Tomer Shekel

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    General Geospatial Inference with a Population Dynamics Foundation Model
    Chaitanya Kamath
    Prithul Sarker
    Joydeep Paul
    Yael Mayer
    Sheila de Guia
    Jamie McPike
    Adam Boulanger
    David Schottlander
    Yao Xiao
    Manjit Chakravarthy Manukonda
    Monica Bharel
    Von Nguyen
    Luke Barrington
    Niv Efron
    Krish Eswaran
    Shravya Shetty
    (2024) (to appear)
    Preview abstract Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations, and researchers to understand and reason over complex relationships between human behavior and local contexts. This support includes identifying populations at elevated risk and gauging where to target limited aid resources. Traditional approaches to these classes of problems often entail developing manually curated, task-specific features and models to represent human behavior and the natural and built environment, which can be challenging to adapt to new, or even related tasks. To address this, we introduce the Population Dynamics Foundation Model (PDFM), which aims to capture the relationships between diverse data modalities and is applicable to a broad range of geospatial tasks. We first construct a geo-indexed dataset for postal codes and counties across the United States, capturing rich aggregated information on human behavior from maps, busyness, and aggregated search trends, and environmental factors such as weather and air quality. We then model this data and the complex relationships between locations using a graph neural network, producing embeddings that can be adapted to a wide range of downstream tasks using relatively simple models. We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements. The approach achieves state-of-the-art performance on geospatial interpolation across all tasks, surpassing existing satellite and geotagged image based location encoders. In addition, it achieves state-of-the-art performance in extrapolation and super-resolution for 25 of the 27 tasks. We also show that the PDFM can be combined with a state-of-the-art forecasting foundation model, TimesFM, to predict unemployment and poverty, achieving performance that surpasses fully supervised forecasting. The full set of embeddings and sample code are publicly available for researchers. In conclusion, we have demonstrated a general purpose approach to geospatial modeling tasks critical to understanding population dynamics by leveraging a rich set of complementary globally available datasets that can be readily adapted to previously unseen machine learning tasks. View details
    Geographical accessibility to emergency obstetric care in urban Nigeria using closer-to-reality travel time estimates
    Aduragbemi Banke-Thomas
    Kerry L. M. Wong
    Tope Olubodun
    Peter M. Macharia
    Narayanan Sundararajan
    Yash Shah
    Mansi Kansal
    Swapnil Vispute
    Olakunmi Ogunyemi
    Uchenna Gwacham-Anisiobi
    Jia Wang
    Ibukun-Oluwa Omolade Abejirinde
    Prestige Tatenda Makanga
    Ngozi Azodoh
    Charles Nzelu, PhD
    Charlotte Stanton
    Bosede B. Afolabi
    Lenka Beňová
    Lancet Global Health (2024)
    Preview abstract Background Better accessibility of emergency obstetric care (CEmOC) facilities can significantly reduce maternal and perinatal deaths. However, pregnant women living in urban settings face additional complex challenges travelling to facilities. We estimated geographical accessibility and coverage to the nearest, second nearest, and third nearest public and private CEmOC facilities in the 15 largest Nigerian cities. Methods We mapped city boundaries, verified and geocoded functional CEmOC facilities, and assembled population distribution for women of childbearing age (WoCBA). We used Google Maps Platform’s internal Directions Application Programming Interface (API) to derive driving times to public, private, or either facility-type. Median travel time (MTT) and percentage of WoCBA able to reach care were summarised for eight traffic scenarios (peak and non-peak hours on weekdays and weekends) by city and within-city (wards) under different travel time thresholds (<15, <30, <60 min). Findings City-level MTT to the nearest CEmOC facility ranged from 18min (Maiduguri) to 46min (Kaduna). Within cities, MTT varied by location, with informal settlements and peripheral areas being the worst off. The percentages of WoCBA within 60min to their nearest public CEmOC were nearly universal; whilst the percentages of WoCBA within 30min reach to their nearest public CEmOC were between 33% in Aba to over 95% in Ilorin and Maiduguri. During peak traffic times, the median number of public CEmOC facilities reachable by WoCBA under 30min was zero in eight of 15 cities. Interpretation This approach provides more context-specific, finer, and policy-relevant evidence to support improving CEmOC service accessibility in urban Africa. View details
    Socio-spatial equity analysis of relative wealth index and emergency obstetric care accessibility in urban Nigeria
    Kerry L. M. Wong
    Aduragbemi Banke-Thomas
    Tope Olubodun
    Peter M. Macharia
    Charlotte Stanton
    Narayanan Sundararajan
    Yash Shah
    Mansi Kansal
    Swapnil Vispute
    Olakunmi Ogunyemi
    Uchenna Gwacham-Anisiobi
    Jia Wang
    Ibukun-Oluwa Omolade Abejirinde
    Prestige Tatenda Makanga
    Bosede B. Afolabi
    Lenka Beňová
    Communications Medicine, 4 (2024), pp. 34
    Preview abstract Background Better geographical accessibility to comprehensive emergency obstetric care (CEmOC) facilities can significantly improve pregnancy outcomes. However, with other factors, such as affordability critical for care access, it is important to explore accessibility across groups. We assessed CEmOC geographical accessibility by wealth status in the 15 most-populated Nigerian cities. Methods We mapped city boundaries, verified and geocoded functional CEmOC facilities, and assembled population distribution for women of childbearing age and Meta’s Relative Wealth Index (RWI). We used the Google Maps Platform’s internal Directions Application Programming Interface to obtain driving times to public and private facilities. City-level median travel time (MTT) and number of CEmOC facilities reachable within 60 min were summarised for peak and non-peak hours per wealth quintile. The correlation between RWI and MTT to the nearest public CEmOC was calculated. Results We show that MTT to the nearest public CEmOC facility is lowest in the wealthiest 20% in all cities, with the largest difference in MTT between the wealthiest 20% and least wealthy 20% seen in Onitsha (26 vs 81 min) and the smallest in Warri (20 vs 30 min). Similarly, the average number of public CEmOC facilities reachable within 60 min varies (11 among the wealthiest 20% and six among the least wealthy in Kano). In five cities, zero facilities are reachable under 60 min for the least wealthy 20%. Those who live in the suburbs particularly have poor accessibility to CEmOC facilities. Conclusions Our findings show that the least wealthy mostly have poor accessibility to care. Interventions addressing CEmOC geographical accessibility targeting poor people are needed to address inequities in urban settings. View details
    Quantifying urban park use in the USA at scale: empirical estimates of realised park usage using smartphone location data
    Michael T Young
    Swapnil Vispute
    Stylianos Serghiou
    Akim Kumok
    Yash Shah
    Kevin J. Lane
    Flannery Black-Ingersoll
    Paige Brochu
    Monica Bharel
    Sarah Skenazy
    Shailesh Bavadekar
    Mansi Kansal
    Evgeniy Gabrilovich
    Gregory A. Wellenius
    Lancet Planetary Health (2024)
    Preview abstract Summary Background A large body of evidence connects access to greenspace with substantial benefits to physical and mental health. In urban settings where access to greenspace can be limited, park access and use have been associated with higher levels of physical activity, improved physical health, and lower levels of markers of mental distress. Despite the potential health benefits of urban parks, little is known about how park usage varies across locations (between or within cities) or over time. Methods We estimated park usage among urban residents (identified as residents of urban census tracts) in 498 US cities from 2019 to 2021 from aggregated and anonymised opted-in smartphone location history data. We used descriptive statistics to quantify differences in park usage over time, between cities, and across census tracts within cities, and used generalised linear models to estimate the associations between park usage and census tract level descriptors. Findings In spring (March 1 to May 31) 2019, 18·9% of urban residents visited a park at least once per week, with average use higher in northwest and southwest USA, and lowest in the southeast. Park usage varied substantially both within and between cities; was unequally distributed across census tract-level markers of race, ethnicity, income, and social vulnerability; and was only moderately correlated with established markers of census tract greenspace. In spring 2019, a doubling of walking time to parks was associated with a 10·1% (95% CI 5·6–14·3) lower average weekly park usage, adjusting for city and social vulnerability index. The median decline in park usage from spring 2019 to spring 2020 was 38·0% (IQR 28·4–46·5), coincident with the onset of physical distancing policies across much of the country. We estimated that the COVID-19-related decline in park usage was more pronounced for those living further from a park and those living in areas of higher social vulnerability. Interpretation These estimates provide novel insights into the patterns and correlates of park use and could enable new studies of the health benefits of urban greenspace. In addition, the availability of an empirical park usage metric that varies over time could be a useful tool for assessing the effectiveness of policies intended to increase such activities. View details
    Revealed versus potential spatial accessibility of healthcare and changing patterns during the COVID-19 pandemic
    Kristina Gligoric
    Chaitanya Kamath
    Daniel Weiss
    Shailesh Bavadekar
    Kevin Schulman
    Evgeniy Gabrilovich
    Nature Communications Medicine (2023)
    Preview abstract Background Timely access to healthcare is essential but measuring access is challenging. Prior research focused on analyzing potential travel times to healthcare under optimal mobility scenarios that do not incorporate direct observations of human mobility, potentially underestimating the barriers to receiving care for many populations. Methods We introduce an approach for measuring accessibility by utilizing travel times to healthcare facilities from aggregated and anonymized smartphone Location History data. We measure these revealed travel times to healthcare facilities in over 100 countries and juxtapose our findings with potential (optimal) travel times estimated using Google Maps directions. We then quantify changes in revealed accessibility associated with the COVID-19 pandemic. Results We find that revealed travel time differs substantially from potential travel time; in all but 4 countries this difference exceeds 30 minutes, and in 49 countries it exceeds 60 minutes. Substantial variation in revealed healthcare accessibility is observed and correlates with life expectancy (⍴=−0.70) and infant mortality (⍴=0.59), with this association remaining significant after adjusting for potential accessibility and wealth. The COVID-19 pandemic altered the patterns of healthcare access, especially for populations dependent on public transportation. Conclusions Our metrics based on empirical data indicate that revealed travel times exceed potential travel times in many regions. During COVID-19, inequitable accessibility was exacerbated. In conjunction with other relevant data, these findings provide a resource to help public health policymakers identify underserved populations and promote health equity by formulating policies and directing resources towards areas and populations most in need. View details
    High Resolution Building and Road Segmentation from Sentinel-2 Imagery
    Abdoulaye Diack
    Abel Tesfaye Korme
    Emmanuel Asiedu Brempong
    Jason Hickey
    Juliana Marcos
    Krishna Sapkota
    Mohammed Alewi Hassen
    Wojciech Sirko
    arXiv, https://arxiv.org/abs/2310.11622 (2023)
    Preview abstract Mapping buildings and roads automatically with remote sensing typically requires imagery of at least 50 cm resolution, which is expensive to obtain and often sparsely available. In this work we demonstrate how public, worldwide imagery from the Sentinel-2 Earth observation mission can be used to carry out this task at a much higher level of detail than the 10 m raw pixel resolution would suggest. To do this, we employ a teacher-student method in which a model with access to a temporal stack of Sentinel-2 images is trained to make the same predictions as a high-resolution model with access to corresponding 50 cm imagery. Evaluating at 50cm resolution, we achieve mIOU of 0.78, equivalent in accuracy to applying a single-frame high resolution model with imagery of 4m resolution. This work opens up new possibilities for using freely available Sentinel-2 imagery for a range of downstream tasks that previously could only be done with high resolution satellite imagery. The model will be made available soon to non-commercial, non-governmental entities at https://sites.research.google/open-buildings/ upon request. View details
    A geospatial database of close to reality travel times to obstetric emergency care in 15 Nigerian conurbations
    Peter M. Macharia
    Kerry L. M. Wong
    Tope Olubodun
    Lenka Beňová
    Charlotte Stanton
    Narayanan Sundararajan
    Yash Shah
    Mansi Kansal
    Swapnil Vispute
    Uchenna Gwacham-Anisiobi
    Olakunmi Ogunyemi
    Jia Wang
    Ibukun-Oluwa Omolade Abejirinde
    Prestige Tatenda Makanga
    Bosede B. Afolabi
    Aduragbemi Banke-Thomas
    Scientific Data, TBD (2023), TBD
    Preview abstract Travel time estimation accounting for on-the-ground realities between the location where a need for emergency obstetric care (EmOC) arises and the health facility capable of providing such services is essential for improving maternal and neonatal health outcomes. Current understanding of travel time to care is particularly inadequate in urban areas where short distances obscure long travel times, and also in low-resource settings. Here, we describe a database of travel times to facilities that can provide comprehensive EmOC in the 15 most populated extended urban areas (conurbations) in Nigeria. The travel times from cells of approximately 0.6 x 0.6km to facilities were derived based on Google Maps Platform’s internal Directions Application Programming Interface (API). The API incorporates estimates of traffic to provide closer-to-reality estimates of travel time. Computations were done to the first, second and third nearest public or private facilities. Travel time estimates for eight traffic scenarios (including peak and non-peak periods) and number of facilities within specific time thresholds were estimated. The database offers a plethora of opportunities for research and planning towards improving EmOC accessibility. View details
    Comparing access to urban parks across six OECD countries
    Talia Kaufmann
    Swapnil Vispute
    Mansi Kansal
    Daniel T. O'Brien
    Evgeniy Gabrilovich
    Gregory A. Wellenius
    Lewis Dijkstra
    Paolo Veneri
    OECD Regional Development Papers (2023)
    Preview abstract This work leverages globally consistent data on parks from Google Maps, in combination with the computational power of Google Maps Directions API to quantify accessibility to parks across nearly 500 metropolitan areas in six countries: Estonia, France, Greece, Mexico, Sweden, and the United States. We combined high resolution population data from Worldpop with parks data and navigation estimates to measure: (1) Fraction of the population with access to parks within a 10-minute walk; and (2) the median walking time to the closest park. We find large differences in access to parks between countries, as well as large variability across cities and their respective commuting zones. To demonstrate how this framework can support cross country comparisons and efforts to track progress towards SDG11, we assessed access to parks by income group in selected countries, finding that the median walking time to a park is shorter for residents of low income neighbourhoods both in French and American metropolitan areas. View details
    Identifying COVID-19 Vaccine Deserts and Ways to Reduce Them: A Digital Tool to Support Public Health Decision-Making
    Rebecca L. Weintraub
    Kate Miller
    Benjamin Rader
    Julie Rosenberg
    Shreyas Srinath
    Samuel R. Woodbury
    Marinanicole Schultheiss
    Mansi Kansal
    Swapnil Vispute
    Stelios Serghiou
    Gerardo Flores
    Akim Kumok
    Evgeniy Gabrilovich
    Iman Ahmad
    Molly E. Chiang
    John S. Brownstein
    American Journal of Public Health (2023)
    Preview abstract A private–academic partnership built the Vaccine Equity Planner (VEP) to help decision-makers improve geographic access to COVID-19 vaccinations across the United States by identifying vaccine deserts and facilities that could fill those deserts. The VEP presented complex, updated data in an intuitive form during a rapidly changing pandemic situation. The persistence of vaccine deserts in every state as COVID-19 booster recommendations develop suggests that vaccine delivery can be improved. Underresourced public health systems benefit from tools providing real-time, accurate, actionable data. (Am J Public Health. 2023;113(4):363–367. https://doi.org/10.2105/AJPH.2022.307198) Public health leaders can make better, more equitable decisions when they can clearly see and understand the problems. Being presented with potential solutions based on evidence further supports their decision-making and can aid in supporting health equity. View details
    An evaluation of Internet searches as a marker of trends in population mental health in the US
    Uma Vaidyanathan
    Yuantong Sun
    Katherine Chou
    Sandro Galea
    Evgeniy Gabrilovich
    Gregory A. Wellenius
    Scientific Reports (2022)
    Preview abstract The absence of continuous, real-time mental health assessment has made it challenging to quantify the impacts of the COVID-19 pandemic on population mental health. We examined publicly available, anonymized, aggregated data on weekly trends in Google searches related to anxiety, depression, and suicidal ideation from 2018 to 2020 in the US. We correlated these trends with (1) emergency department (ED) visits for mental health problems and suicide attempts, and (2) surveys of self-reported symptoms of anxiety, depression, and mental health care use. Search queries related to anxiety, depression, and suicidal ideation decreased sharply around March 2020, returning to pre-pandemic levels by summer 2020. Searches related to depression were correlated with the proportion of individuals reporting receiving therapy (r = 0.73), taking medication (r = 0.62) and having unmet mental healthcare needs (r = 0.57) on US Census Household Pulse Survey and modestly correlated with rates of ED visits for mental health conditions. Results were similar when considering instead searches for anxiety. Searches for suicidal ideation did not correlate with external variables. These results suggest aggregated data on Internet searches can provide timely and continuous insights into population mental health and complement other existing tools in this domain. View details