Hasil untuk "Cartography"

A. Carrara, M. Cardinali, R. Detti et al.

A. Maceachren, M. J. Kraak

Syed Hamad Hassan Shah, Shen Lei, M. Ali et al.

Purpose Over the past decade, the term prosumption (denoting simultaneous consumption and production) has exhibited a dramatic increase in frequency of use in publications in the social sciences and business studies. This paper aims to explore the current state of research into prosumption, particularly related to marketing. Design/methodology/approach This study systematically reviews papers on prosumption extracted from the Web of Science, using two bibliometric analyses on 20 years of data: citation counts from HistCite and bibliographic coupling and cartography analysis from the visualization of similarities software VOSviewer. A total of 75 papers on prosumption were found from the period 1997-2017, and the most influential authors, articles, journals, institutions and countries among these were determined. Furthermore, bibliographic coupling and most co-occurrent keywords in the title, keywords and abstracts were found. Findings This study found that the USA and the UK were the most influential among prosumption publications. Ritzer was the most prominent author and Journal of Consumer Culture was the top-ranking journal. Three clusters were found using bibliographic coupling and cartography analysis: prosumer and co-creation, prosumer and user-generated content and prosumer and informational capital. Research limitations/implications This analysis provided a basis for conceptualizing publications on prosumption related to business and sociology in the discipline of marketing. Content analysis found that prosumption research in marketing is in early stages: little quantitative study has been conducted yet. Researchers have not yet constructed a quantitative measure for prosumption. Practical implications Business firms can engage prosumers to gain market share and competitive advantage, especially relative to value co-creation, with near-zero marginal cost. Originality/value This may be the first bibliometric analysis and systematic review of prosumption research in marketing studies. The achievements of this paper open new avenues for other prosumption researchers.

I. D. Apriliyanti, I. Alon

kepha zablon, Wilfred, Norah

Context and background Watershed natural resources are the major sources of community livelihoods and are also used as an indicator of socioeconomic development thus they need to be conserved for sustainable productivity. Community participation and stakeholders involvement is significant in controlling degradation of this valuable natural resources. A knowledge gap therefore exist on what factors influence their participation in watershed management programs and activities. Goals and objective Several factors that influences community participation have been studied and have been found to vary from one watershed to another. Hence the purpose of this study was to assess the factors influencing community participation in watershed management in upper gucha watershed, Kisii County, Kenya. Methodology The methodologies used include mapping and delineating the boundaries of the watershed using Google Earth satellite images and digitizing using ArcGis software version 10.5. Descriptive survey design and a sample size of 354 household heads was utilized. Survey method was used to collect data via questionnaires. Mean, frequency, and weighted average were used to analyze data. Pie charts, bar graphs and tables were method used to present data. Results The results established that factors such as extension services, watershed management groups, awareness, training, and derived benefits positively influenced community participation. In contrast, cultural practices, attitudes, and farm size did not have a significant impact. The study recommended implementing policies that encourage stakeholder’s collaboration and incorporating economic benefits into conservation programs, in order to increase community participation and manage watersheds effectively.

S. Wilks, Barbara Mühlemann, Xiaoying Shen et al.

During the SARS-CoV-2 pandemic, multiple variants escaping pre-existing immunity emerged, causing concerns about continued protection. Here, we use antigenic cartography to analyze patterns of cross-reactivity among a panel of 21 variants and 15 groups of human sera obtained following primary infection with 10 different variants or after mRNA-1273 or mRNA-1273.351 vaccination. We find antigenic differences among pre-Omicron variants caused by substitutions at spike protein positions 417, 452, 484, and 501. Quantifying changes in response breadth over time and with additional vaccine doses, our results show the largest increase between 4 weeks and >3 months post-2nd dose. We find changes in immunodominance of different spike regions depending on the variant an individual was first exposed to, with implications for variant risk assessment and vaccine strain selection. One sentence summary: Antigenic Cartography of SARS-CoV-2 variants reveals amino acid substitutions governing immune escape and immunodominance patterns.

D. Wood, John Fels, J. Krygier

Marwa Zerrouk, Kenza Ait El Kadi, Imane Sebari et al.

Wetlands, among the most productive ecosystems on Earth, shelter a diversity of species and help maintain ecological balance. However, they are witnessing growing anthropogenic and climatic threats, which underscores the need for regular and long-term monitoring. This study presents a systematic review of 121 peer-reviewed articles published between January 2015 and 30 April 2025 that applied machine learning (ML) and deep learning (DL) for wetland mapping and bird-habitat monitoring. Despite rising interest, applications remain fragmented, especially for avian habitats; only 39 studies considered birds, and fewer explicitly framed wetlands as bird habitats. Following PRISMA 2020 and the SPIDER framework, we compare data sources, classification methods, validation practices, geographic focus, and wetland types. ML is predominant overall, with random forest the most common baseline, while DL (e.g., U-Net and Transformer variants) is underused relative to its broader land cover adoption. Where reported, DL shows a modest but consistent accuracy over ML for complex wetland mapping; this accuracy improves when fusing synthetic aperture radar (SAR) and optical data. Validation still relies mainly on overall accuracy (OA) and Kappa coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>κ</mi></semantics></math></inline-formula>), with limited class-wise metrics. Salt marshes and mangroves dominate thematically, and China geographically, whereas peatlands, urban marshes, tundra, and many regions (e.g., Africa and South America) remain underrepresented. Multi-source fusion is beneficial yet not routine; The combination of unmanned aerial vehicles (UAVs) and DL is promising for fine-scale avian micro-habitats but constrained by disturbance and labeling costs. We then conclude with actionable recommendations to enable more robust and scalable monitoring. This review can be considered as the first comparative synthesis of ML/DL methods applied to wetland mapping and bird-habitat monitoring, and highlights the need for more diverse, transferable, and ecologically/socially integrated AI applications in wetland and bird-habitat monitoring.

Zechao Bai, Yuxiao Qin, Yanping Wang et al.

Monitoring the structural deformation of bridge with high precision during the operation process is crucial for assessing its health. This study proposes a practical strategy for jointly measuring multi-scale periodic dynamic deformation in bridges using both spaceborne and ground-based Interferometric Synthetic Aperture Radar (InSAR) technologies. The proposed strategy involves extracting seasonal periodic deformation by applying thermal expansion components with spaceborne Persistent Scatterer InSAR (PS-InSAR) and capturing daily periodic deformation using a two-stage atmospheric phase screen compensation ground-based InSAR method. This study focuses on a double-tower cable-stayed and rigid frame system bridge to investigate the spatiotemporal evolution of bridge multi-scale periodic dynamic deformation patterns. The monitoring results indicate that the geometric state and deformation pattern of the bridge remained stable, exhibiting significant seasonal and daily dynamic deformations that were either positively or negatively correlated with temperature changes. Seasonal periodic deformation captured by spaceborne InSAR showed maximum displacements near expansion joints, while tower deformation remained constrained within ±5 mm. Daily periodic deformation captured by ground-based InSAR revealed significant displacements at the bridge tower top, contrasting with minimal deformation of ±2 mm near fixed bearings. These deformations exhibited significant correlations with temperature changes. Both the deformation trend and magnitude confirmed to the computational results of the bridge structure design.

Keyu Lu, Xin Zhao, Manchun Li et al.

Urban change detection faces critical challenges in capturing comprehensive transformations across morphological, environmental, social, and economic dimensions. Knowledge graphs demonstrate exceptional compatibility with multimodal geospatial data, providing a novel approach for change detection. However, existing knowledge graphs are predominantly static and lack deep fusion between features, limiting their direct application to change detection. To address these limitations, this study proposes an urban change detection method based on multimodal data and knowledge graph technology. First, the study develops a multimodal bitemporal urban knowledge graph (MBUKG) that integrates multimodal geographical data. Second, the study proposes a dual cross-attention knowledge representation learning (DCKRL) framework to derive knowledge graph entity vectors. Finally, the study constructs change rate indicators based on cosine similarity to quantify the extent of changes in grid entities between 2017 and 2023, thereby enabling urban change detection. The results demonstrated the effectiveness of the proposed framework, achieving an F1 score of 0.917. The DCKRL framework exhibits robust performance with a Hit@10 value of 0.670. The findings reveal that MBUKG successfully integrates multimodal data with different temporal attributes, while DCKRL effectively captures intricate relationships among entities. The proposed method can provide scientific support for urban planning.

Bo Zhao, Shaozeng Zhang, Chunxue Xu et al.

ABSTRACT The developing convergence of Artificial Intelligence and GIScience has raised a concern on the emergence of deep fake geography and its potentials in transforming human perception of the geographic world. Situating fake geography under the context of modern cartography and GIScience, this paper presents an empirical study to dissect the algorithmic mechanism of falsifying satellite images with non-existent landscape features. To demonstrate our pioneering attempt at deep fake detection, a robust approach is then proposed and evaluated. Our proactive study warns of the emergence and proliferation of deep fakes in geography just as “lies” in maps. We suggest timely detections of deep fakes in geospatial data and proper coping strategies when necessary. More importantly, it is encouraged to cultivate a critical geospatial data literacy and thus to understand the multi-faceted impacts of deep fake geography on individuals and human society.

V. Andreotti, Sharon Stein, Cash Ahenakew et al.

R. Roth

ABSTRACT In this article, I review considerations and techniques for approaching cartographic design as visual storytelling. Stories, like maps, are a method for documenting and explaining, for meaningfully abstracting our experiences, for communicating and sharing, and for asserting a particular worldview. I argue that visual storytelling offers an entry point for hybridization in cartography, uniting technology with praxis, product with process, and design with critique while opening rich new avenues for transdisciplinary research and design. I begin by introducing influences on map-based visual storytelling and review ten recurring themes that make visual storytelling different from traditional perspectives on cartographic design. I then offer three of potentially many ways to articulate and organize the design space for map-based visual storytelling: foundational narrative elements and their adaptation to geographic phenomena and processes, visual storytelling genres delineating different story experiences, and visual storytelling tropes used to advance narratives across text, maps, images, and other multimedia. I conclude with a call for future research on visual storytelling in cartography, including visual design, visual ethics, and visual literacy.

L. Moore

M. Reffay, M. Reffay, M. Parrini et al.

P. Kiefer, I. Giannopoulos, M. Raubal et al.

R. Braidotti

A. Turner

George Deroco Martins, Laura Cristina Moura Xavier, Guilherme Pereira de Oliveira et al.

The application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium <i>Azospirillum brasilense</i> has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying <i>A. brasilense</i>. Analyses were carried out in two commercial areas treated with <i>A. brasilense</i>. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R² to be 6.67, 5.42, and 0.88, respectively. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R² to be 9.3, 7.71, and 0.80, respectively. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of <i>A. brasilense</i>.

Christensen Carsten Sander

What is the meaning of legendary or phantom islands? A phantom island is a purposed island which was included on maps for a period of time, but was later found not to exist. Most of these phantom islands appeared on maps from the 1200s until the early 1800s. Some of islands did not disappear on maps until our time. But is an island simply falsely mapped and then turned out to be not existent. This paper analyses the histories, meaning of and the etymologies of these phantom islands in different parts of the world ocean. It can be argued that these mysterious lands were almost all found in the Middle Ages and some centuries later. To which depictions of sea monsters, dragons and Cyclops on various maps from the Middle Ages bear witness? Or these fantasies and hallucinations are in the minds of sailors? This paper also tries to put these legendary islands into a far much larger perspective. Be it in the world of mythology or in the world of fantasy, in the inexplicable universe or simply in weather phenomena under extreme conditions both among the sailors and in the world of nature.