Public Data Portal Research Articles

Abstract C009: An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities

Abstract Introduction: Obesity has been associated with aggressive prostate cancer (PCa). Environments that promote obesity, known as obesogenic environments, may also contribute to PCa progression at the population level. However, studies exploring these factors are limited. The purpose of this study was to examine the association between neighborhood obesogenic factors with PCa mortality rates in Chicago. Methods: We used an ecological study design with Chicago Community Areas (n=77) serving as the geographical area unit of analysis. All community area-level data were obtained from the Chicago Health Atlas public data portal. For each area, two obesogenic factors were examined: food environment and park access. The number of healthy and unhealthy grocery stores was used to characterize the food environment. The retail food establishments data was retrieved for the year 2013. The stores were classified into chain grocery stores, non-chain grocery stores, liquor stores, and convenience stores which included dollar tsores, corner stores and gas stations. Chain and non-chain grocery stores were grouped as healthy food access, and other (convenience store, dollar store and liquor store) as unhealthy food access. The area of public parks per 100,000 community area population was used as a measure of park access. An exclusive park area for each community was calculated by intersecting the community area and park area shape files from the year 2016. The outcome variable was community area-level PCa mortality rate age-adjusted to the community area male population. To normalize the data, all included variables were standardized to zero mean and unit standard deviation. Linear regression models analyzed the association of the standardized measures of obesogenic environment (individually and simultaneously) with age-adjusted rates using the Statistical Analysis Software 9.4. Results: The mean number of healthy (0 - 11) and unhealthy (0 - 10) food stores in each community area were 3.1 and 2.6 respectively and mean park area (14.0 - 4096.5 acres) available in each community area was 453.5 acres per 100,000 population. The mean (0 – 29.6) PCa mortality was 9.8 per 100,000 population. Healthy food access was inversely associated with PCa mortality rates, where for every for one 1-standard deviation increase in healthy food access standard deviation, there is a 0.40 unit decrease in community area PCa mortality rate standard deviation (p=0.002) while accounting for the other obesogenic factors (unhealthy food access and park access). Conclusion: The findings of this study suggest that the neighborhood access to healthy food may be a determinant of neighborhood PCa mortality rates. Ecological studies are helpful to understand the impact at the geographical level. Methodological challenges and opportunity in ecological analyses, such as addressing bias and multivariable control of confounding, will be discussed. Citation Format: Ravneet Kaur, Jiehuan Sun, Nebiyou Y. Tilahun, Vincent L. Freeman. An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C009.

A biologging database of mobulid rays from the Gulf of California, Mexico

We initiated a tagging program in 2004 to determine the large-scale and long-term movement patterns of three species of Mobulid Ray (Mobula mobular, M. munkiana, M. thurstoni). Between 2004 and 2014 we deployed 48 pop-up archival (PAT) tags that recorded temperature, pressure, and light level. Pressure and light level records were then used to calculate animal depth and geolocation. Transmitted and when available recovered raw data files from successful deployments (n = 45) were auto-ingested from the manufacturer into the United States Animal Telemetry Network’s (ATN) Data Assembly Center (DAC). Through the ATN DAC, all necessary metadata were compiled, dataset was prepped for release, and derived geolocation trajectories (n = 43) were visualized within their public facing data portal. These data and the full metadata records are available for download from the ATN portal as well as permanently archived under the DataONE Research Workspace member node.

Clinical Significance of Disulfidptosis-related Genes and Functional Analysis in Gastric Cancer.

Background: Worldwide, gastric cancer (GC) remains intractable due to its poor prognosis and high morbidity and mortality. Disulfidptosis is a novel kind of cell death mediated by abnormal accumulation of intracellular disulphides. The correlation between disulfidptosis and GC is still unknown. Therefore, it is necessary to elucidate the pathogenesis and mechanism of disulfidptosis and GC for clinical diagnosis and intervention. Methods: RNA-sequencing data from several public data portals and clinical samples were collected. We compared the expression levels of four key genes of disulfidptosis, including SLC7A11, SLC3A2, RPN1, and NCKAP1, in GC and selected prognostic genes to build a novel GC prognosis-related nomogram model. The biological functions and immune landscape of the identified prognostic genes were explored. Results: Overexpressed NCKAP1 and SLC7A11 were prognostic disulfidptosis-related genes in GC. We combined these genes and several clinicopathological factors to build a prognostic nomogram model for GC. Meanwhile, the ROC curves showed that NCKAP1 and SLC7A11 were promising biomarkers for GC screening. The biological and cellular functions were focused on actin activities, GTPase and immunoreaction. The tumour immune microenvironment and immune therapy targets were identified. Competing endogenous RNA network was built to explore the downstream regulatory mechanisms. Finally, the elevated NCKAP1 and SLC7A11 expression in GC was validated via qRT-PCR in a cell line and tissue line. Conclusion: In conclusion, NCKAP1 and SLC7A11 are promising prognostic and diagnostic biomarkers for GC that correlate with the activities of actin, energy metabolism of GTPase, immune infiltration and immunotherapy.

Impact of COVID-19 Induced Economic Hardships on Women Participation in MGNREGA Works During the Financial Year 2020–2021

Impact of COVID-19 induced lockdown has been massive for the Indian economy; it witnessed a contraction in gross domestic product and massive employment loss. India being a poor country and a huge population having insignificant to no social security, a large number of those workers who lost employment were not having option to remain unemployed. Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) which is a job-guarantee programme launched a decade earlier and that has been instrumental in providing employment opportunities to rural women casual workers witnessed a huge increase in demand as both men and women flocked to it. For the financial year 2020–2021 the share of women in the person-days worked under MGNREGA fell. The article using data from MGNREGA public data portal and the Centre for Monitoring Indian Economy seeks to explain the reasons behind this fall and suggest that MGNREGA needs to be expanded and modified in favour of women casual workers till the recovery on the employment front does not happen. JEL Codes: H53, I38, J08, J16, J23, J30, J48, J71, J78

Using cancer proteomics data to identify gene candidates for therapeutic targeting.

Gene-level associations obtained from mass-spectrometry-based cancer proteomics datasets represent a resource for identifying gene candidates for functional studies. When recently surveying proteomic correlates of tumor grade across multiple cancer types, we identified specific protein kinases having a functional impact on uterine endometrial cancer cells. This previously published study provides just one template for utilizing public molecular datasets to discover potential novel therapeutic targets and approaches for cancer patients. Proteomic profiling data combined with corresponding multi-omics data on human tumors and cell lines can be analyzed in various ways to prioritize genes of interest for interrogating biology. Across hundreds of cancer cell lines, CRISPR loss of function and drug sensitivity scoring can be readily integrated with protein data to predict any gene's functional impact before bench experiments are carried out. Public data portals make cancer proteomics data more accessible to the research community. Drug discovery platforms can screen hundreds of millions of small molecule inhibitors for those that target a gene or pathway of interest. Here, we discuss some of the available public genomic and proteomic resources while considering approaches to how these could be leveraged for molecular biology insights or drug discovery. We also demonstrate the inhibitory effect of BAY1217389, a TTK inhibitor recently tested in a Phase I clinical trial for the treatment of solid tumors, on uterine cancer cell line viability.

Identification of novel prognostic biomarkers in the TF-enhancer-target regulatory network in hepatocellular carcinoma and immune infiltration analysis.

Background: Hepatocellular carcinoma (HCC) remains notorious for its high malignancy, poor prognosis and high mortality. The exploration of novel therapeutic agents for HCC has remained challenging due to its complex aetiology. Therefore, it is necessary to elucidate the pathogenesis and mechanism of HCC for clinical intervention. Methods: We collected data from several public data portals and systematically analysed the association between transcription factors (TFs), eRNA-associated enhancers and downstream targets. We next filtered the prognostic genes and established a novel prognosis-related nomogram model. Moreover, we explored the potential mechanisms of the identified prognostic genes. The expression level was validated by several ways. Results: We first constructed a significant TF-enhancer-target regulatory network and identified DAPK1 as a coregulatory differentially expressed prognosis-related gene. We combined common clinicopathological factors and built a prognostic nomogram model for HCC. We found that our regulatory network was correlated with the processes of synthesizing various substances. Moreover, we explored the role of DAPK1 in HCC and found that it was associated with immune cell infiltration and DNA methylation. Several immunostimulators and targeting drugs could be promising immune therapy targets. The tumor immune microenvironment was analyzed. Finally, the lower DAPK1 expression in HCC was validated via the GEO database, UALCAN cohort, and qRT-PCR. Conclusion: In conclusion, we established a significant TF-enhancer-target regulatory network and identified downregulated DAPK1 as an important prognostic and diagnostic gene in HCC. Its potential biological functions and mechanisms were annotated using bioinformatics tools.

Comparative analysis of open government data topics and usability.

The online version contains supplementary material available at 10.1007/s11135-023-01630-x.

Hacia la madurez de los portales de datos públicos abiertos en el sector público. Un análisis comparado del nivel local de gobierno en España

Open public data portals have become one of the main technologies of public administrations for the exercise of transparency, accountability, citizen participation and co-production of public services. Few empirical studies have analyzed on a large scale the use of these platforms considering their multidimensional nature. This research offers an empirical analysis of how these technological artifacts are being used at the Spanish local level. An observation of 66 open public data web portals enabled by Spanish municipalities with more than 50,000 inhabitants has been carried out through a framework of indicators and dimensions based on previous literature. The results, derived from a descriptive statistical analysis of the state of evolution of the portals observed, show a certain preference for a technical development linked to transparency, as opposed to a social and reuse dimension. The implications of this study highlight an incipient transformation of local public administration, whereas the paradigm of smart public management -more participative and collaborative- has not yet fully accomplished. Also, this study provides a basis for the development of future research that requires further study of the dimensions of citizen participation and co-production by the advent of open public data.

지하철역사 에너지성능 평가를 위한 클러스터링 분석기법을 활용한 지하철역사 대표모델 선정에 관한 연구

In order to realize the government’s plan to increase zero energy early and play a leading role in the low carbonization of railway facilities, Since 2025, the National Railroad Corporation has established a Zero Energy Roadmap for railway stations to promote zero energy building certification. However, currently, standards and tools for evaluating various railway history, including subways, are insufficient in Korea. In particular, railway history has characteristics that are distinct from general buildings, such as the presence of peak loads, continuous erosion, and the possession of safety and functional elements that must be equipped Accordingly, an alternative is needed to overcome the limitations of existing domestic energy performance evaluation tools developed for general building evaluation. Therefore, this paper uses the clustering technique as the first step to develop a decision-making tool that presents the energy performance evaluation and improvement direction of railway history It can represent the characteristics of subway history. In order to select a representative model, data collection and clustering analysis of all 291 subway stations in the Seoul area were performed using Open Data provided by the public data portal. K-means and GMM algorithms were used as clustering analysis methods. As a result of clustering analysis, the K-means algorithm was difficult to use as a result of classification because the data columns were diverse and were not in the form of circular distribution by cluster. Compared to K-means, GMM algorithms can significantly distinguish characteristics such as area, completion year, and platform type the results were shown.BRThe representative standard stations selected based on the results of GMM clustering were Konkuk University Station (Line 2), Suraksan Station, and Hongdae Station. After that, through simulation of representative models and multiple regression analysis using results, the standard model is developed and used as a tool for initial decision-making such as zero energy construction plan and remodeling plan for subway stations.

A Study on Customized Prediction of Daily Illness Risk Using Medical and Meteorological Data

This study selected the most common illnesses in children and older adults and aimed to provide a customized degree of daily risk for each illness based on patient data for specific regions and illnesses. Sample medical data of one million people provided by the National Health Insurance Corporation and information regarding the meteorological environment and atmosphere from the Korea Meteorological Administration and a public data portal using application programing interface were collected. Learning and predictions were carried out with machine learning. Models with high R2 were selected and tuned to determine the optimal hyperparameter for predicting the degree of daily risk of an illness. Illnesses with an R2 value greater than 0.65 were considered significant. For children, these consisted of acute bronchitis, the common cold, rhinitis and tonsillitis, and middle ear inflammation. For older adults, they consisted of high blood pressure and heart disease, the common cold, esophageal inflammation and gastritis, acute bronchitis, eczema and dermatitis, and chronic bronchitis. This study provides the degree of daily risk for the most common illnesses in each age group. Furthermore, the results of this study are expected to raise awareness of illnesses that occur in certain climates and to help prevent them.

Comparative Analysis of Disaster and Safety Datasets, Data Quality, and Services in Korean and Australian Public Data Portals

The objective of this study is to design improved plans for disaster and safety management through comparative analysis of datasets comprising details regarding the disaster and safety sector that are available in Korean and Australian public data portals and thereby examine the associated portal strategies and national operating systems To this end, based on previous studies and national public data portal evaluation standards, public data portal comparison standards were established. Furthermore, quantitative configuration analysis, data mining analysis, data quality analysis, and various service analyses were conducted. Consequently, it was found that compared to the Australian public data portal, the number and proportion of public data openings for disaster and safety management are significantly smaller in the Korean public data portal. The number of prevention datasets available in both countries accounts for the highest proportion in disaster and safety management. In addition, “supply management and resource support” accounts for the majority of Korean disaster response collaborations, whereas in the case of Australia, “comprehensive situation management” has a high proportion. In terms of data quality, Australian datasets include 69.54% of the total insoluble data, which implies inefficient data quality management. However, there are merely few Korean insoluble datasets. Through comparative analysis of the national operating systems, it was found that detailed guidelines for public data are available in Australia. However, the Korean public data portal is operated in accordance with the comprehensive Framework Act on Intelligence Informatization. Hence, a promotion strategy operation policy has to devise for the Korean public data portal to expand the usage of public data in the disaster and safety sector, along with quantitative growth policies that will increase the number and proportion of public data in the disaster and safety sector.

Comparative Analysis of Disaster and Safety Datasets, Data Quality, and Services in Korean and Australian Public Data Portals

The objective of this study is to design improved plans for disaster and safety management through comparative analysis of datasets comprising details regarding the disaster and safety sector that are available in Korean and Australian public data portals and thereby examine the associated portal strategies and national operating systems To this end, based on previous studies and national public data portal evaluation standards, public data portal comparison standards were established. Furthermore, quantitative configuration analysis, data mining analysis, data quality analysis, and various service analyses were conducted. Consequently, it was found that compared to the Australian public data portal, the number and proportion of public data openings for disaster and safety management are significantly smaller in the Korean public data portal. The number of prevention datasets available in both countries accounts for the highest proportion in disaster and safety management. In addition, “supply management and resource support” accounts for the majority of Korean disaster response collaborations, whereas in the case of Australia, “comprehensive situation management” has a high proportion. In terms of data quality, Australian datasets include 69.54% of the total insoluble data, which implies inefficient data quality management. However, there are merely few Korean insoluble datasets. Through comparative analysis of the national operating systems, it was found that detailed guidelines for public data are available in Australia. However, the Korean public data portal is operated in accordance with the comprehensive Framework Act on Intelligence Informatization. Hence, a promotion strategy operation policy has to devise for the Korean public data portal to expand the usage of public data in the disaster and safety sector, along with quantitative growth policies that will increase the number and proportion of public data in the disaster and safety sector.

High expression of the ferroptosis-associated MGST1 gene in relation to poor outcome and maladjusted immune cell infiltration in uterine corpus endometrial carcinoma.

BackgroundUterine corpus endometrial carcinoma (UCEC) tightly correlates with dysregulated iron homeostasis. MGST1 (microsomal glutathione S‐transferase 1) involves in the regulation of oxidative stress and plays a key role in inhibiting iron‐mediated cell death in cancer cells. Hence, we aimed to illuminate the characteristics of MGST1 expression and prognosis in UCEC using bioinformatics prediction to provide novel perspectives for theoretical supplementation and ferroptosis‐based immunotherapy.MethodsWe retrieved MGST1 expression data via several public data portals. The relationships between MGST1 expression and clinicopathologic characteristics as well as survival time were evaluated via multivariate methods and Kaplan–Meier survival curves. The MGST1‐interacting protein–protein interaction was also established by the STRING website. The TIMER and GEPIA databases were used to illustrate the association between MGST1 expression and infiltrated immune cells. We used the MethSurv website and the UALCAN website to determine the relationship between MGST1 expression and DNA methylation.ResultsMGST1 overexpression in UCEC compared with normal tissues correlates with different histological types, a lack of hormone therapy and poor survival time. MGST1 interacts with several ferroptosis‐related proteins. Overexpression of MGST1 was accompanied by lower levels of NK cell and CD8+ T cell infiltration, higher myeloid‐derived suppressor cell infiltration and different immunocytes with corresponding markers. Hypermethylation and low promoter methylation cooperate to regulate MGST1 expression.ConclusionElevated MGST1 expression is related to tumour development and poor prognosis, as well as dysregulated infiltration of immune cells in UCEC, which can be a potential prognostic indicator and ferroptosis‐based immunotherapy target.

Comparison of Disaster Safety Datasets and Service in Korean and Japanese Public Data Portals

The purpose of this study is to obtain implications through comparative analysis of disaster safety datasets and services of representative public data portals in Korea and Japan. Comparative standards were established first. Then, dataset weight analysis of disaster-type and safety-management -stage components, trend analysis through text mining on data-set descriptions, and data quality and portal services analysis were performed. As a result public data sets were lower in Korea both numerically and proportionally than in Japan. Japan had a high proportion of disaster preparation and recovery datasets in terms of disaster safety management, while Korea had a high proportion of prevention data-sets. In addition, in terms of disaster response collaboration, most of Korea has material management and resource support, but Japan has high proportion of emergency recovery and situation management of damaged facilities. In terms of data quality, Japan has many datasets with four levels of Berners-Lee rating. However Korea has a high proportion of datasets with three levels of Beners-Lee rating. However, Korea has a better data format for big-data utilization. Portal services are mainly centered on natural disasters in Japan, but in Korea, they are centered on social disasters. The results of this study provide a reference for the future direction of disaster safety public data portals in Korea.

A Novel Pyroptosis-related Prognostic Model for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor because of its significant heterogeneity and complicated molecular pathogenesis. Novel prognostic biomarkers are urgently needed because no effective and reliable prognostic biomarkers currently exist for HCC patients. Increasing evidence has revealed that pyroptosis plays a role in the occurrence and progression of malignant tumors. However, the relationship between pyroptosis-related genes (PRGs) and HCC patient prognosis remains unclear. In this study, 57 PRGs were obtained from previous studies and GeneCards. The gene expression profiles and clinical data of HCC patients were acquired from public data portals. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a risk model using TCGA data. Additionally, the risk model was further validated in an independent ICGC dataset. Our results showed that 39 PRGs were significantly differentially expressed between tumor and normal liver tissues in the TCGA cohort. Functional analysis confirmed that these PRGs were enriched in pyroptosis-related pathways. According to univariate Cox regression analysis, 14 differentially expressed PRGs were correlated with the prognosis of HCC patients in the TCGA cohort. A risk model integrating two PRGs was constructed to classify the patients into different risk groups. Poor overall survival was observed in the high-risk group of both TCGA (p < 0.001) and ICGC (p < 0.001) patients. Receiver operating characteristic curves demonstrated the accuracy of the model. Furthermore, the risk score was confirmed as an independent prognostic indicator via multivariate Cox regression analysis (TCGA cohort: HR = 3.346, p < 0.001; ICGC cohort: HR = 3.699, p < 0.001). Moreover, the single-sample gene set enrichment analysis revealed different immune statuses between high- and low-risk groups. In conclusion, our new pyroptosis-related risk model has potential application in predicting the prognosis of HCC patients.

Classification of Covid-19 Survillance Datasets using the Decision Tree Algorithm

Covid-19 is a new type of mutated virus that has been discovered and studied throughout the world. For the time being, no effective drug has been found to treat or prevent this disease. One way that governments around the world are doing is limiting physical contact with people with COVID-19. Data mining is a computer science to study data and perform extraction to get new knowledge. One technique in data mining is classification. C45 is one of the best classification algorithms. The result of the c45 algorithm can be a decision tree. Decision trees are used because the results can be well represented. and can be easily understood in human language. This study classified the Covid-19 surveillance dataset using the Decission tree. The Covid-19 surveillance dataset was obtained from a public data portal, namely the UCI machine learning repository. This study resulted in better accuracy than previous studies using the same dataset. The level of accuracy obtained by using the decision tree algorithm is 65%. Although in this study the accuracy value has increased by 10%, the level of accuracy is still relatively low. The low level of accuracy is due to the dataset used only has 7 attributes and 14 records

Social Protection Network Across Indian States

This contribution of the Social Change Indicators forms the eleventh part of the series. Over the last three segments, we have been focussing on the social and economic challenges arising out of the COVID-19 pandemic and lockdowns. The topics previously covered were on the themes of vulnerable households across social classes, poverty and migration and living conditions for social distancing. In this part, we highlight the spread of the social protection network in various states of India. Aspects such as the percentage of households having a below poverty line (BPL) card, percentage of households having a health scheme or health insurance, percentage of households having a bank or post office account, Aadhaar card saturation, percentage of families/persons covered under the targetted Public Distribution System, percentage of Aadhaar-seeded ration cards, allocation of work under MGNREGA (Mahatma Gandhi National Rural Employment Guarantee Act) and the number of fair price shops per thousand population in 2021 have been examined. The required data has been collected from the Aadhaar Saturation Report provided by the Unique Identification Authority of India, the Food Grain Bulletin of the Ministry of Consumer Affairs, Food and Public Distribution, Ration Card Dashboard of the National Food Security Act, the public data portal of MGNREGA and the Fourth Round of the National Family Health Survey’s state volumes.

Linked Method of Open Government Data by Datasets Oriented

In order to make public data more useful, it is necessary to provide relevant data sets that meet the needs of users. We introduce the method of linkage between datasets. We provide a method for deriving linkages between fields of structured datasets provided by public data portals. We defined a dataset and connectivity between datasets. The connectivity between them is based on the metadata of the dataset and the linkage between the actual data field names and values. We constructed the standard field names. Based on this standard, we established the relationship between the datasets. This paper covers 31,692 structured datasets (as of May 31, 2020) among the public data portal datasets. We extracted 1,185,846 field names from over 30,000 datasets. We extracted 1,185,846 field names from over 30,000 datasets. As a result of analyzing the field names, the field names related to spatial information were the most common at 35%. This paper verified the method of deriving the relation between data sets, focusing on the field names classified as spatial information. For this reason, we have defined spatial standard field names. To derive similar field names, we extracted related field names into spaces such as locations, coordinates, addresses, and zip codes used in public datasets. The standard field name of spatial information was designed and derived 43% cooperation rate of 31,692 datasets. In the future, we plan to apply similar field names additionally to improve the data set cooperation rate of the spatial information standard.

공공데이터포털에 공개된 지방자치단체의 공공데이터 활용 방안

대한민국 정부는 지속적으로 공공데이터를 개방하고 있고, 산업적 활용을 위해 다양한 정책을 추진하고 있다. 정부가 공공데이터를 지속적으로 개방하고 있음에도, 데이터 활용은 여전히 제한적이다. 공공데이터는 낮은 수준의 데이터 품질, 이종의 데이터를 연계하기 위한 표준 등 다양한 이슈를 갖고 있다. 한편, 데이터 수요를 적시에 파악하고 개방하기 위한 프로세스, 개방된 공공데이터의 활용 현황의 진단 등 공공 데이터 환경에 대한 포괄적 진단이 필수적이다. 본 논문은 공공데이터포털에 개방된 지방자치단체의 데이터세트의 활용도를 평가한다. 데이터 활용도는 개방한 데이터를 사용자의 조회와 다운로드한 수치로 측정한다. 공공데이터가 지속적으로 공개되고 있지만, 개방된 데이터가 민간 또는 기업에 의해 실제 활용되지 않고 있다. 본 논문은 지방자치단체의 공공데이터의 활용 현황을 진단하고, 공공데이터 활용 환경을 개선하기 위한 방안을 제안한다.

Mapping Drainage Structures Using Airborne Laser Scanning by Incorporating Road Centerline Information

Wide-area drainage structure (DS) mapping is of great concern, as many DSs are reaching the end of their design life and information on their location is usually absent. Recently, airborne laser scanning (ALS) has been proven useful for DS mapping through manual methods using ALS-derived digital elevation models (DEMs) and hillshade images. However, manual methods are slow and labor-intensive. To overcome these limitations, this paper proposes an automated DS mapping algorithm (DSMA) using classified ALS point clouds and road centerline information. The DSMA begins with removing ALS ground points within the buffer of the road centerlines; the size of the buffer varies according to different road classes. An ALS-modified DEM (ALS-mDEM) is then generated from the remaining ground points. A drainage network (DN) is derived from the ALS-mDEM. Candidate DSs are then obtained by intersecting the DN with the road centerlines. Finally, a refinement buffer of 15 m is placed around each candidate DS to prevent duplicate DS from being generated in close proximity. A total area of 50 km2, including an urban site and a rural site, in Vermont, USA, was used to assess the DSMA. Based on the road functional classification scheme of the Federal Highway Administration (FHWA), the centerline information regarding FHWA roads was obtained from a public data portal. The centerline information on non-FHWA roads, i.e., private roads and streets, was derived from the impervious surface data of a land cover dataset. A benchmark DS dataset was gathered from the transport agency of Vermont and was further augmented using Google Earth Street View images by the authors. The one-to-one correspondence between the benchmark DS and mapped DS for these two sites was then established. The positional accuracy was assessed by computing the Euclidian distance between the benchmark DS and mapped DS. The mean positional accuracy for the urban site and rural site were 13.5 m and 15.8 m, respectively. F1-scores were calculated to assess the prediction accuracy. For FHWA roads, the F1-scores were 0.87 and 0.94 for the urban site and rural site, respectively. For non-FHWA roads, the F1-scores were 0.72 and 0.74 for the urban site and rural site, respectively.