Data and Code for: Subways and Urban Air Pollution
Principal Investigator(s): View help for Principal Investigator(s) Nicolas Gendron-Carrier, McGill University; Marco Gonzalez-Navarro, University of California-Berkeley; Stefano Polloni, Mapdwell; Matthew A. Turner, Brown University
Version: View help for Version V1
Name | File Type | Size | Last Modified |
---|---|---|---|
DEM | 12/04/2020 02:04:PM | ||
MODIS_AOD | 12/08/2020 03:13:PM | ||
PWT | 12/04/2020 02:04:PM | ||
UN_cities | 12/04/2020 02:04:PM | ||
WHO_mortality | 12/04/2020 02:04:PM | ||
boundaries | 12/04/2020 02:04:PM | ||
lights | 12/04/2020 02:04:PM | ||
merge | 12/04/2020 02:07:PM | ||
pollution | 12/04/2020 02:04:PM | ||
ridership | 12/04/2020 02:04:PM |
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Project Description
Summary:
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The code and data in this repository generate the results of the paper “Subways and Urban Air Polution” by Gendron-Carrier, Gonzalez-Navarro, Polloni and Turner published in the American Economic Journal: Applied Economics.
The source satellite data from MODIS are quite large and are housed in a separate repository because of space constraints. They are available at https://doi.org/10.5281/zenodo.4317553.
The archive folder “dofiles” contains the code that organizes the MODIS data and matches it to other data sources. It results in the workhorse dataset "data/merge/analyze_me.dta"
The archive folder “dofiles” contains the code that organizes the MODIS data and matches it to other data sources. It results in the workhorse dataset "data/merge/analyze_me.dta"
The folder “data_analysis” contains the Stata dofiles that operate on “data/merge/analyze_me.dta” as well as archive folder “data/WHO_mortality” to generate tables and graphs presented in the paper.
Scope of Project
Subject Terms:
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AOD;
subways ;
air pollution;
aerosol optical depth;
public transit
JEL Classification:
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L91 Transportation: General
R10 General Regional Economics (includes Regional Data)
R11 Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
R40 Transportation Economics: General
L91 Transportation: General
R10 General Regional Economics (includes Regional Data)
R11 Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
R40 Transportation Economics: General
Geographic Coverage:
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Global
Universe:
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Cities that inaugurated a subway system between August 2001 and July of 2016.
Data Type(s):
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geographic information system (GIS) data;
observational data
Methodology
Response Rate:
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100% due to observability of pollution from satellite data.
Sampling:
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All cities that opened a subway in the relevant time period are included.
Data Source:
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Feenstra, Robert, Robert Inklaar, and Marcel Timmer. 2015. "The next generation of the Penn World Table" American Economic Review, 105(10), 31503182.
Google. 2020. Google maps. https://www.google.com/maps. Accessed: February 2020
Gonzalez-Navarro, Marco and Matthew A Turner. 2018. "Subways and urban growth: Evidence from Earth" Journal of Urban Economics 108: 85–106. Available for download at: Gonzalez-Navarro, Marco and Matthew A Turner. 2019. Replication data for: "Subways and urban growth: Evidence from earth" url https://doi.org/10.7910/DVN/VHPRTA
Harris, I., P.D. Jones, T.J. Osborn, and D.H. Lister. 2014. "Updated high-resolution
World Health Organization. 2014. Ambient air pollution database.
World Health Organization. 2016. Ambient air pollution database.
DESA Population Division, UN. 2018. "World urbanization prospects: the 2018 revision" url https://esa.un.org/unpd/wup/. Accessed: February 2020.
Feenstra, Robert, Robert Inklaar, and Marcel Timmer. 2015. "The next generation of the Penn World Table" American Economic Review, 105(10), 31503182.
Available for download at www.ggdc.net/pwt Accessed: 2019-09-16.
Google. 2020. Google maps. https://www.google.com/maps. Accessed: February 2020
Gonzalez-Navarro, Marco and Matthew A Turner. 2018. "Subways and urban growth: Evidence from Earth" Journal of Urban Economics 108: 85–106. Available for download at: Gonzalez-Navarro, Marco and Matthew A Turner. 2019. Replication data for: "Subways and urban growth: Evidence from earth" url https://doi.org/10.7910/DVN/VHPRTA
Harris, I., P.D. Jones, T.J. Osborn, and D.H. Lister. 2014. "Updated high-resolution
grids of monthly climatic observations - the CRU TS3.26 dataset" International Journal of Climatology, 34(3), 623-642. Available for download at https://crudata.uea.ac.uk/cru/data/hrg/ Accessed: 20181028.
Levy, Rob, Christina Hsu, and al. 2015a. "MODIS atmosphere l2 aerosol product,
NASA MODIS adaptive processing system", Goddard space flight center, USA.
http://dx.doi.org/10.5067/MODIS/MYD04_L2.006. Accessed: November 2018.
Urbanrail. 2020. Urbanrail.net. http://urbanrail.net/. Accessed: February 2020.
Urbanrail. 2020. Urbanrail.net. http://urbanrail.net/. Accessed: February 2020.
Wentz, F., J. Scott, R. Hoffman, M. Leidner, R. Atlas, and J. Ardizzone. 2015. "Remote sensing systems cross-calibrated multiplatform (ccmp) 6-hourly ocean vector wind analysis product on 0.25 deg grid, version 2.0" Remote Sensing Systems, Santa Rosa, CA. Available online at www.remss.com/measurements/ccmp.
World Health Organization. 2011. Urban outdoor air pollution database.
en/. Accessed: 2013-04-04.
World Health Organization. 2014. Ambient air pollution database.
en/. Accessed: 2015-11-10.
World Health Organization. 2016. Ambient air pollution database.
en/. Accessed: 2017-04-10.
Note: All WHO links are now broken but the source datasets are included in the replication archive in data/pollution/source/WHO/.
Collection Mode(s):
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remote sensing;
web scraping
Unit(s) of Observation:
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Metropolitan areas
Geographic Unit:
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Metropolitan area
Related Publications
Published Versions
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This material is distributed exactly as it arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.