OpenStreetMap: Access, data mining and data manipulation

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OpenStreetMap: Access, data mining and data manipulation

OSM OpenStreetMap (OSM) is a wonderful source of geospatial data, constantly growing with the support of amazing volunteers. To use the data for any purpose, I like to query it via overpass-turbo by using its overpass query language (OQL). For a small number of requests, we can use the default web-page for our query and export the data directly in a geojson (gpx and kml are also possible). We can also access data programmatically with Python very easily. overpy assist in the access and basic processing of this data. An example can be found in a previous post where in the first part, we get the data from OSM using OQL itself. Additionally, this is a great article, to begin with OQL in Python. For heavy usage, I would recommend setting your own overpass server, which with docker, is quite easy. You can check out the docker setup from the github which is forked from an existing repository which I basically updated for a newer working version of the overpass and some notes to troubleshoot. If you are on a smaller memory machine then you might quickly run out of memory. To overcome this, configure the docker settings along with the smaller OSM file (e.g. cropping the OSM data to a city level). One of the alternatives is to crop the data to an ROI is described in the following.

The OSM dataset can be downloaded from the generous servers of geofgeofabrik. They have organized the OSM data at different levels (e.g. planet>continent>country>state). For some regions (e.g. India), the dataset is not divided into states. In addition to it, in a certain situation (like to out of memory issue with docker as above-mentioned), we might only want to have the only subset of the entire dataset (e.g. a city). In these scenarios, osmconvert comes handy. The binaries can directly be downloaded and then we are good to go. With this tool, we can define a rectangle (polygon is also possible) to crop the dataset. For a detailed list of options just execute: osmconvert --help. After downloading data for India, we can crop the approximate region for Delhi by:

osmconvert india-latest.osm.pbf -b=76.908869,28.345084,77.443079,28.732346 --complete-ways -o=delhi.pbf

It is important to note that we have to provide the southwestern corner first and then the northeastern corner. Besides, we need to switch the coordinates what we get from google map.

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Setting up an Overpass API server with Docker

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You might have already seen my blog on OpenStreetMap from 2020. In that post, I briefly talked about Overpass API server with a pointer to a GitHub repo to setting up locally. However, that setup might fail if we try to build for entire planet. Additionally, source repository hasn’t been updated for 5 years. I’ve received some requests to assist in setting up the Overpass server. Therefore, this short post basically illustrates the process of local Overpass server.

Starting your own Overpass API server

Currently, the process has been very simple and straightforward, thanks to this docker image docker image.

Building the server from raw OSM files

This approach is beneficial when our region of interest in small (compared to entire world).

docker run -e OVERPASS_META=yes -e OVERPASS_MODE=init -e OVERPASS_PLANET_URL=http://download.geofabrik.de/europe/monaco-latest.osm.bz2 -e OVERPASS_DIFF_URL=http://download.openstreetmap.fr/replication/europe/monaco/minute/ -e OVERPASS_RULES_LOAD=10 -v /overpass_db/:/db/overpass_clone_db -p 8888:80 -it --name overpass_monaco wiktorn/overpass-api

This usually takes 5 minutes on a normal computer including the downloading image from dockerhub, downloading OSM file from geofabrik and building the database for a 700 KB file. All the generated builds can be used with the server. At the end of this build, docker container will be stopped and need to be started again. For the same, either one can assign a name to container in the previous step or can look at auto-generated name with docker ps --all command. After grabbing the name, simply start the container as docker start <CONTAINER NAME>. Alternatively, one can pass -e OVERPASS_STOP_AFTER_INIT false option so that we can continue the instance after flushing database.

img

With that, we can query for a pizza shop: -.

In the above, we didn’t use a custom region as shown here obtained from OSM tool without tweaks. But, workaround should be simple, i.e. either providing file as file:/// as mention here or hosting file with local HTTP server.

Note: So far, I am not able to circumvent the issue when I try to mount a local folder in current directory. Although build was successful however during the query server results in error. This is specific to windows only.

Cloning for entire world

When we need to scale up to entire world, then cloning is a better option compared to building from the raw OSM file. In this case, we can pass the option in OVERPASS_MODE for clone. The data will be cloned from the Overpass API server with the defined replication. You can check out available replication frequency on the OSM wiki.

docker run -e OVERPASS_MODE=clone -e OVERPASS_DIFF_URL=https://planet.openstreetmap.org/replication/day/ -v /big/docker/overpass_clone_db/:/db -p 8888:80 -it --name overpass_world wiktorn/overpass-api

This process took approx 2 hours with a good internet speed on a Linux machine, and it took approx 204 GB. For sure, this number will grow with more contributions. We can now query for nearby Indian restaurants from our POI with query: -

Using in Python

I have a sample REST API with Flask which basically finds the nearest toilets from the query point. To use the above server is easy, we just need to change the URL of Overpass API server mentioned here to self.sever = 'http://localhost:8888/api/interpreter'.

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Machine Learning Applied to Stock Market Prediction: A Comparison between LSTM and ESN

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I have been working with various neural networks for a while and always find recurrent neural network (RNN) very special. Whenever, there is a dependency with previous data points, then these networks shines out. Time-series problem especially fits here for e.g. predicting the weather pattern or stock market. In the past, I have done work to compare various RNNs for such tasks and my work concluded that ESN works quite well compared to simple deep neural network and some RNNs like LSTM or GRU (paper-1, paper-2 and paper-3) (of course this statement vary with the domain). Those works were done mostly with the scientific data for turbulent flow for thermal plumes, which has significant effects on weather (Wikipedia). Now, I was curious to see how these network can work for practical purpose.

Deep learning on HPC cluster with LSF queue

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This is rather a short documentation to run the TensorFlow jobs on a high performance computing (HPC) cluster which is using LSF Load Sharing Facility. This can be extrapolated to other HPC systems with some tweaks. If you’re new to LSF and HPC jobs with DL then this post nicely summerizes the jargon.