Search Results (98)

The big data revolution transformed how we think of data analytics in many ways. Critical amongst them are the somewhat interconnected ideas of volunteered geographic information, crowdsourcing, and the big data property of variety. The robust literature concerning conceptual neighborhood graphs in two of these cases considers objects whose datatypes are held stable between the relations under consideration. This, however, is a limiting factor in these three application spaces due to the unknown form that data will take. This paper considers two avenues for the conceptual neighborhood graph to take as directions to address current complications facing reasoning tasks within a practically dirty world motivated by various sources of data: discretization conceptual neighborhood graphs (changing between corresponding vector and raster spaces) and cartographic generalization conceptual neighborhood graphs (changing the form of the objects in question). This paper provides insights as to what considerations should be considered when embarking upon this idea and demonstrates these concepts applied to prior conceptual neighborhood graphs. Full article

Geospatial Artificial Intelligence (GeoAI) enables the automated generation of built environment map features, such as building outlines/footprints, on a global scale. However, the integration of these AI-generated datasets into Volunteered Geographic Information (VGI) platforms like OpenStreetMap (OSM) risks incorporating ‘AI slop’, consisting of geometrically inconsistent/unreliable data, into the online map. While the OSM “Code of Conduct for Automated Edits” provides a policy framework for data ingestion, it lacks a machine-enforceable mechanism for real-time quality gating. This paper proposes a GeoAI-Gatekeeper to perform this task—an automated process that applies empirical Acceptable Quality Thresholds (AQT) to address the GeoAI data governance problem. Because the Gatekeeper utilizes an intrinsic, no-reference evaluation of geometric fidelity, it can assess incoming AI-generated data streams in real-time without requiring ground-truth benchmarks. Importantly, it focuses exclusively on the geometric validation of building footprints, acknowledging for now that semantic enrichment, such as tagging, remains a human-centric task. The presented GeoAI-Gatekeeper is a working prototype developed for a specific urban area, systematically triaging incoming AI-generated data into three tiers; Auto-Accept, Manual Review, and Reject. It provides a Web-GIS interface for Human-in-the-Loop (HITL) functionality to ensure the OSM community remains the final arbiter of acceptable data quality. Testing the Gatekeeper in Dublin (Ireland) demonstrates that our solution can auto-ingest 93.6% of features with a 14x reduction in human review effort while still adhering to OSM’s cartographic integrity standards. By implementing qualitative community guidelines into machine-enforceable thresholds, our approach introduces a viable methodology for next-generation hybrid VGI systems. Importantly, it ensures that the transition towards automated data ingestion reinforces, rather than undermines, the reliability of global crowd-source mapping datasets. Full article

Automatic license plate reader (ALPR) cameras and gunshot detection system (GDS) technology represent rapidly expanding forms of surveillance. Despite their prevalence, empirical literature regarding these tools remains limited, particularly concerning their geographic distribution across the United States. This study addresses this gap by conducting a geospatial analysis of crowdsourced ALPR and GDS locations throughout Virginia. Utilizing Geographic Information Systems (GIS), we mapped the concentrations of this technology and analyzed the racial demographic profiles of the most heavily surveilled communities. Our results identify distinct clusters of surveillance technology hubs across Virginia. In these high-intensity areas, surveillance technology is frequently concentrated in and around communities of color. These findings carry an array of implications, including the risk that over-surveilled neighborhoods may disproportionately suffer from the abuse or misuse of these tools. Furthermore, this distribution reflects a historical legacy within the criminal justice system of disproportionately monitoring marginalized populations. The limitations of this analysis are equally revealing: the reliance on crowdsourced data due to a lack of verifiable, publicly accessible coordinates underscores an ongoing lack of transparency. Full article

The objective of this paper is to develop an adaptable and affordable technical tool for managing small urban areas. It demonstrates a low-cost, reliable, and fast method for integrating BIMs, IFC data, and GIS to support fit-for-purpose, crowdsourcing, and participatory applications through an online dashboard. Open data and existing geoportals are used to create the necessary geospatial infrastructure. Geometric information such as building area size and volume is combined with other data from multiple sources such as market values and CO₂ emissions, which can be updated dynamically through real-time interactions. A case study is presented for a small urban neighborhood in Athens. Full article

Street design quality and socio-economic factors jointly influence housing prices, but their intertwined effects and spatial variations remain under-quantified. Housing prices not only reflect residents’ neighborhood experiences but also stem from the spillover value of public streets perceived and used by different users. This study takes Tianjin as a case and views the street environment as an immediate experience proxy for through-travelers, combining street view images and crowdsourced perception data to extract both subjective and objective indicators of the street environment, and integrating neighborhood and location characteristics. We use Geographical-XGBoost to evaluate the relative contributions of multiple factors to housing prices and their spatial variations. The results show that incorporating both subjective and objective street information into the Hedonic Pricing Model (HPM) improves its explanatory power, while local modeling with G-XGBoost further reveals significant heterogeneity in the strength and direction of effects across different locations. The results indicate that incorporating both subjective and objective street information into the HPM enhances explanatory power, while local modeling with G-XGBoost reveals significant heterogeneity in the strength and direction of effects across different locations. Street greening, educational resources, and transportation accessibility are consistently associated with higher housing prices, but their strength varies by location. Core urban areas exhibit a “counterproductive effect” in terms of complexity and recognizability, while peripheral areas show a “barely acceptable effect,” which may increase cognitive load and uncertainty for through-travelers. In summary, street environments and socio-economic conditions jointly influence housing prices via a “corridor-side–community-side” dual-pathway: the former (enclosure, safety, recognizability) corresponds to immediate improvements for through-travelers, while the latter (education and public services) corresponds to long-term improvements for residents. Therefore, core urban areas should control design complexity and optimize human-scale safety cues, while peripheral areas should focus on enhancing public services and transportation, and meeting basic quality thresholds with green spaces and open areas. Urban renewal within a 15 min walking radius of residential areas is expected to collaboratively improve daily travel experiences and neighborhood quality for both residents and through-travelers, supporting differentiated housing policy development and enhancing overall quality of life. Full article

Geospatial Artificial Intelligence (GeoAI) offers a scalable solution for automating the generation and updating of volunteered geographic information (VGI) maps—addressing the limitations of manual contributions to crowd-source mapping platforms such as OpenStreetMap (OSM). This study evaluates the accuracy of GeoAI-generated buildings specifically, using two Generative Adversarial Network (GAN) models. These are OSM-GAN—trained on OSM vector data and Google Earth imagery—and OSi-GAN—trained on authoritative “ground truth” Ordnance Survey Ireland (OSi) vector data and aerial orthophotos. Altogether, we assess map feature completeness, shape accuracy, and positional accuracy and conduct qualitative visual evaluations using live OSM database features and OSi map data as a benchmark. The results show that OSi-GAN achieves higher completeness (88.2%), while OSM-GAN provides more consistent shape fidelity (mean HD: 3.29 m; σ = 2.46 m) and positional accuracy (mean centroid distance: 1.02 m) compared to both OSi-GAN and the current OSM map. The OSM dataset exhibits moderate average deviation (mean HD 5.33 m) but high variability, revealing inconsistencies in crowd-source mapping. These empirical results demonstrate the potential of GeoAI to augment manual VGI mapping workflows to support timely downstream applications in urban planning, disaster response, and many other location-based services (LBSs). The findings also emphasize the need for robust Quality Assurance (QA) frameworks to address “AI slop” and ensure the reliability and consistency of GeoAI-generated data. Full article

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

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

Environmental noise is a major environmental concern in metropolitan cities. The rapid social and economic growth in the 20th century is not always accompanied by adequate land planning and environmental management measures. As a consequence of rapid urbanization processes, cities are facing an increase in noise pollution. Noise is being recognized as a serious environmental problem and one which must be accounted for in a sustained development policy designed to improve the quality of life for citizens. Therefore, the monitoring of noise is a crucial aspect of urban planning to allow urban planners to create harmonious and livable environments for communities worldwide. This research aims at assessing the noise levels and associated sources of noise in downtown areas through the involvement of crowdsourcing techniques. The incorporation of noise mapping and increased public awareness are achieved by a framework that enables a comparative scheme between two cities: Alexandria, Egypt and Zagreb, Croatia. The methodology depends on combining crowdsourcing techniques using mobile applications and geographic information system (GIS) tools to detect and analyze noise. Finally, this research provides a comparison between the two cities using the adopted methodology and introduces recommendations to enhance urban planning decisions. 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

Climate change, population growth and urban sprawl have put a strain on water supplies across the world, making it difficult to meet water demand, especially in city regions where more than half of the world’s population now reside. Due to the complex urban fabric, conventional techniques should be developed to diagnose water shortage risk (WSR) by engaging crowdsourcing. This study aims to develop a novel approach based on public participation (PP) with a geographic information system coupled with machine learning (ML) in the urban water domain. The approach was used to detect (WSR) in two ways, namely, prediction using ML models directly and using the weighted linear combination (WLC) function in GIS. Five types of ML algorithm, namely, support vector machine (SVM), multilayer perceptron, K-nearest neighbour, random forest and naïve Bayes, were incorporated for this purpose. The Shapley additive explanation model was added to analyse the results. The Water Evolution and Planning system was also used to predict unmet water demand as a relevant criterion, which was aggregated with other criteria. The five algorithms that were used in this work indicated that diagnosing WSR using PP achieved good-to-perfect accuracy. In addition, the findings of the prediction process achieved high accuracy in the two proposed techniques. However, the weights of relevant criteria that were extracted by SVM achieved higher accuracy than the weights of the other four models. Furthermore, the average weights of the five models that were applied in the WLC technique increased the prediction accuracy of WSR. Although the uncertainty ratio was associated with the results, the novel approach interpreted the results clearly, supporting decision makers in the proactive exploration processes of urban WSR, to choose the appropriate alternatives at the right time. Full article