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.

Urban structure can be better comprehended through analyzing its cores. Geospatial big data facilitate the identification of urban centers in terms of high accuracy and accessibility. However, previous studies seldom leverage multi-source geospatial big data to identify urban centers from a topological perspective. This study attempts to identify urban centers through the spatial integration of multi-source geospatial big data, including nighttime light imagery (NTL), building footprints (BFP) and street nodes of OpenStreetMap (OSM). We use a novel topological approach to construct complex networks from intra-urban hotspots based on the theory of centers by Christopher Alexander. We compute the degree of wholeness value for each hotspot as the centric index. The overlapped hotspots with the highest centric indices are regarded as urban centers. The identified urban centers in New York, Los Angeles, and Houston are consistent with their downtown areas, with overall accuracy of 90.23%. In Chicago, a new urban center is identified considering a larger spatial extent. The proposed approach can effectively and objectively prevent counting those hotspots with high intensity values but few neighbors into the result. This study proposes a topological approach for urban center identification and a bottom-up perspective for sustainable urban design.

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.

The hierarchical structure of geographic or urban space can be well-characterized by the concept of living structure, a term coined by Christopher Alexander. All spaces, regardless of their size, possess certain degrees of livingness that can be mathematically quantified. While previous studies have successfully quantified the livingness of small spaces such as images or artworks, the livingness of geographic space has not yet been characterized in a recursive manner. Zipf’s law has been observed in urban systems and intra-urban structures. However, whether Zipf’s law is applicable to the hierarchical substructures of geographic space has rarely been investigated. In this study, we recursively extract the substructures of geographic space using global nighttime light imagery. We quantify the livingness of global cities considering the number of substructures (S) and their inherent hierarchy (H). We further investigate the scaling properties of the extracted substructures across scales and the relationships between livingness and population for global cities. The results demonstrate that all substructures of global cities form a living structure that conforms to Zipf’s law. The degree of livingness better captures population distribution than nighttime light intensity values for the global cities. This study contributes in three aspects: First, it considers global cities as a whole to quantify spatial livingness. Second, it applies the concept of livingness to cities to better capture the spatial structure of the population using nighttime light data. Third, it introduces a novel method to recursively extract substructures from nighttime images, offering a valuable tool to investigate urban structures across multiple spatial scales.

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT’s ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be “lying” in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast.

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.

This study evaluates the accuracy of Microsoft HoloLens (Version 1) for small-scale industrial activities, comparingits measurements to ground truth data from a Kuka Robotics arm. Two experiments were conducted to assess its positiontracking capabilities, revealing that the HoloLens device is effective for measuring the position of dynamic objects with smalldimensions. However, its precision is affected by the velocity of the trajectory and its position within the device's field of view.While the HoloLens device may be suitable for small-scale tasks, its limitations for more complex and demanding applicationsrequiring high precision and accuracy must be considered. The findings can guide the use of HoloLens devices in industrialapplications and contribute to the development of more effective and reliable position-tracking systems.

Rooftop photovoltaic panels (RPVs) are being increasingly used in urban areas as a promising means of achieving energy sustainability. Determining proper layouts of RPVs that make the best use of rooftop areas is of importance as they have a considerable impact on the RPVs performance in efficiently producing energy. In this study, a new spatial methodology for automatically determining the proper layouts of RPVs is proposed. It aims to both extract planar rooftop segments and identify feasible layouts with the highest number of RPVs in highly irradiated areas. It leverages digital surface models (DSMs) to consider roof shapes and occlusions in placing RPVs. The innovations of the work are twofold: (a) a new method for plane segmentation, and (b) a new method for optimally placing RPVs based on metaheuristic optimization, which best utilizes the limited rooftop areas. The proposed methodology is evaluated on two test sites that differ in urban morphology, building size, and spatial resolution. The results show that the plane segmentation method can accurately extract planar segments, achieving 88.7% and 99.5% precision in the test sites. In addition, the results indicate that complex rooftops are adequately handled for placing RPVs, and overestimation of solar energy potential is avoided if detailed analysis based on panel placement is employed.

Rooftop photovoltaics have been acknowledged as a critical component in cities’ efforts to reduce their reliance on fossil fuels and move towards energy sustainability. Identifying rooftop areas suitable for installing rooftop photovoltaics-referred to as utilizable areas-is essential for effective energy planning and developing policies related to renewable energies. Utilizable areas are greatly affected by the size, shape, superstructures of rooftops, and shadow effects. This study estimates utilizable areas and solar energy potential of rooftops by considering the mentioned factors. First, rooftops are extracted from LiDAR data by training PointNet++, a neural network architecture for processing 3D point clouds. The second step involves extracting planar segments of rooftops using a combination of clustering and region growing. Finally, utilizable areas of planar segments are identified by removing areas that do not have a suitable size and do not receive sufficient solar irradiation. Additionally, in this step, areas reserved for accessibility to photovoltaics are removed. According to the experimental results, the methods have a high success rate in rooftop extraction, plane segmentation, and, consequently, estimating utilizable areas for photovoltaics.

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where assessments have long been challenging due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT’s ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, a study was conducted where potential opportunities and threats of ChatGPT for programming education were investigated. The question to answer was: What will the consequences be for programming education? 

The study applied a methodological approach inspired by action research and analytic autoethnography to investigate, experiment and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. 

Findings of the study include several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be lying in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT will have a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast.