Cloud computing enables performing computations and analysis tasks and sharing services in web-based computer centres instead of local desktop systems. One of the most used areas of cloud computing is geographic information systems (GIS) applications. Although Desktop GIS products are still used in the community frequently, Web GIS and Cloud GIS applications have drawn attention and have become more efficient for users. In this study, a serverless Cloud GIS framework is implemented for the land valuation platform. In order to store, analyse, and share geospatial data, the Aurora Serverless PostgreSQL database is created on Amazon Web Services (AWS). While adopting Aurora Serverless PostgreSQL as a database management system, a simple point in polygon analysis conducted to compare the performances with Amazon Relational Database Service (RDS) instance. Results showed that the serverless database responded to the query faster and scaled up during high workload to decrease latency. Hence, parcel vector data, which conveys ownership information and land values attributes, is shared directly from the PostGIS database as vector tiles. Besides S3 and AWS Lambda services are used for storing and disseminating raster-based land value map tiles. To visualize all shared data and maps through a web browser, open source web mapping library Mapbox GL JS is used.

Growing urbanization cause environmental problems such as vast amount of carbon emissions and pollution all over the world.Smart Infrastructure and Smart Environment are two significant components of the smart city paradigm that can create opportunities for ensuring energy conservation, preventing ecological degradation, and using renewable energy sources. Since a great portion of the data contains location information, geospatial intelligence is a key technology for sustainable smart cities. We need a holistic framework for the smart governance of cities by utilizing key technological drivers such as big data, Geographic Information Systems (GIS), cloud computing, Internet of Things (IoT). Geospatial Big Data applications offer predictive data science tools such as grid computing and parallel computing for efficient and fast processing to build a sustainable smart city ecosystem. Effective management of big data in storage, visualization, analytics, and analysis stages can foster green building, green energy, and net zero targets of countries. Parallel computing systems have the ability to scale up analysis on geospatial big data platforms which is key for ocean, atmosphere, land, and climate applications. In this study, it is aimed to create the necessary technical infrastructure for smart city applications with a holistic big data management approach. Thus, a smart city model framework is developed for Smart Environment and Smart Governance components and performance comparison of Dask-GeoPandas and Apache Sedona parallel processing systems are carried out. Apache Sedona performed better on the performance test during read, write, join and clustering operations.

On February 6, 2023, the earthquakes centered on Kahramanmaraş-Pazarcık and Kahramanmaraş-Elbistan affected several provinces in the region and generated a vast amount of demolition waste. This study aims to develop a site selection model for the most suitable landfill areas after the earthquake using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM). First, the study identifies decision criteria with a Delphi expert panel. Next, it determines the weights using the BWM based on pairwise comparison. Spatial data of the determinants were obtained from open data portals, and proximity, overlay, and slope analyses were carried out for the disaster region. Combining the output data layer of each criterion, a suitability map of the landfills was produced. As a result, 36,2% (29,826 kms square) of the study area was classified as suitable and very suitable. In this study, a cloud GIS-based decision support platform was developed to act quickly and initiate recovery processes within the scope of disaster management of the destructive earthquake. The suitability map and potential sites were shared with the responsible teams in the disaster area so that the potential sites could be displayed on the map and the suitable areas could be navigated on appropriate routes. The findings of the study revealed that the use of open data and cloud GIS framework in the disaster management process enables quick, reliable, and cost-effective actions in the immediate aftermath of disasters.

GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published. Starting 2016, GI_Forum publishes two issues a Year. Joumal Information is available at: GI-Forum GI_Forum is listed on the Directory of Open Access Journals (DOAJ)