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Kangjae Lee
School of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Korea

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Journal article
Published: 11 June 2021 in Sensors
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The effects of environmental exposure on human health have been widely explored by scholars in health geography for decades. However, recent advances in geospatial technologies, especially the development of mobile approaches to collecting real-time and high-resolution individual data, have enabled sophisticated methods for assessing people’s environmental exposure. This study proposes an individual environmental exposure assessment system (IEEAS) that integrates objective real-time monitoring devices and subjective sensing tools to provide a composite way for individual-based environmental exposure data collection. With field test data collected in Chicago and Beijing, we illustrate and discuss the advantages of the proposed IEEAS and the composite analysis that could be applied. Data collected with the proposed IEEAS yield relatively accurate measurements of individual exposure in a composite way, and offer new opportunities for developing more sophisticated ways to measure individual environmental exposure. With the capability to consider both the variations in environmental risks and human mobility in high spatial and temporal resolutions, the IEEAS also helps mitigate some uncertainties in environmental exposure assessment and thus enables a better understanding of the relationship between individual environmental exposure and health outcomes.

ACS Style

Jue Wang; Lirong Kou; Mei-Po Kwan; Rebecca Shakespeare; Kangjae Lee; Yoo Park. An Integrated Individual Environmental Exposure Assessment System for Real-Time Mobile Sensing in Environmental Health Studies. Sensors 2021, 21, 4039 .

AMA Style

Jue Wang, Lirong Kou, Mei-Po Kwan, Rebecca Shakespeare, Kangjae Lee, Yoo Park. An Integrated Individual Environmental Exposure Assessment System for Real-Time Mobile Sensing in Environmental Health Studies. Sensors. 2021; 21 (12):4039.

Chicago/Turabian Style

Jue Wang; Lirong Kou; Mei-Po Kwan; Rebecca Shakespeare; Kangjae Lee; Yoo Park. 2021. "An Integrated Individual Environmental Exposure Assessment System for Real-Time Mobile Sensing in Environmental Health Studies." Sensors 21, no. 12: 4039.

Research article
Published: 15 February 2021 in Transactions in GIS
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Past studies have failed to address the spatially and temporally varying impacts of environmental factors regarding the uncertain geographic context problem. This study seeks to provide an innovative framework to facilitate the understanding of spatially and temporally varying impacts of multiple contexts on individuals' travel modes using GIS and machine learning techniques. It adopts machine learning techniques to create likelihood maps to predict the spatiotemporal patterns of individual travel behaviors and uses explanatory tools to explore the spatially and temporally varying impacts. The most notable change at a local level in the spatial dimension was that assaults and offenses involving children turned out to be important in two selected communities in Chicago. Regarding the temporally varying impact, batteries, other offenses, and robberies showed negative associations with the walking prediction to some extent at the afternoon peak (5–7:59 p.m.) during weekdays. The proposed approach will enable meaningful interpretation of complex interactions between multiple environmental factors and individual travel behaviors to suggest policies in urban planning and design.

ACS Style

Kangjae Lee; Mei‐Po Kwan. Interpretation of contextual influences with explanatory tools: Travel mode likelihood mapping using GPS trajectories. Transactions in GIS 2021, 1 .

AMA Style

Kangjae Lee, Mei‐Po Kwan. Interpretation of contextual influences with explanatory tools: Travel mode likelihood mapping using GPS trajectories. Transactions in GIS. 2021; ():1.

Chicago/Turabian Style

Kangjae Lee; Mei‐Po Kwan. 2021. "Interpretation of contextual influences with explanatory tools: Travel mode likelihood mapping using GPS trajectories." Transactions in GIS , no. : 1.

Journal article
Published: 13 August 2020 in Applied Sciences
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Address matching is a crucial step in geocoding; however, this step forms a bottleneck for geocoding accuracy, as precise input is the biggest challenge for establishing perfect matches. Matches still have to be established despite the inevitability of incorrect address inputs such as misspellings, abbreviations, informal and non-standard names, slangs, or coded terms. Thus, this study suggests an address geocoding system using machine learning to enhance the address matching implemented on street-based addresses. Three different kinds of machine learning methods are tested to find the best method showing the highest accuracy. The performance of address matching using machine learning models is compared to multiple text similarity metrics, which are generally used for the word matching. It was proved that extreme gradient boosting with the optimal hyper-parameters was the best machine learning method with the highest accuracy in the address matching process, and the accuracy of extreme gradient boosting outperformed similarity metrics when using training data or input data. The address matching process using machine learning achieved high accuracy and can be applied to any geocoding systems to precisely convert addresses into geographic coordinates for various research and applications, including car navigation.

ACS Style

Kangjae Lee; Alexis Richard C. Claridades; Jiyeong Lee. Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques. Applied Sciences 2020, 10, 5628 .

AMA Style

Kangjae Lee, Alexis Richard C. Claridades, Jiyeong Lee. Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques. Applied Sciences. 2020; 10 (16):5628.

Chicago/Turabian Style

Kangjae Lee; Alexis Richard C. Claridades; Jiyeong Lee. 2020. "Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques." Applied Sciences 10, no. 16: 5628.

Journal article
Published: 13 November 2019 in ISPRS International Journal of Geo-Information
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To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on estimates of individuals’ dynamic exposures along their daily trips recorded as GPS trajectories. Thus, using a 7-day GPS dataset collected in the Chicago Regional Household Travel Inventory (CRHTI) Survey, this study addresses the methodological issue of how the associations between environmental contexts and active travel modes (ATMs) as a subset of physical activity vary with GPS-based buffer size. The results indicate that buffer size influences such associations and the significance levels of the seven environmental factors selected as predictors. Further, the findings on the effects of buffer size on such associations and the significance levels are clearly different between the ATMs of walking and biking. Such evidence of the existence of buffer-size effects for multiple environmental factors not only confirms the importance of the uncertain geographic context problem (UGCoP) but provides a resounding cautionary note to all future research on human mobility involving individuals’ GPS trajectories, including studies on physical activity and travel behaviors, especially on the reliable estimation of individual exposures to environmental factors and their health outcomes.

ACS Style

Kangjae Lee; Mei-Po Kwan. The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts. ISPRS International Journal of Geo-Information 2019, 8, 514 .

AMA Style

Kangjae Lee, Mei-Po Kwan. The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts. ISPRS International Journal of Geo-Information. 2019; 8 (11):514.

Chicago/Turabian Style

Kangjae Lee; Mei-Po Kwan. 2019. "The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts." ISPRS International Journal of Geo-Information 8, no. 11: 514.

Journal article
Published: 04 March 2019 in Urban Forestry & Urban Greening
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People who live near more greenspace report less anxiety and depression. Do these findings hold for elderly populations living in care facilities, such as nursing homes? The answer to this question has not been directly examined. Studies on the relationship between greenspace and mental health in this population have focused on nature-based therapy programs rather than on greenspace coverage. Research on outdoor greenspace coverage is important for facility design. Facilities should know whether to prioritize greening investments in indoor atriums where programming can be provided year-round or in outdoor greenspace, which can also promote health by providing restorative views and reducing harmful exposures (e.g., noise and air pollution). To investigate whether nursing homes residents benefit from outdoor greenspace cover, we examined the relationship between tree canopy cover around 9,186 U.S. nursing homes and the percentage of residents suffering from depressive symptoms. Depressive symptoms data were obtained from the 2011 Minimum Data Set, and canopy data were obtained from the 2011 National Land Cover Database. Because facilities with more resources and higher qualities of care might also have more trees, we gathered 2011 data on occupancy rates, staffing ratios, age, sex, percent Medicaid eligibility, care needs, for-profit status, presence of special care units from the Long Term Care Focus dataset as well as air quality and population density and used these potential covariates in adjusted generalized linear mixed models and spatial lag models. We observed an inverse relationship between depressive symptoms and tree cover surrounding facilities. Associations did not vary by aggregated racial or socioeconomic characteristics of residents but did became weaker at greater distances from facilities. These findings provide hypotheses for future testing regarding whether nursing homes should incorporate outdoor greening in addition to nature-based therapy programs for residents’ mental health.

ACS Style

Matthew H.E.M. Browning; Kangjae Lee; Kathleen L. Wolf. Tree cover shows an inverse relationship with depressive symptoms in elderly residents living in U.S. nursing homes. Urban Forestry & Urban Greening 2019, 41, 23 -32.

AMA Style

Matthew H.E.M. Browning, Kangjae Lee, Kathleen L. Wolf. Tree cover shows an inverse relationship with depressive symptoms in elderly residents living in U.S. nursing homes. Urban Forestry & Urban Greening. 2019; 41 ():23-32.

Chicago/Turabian Style

Matthew H.E.M. Browning; Kangjae Lee; Kathleen L. Wolf. 2019. "Tree cover shows an inverse relationship with depressive symptoms in elderly residents living in U.S. nursing homes." Urban Forestry & Urban Greening 41, no. : 23-32.

Research article
Published: 03 October 2018 in Transactions in GIS
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Due to the advancement of tracking technology, a large quantity of movement data has been collected and analyzed in various research domains. In human mobility and physical activity (PA) research, GPS trajectories and the capabilities of geographic information systems (GIS) facilitate a better understanding of the associations between PA and various environmental factors taking individuals’ daily travels into account. PA research, however, needs to widen its focus from the intensity of PA to types of PA, which may provide useful clues for understanding specific health behaviors in particular geographic contexts. This study proposes and develops an algorithm to automatically classify PA types and in‐vehicle status using GPS and accelerometer data. Walking, standing, jogging, biking and sedentary/in‐vehicle statuses are identified through hierarchical classification processes based on machine learning and geospatial techniques. The proposed algorithm achieved high predictive accuracy on real‐world GPS and accelerometer data. It can greatly reduce participants’ and researchers’ burdens by automatically identifying PA types and in‐vehicle status for human mobility research, which is also known as travel mode imputation in transportation research.

ACS Style

Kangjae Lee; Mei-Po Kwan. Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques. Transactions in GIS 2018, 22, 1522 -1549.

AMA Style

Kangjae Lee, Mei-Po Kwan. Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques. Transactions in GIS. 2018; 22 (6):1522-1549.

Chicago/Turabian Style

Kangjae Lee; Mei-Po Kwan. 2018. "Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques." Transactions in GIS 22, no. 6: 1522-1549.

Journal article
Published: 01 October 2018 in Landscape and Urban Planning
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Recent studies find vegetation around schools correlates positively with student test scores. To test this relationship in schools with less green cover and more disadvantaged students, we replicated a leading study, using six years of NDVI-derived greenness data to predict school-level math and reading achievement in 404 Chicago public schools. A direct replication yielded highly mixed results with some significant positive relationships between greenness and academic achievement, some negative, and some null – but accompanying VIF scores in the thousands indicated untenable levels of multicollinearity. An adjusted replication corrected for multicollinearity and yielded stable results; surprisingly, all models then showed near-zero but statistically significant negative relationships between greenness and performance. In low-green, high-disadvantage schools, negative greenness-academic performance links may reflect the predominance of grass in measures of overall greenness and/or insufficient statistical controls for the moderating effect of disadvantage.

ACS Style

Matthew Browning; Ming Kuo; Sonya Sachdeva; Kangjae Lee; Lynne Westphal. Greenness and school-wide test scores are not always positively associated – A replication of “linking student performance in Massachusetts elementary schools with the ‘greenness’ of school surroundings using remote sensing”. Landscape and Urban Planning 2018, 178, 69 -72.

AMA Style

Matthew Browning, Ming Kuo, Sonya Sachdeva, Kangjae Lee, Lynne Westphal. Greenness and school-wide test scores are not always positively associated – A replication of “linking student performance in Massachusetts elementary schools with the ‘greenness’ of school surroundings using remote sensing”. Landscape and Urban Planning. 2018; 178 ():69-72.

Chicago/Turabian Style

Matthew Browning; Ming Kuo; Sonya Sachdeva; Kangjae Lee; Lynne Westphal. 2018. "Greenness and school-wide test scores are not always positively associated – A replication of “linking student performance in Massachusetts elementary schools with the ‘greenness’ of school surroundings using remote sensing”." Landscape and Urban Planning 178, no. : 69-72.

Review
Published: 08 August 2018 in Urban Science
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This review examines disparities in access to urban green space (UGS) based on socioeconomic status (SES) and race-ethnicity in Global South cities. It was motivated by documented human health and ecosystem services benefits of UGS in Global South countries and UGS planning barriers in rapidly urbanizing cities. Additionally, another review of Global North UGS studies uncovered that high-SES and White people have access to a higher quantity of higher quality UGSs than low-SES and racial-ethnic minority people but that no clear differences exist regarding who lives closer to UGS. Thus, we conducted a systematic review to uncover (1) whether UGS inequities in Global North cities are evident in Global South cities and (2) whether inequities in the Global South vary between continents. Through the PRISMA approach and five inclusion criteria, we identified 46 peer-reviewed articles that measured SES or racial-ethnic disparities in access to UGS in Global South cities. We found inequities for UGS quantity (high-SES people are advantaged in 85% of cases) and UGS proximity (74% of cases). Inequities were less consistent for UGS quality (65% of cases). We also found that UGS inequities were consistent across African, Asian, and Latin American cities. These findings suggest that Global South cities experience similar inequities in UGS quantity and quality as Global North cities, but that the former also face inequities in UGS proximity.

ACS Style

Alessandro Rigolon; Matthew H. E. M. Browning; Kangjae Lee; Seunguk Shin. Access to Urban Green Space in Cities of the Global South: A Systematic Literature Review. Urban Science 2018, 2, 67 .

AMA Style

Alessandro Rigolon, Matthew H. E. M. Browning, Kangjae Lee, Seunguk Shin. Access to Urban Green Space in Cities of the Global South: A Systematic Literature Review. Urban Science. 2018; 2 (3):67.

Chicago/Turabian Style

Alessandro Rigolon; Matthew H. E. M. Browning; Kangjae Lee; Seunguk Shin. 2018. "Access to Urban Green Space in Cities of the Global South: A Systematic Literature Review." Urban Science 2, no. 3: 67.

Journal article
Published: 05 April 2018 in ISPRS International Journal of Geo-Information
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Considerable research has been conducted to advance our understanding of how environmental factors influence people’s health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic locations. This study explores the inconsistent findings regarding the associations between environmental exposures and physical inactivity. To address spatial autocorrelation and explore the impact of spatial non-stationarity on research results which may lead to biased estimators, this study uses spatial regression models to examine the associations between leisure-time physical inactivity and different social and physical environmental factors for all counties in the conterminous U.S. By comparing the results with the conventional ordinary least squares regression and spatial lag model, the geographically weighted regression model adequately addresses the problem of spatial autocorrelation (Moran’s I of the residual = 0.0293) and highlights the spatial non-stationarity of the associations. The existence of spatial non-stationarity that leads to biased estimators, which were often ignored in past research, may be another reason for the inconsistent findings in previous studies besides the modifiable areal unit problem and the uncertain geographic context problem. Also, the observed associations between environmental variables and leisure-time physical inactivity are helpful for developing location-based policies and interventions to encourage people to undertake more physical activity.

ACS Style

Jue Wang; Kangjae Lee; Mei-Po Kwan. Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity. ISPRS International Journal of Geo-Information 2018, 7, 143 .

AMA Style

Jue Wang, Kangjae Lee, Mei-Po Kwan. Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity. ISPRS International Journal of Geo-Information. 2018; 7 (4):143.

Chicago/Turabian Style

Jue Wang; Kangjae Lee; Mei-Po Kwan. 2018. "Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity." ISPRS International Journal of Geo-Information 7, no. 4: 143.

Journal article
Published: 01 January 2018 in Computers, Environment and Urban Systems
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ACS Style

Kangjae Lee; Mei-Po Kwan. Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results. Computers, Environment and Urban Systems 2018, 67, 124 -131.

AMA Style

Kangjae Lee, Mei-Po Kwan. Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results. Computers, Environment and Urban Systems. 2018; 67 ():124-131.

Chicago/Turabian Style

Kangjae Lee; Mei-Po Kwan. 2018. "Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results." Computers, Environment and Urban Systems 67, no. : 124-131.

Review
Published: 23 June 2017 in International Journal of Environmental Research and Public Health
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Is the amount of “greenness” within a 250-m, 500-m, 1000-m or a 2000-m buffer surrounding a person’s home a good predictor of their physical health? The evidence is inconclusive. We reviewed Web of Science articles that used geographic information system buffer analyses to identify trends between physical health, greenness, and distance within which greenness is measured. Our inclusion criteria were: (1) use of buffers to estimate residential greenness; (2) statistical analyses that calculated significance of the greenness-physical health relationship; and (3) peer-reviewed articles published in English between 2007 and 2017. To capture multiple findings from a single article, we selected our unit of inquiry as the analysis, not the article. Our final sample included 260 analyses in 47 articles. All aspects of the review were in accordance with PRISMA guidelines. Analyses were independently judged as more, less, or least likely to be biased based on the inclusion of objective health measures and income/education controls. We found evidence that larger buffer sizes, up to 2000 m, better predicted physical health than smaller ones. We recommend that future analyses use nested rather than overlapping buffers to evaluate to what extent greenness not immediately around a person’s home (i.e., within 1000–2000 m) predicts physical health.

ACS Style

Matthew Browning; Kangjae Lee. Within What Distance Does “Greenness” Best Predict Physical Health? A Systematic Review of Articles with GIS Buffer Analyses across the Lifespan. International Journal of Environmental Research and Public Health 2017, 14, 675 .

AMA Style

Matthew Browning, Kangjae Lee. Within What Distance Does “Greenness” Best Predict Physical Health? A Systematic Review of Articles with GIS Buffer Analyses across the Lifespan. International Journal of Environmental Research and Public Health. 2017; 14 (7):675.

Chicago/Turabian Style

Matthew Browning; Kangjae Lee. 2017. "Within What Distance Does “Greenness” Best Predict Physical Health? A Systematic Review of Articles with GIS Buffer Analyses across the Lifespan." International Journal of Environmental Research and Public Health 14, no. 7: 675.

Journal article
Published: 01 March 2017 in Computers, Environment and Urban Systems
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ACS Style

Kangjae Lee; Jiyeong Lee; Mei-Po Kwan. Location-based service using ontology-based semantic queries: A study with a focus on indoor activities in a university context. Computers, Environment and Urban Systems 2017, 62, 41 -52.

AMA Style

Kangjae Lee, Jiyeong Lee, Mei-Po Kwan. Location-based service using ontology-based semantic queries: A study with a focus on indoor activities in a university context. Computers, Environment and Urban Systems. 2017; 62 ():41-52.

Chicago/Turabian Style

Kangjae Lee; Jiyeong Lee; Mei-Po Kwan. 2017. "Location-based service using ontology-based semantic queries: A study with a focus on indoor activities in a university context." Computers, Environment and Urban Systems 62, no. : 41-52.

Journal article
Published: 31 December 2013 in Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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ACS Style

Kangjae Lee; Hye-Young Kang; Jiyeong Lee. Topological Analysis in Indoor Shopping Mall using Ontology. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 2013, 31, 511 -520.

AMA Style

Kangjae Lee, Hye-Young Kang, Jiyeong Lee. Topological Analysis in Indoor Shopping Mall using Ontology. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography. 2013; 31 (6_2):511-520.

Chicago/Turabian Style

Kangjae Lee; Hye-Young Kang; Jiyeong Lee. 2013. "Topological Analysis in Indoor Shopping Mall using Ontology." Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 31, no. 6_2: 511-520.

Journal article
Published: 28 February 2013 in Journal of Korea Spatial Information Society
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ACS Style

Kang-Jae Lee; Jiyeong Lee. A Geocoding Method on Character Matching in Indoor Spaces. Journal of Korea Spatial Information Society 2013, 21, 87 -100.

AMA Style

Kang-Jae Lee, Jiyeong Lee. A Geocoding Method on Character Matching in Indoor Spaces. Journal of Korea Spatial Information Society. 2013; 21 (1):87-100.

Chicago/Turabian Style

Kang-Jae Lee; Jiyeong Lee. 2013. "A Geocoding Method on Character Matching in Indoor Spaces." Journal of Korea Spatial Information Society 21, no. 1: 87-100.