Trending Places in OpenStreetMap!
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doc
.dockerignore
.gitignore
Bubble.py
Caches.py
Database.py
Dockerfile
Fetch2.py
LICENSE
README.md
Reverse_Geocoding.py
Tiles_helper_scripts.sql
Top_Trending.py
Trending_Bot.py
config
crons.conf
main.sh
requirements.txt
run_cron.sh

README.md

Trending-Places-in-OpenStreetMap

Stories in Ready Stories in In Progress

This is an experimental bot to calculate the Trending places in OSM.

Note: The first execution might take a few hours, but it shold become faster thereafter.

Execution with Docker

Prerequsite: docker

Clone this repository and make it your active folder.
If you want the twitter bot to Tweet a status, register as a developer on twitter and obtain the 4 keys to authenticate your twitter bot. The four variables required are mentioned in the sample config file. These need to be set as environment variables system and/or in your docker container. Example: (You need to repeat this for all four or directly input it in the docker container with -e)

export CONSUMER_KEY="somevaluehere"

Then run the following:

docker build -t trendingplaces:v1 .

To run the main program:

docker run -e CONSUMER_KEY -e CONSUMER_SECRET -e ACCESS_TOKEN -e ACCESS_TOKEN_SECRET trendingplaces:v1 ./main.sh

OR
To set it up with a cron job that executes once every day:

docker run -e CONSUMER_KEY -e CONSUMER_SECRET -e ACCESS_TOKEN -e ACCESS_TOKEN_SECRET trendingplaces:v1

Execution manually

Prerequsite: Python 3 and Virtual Environment

Set up a python virtual environment and start with pip install -r requirements.txt
Matplotlib and lxml might need additional dependencies which can be installed by:

apt-get build-dep python-matplotlib
apt-get build-dep python3-lxml

Running the code

Step 1 and 2 are based on previous work done by Lucas Martinelli and Pavel Tyslacki on map-trends.

  • STEP 1: Fetch the Tile Logs with python3 Fetch2.py --date_from=(date_to-7 days back) --date_to=(today-2) >Trends.csv:
  • STEP 2: cat Trends.csv|python3 Bubble.py --date_precision=1d --min_zoom=10 --max_zoom=19 --min_subz=10 --max_subz=10 --no_per_day>Zoom10Tiles.csv
    • The max/min_zoom specify the output zoom level i.e. all views aggregated here. 10 is selected as it is the optimal level to view cities
    • The min/max_subz specify the input levels to aggregate views.
    • Levels 1-10 are ignore as they are considered insignificant, with almost 100% views everyday.
  • STEP 3: cat Zoom10Tiles.csv | python3 Top_trending.py --graph
    • Input: --date (Default 2 days ago unless specified)
      --limit (default 10 for top 10 trending places)
      --period (default 7 for the sampling interval)
      --country(to specify a country to deduce the trending places in)
      --graph(to generate a .png file showing the trending places views)
    • The output: trending_places.png (Optional)
      trending_places.db (containing the lat/lon/count etc)
  • STEP 4: python3 Trending_Bot.py Before running this twitter bot, you will need to set up your twitter developer account and add in the consumer and token access keys as environment variables in your system before this! A sample format is available on github.

Debug the code faster

For Fetch2.py

  • In the first run Fetch2.py gets a few files. This takes longer to run in the first time, but runs faster later on.
  • For testing purposes, you can just run Fetch2.py once and keep those files permanently. (And/or add them to the docker container if needed)
  • The date_to and date_from refer to the range of dates where you want to fetch the log stats from. This can be shortened, in order to run the code faster.

For Bubble.py*

  • The program takes in the parameters --min_zoom=10 --max_zoom=19 --min_subz=10 --max_subz=10. On reducing max/min_subz/zoom to lower zoom levels, the program will run faster, including STEP 3 Top_trending.

For Top_Trending.py

  • Store any file of the resampled values (an example is inside docs) in a folder called 'Cache'. The file must be last modified on the current day of testing otherwise, the Cache will be automatically emptied. So just open and save it once so that you dont have keep pasting it inside Cache again and again.
  • Note 1: When you do this please ensure a --date parameter is set correctly in the command. For example, the date for the sample file provided is 2016-05-04.
  • Note 2: If you use a resampled file, it does not matter what were the contents of the Zoom10Tiles file from Step 2 described under running the code. Thus, Step 1 and 2 of the code can be tested separately as well. As mentioned reducing the range of dates (See date_to and date_fromin ./main.sh) can speed them up for testing as well.

An example for commands for testing the program with faster speed (you can store resampled values in Cache as an additional step the --date must be set correctly):

tile_log_diff="2"
date_diff="1"

date_to=$(date "--date=-${tile_log_diff} day" +%Y-%m-%d)
date_from=$(date "--date=${date_to} -${date_diff} day+1 day" +%Y-%m-%d)

python3 Fetch2.py --date_from=$date_from --date_to=$date_to >Trends.csv &&
cat Trends.csv|python3 Bubble.py --date_precision=1d --min_zoom=5 --max_zoom=10 --min_subz=5 --max_subz=5 --no_per_day>Zoom10Tiles.csv &&
cat Zoom10Tiles.csv | python3 Top_Trending.py --graph --date=$date_to
python3 Trending_bot.py

For further explaination, please read: See http://geometalab.github.io/Trending-Places-in-OpenStreetMap