Open geographic modeling

Abstract

Various problems are occurring and evolving in the earth’s environment, for example, global warming, air/ water/soil pollution, floods, traffic congestion, and so forth. Moreover, decision-making and planning demands in industry and governance areas are also dependent on reasonable understandings of the environment. Geographic modelling (can also be expressed as environmental modelling as humans are living in a geographical environment) is an important approach in exploring solutions for solving problems and supporting decision-making. Climate models, air/water/soil quality assessment models, traffic management models, watershed models, urban explanation models, and other more different types of models have been and are being designed and developed. They can be applied in simulating the global/local environments and helping people formulate better solutions. However, modelling and simulating ability of a single model is limited. Interdisciplinary knowledge and collaborative exploration are generally required when solving complicated problems. With regard to this, integrated environmental modelling (Laniak et al. 2013), collaborative modelling (Hurrell et al. 2013; Chen et al. 2019), and participatory modelling (Bakhanova et al. 2020; Yue et al. 2020) are all effective approaches. In addition, studies on Future Earth (FE), Virtual Geographic Environment (VGE), and E-Science also promote modelling activities towards more comprehensive applications (Lin, Chen, and Lu 2013; Lü et al. 2019). The idea of implementing modelling work in an open style emerges as a communitybased approach to lower the barrier when collaborating among different modelling fields (Chen et al. 2020; Barton et al. 2020; Zhu et al. 2021). On the technical side, ‘open’ can be interpreted as sharing modellingrelated knowledge via information means as openaccessible resources (web services, cloud computing, open-source platforms, etc.). On the scientific exploration side, ‘open’ generally describes that different environmental and social disciplines are involved in seeking better solutions. This special issue aims at a collection of state-of-theart research efforts that related to open modelling, including techniques, practices, and applications in the integration of data, models, and methods from geographic information science. The special issue starts with a research article titled ‘Design and development of a web-based EPANET Model catalog and execution environment’ by Tylor Bayer, Daniel P. Ames, and Theodore G Cleveland. This paper presents the design and implementation of a model-sharing repository and a model-viewing application, specifically for the EPANET modelling community using existing open-source cyberinfrastructure. HydroShare is used as the backend data store for the EPANET model programme, model instances, and metadata, and the Tethys Platform framework is used to create a web-based front-end for the repository and viewer. Natural features and human-made features interact with one another across time and space in human settlements, Jeeno Soa George, Saikat Kumar Paul, and Richa Dhawale, in their article ‘A cellular-automata model for assessing the sensitivity of the street network to natural terrain’, describe the influence of terrain, a natural feature, on the configuration of the street network, a human-made feature, by analysing the results of two transition states of cellular automata used to model street networks. Data from open-source projects and open-source applications are used for this study. In the next article on the ‘Machine learning for inference: using gradient-boosting decision tree to assess non-linear effects of bus rapid transit on house prices’, the authors Linchuan Yang, Yuan Liang, Qing Zhu, and Xiaoling Chu discussed the non-linear relationship between Bus Rapid Transit (BRT) and house prices. Using the Xiamen data, this study employs a machine learning technique to scrutinize the non-linear relationship between BRT and house prices. With the open consideration of measurements in the environment and human engineering, this article suggests a nonlinear relationship between BRT and house prices and indicates that GBDT has more substantial predictive power than hedonic pricing models. Habitat spatial distribution is essential to understand where to focus the protection of the seafloor resources. The article ‘Modelling of the reef benthic habitat distribution within the Cabrera National Park (Western Mediterranean Sea)’ by Dulce Mata, Jose Úbeda, and Adrián Fernández–Sánchez applied a semi-automated classification method with GIS techniques in a marine ANNALS OF GIS 2021, VOL. 27, NO. 3, i-iii https://doi.org/10.1080/19475683.2021.1959855