Route-Report
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
usage: route_report.py [-h] -f [route.gpx] [-d [<distance>]] [-c [countries]] [-r] [-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
set if you want to update the already downloaded and preprocessed country files
-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)
Points of Interest
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.
Autodetection of countries
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.
Example
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 1km (-d) you can download the gpx file and then run the command below (route).
>>> python3 route_report.py -f test_route_andorra.gpx -p food-shop,petrol-station -d 1
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.
Future Work
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.
1 Jan 05, 2022
611 Dec 29, 2022
0 Aug 25, 2021
1 Nov 24, 2021
2 Aug 30, 2022
1 Nov 15, 2021
6 Dec 10, 2021
2 Dec 13, 2021
2.3k Jan 04, 2023
67 Dec 28, 2022
2 May 05, 2022
246 Dec 08, 2022
9 Jul 15, 2022
10k Jan 01, 2023
12 Aug 24, 2022
16 Dec 17, 2022
1.3k Dec 26, 2022
5 Apr 29, 2022
466 Jan 09, 2023
7 Sep 20, 2022