Spatial accessibility to urban facilities has been extensively examined in the literature. However, gendered disparities in spatial access remain underexplored, particularly among refugee populations. This research assesses the spatial accessibility of four key urban facilities – education, health, green, and religious – in Konya, Türkiye, using the Three-Step Floating Catchment Area (3SFCA) method. The research quantifies spatial-structural inequalities arising from the distribution of facilities and refugee settlements, independent of behavioural variations. Accessibility scores were further evaluated using Lorenz curves and Gini coefficients to examine distributional inequalities. Findings reveal consistent gender-based spatial disparities. Men exhibit higher accessibility scores across all facility types compared to women, with the greatest inequalities in health and green areas. Gini coefficients indicate that women experience higher within-group spatial inequality, revealing compound disadvantage. This study offers two significant contributions: (1) an empirical application of spatial accessibility methods (3SFCA, Lorenz curves, and Gini coefficients) to assess gendered refugee accessibility, and (2) evidence quantifying baseline spatial-structural inequalities in urban service provision arising from the spatial distribution of facilities and refugee settlements. The results underscore an urgent need for gender-sensitive urban planning and equitable service distribution to address accessibility gaps.

Cycling has seen a remarkable rise, signifying a paradigmatic move towards sustainable, eco-friendly, and efficient commuting alternatives in the contemporary urban setting. Cities also encourage this trend by establishing cycle lanes, bike-sharing programs, and incentives for frequent riders. To enhance these motivations from an urbanistic perspective, it is essential to comprehend the influence of urban characteristics on cycling volume and to incorporate this understanding into data-driven decision-making processes. This research examines the Bicification project data from Istanbul with a spatial perspective. Utilising a comprehensive array of spatial big data, the study explores the impact of urban land use, transport services, land morphology, and sociodemographic factors on cycling volume through a Multi-scale Geographically Weighted Regression (MGWR). With an Adj R² value of 0.68, the model demonstrates a strong relation between cycling volume and several factors, including biking park stations, park and ride points, pier stops, rail stops, transfer points, main roads, elevation, population, industrial facilities, health facilities, sports areas, and residential areas. The findings will serve to develop a data-driven strategic approach to promote cycling in Istanbul.