Privacy Geofencing

A question was posted yesterday on the Leaflet forum about doing geofencing with LeafletJS, so I posted this demo in response:

Screen shot of privacy geofence demo

Privacy Geofence Demo

My demo scenario is that there is a user who likes to share her runs with friends on social media but doesn’t want everybody to see exactly where she lives, so she creates a privacy geofence around her house. The starting and ending sections of her run polyline are hidden once they enter the geofenced area. If the middle section of the run passes through the geofenced area it remains visible.

Here is a shout out to Roadrunners of Kansas City. The geofence boundary in this demo is around Sport+Spine, home-base to many of the Roadrunner runs. Thanks, Coach Amy!

For my example, I turned once again to the Leaflet-pip library, as I did for Demo7 and Demo 8. I use it a couple of different ways in this demo. First, I use it to locate runs for this demo whose start point falls within the demo’s privacy geofenced area. Second, when the user displays a run (by clicking the numbered buttons on the bottom of the page) I use Leaflet-pip again to test which points at the beginning and end of the run fall within the privacy geofence.

In the real world, you would always do the privacy filtering server-side, using something like GeoTools or spatial database queries, so that only the public sections of the polyline are exposed in the GeoJSON. I did this demo on the browser-side because my site is an Amazon S3-hosted static website, and also because the forum question was about doing geofencing with Leaflet.

The code for this demo can all be found in RightSideView.js. The render method does the following things:

  1. Creates the L.featureGroup, geoFence, and adds a L.polygon defining the geofence boundaries.
  2. Displays the geofence on the map.
  3. Loads the running log GeoJSON file from Demo 5.
  4. Uses leafletPip.pointInLayer to find runs that start in the geofenced area, and then adds a paging div to the bottom of the page to open each matching run.
  5. Loads the first run.

The interesting part of the code is in the onActivityFetched function. The “hidden” parts of the polyline are shown in gray on the map, so I needed to define three polylines: the hidden start, the visible middle, and the hidden end.

I loop through the polyline coordinates and add the points to the “fencedStart” array until I hit a point falling outside the geofenced area. After that, all points are added to a “middle” array.

Screen shot of the code to find the hidden start of the run.

Find the Hidden Start of the Run and the Middle

Next, I loop backward over the points collected in the middle array looking for the points at the end of the run to be hidden.

Screen shot of the code to find the ending section of the run to be hidden.

Find the End Section of the Run to Hide

Finally, now that I have the points to hide at the start and the end of the run, I just need to find the points in between to draw on the map in red.

Screen shot of code to find the middle of the run to add to map.

Find the Visible Section of the Run to Display on Map

Go to the demo and click on a few of the runs to see how it works. Doing this demo I learned that “Leaflet.PIP” (point-in-polygon), only works on polygons (go figure). I started out trying to use a for the geofenced area. That didn’t work.

The other shortcoming of this demo is that sometimes the first point outside the privacy geofence is some distance away, causing the visible line to stop short. That could easily be fixed with point projection along the bearing of the line between the two points that span the geofence boundary.

Happy Geofencing.


Browser-side Geospatial Maps

If you know many marathon runners, then you probably know somebody in the Fifty States Marathon Club, like my friend Jennifer. Here is a post from Jennifer on Facebook:

Map of states run

Jennifer’s 50-States Progress Report

What if users could simply use their electronic running log to generate progress maps on-the-fly? And, what if that map could be generated without increasing the load on expensive geospatial database servers?

That is exactly what I’m demonstrating today with Tom McWright‘s Leaflet-pip library. See it live here: This demo reuses a personal running log GeoJSON file from demo 5. It combines my race data with a GeoJSON file containing shapes for the 50 states that I found at Frictionless Data.

The map flow is illustrated below. Keep in mind that I’m doing all of this with static files on my Amazon S3-hosted website.


First, RightSideView.js downloads the running log GeoJSON file and the States GeoJSON file. It passes the states GeoJSON feature collection to StatesMapLayerView.js which loads it into three maps: the mainland, Alaska, and Hawaii.

Next, RacesMapLayerView.js loads the running log GeoJSON feature collection into a Leaflet GeoJson Layer filtering by eventType = “Race” and totalMeters equals the selected race distance. As each point is added to the map, MapEventDispatcher.js is used to publish a RACE_ADDED event.

The RACE_ADDED event triggers the State Map Layer View to call the Leaflet-pip library to find the state polygon containing the race point and change that state’s background color from blue to red.

Image of code used to find the race start point.

Find State Containing Race Start Point

Above the map is the race selector. It uses the Map Event Dispatcher to fire the RACE_SELECTED event. The RACE_SELECTED event triggers the State Map Layer View to remove all state highlighting. It also triggers the Race Map Layer View to re-filter the running log GeoJSON file using the updated race distance. That, in turn, fires RACE_ADDED again, which re-triggers state highlighting.

That’s it. Pretty simple. The geospatial matching to produce this map, that might have been done, say, in Oracle Spatial, is successfully offloaded to the customer’s web browser instead. This leaves expensive servers to do the large-scale geospatial operations they do best.


Breaking Dependence on Vendor Map APIs with Leaflet

A colleague had a question about porting a map written using Bing Maps AJAX 6.2 to Bing Maps AJAX V7. The existing map shows coverage areas that, when highlighted, highlight a corresponding item in a price list. The problem was that the mouse events didn’t work the same in the new version. I’ve become a bit of a Leaflet evangelist these days, so I showed them how to do it using Leaflet and the Leaflet-pip library instead. You can see the demo, pictured below, at this URL: It’s a fun little interactive map.

Screenshot of Shape Event Demo

Screenshot of Shape Event Demo

I grabbed a GeoJSON file of Kansas counties from CivicDashboards for the shapes to be highlighted. There was also a requirement that hovering the mouse above overlapping shapes highlight all of the overlapping shapes, not just the top shape, so I added several large circles to my map representing Kansas universities. The list of county names to the left of the map is meant to represent the price list on my colleague’s map.

Hovering the mouse over a name in the list of counties highlights the corresponding county shape on the map and vice versa. Hovering over the university circles not only highlights the circle , but also the county shape underneath that circle.

The demo came together quickly since I was using Leaflet’s L.geoJson() layer for the county shapes, as I have done in several previous demos. There were just a couple of things that slowed me down: scrolling the list of county names to the highlighted name when hovering over a county shape on the map and propagating the mouse events to all overlapping shapes.

Scrolling Long Lists

Automatically scrolling to a highlighted name should have been easy, but it took me a bit to get it right. I worked up a proof-of-concept in JSFiddle first. You can find the POC code here:  The code in my map demo is even shorter than my proof-of-concept on JSFiddle.

Propagating Mouse Events

I used mouseover and mouseout event handlers on the county shapes and college circles to handle highlighting. The problem was that when the circle received the mouse event the county underneath the circle did not receive an event, so it never got highlighted.

I posted a question to the Leaflet forum and was sent a solution in JSFiddle by user “ghybs”,, that used the leaflet-pip plugin. The leaflet-pip plugin was written by Tom McWright of Mapbox. It, in turn, credits an algorithm documented at Rensselaer Polytechnic Institute in Troy, NY.

Long story short, I added a mousemove listener to the college circles on the map. When the mouse moves over a circle the mousemove event handler uses leaflet-pip to find the county shape containing the point of the mouse event and then explicitly fires the mouseover event of the matching county shape.

Using leaflet-pip.pointInLayer

Using leaflet-pip.pointInLayer

I did not take the time to implement the event propagation the other direction. When you hover over a county name in the list, which highlights a county shape on the map, the college circle above it is not highlighted. This could be done… but I’ve invested enough time in this demo, so I didn’t do that.

I love this plugin. It is useful and easy to use. This gave me an idea for my next demo which will use leaflet-pip to demonstrate a “50-State Marathon Club” style map. You’ve seen them before. People who are trying to run a half or full marathon in all 50-states, or all seven continents, use them to track their progress.

The bulk of the code for this demo can be found here:


Rendering a GeoJSON layer in Leaflet

The addition of a GeoJSON layer showing a Garmin activity polyline completes my example of using Leaflet with Bing, Google, and OSM base layers.

Static GeoJSON Leaflet map with Google, Bing and OSM base layers.

Static GeoJSON Leaflet map with Google, Bing and OSM base layers.

I did a lot of work to get to this point. Some tasks were completely unrelated to the demo, like converting my website to a single-page Backbone application. Other tasks were behind the scenes, like writing a utility to convert Garmin FIT files into GeoJSON. A great deal of my time was spent recreating the Garmin-style map controls to switch between Google, Bing, and OSM. Eventually, I want to add the Google bicycle path layer and fix a bug I found today on the menu controls, but that can wait.

Once I was ready to add the polyline and custom pin icons, coding was a breeze. All that needed to be done was to extend L.Icon for the custom pin icons and drop the polyline GeoJSON into the L.geoJson function as shown below:

    var ActivityMapLayerView = Backbone.View.extend({

        initialize: function(args) {
            var CustomIcon = L.Icon.extend({options: {
                iconSize: [33, 50],
                iconAnchor: [16, 49]
            this.startIcon = new CustomIcon({iconUrl: 'media/pin_start.png'});
            this.endIcon = new CustomIcon({iconUrl: 'media/pin_end.png'});

        render: function() {
            var props = this.model.get('properties');
                [props.get('minLat'), props.get('minLon')],
                [props.get('maxLat'), props.get('maxLon')]
            var style = {
                color: '#FF0000',
                weight: 3,
                opacity: 0.6
            L.geoJson(this.model.toJSON(), {style: style}).addTo(;
            var polyline = this.model.get('geometry').get('coordinates');
            var startPoint = polyline[0];
            var endPoint = polyline[polyline.length - 1];
            L.marker([startPoint[1], startPoint[0]], {icon: this.startIcon}).addTo(;
            L.marker([endPoint[1], endPoint[0]], {icon: this.endIcon}).addTo(;


The GeoJSON is passed into L.geoJson along with style properties to control the color, weight, and opacity of the polyline. That is all it took!

Now it is time to being exploring the plethora of Leaflet plugins supporting interactive maps. I’m looking forward to getting started.

Converting Garmin FIT activities into GeoJSON

My demo accomplished what I wanted to do with Leaflet regarding Bing, OSM, and Google base maps. The demo is at The only base map plumbing that remains is to figure out how to add Google’s bicycle path layer.

The next step is to display the activity polyline as a Leaflet layer instead of adding it directly to Google, Bing, or OSM maps via the vendors’ map APIs. With Leaflet, the base map is simply the background for the vector graphics displayed on a Leaflet map layer in the foreground.

Using GeoJSON with Leaflet

First, I needed to produce GeoJSON as described here: GeoJSON is a JSON standard for geometry types (see The geometry types are:

  • Point
  • LineString
  • Polygon
  • MultiPoint
  • MultiLineString
  • MultiPolygon

Geometry types may be combined with additional properties using Feature or FeatureCollection. Leaflet has a map layer type that can consume Feature JSON or FeatureCollection JSON.

Garmin Activity FIT File in –> GeoJSON out

Modern Garmin fitness devices store data in a compressed binary format called the FIT protocol (see

My first task was to create a utility to read Garmin activity FIT files and produce Feature GeoJSON. The code is on GitHub:

I used my 2012 Little Rock Marathon activity to test the GeoJSON utility. The FIT SDK decodes the activity FIT file. The GeoTools FeatureJSON class outputs the GeoJSON.

There are two steps to creating GeoJSON using the GeoTools API:

  1. Define a feature type (schema) for the feature properties.
  2. Build the feature following the feature type definition.

Defining a Simple Feature Type

The SimpleFeatureTypeBuilder class is used to layout the schema for the Feature.

public SimpleFeatureType getFeatureSchema() {
    final SimpleFeatureTypeBuilder simpleFeatureType = new SimpleFeatureTypeBuilder();
    simpleFeatureType.add("geom", LineString.class, DefaultGeographicCRS.WGS84);
    simpleFeatureType.add("name", String.class);
    simpleFeatureType.add("activityId", Long.class);
    simpleFeatureType.add("activityName", String.class);
    simpleFeatureType.add("sport", String.class);
    simpleFeatureType.add("startTime", String.class);
    simpleFeatureType.add("totalMeters", Double.class);
    simpleFeatureType.add("totalSeconds", Double.class);
    simpleFeatureType.add("minLat", Double.class);
    simpleFeatureType.add("minLon", Double.class);
    simpleFeatureType.add("maxLat", Double.class);
    simpleFeatureType.add("maxLon", Double.class);
    return simpleFeatureType.buildFeatureType();

Building the Feature

The SimpleFeatureBuilder builds the SimpleFeature following the feature schema definition.

public SimpleFeature buildSimpleFeature(final FitActivity fitActivity) {
    final SimpleFeatureType featureSchema = getFeatureSchema();
    final SimpleFeatureBuilder builder = new SimpleFeatureBuilder(featureSchema);
    builder.set("activityId", fitActivity.getActivityId());
    builder.set("sport", fitActivity.getSport());
    builder.set("startTime", fitActivity.getStartTime());
    builder.set("totalMeters", fitActivity.getTotalMeters());
    builder.set("totalSeconds", fitActivity.getTotalSeconds());
    final Coordinate[] polyline = fitActivity.getPolyline().toArray(
        new Coordinate[fitActivity.getPolyline().size()]);
    final Geometry geometry = simplifyLineString(polyline);
    final Coordinate[] boundingBox = generateBoundingBox(geometry);
    builder.set("minLat", boundingBox[0].y);
    builder.set("minLon", boundingBox[0].x);
    builder.set("maxLat", boundingBox[1].y);
    builder.set("maxLon", boundingBox[1].x);
    return builder.buildFeature("0");

The final result, formatted for readability, can be found here:

This is a partial listing:

  "type": "Feature",
  "geometry": {
    "type": "LineString",
    "coordinates": [

... many points omitted.

  "properties": {
    "activityId": 155155867,
    "sport": "RUNNING",
    "startTime": "2012-03-04T14:02Z",
    "totalMeters": 42453.58984375,
    "totalSeconds": 15162.140625,
    "minLat": 34.73176879808307,
    "minLon": -92.34434505924582,
    "maxLat": 34.78602569550276,
    "maxLon": -92.25817699916661
  "id": "0"

Now I’m ready to create a Leaflet GeoJSON layer to display the polyline!

Exploring Leaflet Raster Classes


Below is the class diagram for the Leaflet raster classes. As I prepared this diagram I realized it did not go far enough for me since I don’t have experience working the raster images. I’m going to put my exploration of Leaflet classes on hold and take a detour into the world of raster graphics.

Leaflet Raster Classes

Leaflet Raster Classes

I found several posts on Google map tilesBing map tiles, and OSM map tiles. The article on OSM map tiles referenced the Slippy Map Tile Application, and Mapnik, an open source tool for rendering maps.

If I start with Mapnik I need some raster tiles to render, don’t I? This would be the perfect time to continue my post from June about working with satellite images with TileMill. In that post I installed the following tools on my computer:

  1. GDAL, a low-level GIS toolkit
  2. libgeotiff, to work with geotags
  3. ImageMagick, an image processing package
  4. TileMill, an open-source mapmaking app from Mapbox

For my next post, I’ll pick up where I left off and follow the article Processing Landsat 8 Using Open-Source Tools by Charlie Loyd. Then perhaps I’ll look at GMap Creator and MapTiler. I’m open to suggestions for good raster learning resources.

When I’m done I’ll revisit this Leaflet raster class diagram again.

Penn State Geospatial Revolution MOOC on Coursera


This week I completed the 5-week MOOC, Maps and the Geospatial Revolution, taught by Dr. Anthony Robinson of Penn State on the site Coursera. Over 47,000 students enrolled and over 35,000 students participated, making it the largest GIS course ever taught. I found the course to be a good overall survey of the world of GIS (geographic information systems). I recommend it to anybody wanting to learn about GIS or who wants to see what it’s like to take a MOOC (massive open online course). Below is my brief overview which only skims the surface of what we covered.

In the first week, Dr. Robinson illustrated how geospatial technology has revolutionized the way we navigate, make decisions using geography, and share stories using geographic references, like geotagging and geocoding.

The second week covered reference maps and thematic maps. We saw thematic maps showcasing geographic observations, including choropleth maps, dot mapping, proportional symbol mapping, and isoline maps. The concept of spatial autocorrelation  was introduced (the measure of similarity of observations that are close to one another), as well as Tobler’s first law of geography:

Everything is related to everything else, but near things are more related than distant things.

A cool project highlighted in week two was Ushahidi, a non-profit organization that built a platform for volunteered geographic information in disaster areas. Ushahidi was instrumental in relief efforts in Haiti where online maps were very poor at the time of the earthquake.

Week three introduced GNSS (global navigation satellite systems) ranging from America’s GPS, GLONASS in Russia, to the E.U. Galileo system. We also covered the vector and raster spatial data types, as well as remote sensing, LIDAR, spatial metadata, and ArcGIS Online.

Week four discussed map overlays, first introduced in Ian McHarg’s 1969 book, Design with Nature.  A video explained how Dr. John Snow’s map of the cholera outbreak in 1854, Soho, London, was used to trace the cholera outbreak to a bad well. That was the first major use of cluster detection. We learned that spatial correlation is not causation, and we learned about the MAUP (modifiable areal unit problem), where scale effects how geospatial observations are visualized. We covered how important normalization is to avoid skewing maps by population density. To normalize data you calculate the rates of occurrence as a proportion of the overall population.

Finally, week five covered the art of map making, which starts by clearly understanding the audience, target format, and specific purpose of the map. It’s difficult to give a concise summary, so I’ll just say the lesson taught that it is important to use the right thematic map for the type of data (nominal, ordinal, or interval/ratio data). On the topic of color, we covered sequential colors, diverging colors, and qualitative colors. We then explored data classifications: equal interval classification, quantile classification, and natural break classification.

The final assignment was a map of your choosing. I chose to use to running and cycling data that I extracted to create a surface analysis map. The data has some serious flaws (a triathlon at Clinton Lake skewed my data), but it was adequate for my proposes.


I’m already looking for other MOOCs of interest to me that are offered by Corsera.