Search Results (161)

Research on commuting patterns has mainly focused on urban and metropolitan areas, and such studies are not typically applied to rural and small-town regions, where workers often face longer commutes due to limited job opportunities and inadequate public transportation. By using the Census Transportation Planning Package (CTPP) data, this research fills that gap by analyzing commuting behavior by ethnic group and industry in south-central Minnesota, which is a predominantly rural area of 13 counties in the United States. The results show that both white and minority groups in District 7 experienced an increase in excess commuting from 2006 to 2016, with the minority group in Nobles County showing a significantly higher rise. Analysis by industry reveals that excess commuting in the leisure and hospitality sector (including arts, entertainment, and food services) in Nobles County increased five-fold during this time, indicating a severe spatial mismatch between jobs and affordable housing. In contrast, manufacturing experienced a decline of 50%, possibly indicating better commuting efficiency or a loss of manufacturing jobs. These findings can help city and transportation planners conduct an in-depth analysis of rural-to-urban commuting patterns and develop potential solutions to improve rural transportation infrastructure and accessibility, such as promoting telecommuting and hybrid work options, expanding shuttle routes, and adding more on-demand transit services in rural areas. Full article

Governance challenges, success factors, and stakeholder dynamics are central to the implementation of Positive Energy District (PED) Labs, which aim to develop energy-positive and sustainable urban areas. In this paper, a qualitative analysis combining expert surveys, participatory workshops with practitioners from the COST Action PED-EU-NET network, and comparative case studies across Europe identifies key barriers, drivers, and stakeholder roles throughout the implementation process. Findings reveal that fragmented regulations, social inertia, and limited financial mechanisms are the main barriers to PED Lab development, while climate change mitigation goals, strong local networks, and supportive policy frameworks are critical drivers. The analysis maps stakeholder engagement across six development phases, showing how leadership shifts between governments, industry, planners, and local communities. PED Labs require intangible assets such as inclusive governance frameworks, education, and trust-building in the early phases, while tangible infrastructures become more relevant in later stages. The conclusions emphasize that robust, inclusive governance is not merely supportive but a key driver of PED Lab success. Adaptive planning, participatory decision-making, and digital coordination tools are essential for overcoming systemic barriers. Scaling PED Labs effectively requires regulatory harmonization and the integration of social and technological innovation to accelerate the transition toward energy-positive, climate-resilient cities. Full article

This study aimed to investigate land-use and land-cover change and the associated change in the ecological stability of the model area Dobrá–Opatová (district of Trenčín, Slovakia), where increasing landscape transformation has raised concerns about declining ecological resilience. Despite the importance of sustainable land management, few studies in this region have addressed long-term landscape dynamics in relation to ecological stability. This research fills that gap by evaluating historical and recent LULC changes and their ecological consequences. Four time horizons were analysed: 1850, 1949, 2009, and 2024. Although the selected time periods are irregular, they reflect key milestones in the region’s land development, such as pre-industrial land use, post-war collectivisation, and recent land consolidation. These activities significantly altered the structure of the landscape. To assess future trends, we used the MOLUSCE plug-in in QGIS to simulate ecological stability for the future. The greatest structural landscape changes occurred between 1850 and 1949. Significant transformation in agricultural areas was observed between 1949 and 2009, when collectivisation reshaped small plots into large block structures and major water management projects were implemented. The 2009–2024 period was marked by land consolidation, mainly resulting in the construction of gravel roads. These structural changes have contributed to a continuous decrease in ecological stability, calculated using the coefficient of ecological stability derived from LULC categories. To explore future trends, we simulated ecological stability for the year 2030 and the simulation confirmed a continued decline in ecological stability, highlighting the need for sustainable land-use planning in the area. Full article

With the rapid development of science and technology, the global demand for renewable energy is increasing. In the urban context, solar energy has become one of the key ways to increase urban energy self-sufficiency and reduce carbon emissions due to its flexibility in installation and ease of expansion of applications. Therefore, based on Geographic Information System (GIS) and deep learning modeling, this paper proposes a method to efficiently assess the potential of urban rooftop solar photovoltaic (PV), which is analyzed in a typical area of Lanzhou New District, which is divided into 8774 units with an area of 87.74 km². The results show that the method has a high accuracy for the identification of the roof area, with a maximum maxF_β of 0.889. The annual solar PV potential of industrial and residential buildings reached 293.602 GWh and 223.198 GWh, respectively, by using the PV panel simulation filling method for the calculation of the area of roofs where the PV panels can be installed. Furthermore, the rooftop PV potential of the industrial buildings in the research area provided can cover 75.17% of the industrial electricity consumption. This approach can provide scientific guidance and data support for regional solar PV planning, which should prioritize the development of solar potential of industrial buildings in the actual consideration of rooftop PV deployment planning. Full article

Urban expansion threatens sustainable development in densely populated countries like Bangladesh. This study aims to quantitatively identify and evaluate the key drivers influencing the spatial distribution of urban surfaces (SDUS) in Chattogram City, providing insights into urban growth patterns over 30 years. Using Landsat 5 and 9 imageries, the Normalized Difference Built-up Index (NDBI) was computed for 1993 and 2023 to map urban surface changes. A total of 16 geospatial variables representing potential drivers were analyzed. Four statistical and machine learning methods, including GeoDetector, Distributed Random Forest (DRF), global Geographically Weighted Random Forest (GWRF), and local GWRF, were employed to quantify individual and interactive influences on SDUS. The Geodetector analysis identified the central business district (CBD) as the most influential driver of urban surface distribution, with a q statistic of 0.22, followed by river proximity (q = 0.14) and administrative boundaries (q = 0.13). Across all models, CBD consistently ranked as a dominant factor. In the Distributed Random Forest (DRF) model, CBD showed the highest importance score (0.57), followed by coastlines (0.35) and rivers (0.35). The DRF model achieved the highest performance (R² = 0.612), outperforming the global GWRF (R² = 0.59) and local GWRF (R² = 0.529). Although variables like the proximity of administrative location and forests have low individual impacts, they show a stronger coupled influence. This industrial port-based economy expanded, facing challenges of uncontrolled urbanization, poor governance, and environmental issues. Promoting mixed land use planning, decentralizing urban governance, and improving coordination among implementing agencies may better resolve these issues. This work may help planners and policymakers in planning future cities and developing policies to promote sustainable urban growth. Full article

Tongzhou District is the urban sub-center of Beijing, and the importance of groundwater resources is increasingly prominent. Based on groundwater level data from 1980 to 2020 and water usage data from various sectors in Tongzhou District between 2011 and 2020, this paper utilizes continuous wavelet transform (CWT), geostatistical models, and grey relational analysis (GRA) to explore the spatiotemporal evolution patterns and influencing factors of groundwater levels in Tongzhou District. The study reveals that the groundwater level evolution in Tongzhou District exhibits two primary cycles, and it predicts that the groundwater level at Liyuan Station will decrease and eventually rebound. From 1980 to 2020, the overall trend of groundwater levels in Tongzhou District showed a decline. However, the groundwater levels in the central and southern regions exhibited an upward trend from 2000 to 2020. The groundwater level is mainly influenced by spatial structural factors, with minimal impact from external random factors. Domestic water consumption, water usage in the tertiary sector, and industrial water usage have the greatest impact on groundwater levels, attributed to the rapid growth of the population and regional economy. Agricultural water usage has the least grey relational grade, which is related to changes in agricultural development planning in the study area, as well as reductions in the area of crop planting and the actual utilization area of facility agriculture. Full article

Health and wellness tourism is a rapidly expanding segment of the global tourism industry, driven by increasing consumer awareness of well-being and lifestyle enhancement. As the demand for wellness travel grows, destinations are expected to offer high standards of safety, hygiene, rehabilitation, and holistic experiences. This study aims to identify and evaluate the key attributes and determinants for developing health and wellness tourism destinations by applying the 6As Tourism Development framework: Attractions, Accessibility, Amenities, Activities, Available Packages, and Ancillary Services. A multi-criteria decision-making approach, specifically the TOPSIS, was employed to assess destination potential through a case study of Nakhon Ratchasima Province, Thailand. The results indicate that Attractions, Accessibility, and Amenities are the top three priorities for wellness tourists. Sub-criteria such as natural scenery, cultural significance, accessibility for all, safety, and accommodation quality are particularly influential. Three districts in Nakhon Ratchasima were found to exhibit distinct strengths—Pak Chong is best suited for rehabilitative tourism (e.g., aroma and water therapy), aligning with mind and nutrition wellness components; Wang Nam Khiao is ideal for ecotourism and cultural experiences, supporting environmental and nutritional dimensions; while Mueang Nakhon Ratchasima excels in sports tourism, supporting physical and nutritional well-being. The study offers practical insights for policymakers and tourism stakeholders to design sustainable, visitor-centered wellness destinations. The proposed framework supports strategic planning and resource allocation for health-focused tourism development. Full article

Ensuring the healthy movement of urban and rural factors is a key aspect in promoting urban–rural integration. Defining the mechanisms of factor movement and constructing an urban–rural integration planning model are important for the practice of urban–rural integration work. This study considers the movement of urban and rural factors as its entry point and explores the resource endowments and movement paths of urban and rural factors based on the city–town–village spatial system. The urban–rural integration planning model was constructed using spatial and policy dimensions, and six integration design strategies for ecological, population, industrial, land, transportation, and public service factors were defined. Next, considering the Xiuzhou District of Jiaxing City as a case study, this study combines the current characteristics and integration directions of urban and rural factors to propose integration design goals and measures for six key factors. The movement paths of urban and rural factors were delineated, and the specific tasks of each administrative entity in urban–rural integration development were identified at each level to achieve the breakdown and transmission of the overall urban–rural integration strategy. The study integrated current status assessment, integration design, path construction, and goal breakdown, exploring the formulation of urban–rural integration strategies and work pathways. The aim was to address the current gap between urban–rural integration theory and practice, thus providing a reference and inspiration for related research. Full article

Based on the data of the non-central districts in Shanghai, this paper investigates the determinants of the commercial land leases of district governments from the government–market perspective and how these determinants affect the price and area of commercial land leasing. A kernel density analysis is used to analyze the agglomeration degree and density distribution of commercial land leasing. The variables are considered as the factors impacting commercial land leases based on a literature review and land development in Shanghai. The mathematical models used for multiple linear regression for the leased price and area of the influencing factors of commercial land leases from the perspective of the government and market are proposed. The results show that Shanghai’s multi-center development strategy aims to optimize the city’s commercial layout by developing the key areas of non-central districts. The construction area and plot ratio of land; the distances from the land to the city center, district center, airports, the nearest middle schools, the nearest park, and the nearest industrial zone; and the quantity of subway stations and highways affect commercial land leases. Policies are proposed to improve commercial land lease efficiency, make more suitable land planning strategies, and optimize urban spatial structures. Full article

With continuous urbanization, cities are facing numerous challenges. In addition, the construction and effective management of urban green spaces have become essential for the sustainable development of healthy cities. In previous studies, algorithms were developed to select appropriate locations for parks with distinctive green patches. Although the suitability of urban parks has been discussed from various perspectives, the location and land use around parks have been rarely considered. Therefore, the benefits and importance of parks across various land uses were assessed in this study. Based on the assessment results, improvement strategies for future park planning and development were proposed. By highlighting and integrating the benefits of different parks, urban green spaces can be expanded to deliver diverse benefits and contribute to healthy and sustainable development. We compiled 35 items in four major types of park benefits from a literature review. Using the Delphi method, 24 important benefits of parks were identified. An importance–performance analysis (IPA) was then conducted to create matrix diagrams for parks in different land use zones to understand the key benefits and identify areas that require priority improvement. The IPA results indicated that parks in residential and industrial areas need to improve ecological benefits and environmental functions for sustainable development. Parks in districts have greening and visual appeal but need improvement in environmental education. There are excessive facilities for passive activities in parks, suggesting a need for resource optimization. The results of this study help urban planners find region-specific design solutions for different land uses and effectively manage and optimize resource allocation. Full article

With the acceleration in urbanization, surface hardening has increased, urban flooding and soil erosion problems are frequent, and urban water resource management faces great challenges. Sponge city construction can effectively alleviate these problems by simulating the natural water cycle and constructing blue–green infrastructure. In this study, the analytic hierarchy process (AHP) and the ArcGIS weighted overlay tool were used to construct a framework for assessing the suitability of sponge city construction in Suzhou from the three dimensions of Geo-Smart spatial productive forces, Eco-Dynamic green productive forces, and Resilio-Tech responsive productive forces. A zoning strategy based on new quality productive forces is also proposed. The results show that Suzhou can be divided into three types of construction zones according to the suitability level: key construction zones, secondary key construction zones, and general construction zones. The key construction zones account for about 28.01% of the total land area, mainly covering the built-up areas of Suzhou, covering the developed urban areas such as Gusu District, Xiangcheng, Suzhou Industrial Park, and other key zones such as Northern Kunshan. The secondary key construction area and general construction area, on the other hand, account for 61.94% and 10.05% of the total area, respectively. From the new quality productive forces, this study proposes the following construction guidelines for sponge city zones: (1) enhance the coordinated development of urban planning and sponge city construction; (2) promote blue–green infrastructure development, strengthen inter-departmental cooperation, and ensure ecological and economic co-development; and (3) encourage public participation in governance. This research offers theoretical and practical guidance for sponge city construction in Suzhou and other cities from the perspective of new quality productive forces. Full article

This study proposes an innovative framework integrating geographic information systems (GISs), water quality index (WQI) analysis, and advanced machine learning (ML) models to evaluate the prevalence and impact of organic and inorganic pollutants across the urban–industrial confluence zones (UICZ) surrounding the National Capital Territory (NCT) of India. Surface water samples (n = 118) were systematically collected from the Gautam Buddha Nagar, Ghaziabad, Faridabad, Sonipat, Gurugram, Jhajjar, and Baghpat districts to assess physical, chemical, and microbiological parameters. The application of spatial interpolation techniques, such as kriging and inverse distance weighting (IDW), enhances WQI estimation in unmonitored areas, improving regional water quality assessments and remediation planning. GIS mapping highlighted stark spatial disparities, with industrial hubs, like Faridabad and Gurugram, exhibiting WQI values exceeding 600 due to untreated industrial discharges and wastewater, while rural regions, such as Jhajjar and Baghpat, recorded values below 200, reflecting minimal anthropogenic pressures. The study employed four ML models—linear regression (LR), random forest (RF), Gaussian process regression (GPR), and support vector machines (SVM)—to predict WQI with high precision. SVM_Poly emerged as the most effective model, achieving testing CC, RMSE, and MAE values of 0.9997, 11.4158, and 5.6085, respectively, outperforming RF (0.9925, 29.8107, 21.7398) and GPR_PUK (0.9811, 68.4466, 54.0376). By leveraging machine learning models, this study enhances WQI prediction beyond conventional computation, enabling spatial extrapolation and early contamination detection in data-scarce regions. Sensitivity analysis identified total suspended solids as the most critical predictor influencing WQI, underscoring its relevance in monitoring programs. This research uniquely integrates ML algorithms with spatial analytics, providing a novel methodological contribution to water quality assessment. The findings emphasize the urgency of mitigating the fate and transport of organic and inorganic pollutants to protect Delhi’s hydrological ecosystems, presenting a robust decision-support system for policymakers and environmental managers. Full article

Along with the progression of globalized climate change, flooding has become a significant challenge in low-lying plain river network regions, where urban areas face increasing vulnerability to extreme climate events. This study explores climate-adaptive land use strategies by coupling blue–green infrastructure (BGI) with conventional gray infrastructure, forming blue–green–gray infrastructure (BGGI), to enhance flood resilience at localized and regional scales. By integrating nature-based solutions with engineered systems, this approach focuses on flood mitigation, environmental co-benefits, and adaptive land-use planning. Using the Minhang District in Shanghai as a case study, the research employs geospatial information system (GIS) analysis, hydrological modeling, and scenario-based assessments to evaluate the performance of BGGI systems under projected climate scenarios for the years 2030, 2050, and 2100. The results highlight that coupled BGGI systems significantly improve flood storage and retention capacity, mitigate risks, and provide ecological and social benefits. Water surface-to-catchment area ratios were optimized for primary and secondary catchment areas, with specific increases required in high-risk zones to meet future flood scenarios. Ecological zones exhibited greater adaptability, while urban and industrial areas required targeted interventions. Scenario-based modeling for 2030, 2050, and 2100 demonstrated the scalability, feasibility, and cost-effectiveness of BGI in adapting to climate-induced flooding. The findings contribute to the existing literature on urban flood management, offering a framework for climate-adaptive planning and resilience building with broader implications for sustainable urban development. This research supports the formulation of comprehensive flood management strategies that align with global sustainability objectives and urban resilience frameworks. Full article

Urban green space systems (UGSS) play a crucial role in enhancing citizens’ well-being and promoting sustainable urban development through their ecosystem service values (ESV). However, understanding the spatiotemporal changes, driving factors, and influencing mechanisms of ESV remains a critical challenge for advancing urban green theories and formulating effective policies. This study focuses on Suzhou, China’s third-largest prefecture-level city by economic volume and ecological core city of the Taihu watershed, to evaluate the ESV of its UGSS from 2010 to 2020, identify key driving factors, and analyze their influencing mechanisms. Using the InVEST model combined with the entropy weight method (EWM), we assessed the ESV changes over the study period. To examine the influencing mechanisms, we employed an innovative XGBoost-GWR approach, where XGBoost was used to screen globally significant factors from 37 potential drivers, and geographically weighted regression (GWR) was applied to model local spatial heterogeneity, providing a research perspective that balances global nonlinear relationships with local spatial heterogeneity. The results revealed three key findings: First, while Suzhou’s UGSS ESV increased by 9.92% from 2010 to 2020, the Global Moran’s I index rose from 0.325 to 0.489, indicating that its spatial distribution became more uneven, highlighting the increased ecological risks. Second, climate, topography, landscape pattern, and vegetation emerged as the most significant driving factors, with topographic factors showing the greatest variation (the negatively impacted area increased by 455.60 km²) and climate having the largest overall impact but least variation. Third, the influencing mechanisms were primarily driven by land use changes resulting from urbanization and industrialization, leading to increased ecological risks such as soil erosion, pollution, landscape fragmentation, and habitat degradation, particularly in the Kunshan, Wujiang, and Zhangjiagang Districts, where agricultural land has been extensively converted to constructed land. This study not only elucidates the mechanisms influencing UGSS’s ESV driving factors but also expands the theoretical understanding of urbanization’s ecological impacts, providing valuable insights for optimizing UGSS layout and informing sustainable urban planning policies. Full article

Spatial justice requires equitable construction land allocation to realize disadvantaged regions’ development rights. Construction land reduction (CLR) in economically developed areas is a complex and multi-dimensional process of land spatial optimization. While optimizing the allocation of land resources, this process may also lead to challenges in spatial justice. This study assessed spatial injustice using construction land data from W-district, Shanghai, based on spatial simulation. Planning documents indicated that some areas had a net resident outflow; the simulation showed that promoting CLR decreased mixed land use in these areas. Control of construction land decreased industrial and mining storage and rural residential land; urban residential, commercial, and other construction land increased. Bottom-line planning thinking reduced spatial injustice by approximately 0.0393 overall (the reduction rate was nearly 14.05%). Under territorial spatial planning, construction land stock quotas were optimized; CLR quotas were transferred, creating significant differences in construction land internal structures. Weighted Gini coefficients suggested unfair distribution between urban residential and commercial land, with the latter being more concentrated. Industrial and mining storage, other construction, and urban residential land contribute to spatial injustice. Industrial and mining storage and urban residential land have positive marginal effects; those of commercial, rural residential, and other construction land are negative. Promoting centralized residences has consolidated scattered rural residential land; decreasing rural residential land inhibits spatial injustice reduction. Construction land and the population can be agglomerated simultaneously to reduce construction land inequality. Full article