Neighbourhood roads are critical for developing walkable cities and improving residents' health. This study introduces a prototype system, the Road Evaluation by Desire Path Simulation System (RED-PaSS), based on agent-based modelling (ABM) designed to assess the alignment of roads with pedestrian natural movement. RED-PaSS simulates pedestrian movement to generate optimal desire path systems, representing the most direct and natural paths pedestrians would choose in urban settings. The system evaluates existing roads against these simulated desire paths using key metrics, including road patch distance, road segment alignment with desire paths, and origin–destination (OD) flow efficiency and compares road networks using a ranking index. In the case study, the RED-PaSS was applied to evaluate and rank 708 US neighbourhood-scale road networks using an open-source road dataset from Zillow. Results showed that the roads closely aligned with the simulated desire paths offer more direct and efficient pedestrian routes, enhancing their natural walking movement. In contrast, poorly aligned roads often force pedestrians to take indirect routes, reducing their walkability and efficiency. Incorporating optimal desire paths with road networks also reduces average pedestrian walking distance (for example, by approximately 28%, decreasing from 43 to 31 metres in a sample neighbourhood). Moreover, the ranking of the 708 road networks demonstrated Zipf’s law distribution R2=0.95, indicating that the majority have poor alignment with their optimal desire paths while very few align well. The top-ranked neighbourhoods demonstrated higher alignment scores and better coverage, effectively connecting areas with high pedestrian demand. The RED-PaSS offers actionable insights for designing walkable cities by predicting optimal desire paths and identifying discrepancies between existing roads and the optimal, which can serve as a valuable tool for urban planners creating more pedestrian-friendly urban environments.

Flood maps that show predicted flood extents will always be uncertain regardless of how the modelling is conducted. It is therefore important that these uncertainties are represented and communicated in the maps, and that map users understand the presented information. Through an online user survey, this study evaluates how users make geographic decisions based on nine flood uncertainty maps, represented and designed according to data scheme and semantics associated with their values (dual-ended, sequential and binary), and applied with different mapping techniques (continuous surface, choropleth and graduated symbol mapping). The results show that the type of map and the visual variable used for representation (in terms of colours and values) became important when deciding locations. Higher decision confidence was shown when dual-ended and sequential probability maps were used. Medium-to-dark blue regions in these maps made participants avoid locations, while white, brown and the lightest blue colours made them select locations. The usage of a sequential map represented by grey scale colour showed to be less intuitive for the participants, leading to lower task performance and less confidence in decisions. Despite the different backgrounds of participants, comprehension of the uncertainty maps and the tasks did not vary much from each other. Differences among them were observed in location preferences and time to solve the tasks. The user group that had the most professional experience with maps and GIS was most conservative in their site choices, and took longest time to solve the tasks. Students, on the other hand, opted to take more risk in their decisions and preferred more uncertain locations. Apparently, the effectiveness of the flood uncertainty maps used in this study varied mainly on the representations used. Appropriate design made them comprehendible by different users. However, making decisions based on these maps, as well as confidence in decisions and time to solve the task, may also be dependent on other factors such as domain knowledge, line of work, practical experience in handing problems or making decisions, and possibly culture.

Two key goals for sustainable spatial planning are to promote low-carbon travel in daily life and to enhance human wellbeing through diverse human-environment interactions. Yet, the integration of these goals has been underexplored. This study investigates the potential for experiential diversity via active travel in different residential contexts within the Gävle city-region, Sweden. Over 15 months, we collected spatiotemporal data from 165 participants, analyzing 4,362 reported experiences and 13,192 GPS-derived travel trajectories. Our analysis uncovered a significant spatial discrepancy: while the travelled distances to locations of positive experiences typically ranged from 1.5 km to 5 km, active travel predominated only within 1.5 km. This discrepancy persisted across urban, suburban, and peripheral contexts. Although residents in different contexts reported the same types of experiences, urban dwellers travelled about 50 % farther for nature experiences compared with other positive experiences, whereas peripheral dwellers travelled twice the distance for urbanicity experiences compared with other positive experiences. Consequently, urban residents mostly relied on active travel for urbanicity experiences and motorised travel for nature experiences, with the reverse trend observed among peripheral dwellers. These results illustrate the importance of spatial scale for promoting diverse positive experiences via active travel, regardless of residential context. Effective planning strategies may include enhancing environmental diversity near homes and developing infrastructure that favours active over motorised travel for short to moderate distances.

In well-planned open and semi-open urban areas, it is common to observe desire paths on the ground, which shows how pedestrians themselves enhance the walkability and affordance of road systems. To better understand how these paths are formed, we present an agent-based modelling approach that simulates real pedestrian movement to generate complex path systems. By using heterogeneous ground affordance and visit frequency of hotspots as environmental settings and by modelling pedestrians as agents, path systems emerge from collective interactions between agents and their environment. Our model employs two visual parameters, angle and depth of vision, and two guiding principles, global conception and local adaptation. To examine the model’s visual parameters and their effects on the cost-efficiency of the emergent path systems, we conducted a randomly generated simulation and validated the model using desire paths observed in real scenarios. The results show that (1) the angle (found to be limited to a narrow range of 90–120°) has a more significant impact on path patterns than the depth of vision, which aligns with Space Syntaxtheories that also emphasize the importance of angle for modelling pedestrian movement; (2) the depth of vision is closely related to the scale-invariance of path patterns on different map scales; and(3) the angle has a negative exponential correlation with path efficiency and a positive correlation with path costs. Our proposed model can help urban planners predict or generate cost-efficient path installations in well- and poorly designed urban areas and may inspire further approaches rooted in generative science for future cities.

To understand how the produced flood boundaries from hydraulic modeling can be affected by the resolution of the Digital Elevation Model (DEM) and the roughness values used, and how different performance measures can affect the choice of most optimal models, different simulations were conducted using varying pairs of DEM and Manning’s n. Each result was then compared to the reference flood data and analyzed using different performance measures that account for the sizes of inundation (F-statistics), and the distance between the modeled and reference flood (disparity measures). The results showed that the different pairs of DEM resolution and Manning’s n produced different performance scores and flood extents. High-resolution DEMs performed generally better with higher resolution DEMs, while lower resolution DEMs received better performance when paired with lower Manning’s n. The different model performances also produced different combinations having the most optimal performances depending on the assumptions they applied in the computations. Additionally, the results showed that models from coarser resolution DEMs can produce higher maximum scores than higher resolution DEMs. However, when the maps, cross-sectional profiles, and the water surface elevations from the coarser resolution DEMs were compared with the higher resolution DEMs, they are more inaccurate in representing the terrain, flood boundaries, and the water depths. Thus, interpreting model results only based on performance measure, particularly for lower resolution DEMs, may give incorrect conclusion on the optimality of the result. It is important that the flood extent maps, cross-sectional profiles, and the derived water depths are analyzed together with the performance scores to gain better understanding of the reliability of flood model results.

To inform spatial planning promoting low-carbon travel and well-being, we investigate the potential for experiential diversity by active travel across different residential contexts. We use spatiotemporal tracking and experience data from the Gävle city-region, Sweden, generated by 165 participants over the course of 15 months. Findings reveal a discrepancy between typical travel distances to locations of positive experiences (1.5–5 km) and the distances at which active travel dominates (up to 1.5 km). This discrepancy largely persists across urban, suburban, and peripheral contexts, with urban dwellers travelling further for nature experiences, whereas peripheral dwellers travel further for urbanicity experiences. These results illustrate the importance of spatial scale for promoting diverse positive experiences by active travel, regardless of residential context. Planning strategies include enhancing environmental diversity close to people’s homes and providing infrastructure that promotes switching from motorised to active travel for trips of a few kilometres.

As climate change and urbanization affect current water management systems, new solutions and approaches rooted in public acceptance are needed to ensure future water supply. In this study, we examine public attitudes to reuse of recycled water and associated worldviews, values, and perceptions in a site without historical water issues. A survey of 143 randomly sampled residents in the municipality of the growing Swedish town Knivsta revealed that 81.4% of the respondents had a positive attitude towards using recycled water in general. The results did not indicate any differences in attitudes between those living in and outside the municipality's urban areas. Perceived benefits and risks were found to be significantly related to both attitudes towards using recycled water in general and to the extreme case of using it for drinking purposes. Additionally, trust in public authorities was highly predictive of attitudes towards drinking recycled water. Furthermore, attitudes were found to be related to an environmental worldview and underlying biospheric, altruistic, and hedonic values. This indicates a need to consider the intended purpose as well as engaging with underlying values as part of the technology legitimation process for improving the chances of successful implementation of water recycling technologies.

Examining the complexity of urban form may help to understand human behavior in urban spaces, thereby improving the conditions for sustainable design of future cities. Metrics, such as fractal dimension, ht-index, and cumulative rate of growth (CRG) index have been proposed to measure this complexity. However, as these indicators are statistical rather than spatial, they result in an inability to characterize the spatial complexity of urban forms, such as building footprints. To overcome this problem, this paper proposes a graph-based fractality index (GFI), which is based on a hybrid of fractal theory and deep learning techniques. First, to quantify the spatial complexity, several fractal variants were synthesized to train a deep graph convolutional neural network. Next, building footprints in London were used to test the method, where the results showed that the proposed framework performed better than the traditional indices, i.e., the index is capable of differentiating complex patterns. Another advantage is that it seems to assure that the trained deep learning is objective and not affected by potential biases in empirically selected training datasets Furthermore, the possibility to connect fractal theory and deep learning techniques on complexity issues opens up new possibilities for data-driven GIS science.

The term green infrastructure captures the need to conserve biodiversity and to sustain landscapes’ different ecosystem services. Maintaining green infrastructures through protected areas, management and landscape restoration requires knowledge in geography, spatial data about biophysical, anthropogenic and immaterial values, spatial comprehensive planning, and thus geographical information systems (GIS). To understand land use planning practices and planning education regarding GIS in Sweden we interviewed 43 planners and reviewed 20 planning education programmes. All planners used GIS to look at data but did not carry out spatial analyses of land covers. BSc programmes included more GIS than MSc programmes but very few taught analyses for spatial planning. As key spatial planning actors, municipalities’ barriers and bridges for improved GIS use for collaborative learning about green infrastructures are discussed. A concluding section presents examples of how GIS can support spatial planning for green infrastructures.