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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Ett varmare klimat innebär kraftigare skyfall och fler översvämningar. Men många kommuner är dåliga på att avgöra var risken för höga vattenflöden gör det olämpligt att bygga. Det menar naturgeografen Anders Brandt – som vill se ett kunskapslyft i geografi.

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.

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.

Despite much attention in the literature, knowledge about the dynamics surrounding urban densification and urban greening is still in dire need for architects, urban planners and scientists that strive to design, develop, and regenerate sustainable and resilient urban environments. Here, we investigate countrywide patterns of changes in residential density and residential nature at high spatial resolution over a time period of >20 years (1995-2016), combining a dataset of address-level population data covering all of Denmark (>2 million address points) with satellite image-derived Normalized Difference Vegetation Index (NDVI) data. Our results show that many residential environments across Denmark have witnessed simultaneous densification and greening since the mid-1990s. In fact, the most common change within 500 metre neighbourhoods around individual address points is of joint increases in population and NDVI (28%), followed by increasing NDVI with stable population figures (21%). In contrast, only 8% of neighbourhoods around address points have seen a decline in either population or NDVI. Results were similar in low- middle- and high-density environments, suggesting that trends were driven by climate change but also to some degree enabled by urban planning policies that seek to increase rather than decrease nature in the cities.

Despite much attention in the literature, knowledge about the dynamics surrounding urban densification and urban greening is still in dire need for architects, urban planners and scientists that strive to design, develop, and regenerate sustainable and resilient urban environments. Here, we investigate countrywide patterns of changes in residential density and residential nature at high spatial resolution over a time period of >20 years (1995-2016), combining a dataset of address-level population data covering all of Denmark (>2 million address points) with satellite image-derived Normalized Difference Vegetation Index (NDVI) data. Our results show that many residential environments across Denmark have witnessed simultaneous densification and greening since the mid-1990s. In fact, the most common change within 500 metre neighbourhoods around individual address points is of joint increases in population and NDVI (28%), followed by increasing NDVI with stable population figures (21%). In contrast, only 8% of neighbourhoods around address points have seen a decline in either population or NDVI. Results were similar in low- middle- and high-density environments, suggesting that trends were driven by climate change but also to some degree enabled by urban planning policies that seek to increase rather than decrease nature in the cities.

River flooding and urbanization are processes of different character that take place worldwide. As the latter tends to make the consequences of the former worse, together with the uncertainties related to future climate change and flood‐risk modeling, there is a need to both use existing tools and develop new ones that help the management and planning of urban environments. In this article a prototype tool, based on estimated maximum land cover roughness variation, the slope of the ground, and the quality of the used digital elevation models, and that can produce flood ‘uncertainty zones’ of varying width around modeled flood boundaries, is presented. The concept of uncertainty, which urban planners often fail to consider in the spatial planning process, changes from something very difficult into an advantage in this way. Not only may these uncertainties be easier to understand by the urban planners, but the uncertainties may also function as a communication tool between the planners and other stakeholders. Because flood risk is something that urban planners always need to consider, these uncertainty zones can function both as buffer areas against floods, and as blue‐green designs of significant importance for a variety of ecosystem services. As the Earth is warming and the world is urbanizing at rates and scales unprecedented in history, we believe that new tools for urban resilience planning are not only urgently needed, but also will have a positive impact on urban planning., Bettering life through integrative GIS (BIG)

River flooding and urbanization are processes of different character that take place worldwide. As the latter tends to make the consequences of the former worse, together with the uncertainties related to future climate change and flood‐risk modeling, there is a need to both use existing tools and develop new ones that help the management and planning of urban environments. In this article a prototype tool, based on estimated maximum land cover roughness variation, the slope of the ground, and the quality of the used digital elevation models, and that can produce flood ‘uncertainty zones’ of varying width around modeled flood boundaries, is presented. The concept of uncertainty, which urban planners often fail to consider in the spatial planning process, changes from something very difficult into an advantage in this way. Not only may these uncertainties be easier to understand by the urban planners, but the uncertainties may also function as a communication tool between the planners and other stakeholders. Because flood risk is something that urban planners always need to consider, these uncertainty zones can function both as buffer areas against floods, and as blue‐green designs of significant importance for a variety of ecosystem services. As the Earth is warming and the world is urbanizing at rates and scales unprecedented in history, we believe that new tools for urban resilience planning are not only urgently needed, but also will have a positive impact on urban planning., Bettering life through integrative GIS (BIG)

Despite much attention in the literature, knowledge about the dynamics surrounding urban densification and urban greening is still in dire need for architects, urban planners and scientists that strive to design, develop, and regenerate sustainable and resilient urban environments. Here, we investigate countrywide patterns of changes in residential density and residential nature at high spatial resolution over a time period of >20 years (1995-2016), combining a dataset of address-level population data covering all of Denmark (>2 million address points) with satellite image-derived normalised difference vegetation index (NDVI) data. Our results show that many residential environments across Denmark have witnessed simultaneous densification and greening since the mid-1990s. In fact, the most common change within 500 m neighbourhoods around individual address points is of joint increases in population and NDVI (28%), followed by increasing NDVI with stable population figures (21%). In contrast, only 8% of neighbourhoods around address points have seen a decline in either population or NDVI. Results were similar in low- middle- and high-density environments, suggesting that trends were driven by climate change but also to some degree enabled by urban planning policies that seek to increase rather than decrease nature in the cities.

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

River flooding and urbanization are processes of different character that take place worldwide. As the latter tends to make the consequences of the former worse, together with the uncertainties related to future climate change and flood‐risk modeling, there is a need to both use existing tools and develop new ones that help the management and planning of urban environments. In this article a prototype tool, based on estimated maximum land cover roughness variation, the slope of the ground, and the quality of the used digital elevation models, and that can produce flood ‘uncertainty zones’ of varying width around modeled flood boundaries, is presented. The concept of uncertainty, which urban planners often fail to consider in the spatial planning process, changes from something very difficult into an advantage in this way. Not only may these uncertainties be easier to understand by the urban planners, but the uncertainties may also function as a communication tool between the planners and other stakeholders. Because flood risk is something that urban planners always need to consider, these uncertainty zones can function both as buffer areas against floods, and as blue‐green designs of significant importance for a variety of ecosystem services. As the Earth is warming and the world is urbanizing at rates and scales unprecedented in history, we believe that new tools for urban resilience planning are not only urgently needed, but also will have a positive impact on urban planning., Bettering life through integrative GIS (BIG)

Despite much attention in the literature, knowledge about the dynamics surrounding urban densification and urban greening is still in dire need for architects, urban planners and scientists that strive to design, develop, and regenerate sustainable and resilient urban environments. Here, we investigate countrywide patterns of changes in residential density and residential nature at high spatial resolution over a time period of >20 years (1995-2016), combining a dataset of address-level population data covering all of Denmark (>2 million address points) with satellite image-derived Normalized Difference Vegetation Index (NDVI) data. Our results show that many residential environments across Denmark have witnessed simultaneous densification and greening since the mid-1990s. In fact, the most common change within 500 metre neighbourhoods around individual address points is of joint increases in population and NDVI (28%), followed by increasing NDVI with stable population figures (21%). In contrast, only 8% of neighbourhoods around address points have seen a decline in either population or NDVI. Results were similar in low- middle- and high-density environments, suggesting that trends were driven by climate change but also to some degree enabled by urban planning policies that seek to increase rather than decrease nature in the cities.

Despite much attention in the literature, knowledge about the dynamics surrounding urban densification and urban greening is still in dire need for architects, urban planners and scientists that strive to design, develop, and regenerate sustainable and resilient urban environments. Here, we investigate countrywide patterns of changes in residential density and residential nature at high spatial resolution over a time period of >20 years (1995-2016), combining a dataset of address-level population data covering all of Denmark (>2 million address points) with satellite image-derived Normalized Difference Vegetation Index (NDVI) data. Our results show that many residential environments across Denmark have witnessed simultaneous densification and greening since the mid-1990s. In fact, the most common change within 500 metre neighbourhoods around individual address points is of joint increases in population and NDVI (28%), followed by increasing NDVI with stable population figures (21%). In contrast, only 8% of neighbourhoods around address points have seen a decline in either population or NDVI. Results were similar in low- middle- and high-density environments, suggesting that trends were driven by climate change but also to some degree enabled by urban planning policies that seek to increase rather than decrease nature in the cities.

Despite much attention in the literature, knowledge about the dynamics surrounding urban densification and urban greening is still in dire need for architects, urban planners and scientists that strive to design, develop, and regenerate sustainable and resilient urban environments. Here, we investigate countrywide patterns of changes in residential density and residential nature at high spatial resolution over a time period of >20 years (1995-2016), combining a dataset of address-level population data covering all of Denmark (>2 million address points) with satellite image-derived Normalized Difference Vegetation Index (NDVI) data. Our results show that many residential environments across Denmark have witnessed simultaneous densification and greening since the mid-1990s. In fact, the most common change within 500 metre neighbourhoods around individual address points is of joint increases in population and NDVI (28%), followed by increasing NDVI with stable population figures (21%). In contrast, only 8% of neighbourhoods around address points have seen a decline in either population or NDVI. Results were similar in low- middle- and high-density environments, suggesting that trends were driven by climate change but also to some degree enabled by urban planning policies that seek to increase rather than decrease nature in the cities.

River flooding and urbanization are processes of different character that take place worldwide. As the latter tends to make the consequences of the former worse, together with the uncertainties related to future climate change and flood‐risk modeling, there is a need to both use existing tools and develop new ones that help the management and planning of urban environments. In this article a prototype tool, based on estimated maximum land cover roughness variation, the slope of the ground, and the quality of the used digital elevation models, and that can produce flood ‘uncertainty zones’ of varying width around modeled flood boundaries, is presented. The concept of uncertainty, which urban planners often fail to consider in the spatial planning process, changes from something very difficult into an advantage in this way. Not only may these uncertainties be easier to understand by the urban planners, but the uncertainties may also function as a communication tool between the planners and other stakeholders. Because flood risk is something that urban planners always need to consider, these uncertainty zones can function both as buffer areas against floods, and as blue‐green designs of significant importance for a variety of ecosystem services. As the Earth is warming and the world is urbanizing at rates and scales unprecedented in history, we believe that new tools for urban resilience planning are not only urgently needed, but also will have a positive impact on urban planning., Bettering life through integrative GIS (BIG)

River flooding and urbanization are processes of different character that take place worldwide. As the latter tends to make the consequences of the former worse, together with the uncertainties related to future climate change and flood‐risk modeling, there is a need to both use existing tools and develop new ones that help the management and planning of urban environments. In this article a prototype tool, based on estimated maximum land cover roughness variation, the slope of the ground, and the quality of the used digital elevation models, and that can produce flood ‘uncertainty zones’ of varying width around modeled flood boundaries, is presented. The concept of uncertainty, which urban planners often fail to consider in the spatial planning process, changes from something very difficult into an advantage in this way. Not only may these uncertainties be easier to understand by the urban planners, but the uncertainties may also function as a communication tool between the planners and other stakeholders. Because flood risk is something that urban planners always need to consider, these uncertainty zones can function both as buffer areas against floods, and as blue‐green designs of significant importance for a variety of ecosystem services. As the Earth is warming and the world is urbanizing at rates and scales unprecedented in history, we believe that new tools for urban resilience planning are not only urgently needed, but also will have a positive impact on urban planning., Bettering life through integrative GIS (BIG)

In a previous work we developed GISwaps, a novel method for geospatial decision-making based on Even Swaps. In this paper, we present the results of an evaluation of a visualization framework integrated with this method, implemented within a decision support system. This evaluation is based on two different studies. In the quantitative study, 15 student participants used GISwaps with no visual features, and 15 participants used GISwaps with the integrated visual framework, as the tool in a solar farm site location case study. The results of the quantitative evaluation show positive impact of the visualization in terms of increased coherency in trade-offs. The results also show a statistically significant difference in average trade-off values between the groups, with users from the non-visual group setting on average 20% higher trade-off values compared with the users in the visual group. In the qualitative study, we had one expert in GIS, two experts in decision-making and two experts in solar energy as a focus user group. Data in this study were obtained by observations and semi-structured interviews with the participants. The impact of the visualization framework was assessed positively by all participants in the expert group.

In a previous work we developed GISwaps, a novel method for geospatial decision-making based on Even Swaps. In this paper, we present the results of an evaluation of a visualization framework integrated with this method, implemented within a decision support system. This evaluation is based on two different studies. In the quantitative study, 15 student participants used GISwaps with no visual features, and 15 participants used GISwaps with the integrated visual framework, as the tool in a solar farm site location case study. The results of the quantitative evaluation show positive impact of the visualization in terms of increased coherency in trade-offs. The results also show a statistically significant difference in average trade-off values between the groups, with users from the non-visual group setting on average 20% higher trade-off values compared with the users in the visual group. In the qualitative study, we had one expert in GIS, two experts in decision-making and two experts in solar energy as a focus user group. Data in this study were obtained by observations and semi-structured interviews with the participants. The impact of the visualization framework was assessed positively by all participants in the expert group.

In a previous work we developed GISwaps, a novel method for geospatial decision-making based on Even Swaps. In this paper, we present the results of an evaluation of a visualization framework integrated with this method, implemented within a decision support system. This evaluation is based on two different studies. In the quantitative study, 15 student participants used GISwaps with no visual features, and 15 participants used GISwaps with the integrated visual framework, as the tool in a solar farm site location case study. The results of the quantitative evaluation show positive impact of the visualization in terms of increased coherency in trade-offs. The results also show a statistically significant difference in average trade-off values between the groups, with users from the non-visual group setting on average 20% higher trade-off values compared with the users in the visual group. In the qualitative study, we had one expert in GIS, two experts in decision-making and two experts in solar energy as a focus user group. Data in this study were obtained by observations and semi-structured interviews with the participants. The impact of the visualization framework was assessed positively by all participants in the expert group.

In a previous work we developed GISwaps, a novel method for geospatial decision-making based on Even Swaps. In this paper, we present the results of an evaluation of a visualization framework integrated with this method, implemented within a decision support system. This evaluation is based on two different studies. In the quantitative study, 15 student participants used GISwaps with no visual features, and 15 participants used GISwaps with the integrated visual framework, as the tool in a solar farm site location case study. The results of the quantitative evaluation show positive impact of the visualization in terms of increased coherency in trade-offs. The results also show a statistically significant difference in average trade-off values between the groups, with users from the non-visual group setting on average 20% higher trade-off values compared with the users in the visual group. In the qualitative study, we had one expert in GIS, two experts in decision-making and two experts in solar energy as a focus user group. Data in this study were obtained by observations and semi-structured interviews with the participants. The impact of the visualization framework was assessed positively by all participants in the expert group.