Abstract The definition of accessibility encompasses the role of opportunities at potential destinations that people consider valuable. This study revises the common assumption in empirical studies that residents are equally attracted to all types of employment and examines its implications for public transport evaluation from a social equity perspective. Additionally, the role of the modifiable areal unit problem (MAUP) is also explored in this relationship. The study draws on the case of Greater Mexico City over a ten-year period, in which seven temporal stages of the main public transport network are examined. The key results highlight a significant difference between accessibility measures that account for employment matching and those that do not, though these distinctions diminish when lower spatial resolutions are used. The spatial analysis also shows that the differences are consistently larger for lower-educated populations. In terms of public transport infrastructure evaluation over time, the study confirms that relying on simple measures, such as the global average, may overlook critical transport equity insights. Additionally, the impact of including employment matching in equity analyses varies, with outcomes differing case by case. Depending on the accessibility measure, the analyses show that a transport improvement might have progressive effects with one measure, while another measure may indicate regressive effects, or both measures can sometimes align. Overall, the comparisons between measures suggest their complementarity in equity evaluations. The findings have implications for researchers and policy analysts, given the systematic differences in how transport projects tend to affect less-educated populations and the heterogeneity in the type of population impacted by specific transport projects on a case-by-case basis.

Abstract In the analysis of large spatial datasets, identifying and treating spatial outliers is essential for accurately interpreting geographical phenomena. While spatial correlation measures, particularly Local Indicators of Spatial Association (LISA), are widely used to detect spatial patterns, the presence of abnormal observations may frequently distort the landscape and conceal critical spatial relationships. Traditional influence function (IF) methodologies, commonly used in statistical analysis to measure the impact of individual observations on statistical measures, are not applicable in the spatial context because in this case the influence is determined not only by the value observed in a spatial unit, but also by its location, its connections with neighboring regions, and by the values observed in neighboring observations. In this paper, we introduce a local version of the influence function (called “Local Influence Function”, or LIF for short) that accounts for spatial dependence in robustness analysis. In the paper, we first derive the analytical expression of the local influence function and we suggest a way of summarizing it in a single parameter. We then show, through the analysis of both simulated and real-world datasets, how the LIF provides a more nuanced and accurate description of the effects of spatial outliers in the analysis of spatial correlation and we discuss its interpretation in comparison with the local Moran index.

Abstract Active commuting offers important environmental, economic, and health benefits, yet urban inequalities shape who can access these benefits. This paper analyzes the relationship between pedestrian mobility and mental well-being by examining how environmental and socioeconomic attributes associated with well-being—tree density, vegetation cover (NDVI), air and noise pollution, property values, and safety—are spatially distributed in two contrasting urban contexts: São Paulo (Global South) and Lisbon (Global North). Using a GIS-based framework, we constructed 15-minute walking isochrones for 68,807 pedestrians and integrated environmental and socio-demographic indicators through spatial overlay, correlation, and clustering. Results show that conditions associated with mental well-being are unevenly distributed: greener, safer, and less polluted areas are disproportionately linked to higher-income and more educated groups, while disadvantaged populations face greater exposure to risks such as air pollution and unsafe pedestrian environments. Spatial clustering identified four distinct clusters in São Paulo and three in Lisbon, highlighting how socio-spatial inequalities structure active commuting conditions in both cities. Conceptually, the study demonstrates that opportunities for mental well-being are shaped by the spatial organization of urban environments. Methodologically, it shows how GIScience can operationalize individual exposures and reveal hidden intra-urban disparities, providing a transferable framework for comparative research and actionable evidence for policies aimed at equitable and healthier cities.

Abstract Agriculture is often in conflict with biodiversity conservation, while at the same time depending on the provided ecosystem services (ESS) biodiversity provides. Many of these ESS have a regional scale that extends beyond the local scale of individual farms. This leads to spatial externalities so that the preservation of ESS on one farm benefits neighbouring farms. At the same time, ESS preservation often incurs local costs, creating a trade-off between local costs and regional benefits. In these types of common-resource-management problems, the observed level of biodiversity and ESS is generally below the economically efficient level. A generic spatially explicit agent-based model from literature that focuses on the ESS of pollination is extended to analyse the impact of spatial coordination payments on the land-use dynamics and the regional level of ESS preservation. The economically efficient design that maximises the return (in terms of agricultural profit) on investment (in terms of expenditure for the payments) is determined and analysed as a function of the ecological and economic conditions in the model region.

Abstract Symmetry is an essential feature of many geometric objects. However, the world also contains many asymmetrical or approximately symmetrical objects. Detecting approximate symmetries is a rather weakly defined problem, as computer-detected approximate symmetry may not correspond to human opinion. The situation is even worse if the symmetry is not global but local. This paper investigates whether approximate local reflection symmetries found by a computer in real data are acceptable for human observers. To answer this question, a new simple approximate local reflection symmetry detection is proposed and run on land cadastre data in the form of planar point sets. The resulting symmetries are subject to user tests to study human acceptance of approximate local symmetry. The results show a relatively good correlation between symmetry detected by computers and perceived by humans. This finding provides a solid foundation for integrating both approaches in specific applications. To achieve this, further research is needed on how to utilize specific aspects of human symmetry perception in computer solutions, so that computer symmetry detection can better approximate human perception.