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The effects of the local and regional conditions and inequalities on urban shrinkage: a multilevel analysis focusing on local population decline

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URBAN RESEARCH & PRACTICE 2023年第3期16卷 438-457页

作者： Jeong, Jindo Gim, Tae-Hyoung Tommy Seoul Natl Univ Grad Sch Environm Studies Seoul South Korea Seoul Natl Univ Grad Sch Environm Studies Dept Environm Planning Interdisciplinary Program Landscape Architecture Seoul South Korea Seoul Natl Univ Environm Planning Inst Seoul South Korea

Urban shrinkage is becoming a worldwide issue. However, empirical investigation still lacks an understanding of the spatial extent of the factors that drive local population decline, a prevalent aspect of urban shrinkage. Empirical evidence on multilevel factors relating to population decline is particularly scarce. We investigated the influences by analyzing economic, social, physical, and policy conditions at the local and regional levels. Regional conditions, as well as local conditions, are also related to the decline of the local population. The effect goes beyond economic and demographic conditions;conditions such as the local infrastructure level and development policy also significantly influence.

关键词： Urban shrinkage population decline multilevel factors regional inequalities hierarchical linear modeling

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Sequential action-based dynamic decision-support model for urban ecological planning

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SUSTAINABLE CITIES AND SOCIETY 2024年 101卷

作者： Kim, Eun Sub Yun, Seok Hwan Kim, Ji Yeon Thorne, James H. Hyun, Jung Hee Lee, Dong Kun Seoul Natl Univ Interdisciplinary Program Landscape Architecture Seoul 08826 South Korea Seoul Natl Univ Integrated Major Smart City Global Convergence Pro Seoul 08826 South Korea Seoul Natl Univ Specialized Grad Sch Intelligent Ecosci Dept Landscape Architecture 4 Seoul 08826 South Korea SK forest Smart Green Solut Team Seoul 03161 South Korea Univ Calif Davis Dept Environm Sci & Policy One Shields Ave Davis CA 95616 USA Int Inst Appl Syst Anal Syst Risk & Resilience Res Grp Adv Syst Anal Laxenburg Austria Seoul Natl Univ Dept Landscape Architecture & Rural Syst Engn Seoul 08826 South Korea Seoul Natl Univ Dept Landscape Architecture & Rural Syst Engn Interdisciplinary Program Landscape Architecture Seoul 08826 South Korea

Decision-makers often encounter the challenge of efficient long-term ecological planning to consider economic and environmental trade-offs. This challenge becomes particularly complex while addressing dynamic ecological responses to ongoing urban development. Thus, we developed a sequential action-based dynamic decision -making model to simulate sequential action mitigation measures over time, while considering the dynamic ecological responses that emerge with urban development. We used a multi-objective optimization algorithm to identify optimal plans that maximize landscape connectivity and edge density, while minimizing the imple-mentation costs per action. We focused on utilizing ecological corridors and habitat creation as mitigation measures from 1980s to 2010s. The results show that the initial low-investment strategy adopted in 1980s and sequential planning led to significantly improved ecological benefits beyond the 2000s when compared to the single-action planning approach implemented in 2010s. In contrast, an initial high-cost investment in the 1980s gradual declined the ecological benefits starting from 1995. We have demonstrated that adopting sequential planning provides valuable insights into effective urban ecological planning, especially when analyzing time -series data. This sequential approach enables decision-makers to make informed choices, thereby ultimately facilitating sustainable urban and regional planning.

关键词： Decision-making Ecological resilience NSGA-II Restoration Sequential planning Sustainable development

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Super resolution of historic Landsat imagery using a dual generative adversarial network (GAN) model with CubeSat constellation imagery for spatially enhanced long-term vegetation monitoring

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2023年第1期200卷 1-23页

作者： Kong, Juwon Ryu, Youngryel Jeong, Sungchan Zhong, Zilong Choi, Wonseok Kim, Jongmin Lee, Kyungdo Lim, Joongbin Jang, Keunchang Chun, Junghwa Kim, Kyoung-Min Houborg, Rasmus Seoul Natl Univ Interdisciplinary Program Landscape Architecture Seoul South Korea Seoul Natl Univ Integrated Major Smart City Global Convergence Seoul South Korea Seoul Natl Univ Dept Landscape Architecture & Rural Syst Engn Seoul South Korea Seoul Natl Univ Res Inst Agr & Life Sci Seoul South Korea Univ Virginia Dept Environm Sci Charlottesville VA USA Rural Dev Adm Natl Inst Agr Sci Jeonju South Korea Natl Inst Forest Sci Forest ICT Res Ctr Seoul South Korea Planet Labs PBC San Francisco CA USA

Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earth's surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30-60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In order to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) approach combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term timeseries of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.90, for the dual RSS-GAN maps vs in situ data from all sites). Overall, the dual RSSGAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements;relative bias values of NDVI ranged from - 5.8 % to 0.3 % and - 12.4 % to - 3.7 % for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which reflects fine-scale information from Planet Fusion. Finally, the dual RSS-GAN maps showed both spatial enhancement and r

关键词： CubeSat Deep learning Generative adversarial network Landsat Super -resolution Vegetation index

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Optimizing urban green spaces using a decision-support model for carbon sequestration and ecological connectivity

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 384卷 125058页

作者： Hwang, Hyemee Kim, Daseul Kim, Sanghyuck Kim, Ji Yeon Kim, Eun Sub Yang, Tackang Lee, Nagyeom Piao, Yong Park, Byeong Jin Lee, Dong Kun Seoul Natl Univ Coll Agr & Life Sci Dept Landscape Architecture & Rural Syst Engn 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Interdisciplinary Program Landscape Architecture 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Res Inst Agr & Life Sci 1 Gwanak ro Seoul 08826 South Korea SK Forest Restructuring team Seoul South Korea City Univ Hong Kong Sch Energy & Environm Low Carbon & Climate Impact Res Ctr Hong Kong Peoples R China Yanbian Univ Coll Geog & Ocean Sci Hunchun 133300 Peoples R China

Urban green spaces (UGSs) are vital for enhancing urban ecological health and resident well-being. However, their diverse functions need to be balanced based on spatial limitations and varying stakeholder preferences. Integrated planning approaches are needed to exploit the multiple benefits of UGSs. This study introduces a multi-objective decision-support model designed to optimize UGS planning by simultaneously addressing carbon sequestration, ecological connectivity, and cost constraints. The model incorporates the non-dominated sorting genetic algorithm II to identify Pareto-optimal solutions for tailored decision-making strategies that balance different priorities. The model indicated that ecological connectivity can be improved by 7.57 % while meeting carbon-reduction and budgetary targets. The model effectively balanced trade-offs, underscoring the importance of both the quantity and strategic placement of green space. This decision-support framework empowers decision-makers to rapidly simulate and validate optimal scenarios, effectively balance competing objectives, and provide a scientific basis through verifiable feedback, ultimately promoting the development of sustainable urban environments.

关键词： Decision-making Urban green space Ecological network Carbon sink potential NSGA-II

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Using buffer analysis to determine urban park cooling intensity: Five estimation methods for Nanjing, China

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SCIENCE OF THE TOTAL ENVIRONMENT 2023年第1期868卷 161463页

作者： Xiao, Yi Piao, Yong Pan, Chao Lee, Dongkun Zhao, Bing Nanjing Forestry Univ Coll Landscape Architecture Nanjing 210037 Peoples R China Seoul Natl Univ Interdisciplinary Program Landscape Architecture & Seoul 08826 South Korea Nanjing Agr Univ Coll Econ & Management Nanjing 210095 Jiangsu Peoples R China Seoul Natl Univ Dept Landscape Architecture & Rural Syst Engn Seoul 08826 South Korea Nanjing Forestry Univ Dept Landscape Architecture 159 Longpan Rd Nanjing Jiangsu Peoples R China

Urban parks are part of the blue-green infrastructure of urban ecosystems. Although the cooling effect of urban parks has been widely recognized, the understanding of park cooling intensity (PCI) and its mechanisms remains incomplete. Applicable and accurate quantification could facilitate better design and management of urban parks. We used five methods (equal area method [EAM], equal radius method [ERM], fixed radius method [FRM], turning point method-maximum perspective [TPM-M], and turning point method-accumulation perspective [TPM-A]) to estimate PCI, and established the method selection mechanism, which we evaluated in terms of PCI amplitudes, spatial heterogeneity, and interactions with park landscape features. Using Nanjing as a case study, we employed spatial and statistical analyses to further assess the autocorrelation of PCI and its relationship with park landscape features. The results indicate the following: (1) 62.38 % of Nanjing's urban parks are located above the 90 % confidence level in cold spot areas. (2) Different methods had significant effects on the estimated PCI, were positively correlated, and had similar spatial heterogeneity. (3) All methods revealed that park area (PA), water area proportion (WAP), and the normalized difference vegetation index (NDVI) of the vegetated area (NDVIveg) were the three dominant factors that influenced PCI;WAP and NDVIveg that achieved more effective cooling. (4) The quantification of PCI using the ERM and TPM is recommended over other methods. These findings are essential for landscape planners to understand the formation of PCI and design cooler parks to mitigate the urban heat island (UHI) effect more systematically.

关键词： Urban parks Cooling intensity Buffer analysis landscape features Remote sensing

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Can urban shrinkage contribute to mitigating surface air temperature warming?

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SUSTAINABLE CITIES AND SOCIETY 2024年 114卷

作者： Ma, Fengdi Yoon, Heeyeun Seoul Natl Univ Interdisciplinary Program Landscape Architecture 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Res Inst Agr Life Sci Coll Agr & Life Sci Integrated Major Smart City Gl Dept Landscape Architecture & Rural Syst Engn 1 Gwanak Ro Seoul 08826 South Korea

In recent years, urban areas are increasingly experiencing intense warming. Although urbanization is an important driver of warming in urban environments, it remains unclear whether depopulation trends in shrinking cities mitigate this warming effect. Here, we explored the relationship between shrinking cities and observed warming in China. Of the 356 Chinese cities, 95 were identified as shrinking between 2000 and 2010. We categorized 2419 observation stations into three groups-rural, shrinking, and non-shrinking-and generated a surface air temperature (SAT) anomaly series for each group from 1961 to 2014. Temperature differences between the shrinking and non-shrinking urban stations were investigated. Using segmented generalized least squares regression, the spatiotemporal temperature patterns across the mean (Tmean), maximum (Tmax), and minimum temperatures (Tmin), diurnal temperature range (DTR) indicators, and across seasons-spring, summer, autumn, and winter-were explored. Results revealed a cooling effect in shrinking cities, with decadal decreases of 0.042 degrees C (-0.078 to -0.005 degrees C), 0.083 degrees C (-0.126 to -0.039 degrees C), and 0.029 degrees C (-0.062 to -0.005 degrees C) in regional Tmean, Tmax, and Tmin anomalies, respectively. Moreover, pronounced seasonality was identified in this phenomenon-the cooling effect was most notable for Tmean in spring and Tmax in autumn, less significant in summer, and negligible in winter. These results suggested that the population decline in shrinking cities could alleviate regional warming, having implications that could influence urban planning and climate mitigation policies.

关键词： Shrinking cities Temperature anomalies Regional warming Climate mitigation policies

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The Impact of Greening Strategies on Temperature Reduction at Bus Stop Utilizing Computational Fluid Dynamics (Cfd) Analysis

SSRN

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SSRN 2024年

作者： Kim, Na Youn Kim, Eun Sub Yun, Seokhwan Lee, Dong Kun Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Specialized Graduate School of Intelligent Eco-Science 4 Dept. of Landscape Architecture Seoul National University Seoul08826 Korea Republic of

This study analyzed the effectiveness of a vegetation strategy for mitigating heat within a bus stop shelter by implementing vegetation wind permeability and transpiration cooling processes using a computational fluid dynamics (CFD) simulation. In scenario 1, a pot-type vertical greening system (VGS) without wind permeability had no significant effect on temperature reduction in the bus stop shelter. In contrast, scenario 2, with a mesh VGS allowing wind permeability, resulted in an average temperature reduction of 0.16°C within the interior area of the bus stop shelter, accompanied by shelter improved ventilation. Scenario 3, which entailed the removal of the shelter and the planting of trees, resulted in the least reduction in wind speed and an average temperature reduction of 0.14°C in the bus waiting area. Scenario 2 demonstrated the most effective temperature reduction for interior and surrounding area of the bus stop. Considering the normal effective temperature (NET), Scenario 3 provided the lowest sensation temperature. This indicates that the improved ventilation in Scenario 3 had a substantial impact on NET. Considering the effects of the vegetation strategies proposed in this study, it is essential to select suitable strategies for heat mitigation in bus-stop shelters based on regional and spatial considerations. © 2024, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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Development of a Success Prediction Model for Crowdfunding Based on Machine Learning Reflecting ESG Information

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IEEE ACCESS 2024年 12卷 197275-197289页

作者： Park, Jiyoung Na, Hyung Jong Kim, Hyungjoon Seoul Natl Univ Interdisciplinary Program Landscape Architecture Seoul 08826 South Korea Semyung Univ Dept Accounting & Taxat Jecheon Si 27136 South Korea Changwon Natl Univ Dept Comp Engn Changwon Si 51140 South Korea

This study aims to develop a success prediction model for crowdfunding by integrating ESG (Environmental, Social, and Governance) factors using machine learning techniques. Crowdfunding, a modern financing method conducted through online platforms, has become a popular avenue for raising funds, particularly for creative projects, startups, and social enterprises. Incorporating ESG factors into crowdfunding success prediction models is crucial, as these factors represent environmental responsibility, social accountability, and ethical governance, which are increasingly important to investors and sponsors. To achieve the research objectives, this study employed advanced machine learning algorithms, including XGBoost, LightGBM, AdaBoost, CatBoost, and NGBoost, to analyze data from a prominent reward-based crowdfunding platform in Korea, 'Wadiz.' Text mining techniques were used to quantify unstructured ESG-related data, and the predictive performance of models including ESG factors was compared with models excluding them. The results demonstrated that prediction accuracy significantly improved when ESG factors were incorporated. Among the ESG dimensions, environmental information contributed the highest prediction performance, followed by social activity information and governance information. The findings of this study contribute to academia by expanding the understanding of non-financial factors in crowdfunding success prediction and integrating ESG elements into machine learning models. This research also provides practical implications by offering insights for platform operators, project creators, and investors to improve decision-making processes through the strategic use of ESG factors. The proposed model enhances crowdfunding platforms' reliability and success rates, supports project makers in designing impactful campaigns, and aids investors in making sustainable and data-driven investment decisions. This study advances theoretical understanding and provides ac

关键词： Crowdfunding Predictive models Companies Data models Investment Boosting Accuracy Business Analytical models Text mining environment ESG governance machine learning social activity

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Improvement of the solar radiation prediction accuracy of a light environment simulation according to the positioning of an interpolation application during the process

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JOURNAL OF BUILDING ENGINEERING 2023年 75卷

作者： Lee, Sewon Lee, Kyung Sun Lee, Jae Seung Lee, Ho Young Kim, Sun Hoo Seoul Natl Univ Interdisciplinary Program Landscape Architecture Seoul 151921 South Korea Hongik Univ Sch Architecture Seoul 04066 South Korea Seoul Natl Univ Dept Landscape Architecture & Integrated Major Sma Seoul 08826 South Korea Ten Eleven INC R&D Ctr Seoul 03925 South Korea

Light environment is directly related to energy reduction and improving health and quality of life, and interest in it continues to increase. Therefore, in light-environment design, simulation tools are used to predict and evaluate the light environment early in the planning stage of a project. Consequently, scientific simulations are most effective from the early stages of sustainable design. However, choosing which simulation tools to use can be challenging, and the entry barriers to their use can be high;some tools are also more difficult to use. In addition, if there is no weather data available for the area to be analyzed, data from a nearby location is often used instead, which reduces the accuracy of the solar radiation prediction in the simulation. Therefore, this study aimed to reduce the difficulty in using environmental prediction simulation tools by applying adjusted Kriging. This study also attempted to determine where the interpolation method could be best applied in the solar radiation simulation process to produce better prediction results. The experimental results showed that the simulation values were more accurate when calculating the solar radiation by generating sky patches with the original typical meteorological year (TMY) data than by generating them with the interpolated TMY data. This interpolation position and method can increase a simulation's accuracy by creating new weather data in areas where no solar radiation data are available. BUILDIT, as a simulation tool for sustainable urban or building design solutions, applied these techniques, which helped the performance of sustainable designs in the early stages of projects.

关键词： Light environment Light simulator Simulator accuracy verification Interpolation Kriging Solar radiation Weather data

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Enhancing water management and urban flood resilience using Hazard Capacity Factor Design (HCFD) model: Case study of Eco-Delta city, Busan

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SUSTAINABLE CITIES AND SOCIETY 2024年 115卷

作者： Kim, Jaekyoung Park, Jongpyo Park, Samuel Kang, Junsuk Gangneung Wonju Natl Univ Dept Environm Landscape Architecture Kangnung 25457 South Korea HECKOREA Inc Seoul 08501 South Korea Seoul Natl Univ Res Ctr Reg Climate Crisis Response Seoul 08826 South Korea Seoul Natl Univ Dept Landscape Architecture & Rural Syst Engn Seoul 08826 South Korea Seoul Natl Univ Interdisciplinary Program Landscape Architecture Seoul 08826 South Korea Seoul Natl Univ Interdisciplinary Program Smart City Global Conver Seoul 08826 South Korea Seoul Natl Univ Res Inst Agr & Life Sci Seoul 08826 South Korea

This study evaluated future climate scenarios and the changes in urban flood resistance capacity in Busan EcoDelta City using the hazard capacity factor design (HCFD) model. It analyzed the flood reduction effects of both gray and green infrastructure. Despite existing flood safety systems, there is a growing need to enhance urban flood resilience due to increasing heavy rainfall, unpredictable precipitation, and frequent typhoons driven by climate change. The HCFD model predicted urban flood volumes of Busan Eco-Delta City and analyzed the effectiveness of gray and green infrastructure in flood control. A stormwater management model (SWMM) simulated urban flood resistance capacity under the SSP1-2.6 climate change scenario. Results indicate that for an anticipated 500 mm rainfall over 3 h, green infrastructure can mitigate floods by 9 % to 17.6 %, while gray infrastructure can reduce flooding by 24 % to 32.1 %. The integration of gray and green infrastructure leads to an overall flood mitigation ranging from 47.1 % to 63.3 %. A notable contribution of this research is its predictive analysis of future flood scenarios using model-based scenario analysis and decision support algorithms, offering valuable insights into changes in urban flood resistance capacity and strategies for effective flood control decision-making.

关键词： Hazard capacity factor design (HCFD) model Future climate prediction Smart cities Climate change scenario Flood mitigation Urban flood risk evaluation

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