In this tutorial, I demonstrate how to reduce the size of a spatial data set of GPS latitude-longitude coordinates using Python and its scikit-learn implementation of the DBSCAN clustering algorithm. All my code is in this IPython notebook in this GitHub repo, where you can also find the data.

Traditionally it’s been a problem that researchers did not have enough spatial data to answer useful questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Too many scattered points on a map can overwhelm a viewer looking for a simple narrative. Furthermore, rendering a JavaScript web map (like Leaflet) with millions of data points on a mobile device can swamp the processor and be unresponsive.

## The data set

How can we reduce the size of a data set down to a smaller set of spatially representative points? Consider a spatial data set with 1,759 latitude-longitude coordinates. This manageable data set is not too large to map, but it serves as a useful object for this tutorial (for a more complex example clustering 1.2 million GPS coordinates, see this project).

I have discussed this data set in a series of posts, and reverse-geocoded the coordinates to add city and country data. Here is a simple Python matplotlib scatter plot of all the coordinates in the full data set:

At this scale, only a few dozen of the 1,759 data points are really visible. Even zoomed in very close, several locations have hundreds of data points stacked directly on top of each other due to the duration of time spent at one location. Unless we are interested in time dynamics, we simply do not need all of these spatially redundant points – they just bloat the data set’s size.

## How much data do we need?

Look at the tight cluster of points representing Barcelona around the coordinate pair (2.15, 41.37). I stayed at the same place for a month and my GPS coordinates were recorded every 15 minutes, so I ended up with hundreds of rows in my data set corresponding to the coordinates of my apartment.

This high number of observations is useful for representing the duration of time spent at certain locations. However, it grows less useful if the objective is to represent merely *where* one has been. In that case only a single data point is needed for each geographical location to demonstrate that it has been visited. This reduced-size data set would be far easier to map with an on-the-fly rendering tool like JavaScript. It’s also far easier to reverse-geocode only the spatially representative points rather than the thousands or possibly millions of points in some full data set.

## Clustering algorithms: k-means and DBSCAN

The k-means algorithm is likely the most common clustering algorithm. But for spatial data, the DBSCAN algorithm is far superior. Why?

The k-means algorithm groups *N* observations (i.e., rows in an array of coordinates) into *k* clusters. However, k-means is not an ideal algorithm for latitude-longitude spatial data because it minimizes variance, not geodetic distance. There is substantial distortion at latitudes far from the equator, like those of this data set. The algorithm would still “work” but its results are poor and there isn’t much that can be done to improve them.

With k-means, locations where I spent a lot of time – such as Barcelona – would still be over-represented because the initial random selection to seed the k-means algorithm would select them multiple times. Thus, more rows near a given location in the data set means a higher probability of having more rows selected randomly for that location. Even worse, due to the random seed, many locations would be missing from any clusters, and increasing the number of clusters would still leave patchy gaps throughout the reduced data set.

Instead, let’s use an algorithm that works better with arbitrary distances: scikit-learn’s implementation of the DBSCAN algorithm. DBSCAN clusters a spatial data set based on two parameters: a physical distance from each point, and a minimum cluster size. This method works much better for spatial latitude-longitude data.

## Spatial data clustering with DBSCAN

Time to cluster. I begin by importing necessary Python modules and loading up the full data set. I convert the latitude and longitude coordinates’ columns into a two-dimensional numpy array, called *coords*:

import pandas as pd, numpy as np, matplotlib.pyplot as plt from sklearn.cluster import DBSCAN from geopy.distance import great_circle from shapely.geometry import MultiPoint df = pd.read_csv('summer-travel-gps-full.csv') coords = df.as_matrix(columns=['lat', 'lon'])

Next I compute DBSCAN. The *epsilon* parameter is the max distance (1.5 km in this example) that points can be from each other to be considered a cluster. The *min_samples* parameter is the minimum cluster size (everything else gets classified as noise). I’ll set *min_samples* to 1 so that every data point gets assigned to either a cluster or forms its own cluster of 1. Nothing will be classified as noise.

I use the haversine metric and ball tree algorithm to calculate great circle distances between points. Notice my epsilon and coordinates get converted to radians, because scikit-learn’s haversine metric needs radian units:

kms_per_radian = 6371.0088 epsilon = 1.5 / kms_per_radian db = DBSCAN(eps=epsilon, min_samples=1, algorithm='ball_tree', metric='haversine').fit(np.radians(coords)) cluster_labels = db.labels_ num_clusters = len(set(cluster_labels)) clusters = pd.Series([coords[cluster_labels == n] for n in range(num_clusters)]) print('Number of clusters: {}'.format(num_clusters))

Number of clusters: 138

Ok, now I’ve got 138 clusters. Unlike k-means, DBSCAN doesn’t require you to specify the number of clusters in advance – it determines them automatically based on the *epsilon* and *min_samples* parameters.

## Finding a cluster’s center-most point

To reduce my data set size, I want to grab the coordinates of one point from each cluster that was formed. I could just take the first point in each cluster, but it would be more spatially-representative if I take the point nearest the cluster’s centroid. Note that with DBSCAN, clusters may be non-convex and centers may fall outside the cluster – however, we just want to reduce the cluster down to a single point. The point nearest its center is perfectly suitable for this.

This function returns the center-most point from a cluster by taking a set of points (i.e., a cluster) and returning the point within it that is nearest to some reference point (in this case, the cluster’s centroid):

def get_centermost_point(cluster): centroid = (MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y) centermost_point = min(cluster, key=lambda point: great_circle(point, centroid).m) return tuple(centermost_point) centermost_points = clusters.map(get_centermost_point)

The function above first calculates the centroid’s coordinates. Then I use Python’s built-in *min* function to find the smallest member of the cluster in terms of distance to that centroid. The *key* argument does this with a lambda function that calculates each point’s distance to the centroid in meters, via geopy’s great circle function. Finally, I return the coordinates of the point that was the least distance from the centroid.

To use this function, I map it to my pandas series of clusters. In other words, for each element (i.e., cluster) in the series, it gets the center-most point and then assembles all these center-most points into a new series called *centermost_points*. Then I turn these center-most points into a pandas dataframe of points which are spatially representative of my clusters (and in turn, my original full data set):

lats, lons = zip(*centermost_points) rep_points = pd.DataFrame({'lon':lons, 'lat':lats})

Great! Now I’ve got my set of 138 spatially representative points. But, I also want the city, country, and date information that was contained in the original full data set. So, for each row of representative points, I pull the full row from the original data set where the latitude and longitude columns match the representative point’s latitude and longitude:

rs = rep_points.apply(lambda row: df[(df['lat']==row['lat']) && (df['lon']==row['lon'])].iloc[0], axis=1)

All done. I’ve reduced my original data set down to a spatially representative set of points with full details.

## Final result from DBSCAN

I’ll plot the final reduced set of data points versus the original full set to see how they compare:

fig, ax = plt.subplots(figsize=[10, 6]) rs_scatter = ax.scatter(rs['lon'], rs['lat'], c='#99cc99', edgecolor='None', alpha=0.7, s=120) df_scatter = ax.scatter(df['lon'], df['lat'], c='k', alpha=0.9, s=3) ax.set_title('Full data set vs DBSCAN reduced set') ax.set_xlabel('Longitude') ax.set_ylabel('Latitude') ax.legend([df_scatter, rs_scatter], ['Full set', 'Reduced set'], loc='upper right') plt.show()

Looks good! You can see the 138 representative points, in green, approximating the spatial distribution of the 1,759 points of the full data set, in black. DBSCAN reduced the data by 92.2%, from 1,759 points to 138 points. There are no gaps in the reduced data set and heavily-trafficked spots (like Barcelona) are no longer drastically over-represented.

## Visualizing the cluster-reduced spatial data

This interactive CartoDB map shows both the full and reduced data sets as separate layers, along with informational pop-ups when you hover over a point. You can turn each layer on or off and zoom in to see how well the reduced set of points represents the full data set:

Now I can save the final reduced data set to CSV and use it in other applications that need a low-overhead spatial data set to render a visualization quickly and responsively. In particular, you might be interested in this notebook that uses this technique to cluster 1.2 million spatial data points and this post about that project.

This post was really helpful!! Thank you!

This was exactly what I was looking for. Helpful for beginners. Thanks a ton!

Great post. Very helpful; thank you very much for it.

I have a couple of questions about the parameters of DBSCAN. First of all, what is the unit of the physical distance (eps) here? You set it to be 0.01 but I am not sure what does this value mean physically. I was also wondering how to choose the best combination of epsilon and min_samples. In your case, you did not want to have any points to be classified as noise so you set the minimum samples to be 1. But what if in another application it’s OK to have some noise points. I understand that there might not be one correct answer for this question and perhaps it may depend on the context of the problem; but is there any rule of thumb as to how to choose the best values for the DBCSCAN parameters?

Again, thanks for this great post!

The epsilon parameter (distance) is in the units of your variable. Since my variables are latitude and longitude, the units are degrees. Certainly not ideal for clustering, as degrees represent different physical distances at the equator than at the poles, but good enough for the simple demonstration here. So, an epsilon value of 0.01 means 0.01 degrees. If you project your spatial data to meters, the epsilon value will be in meters. Indeed, I set minimum samples to 1 so every point would be in a cluster (no noise). The best way to parameterize your DBSCAN is according to theory and intention. If in your project a cluster should contain 4 entities, then set min_samples to 4. I used an epsilon of 0.01 degrees to approximately represent neighborhood-scale distance, but it would totally depend on the specific project.

Good post!

Dear Geoff Boeing,

Thank you very much for your post on http://geoffboeing.com/2014/08/clustering-to-reduce-spatial-data-set-size/

Although I was surfing all of your amazing works on data visualization. I was trying to cluster my data set by following this https://github.com/gboeing/data-visualization/blob/master/location-history

/google-location-history-cluster.ipynb. My code and input data are in here:

https://gitlab.com/roypulak/ExperimentalProject/tree/master

But here I am getting following error:

There are 61,510 rows

Number of clusters: 7

Traceback (most recent call last):

File “E:/URVThesisPythonCodes/DBSCANClustering.py”, line 51, in

df_clustered = dbscan_reduce(df_gps, epsilon=eps_rad)

File “E:/URVThesisPythonCodes/DBSCANClustering.py”, line 32, in dbscan_reduce

centermost_points = clusters.map(get_centermost_point)

File “C:\Users\PULAK\Anaconda3\lib\site-packages\pandas\core\series.py”, line 2104, in map

new_values = map_f(values, arg)

File “pandas\src\inference.pyx”, line 1088, in pandas.lib.map_infer (pandas\lib.c:62658)

File “E:/URVThesisPythonCodes/DBSCANClustering.py”, line 16, in get_centermost_point

centroid = (MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)

File “C:\Users\PULAK\Anaconda3\lib\site-packages\shapely\geometry\point.py”, line 56, in x

return self.coords[0][0]

IndexError: list index out of range

Process finished with exit code 1

Another:

MemoryError: bad allocation

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

File “E:/URVThesisPythonCodes/DBSCANClustering_basic.py”, line 22, in

db = DBSCAN(eps=epsilon, min_samples=50, algorithm=’ball_tree’, metric=’haversine’).fit(np.radians(coords))

File “C:\Users\PULAK\Anaconda3\lib\site-packages\sklearn\cluster\dbscan_.py”, line 265, in fit

clust = dbscan(X, sample_weight=sample_weight, **self.get_params())

File “C:\Users\PULAK\Anaconda3\lib\site-packages\sklearn\cluster\dbscan_.py”, line 160, in dbscan

dbscan_inner(core_samples, neighborhoods, labels)

SystemError: returned a result with an error set

Process finished with exit code 1

I executed this code by your summer-travel data-set and it worked fine(I am using PyCharm Edu 3.0.1). As I am very new on python, could you please help me, why this error is happening for my data set.

Best Regards,

Pulak Roy

Hi Pulak,

Have you solved your first error? I met the same one today. If you solved, can you post how to fix it?

Thank you

Ryan

First thanks a ton to Geoff Boeing for his work. Ryan and Baskas you can try my code here https://gitlab.com/roypulak/ExperimentalProject/blob/master/DBScan.py

This code works in my machine.You can try.

Hi,

I am getting the error “global name ‘MultiPoint’ is not defined”. Is there any way to resolve it?

Thanks.

That would completely depend on the specifics of what’s happening to cause it in your case. If it’s an error in my own code, please feel free to open a new issue in the GitHub repo.

Hi, Geoff. Thanks for this great post. Is there a way to report which cluster is the most dense?

If you’re looking for spatial density, you could, for each cluster, construct a convex hull around all the points using geopandas. Then calculate the area of the convex hull, and then calculate the points/area.