The Nachtlichter app was developed within a project called Nachtlicht-BüHNE (Citizen-Helmholtz Network for research on night light phenomena)5, using a co-design process in which academic and citizen scientists met regularly over a several year period. Our co-design process, app methodology, site selection, systematic variability of the observations, data pre-processing and data structure have already been described in detail5. This section therefore briefly covers the data and validation and focuses mainly on the methods unique to the analyses presented here.
Nachtlichter data and validation
In a Nachtlichter observation, participants conducted a ‘survey’ while walking along a ‘transect’, which typically extended from one street corner to the next. The participants used the app to classify and count all of the light sources that they could see. A total of 18 light categories were used for the 2021 experiment (Fig. 2). Depending on the light type selected, participants provided additional information about the size, emission direction (that is, shielding), color and subjective brightness. Transects were pre-defined in most cases and selected and arranged to completely survey the publicly accessible areas covered by a reprojected DNB satellite pixel. We therefore somewhat undercount the total number of installed lights because we did not record lights installed in areas not visible from public spaces (for example, backyards, courtyards and rooftops; Supplementary Fig. 5).
The observation time (of night) was not constrained, but the main experiment took place from 23 August to 14 November 2021, usually over a period of weeks for each pixel51. Additional smaller data-taking campaigns were conducted in the spring and autumn of 2022 to develop a correction for certain lighting types that were found to frequently turn off. The campaign in autumn of 2022 took place immediately after a German law requiring switch offs of some signs was passed22. However, as our statistics were not sufficient to observe a difference to the data taken in 2021, all of the available data were combined for determining the correction factors. The app and training materials were updated in 2023 to perform an experiment directly investigating lighting changes; data from that campaign is not included in the analyses reported here.
Observations were collected mainly in Germany from city centers, suburbs and villages (Extended Data Fig. 7). Region selection was based partly on where citizen scientists were present and able to count, and areas without sharp changes between land use near the boundaries were preferred5. Brighter areas in cities are therefore overrepresented compared to their relative frequency by area, but this means we cover nearly the full range of radiance observed for German communities13. Areas with high-rise buildings were generally avoided because of the difficulty in counting windows, but there were a few cases in which buildings of approximately ten stories were located along the transect. For most of the counting areas, buildings were one to four stories tall. The raw data may be downloaded from within the app itself (https://lichter.nachtlicht-buehne.de), and a processed dataset more suitable for analysis is available from GFZ Data Services52.
Observations were validated by comparing our total counts of streetlights to the numbers reported in public databases5. The values agreed to better than 7% for our areas in Berlin, Cologne and Dresden. In Fulda and Leipzig, the Nachtlichter counts were 40% and 90% larger, respectively. This was due to the presence of streetlights on private roads in these two measurement areas and exemplifies how Nachtlichter data are more complete than existing public lighting databases. Observations were additionally validated by comparing the counts of different participants to each other on the same transect. This was complicated by the fact that participants did not count at identical times, and later observations had fewer lights. The standard deviation for the total number of lights on the two most frequently observed streets was 15% for observations during 19:30–21:30. The counts were more consistent for streetlights than for other types of light, such as signs and windows, for which participants estimated sizes.
Time of night correction
As mentioned above, some light source types turn off during the course of the night5. Different satellite pixels were sampled at different dates of the campaign, and the earliest (by date) observations were acquired later at night, due to the late sunset. We therefore developed an approximate temporal correction to account for the changes and tested a few strategies using a Monte Carlo simulation of counting data. We found that the dataset size limitation would prevent fitting a general function. We therefore decided to model the switch off with a logistic function:
$${p}(t)=1-f+\frac{f}{1+{e}^{-s(t-h)}}$$ (1)
where p is the probability that a light is on at time t (in hours relative to midnight), f is the fraction of lights that turn off, s is a parameter that describes how quickly the lights turn off and h is the time (relative to midnight) at which half of the lights that will turn off have done so (Extended Data Fig. 8).
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