Geospatial Data Storage Research Articles

Assessing Riverbank Change Caused by Sand Mining and Waste Disposal Using Web-Based Volunteered Geographic Information

River water is one of the most important natural resources for economic development and environmental sustainability. However, river water systems are vulnerable in some of the densely populated regions across the globe. Intense sand mining and waste disposal can lead to river changes, loose foundations, and biodiversity loss. This study aims to develop a web-based geographic information system (GIS) platform to monitor river channel changes and their impacts on river environments due to sand mining and waste burial. The system integrates open-source software, Windows Server 2012, a web server, and PostgreSQL with PostGIS plugins for efficient mapping and storage of geospatial data and volunteered reporting of location events. Interferometric methods, including SNAP2STAMPS Automated Algorithm, persistent scatterer interferometry (PSI), small baseline subset (SBAS), and Snap software, were used to analyze spatial changes of subsidence from Sentinel-1 satellite data from 2021 to 2023 in the Gadar River in Oshnavieh, Iran. The results showed that the maximum subsidence at the riverbank was −10.1 cm due to sand mining, and the maximum uplift was 8.2 cm due to waste landfilling. The average subsidence was reported to be −5.1 cm. The results emphasize spatial analysis, showcasing material mining’s impact on subsidence trends and underscoring the significance of public participation in monitoring river health. Three parameters—completeness, correctness, and quality—were used to validate the system. Validation results showed completeness, correctness, and quality rates of 94.15%, 92.48%, and 86.63%, respectively.

Construction of rhombic triacontahedron discrete global grid systems

ABSTRACT Discrete Global Grid System (DGGS) is a new multi-resolution geospatial data modeling and processing scheme for the digital earth. The icosahedron is commonly regarded as an ideal polyhedron for constructing DGGSs with small distortions; however, the shape of its face is triangular, making it difficult to incorporate the matrix structure used for geospatial data storage and parallel computing. To overcome this limitation, this study utilizes the rhombic triacontahedron (RT) as the basic polyhedron to construct DGGSs. An equal-area projection between the surface of RT and the sphere is developed and used to design a grid-generation algorithm for the aperture 4 hexagonal DGGS based on RT. Compared with the equal-area DGGS based on the icosahedron, the proposed scheme results in smaller angular projection distortions, with the mean and standard deviation decreasing by 41.6% and 30.9%, respectively. The grid cells of the RT DGGS also achieve more optimized geometric characteristics in shape compactness, length deviation, and angle deviation than those in the icosahedron DGGS. Additionally, the cross-surface computation efficiency provides advantages in code conversion to latitude and longitude and proximity queries. Furthermore, the use of RT offers a new and better framework within the context of DGGS research and application.

State-of-the-Art Geospatial Information Processing in NoSQL Databases

Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages.

Programa de atualização permanente da base contínua 1:250.000, uma visão de futuro

Este trabajo tiene como objetivo presentar la Base Cartográfica Continua de Brasil en la escala de 1:250,000 - BC250; se inserta en el Programa de Actualización Permanente (PAP) del IBGE. Este programa consiste en la actualización y mantenimiento continuo de la cartografía de todo el territorio brasileño a una escala de 1:250.000, con una vista que se trata de un apoyo estratégico para el gobierno en la planificación regional y también contribuye a los estudios sobre las políticas ambientales. Estas actividades incluyen el uso de PAP, foto sensores de los satélites como el Landsat, CBERS, RapidEye, modelos digitales de elevación, las aportaciones de los organismos asociados y los estudios de campo. La incorporación de la tecnología asociada a los Sistemas de Información Geográfica y Sensores Remotos para el mapeo sistemático contribuyó al desarrollo de una sola base integrada en todo Brasil. El proceso de construcción de BC250 se inició en 2008; con Brasil se divide en bloques de la contratación; y la conclusión de la etapa de la cartografía, en agosto de 2012. Una primera versión de la base continua está disponible para consulta en el sitio web de IBGE (www.ibge.gov.br) desde octubre de 2013 y en la página de la Infraestructura Nacional de Datos Espaciales (INDE) desde diciembre de 2013 <www.inde.gov.br>. Este mapa base es el primero en ser producido en Brasil en la Norma Técnica Especificaciones para los Datos Geoespaciales Estructuración Vectorial (ET-EDGV) que proporciona una nueva estructura para el almacenamiento de datos geoespaciales para la integración de datos en el INDE.

Remap: An online remote sensing application for land cover classification and monitoring

Abstract Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to the maps they need to support their routine decision‐making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to develop ecosystem maps from remote sensing data. We have developed a free and open‐access online remote sensing and environmental modelling application, the Remote Ecosystem Monitoring and Assessment Pipeline (Remap; https://remap-app.org), that enables volunteers, managers and scientists with little or no experience in remote sensing to generate classifications (maps) of land cover and land use change over time. Remap utilizes the geospatial data storage and analysis capacity of Google Earth Engine and requires only spatially resolved training data that define map classes of interest (e.g. ecosystem types). The training data, which can be uploaded or annotated interactively within Remap, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in Remap represent topographic (e.g. slope, elevation), spectral (archival Landsat image composites) and climatic variables (precipitation, temperature) that are relevant to the distribution of ecosystems and land cover classes. The ability of Remap to develop and export high‐quality classified maps in a very short (<10 min) time frame represents a considerable advance towards globally accessible and free application of remote sensing technology. By enabling access to data and simplifying remote sensing classifications, Remap can catalyse the monitoring of land use and change to support environmental conservation, including developing inventories of biodiversity, identifying hotspots of ecosystem diversity, ecosystem‐based spatial conservation planning, mapping ecosystem loss at local scales and supporting environmental education initiatives.

Dynamic searchable symmetric encryption for storing geospatial data in the cloud

We present a dynamic searchable symmetric encryption scheme allowing users to securely store geospatial data in the cloud. Geospatial data sets often contain sensitive information, for example, about urban infrastructures. Since clouds are usually provided by third parties, these data need to be protected. Our approach allows users to encrypt their data in the cloud and make them searchable at the same time. It does not require an initialization phase, which enables users to dynamically add new data and remove existing records. We design multiple protocols differing in their level of security and performance, respectively. All of them support queries containing boolean expressions, as well as geospatial queries based on bounding boxes, for example. Our findings indicate that although the search in encrypted data requires more runtime than in unencrypted data, our approach is still suitable for real-world applications. We focus on geospatial data storage, but our approach can also be applied to applications from other areas dealing with keyword-based searches in encrypted data. We conclude the paper with a discussion on the benefits and drawbacks of our approach.

Spatiotemporal correlation in WebGIS group-user intensive access patterns

ABSTRACTGroup-user intensive access to WebGIS exhibits spatiotemporal behaviour patterns with aggregation features and regularity distributions when geospatial data are accessed repeatedly over time and aggregated in certain spatial areas. We argue that these observable group-user access patterns provide a foundation for improved optimization of WebGIS so that it can respond to volume intensive requests with a higher quality of service and improve performance. Subsequently, a measure of access popularity distribution must precisely reflect the access aggregation and regularity features found in group-user intensive access. In our research, we considered both the temporal distribution characteristics and spatial correlation in the access popularity of tiled geospatial data (tiles). Based on the observation that group-user access follows a Zipf-like law, we built a tile-access popularity distribution based on time-sequence, to express the access aggregation of group-users with heavy-tailed characteristics. Considering the spatial locality of user-browsed tiles, we built a quantitative expression for the correlation between tile-access popularities and the distances to hotspot tiles, reflecting the attenuation of tile-access popularity to distance. Moreover, given the geographical spatial dependency and scale attribute of tiles, and the time-sequence of tile-access popularity, we built a Poisson regression model to express the degree of correlation among the accesses to adjacent tiles at different scales, reflecting the spatiotemporal correlation in tile access patterns. Experiments verify the accuracy of our Poisson regression model, which we then applied to a cluster-based cache-prefetching scenario. The results show that our model successfully reflects the spatiotemporal aggregation features of group-user intensive access and group-user behaviour patterns in WebGIS. The refined mathematical method in our model represents a time-sequence distribution of intensive access to tiles and the spatial aggregation and correlation in access to tiles at different scales, quantitatively expressing group-user spatiotemporal behaviour patterns with aggregation features and a regular distribution. Our proposed model provides a precise and empirical basis for performance-optimization strategies in WebGIS services, such as planning computing resource allocation and utilization, distributed storage of geospatial data, and providing distributed services so as to respond rapidly to geospatial data requests, thus addressing the challenges of volume-intensive user access.

Conceptual Framework for Geospatial Data Security

Due to rapid growth of distributed network and Internet, it becomes easy for data providers and users to access, manage, and share voluminous geospatial data in digital form. With Internet, it becomes very easy to distribute and copy geospatial data. Therefore, it becomes necessary to protect geospatial data from illegal and unauthorized usage. The increase availability of tools and techniques to access and disseminate geospatial data has made more urgent to protect it. Security mechanism should be enforced at geospatial data storage, dissemination, and at data retrieval to protect geospatial data from illicit users. In this paper, conceptual framework for geospatial data security is proposed by considering unique security requirements of geospatial data.