Visualising the History of openSenseMap.org

Norwin Roosen

2019-03-10

This vignette serves as an example on data wrangling & visualization with opensensmapr, dplyr and ggplot2.

# required packages:
library(opensensmapr) # data download
library(dplyr)        # data wrangling
library(ggplot2)      # plotting
library(lubridate)    # date arithmetic
library(zoo)          # rollmean()

openSenseMap.org has grown quite a bit in the last years; it would be interesting to see how we got to the current 3653 sensor stations, split up by various attributes of the boxes.

While opensensmapr provides extensive methods of filtering boxes by attributes on the server, we do the filtering within R to save time and gain flexibility. So the first step is to retrieve all the boxes:

# if you want to see results for a specific subset of boxes,
# just specify a filter such as grouptag='ifgi' here
boxes = osem_boxes()

Plot count of boxes by time

By looking at the createdAt attribute of each box we know the exact time a box was registered. With this approach we have no information about boxes that were deleted in the meantime, but that’s okay for now.

…and exposure

Outdoor boxes are growing fast! We can also see the introduction of mobile sensor “stations” in 2017. While mobile boxes are still few, we can expect a quick rise in 2018 once the new senseBox MCU with GPS support is released.

Let’s have a quick summary:

exposure oldest newest count
outdoor 2015-02-18 16:53:41 2019-03-10 11:32:20 2930
indoor 2015-02-08 17:36:40 2019-03-09 22:30:39 550
mobile 2017-05-24 08:16:36 2019-03-09 20:51:23 153
unknown 2014-05-28 15:36:14 2016-06-25 15:11:11 20

…and grouptag

We can try to find out where the increases in growth came from, by analysing the box count by grouptag.

Caveats: Only a small subset of boxes has a grouptag, and we should assume that these groups are actually bigger. Also, we can see that grouptag naming is inconsistent (Luftdaten, luftdaten.info, …)

grouptag oldest newest count
Luftdaten 2017-03-14 17:01:16 2019-03-10 08:22:41 177
Futurium 2018-11-23 17:21:50 2018-12-21 17:25:30 48
ifgi 2016-06-17 08:04:54 2019-03-05 12:33:48 42
Bad_Hersfeld 2017-07-18 13:32:03 2019-02-12 10:52:19 40
TKS Bonn 2018-06-18 11:21:21 2018-12-10 16:33:10 37
Save Dnipro 2018-12-24 10:47:45 2019-03-07 09:37:49 33
Luchtwachters Delft 2018-03-09 21:39:11 2019-02-14 11:42:03 27
Feinstaub 2017-04-08 06:38:25 2019-03-06 11:12:45 19
luftdaten.info 2017-05-01 10:15:44 2019-02-25 10:48:07 19
Luftdaten.info 2017-04-03 14:10:20 2019-02-14 14:53:17 15
MakeLight 2015-02-18 16:53:41 2018-02-02 13:50:21 15
PGKN 2018-03-22 16:44:00 2018-12-20 13:24:32 12
dwih-sp 2016-08-09 08:06:02 2016-11-23 10:16:04 11
Che Aria Tira? 2018-03-11 10:50:42 2018-03-11 23:11:20 10
IRESA 2019-02-04 13:51:44 2019-02-14 12:37:41 10
Netlight 2019-01-06 18:14:26 2019-03-08 17:46:33 10
Sofia 2017-04-11 04:40:11 2018-06-07 11:00:54 10
IKG 2017-03-21 19:02:11 2018-12-16 07:15:59 9
Raumanmeri 2017-03-13 11:35:39 2017-04-27 05:36:20 9
esri-de 2018-09-15 10:11:25 2018-11-27 13:56:07 9
luftdaten 2017-04-28 06:33:07 2019-01-05 22:03:49 9
GIS-FH 2018-11-02 13:17:01 2018-11-02 13:26:01 8

Plot rate of growth and inactivity per week

First we group the boxes by createdAt into bins of one week:

bins = 'week'
mvavg_bins = 6

growth = boxes %>%
  mutate(week = cut(as.Date(createdAt), breaks = bins)) %>%
  group_by(week) %>%
  summarize(count = length(week)) %>%
  mutate(event = 'registered')

We can do the same for updatedAt, which informs us about the last change to a box, including uploaded measurements. This method of determining inactive boxes is fairly inaccurate and should be considered an approximation, because we have no information about intermediate inactive phases. Also deleted boxes would probably have a big impact here.

inactive = boxes %>%
  # remove boxes that were updated in the last two days,
  # b/c any box becomes inactive at some point by definition of updatedAt
  filter(updatedAt < now() - days(2)) %>%
  mutate(week = cut(as.Date(updatedAt), breaks = bins)) %>%
  group_by(week) %>%
  summarize(count = length(week)) %>%
  mutate(event = 'inactive')

Now we can combine both datasets for plotting:

boxes_by_date = bind_rows(growth, inactive) %>% group_by(event)

ggplot(boxes_by_date, aes(x = as.Date(week), colour = event)) +
  xlab('Time') + ylab(paste('rate per ', bins)) +
  scale_x_date(date_breaks="years", date_labels="%Y") +
  scale_colour_manual(values = c(registered = 'lightgreen', inactive = 'grey')) +
  geom_point(aes(y = count), size = 0.5) +
  # moving average, make first and last value NA (to ensure identical length of vectors)
  geom_line(aes(y = rollmean(count, mvavg_bins, fill = list(NA, NULL, NA))))

We see a sudden rise in early 2017, which lines up with the fast growing grouptag Luftdaten. This was enabled by an integration of openSenseMap.org into the firmware of the air quality monitoring project luftdaten.info. The dips in mid 2017 and early 2018 could possibly be explained by production/delivery issues of the senseBox hardware, but I have no data on the exact time frames to verify.

Plot duration of boxes being active

While we are looking at createdAt and updatedAt, we can also extract the duration of activity of each box, and look at metrics by exposure and grouptag once more:

…by exposure

The time of activity averages at only 197 days, though there are boxes with 1716 days of activity, spanning a large chunk of openSenseMap’s existence.

…by grouptag

grouptag duration_avg duration_min duration_max oldest_box
dwih-sp 828 days 581 days 943 days 943 days
Sofia 450 days 267 days 652 days 698 days
IKG 361 days 0 days 514 days 719 days
Che Aria Tira? 348 days 256 days 364 days 364 days
luftdaten.info 327 days 13 days 648 days 678 days
ifgi 305 days 0 days 788 days 996 days
Luftdaten 300 days 0 days 712 days 726 days
Feinstaub 265 days 0 days 701 days 701 days
Luftdaten.info 250 days 24 days 706 days 706 days
PGKN 202 days 5 days 353 days 353 days
luftdaten 195 days 0 days 680 days 681 days
Bad_Hersfeld 169 days 0 days 595 days 600 days
Luchtwachters Delft 152 days 0 days 366 days 366 days
esri-de 108 days 0 days 176 days 176 days
TKS Bonn 95 days 0 days 265 days 265 days
Futurium 53 days 0 days 107 days 107 days
Raumanmeri 45 days 7 days 318 days 727 days
Netlight 19 days 2 days 63 days 63 days
Save Dnipro 15 days 0 days 76 days 76 days
GIS-FH 0 days 0 days 0 days 128 days
IRESA 0 days 0 days 0 days 34 days

The time of activity averages at only 221 days, though there are boxes with 943 days of activity, spanning a large chunk of openSenseMap’s existence.

…by year of registration

This is less useful, as older boxes are active for a longer time by definition. If you have an idea how to compensate for that, please send a Pull Request!

More Visualisations

Other visualisations come to mind, and are left as an exercise to the reader. If you implemented some, feel free to add them to this vignette via a Pull Request.