Serving interactive geospatial data in browser with Kepler.gl

1 minute read

Published:

Kepler.gl logo

Kepler.gl is a great geospatial data analytic tool coming from the Uber’s data analytics team. In my opinion, it is one of the easiest ways to visualize geospatial data from a variety of formats (csv/json/geojson). Other formats (gpx/xml/..) can easily be converted to these with existing libraries. Kepler.gl is a client-side application which means that we can visualize the data without any upload, thereby avoiding the risk to expose confidential data. The main framework is written in javascript and heavily usage deck.gl which gives us the power to explore large datasets.

Apart from the simple drag and drop function on the default demo page, we can easily integrate it with our development workflow, especially with its Python API which is seamlessly integrated with Jupyter-notebook already and very well documented by the team.

However, there is no CLI/IDE tool that is released by the core team to automatize the whole workflow. However, you can directly download keplergl_cli which is built upon the Kepler.gl python package and will give you more freedom. I slightly modified this package to save a custom HTML map to a user-defined location and it’s available on github.

Both Kepler.gl and keplergl_cli has wonderful documentation, therefore, there is no need to repeat anything. I wanted to serve the created maps in by embedding them directly in the markdown (or HTML). Therefore, I am attempting the same.

For the same, we need to save the config file which will have the settings of different layers in addition to the data. This can be done easily in the demo app (export map in JSON) or by exporting the created map from Python. After having this JSON file (data+map), we need to upload and provide absolute path. For instance, I’ve uploaded the exported data to Github for a road network in Delhi. Then, with Kepler.gl, we can show these data with the help of iframe, directly on the webpage by linking the demo app to our dataset as follow:

<iframe src="https://kepler.gl/#/demo?mapUrl=https://raw.githubusercontent.com/ikespand/ikespand.github.io/master/_data/sample_data/keplergl_road_network.json" style="border:0px #ffffff none;" name="myiFrame" scrolling="no" frameborder="1" marginheight="0px" marginwidth="0px" height="800px" width="600px" allowfullscreen></iframe>

Within this iframe, we can change the settings as per our requirements. Following is the outcome:

You May Also Enjoy

Setting up an Overpass API server with Docker

3 minute read

Published:

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'.

Reach out to me on Instagram for a faster reply! "Buy Me A Coffee"

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

5 minute read

Published:

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

2 minute read

Published:

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.