Publications
My publications in reversed chronological order.
2023
- Appl. Sci.Modeling the Health Benefits of Superblocks across the City of Los AngelesKenan Li, and John P. WilsonApplied Sciences, 2023
Superblocks can help to transform urban areas into pedestrian-centric neighborhoods. First launched in Barcelona, Spain, they are expected to reduce harmful environmental exposures, increase green space access and thereby provide substantial health benefits. However, few studies have examined the practicality and likely benefits of implementing Superblocks in other metropolitan areas. We developed a methodological framework to build a generalizable City of Los Angeles (LA) Superblocks Model and evaluate the predicted health benefits that would follow such an intervention. We derived and used five rules to guide the choice of arterial streets and candidate blocks and the choice of major bounding streets that could facilitate mobility across the metropolitan area. We next used the BenMap-CE model to perform a quantitative assessment of the health and economic benefits that would accompany five scenarios that would transform 5–50% of the residential areas in the City of LA to Superblocks. We found that the creation of superblocks resulted in significant reductions in hospital admissions and significant economic savings. The benefits were strongest when 5–10% of residential areas were transformed, but rapidly decreased as the threshold reached 30%. These results will help stakeholders determine the optimal balance between reduced car traffic and improved health outcomes. Moreover, we illustrated how to develop a Superblocks model for a highly versatile and populated metropolitan area like the City of LA and how the model can be used to assess the potential health benefits and benchmark the relationship between the scale of the Superblock implementation and the accompanying health benefits moving forward.
@article{LiApp1Sci23-01, author = {Li, Kenan and Wilson, John P.}, title = {Modeling the Health Benefits of Superblocks across the City of Los Angeles}, journal = {Applied Sciences}, volume = {13}, year = {2023}, number = {4}, article-number = {2095}, issn = {2076-3417}, doi = {10.3390/app13042095}, dimensions = {true} } - Appl. Sci.Geographic Variations in Human Mobility Patterns during the First Six Months of the COVID-19 Pandemic in CaliforniaKenan Li, Sandrah P. Eckel, Erika Garcia, and 3 more authorsApplied Sciences, 2023
Human mobility influenced the spread of the COVID-19 virus, as revealed by the high spatiotemporal granularity location service data gathered from smart devices. We conducted time series clustering analysis to delineate the relationships between human mobility patterns (HMPs) and their social determinants in California (CA) using aggregated smart device tracking data from SafeGraph. We first identified four types of temporal patterns for five human mobility indicator changes by applying dynamic-time-warping self-organizing map clustering methods. We then performed an analysis of variance and linear discriminant analysis on the HMPs with 17 social, economic, and demographic variables. Asians, children under five, adults over 65, and individuals living below the poverty line were found to be among the top contributors to the HMPs, including the HMP with a significant increase in the median home dwelling time and the HMP with emerging weekly patterns in full-time and part-time work devices. Our findings show that the CA shelter-in-place policy had varying impacts on HMPs, with socially disadvantaged places showing less compliance. The HMPs may help practitioners to anticipate the efficacy of non-pharmaceutical interventions on cases and deaths in pandemics.
@article{LiApp1Sci23-02, author = {Li, Kenan and Eckel, Sandrah P. and Garcia, Erika and Chen, Zhanghua and Wilson, John P. and Gilliland, Frank D.}, title = {Geographic Variations in Human Mobility Patterns during the First Six Months of the COVID-19 Pandemic in California}, journal = {Applied Sciences}, volume = {13}, year = {2023}, number = {4}, article-number = {2440}, issn = {2076-3417}, doi = {10.3390/app13042440}, dimensions = {true} }
2022
- Environ. Pollut.Long-term air pollution and COVID-19 mortality rates in California: Findings from the Spring/Summer and Winter surges of COVID-19Erika Garcia, Brittney Marian, Zhanghua Chen, and 4 more authorsEnvironmental Pollution, 2022
A growing number of studies report associations between air pollution and COVID-19 mortality. Most were ecological studies at the county or regional level which disregard important local variability and relied on data from only the first few months of the pandemic. Using COVID-19 deaths identified from death certificates in California, we evaluated whether long-term ambient air pollution was related to weekly COVID-19 mortality at the census tract-level during the first ∼12 months of the pandemic. Weekly COVID-19 mortality for each census tract was calculated based on geocoded death certificate data. Annual average concentrations of ambient particulate matter <2.5 μm (PM2.5) and <10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) over 2014–2019 were assessed for all census tracts using inverse distance-squared weighting based on data from the ambient air quality monitoring system. Negative binomial mixed models related weekly census tract COVID-19 mortality counts to a natural cubic spline for calendar week. We included adjustments for potential confounders (census tract demographic and socioeconomic factors), random effects for census tract and county, and an offset for census tract population. Data were analyzed as two study periods: Spring/Summer (March 16-October 18, 2020) and Winter (October 19, 2020–March 7, 2021). Mean (standard deviation) concentrations were 10.3 (2.1) μg/m3 for PM2.5, 25.5 (7.1) μg/m3 for PM10, 11.3 (4.0) ppb for NO2, and 42.8 (6.9) ppb for O3. For Spring/Summer, adjusted rate ratios per standard deviation increase were 1.13 (95% confidence interval: 1.09, 1.17) for PM2.5, 1.16 (1.11, 1.21) for PM10, 1.06 (1.02, 1.10) for NO2, and 1.09 (1.04, 1.14) for O3. Associations were replicated in Winter, although they were attenuated for PM2.5 and PM10. Study findings support a relation between long-term ambient air pollution exposure and COVID-19 mortality. Communities with historically high pollution levels might be at higher risk of COVID-19 mortality.
@article{GarciaEnvironmentalPollution22, title = {Long-term air pollution and COVID-19 mortality rates in California: Findings from the Spring/Summer and Winter surges of COVID-19}, journal = {Environmental Pollution}, volume = {292}, pages = {118396}, year = {2022}, issn = {0269-7491}, doi = {https://doi.org/10.1016/j.envpol.2021.118396}, url = {https://www.sciencedirect.com/science/article/pii/S0269749121019783}, author = {Garcia, Erika and Marian, Brittney and Chen, Zhanghua and Li, Kenan and Lurmann, Fred and Gilliland, Frank and Eckel, Sandrah P.}, keywords = {Air pollution, COVID-19, Mortality, Particulate matter, Ozone, Nitrogen dioxide}, dimensions = {true} }
2021
- Pub. Exc.Enough to Eat: The Impact of COVID-19 on Food Insecurity and the Food Environment in L.A. County April 2020 –September 2021Kayla Haye, John Wilson, Wändi Bruin, and 9 more authors2021
@report{KaylaPE21, author = {de la Haye, Kayla and Wilson, John and Bruine de Bruin, Wändi and Li, Kenan and Livings, Michelle and Xu, Mengya and Miller, Sydney and Solanky, Maansi and Weber, Kate and Babboni, Marianna and Wald, Rachel and Frazzini, Alison}, title = {Enough to Eat: The Impact of COVID-19 on Food Insecurity and the Food Environment in L.A. County April 2020 –September 2021}, institution = {Dornsife Public Exchange, University of Southern California}, year = {2021}, } - Sci. Rep.Using dynamic time warping self-organizing maps to characterize diurnal patterns in environmental exposuresKenan Li, Katherine Sward, Huiyu Deng, and 7 more authorsScientific Reports, Dec 2021
Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM2.5) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients were followed for up to 373 days each. Compared to previous SOM algorithms using timestamp alignment on time series data, the DTW-SOM algorithm produced fewer quantization errors and more detailed diurnal patterns. DTW-SOM identified the expected typical diurnal patterns in outdoor temperature which varied by season, as well diurnal patterns in PM2.5 which may be related to daily asthma outcomes. In summary, DTW-SOM is an innovative feature engineering method that can be applied to highly time-resolved environmental exposures assessed by sensors to identify typical diurnal (or hourly or monthly) patterns and provide new insights into the health effects of environmental exposures.
@article{LiScientificReports21, title = {Using dynamic time warping self-organizing maps to characterize diurnal patterns in environmental exposures}, author = {Li, Kenan and Sward, Katherine and Deng, Huiyu and Morrison, John and Habre, Rima and Franklin, Meredith and Chiang, Yao-Yi and Ambite, Jose Luis and Wilson, John P and Eckel, Sandrah P}, journal = {Scientific Reports}, volume = {11}, number = {1}, pages = {24052}, month = dec, year = {2021}, doi = {10.1038/s41598-021-03515-1}, dimensions = {true} } - SensorsW-TSS: A Wavelet-Based Algorithm for Discovering Time Series ShapeletsKenan Li, Huiyu Deng, John Morrison, and 7 more authorsSensors, Dec 2021
Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.
@article{LiSensors21, author = {Li, Kenan and Deng, Huiyu and Morrison, John and Habre, Rima and Franklin, Meredith and Chiang, Yao-Yi and Sward, Katherine and Gilliland, Frank D. and Ambite, José Luis and Eckel, Sandrah P.}, title = {W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets}, journal = {Sensors}, volume = {21}, year = {2021}, number = {17}, article-number = {5801}, url = {https://www.mdpi.com/1424-8220/21/17/5801}, pubmedid = {34502692}, issn = {1424-8220}, doi = {10.3390/s21175801}, dimensions = {true} } - Annu. of Epi.COVID-19 mortality in California based on death certificates: disproportionate impacts across racial/ethnic groups and nativityErika Garcia, Sandrah P. Eckel, Zhanghua Chen, and 2 more authorsANNALS OF EPIDEMIOLOGY, Jun 2021
Purpose: To examine characteristics of coronavirus disease 2019 (COVID-19) decedents in California (CA) and evaluate for disproportionate mortality across race/ethnicity and ethnicity/nativity. Methods: COVID-19 deaths were identified from death certificates. Age-adjusted mortality rate ratios (MRR) were compared across race/ethnicity. Proportionate mortality rates (PMR) were compared across race/ethnicity and by ethnicity/nativity. Results: We identified 10,200 COVID-19 deaths in CA occurring February 1 through July 31, 2020. The most frequently observed characteristics among decedents were age 65 years or above, male, Hispanic, foreign-born, and educational attainment of High School or below. MRR indicated elevated COVID-19 morality rates among Asian/Pacific Islander, Black, and Hispanic groups compared with the White group, with Black and Hispanic groups having the highest MRR at 2.75 (95%CI: 2.54-2.97) and 4.18 (95%CI: 3.994.37), respectively. Disparities were larger at younger ages. Similar results were observed with PMR, and patterns of age-racial/ethnic disparities remained in analyses stratified by education. Elevated PMR were observed in all ethnicity/nativity groups, especially foreign-born Hispanic individuals, relative to U.S.-born non-Hispanic individuals. These were generally larger at younger ages and persisted after stratifying by education. Conclusions: Differential COVID-19 mortality was observed in California across racial/ethnic groups and by ethnicity/nativity groups with evidence of greater disparities among younger age groups. Identifying COVID-19 disparities is an initial step toward mitigating disease impacts in vulnerable communities. (c) 2021 Elsevier Inc. All rights reserved.
@article{GarciaEpidemiology21, author = {Garcia, Erika and Eckel, Sandrah P. and Chen, Zhanghua and Li, Kenan and Gilliland, Frank D.}, title = {COVID-19 mortality in California based on death certificates: disproportionate impacts across racial/ethnic groups and nativity}, journal = {ANNALS OF EPIDEMIOLOGY}, year = {2021}, volume = {58}, pages = {69-75}, month = jun, doi = {10.1016/j.annepidem.2021.03.006}, issn = {1047-2797}, eissn = {1873-2585}, unique-id = {WOS:000655476000011}, dimensions = {true} }
2020
- USC SSILA County-Analytics Pilot: Building a Geo-Cyberinfrastructure for Emergency Management of Trail Systems in Los Angeles CountyAlicia Adiwidjaja, Kenan Li, Adam Owens, and 1 more authorJun 2020
@report{AdiwidjajaSSI21, author = {Adiwidjaja, Alicia and Li, Kenan and Owens, Adam and Wilson, John P}, title = {LA County-Analytics Pilot: Building a Geo-Cyberinfrastructure for Emergency Management of Trail Systems in Los Angeles County}, institution = {Spatial Sciences Institute, University of Southern California}, year = {2020}, }
2019
- SpringerCollaboration Across Boundaries: Reflections on Studying the Sustainability of the Mississippi River Delta as a Coupled Natural-Human SystemNina S.-N. Lam, Y. Jun Xu, R. Kelley Pace, and 9 more authorsJun 2019
We report in this chapter our experience in collaboration across boundaries from working on an interdisciplinary project funded by the National Science Foundation under the Dynamics of Coupled Natural-Human Systems program. The project investigates the sustainability of the Mississippi River Delta (MRD), which is considered one of the most vulnerable coastal zones in the continental United States and the world. Our overarching research question is: will the MRD reach a tipping point that would make it difficult to sustain in the future? The project consists of seven components, each led by investigators from disciplines including hydrology, sedimentology, ecology, geography, political science, economics, and finance. We conducted a survey of the team members to obtain their opinions on the challenges, benefits, and suggestions for improvement regarding collaboration across disciplinary boundaries. The results provide insights into the development of best practice for collaboration across boundaries. Survey results suggest that a successful interdisciplinary project would need a detailed research plan with timelines and expected results stated, and the plan would need to be followed through. Finding collaborators who have similar priorities, can deliver the results on time, and continue engagement in the research is difficult, but the reward in making it happen is gratifying because it will ultimately be beneficial to advancing the science and practice of sustaining complex natural-human systems.
@book{Lam2019, author = {Lam, Nina S.-N. and Xu, Y. Jun and Pace, R. Kelley and Liu, Kam-biu and Qiang, Yi and Narra, Siddhartha and Bianchette, Thomas A. and Cai, Heng and Zou, Lei and Li, Kenan and Joshi, Sanjeev and Mihunov, Volodymyr}, editor = {Perz, Stephen G.}, title = {Collaboration Across Boundaries: Reflections on Studying the Sustainability of the Mississippi River Delta as a Coupled Natural-Human System}, booktitle = {Collaboration Across Boundaries for Social-Ecological Systems Science: Experiences Around the World}, year = {2019}, publisher = {Springer International Publishing}, address = {Cham}, pages = {361--393}, isbn = {978-3-030-13827-1}, doi = {10.1007/978-3-030-13827-1_11}, url = {https://doi.org/10.1007/978-3-030-13827-1_11}, keywords = {chapter}, dimensions = {true} } - JMIR M. U.Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ DataKenan Li, Rima Habre, Huiyu Deng, and 12 more authorsJMIR MHEALTH AND UHEALTH, Feb 2019
Background: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. Objective: We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. Methods: We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into similar to 90% training and similar to 10% holdout testing. Results: In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. Conclusions: In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.
@article{LiJMIR19, author = {Li, Kenan and Habre, Rima and Deng, Huiyu and Morrison, John and Gilliland, Frank D. and Ambite, Jose Luis and Stripelis, Dimitris and Chiang, Yao-Yi and Lin, Yijun and Bui, Alex A. T. and King, Christine and Hosseini, Anahita and Van Vliet, Eleanne and Sarrafzadeh, Majid and Eckel, Sandrah P.}, title = {Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data}, journal = {JMIR MHEALTH AND UHEALTH}, year = {2019}, volume = {7}, number = {2}, month = feb, doi = {10.2196/11201}, article-number = {e11201}, issn = {2291-5222}, researcherid-numbers = {Habre, Rima/AAJ-3385-2020 Chiang, Yao-Yi/N-3628-2013 }, orcid-numbers = {van Vliet, Eleanne/0000-0001-5997-3040 Habre, Rima/0000-0003-2103-1706 Ambite, Jose Luis/0000-0003-0087-080X Lin, Yijun/0000-0002-0815-9636 Chiang, Yao-Yi/0000-0002-8923-0130 Gilliland, Frank/0000-0002-9033-7269 Li, Kenan/0000-0002-4641-6699}, unique-id = {WOS:000458350900001}, dimensions = {true} }
2018
- J. Coastal Res.Extending Resilience Assessment to Dynamic System Modeling: Perspectives on Human Dynamics and Climate Change ResearchNina S. -N. Lam, Yi Qiang, Kenan Li, and 3 more authorsJOURNAL OF COASTAL RESEARCH, May 201815th International Coastal Symposium (ICS), Busan, SOUTH KOREA, MAY 13-18, 2018
It is widely known that the same type and strength of hazard could lead to very uneven impacts on different communities due to their varying vulnerability and resilience capacity. Hence, identifying the factors that make a community more resilient to hazards is critical to its sustainability and is central to climate change research and planning. This paper addresses three questions: what is the best way to measure community resilience to disasters and how to identify the key indicators? How do the resilience indicators dynamically interact in a quantitative manner that would lead to long-term resilience? And how can we translate the scientific results into practical tools for decision making? Using the population change pattern in the Mississippi River Delta as a case study, this paper demonstrates the use of a relatively new resilience assessment method called the Resilience Inference Measurement (RIM) method to measure resilience. Then, a newly developed spatial dynamic model is used to simulate population changes in the study area. The results show that without any changes in the current condition, the coastal portion of the study area will continue to suffer population loss and the region is unlikely to sustain in the future.
@article{LamJCR18, author = {Lam, Nina S. -N. and Qiang, Yi and Li, Kenan and Cai, Heng and Zou, Lei and Mihunov, Volodymyr}, title = {Extending Resilience Assessment to Dynamic System Modeling: Perspectives on Human Dynamics and Climate Change Research}, journal = {JOURNAL OF COASTAL RESEARCH}, year = {2018}, number = {85}, pages = {1401-1405}, month = may, note = {15th International Coastal Symposium (ICS), Busan, SOUTH KOREA, MAY 13-18, 2018}, organization = {Korea Inst Ocean Sci \& Technol; Korea Soc Coastal Disaster Prevent; JCR; Coastal Educ \& Res Fdn}, doi = {10.2112/SI85-281.1}, issn = {0749-0208}, eissn = {1551-5036}, researcherid-numbers = {Cai, Heng/AAK-6514-2020 Zou, Lei/AAV-8227-2020 Lam, Nina/AAQ-1097-2020 Mihunov, Volodymyr/R-7510-2019}, orcid-numbers = {Cai, Heng/0000-0001-6068-5150 Zou, Lei/0000-0001-6206-3558 Mihunov, Volodymyr/0000-0002-1490-6124}, unique-id = {WOS:000441173100281}, dimensions = {true} } - WaterUnderstanding the Mississippi River Delta as a Coupled Natural-Human System: Research Methods, Challenges, and ProspectsNina S-N. Lam, Y. Jun Xu, Kam-biu Liu, and 10 more authorsWATER, Aug 2018
A pressing question facing the Mississippi River Delta (MRD), like many deltaic communities around the world, is: Will the system be sustainable in the future given the threats of sea level rise, land loss, natural disasters, and depleting natural resources? An integrated coastal modeling framework that incorporates both the natural and human components of these communities, and their interactions with both pulse and press stressors, is needed to help improve our understanding of coastal resilience. However, studying the coastal communities using a coupled natural-human system (CNH) approach is difficult. This paper presents a CNH modeling framework to analyze coastal resilience. We first describe such a CNH modeling framework through a case study of the Lower Mississippi River Delta in coastal Louisiana, USA. Persistent land loss and associated population decrease in the study region, a result of interplays between human and natural factors, are a serious threat to the sustainability of the region. Then, the paper describes the methods and findings of three studies on how community resilience of the MRD system is measured, how land loss is modeled using an artificial neural network-cellular automata approach, and how a system dynamic modeling approach is used to simulate population change in the region. The paper concludes by highlighting lessons learned from these studies and suggesting the path forward for analysis of coupled natural-human systems.
@article{LamWater18, author = {Lam, Nina S-N. and Xu, Y. Jun and Liu, Kam-biu and Dismukes, David E. and Reams, Margaret and Pace, R. Kelley and Qiang, Yi and Narra, Siddhartha and Li, Kenan and Bianchette, Thomas A. and Cai, Heng and Zou, Lei and Mihunov, Volodymyr}, title = {Understanding the Mississippi River Delta as a Coupled Natural-Human System: Research Methods, Challenges, and Prospects}, journal = {WATER}, year = {2018}, volume = {10}, number = {8}, month = aug, doi = {10.3390/w10081054}, article-number = {1054}, eissn = {2073-4441}, researcherid-numbers = {Lam, Nina/AAQ-1097-2020 Zou, Lei/AAV-8227-2020 Cai, Heng/AAK-6514-2020 Mihunov, Volodymyr/R-7510-2019 Xu, Y. Jun/A-7681-2014 Qiang, Yi/J-3313-2016 }, orcid-numbers = {Zou, Lei/0000-0001-6206-3558 Cai, Heng/0000-0001-6068-5150 Mihunov, Volodymyr/0000-0002-1490-6124 Xu, Y. Jun/0000-0003-3646-626X Qiang, Yi/0000-0002-6872-8837 Lam, Nina S.N./0000-0002-5344-9368}, unique-id = {WOS:000448462700088}, dimensions = {true} } - IJGISA spatial dynamic model of population changes in a vulnerable coastal environmentKenan Li, and Nina S. N. LamINTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, Aug 2018
This study developed a spatial dynamic model to examine the coupled natural-human responses in the form of changes in population and associated developed land area in the Lower Mississippi River Basin region. The goal was to identify key socioeconomic factors (utility) and environmental factors (hazard damages, elevation, and subsidence rate) that affected population changes, as well as to examine how population changes affected the local utility and the local environment reciprocally. We first applied areal interpolation techniques with the volume-preserving property to transform all the data at Year 2000 into a unified 3km by 3km cellular space. We then built an Elastic Net model to extract 12 variables from a set of 33 for the spatial dynamic model. Afterward, we calibrated the neighborhood effects with a genetic algorithm and use the spatial dynamic model to simulate population and developed land area in 2010. Furthermore, we took a Monte Carlo approach for analyzing the uncertainty of the model outcome. Our accuracy assessment shows that the model on average slightly overpredicts the number of population and the developed land percentage at 2010, as indicated by the low values of mean absolute deviation (MAD) due to quantity. On the other hand, the MADs due to allocation are larger than the MADs due to quantity, with most outliers found in the New Orleans region where population and urban development declined significantly during 2000-2010 after Hurricane Katrina. The proposed model sheds light on the complex relationships between coastal hazards and human responses and provides useful insights to strategic development for coastal sustainability.
@article{LiIJGIS18, author = {Li, Kenan and Lam, Nina S. N.}, title = {A spatial dynamic model of population changes in a vulnerable coastal environment}, journal = {INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE}, year = {2018}, volume = {32}, number = {4}, pages = {685-710}, doi = {10.1080/13658816.2017.1407415}, issn = {1365-8816}, eissn = {1362-3087}, researcherid-numbers = {Lam, Nina/AAQ-1097-2020}, unique-id = {WOS:000423627100003}, dimensions = {true} } - Annu. of AAGGeographically Weighted Elastic Net: A Variable-Selection and Modeling Method under the Spatially Nonstationary ConditionKenan Li, and Nina S. N. LamANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS, Nov 2018
This study develops a linear regression model to select local, low-collinear explanatory variables. This model combines two well-known models: geographically weighted regression (GWR) and elastic net (EN). The GWR model posits that the regression coefficients vary as a function of location and focuses on solving the problem of explaining the relationships under the spatially nonstationary condition, which a global model cannot solve. GWR cannot fulfill the task of variable selection, however, which is problematic when there are many explanatory variables with nonnegligible multicollinearity. On the other hand, the EN model is a member of the regulated regression family. EN can trim the number of explanatory variables and select the most important ones by adding penalty terms in its cost function, and it has been proven to be robust under the high-multicollinearity condition. The EN model is a global model, however, and does not consider the spatial nonstationarity. To overcome these deficiencies, we proposed the geographically weighted elastic net (GWEN) model. GWEN uses the kernel weights derived from GWR and applies EN locally to select variables for each geographical location. The result is a set of locally selected, low-collinear explanatory variables with spatially varying coefficients. We demonstrated the GWEN method on a data set relating population changes to a set of social, economic, and environmental variables in the Lower Mississippi River Basin. The results show that GWEN has the advantages of both the high prediction accuracy of GWR and the low multicollinearity among explanatory variables of EN.
@article{LiAAG18, author = {Li, Kenan and Lam, Nina S. N.}, title = {Geographically Weighted Elastic Net: A Variable-Selection and Modeling Method under the Spatially Nonstationary Condition}, journal = {ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS}, year = {2018}, volume = {108}, number = {6}, pages = {1582-1600}, month = nov, doi = {10.1080/24694452.2018.1425129}, issn = {2469-4452}, eissn = {2469-4460}, researcherid-numbers = {Lam, Nina/AAQ-1097-2020}, unique-id = {WOS:000449077600012}, dimensions = {true} }
2017
2016
- WaterEvaluating Land Subsidence Rates and Their Implications for Land Loss in the Lower Mississippi River BasinLei Zou, Joshua Kent, Nina S. -N. Lam, and 3 more authorsWATER, Jan 2016
High subsidence rates, along with eustatic sea-level change, sediment accumulation and shoreline erosion have led to widespread land loss and the deterioration of ecosystem health around the Lower Mississippi River Basin (LMRB). A proper evaluation of the spatial pattern of subsidence rates in the LMRB is the key to understanding the mechanisms of the submergence, estimating its potential impacts on land loss and the long-term sustainability of the region. Based on the subsidence rate data derived from benchmark surveys from 1922 to 1995, this paper constructed a subsidence rate surface for the region through the empirical Bayesian kriging (EBK) interpolation method. The results show that the subsidence rates in the region ranged from 1.7 to 29 mm/year, with an average rate of 9.4 mm/year. Subsidence rates increased from north to south as the outcome of both regional geophysical conditions and anthropogenic activities. Four areas of high subsidence rates were found, and they are located in Orleans, Jefferson, Terrebonne and Plaquemines parishes. A projection of future landscape loss using the interpolated subsidence rates reveals that areas below zero elevation in the LMRB will increase from 3.86% in 2004 to 19.79% in 2030 and 30.88% in 2050. This translates to a growing increase of areas that are vulnerable to land loss from 44.3 km(2)/year to 240.7 km(2)/year from 2011 to 2050. Under the same scenario, Lafourche, Plaquemines and Terrebonne parishes will experience serious loss of wetlands, whereas Orleans and Jefferson parishes will lose significant developed land, and Lafourche parish will endure severe loss of agriculture land.
@article{ZouWater16, author = {Zou, Lei and Kent, Joshua and Lam, Nina S. -N. and Cai, Heng and Qiang, Yi and Li, Kenan}, title = {Evaluating Land Subsidence Rates and Their Implications for Land Loss in the Lower Mississippi River Basin}, journal = {WATER}, year = {2016}, volume = {8}, number = {1}, month = jan, doi = {10.3390/w8010010}, article-number = {10}, issn = {2073-4441}, researcherid-numbers = {Zou, Lei/AAV-8227-2020 Cai, Heng/AAK-6514-2020 Qiang, Yi/J-3313-2016 Lam, Nina/AAQ-1097-2020}, orcid-numbers = {Zou, Lei/0000-0001-6206-3558 Cai, Heng/0000-0001-6068-5150 Qiang, Yi/0000-0002-6872-8837 }, unique-id = {WOS:000369513100010}, dimensions = {true} } - IJDRSMeasuring County Resilience After the 2008 Wenchuan EarthquakeXiaolu Li, Nina Lam, Yi Qiang, and 4 more authorsINTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE, Dec 2016
The catastrophic earthquake that struck Sichuan Province, China, in 2008 caused serious damage to Wenchuan County and surrounding areas in southwestern China. In recent years, great attention has been paid to the resilience of the affected area. This study applied the resilience inference measurement (RIM) model to quantify and validate the community resilience of 105 counties in the impacted area. The RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure, damage, and recovery conditions. The model then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county’s resilience. Analysis results show that counties located at the epicenter had the lowest resilience, but counties immediately adjacent to the epicenter had the highest resilience capacities. Counties that were farther away from the epicenter returned to normal resiliency quickly. Socioeconomic variables-including sex ratio, per capita GDP, percent of ethnic minority, and medical facilities-were identified as the most influential characteristics influencing resilience. This study provides useful information to improve county resilience to earthquakes and support decision making for sustainable development.
@article{LiIJDRS16, author = {Li, Xiaolu and Lam, Nina and Qiang, Yi and Li, Kenan and Yin, Lirong and Liu, Shan and Zheng, Wenfeng}, title = {Measuring County Resilience After the 2008 Wenchuan Earthquake}, journal = {INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE}, year = {2016}, volume = {7}, number = {4}, pages = {393-412}, month = dec, doi = {10.1007/s13753-016-0109-2}, issn = {2095-0055}, eissn = {2192-6395}, researcherid-numbers = {Zheng, Wenfeng/AAC-7405-2019 Yin, Lirong/ABG-7449-2021 Li, Xiaolu/AAM-3177-2020}, orcid-numbers = {Zheng, Wenfeng/0000-0002-8486-1654 Yin, Lirong/0000-0002-5022-610X Li, Xiaolu/0000-0002-8299-7937}, unique-id = {WOS:000391822700006}, dimensions = {true} } - Nat. Hazards Rev.Measuring Community Resilience to Coastal Hazards along the Northern Gulf of MexicoNina S. N. Lam, Margaret Reams, Kenan Li, and 2 more authorsNATURAL HAZARDS REVIEW, Feb 2016
The abundant research examining aspects of social-ecological resilience, vulnerability, and hazards and risk assessment has yielded insights into these concepts and suggested the importance of quantifying them. Quantifying resilience is complicated by several factors including the varying definitions of the term applied in the research, difficulties involved in selecting and aggregating indicators of resilience, and the lack of empirical validation for the indices derived. This paper applies a new model, called the resilience inference measurement (RIM) model, to quantify resilience to climate-related hazards for 52 U.S. counties along the northern Gulf of Mexico. The RIM model uses three elements (exposure, damage, and recovery indicators) to denote two relationships (vulnerability and adaptability), and employs both K-means clustering and discriminant analysis to derive the resilience rankings, thus enabling validation and inference. The results yielded a classification accuracy of 94.2% with 28 predictor variables. The approach is theoretically sound and can be applied to derive resilience indices for other study areas at different spatial and temporal scales. (C) 2015 American Society of Civil Engineers.
@article{LamNHR16, author = {Lam, Nina S. N. and Reams, Margaret and Li, Kenan and Li, Chi and Mata, Lillian P.}, title = {Measuring Community Resilience to Coastal Hazards along the Northern Gulf of Mexico}, journal = {NATURAL HAZARDS REVIEW}, year = {2016}, volume = {17}, number = {1}, month = feb, doi = {10.1061/(ASCE)NH.1527-6996.0000193}, article-number = {04015013}, issn = {1527-6988}, eissn = {1527-6996}, researcherid-numbers = {Lam, Nina/AAQ-1097-2020}, unique-id = {WOS:000368837300006}, dimensions = {true} } - WaterAssessing Community Resilience to Coastal Hazards in the Lower Mississippi River BasinHeng Cai, Nina S. N. Lam, Lei Zou, and 2 more authorsWATER, Feb 2016
This paper presents an assessment of community resilience to coastal hazards in the Lower Mississippi River Basin (LMRB) region in southeastern Louisiana. The assessment was conducted at the census block group scale. The specific purpose of this study was to provide a quantitative method to assess and validate the community resilience to coastal hazards, and to identify the relationships between a set of socio-environmental indicators and community resilience. The Resilience Inference Measurement (RIM) model was applied to assess the resilience of the block groups. The resilience index derived was empirically validated through two statistical procedures: K-means cluster analysis of exposure, damage, and recovery variables to derive the resilience groups, and discriminant analysis to identify the key indicators of resilience. The discriminant analysis yielded a classification accuracy of 73.1%. The results show that block groups with higher resilience were concentrated generally in the northern part of the study area, including those located north of Lake Pontchartrain and in East Baton Rouge, West Baton Rouge, and Lafayette parishes. The lower-resilience communities were located mostly along the coastline and lower elevation area including block groups in southern Plaquemines Parish and Terrebonne Parish. Regression analysis between the resilience scores and the indicators extracted from the discriminant analysis suggests that community resilience was significantly linked to multicomponent capacities. The findings could help develop adaptation strategies to reduce vulnerability, increase resilience, and improve long-term sustainability for the coastal region.
@article{CaiWater16, author = {Cai, Heng and Lam, Nina S. N. and Zou, Lei and Qiang, Yi and Li, Kenan}, title = {Assessing Community Resilience to Coastal Hazards in the Lower Mississippi River Basin}, journal = {WATER}, year = {2016}, volume = {8}, number = {2}, month = feb, doi = {10.3390/w8020046}, article-number = {46}, eissn = {2073-4441}, researcherid-numbers = {Cai, Heng/AAK-6514-2020 Qiang, Yi/J-3313-2016 Zou, Lei/AAV-8227-2020 Lam, Nina/AAQ-1097-2020}, orcid-numbers = {Cai, Heng/0000-0001-6068-5150 Qiang, Yi/0000-0002-6872-8837 Zou, Lei/0000-0001-6206-3558}, unique-id = {WOS:000371828400010}, dimensions = {true} }
2015
- CAGISA cyberinfrastructure for community resilience assessment and visualizationKenan Li, Nina S. N. Lam, Yi Qiang, and 2 more authorsCartography and Geographic Information Science, Feb 2015
Disaster resilience is a major societal challenge. Cartography and GIS can contribute substantially to this research area. This paper describes a cyberinfrastructure for disaster resilience assessment and visualization for all counties in the United States. Aided by the Application Programming Interface-enabled web mapping and component-oriented web tools, the cyberinfrastructure is designed to better serve the US communities with comprehensive resilience information. The resilience assessment tool is based on the resilience inference measurement model. This web application delivers the resilience assessment tool to the users through applets. It provides an interactive tool for the users to visualize the historical natural hazards exposure and damages in the areas of their interest, compute the resilience indices, and produce on-the-fly maps and statistics. The app could serve as a useful tool for decision makers. This app won the top 10 runners-up in the Environmental Systems Research Institute (ESRI) Climate Resilience App Challenge 2014 and the top 5 in the scientific section of the ESRI Global Disaster App Challenge 2014.
@article{LiCAGIS15, author = {Li, Kenan and Lam, Nina S. N. and Qiang, Yi and Zou, Lei and Cai, Heng}, title = {A cyberinfrastructure for community resilience assessment and visualization}, journal = {Cartography and Geographic Information Science}, volume = {42}, number = {sup1}, pages = {34-39}, year = {2015}, publisher = {Taylor & Francis}, doi = {10.1080/15230406.2015.1060113}, eprint = {https://doi.org/10.1080/15230406.2015.1060113}, dimensions = {true} }
2014
2013
2012
2010
- J. Hazard. Mater.Promoted biodegradation and microbiological effects of petroleum hydrocarbons by Impatiens balsamina L. with strong enduranceZhang Cai, Qixing Zhou, Shengwei Peng, and 1 more authorJOURNAL OF HAZARDOUS MATERIALS, Nov 2010
Phytoremediation is a promising green technology for cleanup of petroleum hydrocarbons (PHCs) in contaminated environment. Based on the objective of identifying special ornamental plants for the effective biodegradation of PHCs, the efficacy of Impatiens balsamina L to phytoremady petroleum contaminated soil from the Shengli Oil Field in Dongying City, Shandong Province, China, was further examined in a field plot-culture experiment under greenhouse conditions. After a 4-month culture period, the average degradation rate of total petroleum hydrocarbons (TPHs) by the plant was up to 18.13-65.03%, greatly higher than that (only 10.20-35.61%) in their corresponding controls by natural degradation. Among petroleum compositions saturated hydrocarbons had the highest degradation. The release of polar metabolic byproducts during phytoremediation of contaminated soils with >= 20,000 mg/kg of PHCs by I. balsamina may occur. Some growth indexes of I. balsamina indicated that the plant had a good tolerance to contaminated soils with <= 10,000 mg/kg of PHCs. Moreover rhizosphere bacteria and fungi became the dominant microbial population in soils with 5000 and 10,000 mg/kg of PHCs and were probably responsible for TPH degradation. Thus, I. balsamina L could be a potential ornamental plant for effective phytoremediation of contaminated soils with <= 10,000 mg/kg of PHCs. (c) 2010 Elsevier B.V. All rights reserved.
@article{CaiJHM10, author = {Cai, Zhang and Zhou, Qixing and Peng, Shengwei and Li, Kenan}, title = {Promoted biodegradation and microbiological effects of petroleum hydrocarbons by Impatiens balsamina L. with strong endurance}, journal = {JOURNAL OF HAZARDOUS MATERIALS}, year = {2010}, volume = {183}, number = {1-3}, pages = {731-737}, month = nov, doi = {10.1016/j.jhazmat.2010.07.087}, issn = {0304-3894}, eissn = {1873-3336}, researcherid-numbers = {cai, zhang/F-9418-2011}, unique-id = {WOS:000282607600093}, dimensions = {true} }