Urban Growth Research Articles

Exploring the LULC dynamics and its relation with land surface temperature variation using split window algorithm: A study of Barasat subdivision, West Bengal, India.

Rapid urban growth in an unplanned way, decreased forest cover and consequential changes in land use in urban and rural areas have become pivotal obstacles for policymakers and urban planners to a great extent. Specifically, in a developing country like India, the repercussions of these changes have generated a substantial alteration in climate in the form of increased land surface temperature (LST), heat islands and associated human health risks. This research was initiated to assess the interrelationship between LST and LULC dynamics using spatial techniques over the Barasat Sub-Division in West Bengal (India). This study encompasses the extraction of LST using satellite images, seasonal variations of LST, the relationship of different land indices (i.e. NDVI, NDBI, NDMI and NDWI) with LST, and hotspot analysis of the landscape to get a comprehensive understanding of LST changes from 1988 to 2023, seasonal patterns, and relationships with LULC dynamics in the study area. The results of this research signify a substantial percentage increase in built-up spaces (51%), a decline in fallow areas (- 73%), relatively higher LST changes (3.41°C) in high-density built-up areas (e.g. Madhyamgram), a positive relationship (e.g. r2 = 0.59) between NDBI and LST, a transition from a cold spot to a hot spot share of 20% and a persisting hot spot of 42% throughout the summer season (1988 to 2023) in the selected landscape. The findings of this study will improve our present knowledge of seasonal fluctuations in land surface temperature (LST) and the variables that influence them. These findings will also draw attention to the growing worries about the fast variations in LST and the climatic and health hazards they pose. It is advised to establish planned urban expansion, efficient land use and land cover (LULC) management laws and encourage green areas that support sustainable development in India's urban surroundings in order to reduce these dangers.

Does improvement of railway network promote urban economic growth? evidence from Northeast China.

The railway network connects different cities and regions and plays an important role in the transportation system. This paper theoretically analyzes the causal effect and mechanisms of the railway network on urban economic growth, and explores the direct effects, threshold effects, and spatial spillover effects of improved centrality in the railway network on urban economic growth in Northeast China. The results show that: (1) There is a similarity between the economic growth level and the trend of the railway network in Northeast China. From a spatial perspective, there are regional differences in the development of the railway network. (2) From the perspective of the direct effects, each unit improvement in railway network centrality leads to a 0.160 unit increase in urban economic growth in Northeast China. The degree of influence of other variables on urban economic growth, in descending order, is labor employment level, material capital investment, information development level, technological development level, human capital, and degree of openness. (3) From the perspective of the threshold effects of the railway network, the threshold values and the number of threshold levels are different for different regions. (4) There are significant spatial spillover effects in the railway network of Northeast China, and an increase in the centrality of a city's network can have negative spatial spillover effects on the economies of surrounding areas. Based on these results, we recommend that Northeast China should focus on the construction and improvement of the railway network, strengthen the connections between regions, and promote the overall economic growth of the region while improving its international competitiveness.

Urban green spaces with high connectivity and complex vegetation promote occupancy and richness of birds in a tropical megacity

Urban growth often leads to land-use changes that result in biodiversity loss and reduced human benefits. In urban zones, green areas facilitate physicochemical processes (such as carbon capture, reducing environmental temperature and noise pollution), offer multiple benefits to human beings (e.g., water filtration and purification), and support numerous vertebrate populations, including birds. In the tropics, the capacity of green spaces to maintain bird populations is regulated by characteristics of these areas (e.g., vegetation structure) and environmental seasonality. In order to generate ecological knowledge to help conserve bird diversity in large urban settlements, this study aimed to (1) identify the most influential variables on the distribution of bird species in green areas of a tropical megacity, and (2) assess how bird richness varies between the dry and rainy seasons. Across two dry and rainy seasons between 2021 and 2022, detection records of 108 bird species were obtained from 101 green areas. Air temperature and sampling time were the primary factors influencing bird detection. Bird occupancy and richness were higher in parks near other green areas in first dry and increased with tree richness during the rainy seasons. Floral abundance explained the occupancy and richness in the second dry season. In 2021, the highest richness was observed during the dry season, while in 2022, the highest richness was estimated during the rainy season. These findings highlight the importance of resource availability and spatial arrangement in urban green areas for bird diversity, offering insights for conservation and maintaining ecosystem benefits in urban environments.

Transforming landscapes: Decoding the impact of universities on urbanization using advanced modeling and perception analysis.

Universities play a crucial role in urban economic and structural development. The government of Bangladesh has undertaken the initiative to establish a public university in each of the 64 districts. These newly founded universities have the potential to impact urban growth significantly. We aimed to project university-induced urban expansion to address this knowledge gap and identify the mechanisms driving urban growth. The classification of supervised and unsupervised methods was employed to analyze urban development for the years 2000, 2010, 2016, and 2022. We used the Cellular Automata and Markov Chain approach to forecast future urban growth and land transition capacity. Additionally, the driving factors and selection of the study area were derived from Focus Group Discussions (FGD), Key Informant Interviews (KII), Probit Model, and Perception Index (PI). The findings of this study reveal a 1.6% urban growth rate within ten years of the establishment of the university, while urban expansion accelerated to 29.78% after ten years. The predictions also indicate a sustained urban growth rate of 4.7% by 2042. Furthermore, the PI index demonstrates that the establishment of the university has resulted in high demand for rental housing, serving as one of the primary drivers of urban expansion. Moreover, the Probit model highlights strong economic capability, proximity to the town, railway station, hospital, and easy access to credit as vital facilitators behind the drivers of urban expansion. Policymakers, the scientific community, and urban planners can benefit from this study in pursuing sustainable city development through university establishment.

Monitoring and assessing of sustainable development in the urban area of Ouagadougou based on SDG 11.3.1 indicator and the city biodiversity index

Urban growth can induce changes in the use and transformation of urban land. In the context of rapid urbanization, monitoring key indicators of sustainable development is essential. The aim of this study was to monitor and assess the level of sustainable development in the city of Ouagadougou between 2002 and 2022, based on global indicators such as Sustainable Development Goal 11.3.1 indicator and the Singapore City Biodiversity Index. A remote sensing and Geographic Information System approach was used to acquire and analyze open-source geospatial data. The results show an increasing inefficiency in land use, with excessive consumption to population growth until 2013. Between 2013 and 2022, this trend declined, but urban biodiversity nevertheless decreased significantly, with a 40% loss of natural areas between 2002 and 2022. Fragmentation of natural areas, due to infrastructure development, has amplified this degradation, posing ecological risks to the city. This study underlines the importance of strengthening the ecological connectivity of urban spaces to improve their sustainability and resilience. It also recommends reorienting urban strategies by integrating nature-based solutions and placing biodiversity preservation at the heart of sustainable urban development policies.

A new optimized procedure for circular WasteWater sustainability: Coastal cities supporting agricultural rural communities

This proposal introduces a novel methodology that addresses the increasing irrigation demands driven by climate change and urban growth. Traditionally water-scarce areas are now facing severe water deficits, while wastewater volumes from treatment plants, often discharged into the sea, contribute to pollution. The proposed hybrid system strategy innovatively reallocates 33 hm3 of water annually to agricultural communities, employing a zero-discharge approach to prevent marine pollution. Evaluated from energetic, environmental, and social perspectives, this methodology shows a remarkable cost-benefit ratio exceeding 12, showing its feasibility. It features technical indicators for optimizing water distribution and regulatory components, applied effectively to 28,424 ha of farmland. This strategy meets 24.1 % of the irrigation needs in these regions while safeguarding coastal areas from degradation. Crucially, it integrates 11.3 GWh of renewable energy annually, underscoring its sustainability and enhancing its replicability for other water-deficient regions.

Climate-Driven vs Human-Driven Land Degradation? The Role of Urbanization and Agricultural Intensification in Italy, 1960–2030

Climate warming, agricultural intensity, and urban growth are main forces triggering land degradation in advanced economies. Being active over different spatial and temporal scales, they usually reflect—at least indirectly—the impact of additional factors, such as wellbeing, demographic dynamics, and social development, on land quality. Using descriptive statistics and a multiple regression analysis, we analyzed the impact of these three processes comparatively over a decadal scale from 1960 to 2020 at the provincial level (Nuts-3 sensu Eurostat) in Italy. We enriched the investigation with a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic approach. Land degradation was estimated adopting the Environmental Sensitive Area Index (ESAI) measured at the spatio-temporal scale mentioned above. Computing on multiple observations at nearly 300,000 locations all over Italy, provinces were regarded as representative spatial units of the territorial pattern of land degradation. Between 1960 and 1990, the three predictors (climate, agriculture, and urbanization) explained a relatively high proportion of variance, suggesting a modest role for any other (unobserved) factor. All of these factors were found to be highly significant predictors of land degradation intensity across provinces, the most impactful being farming intensity. The highest adjusted-R2 coefficient was observed in both 1990 and 2000, and suggests that the three predictors still reflect the most powerful drivers of land degradation in Italy at those times, with a marginal role for additional (unobserved) factors. The impact of farming intensity remained high, with the role of urbanization increasing moderately, and the role of climate aridity declining weakly between 2000 and 2010. In more recent times (2010 and 2020), and in future (2030) scenarios, the adjusted R2 diminished moderately, suggesting a non-negligible importance of external (unobserved) factors and the rising role of spatial heterogeneity. The climate factor became progressively insignificant over time, while increasing the role of urbanization systematically. The impact of farming intensity remained high and significant. These results underlie a latent shift in the spatial distribution of the level of land vulnerability in Italy toward a spatially polarized model, influenced primarily by human pressure and socioeconomic drivers and less intensively shaped by biophysical factors. Climate aridity was revealed to be more effective in the explanation of land degradation patterns in the 1960s rather than in recent observation times.

Land Use and Land Cover Change in East Java Indonesia from 1972 to 2021: Learning from Landsat

This study analyses land use and land cover (LULC) changes during the last five decades (1970–2021) in East Java Province, Indonesia. The changes are analysed by comparing four maps interpreted from Landsat images (MSS 1972, TM 1997, OLI 2013, and OLI 2021). The main research procedures consist of (1) data collection, (2) field survey, (3) image classification, and (4) LULC change interpretation. The classification uses the maximum likelihood algorithm, achieving an overall kappa accuracy of > 75%. The classification produces eight classes, i.e., built-up land (BU), heterogeneous agricultural land (HAL), bare soil (BS), paddy fields (PF), open water (OW), vegetation (VG), shrubland (SH), and wetlands (WL). The analysis shows a significant shift in land use from 1972 to 2021, with HAL declining by 46%, SH by 91%, and BS by 88%. In contrast, PF has increased by 105%, VG (forest and plantation) by 64%, and built-up areas by a remarkable 366%. These changes show a significant shift from dryland agriculture, shrublands, and barren lands to irrigated regions, vegetated areas, and urban growth. Furthermore, there are increases in water bodies due to the construction of several reservoirs to support water availability. In coastal regions, the development of inland aquaculture leads to a proliferation of wetlands (salt evaporation ponds, fish ponds, and rice-fish integrated farming).

The factors influencing waste management for economic development—the perspective of Nelson Mandela bay municipality residents

The rapid increase in volume and variety of solid waste as a result of continuous economic growth, urbanization, and industrialization has become an intractable problem for the public and private sectors, making it difficult to ensure effective and sustainable waste management. The study sought to understand the perspective of Nelson Mandela Bay Municipal residents on their perceived influence of municipal waste management on economic development. The study used a quantitative techniques approach, with a structured questionnaire. Data was collected from 255 respondents. The quantitative question aimed to determine if the influence was positive or negative, and the Likert scale question had a scale of 1 to 5. This research identified various types of waste, assessed challenges, and evaluated waste's potential for resource creation. The study concluded that municipal waste includes organic, paper, plastic, metal, glass, food, garden, hazardous, construction, demolition, and non-recyclable waste. Environmental pollution is a major challenge, and 26 factors positively influence waste to wealth. Waste to wealth can be achieved through social behavior, education/knowledge, MSWM governance, and economic feasibility for MSWM. The research also highlighted the importance of land attributes, budget allocation, trained personnel, and government regulations. The framework proposed aims to minimize urban poverty while preserving the environment and supporting the present urban economy. Urban communities can utilize solid waste management as a mechanism to foster economic development. The fundamental question is whether stakeholders will continue to ignore waste's potential demands and benefits for urban economic growth.

A hierarchical deep reinforcement learning method for solving urban route planning problems under large-scale customers and real-time traffic conditions

As urbanization and economic growth advance, large-scale customers and real-time traffic conditions have become crucial factors in urban route planning. Deep reinforcement learning is considered the most effective method for solving urban route planning problems involving large-scale customers and real-time traffic conditions. Due to memory usage limitations, existing deep reinforcement learning methods cannot identify candidate customers or determine optimal travel routes in large-scale and real-time environments. To tackle these problems, this study introduces a hierarchical deep reinforcement learning method utilizing an improved transformer model (HDRLITF) based on the divide-and-conquer concept. Graph attention networks and gate mechanisms are integrated into the transformer model to capture dynamic features and improve the model’s performance. A two-stage training method, based on the actor-critic algorithm, is proposed to determine the optimal policy function. To evaluate the HDRLITF method, experiments were conducted using datasets from the cities of Shenzhen and Chengdu in China. The experimental results suggest that the HDRLITF method can effectively interact with real-time traffic environments and obtain high-quality solutions compared to other deep reinforcement learning methods. The robustness and reliability of HDRLITF were further validated across multiple traffic scenarios and indicators.

Sustainable Tourism Development Through Religious Tourism in Pasuruan City

The city of Pasuruan holds significant potential for religious tourism, making it a prime candidate for tourism development. Achieving sustainable tourism, which involves finding a harmonious equilibrium between three key aspects - the quality of tourism experiences, the excellence of tourism resources, and the well-being of the local community - is imperative for the region. To this end, well-devised strategies are essential to facilitate urban tourism growth within Pasuruan City. This research endeavors to shift the focus of religious tourism development towards a sustainable paradigm in Pasuruan. Employing qualitative research techniques, the study uses methods such as observation, interviews, literature review, and documentation for data collection. The findings of the research underscore Pasuruan City’s potential for fostering sustainable tourism through the establishment of religious tourism offerings. The establishment of sustainable tourism practices not only promises to bolster the local economy but also enrich the lives of the Pasuruan community, ultimately shaping the city’s identity. This research aspires to serve as a guiding tool for the local government of Pasuruan City, aiding them in implementing tourism development strategies to uplift the region’s economy. Keywords: tourism, sustainable tourism, religious tourism, Pasuruan city

Progress and Potential Drawbacks of Modern Agricultural Technologies: A Literature Review

The production of agriculture has undergone new modifications as a result of agricultural technologies. These not only boost agricultural output but may also significantly raise the caliber of produced food, cut labor expenses, boost farmers' incomes, and achieve agricultural modernization. The use of precision agriculture (PA) is expanding due to the rapid socioeconomic changes that are occurring in certain developing nations. There are enormous ramifications for urbanization, energy consumption, and economic growth in certain developing nations when fundamental changes occur. The research status and current agricultural technology achievements are carefully summarized in this study. In-depth discussions of thirteen significant agricultural technologies are provided in this article. All significant technologies from developed countries are discussed so that under-developed and lower-developed countries will benefit from this paper. Finally, some fresh concepts for each technology are offered, and potential issues in establishing such sophisticated technologies are identified. The main objective of this review is to increase knowledge of modern agriculture and the development process in the agricultural field.

Cardiovascular Disease Risk in the Obese Population in Kuwait: A Systematic Review and Meta-Analysis.

The increasing prevalence of obesity and associated cardiovascular diseases (CVDs) in Kuwait requires a comprehensive analysis of its contributing factors and health outcomes. This systematic review and meta-analysis aimed to synthesize the current evidence on the relationship between obesity and CVDs and identify the main factors driving obesity in the Kuwaiti population. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, multiple databases were systematically searched, identifying 980 articles. After removing duplicates and screening the articles against the inclusion criteria (observational or intervention studies published from 2001 to 2024 in English, conducted in Kuwait or on Kuwaitis, examining obesity (body mass index or validated measures) and CVDs (coronary artery disease, stroke, heart attack, hypertension, dyslipidemia, diabetes), 44 published studies from 2001 to 2024 were included in the final analysis. The studies varied widely in design and population, complicating the estimation of the total number of participants. The pooled prevalence of overweight patients was 36% (95% CI: 25-47) with high heterogeneity (I2=99.86%) and a statistically non-significant difference (p>0.01). Likewise, the pooled prevalence of overweight patients was 31% (95% CI: 23-40,I2=99.03%), with a statistically non-significant difference (p>0.01). The key factors contributing to obesity in Kuwait include a sedentary lifestyle, unhealthy diet, poor sleep quality, stress, genetic predisposition, metabolic disorders, environmental factors, and cultural factors. Sedentary behavior and unhealthy diets, exacerbated by rapid urbanization and economic growth, were prominent contributors. Genetic studies have identified specific genetic variants associated with obesity. Stress and poor sleep quality were significant factors, particularly in urban environments. A strong association was found between obesity and various CVD risk factors, including hypertension, dyslipidemia, and diabetes mellitus, with pooled population attributable risk estimates of 15%-18%, 13%-15%, and 10%, respectively. These findings underscore the multifaceted nature of obesity in Kuwait, which is influenced by lifestyle, dietary habits, genetic factors, and socioeconomic changes. Age- and gender-specific analyses revealed higher obesity rates in adults aged 40-60 years and stronger associations with CVD in females. The consistent link between obesity and CVD risk factors highlights the urgent need for targeted public health interventions. Obesity contributes significantly to the CVD burden in Kuwait, driven by a combination of lifestyle, genetic, and environmental factors. Comprehensive strategies addressing these determinants are essential to mitigate the obesity epidemic and its associated health risks in the Kuwaiti population.

Analysing the Impacts of Rusizi Urban Growth on Kivu Coastline: Evidence in Gihundwe and Kamembe Sectors

Analysing the Impacts of Rusizi Urban Growth on Kivu Coastline: Evidence in Gihundwe and Kamembe Sectors

GIS integration for spatial analysis of urban expansion in Tisaleo canton (Ecuador).

Political or economic interests have led to an empirical zonal delimitation, and the deficient use of Geographic Information Systems does not allow the specific characterisation of the territories. This article aims to determine the spatial analysis of the urban expansion areas of the canton of Tisaleo by integrating Geographic Information Systems, using a quantitative research method. The main components analysed are: the situational diagnosis of the canton through the characterisation of its biophysical component, its human settlements and the public system of facilities and social services that support it. Based on this information, an interrelation of the data collected is carried out to complement a spatial analysis with GIS software (ArcGis/QGis, ESRI, 2018). The results show that in the canton of Tisaleo there are areas suitable for urban growth, but their limits are overdimensioned, i.e. they include areas that do not have the aptitude to be urbanised, as well as rural areas that have the characteristics to be considered urban.

Advancements in green-synthesized transition metal/metal-oxide nanoparticles for sustainable wastewater treatment: techniques, applications, and future prospects

Abstract In order to meet the demand for portable water and replenish depleting water resources caused by industrialization, urbanization, and population growth, wastewater purification has become crucial. Emerging contaminants (ECs), which include organic dyes, pesticides, pharmaceutical drugs, polyaromatic compounds, heavy metal ions, and fertilizers, among others, have caused significant disruptions to environmental balance and severe health complications. As a result, considerable effort has been devoted to the development of technologies that eliminate wastewater from effluents via adsorption, photocatalysis, and other means. However, considering the economic and environmental implications of the adopted technologies, green technology has gained significant attention owing to their eco-friendly approaches, cost-effectiveness, avoiding use of toxic and harmful chemicals and production of less-toxic by-products. Currently green-synthesized nanomaterials have seen tremendous growth in emerging as sustainable nanoadsorbents, nanocatalysts for the removal of the emerging contaminants from wastewater in highly efficient and eco-friendly manner. Thus, this review presents an overview of the various techniques utilized in wastewater treatment with a particular emphasis on the production and application of environmentally friendly transition metal/metal oxide nanoparticles as sustainable tools in wastewater treatment technology. This article also discusses the limitations and future potential of using green-synthesized transition metal/metal oxide based nanoparticles in advancing the technology on a broad scale.

Innovative Pavement Solutions: A Comprehensive Review from Conventional Asphalt to Sustainable Colored Alternatives

Climate change significantly impacts transportation infrastructure, particularly asphalt pavements. Similarly, the heat absorption of paved surfaces, especially conventional black pavements, significantly intensifies the urban microclimate. Paved surfaces, including asphalt pavements, account for over 30% of the covered surfaces and are vulnerable to rising temperatures, which cause not only pavement distress, such as rutting and cracking, but also urban heat islands (UHI). Sustainable pavement solutions, specifically colored pavements, have been investigated for their potential to mitigate these effects. This review presents an extensive overview of current pavement technologies, emphasizing conventional asphalt’s economic, environmental, and functional characteristics. A discussion of the benefits and challenges of colored pavements is also provided, including their ability to reduce UHI, enhance safety, and contribute to sustainable urban growth. This paper discusses advancements in pavement material science, the use of recycled materials, and the application of reflective coatings, providing insights into sustainable infrastructure development. Transitioning from conventional black pavements to sustainable colored alternatives is not merely a matter of material choice but a strategic transition toward resilient urban planning. Increasing demand for environmentally friendly infrastructure could prompt the construction industry to adopt colored pavements as a tool to promote environmental stewardship.

The impact of urban growth boundary on urban sprawl: evidence from China

The impact of urban growth boundary on urban sprawl: evidence from China

Sustainable Human Development and Human Resource Management Strategies in Building Sustainable Cities

While the context of rapid urbanization and increasing challenges faced by cities worldwide, the role of Human Resource Management (HRM) in fostering sustainable urban development has garnered significant attention. This paper presents a comprehensive review of existing literature on HRM strategies aimed at building sustainable cities. Thus, a detailed analysis of the prominent findings of studies on “human resource management” and “sustainable cities” in Google Scholar, Web of Science (WoS) and Scopus databases was presented by a literature review. The review synthesizes insights from a range of sources including academic articles to identify key themes, trends, and best practices in HRM for urban sustainability and reveals that effective HRM strategies play a crucial role in addressing urban challenges such as population growth, environmental sustainability, social inclusion, and economic resilience. Furthermore, the review highlights the importance of aligning HRM strategies with broader urban development goals and policy frameworks and emphasizes the need for collaboration among government agencies, businesses, academia, and civil society to implement integrated HRM approaches that promote sustainable urban growth and enhance the quality of life for urban residents. The findings from this literature review also contribute to a deeper understanding of the role of HRM in building sustainable cities. Therefore, this paper concludes with recommendations for future research directions and practical implications for human resource (HR) professionals, policy makers, and urban planners seeking to create more sustainable urban environment.

Machine learning-aided hybrid technique for dynamics of rail transit stations classification: a case study

Accurate classification of rail transit stations is crucial for successful Transit-Oriented Development (TOD) and sustainable urban growth. This paper introduces a novel classification model integrating traditional methodologies with advanced machine learning algorithms. By employing mathematical models, clustering methods, and neural network techniques, the model enhances the precision of station classification, allowing for a refined evaluation of station attributes. A comprehensive case study on the Chengdu rail transit network validates the model’s efficacy, highlighting its value in optimizing TOD strategies and guiding decision-making processes for urban planners and policymakers. The study employs several regression models trained on existing data to generate accurate ridership forecasts, and data clustering using mathematical algorithms reveals distinct categories of stations. Evaluation metrics confirm the rationality and accuracy of the results. Additionally, a neural network achieving high accuracy on labeled data enhances the model’s predictive capabilities for unlabeled instances. The research demonstrates high accuracy, with the Mean Squared Error (MSE) for regression models (Multiple Linear Regression (MLR), Deep-Learning Neural Network (DNN), and K-Nearest Neighbor (KNN)) remaining below 0.012, while the neural networks used for station classification achieve 100% accuracy across seven time intervals and 98.15% accuracy for the eighth, ensuring reliable ridership forecasts and classification outcomes. Accuracy in rail transit station classification is critical, as it not only strengthens the model’s predictive capabilities but also ensures more reliable data-driven decisions for transit planning and development, allowing for more precise ridership forecasts and evidence-based strategies for optimizing TOD. This classification model provides stakeholders with valuable insights into the dynamics and features of rail transit stations, supporting sustainable urban development planning.