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Den här antologin visar vad som händer när skolans och universitetets världarmöts i genuin samverkan. Inom ramen för ULF‑satsningen vid Lunds universi‑tet har lärare, forskare, skolledare och lärarstudenter samt elever tillsammansutvecklat projekt som utgår från verkliga behov i undervisningen – och somsamtidigt bidrar till ny forskning.Genom konkreta exempel från grundskola, gymnasium och kulturs

This chapter examines trends in child poverty in Sweden through national and European databases, highlighting the achievements and challenges of Swedish welfare. The Swedish model has historically promoted equality through the dual-earner family model, progressive taxes, and generous benefits such as parental leave and publicly funded childcare. While these policies have promoted equality, rising

This article explores meaningful work across two frontline occupations, bank advisors and school­teachers. We build on narrative research focusing on the role of stories and storytelling in the sensemaking and identity construction processes related to work to explore the importance of oth­ers in the perception and construction of everyday work as meaningful. To study this, we examine the narrativ

Enhanced efforts in the transportation sector should be implemented to mitigate the adverse effects of CO2 emissions resulting from zoning-based planning paradigms. The concept of a 15-minute city, emphasizing proximity-based planning, holds promise in reducing unnecessary travel and progressing towards carbon neutrality. However, a critical research question remains inadequately explored: to what

Poverty threatens human development especially for developing countries, so ending poverty has become one of the most important United Nations Sustainable Development Goals (SDGs). This study aims to explore China’s progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning model. The poverty reduction efficiency (PRE) is measured by the di

Predicting the next visited location of an individual is a key problem in human mobility analysis, as it is required for the personalization and optimization of sustainable transport options. Here, we propose a transformer decoder-based neural network to predict the next location an individual will visit based on historical locations, time, and travel modes, which are behaviour dimensions often ov

Location graphs, compact representations of human mobility without geocoordinates, can be used to personalise location-based services. While they are more privacy-preserving than raw tracking data, it was shown that they still hold a considerable risk for users to be re-identified solely by the graph topology. However, it is unclear how this risk depends on the tracking duration. Here, we consider

The spatial distribution of buildings is one of the key factors influencing the local environment within a city. The quantitative measurement of building distribution can provide critical information for exploring local climate patterns in urban areas. Previous studies mainly focused on the two-dimensional spatial distribution of buildings and ignored the differences in height. In this study, a th

Human mobility exhibits power-law distributed visitation patterns; i.e., a few locations are visited frequently and many locations only once. Current research focuses on the important locations of users or on recommending new places based on collective behaviour, neglecting the existence of scarcely visited locations. However, assessing whether a user will return to a location in the future is hig

Detecting travel modes from global navigation satellite system (GNSS) trajectories is essential for understanding individual travel behavior and a prerequisite for achieving sustainable transport systems. While studies have acknowledged the benefits of incorporating geospatial context information into travel mode detection models, few have summarized context modeling approaches and analyzed the si

Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducibility, comparability, and transferability of methods and results in quantitative human mobility anal

Quantifying intra-person variability in travel choices is essential for the comprehension of activity–travel behaviour. Due to a lack of empirical studies, there is limited understanding of how an individual's travel pattern evolves over months and years. We use two high-resolution user-labelled datasets consisting of billions of GPS track points from ∼3800 individuals to analyse individuals’ acti

Human perception of place refers to residents' psychological feelings about urban areas. Many studies of human perceptions have focused on a specific geographic location. Whether the distribution of human perceptions in continuous city space shows specific characteristics and how to disclose these phenomena remains a direction worth exploring. Due to cities' heterogeneity, quantitatively identifyi

The emergence of passively and continuously recorded movement data offers new opportunities to study the long-term change of individual travel behaviour from data-driven perspectives. This study proposes a clustering-based framework to identify travel behaviour patterns and detect potential change periods on the individual level. First, we extract important trips that depict individual characteris

An in-depth analysis of the urban road network structure plays an essential role in understanding the distribution of urban functional area. To concentrate topologically densely connected road segments, communities of urban roads provide a new perspective to study the structure of the network. In this study, based on OpenStreetMap (OSM) roads and points-of-interest (POI) data, we employ the Infoma

Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-a