Traditional methods of counting bicyclists are resource-intensive and generate data with sparse spatial and temporal detail. Previous research suggests big data from crowdsourced fitness apps offer a new source of bicycling data with high spatial and temporal resolution. However, crowdsourced bicycling data are biased as they oversample recreational riders. Our goals are to quantify geographical variables, which can help in correcting bias in crowdsourced, data and to develop a generalized method to correct bias in big crowdsourced data on bicycle ridership in different settings in order to generate maps for cities representative of all bicyclists at a street-level spatial resolution. We used street-level ridership data for 2016 from a crowdsourced fitness app (Strava), geographical covariate data, and official counts from 44 locations across Maricopa County, Arizona, USA (training data); and 60 locations from the city of Tempe, within Maricopa (test data). First, we quantified the relationship between Strava and official ridership data volumes. Second, we used a multi-step approach with variable selection using LASSO followed by Poisson regression to integrate geographical covariates, Strava, and training data to correct bias. Finally, we predicted bias-corrected average annual daily bicyclist counts for Tempe and evaluated the model's accuracy using the test data. We found a correlation between the annual ridership data from Strava and official counts (R 2 = 0.76) in Maricopa County for 2016. The significant variables for correcting bias were: The proportion of white population, median household income, traffic speed, distance to residential areas, and distance to green spaces. The model could correct bias in crowdsourced data from Strava in Tempe with 86% of road segments being predicted within a margin of ±100 average annual bicyclists. Our results indicate that it is possible to map ridership for cities at the street-level by correcting bias in crowdsourced bicycle ridership data, with access to adequate data from official count programs and geographical covariates at a comparable spatial and temporal resolution.

Mobility is an indicator of human movement through space and time. With the increasing availability of geolocated data (from GPS, accelerometers, etc.), it is now possible to examine individual as well as group human mobility patterns. Human mobility is influenced by both intrinsic (i.e. personal motivations) and extrinsic (i.e., events like natural hazards or a pandemic like the COVID-19) factors. However, the intricate relationships between human mobility patterns and sociodemographic characteristics in the context of a pandemic are yet to be fully explored. Our goal is to overcome this gap by using human mobility data at the census block group level from mobile phones and combining those with social vulnerability indicators to examine the overall spread of COVID-19 at local spatial scales. We used 585,878 weekly visits to 37,871 points of interests (POIs) from Safegraph to quantify mobility indices and social distancing metrics in 2,820 census block groups in the city of Los Angeles (LA)-before and during lockdown as well as during the phase1 and phase 2 reopening. Finally, using supervised machine learning algorithms, we classified the census block groups in LA into High, Medium and Low categories that represented the vulnerability of these block groups based on the cumulative number of occurrences of COVID-19 cases till July 24, 2020. Our results indicate that the tree-based classifiers performed well in comparison to the Support Vector Machines and Multinomial Logit models. Gradient Boosting had the highest classification accuracy of 97.4% COVID-19 with an AUC score of 0.987. The block groups with high COVID-19 cases also had a high concentration of socially vulnerable populations, high human mobility index and a low social distancing index. CCS CONCEPTS • Information systems → Geographic information systems; • Computing methodologies → Supervised learning; • Humancentered computing → Empirical studies in collaborative and social computing.

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The ongoing novel coronavirus (COVID-19) pandemic has highlighted the need for individuals to have easy access to healthcare facilities for treatment as well as vaccinations. The surge in COVID-19 hospitalizations during 2020 also underscored the fact that accessibility to nearby hospitals for testing and treatment and vaccination sites is crucial for patients with fever or respiratory symptoms. Although necessary, quantifying healthcare access is challenging as it depends on a complex interaction between underlying socioeconomic and physical factors. In this case study, we deployed a Multi Criteria Decision Analysis (MCDA) approach to uncover the barriers and their effect on healthcare access using an accessibility index. Using the least cost path (LCP) analysis we quantified the cost associated with healthcare access from each census block group in the Los Angeles metropolitan area (LA Metro) to the nearest hospital. Social vulnerability reported by the Centers for Disease Control and Prevention (CDC), the daily number of COVID-19 cases from the Los Angeles open data portal and built environment characteristics (slope of the street, car ownership, population density distribution, walkability, traffic collision density, and speed limit) were used to quantify accessibility. Our results showed that the census block groups with a social vulnerability index above 0.75 (high vulnerability) had low accessibility owing to the higher cost of access to nearby hospitals and were also coincident with the hotspots for COVID-19 cases and deaths. Our findings highlighted the inequitable exposure of socially disadvantaged populations to COVID-19 infections and how the pandemic impacts were exacerbated by the synergistic effect of socioeconomic status and built environment characteristics of the locations where the disadvantaged populations resided. The framework proposed herein could be adapted to geo-target testing/vaccination sites and improve accessibility to healthcare facilities in general and among the socially vulnerable populations to reduce the health risk.