Analyzing Reddit’s Top Posts & Images With Google Cloud (Part 2 – AutoML)

BMW 128i CloudVision

In the last iteration of this article, we analyzed the top 100 subreddits and tried to understand what makes a reddit post successful by using Google’s Cloud ML tool set to analyze popular pictures.

In this article, we will be extending the last article’s premise – to analyze picture-based subreddits with Dataflow – by using Google’s AutoML Vision toolset, training a model, and exposing it via REST to recognize new images.

The source code for this is available on GitHub under the GNU General Public License v3.0.

What is Reddit?

Reddit is a social network where people post pictures of cats and collect imaginary points, so-called “upvotes”.

Reddit (/ˈrɛdɪt/, stylized in its logo as reddit) is an American social news aggregation, web content rating, and discussion website. Registered members submit content to the site such as links, text posts, and images, which are then voted up or down by other members. Posts are organized by subject into user-created boards called “subreddits”, which cover a variety of topics including news, science, movies, video games, music, books, fitness, food, and image-sharing. Submissions with more up-votes appear towards the top of their subreddit and, if they receive enough votes, ultimately on the site’s front page.”(

Reddit is the 3rd most popular site in the US and provides a wonderful basis for a lot of interesting, user-generated data.

Technology & Architecture

We will be partly re-using the architecture of the last article, with some slight adjustments here and there.


As we focus on the image recognition part, we upload a training set of images to Cloud Storage (alongside with our manual classifications), train an AutoML model, and access Reddit data via our Desktop using REST.

The latter part can be automated in subsequent steps, e.g. using Dataflow and PubSub (you can find some prep-work on my GitHub page).

AutoML Vision

Google’s AutoML is a managed machine learning framework. While it is technically still in Beta, it already proves a tremendous advantage: It more or less automates complex segments of “traditional” machine learning, such as image recognition (AutoML Vision) or NLP (AutoML Natural Language and AutoML Translation).

Specifically AutoML Vision enables developers and engineers who are not familiar with the mathematical intricacies of image recognition to build, train, and deploy ML models on the fly – which is why we are going to use it here.

AutoML vs. Cloud Vision

While the Cloud Vision API gives us access to Google’s ever-growing set of data (that naturally is used to train the internal ML models), we can use AutoML to train our very own model with specific use cases that a common-ground approach – such as Cloud Vision – might not capture.

Now, let’s use one of my favorite topics, cars. The following picture shows the output of the Cloud VIsion API, fed with a picture of my car, an E87 BMW 128i.

BMW 128i CloudVision
BMW 128i CloudVision

While it did classify the car as both “BMW” and “car”, it failed to recognize any specifics.

Let’s take another example, my old E85 BMW Z4 3.0i, from when I was living in Germany:

BMW Z4 3.0i CloudVision
BMW Z4 3.0i CloudVision

Once again, it figured out we are dealing with a BMW, but the fact that the massive hood that houses the beauty that is the naturally aspirated three liter I6, nor the fact that the roof is, in fact, missing told Cloud Vision that this must be a Z4.

The main decision criteria here should be: Is it worth spending the extra effort to train your own model? Is your data set so specific and unique that it warrants its own model? Do you have proper Data Scientists in your organization that could do a (better) custom job?

In our case – yes, it is. So, time to train our own model, without having to deal with Tensorflow, massive coding efforts, or a Masters in Statistics.

Getting data & classifying images

As we are trying to extend the idea of image recognition, we first need a set of images to get started on. For that, we will use /r/bmw where people show off their BMWs, mostly E30 and F80 M3s (keep at it folks, I cannot get enough of them). What could go wrong with user-generated content for training sets?

A simple script is used to re-use part of our existing reddit/praw and Python setup to simply pull the top posts from the subreddit, filter by the type “extMedia”, save it as image under /tmp and prepare a CSV file that we will use for classification later.

The resulting images wind up on Google Cloud Storage (GCS).

# encoding=utf8
from __future__ import print_function

import config
import os
import praw
import urllib
import re

from reddit.Main import get_top_posts

__author__ = "Christian Hollinger (otter-in-a-suit)"
__version__ = "0.1.0"
__license__ = "GNU GPLv3"

def unixify(path):
    return re.sub('[^\w\-_\. ]', '_', path)

def get_image(post, img_path):
    filename = unixify(post.title)
    tmp_uri = '{}{}'.format(img_path, filename)
    print('Saving {url} as {tmp}'.format(url=post.content, tmp=tmp_uri))
    urllib.urlretrieve(post.content, tmp_uri)
    return tmp_uri, filename

def write_gcp(_input, _output, bucket_name):
    from import storage
    # Instantiates a client
    storage_client = storage.Client()

    # Gets bucket
    bucket = storage_client.get_bucket(bucket_name)
    blob = bucket.blob(_output)

    # Upload

    print('Uploading {} to bucket {}'.format(_output, bucket_name))

def csv_prep(gcs_loc):
    return '{gcs_loc}|\n'.format(gcs_loc=gcs_loc).encode('utf-8')

def main():
    import sys

    # Get reddit instance
    reddit = praw.Reddit(client_id=config.creddit['client_id'],
    # Set GCP path
    os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = config.cgcp['api_key']
    LIMIT = config.limit
    bucket_name = config.cgcp['images']

    # Settings

    # Get top posts
    top_posts = get_top_posts(subreddit, reddit, LIMIT)

    # Filter images
    images = filter(lambda p: p.type == 'extMedia', top_posts)

    csv = ''
    # Download images
    for post in images:
        tmp, filename = get_image(post, img_path)
        write_gcp(tmp, subreddit + '/' + filename, bucket_name)
        csv += csv_prep('gs://{bucket_name}/{subreddit}/{filename}'
                        .format(bucket_name=bucket_name, subreddit=subreddit, filename=filename))

    # Dump pre-classifier CSV
    with open(img_path+'images.csv', 'a') as file:

if __name__ == "__main__":

Now, here’s where the fun begins – we need to classify the training set by hand. It is generally recommended to have at least 100 images per category (Google actually offers a human-driven service for this!), but we are going to stick to less – it’s Saturday.

In order to simplify the model, I dumbed-down my categories – 1 and 2 series, 3 and 4 series, 5 and 6 series, concept and modern, Z3, Z4, and Z8 as well as classics, such as the iconic M1 or 850Csi. The latter introduces way to much noise, however, having a folder full of those cars is fun on its own.

➜  bmw ls -d */

1-2series/  3-4series/ 5-6series/  classics/ concept-modern/  z3-4-8/

Setting up AutoML

After labeling the images, we can proceed to AutoML and point to our CSV file. As the CSV contains the gs:// path and label, we are presented with an overview that looks like this:

Reddit AutoML Data
Reddit AutoML Data

Once the labeling is complete, we can train the model from the Web UI. It will warn you that you don’t have enough labels per image.

Label Warning
Label Warning

After the training is complete, we can see the model performance.

Model Performance (Reddit)
Model Performance (Reddit)

That does not look good. How did this happen?

The answer is fairly simple: All of reddit’s images are taken from various angles, in various lighting conditions, and with various model years. The noise in the images is too high to achieve a good result and we don’t have enough data to properly train the model.

Image Recognition Theory

In order to understand the issue, let’s talk theory for a second. Cloud ML uses TensorFlow to classify our images, exposing the model via an easy API.

As usual, this is not a scientific paper – I’m one of those engineer-folks who use the research output, not the researcher. Things will simplified and maybe even wrong. But hey, it works in the end!

What is TensorFlow?

“TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. […] In a TensorFlow graph, each node has zero or more inputs and zero or more outputs, and represents the instantiation of an operation. Values that flow along normal edges in the graph (from outputs to inputs) are tensors, arbitrary dimensionality arrays where the underlying element type is specified or inferred at graph-construction time. Special edges, called control dependencies, can also exist in the graph: no data flows along such edges, but they indicate that the source node for the control dependence must finish executing before the destination node for the control dependence starts executing.” (Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G.S.; Davis, A.; Dean, J.; Devin, M.: Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv:1603.04467)

While AutoML uses Google’s NASNet approach to find the right architecture –

“Our work makes use of search methods to find good convolutional architectures on a dataset of interest. The main search method we use in this work is the Neural Architecture Search (NAS) framework proposed by [71]. In NAS, a controller recurrent neural network (RNN) samples child networks with different architectures. The child networks are trained to convergence to obtain some accuracy on a held-out validation set. The resulting accuracies are used to update the controller so that the controller will generate better architectures over time. The controller weights are updated with policy gradient (see Figure 1).”

Figure 1
Figure 1

(Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le: Learning Transferable Architectures for Scalable Image Recognition. arXiv:1707.07012v4)

…we will quickly talk about Convolutional Neural Networks.

“CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics.

Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.

CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns the filters that in traditional algorithms were hand-engineered. This independence from prior knowledge and human effort in feature design is a major advantage.”


Given the nature these networks work – by analyzing an images binary components in a set of computational layers – it is easy to confuse the network with seemingly different, albeit actually very similar images.

Bharath Ray’s article (link below) explains it as follows:

“It finds the most obvious features that distinguishes one class from another. Here [an example about cars taken from different angles], the feature was that all cars of Brand A were facing left, and all cars of Brand B are facing right”

Check out this article by Bharath Ray on Medium for more details on how do overcome this manually.

Adjusting the Model

The solution to our problem is fairly simple – we just need a better training set with more data from more images. Car pictures tend to be taken from very similar angles, in similar lighting conditions, and with similar trim styles of vehicles.

First, we’ll use to get ourselves proper training images from Google. Ironic, isn’t it?

Download Training Set
Download Training Set

Next, simply create a mock-CSV and quickly populate the classifiers.

cd '/tmp/bmw/BMW 3 series/'
gsutil -m cp  "./*" gs://calcium-ratio-189617-vcm/bmw-1s
gsutil ls gs://calcium-ratio-189617-vcm/bmw-1s >> ../1series.csv

After getting enough training data, we can go back to AutoML Vision and create a new model based on our new images.

Classification CSV
Classification CSV

After importing the file, we are greeted with a much more usable training set:

Google AutoML Data
Google AutoML Data

Now, when we evaluate the model, it looks a lot less grim:

Model Performance
Model Performance

Using our model with Reddit data

After we figured out the issue with our training set, let’s try out the REST API.

We’ll use this image from reddit:

by /u/cpuftw at

And simply throw a REST request at it, using a simple Python 2 script:

import sys

from import automl_v1beta1
from import service_pb2

This code snippet requests a image classification from a custom AutoML Model
Usage: python2 $img $project $model_id

def get_prediction(_content, project_id, model_id):
    prediction_client = automl_v1beta1.PredictionServiceClient()

    name = 'projects/{}/locations/us-central1/models/{}'.format(project_id, model_id)
    payload = {'image': {'image_bytes': _content }}
    params = {}
    request = prediction_client.predict(name, payload, params)
    return request  # waits till request is returned

if __name__ == '__main__':
    file_path = sys.argv[1]
    project_id = sys.argv[2]
    model_id = sys.argv[3]

    with open(file_path, 'rb') as ff:
        _content =

    print(get_prediction(_content, project_id,  model_id))

And the results are…

Results M3
Results M3

On point! Well, it is an M3, but that is still a 3 series BMW.

Next, remember my old Z4?

payload {
classification {
    score: 0.999970555305
    display_name: "bmwz4"

Yes, sir! That is, in fact, a Z4.


Now, what did we learn?

First off, using the Cloud Vision API simplifies things tremendously for the overwhelming majority of use cases. It gives you a very accurate output for most standard scenarios, such as detected images not appropriate for your user base (for filtering user-generated content) or for classifying and detecting many factors in an image.

However, when the task becomes too specific, AutoML helps us to build our custom model without having to deal with the intricacies of a custom TensorFlow model. All we need to take care of is good training data and careful labeling before training the model. The simple REST API can be used just like the Cloud Vision API in your custom software.

I don’t know about you, but I’m a big fan – we managed to build a system that would otherwise require a lot of very smart Data Scientists. Granted, it will not achieve the accuracy a good Data Scientists can (on AutoML or not) – these folks know more than I do and can figure out model issues that I cannot; however, this is the key point. Any skilled Engineer with a basic understanding of ML can implement this system and advance your project with a custom ML model. Neato!

All development was done under Arch Linux on Kernel 4.18.12 with 16 AMD Ryzen 1700 vCores @ 3.6Ghz and 32GiB RAM

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Analyzing Twitter Location Data with Heron, Machine Learning, Google’s NLP, and BigQuery


In this article, we will use Heron, the distributed stream processing and analytics engine from Twitter, together with Google’s NLP toolkit, Nominatim and some Machine Learning as well as Google’s BigTable, BigQuery, and Data Studio to plot Twitter user’s assumed location across the US.

We will show how much your Twitter profile actually tells someone about you, how it is possible to map your opinions and sentiments to parts of the country without having the location enabled on the Twitter app, and how Google’s Cloud can help us achieve this.

About language, locations, social media, and privacy

While it is safe to assume that most Twitter users do not enable the Location Services while using the Social network, we can also assume that a lot of people still willingly disclose their location – or at least something resembling a location – on their public Twitter profile.

Furthermore, Twitter (for the most part) is a public network – and a user’s opinion (subtle or bold) can be used for various Data Mining techniques, most of which do disclose more than meets the eye.

Putting this together with the vast advances in publicly available, easy-to-use cloud-driven solutions for Natural Language Processing (NLP) and Machine Learning (ML) from the likes of Google, Amazon or Microsoft, any company or engineer with the wish to tap this data has more powerful tool sets and their disposal than ever before.

Scope of the Project

For this article, we will write a Heron Topology that does the following –

  • Read from Twitter, given certain seed keywords, filtering out users that do not disclose any location information, either as metadata or profile data
  • Use the Google NLP service to analyze the tweets and a user’s location data
  • Use Nominatim (based on OpenStreetMap data) to apply reverse-geocoding on the results
  • Use the DBSCAN cluster with a Haversine distance metric to cluster our results
  • Write the results back to Google BigTable or BigQuery

Then, we’ll visualize the results with Cloud Studio.


The architecture for this process is fairly simple:

Architecture (simplified)

Heron serves as our Stream processing engine and local ML, Nominatim on Postgres serves as Geo-Decoder.

On Google Cloud, we use the NLP API to enrich data, BigTable and BigQuery for storage and Data Studio for visualization.

BigTable (think HBase) is used for simple, inexpensive mass-inserts, while BigQuery is used for analytics. For the sake of simplicity, I’ll refer to one of my old articles which explains quite a bit about when to use BigTable/Hbase and when not to.

Hybrid Cloud

While the notion of “Hybrid Cloud” warrants its own article, allow me to give you an introduction what this means in this context.

For this article, I heavily utilized the Google Cloud stack. The Google NLP API provides me simple access to NLP libraries, without extensive research or complex libraries and training sets.

Google BigTable and BigQuery provide two serverless, powerful data storage solutions that can be easily implemented in your programming language of choice – BigTable simply uses the Apache HBase Interface.

Google Data Studio can access those Cloud-based sources and visualize them similar to what e.g. Tableau can achieve, without the need to worry about the complexity and up-front cost that come with such tools (which doesn’t imply Data Studo can do all the things Tableau can).

At the same time, my Nominatim instance as well as my Heron Cluster still run on my local development machine. In this case, the reason is simply cost – setting up multiple Compute Engine and/or Kubernetes instances simply quickly exceeds any reasonable expense for a little bit of free-time research.

When we translate this into “business” terminology – we have a “legacy” system which is heavily integrated in the business infrastructure and the capital expense to move to a different technology does not warrant the overall lower TCO. Or something along those lines…

The following section describes the implementation of the solution.

Reading from Twitter

First off, we are getting data from Twitter. We use the twitter4j library for this. A Heron Spout consumes the data and pushes it down the line. We use a set of keywords defined in the to consume an arbitrary topic.

Here, we ensure that a tweet contains location information, either from Twitter or via the user’s own profile.

if (location != null) {
    Document locDoc = Document.newBuilder()

    List<Entity> locEntitiesList = language.analyzeEntities(locDoc, EncodingType.UTF16).getEntitiesList();
    String locQuery = getLocQueryFromNlp(locEntitiesList);

    // Build query
    NominatimSearchRequest nsr = nomatimHelper.getNominatimSearchRequest(locQuery);

    // Add the results to a query, if accurate
    JsonNominatimClient client = nomatimHelper.getClient();
    ArrayList<String> reverseLookupIds = new ArrayList<>();
    List<Address> addresses = new ArrayList<>();
    if (!locQuery.isEmpty()) {
        addresses =;
        for (Address ad : addresses) {
            logger.debug("Place: {}, lat: {}, long: {}", ad.getDisplayName(),

            Location loc = LocationUtil.addressToLocation(ad);

            // Filter out points that are not actual data points
            String osmType = ad.getOsmType();

            if (osmType != null && (osmType.equals("node") || osmType.equals("relation") ||
                    osmType.equals("administrative")) && loc.isWithinUSA()) {

You could re-route users that do not use location data to an alternate pipeline and still analyze their tweets for sentiments and entries.

Understanding Twitter locations

The next bolt applies the Google Natural Language Toolkit on two parts of the tweet: It’s content and the location.

For an example, let’s use my own tweet about the pointless “smart” watch I bought last year. If we analyze the tweet’s text, we get the following result:

(Granted, this isn’t the best example – I simply don’t tweet that much)

For this article, we won’t focus too much on the actual content. While it is a fascinating topic in itself, we will focus on the location of the user for now.

When it comes to that, things become a little more intriguing. When people submit tweets, they have the option to add a GPS location to their tweet – but unless you enable it, this information simply returns null. Null Island might be a thing, but not a useful one.

However, we can also assume that many users use their profile page to tell others something about themselves – including their location. As this data gets exposed by Twitter’s API, we get something like this:

While a human as well as any computer can easily understand my profile – it means Atlanta, Georgia, United States Of America, insignificant little blue-green planet (whose ape-descended lifeforms are so amazingly primitive that they still think digital smart watches are a great idea), Solar System, Milky Way, Local Group, Virgo Supercluster, The Universe – it’s not so easy for the more obscure addresses people put in their profile.

A random selection –

  • What used to be Earth
  • Oregon, (upper left)
  • in a free country
  • MIL-WI
  • Oban, Scotland UK 🎬🎥🎥🎬
  • United States
  • your moms house
  • Between Kentucky, Ohio
  • Savannah Ga.
  • Providece Texas A@M Universty
  • USA . Married over 50 yrs to HS Sweetheart
  • 24hrs on d street hustling

(All typos and emojis – the tragedy of the Unicode standard – were copied “as is”)

In case you are wondering, “your moms house” is in Beech Grove, IN.

The sample set I took most of this data from had only 25 entries and were parsed by hand. Out of those, 16 used ANSI standard INCITS 38:2009 for the state names, but in various formats.

A whole 24% used what I called “other” – like “your moms house”.

On a bigger scale, out of a sample set of 22,800 imported tweets, 15,630 (68%) had some type of location in their profile, but only 11 had their actual GPS location enabled.

Points scored

For the time, we can conclude that most Twitter users tell us something about their location – intentional or not. For a more scientific approach, here’s a link – keep in mind that my data is a random selection.

However – using user-entered data always results in messy, fuzzy, non-structured data. This has been a problem way before the terms “Machine Learning” or “Analytics” exceeded any marketing company’s wildest dreams. Levenshtein-Distance record matching, anyone?

Using NLP to identity entities & locations

At this point, Google’s NLP toolkit comes into play again. We use the NLPT to get all locations from the user’s self-entered “place” to identify everything that has the LOCATION metadata flag.

This is simple for something like this:

“Oregon” is clearly the location we need. We were able to strip of “upper left” – and could even weigh this based on the specific salience value.

However, more obscure queries result in more random results:

But even here, we get the core of the statement – USA – with a high confidence level.

The more random place descriptions (such as “a free country”) naturally only produce low-quality results – which is why we should filter results with a low salience score. While this only means “relative to this set of text, this entity is relatively important/unimportant”, it does serve as a rough filter.

In order to use more standardized data, we can also use the wikipedia_url property of the NLP toolkit (if available) and extract a more useful string. This results in “Baltimore, MD” to be turned into “Baltimore Maryland”, for instance.

However, “Atlanta” turns into “Atlanta (TV Series)” – so use it with caution.

public static List<Cluster<Location>> dbscanWithHaversine(ArrayList<Location> input) {
    DBSCANClusterer<Location> clusterer = new DBSCANClusterer<>(EPS, MIN_POINTS, new HaversineDistance());
    return clusterer.cluster(input);
public class HaversineDistance implements DistanceMeasure {
    public double compute(double[] doubles, double[] doubles1) throws DimensionMismatchException {
        if (doubles.length != 2 || doubles1.length != 2)
            throw new DimensionMismatchException(doubles.length, doubles1.length);

        Location l1 = new Location("A", doubles[0], doubles[1],0,"N/A");
        Location l2 = new Location("B", doubles1[0], doubles1[1],0,"N/A");
        return MathHelper.getHaversineDistance(l1, l2);
public static double getHaversineDistance(Location loc1, Location loc2) {
    Double latDistance = toRad(loc2.getLatitude() - loc1.getLatitude());
    Double lonDistance = toRad(loc2.getLongitude() - loc1.getLongitude());
    Double a = Math.sin(latDistance / 2) * Math.sin(latDistance / 2) +
            Math.cos(toRad(loc1.getLatitude())) * Math.cos(toRad(loc1.getLatitude())) *
                    Math.sin(lonDistance / 2) * Math.sin(lonDistance / 2);
    Double c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
    return R * c;

Reverse Geocoding & Clustering indecisive answers

The next step, assuming we received unstructured location data, we will try to convert that data into a query for a location service that supports reverse geocoding. Location services and their respective APIs are plentiful online – as long as it is able to convert a query of location data into a set of potential matches, we will be able to use that data.

In this case, we are using the Nominatim client from OpenStreetMap. Given the data volume, it is advisable to host Nominatim on Kubernetes or your local development machine – the openstreetmap servers will block your IP if you accidentally DDos them or simply don’t respect their Fair Use Policy – and the velocity of streaming tends to violate basically every Fair Use clause in existence.

OpenStreetMap will return a list of potential matches. Take this example when our location is “Springfield” and we limit the results to 20:

Springfield data points

As you can see, this is not conclusive. So we need to find a way to figure out which result is most accurate.

Fun Fact: Without a country boundary on the US with Nominatin, this is what “Detroit Michigan” produces:

Using clustering to approximate locations

In order to figure out where on the map our result is, we use Density-based spatial clustering of applications with noise (DBSCAN), a clustering algorithm that maps points by their density and also takes care of any outliers.

DBSCAN illustration

I found this article’s description of the algorithm most conclusive. Short version – for a dataset of n-dimensional data points, a n-dimensional sphere with the radius ɛ is defined as well as the data-points within that sphere. If the points in the sphere are > a defined number of min_points, a cluster is defined. For all points except the cener, the same logic is applied recursively.

As DBSCAN requires the ɛ parameter to be set to the maximum distance between two points for them to be considered as in the same neighborhood. In order to set this parameter to a meaningful value, we use the Haversine distance to get the orthodromic distance on a sphere – in our case, a rough approximation of the earth and therefore a result in kilometeres between locations.

The Haversine function is defined as such –


  • d is the distance between the two points (along a great circle of the sphere; see spherical distance),
  • r is the radius of the sphere,
  • φ1, φ2: latitude of point 1 and latitude of point 2, in radians
  • λ1, λ2: longitude of point 1 and longitude of point 2, in radians

In our case , r is defined as the radius of the earth, 6,371 km.

To combine those, we can use the package. All we need to do is implement the DistanceMeasure interface (as well as the function for the Haversine distance itself).

public static List<Cluster<Location>> dbscanWithHaversine(ArrayList<Location> input) {
    DBSCANClusterer<Location> clusterer = new DBSCANClusterer<>(EPS, MIN_POINTS, new HaversineDistance());
    return clusterer.cluster(input);
public class HaversineDistance implements DistanceMeasure {
    public double compute(double[] doubles, double[] doubles1) throws DimensionMismatchException {
        if (doubles.length != 2 || doubles1.length != 2)
            throw new DimensionMismatchException(doubles.length, doubles1.length);

        Location l1 = new Location("A", doubles[0], doubles[1],0,"N/A");
        Location l2 = new Location("B", doubles1[0], doubles1[1],0,"N/A");
        return MathHelper.getHaversineDistance(l1, l2);
public static double getHaversineDistance(Location loc1, Location loc2) {
    Double latDistance = toRad(loc2.getLatitude() - loc1.getLatitude());
    Double lonDistance = toRad(loc2.getLongitude() - loc1.getLongitude());
    Double a = Math.sin(latDistance / 2) * Math.sin(latDistance / 2) +
            Math.cos(toRad(loc1.getLatitude())) * Math.cos(toRad(loc1.getLatitude())) *
                    Math.sin(lonDistance / 2) * Math.sin(lonDistance / 2);
    Double c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
    return R * c;

By putting 2 and 2 together, we get a DBSCAN implementation that accepts an ɛ in kilometers.

While choosing the parameters for the DBSCAN algorithm can be tricky, we use 150km / 93mi as the max. Radius of a cluster and assume that a single point is valid for a cluster. While this, in theory, produces a lot of noise, it is an accurate statement for clustering our location set.

For interpreting our clusters (and choosing the one that is seemingly correct), we rely on the average “importance” value of OpenStreetMap, which aggregates multiple important metrics (from e.g., Wikipedia) to “score” a place.

If the user’s location contains part of a state as String (e.g., Springfield, IL), we increase the importance score during execution.

Springfield, data points


Springfield, avg. importance by cluster

In our Springfield example, Springfield, IL is the state capital of Illinois – and that is one of the reasons why the OpenStreetMap data ranks it higher than the entire Springfield, MA cluster (which consists of Springfield, MA, Springfield, VT and Springfield, NH – despite their combined population being bigger than Illinois’ state capitol).

Assuming we have multiple points in a cluster, we take the average between all coordinates in it. This is accurate enough in a ~90mi radius and results in the rough center of the (irregular) polygon that is our cluster.

While I’ve just explained that Springfield, IL could be considered an accurate result, in order to illustrate the averaging, we simply remove Springfield, IL from the equation and our “best” cluster looks like this:

Sample Cluster for Springfield, MA

(The red dot in the middle is the average coordinate result)

Finally, we retrofit the calculated location data to a US state. For this, we have 2 options –

  • Call Nominatim again, resulting in another relatively expensive API call
  • Approximate the result by using a local list of rough state boundaries

While both methods have their pros and cons, using a geo provider undoubtedly will produce more accurate results, especially in cities like NYC or Washington DC, were we have to deal with close state borders to any given point.

For the sake of simplicity and resource constraints, I’m using a singleton implementation of a GSON class that reads a list of US states with rough boundaries that I’ve mapped from XML to JSON.

In our case, the result is either New Hampshire or Illinois, depending if we remove Springfield, IL nor not.

Other examples

Now, what tells us that somebody who states they are from “Springfield” simply likes the Simpsons?

Well, nothing. While it is advisable to store multiple potential location results and re-visit that data (or even use a different algorithm with a proper training set based on that), the architecture and algorithms works surprisingly well – some random profiling produced mostly accurate results, despite various input formats:

Sample Tweet locations across the US by quantity

(The big dot in the middle represents the location “USA”)

Original Location Result Accurate
Philadelphia Philadelphia, Philadelphia County, Pennsylvania, United States of America TRUE
Brooklyn, NY BK, Kings County, NYC, New York, 11226, United States of America TRUE
nebraska Nebraska, United States of America TRUE
United States United States of America TRUE
Truckee, CA Truckee, Donner Pass Road, Truckee, Nevada County, California, 96160, United States of America TRUE
Lafayette, LA Lafayette, Tippecanoe County, Indiana, United States of America TRUE
Minot, North Dakota Minot, Ward County, North Dakota, United States of America TRUE
Rocky Mountain hey! Rocky Mountain, Harrisonburg, Rockingham County, Virginia, United States of America TRUE
Living BLUE in Red state AZ! Arizona, United States of America TRUE
Earth Two, Darkest Timeline Earth Tank Number Two, Fire Road 46, Socorro County, New Mexico, United States of America FALSE
The Golden State Golden, Jefferson County, Colorado, United States of America FALSE
Atlanta, GA Atlanta, Fulton County, Georgia, United States of America TRUE
thessaloniki Thessaloniki Jewelry, 31-32, Ditmars Boulevard, Steinway, Queens County, NYC, New York, 11105, United States of America FALSE
newcastle Newcastle, Placer County, California, 95658, United States of America TRUE
Afton, VA Afton, Lincoln County, Wyoming, 83110, United States of America FALSE
Gary, IN / Chicago, IL Chicago, Cook County, Illinois, United States of America TRUE
Canada Canada, Pike County, Kentucky, 41519, United States of America FALSE
Southern California Southern California Institute of Architecture, 960, East 3rd Street, Arts District, Little Tokyo Historic District, LA, Los Angeles County, California, 90013, United States of America TRUE
San Francisco Bay Area San Francisco Bay Area, SF, California, 94017, United States of America TRUE
Southern CA Southern California Institute of Architecture, 960, East 3rd Street, Arts District, Little Tokyo Historic District, LA, Los Angeles County, California, 90013, United States of America TRUE

(All of these results come with latitude and longitude, state data and the full user profile and tweet metadata)

More importantly, tuning those results is just an exercise in careful profiling. We could filter out obvious countries that are not the US, tune the model parameters or the API calls.

Tweet locations across the US by avg. importance

(In this example, big points indicate a high confidence level; often points in the geographical center of a state hint that the user simply said they were from e.g. “Arizona”, “AZ” or “Living BLUE in Red state AZ!”)


While the example shown here is a simple proof of concept, extending the concept has plenty of opportunities –

  • Fine-tune the model, filtering obvious outliers
  • Build a data model that connects location data with the tweets, all other available metadata
  • Store multiple salience values per analysis, tuning the model based on the data
  • Run the topology on scale and apply it to all tweets concerning a certain topic, analyzing the big picture and calculating for false positives
  • Run regression or other analysis over users entries with the same ID and potential mismatches, tracking changes in the location; write a pipeline that flags users which use their location and retrofit all old results to an accurate GPS
  • Store users without location data and apply the above logic to those

One thing we can conclude is that using a combination of well-known, powerful local Big Data tools in combination with managed, equally powerful Cloud solutions opens the door for a massive variety of new analytics opportunities that required a much higher level of involvement and cost only a few years ago.

The next steps will be to combine this data with the actual location results, create heatmaps, fine-tune the model, and eventually move the whole solution to the Google Cloud Platform.

Sample entity analysis

All development was done under Fedora 27 with 16 AMD Ryzen 1700 vCores @ 3.2Ghz and 32GiB RAM. Nominatim planet data from 2018-03-15 was stored on a 3TB WD RED Raid-1 Array

Software Stack: Heron 0.17.6, Google Cloud Language API 1.14.0, Google Cloud BigTable API 1.2.0, Google Cloud BigQuery API 0.32.0-beta, Google Data Studio, Nominatim 3.1.0, PostgreSQL 9.6.8

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