arXiv subjects

Saeed Saleh Namadi

Publications and source records attributed to Saeed Saleh Namadi.

5 recordsLinked to original sources

arXiv2025

Access to healthcare for people with Alzheimer's Diseases and related dementias

Background: Alzheimer's Disease and Related Dementias (ADRD) affects millions worldwide. Significant disparities exist in ADRD diagnosis and care, disproportionately impacting minority and socioeconomically vulnerable populations Objective: In this study, we investigate the relationship between ADRD density and accessibility to healthcare. We identify underserved and overserved areas in Maryland based on diagnosed cases and mortality due to ADRD, focusing on geographic disparities in care. Methods: 2023 Maryland ADRD patients were identified using ICD-10 codes from. Accessibility was measured using the Kernel Density Two-Step Floating Catchment Area (KD2SFCA) method. The Gini index and t-tests were used to analyze disparities between urban and rural areas. Hot Spot Analysis Getis-Ord Gi* and local bivariate relationships analysis were applied to assess spatial correlations. Principal component analysis (PCA) was applied to calculate the health risk index. Results: Hospital accessibility was unevenly distributed. Mortality rates from ADRD were higher in underserved areas with fewer hospitals. Hot spot analysis shows eastern and southern Maryland have zones with high mortality per population and per ADRD patient, surrounded by similarly high-rate zones. Central Maryland shows lower death rates per patient but more hospital facilities. In eastern Maryland, higher poverty areas are surrounded by zones with lower accessibility and higher health risk indices. Conclusion: Hospital accessibility is unevenly distributed, creating major rural disparities. Underserved regions in terms of access to healthcare facilities, particularly in eastern and southern Maryland, exhibit high ADRD mortality rates despite low diagnosis rates. This suggests that many ADRD cases remain undiagnosed, underdiagnosed, or subject to delayed treatment.

stat.AP↗

arXiv2024

Integrating En Route and Home Proximity in EV Charging Accessibility: A Spatial Analysis in the Washington Metropolitan Area

This study evaluates the accessibility of public EV charging stations in the Washington metropolitan area using a comprehensive measure that accounts for both destination-based and en route charging opportunities. By incorporating the full spectrum of daily travel patterns into the accessibility evaluation, our methodology offers a more realistic measure of charging opportunities than destination-based methods that prioritize proximity to residential locations. Results from spatial autocorrelation analysis indicate that conventional accessibility assessments often overestimate the availability of infrastructure in central urban areas and underestimate it in peripheral commuting zones, potentially leading to misallocated resources. By highlighting significant clusters of high-access and low-access areas, our approach identifies spatial inequalities in infrastructure distribution and provides insights into areas requiring targeted interventions. This study underscores the importance of incorporating daily mobility patterns into urban planning to ensure equitable access to EV charging infrastructure and suggests a framework that other regions could adopt to enhance sustainable transportation networks and support equitable urban development.

physics.soc-ph↗

arXiv2024

Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Due to Covid-19

COVID-19 has deeply affected daily life and travel behaviors. Understanding these changes is crucial, prompting an investigation into socio-demographic and socio-economic factors. This study used large-scale mobile device location data in Washington, D.C., Maryland, and Virginia (DMV area) to unveil the impacts of these variables on commute trip changes. It reflected short and long-term impacts through linear regression and geographically weighted regression models. Findings indicated that counties with a higher percentage of people using walking and biking during the initial phase of COVID-19 experienced greater reductions in commute trips. For the long-term effect in November, the impact of active modes became insignificant, and individuals using public modes showed more significant trip reductions. Positive correlations were observed between median income levels and reduced commute trips. Sectors requiring ongoing outdoor operations during the pandemic showed substantial negative correlations. In the DMV area, counties with a higher proportion of Democratic voters experienced less trip reduction. Applying Geographically Weighted Regression models captured local spatial relationships, showing the emergence of local correlations as the pandemic evolved, suggesting a geographical impact pattern. Initially global, the pandemic's impact on commuting behaviors became more influenced by spatial factors over time, showing localized effects.

stat.AP↗

arXiv2023

A Big Data Driven Framework for Duplicate Device Detection from Multi-sourced Mobile Device Location Data

Mobile Device Location Data (MDLD) has been popularly utilized in various fields. Yet its large-scale applications are limited because of either biased or insufficient spatial coverage of the data from individual data vendors. One approach to improve the data coverage is to leverage the data from multiple data vendors and integrate them to build a more representative dataset. For data integration, further treatments on the multi-sourced dataset are required due to several reasons. First, the possibility of carrying more than one device could result in duplicated observations from the same data subject. Additionally, when utilizing multiple data sources, the same device might be captured by more than one data provider. Our paper proposes a data integration methodology for multi-sourced data to investigate the feasibility of integrating data from several sources without introducing additional biases to the data. By leveraging the uniqueness of travel pattern of each device, duplicate devices are identified. The proposed methodology is shown to be cost-effective while it achieves the desired accuracy level. Our findings suggest that devices sharing the same imputed home location and the top five most-visited locations during a month can represent the same user in the MDLD. It is shown that more than 99.6% of the sample devices having the aforementioned attribute in common are observed at the same location simultaneously. Finally, the proposed algorithm has been successfully applied to the national-level MDLD of 2020 to produce the national passenger origin-destination data for the NextGeneration National Household Travel Survey (NextGen NHTS) program.

cs.CY↗

arXiv2023

A Big-Data Driven Framework to Estimating Vehicle Volume based on Mobile Device Location Data

Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance plans and more. Traditional methods of quantifying vehicle volume rely on manual counting, video cameras, and loop detectors at a limited number of locations. These efforts require significant labor and cost for expansions. Researchers and private sector companies have also explored alternative solutions such as probe vehicle data, while still suffering from a low penetration rate. In recent years, along with the technological advancement in mobile sensors and mobile networks, Mobile Device Location Data (MDLD) have been growing dramatically in terms of the spatiotemporal coverage of the population and its mobility. This paper presents a big-data driven framework that can ingest terabytes of MDLD and estimate vehicle volume at a larger geographical area with a larger sample size. The proposed framework first employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, i.e., Annual Vehicle-Miles of Travel (AVMT), and Annual Average Daily Traffic (AADT). The proposed framework is implemented on the all-street network in the state of Maryland using MDLD for the entire year of 2019. Results indicate that our proposed framework produces reliable vehicle volume estimates and also demonstrate its transferability and the generalization ability.

cs.CY↗