A GIS tool for spatiotemporal modeling under a knowledge synthesis framework

Abstract

In recent years, there has been a fast growing interest in the space-time data processing capacity of Geographic Information Systems (GIS). In this paper we present a new GIS-based tool for advanced geostatistical analysis of space-time data; it combines stochastic analysis, prediction, and GIS visualization technology. The proposed toolbox is based on the Bayesian Maximum Entropy theory that formulates its approach under a mature knowledge synthesis framework. We exhibit the toolbox features and use it for particulate matter spatiotemporal mapping in Taipei, in a proof-of-concept study where the serious preferential sampling issue is present. The proposed toolbox enables tight coupling of advanced spatiotemporal analysis functions with a GIS environment, i.e. QGIS. As a result, our contribution leads to a more seamless interaction between spatiotemporal analysis tools and GIS built-in functions; and utterly enhances the functionality of GIS software as a comprehensive knowledge processing and dissemination platform.

Open source QGIS toolkit for the Advanced Research WRF modelling system

“…In recent years, the potential for adding functionalities to GIS (Geographical Information System) software for environmental modelling applications has been highlighted by several tools such as 5 QSWAT (Dile et al, 2016), UMEP (Lindberg et al, 2018), WET (Nielsen et al, 2017), QMorphoStream (Tebano et al, 2017) and STAR-BME (Yu et al, 2016). A review by Chen et al (2010), has positively found QGIS (QGIS Development Team, 2018a) Here, we present GIS4WRF, a free, open source, and cross-platform toolkit developed as a QGIS Python plug-in to help scientists and practitioners with their WRF workflows in pre-and postprocessing data, simplifying the simulation steps and visualizing and post-processing their model results.…”

Terrain characterization of small island using publicly available data and open- source software: a case study of Marinduque, Philippines

“…Mantovani et al (2010) combined QGIS with WebGIS to develop landslide geomorphological maps for the Olvera area in Cadiz, Spain. Yu et al (2015) integrate QGIS with bayesian maximum entropy (BME) to map particulate matter concentration across Taiwan. Landuyt et al (2015) mapped ecosystem services in Grote Nete basin in Belgium using QGIS.…”

Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method

“…Moreover, BME is capable of considering uncertainties contained in the data. The method has been successfully applied to numerous areas, such as air pollution, soil properties, water demand and disease [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. It has also achieved good results in the gap-filling of remote sensing data [62][63][64].…”