Publications

Abstract

자카르타는 급속한 도시화가 진행 중인 해안 도시로, 기후변화의 영향으로 홍수 위험이 지속적으로 증가하고 있다. 기존 연구들은 자카르타의 홍수 위험을 분석해 왔으나, 주로 설문조사나 집계 자료에 의존하여 미시적 단위에서의 지속적 홍수 위험을 충분히 반영하지 못하는 한계가 있었다. 이에 본 연구는 2050년 기후변화 시나리오를 고려하여, 홍수에 취약한 도시인 자카르타의 홍수 위험을 평가⋅예측하기 위한 홍수위험지수(Flood Risk Index, FRI)를 도출하였다. inaRISK와 PODES의 과거 홍수 발생 자료를 물리적⋅기상학적 요인과 통합하여 격자 기반의 홍수 위험을 분석하였다. 예측 모델은 2015-2020년 자료로 학습하고 2021년 자료로 검증한 뒤 최종적으로 R2가 높은 Extra Trees Regressor (88.85%)를 활용하였다. 분석 결과, Danau Sunter Dll, Rawa Buaya, Bungur 지역이 주요 홍수 고위험 지역으로 나타났으며, 이들은 해안 근접성, 지반 침하, 그린 인프라 부족이 복합적으로 작용하여 취약하다 판단하였다. 본 연구 결과는 그린 인프라 …

Abstract

This study addresses the critical issue of road safety in urban environments, with a specific focus on the Greater London Area. Utilizing a novel, theory-driven approach, the study investigates the multifaceted impact of urban fabric factors on road safety, operationalized through a severity-weighted index of road accident frequency per capita. Through factorial analysis, six key factors (Urban Integration, Socioeconomic Challenges, Urban Amenities, Commuter Patterns, Housing and Mobility Barriers, and Major Urban Infrastructure) are identified. These factors are examined in relation to road safety using a structural equation model to uncover theoretical relationships, which inform predictive modeling with an XGBoost machine learning framework, enhanced by SHAP value analysis. Our findings reveal significant insights into the interplay between urban physical and social environments and road safety, revealing that …

Abstract

Effective water quality monitoring is important for environmental protection and public health, yet conventional field and laboratory methods each present significant limitations. Field tools such as colorimetric test strips offer affordability and accessibility but are prone to subjective interpretation and environmental variability. In contrast, laboratory-based techniques provide high precision but are costly, resource-intensive, and less feasible in decentralized contexts. This study presents a hybrid human–machine methodology that improves the accuracy and reproducibility of colorimetric test strip analysis while maintaining field-level accessibility. A total of 34 water samples collected along a 7-km stretch of Seunggi Stream in Incheon, South Korea, were analyzed using a web-based platform that extracts RGB values from images of test strips and reference charts. To translate color into concentration, the system …

Abstract

Human behavior is a dominant factor in road accidents, contributing to more than 70% of such incidents. However, gathering detailed data on individual drivers’ behavior is a significant challenge in the field of road safety. As a result, researchers often narrow the scope of their studies thus limiting the generalizability of their findings. Our study aims to address this issue by identifying demographic-related variables and their indirect effects on road accident frequency. The theoretical basis is set through existing literature linking demographics to risky driving behavior and through the concept of “close to home” effect, finding that the upwards of 62% of accidents happen within 11km of a driver’s home. Using regression-based machine learning models, our study, looking at England, UK, explores the theoretical linkages between demographics of an area and road accident frequency, finding that census data is able to explain over 28% of the variance in road accident rates per capita. While not replacing more in-depth research on driver behavior, this research validates trends found in the literature through the use of widely available data with the use of novel methods. The results of this study support the use of demographic data from the national census that is obtainable at a large spatial and temporal scale to estimate road accident risks; additionally, it demonstrates a methodology to further explore potential indirect relationships and proxies between behaviors and road accident risk.

Abstract

By drawing on the concept of Food Deserts, this study provides a unique lens and methodology to understand the distribution and accessibility of urban parks through the proposed Park Desert Index, and thus, embrace more inclusive park planning and management in Seoul, South Korea. The index combines park accessibility with social deprivation indicators through a methodology taking advantage of hexagonal grids, advanced spatial interpolation, and Gaussian Mixture Modeling for clustering areas based on their population-weighted Park Desert Index score. The findings highlight significant disparities in park equity across Seoul revealing areas requiring policy focus to achieve equitable park distribution. This research offers a novel multidimensional tool for planners and policy makers and contributes to the discourse on urban planning and environmental justice, aiming to improve urban quality of life …

Abstract

포용적 도시공원 조성을 위한 공원사막지수 개발 및 적용 Development and Application of Park Desert Index fo Page 1 16 포용적 도시공원 조성을 위한 공원사막지수 개발및 적용 Development and Application of Park Desert Index for the Establish of Inclusive Urban Parks ● 정민주* (Minju Jeong) · 다코타 애론 멕카티** (Dakota McCarty) · 김현우*** (Hyun Woo Kim) 도시공원은 현대 도시에서 환경적 문제를 해결할 뿐만 아니라 다차원적인 측면에서 도시문제를 해결할 수 있 는 핵심적인 사회간접자본으로 환경적 측면에서는 열섬효과를 완화하기도 하며 대기 환경을 개선하고 미세먼지 를 저감하는 등 공중보건 개선에 기여하고 있다. COVID-19 이후에는 감염병의 전염을 저감시키는 생태백신의 역할을 수행하기도 하였으며 어린이·노인의 외부활동을 장려하고 도시민 삶의 질에 기여하기도 하는 등 그 기능 이 사회적 측면으로 확대되고 있다. 이처럼 사회간접자본으로서 공원녹지의 기능이 강조되고 있는 시점에서 공 원이 제공하고 있는 서비스…

Abstract

Point of interest (POI) data refers to information about the location and type of amenities, services, and attractions within a geographic area. This data is used in urban studies research to better understand the dynamics of a city, assess community needs, and identify opportunities for economic growth and development. POI data is beneficial because it provides a detailed picture of the resources available in a given area, which can inform policy decisions and improve the quality of life for residents. This paper presents a large-scale, standardized POI dataset from OpenStreetMap (OSM) for the European continent. The dataset's standardization and gridding make it more efficient for advanced modeling, reducing 7,218,304 data points to 988,575 without significant resolution loss, suitable for a broader range of models with lower computational demands. The resulting dataset can be used to conduct advanced analyses …

Abstract

Although several previous studies have examined factors influencing crime at a specific point in time, limited research has assessed how factors influencing crime change in response to social disasters such as COVID-19. This study examines factors, along with their relative importance and trends over time, and their influence on 112 commercial crime reports (illegal street vendors, dining and dashing, minor quarrels, theft, drunkenness, assault, vagrancy and disturbing the peace) in Seoul’s alley commercial districts between 2019 and 2021. Variables that may affect the number of commercial crime reports are classified into four characteristics (socioeconomic, neighborhood, park/greenery and commercial district attributes), explored using machine learning regression-based modeling and analyzed through the use of Shapley Additive exPlanations to determine the importance of each factor on crime reports. The Partial Dependence Plot is used to understand linear/non-linear relationships between key independent variables and crime reports. Among several machine learning models, the Extra Trees Regressor, which has the highest performance, is selected for the analysis. The results show a mixture of linear and non-linear relationships with the increasing crime rates, finding that store density, dawn sales ratio, the number of gathering facilities, perceived urban decline score, green view index and land appraisal value may play a crucial role in the number of commercial crimes reported, regardless of social trends. The findings of this study may be used as a basis for building a safe commercial district that can respond resiliently to social …

Abstract

N/A

Abstract

The urban heat island effect has been studied extensively by many researchers around the world with the process of urbanization coming about as one of the major culprits of the increasing urban land surface temperatures. Over the past 20 years, the city of Dallas, Texas, has consistently been one of the fastest growing cities in the United States and has faced rapid urbanization and great amounts of urban sprawl, leading to an increase in built-up surface area. In this study, we utilize Landsat 8 satellite images, Geographic Information System (GIS) technologies, land use/land cover (LULC) data, and a state-of-the-art methodology combining machine learning algorithms (eXtreme Gradient Boosted models, or XGBoost) and a modern game theoretic-based approach (Shapley Additive exPlanation, or SHAP values) to investigate how different land use/land cover classifications impact the land surface temperature and park cooling effects in the city of Dallas. We conclude that green spaces, residential, and commercial/office spaces have the largest impacts on Land Surface Temperatures (LST) as well as the Park’s Cooling Intensity (PCI). Additionally, we have found that the extent and direction of influence of these categories depends heavily on the surrounding area. By using SHAP values we can describe these interactions in greater detail than previous studies. These results will provide an important reference for future urban and park placement planning to minimize the urban heat island effect, especially in sprawling cities.

Abstract

The ability to rapidly produce accurate land use and land cover maps regularly and consistently has been a growing initiative as they have increasingly become an important tool in the efforts to evaluate, monitor, and conserve Earth’s natural resources. Algorithms for supervised classification of satellite images constitute a necessary tool for the building of these maps and they have made it possible to establish remote sensing as the most reliable means of map generation. In this paper, we compare three machine learning techniques: Random Forest, Support Vector Machines, and Light Gradient Boosted Machine, using a 70/30 training/testing evaluation model. Our research evaluates the accuracy of Light Gradient Boosted Machine models against the more classic and trusted Random Forest and Support Vector Machines when it comes to classifying land use and land cover over large geographic areas. We found that the Light Gradient Booted model is marginally more accurate with a 0.01 and 0.059 increase in the overall accuracy compared to Support Vector and Random Forests, respectively, but also performed around 25% quicker on average.

Abstract

This study develops multiple evaluation indexes in the context of sustainable urban regeneration through introducing smart technologies/infrastructures and assesses 63 local urban regeneration strategic plans by using the content analysis method. A total of 107 indexes are developed based on the four aspects (economy, society and culture, environment, and livability) of sustainability. From our findings, the average plan quality score of 54 local governments’ plans is 17.5 out of 50, with the metropolitan governments’ plans averaging 16.8, which indicates that the plans currently sampled do not sufficiently reflect the basic concepts of sustainable and smart urban regeneration. The contents of most of the plans generally focus on specific sectors, such as society, culture, and housing, whereas smart technology-related information and policies are relatively deficient. Among the five plan components (factual bases, goals/objectives, policies/strategies, implementation, coordination) reviewed, the implementation component receives the highest score, while indicators related to action strategies are mentioned least often. In particular, the results reveal that indexes relating to the energy and transportation sectors are not frequently mentioned; as such, each municipality is recommended to work to increase awareness of smart technologies and policies. For urban regeneration projects to be sustainable, multi-faceted policies must be implemented by various stakeholders with a long-term perspective. The results of this study can be used as a base for local planners and decision-makers when adopting and supplementing existing regeneration plans, and …

Abstract

Incheon, South Korea, is a rapidly growing port city that has long held an important role in the country. While for most of its history it has been considered more of a coastal extension of Seoul, it is now trying to grow from that role and become a global city. National and local initiatives and acts have led to the Incheon Free Economic Zone (IFEZ). This large zone connects three smaller districts into one large project. The goal of the project is to assert Incheon as its own city and go beyond its role as merely a gateway to Seoul. However, as most large-scale projects go, there are multiple issues and constraints faced by the IFEZ. This paper analyzes the project and gives critique on how the project could possibly achieve its goal more quickly.

Abstract

We designed a survey to better understand the experiences and characteristics of homeless youth in Dallas. This provides us with a firsthand account of their living experience, and quality of life. Our dependent variable happiness is measured on a scale of 1 to 3, where 1=“not happy,” 2=“pretty happy,” 3=“very happy.” The happiness question asks: Taken all together, how would you say things are these days–would you say that you are very happy, pretty happy, or not too happy?