Global Impact Journal: Geographical Studies

When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile

Cesar Marin-Flores — 2026-05-12

Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks enables large-scale, dynamic analyses of how people navigate cities. In this study, we employ eXtended Detail Records (XDRs) from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. We identify residential and work locations and model commuting routes by public transport and walking using the R5 multimodal routing engine. Spatial patterns are examined using bivariate local indicators of spatial association (LISA) alongside regression techniques to identify distinct commuting behaviors and their alignment with vulnerable population groups. Our results show that while average public transport commuting times do not differ significantly across socioeconomic groups, marked inequalities emerge when accessibility is considered. High-income neighborhoods consistently exhibit high accessibility, whereas low-income areas show substantially lower levels. Importantly, these disparities do not translate into longer commuting times for lower-income groups, indicating a weak relationship between proximity to opportunities and observed travel times. The analysis also reveals significant disparities across sociodemographic groups, particularly in relation to Indigenous populations and gender. The proposed approach is readily scalable and can support evaluations of changes in commuting patterns and the impacts of urban interventions.

Focal-Feature Regression Kriging

Peng Luo — 2026-05-06

Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models-such as Ordinary Kriging (OK)-assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables. A common solution is trend surface modeling (e.g., Regression Kriging, RK), which relies on external explanatory variables to model the trend and then applies geostatistical interpolation to the residuals. However, this approach requires high-quality and readily available explanatory variables, which are often lacking in many spatial interpolation scenarios-such as estimating heavy metal concentrations underground. This study proposes a Focal Feature Regression Kriging (FFRK) method, which automatically extracts geospatial features to construct a regression-based trend surface without requiring external explanatory variables. We conducted experiments on the spatial prediction of three heavy metals in a mining area in Australia. In comparison with 17 classical interpolation methods, the results indicate that FFRK, which relies solely on extracted geospatial features, consistently outperforms both conventional Kriging techniques and machine learning models that depend on explanatory variables. This approach effectively addresses spatial nonstationarity while reducing the cost of acquiring explanatory variables, improving both prediction accuracy and generalization ability.

Smoothing the Edges: Reconceptualizing Space and Dealing With Modifiable Areal Unit Problems in (Un)Related Variety Research

Mi Hyun Seong — 2026-05-05

Related variety studies in Economic Geography reveal regional diversification mechanisms for regional development, but often overlook geographic fundamentals. By relying on administrative units, the studies may fail to account for spatial continuity and interdependence, which can lead to Modifiable Areal Unit Problems. In this regard, this article introduces an alternative method ‘smoothing the edges’ as proof of concept to strengthen spatial conceptualization. Instead of using administrative units, we construct high-resolution grid cells and define Local Economic Environments (LEEs) around them to calculate economic factors. LEEs capture the conditioning economic context to which each grid cell is exposed. We compare Ordinary Least Squares regression outcomes across three LEE scales, equivalent to NUTS 2, NUTS 3, and municipality levels, and examine how (un)related variety effects behave across scales under the new conceptual framework. We apply two stylized facts from the literature: (Un)Related variety associates with (1) industrial specialization, and with (2) employment growth. A case study with Dutch establishment microdata LISA reveals that effects of (un)related variety are sensitive to scale, particularly in employment growth analysis. These findings highlight the importance of understanding contextual settings, which is critical in informed policy making.

Coarse-to-Fine Spatial Modeling: A Scalable, Machine-Learning-Compatible Framework

Daisuke Murakami — 2026-05-07

This study proposes coarse-to-fine spatial modeling (CFSM) as a scalable and machine learning-compatible alternative to conventional spatial process models. Unlike conventional covariance-based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models. To ensure stable model training, larger-scale patterns that are easier to learn are modeled first, followed by smaller-scale patterns, with training terminated once the validation score stops improving. The training procedure, which is based on holdout validation, can be easily integrated with other machine learning algorithms, including random forests and neural networks. CFSM training is computationally efficient because it avoids explicit matrix inversion, which is a major computational bottleneck in conventional spatial Gaussian processes. Comparative Monte Carlo experiments demonstrated that the CFSM, as well as its integration with random forests, achieved superior predictive performance compared to existing models. Finally, we applied the proposed methods to an analysis of residential land prices in the Tokyo metropolitan area, Japan. The CFSM is implemented in an R package spCF (https://cran.r-project.org/web/packages/spCF/).

SCADDA: Spatiotemporal Cluster Analysis With Density-Based Distance Augmentation and Its Application to Fire Carbon Emissions

Ben Moews — 2026-05-05

Spatiotemporal clustering occupies an established role in various fields dealing with geospatial analysis, spanning from health-care analysis to environmental science. One major challenge is application in which cluster assignments are dependent on local densities, meaning that higher-density areas should be treated more strictly for spatial clustering and vice versa. We describe and implement an extended method that covers continuous and adaptive distance rescaling based on kernel density estimates and the orthodromic metric, as well as the distance between time series via dynamic time warping (DTW). In doing so, we provide the wider research community, as well as practitioners, with a way to solve an existing challenge as well as an easy-to-handle and robust open-source software tool. The resulting implementation is highly customizable to suit different application cases, and we verify and test the latter on both an idealized scenario and the recreation of prior work on broadband antibiotics prescriptions in Scotland to demonstrate well-behaved comparative performance. Following this, we apply our approach to fire emissions in Sub-Saharan Africa using data from Earth-observing satellites, and show our implementation’s ability to uncover seasonality shifts in carbon emissions of subgroups as a result of time series-driven cluster splits.