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Jankowska MM, Yang JA, Luo N, Spoon C, Benmarhnia T. Accounting for space, time, and behavior using GPS derived dynamic measures of environmental exposure. Health Place 2023; 79:102706. [PMID: 34801405 PMCID: PMC9129269 DOI: 10.1016/j.healthplace.2021.102706] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 10/19/2022]
Abstract
Time-weighted spatial averaging approaches (TWSA) are an increasingly utilized method for calculating exposure using global positioning system (GPS) mobility data for health-related research. They can provide a time-weighted measure of exposure, or dose, to various environments or health hazards. However, little work has been done to compare existing methodologies, nor to assess how sensitive these methods are to mobility data inputs (e.g., walking vs driving), the type of environmental data being assessed as the exposure (e.g., continuous surfaces vs points of interest), and underlying point-pattern clustering of participants (e.g., if a person is highly mobile vs predominantly stationary). Here we contrast three TWSA approaches that have been previously used or recently introduced in the literature: Kernel Density Estimation (KDE), Density Ranking (DR), and Point Overlay (PO). We feed GPS and accelerometer data from 602 participants through each method to derive time-weighted activity spaces, comparing four mobility behaviors: all movement, stationary time, walking time, and in-vehicle time. We then calculate exposure values derived from the various TWSA activity spaces with four environmental layer data types (point, line, area, surface). Similarities and differences across TWSA derived exposures for the sample and between individuals are explored, and we discuss interpretation of TWSA outputs providing recommendations for researchers seeking to apply these methods to health-related studies.
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Affiliation(s)
| | - Jiue-An Yang
- Population Sciences, Beckman Research Institute, City of Hope, USA
| | - Nana Luo
- Scripps Institute of Oceanography, University of California San Diego, USA
| | - Chad Spoon
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, USA
| | - Tarik Benmarhnia
- Scripps Institute of Oceanography, University of California San Diego, USA
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Spelta A, Flori A, Pierri F, Bonaccorsi G, Pammolli F. After the lockdown: simulating mobility, public health and economic recovery scenarios. Sci Rep 2020; 10:16950. [PMID: 33046737 PMCID: PMC7550600 DOI: 10.1038/s41598-020-73949-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/23/2020] [Indexed: 12/24/2022] Open
Abstract
The spread of SARS-COV-2 has affected many economic and social systems. This paper aims at estimating the impact on regional productive systems in Italy of the interplay between the epidemic and the mobility restriction measures put in place to contain the contagion. We focus then on the economic consequences of alternative lockdown lifting schemes. We leverage a massive dataset of human mobility which describes daily movements of over four million individuals in Italy and we model the epidemic spreading through a metapopulation SIR model, which provides the fraction of infected individuals in each Italian district. To quantify economic backslashes this information is combined with socio-economic data. We then carry out a scenario analysis to model the transition to a post-lockdown phase and analyze the economic outcomes derived from the interplay between (a) the timing and intensity of the release of mobility restrictions and (b) the corresponding scenarios on the severity of virus transmission rates. Using a simple model for the spreading disease and parsimonious assumptions on the relationship between the infection and the associated economic backlashes, we show how different policy schemes tend to induce heterogeneous distributions of losses at the regional level depending on mobility restrictions. Our work shed lights on how recovery policies need to balance the interplay between mobility flows of disposable workers and the diffusion of contagion.
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Affiliation(s)
- Alessandro Spelta
- Department of Economics and Management, University of Pavia, Via San Felice 7, 27100, Pavia, Italy.
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy.
| | - Andrea Flori
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy
| | - Francesco Pierri
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy.
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Giuseppe Ponzio 34/5, 20133, Milan, Italy.
| | - Giovanni Bonaccorsi
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy
| | - Fabio Pammolli
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy
- CADS, Joint Center for Analysis, Decisions and Society, Human Technopole, Via Cristina Belgioioso, 171, 20157, Milan, Italy
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