Local Data Research Articles

An Automated Machine Learning Approach to the Retrieval of Daily Soil Moisture in South Korea Using Satellite Images, Meteorological Data, and Digital Elevation Model

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea.

Mapping Field-Level Maize Yields in Ethiopian Smallholder Systems Using Sentinel-2 Imagery

Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R2 up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze.

The Rise of Xylazine Shows Why Local Data Must Inform Policy and Spending Decisions.

Local governments have been given direct control over a substantial amount of opioid settlement funding. While state-level data sources are valuable inputs, city and county leaders should consider local data sources when making decisions to ensure timely responses to emerging threats, as is exemplified by the rise of xylazine.

A two-level semi-hybrid-mixed model for Stokes–Brinkman flows with divergence-compatible velocity–pressure elements

A two-level version for a recent semi-hybrid-mixed finite element approach for modeling Stokes and Brinkman flows is proposed. In the context of a domain decomposition of the flow region Ω, composite divergence-compatible finite elements pairs in H(div,Ω)×L2(Ω) are utilized for discretizing velocity and pressure fields, using the same approach previously adopted for two-level mixed Darcy and stress mixed elasticity models. The two-level finite element pairs of spaces in the subregions may have richer internal resolution than the boundary normal trace. Hybridization occurs by the introduction of an unknown (traction) defined over element boundaries, playing the role of a Lagrange multiplier to weakly enforce tangential velocity continuity and Dirichlet boundary condition. The well-posedness of the method requires a proper choice of the finite element space for the traction multiplier, which can be achieved after a proper velocity FE space enrichment with higher order bubble fields. The method is strongly locally conservative, yielding exact divergence-free velocity fields, demonstrating pressure robustness, and facilitating parallel implementations by limiting the communication of local common data to at most two elements. Easier coupling strategies of finite elements regarding different polynomial degree or mesh widths are permitted, provided that mild mesh and normal trace consistency properties are satisfied. Significant improvement in computational performance is achieved by the application of static condensation, where the global system is solved for coarse primary variables. The coarse primary variables are a piecewise constant pressure variable over the subregions, velocity normal trace and tangential traction over subdomain interfaces, as well as a real number used as a multiplier ensuring global zero-mean pressure. Refined details of the solutions are represented by secondary variables, which are post-processed by local solvers. Numerical results are presented for the verification of convergence histories of the method.

The effect of education based on the theory of planned behavior to prevent the consumption of fast food in a population of teenagers

BackgroundConsidering the increasing prevalence of fast food consumption among teenagers, providing the necessary training and self-awareness is the best solution for institutionalizing the prevention of fast food consumption by this group. Therefore, this issue should be taken seriously, as should the implementation of educational interventions to prevent the consumption of fast food. The theory of planned behavior (TPB) is one of the various health education styles and methods that works well. However, given that a number of local data points are crucial in establishing the efficacy of the model employed, this study looks into the impact of TPB-based education in preventing fast food intake among teenagers.MethodsA total of 180 male students, ages 15 to 18, participated in this quasi-experimental study in Shiraz, Iran. Cluster sampling was used in the study, and participants were randomly assigned to two groups: the experimental group (n = 90) and the control group (n = 90). A theory-based questionnaire was used to gather data, and both the control and experimental groups had to complete it before and three months after the intervention. The data were examined using paired t, independent t, and chi-square statistical tests after being entered into SPSS-24.ResultsThe results of the independent t-test showed that there was no significant difference between the two study groups before the intervention in terms of knowledge (P = 0.14), attitude (P = 0.57), subjective norms (P = 0.94), perceived behavioral control (P = 0.81), behavioral intention (P = 0.42), or behavior (P = 0.25). However, following the intervention, there was a significant difference between the two groups with regard to the noted variables (P = 0.001). Furthermore, the McNemar test results demonstrated that the experimental group’s consumption of fast food varied significantly before and after the intervention (P = 0.001), but not in the control group (P = 0.07).ConclusionThe study’s findings demonstrated that TPB-based treatments are successful in helping male students change their habits of consuming fewer amounts of fast food. This study demonstrated that the instructional techniques employed improved the fast food consumption behavior of the intervention group as well as the TPB’s structure (attitude, subjective norms, behavioral intention, and perceived behavioral intention).

FBLearn: Decentralized Platform for Federated Learning on Blockchain

In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision making, exhibit common features that render them complementary. At the same time, machine learning has become a robust and influential technology, adopted by many companies to address non-trivial technical problems. This adoption is fueled by the vast amounts of data generated and utilized in daily operations. An intriguing intersection of blockchain and AI occurs in the realm of federated learning, a distributed approach allowing multiple parties to collaboratively train a shared model without centralizing data. This paper presents a decentralized platform FBLearn for the implementation of federated learning in blockchain, which enables us to harness the benefits of federated learning without the necessity of exchanging sensitive customer or product data, thereby fostering trustless collaboration. As the decentralized blockchain network is introduced in the distributed model training to replace the centralized server, global model aggregation approaches have to be utilized. This paper investigates several techniques for model aggregation based on the local model average and ensemble using either local or globally distributed validation data for model evaluation. The suggested aggregation approaches are experimentally evaluated based on two use cases of the FBLearn platform: credit risk scoring using a random forest classifier and credit card fraud detection using a logistic regression. The experimental results confirm that the suggested adaptive weight calculation and ensemble techniques based on the quality of local training data enhance the robustness of the global model. The performance evaluation metrics and ROC curves prove that the aggregation strategies successfully isolate the influence of the low-quality models on the final model. The proposed system’s ability to outperform models created with separate datasets underscores its potential to enhance collaborative efforts and to improve the accuracy of the final global model compared to each of the local models. Integrating blockchain and federated learning presents a forward-looking approach to data collaboration while addressing privacy concerns.

Early Local Community Data on Safety and Efficacy of Fruquintinib in Metastatic Colorectal Cancer

Early Local Community Data on Safety and Efficacy of Fruquintinib in Metastatic Colorectal Cancer

The effect of fixed and functional remodelling on conduction velocity, wavefront propagation, and rotational activity formation in atrial fibrillation.

Pathophysiology of atrial fibrillation (AF) remains unclear. Interactions between scar and conduction velocity (CV) and their impact on wavefront propagation in sinus rhythm (SR) and rotational activity burden in AF were evaluated. Local activation times (LATs) and voltage data were obtained from patients undergoing ablation for persistent AF. Omnipolar voltage (OV) and bipolar voltage (BV) data were obtained during AF and SR at pacing intervals of 600 and 250 ms. Local activation times were used to determine CV dynamics and their relationship to the underlying voltage and pivot points in SR. Computational modelling studies were performed to evaluate the impact of CVs and fibrosis on rotational activity burden in AF. Data from 60 patients with a total of 2 768 400 LAT and voltage points were analysed (46 140 ± 5689 points/patient). Voltage determined CV dynamics. Enhanced CV heterogeneity sites were predominantly mapped to low-voltage zones (LVZs) (0.2-0.49 mV) (128/168, 76.2%) rather than LVZs (<0.2 mV) and frequently co-located to pivot points (151/168, 89.9%). Atrial fibrillation OV maps correlated better with SR BV 250 ms than 600 ms maps, thereby representing fixed and functional remodelling. Sinus rhythm maps at 250 ms compared with 600 ms harboured a greater number of pivot points. Increased CV slowing and functional remodelling on computational models resulted in a greater rotational activity burden. Conduction velocity dynamics are impacted by the degree of scar. Conduction velocity heterogeneity and functional remodelling impacts wavefront propagation in SR and rotational activity burden in AF. This study provides insight into the pathophysiology of AF and identifies potential novel ablation targets.

Framework to process vehicles uncertain locations for intelligent transportation

Many factors affect the precision and accuracy of location data. These factors include, but not limited to, environmental obstructions (e.g., high buildings and forests), hardware issues (e.g., malfunctioning and poor calibration), and privacy concerns (e.g., users denying consent to fine-grained location tracking). These factors lead to uncertainty about users’ location which in turn affects the quality of location-aware services. This paper proposes a novel framework called UMove, which stands for uncertain movements, to manage the trajectory of moving objects under location uncertainty. The UMove framework employs the connectivity (i.e., links between edges) and constraints (i.e., travel time and distance) on road network graphs to reduce the uncertainty of the object’s past, present, and projected locations. To accomplish this, UMove incorporates (i) a set-based pruning algorithm to reduce or eliminate uncertainty from imprecise trajectories; and (ii) a wrapper that can extend user-defined probability models designed to predict future locations of moving objects under uncertainty. Intensive experimental evaluations based on real data sets of GPS trajectories collected by Didi Chuxing in China prove the efficiency of the proposed UMove framework. In terms of accuracy, for past exact-location inference, UMove achieves rates from 88% to 97% for uncertain regions with sizes of 75 meters and 25 meters respectively; for future exact-location inference, accuracy rates reach up to 72% and 82% for 75 meters and 25 meters of uncertain regions.

A Deep Learning-Based Hybrid CNN-LSTM Model for Location-Aware Web Service Recommendation

Advertising is the most crucial part of all social networking sites. The phenomenal rise of social media has resulted in a general increase in the availability of customer tastes and preferences, which is a positive development. This information may be used to improve the service that is offered to users as well as target advertisements for customers who already utilize the service. It is essential while delivering relevant advertisements to consumers, to take into account the geographic location of the consumers. Customers will be ecstatic if the offerings displayed to them are merely available in their immediate vicinity. As the user’s requirements will vary from place to place, location-based services are necessary for gathering this essential data. To get users to stop thinking about where they are and instead focus on an ad, location-based advertising (LBA) uses their mobile device’s GPS to pinpoint nearby businesses and provide useful information. Due to the increased two-way communication between the marketer and the user, mobile consumers’ privacy concerns and personalization issues are becoming more of a barrier. In this research, we developed a collaborative filtering-based hybrid CNN-LSTM model for recommending geographically relevant online services using deep neural networks. The proposed hybrid model is made using two neural networks, i.e., CNN and LSTM. Geographical information systems (GIS) are used to acquire initial location data to collect precise locational details. The proposed LBA for GIS is built in a Python simulation environment for evaluation. Hybrid CNN-LSTM recommendation performance beats existing location-aware service recommender systems in large simulations based on the WS dream dataset.

Scenario-aware clustered federated learning for vehicle trajectory prediction with non-IID data

In recent years, Federated Learning (FL) has attracted much attention in Vehicle Trajectory Prediction (VTP) as it can resolve the critical issues of insufficient data, data privacy, and training efficiency. However, compared with centralized training, the model trained by FL may have insufficient prediction performance. This important issue comes from a statistical heterogeneity distribution of the local data in the participating clients, that is, non-IID. Therefore, this paper introduces a Clustered Federated Learning (CFL) approach for the VTP model to mitigate the influence of non-IID data. The proposed approach consists of federated trajectory clustering and federated VTP model training. In federated trajectory clustering, the optimal trajectory scenario discriminator is produced using federated K-means clustering without direct access to private data. In the federated VTP model training, multiple VTP models for specific trajectory scenarios are trained to deal with the influence of non-IID data. Experimental results reveal that our approach outperforms the state-of-the-art FL method on both NGSIM and HighD datasets, achieving up to 13.82% convergence acceleration and 12.47% RMSE reduction.

Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT Networks

Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous works, some online multi-armed bandit (MAB)-based FL frameworks were proposed to enable dynamic client scheduling for improving the efficiency of FL in underwater wireless IoT networks. However, the security of online dynamic scheduling, which is especially essential for underwater wireless IoT, is increasingly being questioned. In this work, we study secure dynamic scheduling for FL frameworks that can protect against malicious clients in underwater FL-assisted wireless IoT networks. Specifically, in order to jointly optimize the communication efficiency and security of FL, we employ MAB-based methods and propose upper-confidence-bound-based smart contracts (UCB-SCs) and upper-confidence-bound-based smart contracts with a security prediction model (UCB-SCPs) to address the optimal scheduling scheme over time-varying underwater channels. Then, we give the upper bounds of the expected performance regret of the UCB-SC policy and the UCB-SCP policy; these upper bounds imply that the regret of the two proposed policies grows logarithmically over communication rounds under certain conditions. Our experiment shows that the proposed UCB-SC and UCB-SCP approaches significantly improve the efficiency and security of FL frameworks in underwater wireless IoT networks.

Enhancing Autism Spectrum Disorder Classification with Lightweight Quantized CNNs and Federated Learning on ABIDE-1 Dataset

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that presents significant diagnostic challenges due to its varied symptoms and nature. This study aims to improve ASD classification using advanced deep learning techniques applied to neuroimaging data. We developed an automated system leveraging the ABIDE-1 dataset and a novel lightweight quantized one-dimensional (1D) Convolutional Neural Network (Q-CNN) model to analyze fMRI data. Our approach employs the NIAK pipeline with multiple brain atlases and filtering methods. Initially, the Regions of Interest (ROIs) are converted into feature vectors using tangent space embedding to feed into the Q-CNN model. The proposed 1D-CNN is quantized through Quantize Aware Training (QAT). As the quantization method, int8 quantization is utilized, which makes it both robust and lightweight. We propose a federated learning (FL) framework to ensure data privacy, which allows decentralized training across different data centers without compromising local data security. Our findings indicate that the CC200 brain atlas, within the NIAK pipeline’s filt-global filtering methods, provides the best results for ASD classification. Notably, the ASD classification outcomes have achieved a significant test accuracy of 98% using the CC200 and filt-global filtering techniques. To the best of our knowledge, this performance surpasses previous studies in the field, highlighting a notable enhancement in ASD detection from fMRI data. Furthermore, the FL-based Q-CNN model demonstrated robust performance and high efficiency on a Raspberry Pi 4, underscoring its potential for real-world applications. We exhibit the efficacy of the Q-CNN model by comparing its inference time, power consumption, and storage requirements with those of the 1D-CNN, quantized CNN, and the proposed int8 Q-CNN models. This research has made several key contributions, including the development of a lightweight int8 Q-CNN model, the application of FL for data privacy, and the evaluation of the proposed model in real-world settings. By identifying optimal brain atlases and filtering methods, this study provides valuable insights for future research in the field of neurodevelopmental disorders.

Is hyponatremia caused by Chinese herbal medicine a myth? A scoping review and a review of local data in Hong Kong

AbstractIntroductionHyponatremia is one of the commonly inquired adverse effects related to Chinese herbal medicine (CHM) received by the Hong Kong Poison Information Center. However, it has never been investigated or reviewed systemically. The purpose of this review is to look into the relationship between CHM and hyponatremia.MethodDatabases, including PubMed, the Cochrane library, Embase, and the traditional Chinese medicine database system, were searched for cases reporting hyponatremia after the use of herbs. Inquiries received by the Hong Kong Poison Information Center about CHM‐related hyponatremia from 2008 to 2022 July were reviewed retrospectively by two independent doctors (one physician and one clinical toxicologist) using the Adverse Drug Reaction Probability Scale (Naranjo algorithm). Quadratic weighted Kappa was used to test interrater reliability.ResultsThree case reports or series about herb‐related hyponatremia were identified in the scoping review. Seventy‐two patients with hyponatremia and CHM intake were evaluated to determine the possibility of CHM causing hyponatremia. Around 90% of the patients had biochemical profiles compatible with the syndrome of inappropriate secretion of antidiuretic hormones. Most of the patients had a probability likelihood of “possible” as ranked by the Naranjo algorithm. In 19.5% of the hyponatremic patients taking CHM, no other apparent cause was found.ConclusionThe scarcity of literature in scoping review indicates a potential research gap. CHM might be associated with hyponatremia, for which no alternative cause was found. However, meticulous measures must still be made to determine that no other alternative causes are present in a patient with hyponatremia receiving CHM.

Blockchain Based Analytical Bug Tracking in Software Engineering

This article examines a blockchain-based bug tracking system in detail, focusing on its four main components: the Blockchain Platform, the Consensus Mechanism (also known as Proof of Work, PoW), the Data Structure, and Smart Contracts. To improve the efficiency, security, and transparency of bug tracking in software development, we use mathematical models to dissect the operational framework and functionalities that underpin this cutting-edge system. The Blockchain Platform is the foundational layer. It stores data in multiple locations, cannot be changed, and provides a secure environment for fixing software bugs. We use mathematical abstractions to demonstrate how the blockchain is put together, emphasizing the importance of cryptographic hashes in maintaining data integrity and connecting blocks. People pay close attention to the Consensus Mechanism, particularly Proof of Work (PoW), because it is so important for verifying transactions and maintaining the blockchain's integrity. Using a mathematical model, we demonstrate how Proof of Work (PoW) prevents bad behavior while keeping the network safe and democratic by requiring work to be done on a computer to add blocks. The Data Structure module is examined using a model that depicts how bug reports are stored and found on the blockchain. This model demonstrates the efficiency of storing bug reports as transactions, which allows for quick access and unchangeable record-keeping. People associate smart contracts with self-running programs that manage permissions, automate workflows, and provide incentives for reporting bugs. Mathematical models are used to demonstrate how these contracts' conditions and actions are documented, emphasizing their importance in automating tasks and improving system reliability. This article uses mathematical modeling to provide a clear and structured understanding of a blockchain-based bug tracking system and demonstrate how it could change the way bugs are tracked by leveraging blockchain technology's strengths.

Variability in Heating Demand Predictions: A Comparative Study of PHPP and Mc001-2022 in Existing Residential Buildings

The construction industry is a key driver of environmental change due to its extensive use of resources and high emissions, thus significantly burdening global efforts towards sustainable development targets. A large portion of the environmental footprint of buildings results from the energy required to sustain indoor comfort levels. Thus, enhancing the energy efficiency of existing buildings becomes critical in reducing their environmental impact. This study explores the impact of thermal performance improvements on the heating demand, employing numerical modeling and two energy performance methodologies, PHPP and Mc001-2022, across various climatic datasets and case studies in Romania. The results show substantial variability in heating demand predictions: Mc001-2022 predicts up to 27.2% higher continuous heating demands and 21.0% higher intermittent demands compared to PHPP in one case study. In the second case study, the differences range from 8.1% higher to 6.9% lower for continuous heating and from 3.3% higher to 9.9% lower for intermittent heating, depending on the scenario. These findings underscore the importance of the methodological choice and localized climatic data in heating demand assessments, highlighting the need for a tailored, context-specific approach to energy performance assessment, integrating multiple energy efficiency measures suited to the unique characteristics of each building.

Sustainable Business Models for Innovative Urban Mobility Services

Any sharing mobility service aims to make urban mobility sustainable to help reduce environmental impacts and improve the quality of life for all in cities. Many transport services are not currently self-sustainable. The Life for Silver Coast (LifeSC) opened its mobility services on 22 May 2021 and offered electric mobility services during the summer for a few cities in Tuscany. E-bikes and e-scooters can be financially neutral, and even profitable, thanks to the low costs of the vehicles, but they only see a high utilization rate in winter. Shared electric cars, meanwhile, are not profitable. A new shared service that is viable must be profitable to become widely adopted and significantly contribute to sustainability. A few key characteristics have been identified, and one has been tested with a new business model that combines ride-sharing and car-sharing. The innovative Ride Sharing Algorithm (RSA) has been tested based on data from a potential city, Monterondo, where many commuters travel daily to Rome by train. The Italian census and local survey data allowed for the simulation of the scheduling of vehicle rides and an evaluation of the economic results, which could be positive if enough interest for such a system exists among the people, as at least 400 commuters from Monterotondo go to the train station daily in the morning and return in the afternoon. Such a transport demand would justify a new commercial sharing service by using the model tested with the RSA algorithm.

Do hypothetical and actual service distances align for urban fringe parks? An investigation of Shanghai using big data

In China's transition from rapid urbanization to high-quality development, urban peripheral parks are increasingly crucial for ecological services and quality of life. However, a disparity persists between their intended function and actual usage, a common occurrence in megacities of China. This study aims to identify the actual service distance and accessibility of parks in fringe areas, using the southwestern sector of Shanghai's main urban fringe as an example. By utilizing mobile location data, it explores the multilevel service attributes of urban fringe parks. Analyzing spatial characteristics from the perspectives of clustering distances and life circle service capabilities, it concludes that: 1) fringe parks exhibit lower service capacities compared to central ones, mainly providing routine services; 2) fixed service range models are inadequate, especially for fringe parks; and 3) community and district-level parks have comparable service distances, highlighting the need to enhance district-level parks. This research aims to optimize park site selection and functions in urban peripheries, providing insights for sustainable planning.

Balancing National Security and Privacy: Examining the Use of Commercially Available Information in OSINT Practices

Open source intelligence (OSINT) researchers utilize specialized tools to access vast amounts of data from multiple sources simultaneously. These tools, equipped with (paid) modules, allow users to tap into aggregated data sets containing commercially available information, such as location data from mobile phone users. The utilization of commercially available information from OSINT tools by intelligence and security services impacts fundamental rights and freedoms; more specifically, the right to personal data protection. Drawing from prior experience working on this topic within a Dutch oversight committee on the intelligence and security services and international developments in OSINT practice, insights are provided on this new OSINT practice and the responses of oversight authorities. Rather than advocating for a categorical ban, a more refined approach to process commercially available information from OSINT tools is suggested. Building on the work of a Dutch oversight authority and the work of the U.S. Office of the Director of National Intelligence, four recommendations are provided to intelligence and security services to responsibly handle commercially available information in OSINT practices.

Epidemiology of Chronic Hepatitis B Virus Infection, Alameda County, California, 2017-2021.

In the United States, chronic hepatitis B (CHB) virus infection predominantly affects ethnic minorities and vulnerable populations. We analyzed surveillance data to describe CHB epidemiology to guide hepatitis B virus (HBV) elimination efforts in Alameda County. Persons with CHB during 2017-2021 were identified by ≥2 positive HBV tests (ie, HBsAg, HbeAg, and HBV DNA) ≥180days apart using data from California Reportable Disease Information Exchange. Persons with CHB were stratified by age, race/ethnicity, zip code-based Healthy Places Index (HPI), and federally designated Medically Underserved Areas (MUA). Comparisons of reported CHB prevalence between groups utilized z test; comparisons between MUA regions utilized chi-square testing. A total of 8122 persons with CHB were identified; reported 5-year CHB prevalence was 0.53% (95% confidence interval: 0.52%-0.54%). Reported CHB 5-year prevalence was significantly higher among persons aged 50 to 69years old than persons aged 30 to 49years old (0.99%, 0.78%; P <.001) and Asians, Native Hawaiian/Pacific Islanders, African Americans compared to Whites (1.22%, 0.56%, 0.18%, 0.06%; P <.001). Reported CHB 5-year prevalence in the 2 lowest and most disadvantaged HPI quartiles was significantly higher than the 2 highest quartiles (0.55%, 0.68%, 0.37%, 0.42%; P <.001). The 1918 persons with CHB in MUA, compared to 5859 non-MUA persons, were significantly more likely to be African American (7%, 3%; P <.001) and from the lowest HPI quartile (79%, 19%; P <.001). Reported CHB 5-year prevalence in Alameda County was 1.5 times the national prevalence estimates, and reported prevalence was highest among Asians and persons in more disadvantaged areas. Analysis of local CHB surveillance data can guide public health efforts toward HBV elimination.