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Henrikki Tenkanen

Publications and source records attributed to Henrikki Tenkanen.

4 recordsLinked to original sources

Recurrent visitations reveal selectivity beyond the 15-Minute City vision

In the transition towards sustainable and equitable urban living, proximity-centred planning has been adopted in cities worldwide. Exemplified by the 15-Minute City (15mC), this planning paradigm often assumes that local amenity provision translates into local use, yet behavioural evidence on recurrent visitation remains limited. To address this gap, we introduce K-Visitation, a scalable, behaviourally informed framework for comparing recurrent visitation with proximity-based expectations, using 18 months of mobile phone data from 720,000 users in Finland. For each individual, K-Visitation compares recurrent destinations ($K_{freq}$) with nearest options ($K_{dist}$), and evaluates travel time differences between them and their neighbourhood proximity baseline. Across Finnish cities, individuals repeatedly bypassed nearby visited options that already exposed them to required daily amenity categories, incurring measurable travel time costs beyond what local opportunities could support. The sharpest divergence appears in amenity-rich urban cores: where the physical conditions for 15-minute living are strongest, recurrent destinations depart most from nearest options. A density-aware null model and destination-level classifier show that this divergence reflects selective behaviour: non-nearest recurrent destinations are not explained by amenity density alone and are often amenity-rich but functionally specialised. Amenity-specific analysis further reveals a hierarchy in which routine anchors adhere more closely to proximity, while specialised and infrastructural functions depend on wider catchments and connectivity. Our findings show that proximity is a necessary but insufficient condition for local living. The 15mC should therefore secure routine local access while using behavioural evidence to identify where proximity is bypassed and where wider connectivity remains necessary.

physics.soc-ph

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

Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks now enables large-scale, dynamic analyses of how people navigate cities. This study uses eXtended Detail Records (XDRs) derived from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. First, we identify residential and work locations and model commuting routes using the R5 multimodal routing engine, which combines public transport and walking. To explore spatial patterns, we apply a bivariate spatial clustering analysis (LISA) alongside regression techniques to identify distinct commuting behaviors and their alignment with vulnerable population groups. Our findings reveal that average commuting times remain consistent across socioeconomic groups. However, despite residing in areas with greater opportunity density, higher-income populations do not consistently experience shorter commuting times. This highlights a disconnect between spatial proximity to opportunities and actual travel experience. Our analysis reveals significant disparities between sociodemographic groups, particularly regarding the distribution of indigenous populations and gender. Overall, the findings of our study suggest that commuting and accessibility inequalities in Santiago are closely linked to broader social and demographic structures.

cs.CY

Assessing the livability within the 15-minute city concept based on mobile phone data

Many cities promote walkability through concepts such as the compact city and 15-minute city to enhance urban livability, yet few methods link spatial walkability features to empirically measured livability and account for temporal dynamics. The method developed for this study uses mobile phone data from the Helsinki Metropolitan Area (Finland) to assess whether commonly used, literature-derived livability indicators (diversity, density, proximity, accessibility) predict observed human activity patterns across different times of day. We constructed two key dimensions of livability: attractiveness and walkability with quantifiable sub-indicators that were selected based on literature. Our analysis shows that walkability, and even more so the combined livability index, correlates with activity patterns, outperforming the pure attractiveness perspective. However, this relationship is temporally unstable, significantly weakening at night and fluctuating daily. Moreover, based on Geographically Weighted Regression analysis, our results reveal significant spatial variation in the relationship between livability and the intensity of human activities. The findings suggest that traditional urban planning goals, such as functional diversity to enhance walkability, contribute to livability but have a limited impact on the 15-minute city's overall sustainable mobility objectives, necessitating a larger-scale perspective and more functionally profiled approaches for urban development.

physics.soc-ph

GROKE: Vision-Free Navigation Instruction Evaluation via Graph Reasoning on OpenStreetMap

The evaluation of navigation instructions remains a persistent challenge in Vision-and-Language Navigation (VLN) research. Traditional reference-based metrics such as BLEU and ROUGE fail to capture the functional utility of spatial directives, specifically whether an instruction successfully guides a navigator to the intended destination. Although existing VLN agents could serve as evaluators, their reliance on high-fidelity visual simulators introduces licensing constraints and computational costs, and perception errors further confound linguistic quality assessment. This paper introduces GROKE(Graph-based Reasoning over OSM Knowledge for instruction Evaluation), a vision-free training-free hierarchical LLM-based framework for evaluating navigation instructions using OpenStreetMap data. Through systematic ablation studies, we demonstrate that structured JSON and textual formats for spatial information substantially outperform grid-based and visual graph representations. Our hierarchical architecture combines sub-instruction planning with topological graph navigation, reducing navigation error by 68.5% compared to heuristic and sampling baselines on the Map2Seq dataset. The agent's execution success, trajectory fidelity, and decision patterns serve as proxy metrics for functional navigability given OSM-visible landmarks and topology, establishing a scalable and interpretable evaluation paradigm without visual dependencies. Code and data are available at https://anonymous.4open.science/r/groke.

cs.CL