Using the QGIS Gaussian Filter on Wildfire Risk Data

I thought was done learning new QGIS tools for a while.  Turns out I needed to learn one more trick with QGIS — the Gaussian filter tool.  The Gaussian filter is sparsely documented basically undocumented, so I figured I’d write up an post on how I used it to turn a raster image into vector layers of gradient bands.

Motivation:  In my spare time I’m adding more layers to the site I’ve been building which maps out disaster risks.  California was mostly on fire last year, so I figured wildfires were a pretty hot topic right now.

The most useful data-source I found for wildfire risk was this USDA-sourced raster data of overall 2018 wildfire risk, at a pretty fine gradient level.  I pulled this into QGIS:

(I’m using the continuous WHP from the site I linked).  Just to get a sense of what the data looked like, I did some basic styling to make near-0 values transparent, and map the rest of the values to a familiar color scheme:

This actually looks pretty good as a high-level view, but the data is actually super grainy when you zoom in (which makes sense — the data was collected to show national maps):

This is a bit grainy to display as-is at high zoom levels.  Also, raster data, although very precise is (1) slow to load for large maps and (2) difficult to work with in the browser — in MapBox I’m not able to remap raster values or easily get the value at a point (eg, on mouse click).  I wanted this data available as a vector layer, and I was willing to sacrifice a bit of granularity to get there.

The rest of this post will be me getting there.  The basic steps will be:

  • Filtering out low values from the source dataset
  • Using a very slow, wide, Gaussian filter to “smooth” the input raster
  • Using the raster calculator to extract discrete bands from the data
  • Converting the raster to polygons (“polygonalize”)
  • Putting it together and styling it

The first thing I did was filter values out of the original raster image below a certain threshold using the raster calculator.  The only justification I have for this is “the polygonalization never finished if I didn’t”. Presumably this calculation is only feasible for reasonably-sized raster maps:  

(I iterated on this, so the screenshot is wrong: I used a threshold of 1,000 in the final version).  The result looks like this:

Next step is the fancy new tool — the Gaussian filter.  A Gaussian filter, or blur, as I’ve seen elsewhere, is kind of a fancy “smudge” tool.  It’s available via Processing → Toolbox → SAGA → Raster filter.  

This took forever to run.  Naturally, the larger values I used for the radius, the longer it took.  Iterated on the numbers here for quite a while, with no real scientific basis;  I settled on 20 Standard Deviation and 20 search radius (pixels), because it worked.  There is no numerical justification for those numbers. The result looks like this: 

Now, we can go back to what I did a few weeks ago — turning a raster into vectors with the raster calculator and polygonalization.  I did a raster calculator on this layer (a threshold of .1 here, not shown):

These bands are actually continuous enough that we can vectorize it without my laptop setting any polar bears on fire.  I ran through the normal Raster → Conversion → Polygonalize tool to create a new vector layer:

This looks like what we’d expect:

Fast forward a bit, filtering out the 0-value shape from the vector layer, rinse-and-repeating with 3 more thresholds, and adding some colors, it looks pretty good:

I want this on Mapbox, so I uploaded it there (again, see my older post for how I uploaded this data as an mbtiles file).  Applied the same color scheme in a Style there, and it looks nice: 

Just as a summary of the before and after, here is Los Angeles with my best attempt at styling the raw raster data: 

You get the general idea, but it’s not really fun when you zoom in.  Here’s it is after the Gaussian filter and banding:

I found these layers a lot easier to work with, and a lot more informative to the end user.  It’s now visible as a layer on

I thought this tool was nifty, so hopefully this helps someone else who needs to smooth out some input rasters.

Firebase is cool — Easy IP geolocation on Mapbox GL JS page-load

(warning: this all is probably obvious to people who know Firebase, but I didn’t see any direct references to this feature, so I figured I’d write it up)

Over the past few weeks I’ve been learning my way around Firebase; I use it to host my current side-project, a webapp (, which uses Mapbox GL JS to render an interactive map of natural disasters, nuclear targets, and the like.

Today I took a shot at adding a convenience feature; during the initial page load, I wanted to zoom to the user’s actual location, instead of just defaulting to the center of the US.  Mapbox and Firebase have made this project stupidly easy so far, so I was optimistic this would also be easy.

Precise geolocation is certainly possible through Mapbox GL JS, but I’d have to use the actual browser location APIs; those require permissions, which is a noisy user-experience if it happens during initial page-load: 

(and frankly, people have no reason to give my random webapp their location.  I’m not that important.)

A lighter-weight version of geolocation would be to just geo-locate based on the user’s IP address.  IP geolocation isn’t very accurate — IP addresses move around, so I’m not going to get more precision than a city.  For my purposes, that’s fine. And unlike real location, I don’t have to get permission to see a user’s IP address.*

Mapping IP address to a location still takes a dataset and a bit of work though.  A number of sites offer IP to location services, but I wasn’t really thrilled about creating an account with a location service, managing an API key, and giving my credit card to internet randos just for this convenience.

Luckily, I discovered an easier way: it turns out that even though I’m using Firebase and not AppEngine, all the AppEngine request headers are attached to my Firebase function requests.  Among those is x-appengine-citylatlong, which is (more or less) exactly what I want. 

So, I built a tiny Firebase function which does nothing except listen for requests and pipe the location back into the response so I can use it in Mapbox:

'use strict';

const functions = require('firebase-functions');
const admin = require('firebase-admin');

const cors = require('cors')({
  origin: true

exports.getCoordinates = functions.https.onRequest((req, res) => {
  cors(req, res, () => {
      "data": {
        "coords": req.headers['x-appengine-citylatlong']

(this function ended up being pretty trivial, but I struggled for a bit because it wasn’t obvious (to me) how to directly return JSON from a Firebase function.  Firebase functions are (rightfully) built around the idea of returning Promises, because most Firebase functions proxy async services — storing data in database, putting it on GCS, etc.  It’s pretty unusual that a function is able to do what I do here — respond immediately, based only on the headers.)

Anyway, this function does exactly what I want it to do; it returns the coordinates of the request:

$ curl


On the Mapbox side, we can use this to flyTo the coordinates as soon as the map is loaded:

//  wait until the map is loaded
map.on('load', function () {

    // fetch the user coordinates from firebase
    var getCoordinates = firebase.functions().httpsCallable('getCoordinates');
    getCoordinates({}).then(function (result) {

      if ( {

          let latLong =",");

            //  note that lat/long are reversed in appengine
              center: [
              zoom: 11

Really, that’s it.  I’ve plugged a slightly more complicated version of this code into, and now it zoom to (roughly) the user’s location after the map loads.  With this trick, the geolocation is easy, cheap and simple, my favorite kind of trick : ) 

* do not try to teach me about GDPR.  I do not care.

More QGIS – Hurricane maps (lines to points and vector overlaps)

I posted a couple days ago about how I used QGIS to generate heatmaps of tornado activity based on raw point data.  Since I had invested time (kind of) learning the tool, I figured I should put together a few similar layers.

The most obvious choice was hurricane risk.  I ended up using a pretty similar procedure to when I generated the tornado heatmap, but massaging the data took a few extra steps:

  • The input data came as vectors instead of points
  • My data covered the whole globe, but I wanted the final vectors to only cover land areas

Again, I was happy with the result, so I figured I’d write it up.  

Similar to what I ran into with the tornado risk data, I couldn’t find any hurricane hazard GIS shapefiles.  I did again find a raw dataset of all hurricanes the NOAA has records on, which was enough to get started.

Importing all the vectors (I think there were about 700,000) from this MapServer took a while, and the result was, as expected, a bit cluttered:

There’s probably a better way to filter the data down, but I ended up exporting the records to shapefiles so I could filter on attributes.  The dataset had a lot of tropical storm data, and I filtered out everything except proper hurricanes (H1-H5).

Here things got a bit different.  The heatmap function I used for tornadoes only works on points, and these vectors were all lines.  Luckily, there was a brute force but straightforward solution: turn the line into a bunch of points.  QChainage is a simple plugin that does exactly that.  Once it’s installed, it’s available from the Vector  →  QChainage menu.

The above screenshot is a bit deceptive — I ended up using a point spacing of 20km in the final version.  The only main downside of a higher frequency is longer processing time when generating the heatmap. The result kind of looks a mess from above:

But looks a lot better once I zoom in:

From here, I’ll fast forward through the same stuff I did last time; I used the points to generate a heatmap, this time using 250km point radii, and pulled a vector out of it.  I iterated on thresholds until the most expansive layer more-or-less lined up with other reputable sources. My layer:

Compared to what I found online (source): 

Except for a few lumps in Virginia and Maine, it looks pretty comparable. 

Jumping forward a bit more, I again went with four gradients to get a map that looked like this:

I was a bit torn.  While this looks cool, the highlights on the ocean are distracting when the goal is to highlight risk areas on land; I needed to filter the shapes down to only land areas.

It turns out, intersecting vectors in QGIS is pretty easy.  I found a simple shapefile of all land areas on earth here (it wasn’t even a big download — less than 10MB).  Once this data was imported, I could use the Vector → Geoprocessing tools → Intersect tool to generate an intersection layer:

This did exactly what I wanted.  I repeated this for all four layers and ended up with a gradient only over land areas, exactly what I wanted.  I didn’t bother styling the layers, since I’ll just handle that in Mapbox later.

Just as a sanity check, I swapped back in the openmaptiles background to make sure the coastlines lined up correctly (they did, except a few hundred meters here and there on the coastline).

A nice bonus from this NOAA data: this time the data covered the whole globe.  All the other datasets I’ve found for other natural disaster risks are US-specific (and usually only the continental US):

I won’t go through the details on loading this into Mapbox; everything from here mirrored what I did last time.  You can see the result as a layer on

Once again I was pleasantly surprised at how easy it was to get (relatively) nice looking graphics from QGIS with minimal experience.  

At this point I’ve added data for most the layers I was interested in displaying (although I’m open to suggestions).  I’ll likely get back to the actual web-dev side of this project and clean up a few loose ends over the next couple weekends.