Route report is a command-line utility that can be used to locate points-of-interest near your planned route (gpx). The results are based on the database by OpenStreetMap.
If the metadata for the requested countries is not present then Route-Report
first downloads OpenStreetMap metadata. Then, we use
osmosis in the background to
filter through the metadata and extract relevant locations. This has to be done
only once for each country you want to use and the resulting, filtered file is
quite small (<1MB for Germany). If you want to retrieve an up-to-date version
of the files you can use the -r flag.
Note that the metadata files in this repo are only as up-to-date as their
change date. You may want to download more recent files (-r flag).
Supermarkets don't move often though :P
usage: route_report.py [-h] -f [route.gpx] [-d <distance>] [-c [countries]] [-r] [-m] [-o print|csv|google-sheets|pdf|1D-map]
[-p food-shop|petrol-station|water]
Finds stuff next to your route.
optional arguments:
-h, --help show this help message and exit
-f [route.gpx], --input-file [route.gpx]
used to supply your gpx file
-d <distance>, --search-distance <distance>
defines approx. search radius around route in kilometers (default=1km)
-c [countries], --country-codes [countries]
comma separated list of country codes (ISO 3166-1 Alpha-2 --> see Wikipedia), e.g., DE,US,FR (default=AUTO --> autodetection)
-r, --redownload-files
if you want to redownload the large file from the openstreetmap repository. This does not include processing of the file. Regardless
of this option files will be downloaded automatically if they do not exist.
-m, --reprocess-files
if you wat to reprocess the large openstreetmap file into the metadata file that is used for finding points of interest. Regardless
of this option files will be processed automatically if the processed file does not exist.
-o print|csv|google-sheets|pdf|1D-map, --output-modes print|csv|google-sheets|pdf|1D-map
comma separated list of output modes, e.g., print,csv (default=print)
-p food-shop|petrol-station|water, --points-of-interest food-shop|petrol-station|water
comma separated list of points-of-interest the program is supposed to look for along the route (default=food-shop)
Poi-groups are a collection of OpenStreetMap (OSM) tags are grouped together in
our program. For example the poi-group food-shop represents convenience
stores, grocery stores, bakeries, etc.
The right column in the file ./other_data/osm_tags.csv shows you poi-groups
you can search for along your route using the -p flag (see Example). The left
column in that file represents all OSM tags that we search for given a specific
poi-group(s).
You can change ./other_data/osm_tags.csv however you like, just be aware that
the metadata files in this repository only contain locations with the tags we
are using. If you wish to use your own tags you can refresh your metadata files
using the -r flag after you have changed ./other_data/osm_tags.csv.
We autodetect countries based on the gpx file you provide using
the thematicmapping
dataset. If you wish to use only a subset of country datasets you can specify
them using the -c flag.
Autodetection of countries takes about 30s (on my laptop) for a 1000km route.
This will take even longer for longer routes. Therefore, I suggest you directly
specify countries with the -c if computing resources are scarce.
By default, you can just supply a gpx-file with the -f option and the program
will take care of the rest. For illustration, the below example is more complex.
Assuming you have route planned on Komoot
and you want to know about food-shop and petrol-station (-p) next to your
route that are within 2km (-d) you can download the gpx file and then run the
command below. Additionally, you would like the program to print the output to
stdout (-o print) and create a html map with the results (-o html-map)
>>> python3 route_report.py -f gpx_test_files/test_route_andorra.gpx -p food-shop,petrol-station -d 2 -o print,html-map
cum_distance_km poi_name poi_distance_to_route poi_lat poi_long poi_group
20 0 Consciència 0.085418 42.508222 1.520737 food-shop
11 0 Eco Supermacats 0.474783 42.505049 1.514742 food-shop
22 0 Fleca Font 0.006591 42.507441 1.521643 food-shop
30 0 NaN 0.118936 42.506687 1.523430 food-shop
5 0 NaN 0.658057 42.501832 1.515404 food-shop
59 1 Andorra 2000 0.320416 42.505714 1.529197 food-shop
89 1 Biocoop Andorra 0.225353 42.508006 1.537685 food-shop
81 1 Caprabo 0.133882 42.508700 1.534714 food-shop
66 1 E. Leclerc 0.070915 42.508874 1.532163 food-shop
92 1 Fleca font 0.088633 42.509274 1.538085 food-shop
73 1 Santa Glòria 0.187045 42.508125 1.533945 food-shop
60 1 Super U 0.088410 42.507963 1.530428 food-shop
59 1 bonÀrea (Andorra) 0.260034 42.506250 1.529328 food-shop
59 1 de bon Gra 0.157387 42.507171 1.529441 food-shop
60 1 NaN 0.070890 42.508139 1.530399 food-shop
113 2 13-th street 0.013526 42.509196 1.540867 food-shop
115 2 Artal 0.107198 42.508185 1.539805 petrol-station
145 2 Artal 2 0.121834 42.510551 1.548264 petrol-station
130 2 Repsol 0.103972 42.508329 1.545053 petrol-station
126 2 NaN 0.006941 42.509005 1.543588 food-shop
208 4 BP 0.018608 42.522095 1.559524 petrol-station
207 4 Cepsa 0.024718 42.521652 1.559482 petrol-station
248 6 Cepsa 0.020690 42.531754 1.577210 petrol-station
251 6 Comer la Clementina 0.171664 42.533281 1.579239 food-shop
292 7 BP 0.011910 42.536710 1.589220 petrol-station
273 7 BP 0.021828 42.533517 1.585820 petrol-station
292 7 Comerç les Bons 0.234051 42.537693 1.586538 food-shop
267 7 ECO 0.387443 42.536011 1.582085 food-shop
266 7 Repsol 0.037308 42.533489 1.584708 petrol-station
267 7 NaN 0.388133 42.536065 1.582158 food-shop
305 8 Avenida Doctor Mitjavila, 3- 0.643809 42.542483 1.599984 food-shop
310 8 Esso 0.019175 42.542198 1.591422 petrol-station
433 11 Caprabo 0.016012 42.566131 1.598642 food-shop
434 11 Les delícies del Jimmy 0.026433 42.566201 1.598758 food-shop
451 11 Total 0.031216 42.566991 1.600830 petrol-station
536 15 BP 0.513669 42.579580 1.640062 petrol-station
Ignore the leftmost column. The column cum_distance_km represents
the point of the route where the grocery store has been found and the column
shop_distance_to_route represents how far away the shop is from the route in
kilometers. For example, after riding this route for 11 kilometers you will
encounter a Caprabo (food-shop) 16m next to the route.
The filtering part (with osmosis) only works on Linux for now. I plan on
supplying either already filtered files for each country or some alternative
that works on Windows/Mac in the future. Note that the rest of the program
should still work on other platforms.
There are many minor touches missing, e.g., a nicer output, creating an executable, custom alerts, or supporting the imperial system.