Recent research in the field of geographic information science shows that reproducibility and replicability of publications have substantial room for improvement and proposes various actions to improve the situation. However, the impact of these actions remains unclear. This study investigates the combined effect of novel author guidelines and workflow review process, which award badges for successful reproductions, on the potential reproducibility of articles published in the AGILE conference series proceedings over the past decade. While replicating the approach of previous studies, this work expands the scope of prior reproducibility assessments and systematically compares the findings for the AGILE conference with those of the GIScience conference series proceedings, which has not undergone similar changes to guidelines or procedures. Results indicate that the reproducibility guidelines and the review process measurably improved the potential reproducibility of AGILE publications. The comparison with GIScience papers further suggests that clear and enforced guidance is a key driver for change. Our findings demonstrate the value of institutional policies and community norms in fostering reproducible research in the GIScience field and identify pathways for its ongoing improvement.
The proliferation of location-sensing technologies, including smartphones and standalone GPS devices, has transformed human mobility studies. A central methodological concern in these studies is positioning accuracy, as errors in positioning can bias estimates of mobility patterns and environmental exposure. While the recent rollout of next-generation 5G technology promises sub-meter accuracy, most evidence comes from simulations or controlled trials, leaving everyday mobility performance largely untested, particularly in comparison with 4G smartphones and standalone GPS devices. This study systematically evaluates the positioning errors of 5G New Radio (NR) smartphones compared with 4G smartphones and standalone GPS devices in everyday mobility scenarios. Data were collected from 27 participants across walking, biking, and driving routes on the Western University campus, encompassing five environmental contexts: open space, between buildings, under tree canopy, in building, and underground. Positioning errors were assessed using three complementary approaches: point-based, path-based, and area based analyses. Results demonstrate that 5G smartphones consistently outperform 4G devices and standalone GPS in accuracy, particularly in building and underground, achieving lower median errors, higher spatial path fidelity, and improved indoor localization metrics. This advancement broadens the applicability of 5G smartphones across diverse research in health geography and urban planning.
This paper demonstrates an approach to the application of generalized additive models (GAMs) with space-time smooths to model coefficient processes that vary over space and time. The approach is to create and evaluate multiple GAMs, each with the predictor variables specified in different ways. It emphasizes the need to determine the nature of the space-time dependencies present in the data relationships rather than to assume them, based on the perceived data generating process, especially if this is unknown. The approach is explored using simulated coefficient data with known space-time dependencies. The GAMs are compared with multiscale geographically and temporally weighted regression (MGTWR) models and are shown to have marginally weaker predictive performance and to be marginally better at coefficient recovery. The inferential costs of misspecifying the target-to-predictor variable relationships in the GAMs is quantified both for individual variable main effects and interacting misspecifications. The approach is then applied to an empirical case study of NDVI (as a proxy for forest productivity) informed by precipitation and temperature in the Chaco dry rainforest of South America. The best GAM is determined and its space-time varying coefficient estimates are investigated. The methods and results are discussed and several areas of further work and enhancements to the stgam R package used to undertake this analysis are identified.
The generation of uniform, gridded data from spatially discontinuous station values and the assessment of their accuracy are essential for water resources assessment and management, and for climate change studies, especially in semi-arid environments. Spatial and temporal grids have been generated in recent years as a basis for several studies. This work has two objectives: a) to evaluate the accuracy of the grids available for Spain by comparing their monthly values with stations not used in their estimation or prediction, and b) to verify the improvement in accuracy using Multi-Model Ensembles based on machine learning. A dual ensemble approach is presented: (i) multiple individual Random Forest (RF) ensembles per weather station, using only the information from the station, and (ii) spatially distributed grid prediction using a single ensemble model that incorporates all the information from the nearest stations and their distances using Random Forest Spatial Interpolation -RFSI-). Both models were used to generate monthly data grids of maximum, minimum and mean temperature, and total precipitation, with high spatial resolution (5 km). Seven datasets: Iberia01, STEAD, AEMET, SIMPA, EOBSv27 and STEAD, were used as predictors. Accuracy was estimated using the root mean square error, the percentage bias and the Nash-Sutcliffe efficiency index obtained using block cross-validation buffering (LOOBUF-CV), robust to spatial autocorrelation. The significance of the differences was assessed using ANOVA with heteroscedasticity correction in the residuals. Preliminary results indicate that multi-model ensembles using RF outperform individual grids. Among other reasons, ensembles aggregate the different representations of meteorological processes included in each grid and reduce the uncertainty associated with each individual grids.
This paper addresses the problem of constructing an ideal projection of an ellipsoid of revolution onto a sphere based on the Airy criterion. General equations required to solve the problem are derived. In particular, the Euler-Urmaev system is obtained, allowing a clear illustration of Gauss's theorem that a distortion-free projection between these surfaces cannot exist. The Euler-Ostrogradsky system is also derived to find the projection that minimizes distortion according to the Airy criterion. Natural boundary conditions for the ideal projection are analyzed. It is shown that on the boundary of the mapping region, Tissot's indicatrices are aligned either along the normals or tangents to the boundary, and one of the extremal linear scale factors is equal to unity. Since the value of the Airy criterion depends not only on the projection's mapping functions but also on the radius of the sphere, an additional integral condition is introduced alongside the Euler-Urmaev system, the Euler-Ostrogradsky system, and the natural boundary conditions. According to this condition, the integral of the area distortion over the entire mapping region of the ideal projection must be equal to zero. Two specific cases are examined in detail: projection of the entire ellipsoid and of a region bounded by a parallel. For comparison, conformal projections optimized according to the Airy criterion were also constructed for the same mapping regions. The resulting ideal projections can be used in geodesy for solving direct and inverse geodetic problems, and in cartography for constructing double projections.
