Usability engineering for GIS: learning from a screenshot

In this paper, the focus is on the concept of Usability Engineering for GIS – a set of techniques and methods that are especially suitable for evaluating the usability of GIS applications – which can be deployed as part of the development process. To demonstrate how the framework of Usability Engineering for GIS can be used in reality, a screenshot study is described. Users were asked to provide a screenshot of their GIS during their working day. The study shows how a simple technique can help in understanding the way GIS is used in situ

Reproducible Research and GIScience: An Evaluation Using GIScience Conference Papers

Ponencia presentada en: 11th International Conference on Geographic Information Science (GIScience 2021)GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments

Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature T-s and Normalized Difference Vegetation Index NDVI (). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: - desert ecosystem ETa R-2 = 0.94; oasis ecosystem ETa R-2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands

Spatial thinking in geographic information science: a review of past studies and prospects for the future

AbstractIn recent years, the relationship between geographic information science (GIScience) and spatial thinking has attracted much attention in English-speaking countries. Nevertheless, vagueness remains concerning the concept of spatial thinking and its components. The aim of this paper is to review previous studies on the relationship between GIScience and spatial thinking, and to clarify the elements of spatial thinking and related terms. After discussing the basic elements of spatial thinking, it explores the relationship between GIScience and spatial thinking by dividing it into two aspects: the role of geographic information systems (GIS) in education on spatial thinking, and the role of spatial thinking in GIScience. Concerning the former, potential roles of GIS in spatial thinking education, particularly in geography and STEM disciplines, are suggested. Concerning the latter, the relationships between the body of knowledge on GIS education and the elements of spatial thinking are examined. Finally, the present situation and future prospects for studies on spatial thinking and GIScience in Japan are briefly discussed

Towards general spatial intelligence

The goal of General Spatial Intelligence is to present a unified theory to support the various aspects of spatial experience, whether physical or cognitive. We acknowledge the fact that GIScience has to assume a particular worldview, resulting from specific positions regarding metaphysics, ontology, epistemology, mind, language, cognition and representation. Implicit positions regarding these domains may allow solutions to isolated problems but often hamper a more encompassing approach. We argue that explicitly defining a worldview allows the grounding and derivation of multi-modal models, establishing precise problems, allowing falsifiability. We present an example of such a theory founded on process metaphysics, where the ontological elements are called differences. We show that a worldview has implications regarding the nature of space and, in the case of the chosen metaphysical layer, favours a model of space as true spacetime, i.e. four-dimensionality. Finally we illustrate the approach using a scenario from psychology and AI based planning

Addressing the challenges of a quarter century of giscience education: A flexible higher education curriculum framework

A wide range of geographic information science (GIScience) educational programs currently exist, the oldest now over 25 years. Offerings vary from those specifically focussed on geographic information science, to those that utilise geographic information systems in various applications and disciplines. Over the past two decades, there have been a number of initiatives to design curricula for GIScience, including the NCGIA Core Curriculum, GIS&T Body of Knowledge and the Geospatial Technology Competency Model developments. The rapid developments in geospatial technology, applications and organisations have added to the challenges that higher educational institutions face in order to ensure that GIScience education is relevant and responsive to the changing needs of students and industry. This paper discusses some of the challenges being faced in higher education in general, and GIScience education in particular, and outlines a flexible higher education curriculum framework for GIScience

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics