented using R for part of the processing and Google Earth Engine. The final app is openly available to the public for accessing the data at various scales.

Hong-fu Chou, Vu Nguyen Ha, Prabhu Thiruvasagam et al.

Earth Observation (EO) systems are crucial for cartography, disaster surveillance, and resource administration. Nonetheless, they encounter considerable obstacles in the processing and transmission of extensive data, especially in specialized domains such as precision agriculture and real-time disaster response. Earth observation satellites, outfitted with remote sensing technology, gather data from onboard sensors and IoT-enabled terrestrial objects, delivering important information remotely. Domain-adapted Large Language Models (LLMs) provide a solution by enabling the integration of raw and processed EO data. Through domain adaptation, LLMs improve the assimilation and analysis of many data sources, tackling the intricacies of specialized datasets in agriculture and disaster response. This data synthesis, directed by LLMs, enhances the precision and pertinence of conveyed information. This study provides a thorough examination of using semantic inference and deep learning for sophisticated EO systems. It presents an innovative architecture for semantic communication in EO satellite networks, designed to improve data transmission efficiency using semantic processing methodologies. Recent advancements in onboard processing technologies enable dependable, adaptable, and energy-efficient data management in orbit. These improvements guarantee reliable performance in adverse space circumstances using radiation-hardened and reconfigurable technology. Collectively, these advancements enable next-generation satellite missions with improved processing capabilities, crucial for operational flexibility and real-time decision-making in 6G satellite communication.

Simon Streicher

Probabilistic graphical models (PGMs) are tools for solving complex probabilistic relationships. However, suboptimal PGM structures are primarily used in practice. This dissertation presents three contributions to the PGM literature. The first is a comparison between factor graphs and cluster graphs on graph colouring problems such as Sudokus - indicating a significant advantage for preferring cluster graphs. The second is an application of cluster graphs to a practical problem in cartography: land cover classification boosting. The third is a PGMs formulation for constraint satisfaction problems and an algorithm called purge-and-merge to solve such problems too complex for traditional PGMs.

S. Klosterkamp

<p>Based on an ethnographic study of anti-terror trials at higher regional appeal courts in Germany, conducted in 2015–2020, this article examines the interrelation between the German penal system and criminal trials as mutually constitutive, governmentally guided, and highly secured elements of a state-induced and Islam-centred terrorism prevention. This includes the physical nature of the courthouses, as well as discourses of risk inscribed within them, which are linked to corresponding racialized and gender-rendered readings of the ‚need for custody‘. Under the auspices of a ‚new penology‘ and legitimized as an elimination of ‚state-endangering actions‘, two logics emerge in the course of these proceedings that emphasize either a ‚rectification of the reformable‘ or a ‚confinement of the incorrigible‘, illustrating how a reshaped field of crime control and criminal justice can currently be observed that makes permanent incarceration the guarantor of a promise of security.</p>

M. Mießner, M. Naumann

<p>Not only since the Covid-19 pandemic, rural areas have received new attention as supposedly healthier and attractive places of residence. Regions previously characterized as shrinking are experiencing a highly selective influx of urban middle-class households and an increase in real estate and rental prices. These influxes and housing market developments raise the question of value increase and displacement. English-speaking, and especially British, human geographers have been studying the phenomenon of ”rural gentrification” for several decades. This article therefore aims to systematize this state of the art in terms of its conceptual framework and empirical objects. Based on this, the article explains possible connections for German research on rural gentrification and discusses starting points for future research.</p>