nightlightstats

This package was built using the code of the paper "The Elusive Banker. Using Hurricanes to Uncover (Non-)Activity in Offshore Financial Centers" by Jakob Miethe but while that paper only focuses on small island economies, this package allows constructing nighttime light statistics for most geospatial units on the planet, which is hopefully useful for researchers in other areas. Calculations using nighttime lights can be performed via the function nightlight_calculate. Plots of nighttime lights can be created with nightlight_plot.

To install the package, run devtools::install_github("JakobMie/nightlightstats").

You can either work with yearly DMSP data ranging from 1992 to 2013 (https://www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html - Image and data processing by NOAA's National Geophysical Data Center, DMSP data collected by US Air Force Weather Agency) or monthly VIIRS data beginning in April 2012 (https://eogdata.mines.edu/products/vnl/ - Earth Observation Group, Payne Institute for Public Policy, Elvidge et al., 2013). Yearly VIIRS data are available at https://eogdata.mines.edu/nighttime_light/annual/v20/. At the moment, the package does not process these data. The package uses administrative boundary shapefiles from GADM (https://gadm.org/data.html).

If you experience trouble with the calculation capacities of your computer because a region you want to investigate is too large, you can take a look at the auxiliary function slice_shapefile which cuts a region into smaller pieces.

nightlight_calculate

This function allows to perform calculations using nighttime lights of a set of areas in a given time span. The output will be an aggregated dataframe in your environment, or if desired an additional dataframe for each area provided in area_names. To get these single dataframes, you can set the argument single_dataframes to TRUE.

For example, if you would like to get a dataframe for the DMSP nighttime lights in adm 1 regions of Luxembourg between 1992 and 1995, you can input:

nightlight_calculate(
area_names = "Luxembourg",
time = c("1992", "1995"),
shapefile_location = "D:/shapefiles",
light_location = "D:/nightlights",
admlevel = 1)

which will give you this dataframe called "lights" in the R environment:

The area in square kilometers will automatically be calculated based on your shapefiles or coordinates. NAME_1 indicates the names of the adm 1 regions of Luxembourg. "mean_obs" refers to the average number of observations per pixel that went into the aggregated image for a time period in a given area.

Default calculations are the sum of the light values and their average. Outliers can be identfied with the minimum and maximum light values. You can use any desired function for the calculations via the argument functions_calculate. The function has to accept an na.rm argument. In case it does not, you have to wrap the function accordingly. If encountering problems, check the documentation of raster::extract.

Other arguments in nightlight_calculate, for which you can consult the helpfiles for further details are:

• rawdata: This argument allows to output a dataframe with the raw light pixels for each region and time period additionally to the processed dataframe.

• cut_low, cut_high, cut_quality: These arguments allow to exclude certain pixels from the calculation. If desired, any values smaller than cut_low, any values larger than cut_high and any pixels with number of observations less or equal to cut_quality will be set to NA. The default setting for cut_quality is 0.

• rectangle_calculate: In case your shapefile does not feature an enclosed area, the code will transform your shapefile into a rectangle based on the minimum/maximum x- and y-coordinates. The default value is NULL, which activates automatic detection, but for non-standard shapefiles the detection might fail. In such a case, you can set this argument to TRUE or FALSE manually. Below, you can see an illustration of what the code would do in case you input a shapefile with the railway system of Ulm, Germany.

nightlight_plot

This function allows to plot a shapefile with its nighttime lights for a given time span. Note: even though it is possible to produce multiple plots by using multiple inputs for area_names and a time span for time, you should pay attention to the number of plots that will be produced this way. All plots will be loaded into your global environment as ggplot objects. As an example, if you input:

nightlight_plot(
area_names = "Germany",
time = "1992",
shapefile_location = "D:/shapefiles",
light_location = "D:/nightlights",
admlevel = 1)

you get the following image:

In case you want to plot a region that is not available on GADM (i.e., a region that is not a country), you must have the downloaded shapefile in your shapefile_location, so the function can detect it according to the name you give in area_names. If this fails, there is always the option to use the shapefiles argument and provide the filenames of the shapefiles instead. This applies to nightlight_calculate as well.

In case you input a set of coordinates, you will get an image with a rectangular shapefile constructed from your coordinates.

nightlight_download

You can download DMSP nighttime light data with this function. As the Colorado School of Mines has changed the access to their VIIRS nighttime light data to require a free user account, you have to download the VIIRS data manually via their website.

The DMSP data are of lower resolution than the VIIRS data and take up less space (1 global DMSP image = 1/16 space of a global VIIRS image). Quality files (recognizable by the ending cf_cvg) show how many observations went into the value of a pixel in the aggregated nighttime light image in a given period.

Limitations, additional notes, and alternative data sources

• The code is not explicitly written for fast performance.

• The DMSP data are of suboptimal quality. Problems are for example a lower resolution and more blooming/blurring of the lights compared to the VIIRS data. Moreover, the DMSP data feature a discrete scale that is top-coded at a digital number of 63, while the VIIRS data have a continuous scale and no top-coding. Detection for low illumination ranges is also better with VIIRS.

• There is a Matlab code to de-blur the DMSP data (see Abrahams et al., 2018).

• You could use a Pareto distribution to circumvent top-coding and extrapolate light values in city centers (see Bluhm & Krause, 2022). You can use these images by setting corrected_lights to TRUE. The files are available via the website of Melanie Krause at https://www.melanie-krause.de/data.html.

• Temporal consistency is not an issue with the VIIRS data, since the light values are consistently calibrated and standardized to describe a fixed unit of radiance (nano Watts / cm2 / steradian).

• Temporal consistency can be an issue with the DMSP data. There are several versions of DMSP satellites. For some years, these versions overlap and there are two images available for a year. nightlight_download always downloads both. Then, for nightlight_calculate and nightlight_plot, the versions to be used will be chosen based on the time span you input into the functions. If possible, a consistent version for your time span will be chosen. If 2 consistent versions are available for your time span, the newer one will be selected. If no consistent version is available, the newest version for each year will be chosen. You will be notified about the selected DMSP versions. Using one version is generally desirable, since the light intensity scale is not consistently calibrated and the measured values could thus not be perfectly comparable across satellite versions (see e.g., Doll, 2008). In an econometric analysis using DMSP data, satellite version fixed effects and year fixed effects are advisable (see e.g., Gibson et al., 2021).

• Natural factors influence data quality. Cloud coverage affects the number of observed nights that went into an aggregated image and can be especially strong in tropical regions. The data often lack values for summer months for regions close to the Poles due to stray light during summer nights. Aurora light and snowfall could influence observed lights for regions close to the Poles. To increase coverage during summer months for the monthly VIIRS data, you can use a straylight-corrected VIIRS version (see Mills et al., 2013) by setting corrected_lights to TRUE. These are different VIIRS images, so you have to manually download them first.

• You can also work with a harmonized DMSP-VIIRS yearly dataset spanning from 1992 onwards (see Li et al., 2020, available at https://figshare.com/articles/dataset/Harmonization_of_DMSP_and_VIIRS_nighttime_light_data_from_1992-2018_at_the_global_scale/9828827/7). To use these data, you have to set harmonized_lights to TRUE in all functions. The harmonized dataset is built with the non-straylight-corrected VIIRS data. The VIIRS data are cleaned from disturbances due to aurora and temporal lights and then matched to the resolution and top-coding of the DMSP data. The DMSP data are calibrated in a temporally consistent way.

References

• Abrahams, A., Oram, C., & Lozano-Gracia, N. (2018). Deblurring DMSP nighttime lights: A new method using Gaussian filters and frequencies of illumination. Remote Sensing of Environment, 210, 242-258.

• Bluhm, R. & Krause, M. (2022). Top lights - Bright cities and their contribution to economic development. Journal of Development Economics 157, 102880.

• Doll, C. (2008). CIESIN thematic guide to night-time light remote sensing and its applications. Center for International Earth Science Information Network, Columbia University, New York.

• Elvidge, C. D., Baugh, K. E., Zhizhin, M., & Hsu, F.-C. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights. Asia-Pacific Advanced Network 35(35), 62.

• Gibson, J., Olivia, S. Boe-Gibson, G. (2021). Which night lights data should we use in economics, and where?. Journal of Development Economics, 149 (102602), 1-12.

• Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. Scientific Data, 7(1).

• Mills, S., Weiss, S., & Liang, C. (2013). VIIRS day/night band (DNB) stray light characterization and correction. Earth Observing Systems XVIII.