Search Results (93)

Comprehending the alterations in wintering grounds of migratory birds amid global change and anthropogenic influences is pivotal for advancing wetland sustainability and ensuring avian conservation. Frequent extreme droughts in the middle and lower Yangtze River region of China have posed severe ecological and socio-economic dilemmas. The integration of internet-derived, crowdsourced geographic data with remote-sensing imagery now facilitates assessments of these avian habitats. Poyang Lake, China’s largest freshwater body, suffered an unprecedented drought in 2022, offering a unique case study on avian habitat responses to climate extremes. By harnessing social and online platforms’ media reports, we analyzed the types, attributes and proportions of migratory bird habitats. This crowdsourced geographic information, corroborated by Sentinel-2 optical remote-sensing imagery, elucidated the suitability and transformations of these habitats under drought stress. Our findings revealed marked variations in habitat preferences among bird species, largely attributable to divergent feeding ecologies and behavioral patterns. Dominantly, shallow waters emerged as the most favored habitat, succeeded by mudflats and grasslands. Remote-sensing analyses disclosed a stark 60% reduction in optimal habitat area during the drought phase, paralleled by a 1.5-fold increase in unsuitable habitat areas compared to baseline periods. These prime habitats were chiefly localized in Poyang Lake’s western sub-lakes. The extreme drought precipitated a drastic contraction in suitable habitat extent and heightened fragmentation. Our study underscores the value of crowdsourced geographic information in assessing habitat suitability for migratory birds. Retaining sub-lake water surfaces within large river or lake floodplains during extreme droughts emerges as a key strategy to buffer the impacts of hydrological extremes on avian habitats. This research contributes to refining conservation strategies and promoting adaptive management practices of wetlands in the face of climate change. Full article

Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1 score in classifying Twitter/X posts, integrated with an interactive web platform that maps emotional trends and crisis situations across geographic regions. The system’s dynamic visualization capabilities allow authorities to monitor situational developments through an interactive map, facilitating targeted response coordination. The experimental results show the model’s effectiveness in extracting actionable intelligence from Twitter/X posts during natural disasters. Full article

Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the specific conditions of the selected area. To address this, the aim of this study was to explore the potential of using crowdsourced data combined with geographic information system (GIS) solutions to support beekeepers’ decision-making on a larger scale. This study investigated possible methods for processing open geospatial data from the OpenStreetMap (OSM) database for the environmental analysis and assessment of the suitability of selected areas. The research included developing methods for obtaining, classifying, and analyzing OSM data. As a result, the structure of OSM data and data retrieval methods were studied. Subsequently, an experimental spatial data classifier was developed and applied to evaluate the suitability of territories for beekeeping. For demonstration purposes, an experimental prototype of a web-based GIS application was developed to showcase the results and illustrate the general concept of this solution. In conclusion, the main goals for further research development were identified, along with potential scenarios for applying this approach in real-world conditions. Full article

With the development of technology, Earth Science has entered a new era. Continuous research has generated a large amount of Earth Science data, including a significant amount of semi-structured and unstructured data, which contain information about locations, geographical concepts, geological characteristics of mineral deposits, and relationships. Efficient management of these Earth Science data is crucial for the development of digital earth systems, rational planning of resource industries, and resource security. By representing entities, relationships, and attributes through graph structures, knowledge graphs capture and present concepts and facts about the real world, facilitating efficient data management. However, due to the highly specialized and complex nature of Earth Science data and disciplinary differences, the methods used to construct general-purpose knowledge graphs cannot be directly applied to building knowledge graphs in the field of geological science. Therefore, this paper summarizes a “pipeline” approach to constructing an Earth Science knowledge graph in order to clarify the complete construction process and reduce barriers between data and technology. This approach divides the construction of the Earth Science knowledge graph into two parts and designs functional modules under each part to specify the construction process of the knowledge graph. In addition to proposing this approach, a knowledge graph of iron ore deposits is automatically constructed by integrating geographic and geological data related to iron ore deposits using deep learning techniques. The systematic approach presented in this paper reduces the threshold for constructing geological science knowledge graphs, provides methodological support for specific disciplines or research objects in Earth Science, and also lays the foundation for the construction of large-scale Earth Science knowledge graphs that combine crowdsourcing and expert decision-making, as well as the development of intelligent question-answering systems and intelligent decision-making systems covering the entire field of Earth Science. Full article

With the rise of the Internet of Things (IoT), mobile crowdsourcing has become a leading application, leveraging the ubiquitous presence of smartphone users to collect and process data. Spatial crowdsourcing, which assigns tasks based on users’ geographic locations, has proven to be particularly innovative. However, this trend raises significant privacy concerns, particularly regarding the precise geographic data required by these crowdsourcing platforms. Traditional methods, such as dummy locations, spatial cloaking, differential privacy, k-anonymity, and encryption, often fail to mitigate the risks associated with the continuous disclosure of location data. An unauthorized entity could access these data and infer personal information about individuals, such as their home address, workplace, religion, or political affiliations, thus constituting a privacy violation. In this paper, we propose a user mobility model designed to enhance location privacy protection by accurately identifying Points of Interest (POIs) and countering inference attacks. Our main contribution here focuses on user mobility modeling and the introduction of an advanced algorithm for precise POI identification. We evaluate our contributions using GPS data collected from 10 volunteers over a period of 3 months. The results show that our mobility model delivers significant performance and that our POI extraction algorithm outperforms existing approaches. Full article

Geographic information systems (GISs) based on WebGIS architectures have transformed geospatial data visualization and analysis, offering rapid access to critical information and enhancing decision making across sectors. This study conducted a bibliometric review of 358 publications using the Web of Science database. The analysis utilized tools, such as Bibliometrix (version R 4.3.0) and Biblioshiny (version 1.7.5), to study authors, journals, keywords, and collaborative networks in the field of information systems. This study identified two relevant clusters in the literature: (1) voluntary geographic information (VGI) and crowdsourcing, focusing on web integration for collaborative mapping through contributions from non-professionals and (2) GIS management for decision making, highlighting web-based architectures, open sources, and service-based approaches for storing, processing, monitoring, and sharing geo-referenced information. The journals, authors, and geographical distribution of the most important publications were identified. China, Italy, the United States, Germany, and India have excelled in the application of geospatial technologies in areas such as the environment, risk, sustainable development, and renewable energy. These results demonstrate the impact of web-based GISs on forest conservation, climate change, risk management, urban planning, education, public health, and disaster management. Future research should integrate AI, mobile applications, and geospatial data security in areas aligned with sustainable development goals (SDGs) and other global agendas. Full article

Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation. Full article

Crowdsourced spatial analytics is a rapidly developing field that involves collecting and analyzing geographical data, utilizing the collective power of human observation. This paper explores the field of spatial data analytics and crowdsourcing and how recently developed tools, cloud-based GIS, and artificial intelligence (AI) are being applied in this domain. This paper examines and discusses cutting-edge technologies and case studies in different fields of spatial data analytics and crowdsourcing used in a wide range of industries and government departments such as urban planning, health, transportation, and environmental sustainability. Furthermore, by understanding the concerns associated with data quality and data privacy, this paper explores the potential of crowdsourced data while also examining the related problems. This study analyzes the obstacles and challenges related to “geospatial crowdsourcing”, identifying significant limitations and predicting future trends intended to overcome the related challenges. Full article

Specific environmental characteristics can encourage active leisure travel and increase physical activity. However, existing environment-travel studies tend to ignore the differences in environmental characteristics associated with route choice and travel distance, of which the latter could be more important for health benefits, since longer trips are associated with increased exercise. Additionally, the most recent studies focus on leisure walking and leisure cycling, and activities such as hiking, climbing, and running are examined less frequently. This study, therefore, compares the similarities and differences of the environmental factors associated with route selection and travel distance through non-parametric tests and Cox proportional hazard models. The results show that two intersecting sets of environmental elements relate to both the route chosen and the distance traveled. Land use diversity and varied topography are appealing for both leisure trips and trip length. In addition, the differences in environmental characteristics among specific leisure travels may be attributed to variations in physical activity requirements, preferences for landscape viewing, and/or sensitivity to crowding. Therefore, conclusions drawn without considering the different types of leisure travel could be skewed. Whether particular surroundings may effectively increase physical activity remains uncertain. A more holistic perspective could be beneficial when studying the connection between the environment, active travel, and health. Full article

Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world’s population resides in urban areas, contributing to the prosperity of cities. However, it also brings more crime risks to the city. Crime prediction based on spatiotemporal data, along with the implementation of multiple unmanned drone patrols and responses, can effectively reduce a city’s crime rate. This paper utilizes machine learning and data mining techniques, predicts crime incidents in small geographic areas with short timeframes, and proposes a random forest algorithm based on oversampling, which outperforms other prediction algorithms in terms of performance. The research results indicate that the random forest algorithm based on oversampling can effectively predict crimes with an accuracy rate of up to 95%, and an AUC value close to 0.99. Based on the crime prediction results, this paper proposes a multi-drone patrol response strategy to patrol and respond to predicted high-crime areas, which is based on target clustering and combined genetic algorithms. This strategy may help with the pre-warning patrol planning within an hourly range. This paper aims to combine crime event predictions with crowd-sourced cruise responses to proactively identify potential crimes, providing an effective solution to reduce urban crime rates. Full article

Currently, a significant portion of published research on online hate speech relies on existing textual corpora. However, when examining a specific context, there is a lack of preexisting datasets that include the particularities associated with various conditions (e.g., geographic and cultural). This issue is evident in the case of online anti-immigrant speech in Mexico, where available data to study this emergent and often overlooked phenomenon are scarce. In light of this situation, we propose a novel methodology wherein three domain experts annotate a certain number of texts related to the subject. We establish a precise control mechanism based on these annotations to evaluate non-expert annotators. The evaluation of the contributors is implemented in a custom annotation platform, enabling us to conduct a controlled crowdsourcing campaign and assess the reliability of the obtained data. Our results demonstrate that a combination of crowdsourced and expert data leads to iterative improvements, not only in the accuracy achieved by various machine learning classification models (reaching 0.8828) but also in the model’s adaptation to the specific characteristics of hate speech in the Mexican Twittersphere context. In addition to these methodological innovations, the most significant contribution of our work is the creation of the first online Mexican anti-immigrant training corpus for machine-learning-based detection tasks. Full article

Radio maps, which can provide metrics for signal strength at any location in a geographic space, are useful for many applications of 6G technologies, including UAV-assisted communication, network planning, and resource allocation. However, current crowd-sourced reconstruction methods necessitate large amounts of privacy-sensitive user data and entail the training of all data with large models, especially in deep learning. This poses a threat to user privacy, reducing the willingness to provide data, and consuming significant server resources, rendering the reconstruction of radio maps on resource-constrained UAVs challenging. To address these limitations, a self-supervised federated learning model called RadioSRCNet is proposed. The model utilizes a super-resolution (SR)-based network and feedback training strategy to predict the pathloss for continuous positioning. In our proposition, users retain the original data locally for training, acting as clients, while the UAV functions as a server to aggregate non-sensitive data for radio map reconstruction in a federated learning (FL) manner. We have employed a feedback training strategy to accelerate convergence and alleviate training difficulty. In addition, we have introduced an arbitrary position prediction (APP) module to decrease resource consumption in clients. This innovative module struck a balance between spatial resolution and computational complexity. Our experimental results highlight the superiority of our proposed framework, as our model achieves higher accuracy while incurring less communication overheads in a computationally and storage-efficient manner as compared to other deep learning methods. Full article

The primary objective of this study was to examine the quality of volunteered geographic information (VGI) data for flood mapping of Hurricane Harvey. As a crowdsourcing platform, the U-Flood project mapped flooded streets in the Houston metro area. This research examines the following: (1) If there are any significant differences in water depth (WD) among the hydraulic and hydrologic (H&H) model, the Federal Emergency Management Agency (FEMA) reference floodplain map, and the VGI? (2) Are there any significant differences in the inundated areas between the floodplain modeled by the VGI and hydraulic simulation? This study used HEC-RAS to simulate flood inundation maps and validated the results with high water marks (HWM) and the FEMA-modeled floodplain after Hurricane Harvey. The statistical results showed that there were significant differences in the WD, the inundated road count, and the length inside/outside of HEC-RAS-modeled floodplain. The results also showed that a less consistent decreasing trend between the U-Flood data and the modeled floodplain over time and space. This study empirically evaluated the data quality of the VGI based on observed and modeled data in flood monitoring. The findings from this study fill the gaps in the literature by assessing the uncertainty and data quality of VGI, providing insights into using supplementary data in flood mapping research. Full article

Over the past 10 years, urban floods have increased in frequency because of extreme rainfall events and urbanization development. To reduce the losses caused by floods, various urban flood models have been developed to realize urban flood early warning. Using CiteSpace software’s co-citation analysis, this paper reviews the characteristics of different types of urban flood models and summarizes state-of-the-art technologies for flood model development. Artificial intelligence (AI) technology provides an innovative approach to the construction of data-driven models; nevertheless, developing an AI model coupled with flooding processes represents a worthwhile challenge. Big data (such as remote sensing, crowdsourcing geographic, and Internet of Things data), as well as spatial data management and analysis methods, provide critical data and data processing support for model construction, evaluation, and application. The further development of these models and technologies is expected to improve the accuracy and efficiency of urban flood simulations and provide support for the construction of a multi-scale distributed smart flood simulation system. Full article

Helicobacter pylori is usually acquired during childhood. The reports from the last two decades pointed out a decrease in H. pylori prevalence across geographical areas worldwide compared to previously reported data. Most of the studies performed in America found an overall H. pylori infection prevalence of approximately 50%. The most important risk factors in America include being male, poor adherence or difficult access to treatment, and the lack of in-home water service. Despite the descending trend in prevalence worldwide, the overall prevalence in Africa remains very high (70%). Nevertheless, the prevalence of H. pylori in children without gastrointestinal who underwent screening was reported to be only 14.2%. The main risk factors in Africa are having a traditional pit or no toilet, poverty, birth order, source of drinking water, or being a farmer. Asia seems to have the widest variations in terms of H. pylori prevalence. Several risk factors were reported in Asia to be associated with this infection, such as lower income and educational level, house crowding, rural residence, ethnicity, the use of tanks as water supplies, alcohol drinking, active smoking, eating spicy food or raw uncooked vegetables, poor living conditions and sanitation. The overall prevalence of H. pylori infection in European children is almost 25%. Portugal has the highest prevalence of all European countries at 66.2% in children 13 years of age. The risk factors in European individuals consist of living in rural areas, eating unwashed fruits and vegetables, not washing hands after school, low parental education and unemployment, and short education duration. Further studies are required to identify the precise mechanisms involved in the discrepancies of H. pylori prevalence worldwide. Full article