Mappt v2.1 Now Available

Version 2.1 has finally found it’s way to the Google Play store!  This version contains range of improvements and bug fixes.

As always, you can download the latest version of Mappt from the Google Play store:



We’ve been busy!  Read on to find out what we done did.

Marker Icons Can Now Be Customised

You can now customise the icon used to display markers, just in case the standard map pin wasn’t doing it for you.  This offers an extremely helpful at-a-glance aspect to markers, as demonstrated in this image below.

Screenshot showing customisable marker icons in Mappt

New Line Styles for Lines and Polygons

Lines and polygons can now be drawn using a set of predefined line styles, such as dotted, dashed, and even dotted and dashed!  At the same time!  Welcome to 2014.

We also support several other line styles, as seen in the screenshot, below.

Screenshot showing line and polygon styles in Mappt

Line styles are an early access preview, so you may find some quirks here and there.  As always, we are welcome to suggestions, so be sure to let us know your feedback!

Feature Labels

You can now specify a label to display for features!  This is specified in the properties of the containing layer, allowing you to choose the value of an attribute, or even the name of the feature itself.

Here’s our previous Mines example, this time with labels turned on.

Screenshot showing feature labels in Mappt

Bug Fixes, Other Improvements and a Goat

As always, we’ve eliminated bugs and made small tweaks here and there to improve the user experience.

We’re now hard at work on the next version of Mappt, much like this goat is hard at work on goating like a goat.

Goat standing on a tree stump.

We’re running out of simian pics, so we’re breaking tradition and going with a goat instead!

Using Mappt to Collect Data in the Field, Part 3 of 3

This is the final installment in our 3-part series, which has been offering a simple user story that could form a workflow basis to be adopted by new or existing Mappt users.

If you haven’t read them, take a look at part 1, “Preparation,” here and part 2, “In the Field,” here.  Part 1 dealt with preparing your tablets and datasets, while part 2 covered importing, updating and exporting job data while in the field.

Returning to Base

Once you return to base, you will want to get your captured data off the tablet.

Within Mappt, this can be achieved by selecting a layer and then exporting the layer to email. Your tablet will then present a list of installed apps that will offer to transmit the data for you. This will include apps that are not email-centric, but are otherwise great options for sending data. For example, if you have Google Drive or other cloud-based storage solution installed, you will be able to upload your data there.

Google Drive (and other cloud-based storage apps) provide great ways to collaborate on data and combine datasets.

Another option is to simply email the data, perhaps to a team leader or other staff member responsible for coordinating data changes.

Screenshot of the Open From Google Drive button in Mappt

You can also export the data to a removable flash card and copy the files to the computer where you may have tools for integrating back into the project.

A final option is to plug your tablet into a computer via the USB cord and copy the files that way.


Integrating the data back into your project datasets is a matter of much greater discussion, involving concerns such as conflicts, merging, authority, etc. and will not be covered here.  As suggested above, this may be something that is handled by a nominated member of your team, using tools designed specially for this purpose.


We hope that the topics covered in this 3-part series have provided some tips on developing your own workflows.  Be sure to post your thoughts in the comments, as we love hearing user stories!

Using Mappt to Collect Data in the Field, Part 2 of 3

This is part 2 in a 3-part series that offers a simple User Story that could form the basis of a workflow to be adopted by new or existing Mappt users.

If you haven’t already, we suggest reading part 1, “Preparation” here, which discussed preparing your tablets and datasets for work in the field.

In the Field

When performing your duties in the field, you will import your Project Datasets into Mappt to assist in locating assets and referring to job information.  As suggested in phase 1, project datasets should not be edited; instead, data captured in the field should be logged into smaller, job template datasets.

When commencing a job, a new template should be imported and updated as the job progresses.

A good job template will allow you to easily capture new data while providing the structure necessary to capture quality, error-free data.  For Shapefiles, this would mean that the job template contains a pre-defined set of attributes, guiding the user to enter relevant data into the correct places.

If your work is conducted in an Internet-connected area, you may consider hosting and distributing your datasets over Google Drive via a shared folder.  Doing this will allow your administrative teams to provide consistently accurate datasets to your team, without the need to redistribute datasets via email or other manual methods.

Custom offline imagery can be loaded to assist with navigation in combination with the tablet’s GPS hardware. If your imagery is high-quality and correctly geo-referenced, you can use the imagery to position features on the map with a high degree of accuracy. This is perfect for when the GPS hardware is not accurate enough.

Thematic Mapping (previously known as Classifications) can be applied to larger datasets to locate features with certain attributes.  For example, consider a dataset containing markers that represent assets to be inspected, with an INSPECTED YES/NO attribute, indicating whether the asset requires inspection.  Using Thematic Mapping, the markers could be styled green to highlight the markers to be inspected, providing an easy method to visually indicate work to be done.

thematic mapping

Completing the Job

Once data has been obtained and the job complete, any captured data layers can be exported to the local storage of the tablet before moving on to the next job.

In phase 3, we will outline some options for retrieving the recorded data from the tablet for re-integration back into the project.

Using Mappt to Collect Data in the Field, Part 1 of 3

In the Mappt cave we are always interested in the workflows and procedures our customers employ when using Mappt in the field.

We find that most of these workflows consist of the same base necessities, regardless of the industry-specific nature of the work.

Over the next couple of posts, we will outline a simple 3-phase User Story that could form the basis of a workflow to be adopted by new or existing Mappt users. This workflow is presented from the perspective of managing a small team of Mappt users for a particular project, but applies equally to one Mappt user performing a single job.

The phases are:

  • Preparation
  • In the Field
  • Returning to Base

This post will cover phase 1, “Preparation,” with posts for phase 2 and 3 coming in the following weeks.


The Preparation Phase would involve gathering the relevant datasets and imagery to be deployed to the tablets. Ideally, the datasets and other files created during this phase would be deployed to the tablets just once, and remain relevant for the duration of the project.

One suggestion is to categorise your data into these groups:

Project Datasets

Project datasets (Shapefiles, ECW files, etc) that are relevant to the project as a whole. For example, a dataset for a project involving inspection parks may contain a spatial database of all parks to be inspected throughout the course of the project.

Screenshot showing an example Project Dataset

In this example, the Project Dataset consists of all of the parks to be inspected by field staff.

Job Templates

Job Templates are generally near-empty Shapefiles or Mappt Project files whose main purpose is to act as a template for data entry. This allows data to be collected in a uniform way, which is then exported on a per-job basis for re-integration into project datasets back at base.

Screenshot showing an example Job Template.

Note that as in the example above, Job Template Shapefiles must have at least one dummy feature defined; an empty Shapefile can not exist.  Once imported into Mappt, this dummy feature can be deleted.


The focus in the Preparation Phase is to create datasets that will require minimal manual handling or administrative intervention once the project commences. These datasets are deployed to tablets before the tablets are issued to staff.

This is especially useful for projects that require long-term disconnection from the Internet, whereby all relevant datasets can be copied to the device back at base, before transporting the tablets to the remote location.

Tune in next week for Phase 2: In the Field

Giving Your Shapefile Features Friendlier Titles

If you load a Shapefile into Mappt that has a “Name”, “Title”, “Label” or “ID” attribute, Mappt will take a best guess at using one of those as each feature’s title in the layer list. This allows you to assign meaningful titles to your features, rather than the standard Polygon 0, Polygon 1, etc.

As an example, let’s load a Shapefile where I have stripped out all of the attributes. You will see that Mappt titles the loaded features by the feature type (in this case, polygons) and the order they appeared in the file, which is not very helpful at all:

Screenshot of polygons named by their order in a shapefile.

Polygon 1 and Polygon 5 are having a fling, but don’t tell Polygon 6.

Restoring the attributes in the Shapefile, we can see that one of the attributes contains a nice name that would help identify the features, and is conveniently called “NAME”, so we don’t need to make any modifications to the Shapefile:

Screenshot showing a Shapefile with a title attribute

The NAME key in this Shapefile will be used to title the features.

Importing the Shapefile results in a feature list that is much easier to work with:

Screenshot showing features titled by attribute.

Naming your features makes them much easier to work with.

Shapefiles with a reasonable quality of attribute data will often contain a key suitable for titling the features, as demonstrated here with the NAME key, so this has proven to be an effective way to title features without requiring user intervention.


Mappt v2.0.0.9 Now Available

Version has been released!  This version contains a few bug fixes and improvements and brings with a it new licensing model.

You can download the latest version of Mappt from the Google Play store:


Read on to find out more about this version!

Features Now Display Their Styling

The layer list now shows the styling applied to a particular item, making it easier to pair the listed features with their visual representations on the map.

This works well when combined with our Thematic Mapping tool (previously known as Classifications), as can be seen in the following image, which shows geographical areas styled by their area.

Screenshot showing feature style indicators.

The colouring used in the icons on the left matches the styling used in the rendered features on the right.

The New Tier-Based Licensing Model

Given the volume of functionality that we have added over the past year-and-a-bit, as well as the exciting upcoming features on our roadmap, we have decided to split the Mappt licensing model into a tier-based model.

A tiered model allows you to choose the level of functionality in Mappt that you need.  The tiers, and their features, are listed below.

Mappt Features by Licence Tiers

Bug Fixes

As always, we tweaked a few things here and there and fixed more bugs than we introduced: net result positive.

Our unrelated simian picture for this post is whatever this thing is:

Lar Gibbon

It’s an unhappy gibbon, apparently.

, ,

Mappt ECW and JP2 Support Demonstrated at HxGN Live

Would you like to see offline mobile imagery support for Mappt? You’re not the only one. Offline mobile imagery support, specifically ECW, is one of the most commonly requested features by many of our users.

Taking up the call, Mappt recently engaged with Hexagon Geospatial to prototype their new ECW/JP2 SDK for mobile platforms. This development is still in beta mode although an early version of Mappt was demonstrated at HxGN Live (Las Vegas, Nevada, from 2 – 5 June, 2014) in front of thousands of delegates.

Stage presentation at HxGN Live

Mappt with offline ECW and JP2 support will enable users to simply load and have imagery for their areas of interest at their fingertips while in the field. Using your own imagery as a core reference point, this allows you to ensure the data you are collecting reflects the surroundings you are in accurately.

We are excited to be working with Hexagon Geospatial in embedding ECW/JP2 support into Mappt. Expect this support to be released in early July 2014. For more information about Mappt visit our product site at

For more details on HxGN Live, see the website.

For more information on the Enhanced Compression Wavelet (ECW) file format, see here.

GPS Tips and Tricks in Mappt for Android

One of the most-used features in Mappt is the ability to capture location data from internal or external GPS devices. With Mappt, users can record their movements throughout an area, turning this GPS-captured information into features.  These features can then be manipulated and annotated, then ultimately exported as Shapefile or KML, to be sent via email or uploaded to cloud-based technologies.

Based on the feedback we’ve received from Mappt users “in the field,” we’ve decided to highlight some tips and tricks when working with the GPS functionality in Mappt.

Image of a Baboon Sitting on a Cliff

Clearly lost, this baboon ponders the power of Mappt’s GPS capabilities.

Tip #1: “Walking Out” an Area

Did you know that, when you are in “polygon drawing mode” or “line drawing mode,” you can drop a new vertex at your current location? This is handy for “walking out” an area when you are in the field.  In the image below, I took a casual stroll around a sand pit, adding vertices at my current GPS location to a polygon as I went.

Partially-drawn polygon being mapped from the user's movements.

A partially-drawn boundary of the sand pit, using points dropped at my GPS position.

The resulting polygon is a bit messy, being subject to GPS inaccuracies, but could easily be tidied up within Mappt, or exported and tidied up on a desktop machine.

Polygon Created by "Walking Out" the Boundary

The completed polygon.

This minor feature provides a range of applications, from mapping boundaries as demonstrated above, to measuring paths or areas, to simply logging landmarks as markers on a map.

Tip #2: Take a Break on Large Trips

Mappt is capable of handling captured GPS paths with tens of thousands of vertices, but eventually performance will degrade under such weight.  We recommend pausing, saving then restarting the GPS tracker every hour or so, which will split your path into smaller segments.  These can later be stitched back together if necessary.  This will also allow you to hide unimportant segments using the visibility toggle button, which reduces the workload on Mappt and promotes responsiveness.

Screenshot of a segment of a captured GPS path with over 7000 points

Mappt will remain responsive, even when working with captured GPS paths with thousands of points

The rate of vertex collection will depend on several factors, such as speed and overall GPS activity, so you may want to experiment with the amount of time between saves.

Tip #3: Ditch the GPS and Use High-Res Imagery For Increased Accuracy

This tip may seem a bit out of place in a blog post about GPS tips, but it all falls under the category of georeferencing features in Mappt.  If you have high-res and accurately-georeferenced imagery of your remote location loaded into Mappt, you can use visual inspection of your surroundings to accurately place features on to the map. For example, you could determine your location by picking a nearby tree or rock formation and finding it in your offline imagery loaded into Mappt.  You can then be sure that a feature placed at that location in Mappt will have reasonable geospatial accuracy (as long as the georeferencing of the imagery is accurate!).

Tip #4: Mappt Will Continue to Capture GPS Data in the Background

As long as you leave the GPS tracking enabled within Mappt, it will continue to capture GPS data, even if you minimise or switch to another app.

Screenshot of the Mappt Background Service notification area item

Mappt will put an item in the Notification Area to let you know it is capturing GPS data.

Note that if you exit Mappt from within the menu (Menu -> Exit Mappt), Mappt will shut down the GPS and stop capturing points before it exits.


Visually Classifying Your Maps by Attributes

Tired of staring at the same, drab, mess of lines and polygons? Having trouble finding the shapes you want? Mappt now has support for Thematic mapping, allowing you to style your features according to the numeric or text values in the layer’s attributes.

Here we have some geological zones. As is typical with datasets, it’s not very pretty to look at. Worse, we can’t really tell much by just glancing at it!


Looking at the attributes defined in the layer, we can see there is an AREA key defined.

The list of attribute values for one of the zones.

The list of attribute values for one of the zones.

Let’s say we want to easily see the zones with the smallest areas. To do this, we open the layer’s properties, then navigate to the Classifications tab. Here, we can specify how we want to classify the data. In this case, we want to find highlight the features with the smallest AREA attribute value. Let’s do some experimenting!

First, we’ll try to classify the AREA by “Distinct Values”, which will give us a class for every unique AREA value in the layer.

The Classifications screen, showing how to classify by Distinct Value.

The Classifications screen, showing how to classify by Distinct Value.

When we hit Apply, the classes are generated, and we are taken to the Class Styles tab, which shows the styling applied to each of the determined classes. In the screenshot below, we can see that there were quite a few unique values, so much so that we haven’t really achieved anything by classifying them!  Perhaps Distinct Values wasn’t such a great choice!

There are too many classes to fiddle with. We can see that classifying a numeric attribute by Distinct Value was a bad idea!

There are too many classes to fiddle with. We can see that classifying a numeric attribute by Distinct Value was a bad idea!

Let’s try again, this time using “Equal Intervals”, which instructs Mappt to classify the features into x number of classes, with x being chosen by us. So, let’s try Equal Intervals.

Let’s try that again, this time with Equal Intervals.

Let’s try that again, this time with Equal Intervals.

This will give us 5 nice classes, evenly spread across the range of values found in the AREA attribute of the features in the layer.  We can apply styling to each class, as seen in the screenshot below, where I have used the colour blue to denote the lowest-range class, and yellow for the rest.  Also note that Mappt shows us the range of each class, as well as how many feature are in it, which is handy when fine-tuning your classification parameters.

Highlighting the lower-fifth zones by area in blue.

Highlighting the lower-fifth zones by area in blue.

Closing the layer properties dialog, we can see the styling has been applied to the map. Because of the settings we choose, we have effectively highlighted, in blue, the zones that are in the lower 20% of overall zone sizes.

The smallest fifth of the zones, by area, can now easily be seen!

The smallest fifth of the zones, by area, can now easily be seen!

Using another example, here I have taken a dataset of the world’s volcanoes and classified them by elevation, using Manual Breaks defined at -4000, -2000, 0, 2000 and 4000 feet, allowing me to see which volcanoes are the highest, with increasing blue being below sea level, and increasing red being above sea level.

edit mappt layer

On the map, we can easily see which volcanoes are above or below sea level, as well as how far above or below, simply from their colour.


One last example shows the path of hurricanes in the Atlantic, coloured by wind speed, with redder being faster.


So in summary:

Thematic Mapping: Mappt, pretty.