Using an Open-source Spatial Database and GIS to Manage Multi-scale Land-use/ Land-cover Data in Laos PDR
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Managing multi-level geospatial datasets, particularly the land-use and land cover data in highly changeable environments and social entities, such in Lao PDR is, always a challenging task for GIS specialists. They have to handle a variety of data types such as vector, raster, and non-spatial data from various data sources to fulfil the tasks. Over the years, GIS has developed rapidly and is considered one of the fastest growing industries; its market value was estimated at 10.8 billion USD in 2018 and continues to grow very strongly (Prescient & Strategic (P&S) Intelligence Private Limited, 2019). The commercial GIS enterprise provides complete package solutions for organisations; however, they have limitations and often come with a high cost and maintenance fee. This cost is a prohibitive factor for financial-constraint countries such as Lao PDR, and the package tends to lack flexibility for integrating with other applications as well as scalability. In the open-source geospatial communities, developers have been collaborating to deliver commercial-grade products that are freely available to the public. For instance, under Open Source Geospatial Foundation (OSGeo) there are a number of powerful geospatial libraries such as GDAL, Orfeo Toolbox, PostGIS, QGIS, and MapServer. Taking advantage of this technology to fulfil the gap and fit the context of a financial-restricted environment is an approach worth investigating. This research examined the potentials of the open-source relational database PostgreSQL and its geographical extension PostGIS (hereafter referred to as PostGIS database) in concurrent use with desktop GIS application, QGIS by storing and managing multi-level land-use and land cover datasets with an aim for increased performance and efficiency over the existing workflows. The research was carried out at a governmental institution in Lao PDR where substantial proportion of land-use and land cover data were located. The research findings indicated that the open-source spatial database resulted in 70 percent increase in performance and efficiency over the existing approach, as well as delivering opportunities for enhanced data security/integrity and accessibility. Additionally, the research findings supported the notion that the spatial database has greater flexibility for future integration and scalability, which were in line with the participant interview-based results.