Urban expansion is strongly determined by changes in urban population and the density in which new residents settle. Consequently, assessments of future changes in urban area depend on population and density projections. While the former are well-established, the latter have received much less research attention. To fill this void, we set up a time series representing most countries around the globe describing urban population density and several explanatory variables. Using a panel regression approach with country-fixed effects we explain the variation in density levels over time. The results indicate that urban density increases with growing total urban population and decreases with increasing levels of urbanisation. The latter effect diminishes, however, when the urban population fraction increases even further. These results indicate a sequence of increasing, decreasing and stabilising urban densities with increasing urban population shares. This succession can be linked to subsequent development stages of urbanisation, suburbanisation and eventually reurbanisation. The latter stage remains rare in our time series, however, as only a few countries reach the corresponding high urbanisation rates. Furthermore, we find that urban densities decrease with growing national income per capita. This may indicate that higher incomes enable suburbanisation to more dispersed urban areas with larger residences but can also be associated with the advent of smaller households and the growth of non-residential sectors such as industry and commerce. Conversely, urban density increases with higher agricultural land values that we proxy with a novel primary production intensity variable. We use our explanatory results to determine country-specific future urban areas following a commonly applied socio-economic scenario (SSP2) and suggest the total global urban area may triple from 2015 to 2100.

Urban expansion studies typically distinguish three main processes: infill, edge expansion, and outlying development. These general processes do not specifically capture another type of urban expansion: the clumping or coalescing of multiple individual urban areas (patches) into one larger urban agglomeration. This paper develops a new Urban Patch Expansion (UPE) framework that characterises coalescence as an urban expansion process that occurs simultaneously with the three commonly distinguished processes. We characterise two types of coalescence: merging and joining. Merging is defined as the amalgamation of at least two initial patches whose combined area is smaller than the newly added urban area merging them, while joining refers to cases where the total area of the initial urban patches is larger than the newly developed urban area connecting them. In an application of this framework, we analyse urban development in the functional urban area of Bandung (Indonesia) in the period 1975-2015 focussing on the variation in urban development processes along the gradient from the urban centre to the most outward edges of the urban agglomeration. The analysis employs the latest release of the Global Human Settlement Layer (GHSL) that provides a highly detailed rasterised account of urban development since 1975. This new spatial analysis approach enriches our understanding of the spatiotemporal dynamics of urban expansion by providing a more specific account of when and where specific development types prevail. Our research characterises urban development in the Bandung area as a sequence of phases. Outlying development is the most important process in the initial period (1975-1990), highlighting a phase of diffusion. From 1990 coalescence sets in, with joining as the prevalent process in the second period (1990-2000). Infill and edge expansion are steady processes throughout the analysis period, where the latter gradually increases in importance as the urban area continues to grow. Both processes further compact the urban landscape. The final period (2000-2015) shows approximately equal shares of joining, infill and edge expansion. Joining can thus be seen as the first step in the transition from the diffusion to the coalescence stage, whereas the infilling of urban voids and the expansion of city at its edges reflect a more continuous growth process. We observe a similar development sequence over space: edge expansion dominates the zone between 5 and 9 km from the core, joining in the zone up to 17 km and outlying beyond that. The latter process, however, became less important over time. Our approach can be used to compare refined development trajectories in different periods and regions and thus supports our understanding of urban growth.

Understanding urban state transitions is critical for sustainability, yet a unified framework to characterize the interactive dynamics of key urban components—green vegetation (GV), impervious surface (ISA), and bare soil (BS)—has been lacking at a national scale. To bridge this gap, we developed a novel framework that moves beyond static mapping to directly quantify the dynamic interactions within the V-I-S nexus. Leveraging over 200 000 Landsat images on Google Earth Engine, we generated the first continuous, annual V-I-S fraction maps for China (1990–2019) using a unified spectral endmember space with change trajectory model to characterize and quantify multidirectional urban state transitions. Through our pixel-level trajectory analysis, we integrated the magnitude, timing, and trend of V-I-S changes into a set of conversion rules to systematically identify five transition archetypes: stable, expansion, intensification, green recovery, and soil exposure. This framework demonstrated superior capability in tracking the complex intraurban landscape components with high accuracy (i.e., smaller mean squared error of –2.07% to 3.44%) and low uncertainties (i.e., fractional biases less than 0.10), as well as achieving highly accurate historical state transitions mapping (87.29%) at fine-scale feature resolution. Our analysis reveals a national-scale GV loss (–31 109 km2) alongside ISA (+28 294 km2) and BS (+2814 km2) gains, primarily driven by expansion (46%) and intensification (28%). Crucially, we uncover a pivotal shift from expansive growth to intensification and greening as cities develop. This study provides the first spatially explicit, nationally consistent record of intraurban dynamics, offering a powerful tool for modeling urban social-ecological systems and guiding targeted governance for sustainable urban futures.

This study investigates car dependence for short trips (travel time <= 15 min) using an integrated Partial Least Squares-Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN) approach applied to Dutch travel diary data (ODiN 2022). The observed outcome of the present study demonstrates that short trips are associated with lower car dependence (car ownership and frequency of car use), particularly for non-work destinations (e.g., educational institutions, hospitals, supermarkets). However, employed individuals exhibit higher car dependence, likely due to workplace commuting demands. Car ownership emerges as the critical mediator, shaping travel patterns for age, employment, household size, income, education, and residential location. While PLS-SEM identifies employment status as the most influential factor affecting frequency of car use, ANN prioritizes car ownership, suggesting unmeasured contextual factors (e.g., urban form, travel habits) influence these relationships in ways linear models cannot fully capture. The study makes three key contributions: (1) it quantifies car-reduction potential for short trips through advanced hybrid modelling, (2) establishes car ownership as a pivotal leverage point for sustainable mobility policies, and (3) identifies the importance of contextual factors in shaping short-trip travel behaviour. The research findings emphasise the need to locate both essential amenities and workplaces in shorter distance to residences to achieve sustainable urban mobility goals in direction of removing car dependence.

Understanding urban state transitions is critical for sustainability, yet a unified framework to characterize the interactive dynamics of key urban components-green vegetation (GV), impervious surface (ISA), and bare soil (BS)-has been lacking at a national scale. To bridge this gap, we developed a novel framework that moves beyond static mapping to directly quantify the dynamic interactions within the V-I-S nexus. Leveraging over 200 000 Landsat images on Google Earth Engine, we generated the first continuous, annual V-I-S fraction maps for China (1990-2019) using a unified spectral endmember space with change trajectory model to characterize and quantify multidirectional urban state transitions. Through our pixel-level trajectory analysis, we integrated the magnitude, timing, and trend of V-I-S changes into a set of conversion rules to systematically identify five transition archetypes: stable, expansion, intensification, green recovery, and soil exposure. This framework demonstrated superior capability in tracking the complex intraurban landscape components with high accuracy (i.e., smaller mean squared error of -2.07% to 3.44%) and low uncertainties (i.e., fractional biases less than 0.10), as well as achieving highly accurate historical state transitions mapping (87.29%) at fine-scale feature resolution. Our analysis reveals a national-scale GV loss (-31 109 km(2)) alongside ISA (+28 294 km(2)) and BS (+2814 km(2)) gains, primarily driven by expansion (46%) and intensification (28%). Crucially, we uncover a pivotal shift from expansive growth to intensification and greening as cities develop. This study provides the first spatially explicit, nationally consistent record of intraurban dynamics, offering a powerful tool for modeling urban social-ecological systems and guiding targeted governance for sustainable urban futures.

Projections of future urban land change are essential for a range of sustainability assessments, including those related to biodiversity loss, carbon emissions, and agricultural land conversion. However, to what extent and where current projections agree or disagree remains unknown. Here, we systematically compare existing global projections that are consistent with the Shared Socioeconomic Pathways. We find that the total global urban land area is expected to increase by 112% between 2020 and 2100 (averaged across all projections), with a coefficient of variation of 0.81. This variation is mostly caused by the selection of the underlying drivers that are included in the different models. Regionally, the highest average growth rates are found in sub-Saharan Africa (+679% to +730%), while this region also has the highest variation across projections (coefficient of variation ranging from 2.02 to 2.18). When ranking scenarios within a study from the highest to the lowest projected increase in urban land, rankings are relatively similar for regions in the Global North, but not for regions in the Global South. The large disagreement across projections can lead to high uncertainties in assessments of future urban land change impacts, which can undermine the effectiveness of long-term planning, policymaking, and resource management decisions.

This paper presents a spatial analysis approach to uncover a range of residential (re)development processes using a complete registration of individual building level developments. By analysing 30 million housing-stock mutations in the Dutch national building registry between 2012 and 2025, we provide a full account of national housing stock changes and their preferred locations. We find a net addition of nearly one million dwellings (+12% of housing stock), with three-quarters realised within the existing urban fabric. Replacement (demolition followed by new construction on the same parcel) accounts for a growing share of net additions over time, while new-build development declines slightly. High-density neighbourhoods, covering just 7% of Dutch land, host five times more replacements per square kilometre than low-density areas. Regression models reveal that neighbourhood-level housing growth correlates positively with higher social-rent shares, urban attractiveness, and heritage values, but negatively with land availability, indicating that scarcity incentivises densification. Effect sizes differ markedly across density categories and development processes. The influence of urban attractiveness, for example, becomes more salient in lower-density areas, where higher amenity levels can partially offset otherwise limited housing increase. The study contributes by: (1) providing a reproducible micro-scale method to detect residential development processes; (2) demonstrating that densification is a substantial and growing component of Dutch housing supply; and (3) investigating the association between neighbourhood characteristics and densification and the balance between densification and expansion. These insights support spatial strategies that blend targeted infill with selective outward growth to meet future housing demand.

Urban expansion is considered to be a major driver of ecosystem services (ESs) loss, and variation of ESs in rapidly urbanizing areas are of great concern. Clarifying the relationship between urban expansion and ecosystem services (ESs) and understanding the impact of socio-ecological drivers on ESs are crucial for sustainable urban development and ecological conservation. However, the differential impacts of urban expansion on the variation of ecosystem services across different urban expansion patterns and the dominant drivers of these variation, have not been fully explored, hampering the formulation of sustainable urban development plans. To address these knowledge gaps, we assessed the differential impact of three urban expansion patterns (edge-spreading, interior-filling and leap-frogging) on five representative ESs—carbon sequestration, food production, habitat quality, soil retention and water yield—in the Yangtze River Delta region (YRDR) of China. We applied random forest model to quantify the impact of ten social-ecological drivers on the variation of ESs across three urban expansion patterns. Our findings revealed that edge-spreading is the main pattern of urban expansion and it leads to higher losses of the three key ESs, which is therefore more significant than that caused by interior-filling and leap-frogging. Although the loss of ESs due to urban expansion was primarily driven by changes in natural drivers, social drivers had a variable and sometimes powerful influence on ESs. The differences in the impact of dominant socio-ecological drivers on the variation of ESs across different expansion patterns are mainly reflected in the relative importance. Moreover, the overall explanatory power of socio-ecological drivers on the variation of ESs under leap-frogging expansion was low. We suggest that different ESs protection strategies should be developed and implemented for different urban expansion patterns to achieve a balance between urban development and ecosystem protection. This study can provide an opportunity to formulate refined urban development plans for the sustainable development of human-environment systems in rapidly urbanizing regions.

Private gardens are an important aspect of small urban green space and help provide a wide range of ecosystem services. This contribution is increasingly recognised and several local governments now stimulate residents to replace garden pavements with vegetation. Tracing such greening efforts is difficult, however, because of their small scale and absence in official mapping efforts. Therefore, we propose an approach to map the greening of gardens by analysing a time series of highly detailed aerial photographs. Our approach overcomes several challenges: the temporal variation in the green signal because of seasonal differences in green cover or shade, the small size of gardens and the obstruction of vision by overhanging trees. We present the results of this analysis and assess their accuracy by using a confusion matrix.

Empirical studies of logistics location choice have largely focused on logistics as a single sector. This research attempts to address this research gap by analysing the heterogeneity in locational preferences of logistics across facility types and sizes. We estimate a multinomial logistic regression model to study the relative impact of various spatial drivers on logistics development in the Netherlands. We explicitly assess the role of a government policy aimed at stimulating logistics growth. We find that factors such as highway and rail accessibility, proximity to consumers and urban areas, land availability, and proximity to other logistics firms have a positive effect across all logistics categories while restrictive zoning measures have a negative effect. On the contrary, the effects of factors such as access to seaports and freight terminals, urban attractiveness, and land price are more heterogeneous and vary with the function and size of logistics. Finally, our analysis also reveals positive effects of the logistics growth stimulating spatial policy. Using our estimated parameters, we also map the predicted probabilities to identify potential future locations for logistics development.

Combatting land damage has become a global priority, and China has adopted a series of ecological engineering measures, especially in the agro-pastoral area with fragile ecological environment. The effectiveness of ecological engineering construction (EEC), from a comprehensive recognition encompassing its quality, quantity, and function, has remained largely unknown. To this end, Zhangbei County, a typical agro-pastoral ecotone of northern China, was chosen as our focal area. After summarizing the timelines, aims and results of the EEC during various periods in Zhangbei, the linear spectral mixture analysis was employed to process Landsat 5 TM images in 2000 and 2010, as well as Landsat 8 OLI images in 2020. Then, a comprehensive evaluation framework of EEC was established from the perspective of "quantity-quality-function", and the ecological effectiveness of EEC was evaluated from 2000 to 2020 in Zhangbei. Results revealed that EEC played a critical role in enhancing quantity, quality and function, in spite of that, there were still numerous regions showing varying degrees of degradation in terms of these aspects. Then, by extending the three-dimensional cube as the theoretical basis for the zoning management of EEC, we merged four zones according to the space matching relationship among quantity, quality and function of EEC, namely, Ecological conservation area, Ecological improvement area, Ecological restoration area and Ecological remodeling zone. More targeted ecological measures were required for specific matching relationship among quantity, quality and function of EEC. This study is expected to present an empirical case for assessing the ecological effectiveness of EEC in areas or countries with similar restoration demand and support regional management.

Urban parks and public open spaces enhance the quality of life for citizens by offering many different services. This is especially important in growing metropoles, where green space is ever scarcer. We assess the impact of park proximity on house prices, paying specific attention to the impact of park size, the role of other green and blue spaces as possible substitutes, and changes in valuation over time. The study relies on an extensive database of residential property transactions of the past 10 years for the area inside the 5th ring road of Beijing. The data captures the housing market in a dynamic period of rapid population increase and surging house prices. We find that large parks have a much larger impact than small parks. For parks larger than 20 ha, price increases can be as large as 6-7 %, compared to <1 % averaged over all parks. Park value decreases significantly, however, with increasing availability of other green and blue spaces, confirming the idea that both are substitutes. Finally, we observe that the appreciation of parks increases over time, and most so for large parks.

Urban densification is a key strategy to accommodate rapid urban population growth, but emerging evidence suggests serious risks of urban densification for individuals’ mental health. To better understand the complex pathways from urban densification to mental health, we integrated interdisciplinary expert knowledge in a causal loop diagram via group model building techniques. Six subsystems were identified: five subsystems describing mechanisms on how changes in the urban system caused by urban densification may impact mental health, and one showing how changes in mental health may alter urban densification. The new insights can help to develop resilient, healthier cities for all.

Intensive agriculture is increasingly associated with environmental degradation that may jeopardise long-term environmental and economic sustainability. The high-dike system in the upper Mekong delta that has enabled intensive rice cultivation represents a prime example of these potential negative feedbacks. The lack of seasonal flooding and the associated depletion of nutrients is expected to affect farmer income as productivity declines and more fertiliser is required. Therefore, emphasis has shifted towards more sustainable, flood-based agriculture, however farmer uptake has its challenges. Based on a compilation of different household surveys we first analyse rice farmers’ ability and willingness to transition and subsequently study the economic sustainability of intensive rice-based livelihoods. A Motivation and Ability (MOTA) survey reveals that two-thirds of the surveyed rice farmers are reluctant to change to flood-based farming systems, as they consider rice cultivation to be economically viable in the near future. They also mention financial and technical ability as key constraints. Subsequently, we analyse yield and fertiliser developments for a large sample of farming households under different dike systems between 2008 and 2015. This shows that income from rice farming grew steadily under high-dike systems as productivity growth compensated for higher input requirements. This growth is partly dampened by the slightly higher negative impacts of potential flood damage in high-dike areas, compared low-dike areas. A counterintuitive effect that is related to the fact that high dikes remain prone to dike overtopping or breaching in the flooding season, resulting in potentially higher damage than low-dike areas that are able to crop flood-based alternatives. The observed growth in income is a likely explanation for the reluctance to change in the studied period. Our analysis also shows that rice income growth is unequally distributed in high-dike areas, with lower incomes being associated with new high-dike systems and slower growth of incomes of smallholder rice farmers compared to large-scale farms. This makes smallholder rice farmers in high-dike areas especially vulnerable to changing conditions, and thus a priority target group for policy makers promoting flood-based alternatives. Recent commune level yield data show that the past productivity growth has stalled, increasing the prospects for alternative flood-based agriculture. This transition can be facilitated, by enhancing the economic viability of flood-based crops and, particularly for smallholder farmers, by improving their financial and technical capabilities through supportive policies.

Adverse impacts of climate and environmental hazards are unevenly distributed between socioeconomic groups due to differences in exposure, vulnerability and resilience. This study examines the distribution of vulnerability and resilience to drought and salt intrusion impacts among rice farmers in the rural Mekong Delta in Vietnam. By defining both aspects independently, we can study potential differences in the socio-economic factors that steer them and analyse how these two aspects of adaptive capacity are related. Using fixed-effect regressions, we find that poorer communes are more vulnerable to direct environmental impacts (loss in rice yield). Several household characteristics that reflect a low socioeconomic status, such as low asset values, small plots, and limited education level, are linked with higher vulnerability to direct drought impacts. High vulnerability does, however, not necessarily translate to low resilience, which we proxy by measuring indirect impacts (loss in household income). Several household activities and characteristics help mitigate indirect impacts. Our results suggest that the least resilient household group consists of smallholder, asset-low households that are unable to diversify to non-crop agriculture or off-farm employment. Supportive policies targeting this particular socioeconomic group to enable transition to non-crop or off-farm labour would substantially improve their resilience to future environmental events. Distinguishing between resilience and vulnerability enables a broader understanding of the mechanisms influencing the distribution of direct and indirect adverse impacts, which enables drafting targeted policy measures for specific socioeconomic groups.

Abstract. Parking management plays a critical role in keeping urban spaces accessible and urban managers strive for an optimal balance between not enough and too much parking. Deciding which parking space can be liberated or needs to be extended requires detailed data on parking occupancy trends. In person inspection and in-situ sensors can provide such data but are too costly for city wide deployment. High-resolution satellite imagery is becoming more affordable, has the advantage of instantaneously collecting information from the whole city, is continuously being updated, and available for several years now to allow building a time series. Yet, identifying cars in satellite imagery is not a trivial task. We propose a method for classifying parking spot occupancy based on thresholding the reflectance range. The method requires individual parking spot data to be available and analyses each parking zone individually. We tested the method on a 0.5 metre resolution image (Pleiades satellite) that was specifically ordered for this purpose during a clear spring day in a medium-size city. The method has the advantage of not requiring extensive training data and is non-parametric. To assess accuracy, we collected ground truth data for the exact same moment as the image was ordered. The colour bands (blue, green, and red) performed equally well, while NIR seriously underperformed. We achieved a F1 score of 0.82 for all parking spots in the ground truth. The method is sensitive to tree canopy. When removing the tree obscured spots, the F1 score increased to 0.85. Tree canopy spots were automatically determined and filtered using NDVI.

High resolution models are essential to assess the localised impacts of global environmental change. To enable the estimation of the impacts of location-specific change, this paper presents a new modelling approach that disaggregates scenario-based national-level urban population estimates derived from the often-applied Shared Socioeconomic Pathways (SSPs) to high-resolution spatial grid (30 arc sec) representations of urban area and local population. By combining high-resolution spatial data, a sophisticated statistics-based calibration approach and a computationally efficient modelling framework, we can coherently describe local changes in urban area and population across the globe. Both developments are modelled simultaneously using the same scenario assumptions, but divergent patterns are shown to arise when, for example, population decline coincides with decreasing densities that yield an increase in urban area. The model can be applied in integrated global assessment studies focused on trends in local exposure to natural hazards or environmental impacts of urban change. The improved resolution of this new model is crucial to assess the impacts of future population growth in developing regions where most of the planet's biodiversity concentrates near large populations that live in clusters of smaller urban areas previously not captured by coarser resolution models.

Abstract. The increasing demand for logistics real estate calls for a better understanding of the location dynamics of logistics firms. Previous empirical studies have largely focused on describing the spatial patterns of logistics but not on explaining the factors that lead to them. To fill this void, we develop a unique dataset of logistics buildings in the Netherlands and employ it in a multinomial logistic regression model to study the impact of key spatial factors on logistics development in the Netherlands during the period 1990-2020. In general, we find a positive influence of highway accessibility on logistics development. Contrary to previous studies in the US, we find a positive influence of rail accessibility and a negative influence of accessibility to airports. The effect of port accessibility and other factors varies with the type of logistics development. Finally, we also present probability maps that illustrate the combined effect of these factors.

The urban-rural fringe is a dynamic environment where urban expansion limits the provision of landscape services. Economic valuation of these services is proposed to quantify the impact of urbanisation and inform planners of the potential losses that attribute to these land-use changes. However, most evaluation methods remain controversial regarding shortcomings in providing reliable results. This study applies market price, contingent valuation and value transfer methods and compares their performance in assessing the economic impact of land-use changes on the urban-rural fringe of the Amstelland (the Netherlands). Results with these applied methods differ greatly due to their respective advantages in revealing use values or non-use values of landscape services and dependence on land-use change. Thus, results are sensitive to value types, the scarcity of landscape services, scale of the study area, and involved stakeholders. This paper reflects on the strengths and weaknesses of these methods in different planning contexts.

Transport development is widely recognized as one of the major drivers in shaping urban forms. While recent literature has documented the urban expansion effect of transport networks between cities, little is known about the effect within larger metropolitan areas. This study aims to find the causal relationships between highway expansion and two different aspects of urban development: urban expansion and urban sprawl in the Jakarta Metropolitan Area (JMA). To obtain the causal effect, we employ historic transport infrastructure as instrumental variables. Our results show that improvement in highway access increases urban sprawl within the JMA, particularly within 40-50 km radius from the city centre. The results also confirm the existence of highway-led urban expansion in several districts within the JMA over the last three decades. This paper adds a piece of enticing evidence that highway expansion leads to a sprawling development of urban areas even within a large metropolitan area.