Expansion Of Urban Areas Research Articles

Exploring the nexus between water quality and land use/land cover change in an urban watershed in Uruguay: a machine learning approach.

The expansion of urban areas contributes to the growth of impervious surfaces, leading to increased pollution and altering the configuration, composition, and context of land covers. This study employed machine learning methods (partial least square regressor and the Shapley Additive exPlanations) to explore the intricate relationships between urban expansion, land cover changes, and water quality in a watershed with a park and lake. To address this, we first evaluated the spatio-temporal variation of some physicochemical and microbiological water quality variables, generated yearly land cover maps of the basin adopting several machine learning classifiers, and computed the most suitable landscape metrics that better represent the land cover. The main results highlighted the importance of spatial arrangement and the size of the contributing watershed on water quality. Compact urban forms appeared to mitigate the impact on pollutants. This research provides valuable insights into the intricate relationship between landscape characteristics and water quality dynamics, informing targeted watershed management strategies aimed at mitigating pollution and ensuring the health and resilience of aquatic ecosystems.

Going urban: variation in personality traits of an invasive species along an urbanization gradient

Abstract The fast-growing expansion of urban areas pose severe pressures on wildlife. However, some generalist native mammals thrive in urbanized landscapes, while invasive alien species (IAS) are often more abundant in cities than in rural habitats. Variation in phenotypes—such as differences in personality traits—can influence the probability to colonize anthropized areas, especially in the case of IAS where certain traits are related to invasion success. Here, we investigated the expression of activity, exploration, and social tendency in the invasive alien Eastern Gray Squirrel (Sciurus carolinensis) in replicated study sites along a rural–urban gradient in Northern Italy. Personality traits expression was estimated using an open field test (OFT) followed by a mirror image stimulation (MIS) test. We found that squirrels were more active in urban than in suburban and rural sites, and behaved more sociable in urban than in suburban sites. Conversely, exploration did not differ along the gradient. Results also reveal the presence of a behavioral syndrome: activity (OFT), social tendency, and activity–exploration (MIS). We suggest that variation in activity level and social behavior in urban habitats could favor some species to respond positively to urbanization. This study sheds light on the underexplored personality–urbanization relationship in IAS, helping to understand the complex process of invasion.

Patterns of Urban Sprawl and Agricultural Land Loss in Sub-Saharan Africa: The Cases of the Ugandan Cities of Kampala and Mbarara

Sub-Saharan Africa (SSA) is undergoing rapid urbanization, yet research comparing urban expansion and agricultural land loss in peri-urban areas is scarce. This study utilizes multi-temporal Landsat imagery to examine the impact of urban growth on agricultural land and fragile ecosystems in Kampala (a mega city) and Mbarara (a regional urban center) in Uganda. We distinguish between random and systematic land-use and land-cover (LULC) transitions in the landscape. The results reveal substantial urban expansion. Kampala’s urban area surged from 7.14% in 1989 to 55.10% in 2015, while Mbarara increased from 6.37% in 2002 to 30.95% in 2016. Correspondingly, agricultural land decreased, from 48.02% to 16.69% in Kampala, and from 39.92% to 32.08% in Mbarara. Notably, a significant proportion of urban growth in both cities encroached upon agricultural land (66.7% in Kampala and 57.8% in Mbarara). The transition from agricultural to built-up areas accounted for 14.72% to 28.45% of the landscapes. Additionally, unsustainable practices led to the conversion of wetlands and forests to agricultural land, with approximately 13% of wetlands and 23% of Savannah and forests being converted between 2001 and 2015. These findings underscore the necessity of monitoring LULC changes for sustainable urban growth management, emphasizing the importance of preserving agricultural land and ecosystems to ensure present and future food security. This research contributes to the understanding of urbanization’s impact on peri-urban agricultural land and ecosystems in SSA, providing insights that are crucial for informed urban planning and policy formulation aimed at sustainable development in the region.

Integrating Spatio-Temporal Graph Convolutional Networks with Convolutional Neural Networks for Predicting Short-Term Traffic Speed in Urban Road Networks

The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors and perpetually changing conditions, making comprehensive prediction of road network behavior challenging. Recent research has leveraged deep learning techniques to identify and forecast traffic flow and road network conditions, enhancing prediction accuracy by extracting key features from diverse factors. In this study, we performed short-term traffic speed predictions for road networks using data from Mobileye sensors mounted on taxis in Daegu City, Republic of Korea. These sensors capture the road network flow environment and the driver’s intentions. Utilizing these data, we integrated convolutional neural networks (CNNs) with spatio-temporal graph convolutional networks (STGCNs). Our experimental results demonstrated that the combined STGCN and CNN model outperformed the standalone STGCN and CNN models. The findings of this study contribute to the advancement of short-term traffic speed prediction models, thereby improving road network flow management.

Modelling volumetric growth of emerging urban areas around new transit stations

Cities in developing countries are fast transforming from area-based expansion, representing spatial growth, to volumetric expansion, representing a higher skyline. Existing urban growth prediction models predict only spatial or two-dimensional growth. This paper demonstrates a volumetric urban growth model, incorporating the vertical expansion of urban areas. Two separate models were calibrated for spatial and built-up height growth, using historical growth patterns of transit-triggered new town development in India. The models were applied to upcoming transit station areas in India to predict the volumetric urban expansion for the next twenty years.

Estimation of apparent thermal inertia of roofing materials from aerial thermal imagery

The rapid expansion of urban areas and soil sealing is enhancing the Urban Heat Island (UHI) phenomenon, especially during heat waves. The different thermal inertia of the building materials compared to natural surfaces is one of the major driving factors of UHI. The present contribution aims to test a methodology for mapping the Apparent Thermal Inertia (ATI)—a proxy that can be derived from remote sensing data—of roofing surfaces at the scale of an entire city and with a high spatial resolution. Day and night aerial thermal images with the resolution of 0.5 m were acquired over two test areas in Bologna (Italy), together with satellite multispectral data. Statistics on the buildings in the test areas are computed considering different classes of roofing materials (e.g. bituminous sheath, clay tiles, metal sheet, gravel tiles). Observed median ATI values for each class range from 0.03 to 0.09 K-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} with interquartile ranges between 0.02 and 0.14 K-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}, so the intra-class variability in some cases appears higher than the variability among different material classes, proving the importance of ATI mapping for UHI investigations.

Refining Long-Time Series of Urban Built-Up-Area Extraction Based on Night-Time Light—A Case Study of the Dongting Lake Area in China

By studying the development law of urbanization, the problems of disorderly expansion and resource wastage in urban built-up areas can be effectively avoided, which is crucial for the long-term sustainable development of cities. This study proposes a high-precision urban built-up-area extraction method for county-level cities for small and medium-sized towns in county-level regions. Our process is based on the Defense Meteorological Satellite/Operational Linescan System (DMSP/OLS) and the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS), which develops long-term series of coordinated night-time light (NTL) datasets. We then combined this with the Normalized Vegetation Index (NDVI) to calculate the Vegetation-Adjusted NTL Urban Index (VANUI). We combine land use data and a support vector machine (SVM) for semi-supervised classification learning to propose a high-precision urban built-up-area extraction method for county-level cities. We achieved the following results: (1) we fit binary polynomials to the DMSP/OLS and VIIRS NTL datasets based on the correspondence of the mean values to construct a consistent time series of NTL data. (2) Our method effectively improves the accuracy of urban built-up-area extraction, especially for county-level cities, with an overall accuracy of 91.84% and a Kappa coefficient of 0.83. (3) Our method can perform a long-time series of urban built-up-area extraction, and, by studying the spatial and temporal changes in urban built-up areas, it can provide valuable information for sustainable urban development and urban planning.

Urban transformation in Lahore: three decades of land cover changes, green space decline, and sustainable development challenges

Pakistan has experienced significant urbanization, characterized by rapid urban population growth and unplanned urban expansion, making it the most urbanized country in South Asia. This study focuses on Lahore, the secondlargest megacity of Pakistan, and evaluates land cover changes over the last three decades (1990-2020). It also analyzes the relationship between urban green landscapes and unregulated urban expansion. The study reveals significant changes in the ecology of Lahore’s urban landscape using Landsat imagery, including Landsat 5 TM, Landsat 8 OLI, and a 30m spatial resolution, along with population data from the Pakistan Bureau of Statistics. In particular, the study reveals a decline in urban green spaces and a significant expansion of urban built-up areas in Lahore. The annual urban area expansion rates were 24.2 km2 (1990- 2000), 12.1 km2 (2000-2010), and 26.4 km2 (2010-2020), while vegetation cover decreased 33.45 km2 (1990-2000), 20 km2 (2000-2010) in the first two decades but slightly increased from 2010 to 2020 at an annual rate of 14.17 km2. As a result, there is a serious concern about the rapid decline of green space in Lahore. It is recommended that the administrative authorities follow the World Health Organization’s guidelines regarding the need for green spaces. This study contributes to achieving the United Nations’ Sustainable Development Goal 11th, indicator 11.3.1, and provides guidelines for conserving natural, social, and economic resources in the face of rapid urbanization.

An Improved Method to Identify Built-Up Areas of Urban Agglomerations in Eastern and Western China Based on Multi-Source Data Fusion

The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or on global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image-processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east, and the Chengdu–Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower, at 80.33%. This study provides an objective and accurate assessment of the distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.

Maximizing N-Nitrosamine Rejection via RO Membrane Plugging with Hexylamine and Hexamethylenediamine.

The rapid expansion of urban areas and the increasing demand for water resources necessitate substantial investments in technologies that enable the reuse of municipal wastewater for various purposes. Nonetheless, numerous challenges remain, particularly regarding disinfection by-products (DBPs), especially carcinogenic compounds such as N-nitrosamines (NTRs). To tackle the ongoing issues associated with reverse osmosis (RO) membranes, this study investigated the rejection of NTRs across a range of commercially available RO membranes. In addition, the research aimed to improve rejection rates by integrating molecular plugs into the nanopores of the polyamide (PA) layer. Hexylamine (HEX) and hexamethylenediamine (HDMA), both linear chain amines, have proven to be effective as molecular plugs for enhancing the removal of NTRs. Given the environmental and human health concerns associated with linear amines, the study also aimed to assess the feasibility of diamine molecules as potential alternatives. The application of molecular plugs led to changes in pore size distribution (PSD) and effective pore number, resulting in a decrease in membrane permeability (from 5 to 33%), while maintaining levels suitable for RO processes. HEX and HDMA exhibited a positive effect on NTR rejection with ACM1, ACM5 and BW30LE membranes. In particular, NDMA rejection, the smallest molecule of the tested NTRs, with ACM1 was improved by 65.5% and 70.6% after treatment with HEX and HDMA, respectively.

Climate and urbanization drive changes in the habitat suitability of Schistosoma mansoni competent snails in Brazil

Schistosomiasis is a neglected tropical disease caused by Schistosoma parasites. Schistosoma are obligate parasites of freshwater Biomphalaria and Bulinus snails, thus controlling snail populations is critical to reducing transmission risk. As snails are sensitive to environmental conditions, we expect their distribution is significantly impacted by global change. Here, we used machine learning, remote sensing, and 30 years of snail occurrence records to map the historical and current distribution of forward-transmitting Biomphalaria hosts throughout Brazil. We identified key features influencing the distribution of suitable habitat and determined how Biomphalaria habitat has changed with climate and urbanization over the last three decades. Our models show that climate change has driven broad shifts in snail host range, whereas expansion of urban and peri-urban areas has driven localized increases in habitat suitability. Elucidating change in Biomphalaria distribution—while accounting for non-linearities that are difficult to detect from local case studies—can help inform schistosomiasis control strategies.

Mapping urban heat: Unveiling nature-based solutions in Sari City, Iran.

Amidst growing concerns surrounding escalating temperatures and the rapid expansion of urban areas, researchers are delving into the intricate interplay between urban morphology, the intensifying Urban Heat Island (UHI) effect, and the efficacy of Nature-based Solutions (NbS). Despite sustained research efforts, a critical knowledge gap persists in precise modeling and predicting UHI mitigation through NbS interventions. This gap is primarily attributed to nuanced spatial variations at the neighborhood level, where these interventions are commonly implemented. This study addressed the challenge by employing a comprehensive grid structure consisting of 699 cells, covering the entirety of Sari City, Iran, as the spatial scale. The investigation identified five hot zones within the study area. To scrutinize the relationships between the UHI effect and various influencing factors of neighborhood morphology, diverse models such as Ordinary Least Squares (OLS), Random Forest Regression (RFR), Geographically Weighted Regression (GWR), and Mixed Geographically Weighted Regression (MGWR) were applied. Remarkably, the modeling fit for simulated data experienced a significant enhancement with the implementation of MGWR models. Five NbS scenarios were created and simulated using model outputs. The analysis revealed diverse responses across different segments. Notably, green roofs emerged as the most effective mid-term scenario for mitigating the UHI effect.

Dynamics of China’s natural cities and their living structures derived from nighttime lights and populated grids

The term “natural cities” refers to human settlements or human activities in general that are naturally or objectively defined. In this study, we utilized the head/tail breaks method to identify natural cities and characterize their living structures in mainland China from 2000 to 2020. This identification was based on nighttime light (NTL) and gridded population distribution (GPD) data, which, respectively, indicate urban areas and residential settlements. Furthermore, we explicitly identified the evolutions of two distinct categories of natural cities and the dynamics of their inherent living structures. Our objective was to verify that the head/tail breaks method is a powerful approach to deriving natural cities that signify various types of human settlements from diverse data sources, while also effectively characterizing their living structures. Also, this article contributes a novel perspective to examine the inequality between the urban-area expansion and the evolution of residential settlements in urban areas (or urban settlements). Our findings reveal a substantial increase in both the number and sizes of urban areas over time. However, there was an intriguing trend observed in urban settlements, where an increasing number corresponded to a gradual decrease in size. Additionally, the inequality exhibited regional disparities and rapidly developing regions showed a higher potential to enter a turning year of urban areas surpassing urban settlements. The dynamics of living structures show that urban settlements always had more hierarchical levels, more substructures, a much higher degree of order, and a much more living (orderly or beautiful) structure than urban areas. The urban-area expansion in mainland China was extremely rapid over the past decades, but the evolution of urban settlements was more “natural” than that (i.e., it consistently generated a living structure characterized by a higher degree of order).

Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing

Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 km2 and 2293 km2, while the maximum nighttime differences were 484 km2 and 265 km2. Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands.

Land Use/Cover Dynamics and its Implication on the Sustainability of Urban Agriculture in Selected Urban Centers of Ethiopia

Abstract Analysis of urban Land Use/Land Cover (LULC) dynamics in light of urban agriculture (UA) helps to understand its implication for UA practice and in turn making the necessary interventions. Therefore, the main objective of this study was to examine the LULC dynamics in selected urban centers of Ethiopia (Addis Ababa, Bahir Dar, Adama, and Hawassa cities) for the last seventeen years (2006–2022). SPOT 5 satellite imagery for the year 2006 and 2016 and Sentinel image for the year 2022 were analyzed. In addition, data from key informant interviews, focus group discussions, and field observations were used to triangulate LULC analyses information and to identify the main causes of LULC dynamics in the studied urban centers. The findings reveled that there were rapid expansion of urban built-up areas at the expense of other urban LULC types mainly of peri urban horticultural lands for the last 17 years (2006‒2022) in Addis Ababa, Hawassa, Bahir Dar, and Adama cities of Ethiopia. This has adverse impact on the sustainability of UA. Rapid urban population growth mainly because of high rural urban migration, expansion of squatter settlement, and increment of investment were the main driving forces of LULC dynamics. Based on the findings of this study (changes in LULC and driving factors) the studied cities administrations or authorities need to develop sustainable development plans by considering UA.

A review on Comparative study of using Silica Fume and Areca Fiber on Soil Stabilization

Abstract The rapid expansion of urban areas and the rise in construction activities have led to a shortage of land with suitable soil conditions. This has forced people to utilize weak soils available locally for construction purposes, employing various stabilization techniques. Urban expansion and construction demand the utilization of local weak soils for building, necessitating the application of soil stabilization techniques. These methods enhance soil strength by altering its properties. This study evaluates silica fume and areca fiber as stabilizers. Silica fume is a pozzolanic and supplementary cementitious material that enhances material properties such as strength, durability, and resistance to chemical attacks and abrasion. Few studies have examined areca fiber as soil reinforcement. Areca fiber (20-30 mm in length) upto 0.6% of dry weight of soil significantly improves engineering properties of soil. Varied weight ratios of silica fume and areca fiber are used based on soil type for optimal conditions. Silica fume stabilizes, and areca fiber reinforces problematic soil, as demonstrated through compaction, UCS, and CBR tests. Utilizing such waste materials is a cost-effective and more sustainable approach in the field of engineering. This research investigates the compaction and strength properties of various problematic soils stabilized with silica fume and areca fiber, providing a comprehensive review of the findings.

SPATIAL ANALYSIS OF REGIONAL DEVELOPMENT OF NGEMPLAK DISTRICT 2013-2023

Development policies influence the rate of regional development. Population growth and the affordability of areas to urban centers determine development policies. Urban areas that have experienced rapid development will gradually influence the surrounding peri-urban areas. The expansion of urban areas leads to suburban areas, starting from the expansion of residential areas. This research aims to determine regional development and regional development priorities in Ngemplak District. This research is qualitative research with comparative and spatial analysis. The results of this research are 1) the level of regional development in Ngemplak District is categorized into 3, namely developed, medium and underdeveloped areas. Villages in the advanced category include Sawahan Village, Ngesrep Village, Pandeyan Village, Kismoyoso Village, Donohudan Village and Gagaksipat Village. Those included in the medium category are Dibal Village, Manggung Village and Sobokerto Village, and those included in the underdeveloped villages are Sindon Village, Ngargorejo Village and Giriroto Village. 2) There are 3 development priorities, namely priority 1 for underdeveloped villages, priority II for medium level villages and priority III for advanced villages.

Influence of urbanization on meteorological conditions and ozone pollution in the Central Plains Urban Agglomeration, China

Changes in aerodynamic and thermal conditions caused by urbanization can impact regional meteorological conditions, subsequently affecting air quality. Updated Moderate-resolution Imaging Spectroradiometer (MODIS) land use data and coupled with the urban canopy models (UCMs) in the Weather Research and Forecasting (WRF) model. This enabled the impact of urban land expansion on meteorological conditions and ozone (O3) concentrations to be evaluated. Urban expansion increased the temperature at 2 m (T2) and the probability of precipitation in urban expansion areas, and enhanced the surface urban heat island at night. As the expansion areas became progressively larger, the increase in T2 became more pronounced. The proportions of urban surfaces in June 2016, 2018, and 2020 compared to 2001 increased by 0.69%, 0.83%, and 1.04%, respectively, while T2 increased by 0.12, 0.19, and 0.20 °C in urban areas, respectively. With urban expansion, the O3 concentration increased by 1.12, 1.37, and 0.76 μg/m3 (three-year averages) in urban, suburban, and rural areas, respectively. After coupling a multi-layer urban canopy model (building effect parameterization, BEP), or a multi-layer urban canopy model with a building energy model including anthropogenic heat due to air conditioning (BEP + BEM, abbreviated as BEM simulation), the O3 concentration changed significantly in the urban expansion area, compared to the results of a single-layer urban canopy model (UCM). O3 concentrations decreased most in the BEP simulation (−0.77 μg/m3), while O3 concentrations increased most in the BEM simulation (+1.85 μg/m3). The average observed O3 concentration was 108.35 μg/m3 (three-year average), while the simulated value was 75.65–83.72 μg/m3 (R = 0.69−0.77). The validation results in the BEM and Global Optimal Scenario (GOS) simulations were relatively good, with the GOS simulation producing slightly better results than the BEM. The simulation of O3 in urban agglomerations could be improved by integrating the results of the UCMs.

Residential segregation in two cities in Peru’s southern macro-region

Studies on residential segregation in Peru have privileged the study of the capital city, to the detriment of cities with other scales and contexts. This article analyzes the residential segregation levels and patterns in two Peruvian cities in the southern macro-region: Arequipa and Tacna. Based on data provided by the 2007 and 2017 censuses, synthetic and spatial segregation indicators were analyzed in both cities, taking as reference the educational level of the head of household and calculating the data on the block, census area, and district scales. The results show that inherited segregation patterns are maintained, while at the same time, the growing middle-class sectors are included in the more consolidated working-class settlements. In addition, processes that reinforce segregation were identified, such as the modification of the role of central areas, urban land production for the upper-class areas, and homogeneity of disadvantages in new areas of urban expansion, which are linked, particularly in the city of Arequipa, to the occupation of risk areas. The study suggests that these processes shape a low-scale urban segregation or fragmentation pattern in both cities, which coexists with inherited patterns.

African food system and biodiversity mainly affected by urbanization via dietary shifts

The rapid urbanization in Africa profoundly affects local food and ecological systems. According to earlier research, urbanization may cause food production and biodiversity losses as agricultural or natural lands are absorbed by expanding cities. Land-use displacement effects may buffer agricultural production losses or may lead to additional biodiversity losses but are often overlooked. Moreover, impacts of dietary changes associated with urbanization are rarely considered. To address this, we combined spatially explicit projections of African urban area expansion with observed rice consumption shifts to inform a partial equilibrium model (the Global Biosphere Management Model). We demonstrate the importance of displacement effects to identify potential food production or biodiversity issues until 2050 and argue for their integration in land-use planning and policymaking across spatial scales. We identify that because of agricultural displacement, the impact of urban area expansion on food production losses is probably limited (<1%)—at the cost of additional losses of natural lands by 2050 (up to 2 Mt). We also show that considering dietary shifts associated with urbanization increases rice consumption, production (+8.0%), trade (up to +2 Mt of required import) and agricultural methane emissions (up to +12 MtCO2-equivalent yr–1), thereby underscoring the need for a systems approach in future sustainability studies.