Urban Patterns Research Articles

Changes of ecological vulnerability in areas with different urban expansion patterns— A case study in the Yanhe river basin, China

Urban expansion has the potentiality to disrupt ecosystems and form highly fragile urban landscapes. However, studies investigating the impact of different urban expansion patterns on the ecological environments are relatively limited. Taking the Yanhe river basin, a typical basin in a loess region, as a case study, we developed an ecological vulnerability assessment system as well as assessed the main drivers of ecological vulnerability for different time periods (1990, 2000, 2010 and 2018). Additionally, we classified each urban expansion region into three different patterns according to the landscape expansion index, and analyzed changes in the ecological vulnerability under these three diverse patterns. Finally, the Kruskal-Wallis rank sum test was applied to compare the factors for the different changes in ecological vulnerability across different urban expansion patterns. Our investigation also aimed to elucidate the impacts of different urban expansion patterns on ecological vulnerability and identify key physical-social-economic-climatic drivers. The results indicate that the ecological vulnerability index (EVI) of the study area is decreasing gradually from the peak value of 0.459 in 2000 to 0.383 in 2018. Habitat quality index is found to be the most influencing factor, followed by aridity index and building density (mean q of 0.53, 0.46, and 0.42, respectively). Our study also reveals that the outlying expansion areas have the greatest increase in EVI at 0.38, with edge and infill expansions at 0.31 and 0.27, respectively. It is also found that when the overall environment is improving, the outlying expansion areas have the smallest decrease in EVI. Initial ecological vulnerability and key drivers may explain this difference. Therefore, results of this study indicate that the ecological impacts of diverse urban expansion patterns are significantly different, among which outlying expansions should receive prioritized attention.

Hyper pooling private trips into high occupancy transit like attractive shared rides

The size of the solution space associated with the trip-matching problem has made the search for high-order ride-pooling prohibitive. We introduce hyper-pooled rides along with a method to identify them within urban demand patterns. Travellers of hyper-pooled rides walk to common pick-up points, travel with a shared vehicle along a sequence of stops and are dropped off at stops from which they walk to their destinations. While closely resembling classical mass transit, hyper-pooled rides are purely demand-driven, with itineraries (stop locations, sequences, timings) optimised for all co-travellers. For 2000 trips in Amsterdam the algorithm generated 40 hyper-pooled rides transporting 225 travellers. They would require 52.5 vehicle hours to travel solo, whereas in the hyper-pooled multi-stop rides, it is reduced sixfold to 9 vehicle hours only. This efficiency gain is made possible by achieving an average occupancy of 5.8 (and a maximum of 14) while remaining attractive for all co-travellers.

Study on temperature regulation function of green spaces at community scale in high-density urban areas and planning design strategies

In recent years, the frequency of extreme high-temperature events has gradually increased. To better understand the impact of urban green space coverage's spatial pattern on land surface temperature (LST), this study selected three sample areas along the urban-rural gradient in Shanghai. Using LST inversion and resampling methods, LST data for different grid sizes were obtained for spring, summer, and winter in the 2010s, 2015s, and 2020 s. A boosting regression tree model was employed to determine the key indicators affecting LST and effective cooling thresholds. The impact of green quality, structure, and pattern at the community scale on LST was discussed, providing feasible suggestions for green space planning and design in different urban spaces. The study found significant differences in the spatial patterns of green areas and LST among the three sample regions. The quantity and integrity of green spaces in the Huangpu, Minhang, and Songjiang sample areas have progressively improved, with overall green coverages of 19 %, 38 %, and 43 %, respectively. As grid size increases, the relative influence of fractional vegetation coverage (FVC) on LST generally decreases, whereas the relative influence of green structure and patterns gradually increases. Taking the summer of the 2020 s as an example, the influence of FVC on the three study sample areas was 80.79, 83.36, and 87.18 at a 30 m grid size, which decreased to 48.87, 40.59, and 47.64 at a 120 m grid size. The green structure's impact rose from 13.09, 15.22, and 10.6–30.01, 51.82, and 38.5; the influence of green patterns increased from 6.12, 1.42, and 2.21–21.13, 7.6, and 13.86. Key indicators affecting LST include FVC (Fractional Vegetation Coverage), AREA (Green Patch Area), PD (Patch Density), COHESION (Patch Cohesion Index), and ED (Edge Density). High temperatures in summer are one of the ecological issues that need special attention in Shanghai's green space design. Setting the green space proportion to 35 % while avoiding fragmentation and low vegetation coverage can achieve effective cooling. This study's main advancement lies in utilizing machine learning algorithms to identify the principal green spatial pattern impact factors and key thresholds influencing LST at the community scale in Shanghai. The related results and proposed strategies provide a research framework and strong basis for special regulations in urban green space system planning concerning urban thermal environments. They offer references for urban renewal and new town planning to address climate change and urban high-temperature issues, such as clear requirements for optimal green space area at the community scale, community park spatial layout and quantity regulations, and urban park planning design suggestions. This study also highlights obstacles in the practical application of planning and design strategies, which can help avoid difficulties in implementing planning policies. It attempts to set goals for community-scale vegetation planning in China from a policy perspective and provides relevant recommendations.

Can government-led urban expansion simultaneously alleviate pollution and carbon emissions? Staggered difference-in-differences evidence from Chinese firms

The outward expansion of city centers is a typical feature of rapid urbanization in developing countries, leading to complex environmental pollution effects due to evolving urban spatial patterns. This study examines the impact of government-led urban expansion on pollution and carbon emissions by firms, utilizing firm and city-level data. Employing the staggered difference-in-differences (DID) method and taking the county-to-district conversion (CDC) as a quasi-natural experiment, the study finds that CDC reduced SO2 emissions by 80.95% and CO2 emissions by 13.12%. The key to this synergistic emission reduction lies in a series of source control strategies, including enhancing energy efficiency and innovation, industrial restructuring, and production reduction. Furthermore, the study reveals that CDC has a more significant effect on emission reduction for NOEs and small firms. Additionally, large cities, cities with high fiscal self-sufficiency, and cities with high per capita GDP exhibit greater environmental regulatory strength, leading to more effective emissions reduction. This paper seeks to explore potential gaps in research on the environmental impacts of government-led urban expansion, and it may provide empirical support for relevant policies in developing countries.

Spatiotemporal evolution of land use efficiency in 357 cities across mainland China from 2000 to 2020 based on SDG 11.3.1

High-efficiency land use facilitates the maximization of land utilization, lowers urban construction costs, and optimizes urban functional patterns. The Sustainable Development Goal 11.3.1 (SDG 11.3.1) can be used to assess land use efficiency (LUE), understand the current state of land use, and identify the potential for optimization. This study combines SDG 11.3.1 with other supplementary indicators to establish a land use efficiency evaluation system. This system provides a more precise understanding of internal city changes and enables a scientific assessment of urban LUE in Mainland China. The results showed that: (1) A significant number of cities were growing cities, particularly in the eastern region, with the population of built-up areas increased by 2.92 times from 2000 to 2020; (2) From 2000 to 2020, cities in China underwent rapid urban expansion, with the most significant urban expansion index in 2015–2020; (3) The coordination between population growth rate (PGR) and land consumption rate (LCR) worsened in the western region, while the central and eastern regions showed better coordination. (4) As the urban expansion index increased, the compactness index of the cities in the above three regions decreased and were at lower levels. This study establishes an evaluation system to assess the LUE and reveals the spatial and temporal characteristics of urban and population change. It holds paramount significance in enhancing LUE and encouraging sustainable development in Mainland China and serves as a valuable reference for global urban management.

Urban growth simulation and scenario projection for the arid regions using heuristic cellular automata

Arid regions tend to form compact urban patterns that have significant implications on urban growth and future urban patterns. Spatial simulation and projection using cellular automata (CA)-based models are important for achieving sustainable urban development in arid regions. In response to this need, we developed a new CA model (GSA-CA) using the gravitational search algorithm (GSA) to capture and project urban growth patterns in arid regions. We calibrated the GSA-CA model for the arid city of Urumqi in Northwest China from 2000 to 2010, and validated the model from 2010 to 2020, and then applied to project urban growth in 2040. The results indicated that the optimal performance of the model was achieved when the fraction of the population was 0.5. GSA-CA achieved an overall accuracy of 98.42% and a figure of merit (FOM) of 43.03% for the year 2010, and an overall accuracy of 98.52% with FOM of 37.64% for 2020. The results of the study help to adjust urban planning and development policies. The developed model has the potential to be employed in simulating urban growth and future scenarios in arid regions globally, including Northwest China and Africa.

The Development Potential of Urbanization in China's Third-tier Cities: A Case Study of Taizhou Area

With the acceleration of China's urbanization process, the development potential of third-tier cities has become increasingly prominent, becoming an important part of the national urbanization strategy. Taking the Taizhou area as an example, this study first conducts a comprehensive evaluation of the current status of urbanization development in Taizhou, aiming to reveal its development level and characteristics in various aspects such as economy, society, culture, and environment. On this basis, a multi-dimensional evaluation system including urbanization level, quality of citizenization, urban functions, and urban governance is constructed to comprehensively consider Taizhou's performance in several key areas of urbanization development. In terms of data processing, the study uses the Taizhou Statistical Yearbook from 2018 to 2022 and other related reports as the main data sources to ensure the comprehensiveness and accuracy of the evaluation. Through the dimensionless processing of data and the weights of indicators determined by the Analytic Hierarchy Process (AHP), the development potential of urbanization in Taizhou was quantified and evaluated. The research results show that the urbanization development in Taizhou has shown a steady growth trend, especially significant progress in the quality of citizenization and urban functions, but there is still room for improvement in some key areas. Based on the research results, targeted policy recommendations are proposed: first, to improve the quality of citizenization, including deepening the reform of the household registration system and improving the level of public services; second, to optimize the pattern of urbanization, strengthen regional collaborative development and county town construction; third, to promote the construction of new types of cities, covering the improvement of urban innovation capabilities and the construction of smart cities; fourth, to deepen the integrated development of urban and rural areas, promote resource sharing and coordinated economic growth, aiming to guide Taizhou to achieve more balanced and sustainable urbanization development, and provide a reference for similar cities.

The Development Potential of Urbanization in China's Third-tier Cities: A Case Study of Taizhou Area

With the acceleration of China's urbanization process, the development potential of third-tier cities has become increasingly prominent, becoming an important part of the national urbanization strategy. Taking the Taizhou area as an example, this study first conducts a comprehensive evaluation of the current status of urbanization development in Taizhou, aiming to reveal its development level and characteristics in various aspects such as economy, society, culture, and environment. On this basis, a multi-dimensional evaluation system including urbanization level, quality of citizenization, urban functions, and urban governance is constructed to comprehensively consider Taizhou's performance in several key areas of urbanization development. In terms of data processing, the study uses the Taizhou Statistical Yearbook from 2018 to 2022 and other related reports as the main data sources to ensure the comprehensiveness and accuracy of the evaluation. Through the dimensionless processing of data and the weights of indicators determined by the Analytic Hierarchy Process (AHP), the development potential of urbanization in Taizhou was quantified and evaluated. The research results show that the urbanization development in Taizhou has shown a steady growth trend, especially significant progress in the quality of citizenization and urban functions, but there is still room for improvement in some key areas. Based on the research results, targeted policy recommendations are proposed: first, to improve the quality of citizenization, including deepening the reform of the household registration system and improving the level of public services; second, to optimize the pattern of urbanization, strengthen regional collaborative development and county town construction; third, to promote the construction of new types of cities, covering the improvement of urban innovation capabilities and the construction of smart cities; fourth, to deepen the integrated development of urban and rural areas, promote resource sharing and coordinated economic growth, aiming to guide Taizhou to achieve more balanced and sustainable urbanization development, and provide a reference for similar cities.

Impacts of urbanization on urban-rural VPD disparities across different subregions in China: A quantitative analysis based on station-pairing and partial differential methods

The climatic disparities between urban cores and surrounding rural areas have become increasingly pronounced due to rapid urbanization. The vapor pressure deficit (VPD) serves as a critical meteorological indicator reflecting atmospheric aridity, and identifying the contributions of urban and rural climate factors on the urban-rural VPD disparities plays an important role in understanding urban climate and ecosystem development patterns. This study introduces a quantitative method, utilizing partial differential equations, to assess the relative contributions of two crucial meteorological factors—air temperature (T) and relative humidity (RH)—toward urban-rural differences in VPD. Analyzing daily scale data from 116 pairs of urban and rural stations across mainland China for the period 2000–2017, we derived several key findings: Urban areas generally exhibit higher VPD than surrounding rural areas, with a relative difference in annual VPD of about 21.6 %, and the disparity increases over time. Furthermore, the influence of urbanization on VPD responds differently across seasons and geographical conditions. The simulated trends in VPD urban-rural differences using the partial differential method align closely with the actual observed trends, validating the effectiveness of our quantitative analysis approach. The VPD urban-rural disparity shows greater sensitivity to shifts in RH, with sensitivity coefficients exceeding 1.5 times to changes in T in most cases. RH is the primary factor causing VPD urban-rural differences in mainland China, and the relative contribution of RH is about 2.5 times of T. The patterns of sensitivity and the contribution of factors exhibit notable variation among subregions. This study enhances the understanding of how urbanization affects regional climate and provides insights for assessing the environmental impacts of future urban expansion.

Morphological and Functional Polycentric Urbanization in Colombo Metropolitan of Sri Lanka Using Time-Series Satellite Images from 1988–2022

Examining urban growth in metropolitan areas from both morphological and functional perspectives is paramount for sustainable development in developing and developed countries. As a collaborative and critical approach, polycentrism operates concurrently at local, regional, and national levels. However, the decentralized morphologies and functional attributes of polycentric urban spaces are yet to be adequately explored, despite their significant influence on sustainable urban planning and management. Colombo, the only metropolitan area in Sri Lanka, has experienced considerable yet ambiguously imbalanced spatial growth due to insufficient data. This study employed time-series satellite imagery from 1988 to 2022, in conjunction with a network model, to elucidate Colombo’s uneven urban patterns, while achieving an accuracy of over 80% throughout the period. The results revealed that the metropolis has evolved into a polynuclear city, complicating the identification of centers and their corresponding developmental stages. Nevertheless, the study successfully pinpointed the morphological and functional Grama Niladhari divisions, disclosing the morphological features of centralized dispersion and the functional traits of decentralized concentration. These insights provide significant implications for local government, guiding urban planning initiatives to achieve sustainable development goals.

Analyzing urban traffic crash patterns through spatio-temporal data: A city-level study using a sparse non-negative matrix factorization model with spatial constraints approach

Urban traffic crashes represent a significant challenge affecting public safety and urban mobility worldwide. This study introduces a novel application of Sparse Non-negative Matrix Factorization with spatial constraints to analyze spatio-temporal patterns of traffic crashes at a city level. Using comprehensive crash data from Denver and Manhattan during 2020, we developed and validated a model capable of capturing distinct temporal dynamics and spatial distributions of traffic crashes. Unlike traditional methods, our approach integrates sparsity and spatial constraints, enhancing the model's ability to handle the inherent sparsity and geographical dependencies found in urban traffic data. The results demonstrate the model's effectiveness in identifying high-risk areas and times, providing actionable insights that can inform urban planning and targeted safety interventions. The study underscores the potential of advanced data-driven techniques in urban traffic analysis and contributes to the broader efforts of improving traffic safety through informed decision-making and policy development.

Sleeping behaviors are determined by lockdown and not work-from-home arrangements

Lockdowns and work-from-home arrangements became abrupt realities for people at an unprecedented scale during the COVID-19 pandemic. Here, considering the case of Singapore, we study how peoples’ sleep behaviors—which are closely linked to their mental health—varied as a result. However, different from most studies, this paper uses household electricity consumption data to estimate the sleeping behaviors of nearly 10,000 households in the city-state. With this, we study how the residents’ daily sleep durations changed dynamically during the lockdown and afterwards when restrictions were progressively eased, and show their strong connection to major changes in the public health policy and current events during this period. Our results add to the evidence for the stress endured by the populace during the lockdown; we find that sleep durations for all demographics, while higher than before the lockdown, became more fluctuating across days. A major, and surprising, finding is that it was the lockdown that determined the residents’ sleeping duration, rather than simply working-from-home arrangements. That is, the sleeping durations largely reverted back to their pre-pandemic levels when the lockdown was lifted—with small variations based on demographic factors—although a vast majority of people continued to work from home. This highlights the resilience of the daily routines of the Singapore populace. While providing insights into how a pandemic influences the dynamics of urban sleep patterns, our finding also has broader implications regarding the efficiency of the workforce, suggesting that concerns about asynchronous work routines and productivity may be overblown.

Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data

Accurate traffic prediction is crucial for urban planning, especially in rapidly growing cities. Traditional models often struggle to account for sudden traffic pattern changes, such as those caused by the COVID-19 pandemic. Neural networks offer a powerful solution, capturing complex, non-linear relationships in traffic data for more precise prediction. This study aims to create a neural network model for predicting vehicle numbers at main intersections in the city. The model is created using real data from the sensors placed across the city of Zilina, Slovakia. By integrating pandemic-related variables, the model assesses the COVID-19 impact on traffic flow. The model was developed using neural networks, following the data-mining methodology CRISP-DM. Before the modelling, the data underwent thorough preparation, emphasising correcting sensor errors caused by communication failures. The model demonstrated high prediction accuracy, with correlations between predicted and actual values ranging from 0.70 to 0.95 for individual sensors and vehicle types. The results highlighted a significant pandemic impact on urban mobility. The model’s adaptability allows for easy retraining for different conditions or cities, making it a robust, adaptable tool for future urban planning and traffic management. It offers valuable insights into pandemic-induced traffic changes and can enhance post-pandemic urban mobility analysis.

Evaluating the impact of urban traffic patterns on air pollution emissions in Dublin: a regression model using google project air view data and traffic data

Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.

Gender Issues, Social Equity, and the Sustainable Management of Urban Transport Networks: An Italian Case Study

The paper contains transport and environmental analyses for the Gallipoli area and presents results from a consultation process involving transport operators, citizens, tourists, public employees, and stakeholders. Through statistical analysis of the data, the paper provides insights into the needs of specific user categories, highlighting gender issues and social equity as key aspects of urban policies. This paper further investigates users’ potential willingness to pay for general improvements in bus service quality and environmental conditions using discrete choice modeling. This study aims to address the challenge of understanding urban travel patterns and the underlying forces influencing user attitudes, which are often found in city surveys. Specifically, a Random Utility Model was used to profile users based on specially collected survey data. The results indicate the potential to meet latent systemic transport demand with more sustainable collective transport modes, particularly among women aged 19-29 who own a car. Simultaneously, there is a willingness to pay higher public transport tariffs for improved service, which is also largely environmentally sustainable. The paper can serve as a valuable resource for professionals in the transport and environmental sectors as well as for policymakers.

Understanding the spatial pattern and structural characteristics of cities in late feudal China: evidence from the local chronicle maps of the qing dynasty (1644–1911)

ABSTRACT Our study analyzes the spatial pattern and structural characteristics of cities in late feudal China based on multiple correlation and geographic information system spatial analysis, using 58 local Chinese chronicle maps of Jiangsu in the Qing Dynasty as research material. The elements in the city map were dominated by the types of official buildings, transportation facilities, and religious buildings. The urban spatial pattern can be classified into four types, such as extraverted and introverted, with each type having an aggregated spatial distribution and a gradual introverted orientation in time. The urban spatial structure can also be divided into four types: traditional, axial, random, and composite. Spatial distribution of each type is relatively stable, with little change over time. The geographic environment and the traditional concept of city building are the basic reasons for the formation of the spatial pattern and structure. The flood disaster, commercial and military factors contribute to the diverse structures. Our study demonstrates the feasibility of quantifying the ancient Chinese chronicles’ city maps. The findings have important value for contemporary ancient city restoration and shaping urban characteristics.

Urban sprawl, urban form, and urban land use pattern: examining urban planning response to the causes and effects of urban sprawl in Kumasi, Ghana

ABSTRACT Unplanned urbanization continues to threaten the sustainability of cities in the global south. This study seeks to assess the urban planning response to the effects of sprawl on the urban form and land use pattern of Kumasi. The study relied on geospatial techniques to determine changes in land use patterns, landforms, and sprawl. A qualitative research approach was used to examine urban planning response to the causes and effects of urban sprawl in Kumasi. The results of this study show that the higher the urbanization rate, the more the built-up area increases, causing a significant loss of natural assets such as farmlands. In terms of shape, the research showed that the Greater Kumasi Metropolis has shifted from the historically monocentric city structure to a polycentric city. The city currently has multiple centers or subcentres for commercial, cultural, and social activities. In spite of all the threats posed by urban sprawl on the metropolis, there no clear policy intervention put in place to address the adverse effects of sprawl in the Greater Kumasi Metropolis. The researchers recommend that city authorities should adopt the compact city approach to control the high rate of sprawl to promote densification.

Review on Potential Urban Development Site Selection Using Geospatial-Based Multi-Criteria Decision Analysis (MCDA) Techniques in the Context of Ethiopia

This review paper provides a comprehensive assessment of the opportunities and challenges in leveraging geospatial-based technologies, particularly Multi-Criteria Decision Analysis (MCDA) techniques, to enhance the effectiveness of urban development site selection in Ethiopia. The overarching findings highlight several critical focus areas for improvement. Firstly, the review emphasizes the need to strengthen the availability, quality, and accessibility of geospatial data through the development of robust data infrastructures and innovative data collection methods. Secondly, it recommends adopting a more inclusive and collaborative approach to stakeholder engagement, coupled with targeted capacity-building initiatives to enhance the technical skills and knowledge of urban planners, GIS experts, and local stakeholders. Thirdly, the paper suggests incorporating advanced spatial modeling and simulation techniques to capture the dynamic nature of urban growth patterns, as well as integrating climate change impact assessments and disaster risk analyses into the decision-making framework. Fourthly, it emphasizes the importance of strengthening institutional and organizational frameworks to promote cross-sectoral collaboration, data sharing, and integrated decision-making processes. Finally, the review underscores the significance of designing user-friendly and customizable Spatial Decision Support Systems (SDSS) that cater to the specific needs and constraints of the Ethiopian urban development context. The review concludes that the use of geospatial-based MCDA approaches holds immense potential in addressing Ethiopia's complex urban development challenges, as it enables a comprehensive evaluation of suitability factors and the exploration of alternative urban development strategies.

Relationship between Urban Forest Fragmentation and Urban Shrinkage in China Differentiated by Moisture and Altitude

Forest fragmentation and urban shrinkage have become the focus of attention in global ecological conservation, with the goal of achieving sustainable development. However, few studies have been concerned with urban forest patterns in shrinking cities. It is necessary to explore whether the loss of the population will mitigate urban forest degradation. Thus, in this study, 195 shrinking cities were identified based on demographic datasets to characterize the spatiotemporal patterns of urban forests in China against a depopulation background. To illustrate the explicit spatial evolution of urban forests in shrinking cities in China, in this study, we reclassified land-use products and determined the annual spatial variations from 2000 to 2022 using area-weighted centroids and landscape pattern indexes. The effects of different climatic and topographical conditions on the spatiotemporal variations in the urban forest patterns against population shrinkage were discussed. The results demonstrated that the forest coverage rate in the shrinking cities of China increased from 40.05 to 40.47% with a generally southwestern orientation, and the most frequent decrease appeared from 2010 to 2015. Except for the temperate humid and sub-humid Northeast China, with plains and hills, all geographical sub-regions of the shrinking cities exhibited growing urban forests. Relatively stable movement direction dynamics and dramatic area changes in climatic sub-regions with large forest coverage were observed. The urban forest centroids of shrinking cities at a lower elevation exhibited more fluctuating changes in direction. The urban forests in the shrinking cities of China were slightly fragmented, and this weakened condition was identified via the decelerating fragmentation. The urban forests of the shrinking cities in the warm-temperate, humid, and sub-humid North China and basin regions exhibited the most pattern variations. Therefore, it is emphasized that the monitoring of policy implementation is essential due to the time lag of national policies in shrinking cities, especially within humid and low-altitude regions. This research concludes that the mitigation of urban deforestation in the shrinking cities of China is greatly varied according to moisture and altitude and sheds light on the effects of the population density from a new perspective, providing support for urban forest management and improvements in the quality of residents’ lives.

Transit-oriented development (TOD) or development-oriented transit (DOT): how does it matter for sustainable urban growth?

ABSTRACT Urban growth patterns occur in various ways with tangible practical consequences. A key dilemma with regard to development and transportation revolves around whether the former follows the latter or vice versa. This study assessed this nexus with transit-oriented development (TOD) and development-oriented development (DOT) cases by using the land use/land cover (LU/LC) classification technique in a developing country example of a city in Turkey. Spatial data included high-resolution Sentinel 2A, Rapid Eye, and two SPOT satellite images and CORONA air photos acquired in 2021, 2013, 2000, 1989, and 1979, respectively, within three buffer zones (500, 750, and 1000 m) for both development forms. While the study acknowledges unorthodox TOD selection criteria and generalizability concerns, the results demonstrate that the TOD drastically reduced green areas (as opposed to ideal TOD requirements), while the DOT excessively grew commercial land uses and street networks. Both developments commonly impaired agricultural, vacant, and water land use. Such transformations direct local governments, transit agencies, and city planners to resolve ideal sustainable urban growth patterns by using remote sensing data and quantitative analysis.