Results from the Karonga Mapathon

Undefined
Karonga Open Street Map buildings before and after the mapathon

It’s a common problem that an area that looks entirely desolate on a map might in reality be a bustling little village or town. Why is this? Well, many maps are out of date, many settlements are informal (and thus the government may actively avoid giving legitimacy to an area by putting it on the map), and many organisations that make maps do so for commercial purposes - so a small town in a devleoping country might not be a prioirty area. 

This issue is a minor irritation for most, but imagine you are an emergency responder, shipped out to a town in the aftermath of a natural disaster, and it is your job to work out what has been damaged and who needs to go where. Or perhaps you’re a doctor, charged with setting up clinics to respond to a Cholera outbreak, and you look at the map and realise you have no idea where people actually live. These are real problems faced by groups such as the Red Cross and Medicins sans Frontieres. But things are changing – we are no longer limited to using static paper maps, or even digital maps created by others. In recent years, the open source mapping community has really taken off, and it is now possible for anyone with a computer to join the millions-strong community of mappers in contributing to Open Street Map. If you’ve not heard of Open Street Map, think Google Maps meets Wikipedia – it’s an online map, but anyone can add, edit and use the data for pretty much any purpose.

This community of open street mappers has been going for a decade now, and is becoming increasingly more organised and recognised as an important tool in responding to disaster events. Following the 2010 Haiti earthquake, a network of around 600 people sat at their computers around the world, and collaboratively created the best base map of Haiti which was used by responders and local government. Fast forward to 2014, at the Humanitarian Open Street Map initiative has been founded, and around 1000 volunteers add roads and buildings to the basemap of the Phillipines before Typhoon Haiyan strikes. This concept of preparing and gathering information before a hazard event occurs fits in nicely with the resilience paradigm – and certainly one could argue that putting vulnerable people and places on the map may have all sorts of social and economic benefits in addition to disaster preparedness. This is where the Missing Maps project comes in – their aim is to put the most vulnerable places in the developing world on the map, and develop communities mapping skills.

So, armed with our laptops and a small budget for pizza (this, I hear is an extremely important fuel for mappers!), PhD student Michele Ferretti and I organised a Missing Maps Mapathon. This is where members of the public meet up and work together on a specific mapping task. In this case, we worked on the Urban ARK case study town of Karonga in Malawi.

Karonga is susceptible to many types of hazards (earthquakes, floods, extreme weather, WASH) and is also growing in population at quite a considerable rate. But in most maps, Karonga looks little more than a couple of crossroads. This is particularly challenging for the work we are doing in Urban ARK work package 2 that looks at spatially modelling the impact of hazards on infrastructure networks. We know the infrastructure such as roads, buildings and power is there, but we do not have freely available maps to do our modelling!

We were delighted to welcome around 40 students, staff, NGO workers and members of the public to our mapathon, which took place on Monday 21st March. Although we didn’t complete the task, you can see from the before and after map that we have really added a lot of useful information, which can now also be used by our research counterparts at Mzuzu university who are working on WP1.

For those of you interested in GIS, Michele has an excellent blog post explaining some of the ins-and-outs of running a mapathon and the GIS data. Following the success of this activity, we plan to arrange further mapathons to add richness to the maps for other Urban ARK towns and cities. Until then, you can try Open Street Mapping yourself, and help complete our Karonga task, by visiting the Humanitarian Open Street Map tasking manager website. Happy mapping!

Tags: 

Standfirst: 

I’m sure many of you who have done work in Africa have experienced the issue of arriving in an area armed with a map, only to see that the area is almost entirely beyond recognition to what it looked like on paper.

Posted in Uncategorized

Results from the Karonga Mapathon

Undefined
Karonga Open Street Map buildings before and after the mapathon

It’s a common problem that an area that looks entirely desolate on a map might in reality be a bustling little village or town. Why is this? Well, many maps are out of date, many settlements are informal (and thus the government may actively avoid giving legitimacy to an area by putting it on the map), and many organisations that make maps do so for commercial purposes - so a small town in a devleoping country might not be a prioirty area. 

This issue is a minor irritation for most, but imagine you are an emergency responder, shipped out to a town in the aftermath of a natural disaster, and it is your job to work out what has been damaged and who needs to go where. Or perhaps you’re a doctor, charged with setting up clinics to respond to a Cholera outbreak, and you look at the map and realise you have no idea where people actually live. These are real problems faced by groups such as the Red Cross and Medicins sans Frontieres. But things are changing – we are no longer limited to using static paper maps, or even digital maps created by others. In recent years, the open source mapping community has really taken off, and it is now possible for anyone with a computer to join the millions-strong community of mappers in contributing to Open Street Map. If you’ve not heard of Open Street Map, think Google Maps meets Wikipedia – it’s an online map, but anyone can add, edit and use the data for pretty much any purpose.

This community of open street mappers has been going for a decade now, and is becoming increasingly more organised and recognised as an important tool in responding to disaster events. Following the 2010 Haiti earthquake, a network of around 600 people sat at their computers around the world, and collaboratively created the best base map of Haiti which was used by responders and local government. Fast forward to 2014, at the Humanitarian Open Street Map initiative has been founded, and around 1000 volunteers add roads and buildings to the basemap of the Phillipines before Typhoon Haiyan strikes. This concept of preparing and gathering information before a hazard event occurs fits in nicely with the resilience paradigm – and certainly one could argue that putting vulnerable people and places on the map may have all sorts of social and economic benefits in addition to disaster preparedness. This is where the Missing Maps project comes in – their aim is to put the most vulnerable places in the developing world on the map, and develop communities mapping skills.

So, armed with our laptops and a small budget for pizza (this, I hear is an extremely important fuel for mappers!), PhD student Michele Ferretti and I organised a Missing Maps Mapathon. This is where members of the public meet up and work together on a specific mapping task. In this case, we worked on the Urban ARK case study town of Karonga in Malawi.

Karonga is susceptible to many types of hazards (earthquakes, floods, extreme weather, WASH) and is also growing in population at quite a considerable rate. But in most maps, Karonga looks little more than a couple of crossroads. This is particularly challenging for the work we are doing in Urban ARK work package 2 that looks at spatially modelling the impact of hazards on infrastructure networks. We know the infrastructure such as roads, buildings and power is there, but we do not have freely available maps to do our modelling!

We were delighted to welcome around 40 students, staff, NGO workers and members of the public to our mapathon, which took place on Monday 21st March. Although we didn’t complete the task, you can see from the before and after map that we have really added a lot of useful information, which can now also be used by our research counterparts at Mzuzu university who are working on WP1.

For those of you interested in GIS, Michele has an excellent blog post explaining some of the ins-and-outs of running a mapathon and the GIS data. Following the success of this activity, we plan to arrange further mapathons to add richness to the maps for other Urban ARK towns and cities. Until then, you can try Open Street Mapping yourself, and help complete our Karonga task, by visiting the Humanitarian Open Street Map tasking manager website. Happy mapping!

Tags: 

Standfirst: 

I’m sure many of you who have done work in Africa have experienced the issue of arriving in an area armed with a map, only to see that the area is almost entirely beyond recognition to what it looked like on paper.

Posted in Uncategorized

Research Associate – ABM, Food and Land Use

This week we started advertising a post-doctoral Research Associate position to work with James on a project looking at the global food system, local land use change and how they’re connected. The successful candidate will drive the development and application of an integrated computer simulation model that represents land use decision-making agents and food commodity trade flows as part of the Belmont Forum (NERC) funded project, ‘Food Security and Land Use: The Telecoupling Challenge’.

Telecoupling is the conceptual framework of socioeconomic and environmental interactions between coupled human and natural systems (e.g., regions, nations) over distances and across scales. Telecouplings take place through socioeconomic and/or biophysical processes such as trade, species invasions, and migration. For example, while a number of countries such as China have experienced a shift from net forest loss to net forest recovery, this forest transition has been often at the cost of deforestation in other countries, such as Brazil where forested land is converted to meet global food demands for soybean and beef.

The goal of the project is to apply the telecoupling framework to understand the direct and collateral effects of feedbacks between food security and land use over long distances. To help achieve this the successful candidate will contribute to the development and application of an innovative computer simulation model that integrates data and analysis to represent coupled human and natural system components across scales, including local land use decision-making agents and global food commodity trade flows.

We’re looking for a quantitative scientist with a PhD (awarded or imminent) or equivalent in Geography, Computer Sciences, Earth Sciences or other related discipline. You should have experience in computer coding for simulation model development, preferably including agent-based modelling. Previous experience studying land use/cover change processes and dynamics or food production, trade and security is desirable.

This is a full-time position, with fixed term for up to 18 months. The deadline for applications is midnight on 19 April 2016. Interviews are scheduled to be held the week commencing 9 May 2016. For more details and how to apply see http://www.jobs.ac.uk/job/ANG825/research-associate/ and direct questions to James via email: james.millington at kcl.ac.uk

If this doesn’t sound quite like your thing, maybe you would be interested in one of the other positions we currently have open (with application deadline 30 March).

Image credit: Liu et al. (2015) Science doi: 10.1126/science.1258832


Talk: Urban hierarchies and scaling laws

This afternoon’s seminar by CASA’s Dr. Elsa Arcaute will be of interest to a wide range of students and staff at King’s – with a background in theoretical physics and complexity, Elsa now studies how urban and regional systems scale and divide, and how these aspects are expressed in infrastructure and the built environment. To put it another way: where does London end? 4:30pm today in the Pyramid Room (K4U.04) and followed by wine and soft drinks.

Abstract

In this talk we look at the different ways to obtain definitions of cities and their relevance to urban scaling laws. We also look at the hierarchical structure of Britain through a percolation process on the road network. We observe how at a large scale the divisions relate to well-known fractures of Britain, such as the North-South divide, while at small scales cities can be observed at a transition where the fractal dimension of the clusters has a maximum. The clusters defined at this distance threshold are in excellent correspondence with the boundaries of cities recovered from satellite images and the previous method.

About Elsa

Elsa Arcaute, is a Lecturer in Spatial Modelling and Complexity at the Centre for Advanced Spatial Analysis (CASA) at University College London. She is a physicist with a masters and a PhD in Theoretical Physics from the University of Cambridge. She decided to move to the field of Complexity Sciences and joined the Complexity and Networks group at Imperial College London. There she developed models on self-regulation for social systems, extracting fundamental behaviours from experiments on ant colonies to test on robots, and to implement for an intervention in an Irish eco-village. In 2011, Elsa moved to CASA, joining a project funded by the European Research Council and led by Prof. Michael Batty, on morphology, energy and climate change in the city. Since then Elsa has been working on applying complexity sciences to urban systems.


Mobile Apps & Tech for Fieldwork

Last week several members of King’s Geocomputation activity hub participated and contributed to a fieldwork mapping and monitoring party held at The Royal Geographical Society in London. Presentations and demos included crowdsourcing & OpenStreetMap, low-cost research drones and Arduino micro-controllers. This blog post summarises another presentation that explored the options for using mobile apps for fieldwork .

My contribution at the mapping and monitoring party was to look at mobile apps for fieldwork. I’ve posted my slides from the presentation online in pdf and Gslides formats and provide a summary of some of the apps below. I provide plenty of links to the apps I refer to in both my presentation slides and this summary.

I focus on android apps but Faith Taylor at King’s focused on Apple apps and used her massive iPad at the party to highlight what she finds useful to have on her real iPad in the field. Also at the party Michele Ferretti gave a quick highlight of using the OpenStreetMap Overpass API to obtain field site data and Tom Smith demonstrated auto-tweeting arduinos for monitoring soil moisture.

 

There was lots of other interesting stuff at the party on twitter, which you can get a taste of from the #rgsfieldtech hashtag on social media. As you can see from the twittersphere it was a great event and we look forward to the next!

Mobile Apps for Fieldwork

I suggested in my presentation we can think about using apps for fieldwork in a few different ways:

  • Planning where and when to go in the field
  • Managing data collection and storage using GPS
  • Measurement using device sensors

Planning

Considering what the weather/tide conditions will be like when you are in the field is an important part of fieldwork preparations. There are a plethora of apps to help with this. Weather Forecast UK is my personal favourite for UK weather and the paid version includes observation and forecast maps. LunaSolCal Mobile is great for finding out rising and setting of sun and moon, whereas Sun Position can also show the solar and lunar path on an augmented reality camera view for any day of the year at your current location.

 

Mapping is another important aspect of preparation – where will you go in the field? Several apps are useful for both planning where to go, but also tracking where you have been in the field (and add notes, photos, etc as you go). OruxMaps is possibly the best Andoid app for tracking while adding notes, photos, video, audio in a single integrated package. Alternatively use a light-weight tracker (such as GPS logger for Android) and then additional apps for photos (turn on ‘save location’ in settings in your device camera app), notes (e.g. MAP note) and other recording.

Managing

When in the field you will likely be collecting data. One of the best apps for data collection during UK fieldwork is Fieldtrip GB which is built on Ordnance Survey map data. The app allows you to capture georeferenced notes, photos, tracks, download maps for offline use, save data to your device which can later be snyced to your dropbox account later. One of the nicest features of the app is the ability to create your own custom forms for data collection.

Collector

The main drawback of the Fieldtrip GB app is that it only works in the UK as uses it uses OS mapping. When venturing beyond the UK, you could try Map It for recording data collected in the field. Map It allows multiple (global) map sources and export formats, map polygons and the like. It also allows you to create custom forms for data collection/recording. If doing human geography data collection, the Collect app is designed specifically for questionnaires or surveys. Again, it allows you to set up custom forms that match the questions you wish to ask – this can be done on a PC before you visit the field and results are automatically synced to a database for later desk analysis.

Measuring

Moving on from managing data in the field, we can also think about apps to actually make measurements with sensors on current mobile devices. There are many possibilities for using mobile devices for surveying. For example, the theodolite app Measure Angle provides functionality to view data lat, long, azimuth, angles, and more in an augmented reality perspective. The precision of these apps are dependent on the hardware on which they are used – don’t necessarily expect professional grade precision, but they are good for the fraction of the price of professional equipment (or even free!).

There are also many stand-alone apps useful for measuring different physical properties in the field:

  • Slope: use device accelerometers to evaluate orientation – these apps possibly require calibration and you may want to assess their accuracy before use
  • Aspect: there’s no dedicated app that I know of for this but aspect could be readily measured by combining compass/theodolite and sun position apps (links above)
  • Albedo: there are few apps for measuring this out there, but according to Dr Tom Smith at KCL this one is quite good

 

There are several apps out there useful for assessing the geology and soil in your study area. Several of these have been developed by British research groups. For example, iGeology and mySoil are developed by the British Geological Survey (with partners). Other apps enable you can use your smartphone or tablet in the same way you would use a brunton compass. You simply orient the phone or tablet along the planar or linear feature, choose a symbol, and tap. The device’s built-in compass and orientation sensors instantly record the strike, the dip, and dip direction.

iGeology

Finally, when in the field you may need to identify flora and fauna. There are two general types of app for this. First, those that recreate traditional guide books but in digital format (including possibly with sound or video). Be sure to select these apps as appropriate for the region you are visiting. Second, there are apps that attempt to use sound and video detected by your mobile device to identify species. The success of these is variable. Google Goggles is one of the most widely known for identifying the contents of images taken by your device camera (I have used with mixed success). For birds there are apps like Warblr that do something similar to Shazam, attempting to identify birds from recordings of their song. Bird Song Id claims 85% success!

General Tips for using apps in the field

Test your apps before you start your fieldwork proper. This is important both so you understand and feel comfortable with how the app works, but also so that you know what it can do and how accurate it is likely to be. You may want to do some benchmarking before you go in the field, for example testing the app against known conditions (e.g. known slope angles).

Think about the need for internet connection – check what data connection apps need before going in the field. Sometimes you may have a data connection, but in many places you may not. Both Connectivity and testing are particularly important if you are linking data to cloud or online databases

Consider upgrading hardware – a device with additional core processors or memory will be worth it if you will be doing a lot of fieldwork. Also consider buying a storage card to expand built-in device storage and for tablets consider getting a device that can use a SIM to connect to cellular data networks.Some apps may need specialist sensors that expensive smartphones have but others do not.

Experiment with alternatives and buy pro versions – they often don’t cost much (relative to professional field equipment) and can offer much better functionality, precision and ease of use. However, beware some apps require in-app purchases and also look out for ‘expert id’ species identification apps in which identification is not automatic (compared to a database) but actually sent to a human to identify. There are two main limitations to this; first, it can take time to receive results (hours to days), and second you will likely have to pay for each id. So this might be good for particularly difficult species but not for general ID.

Finally, get creative with your use of apps. Just because an app is branded for one thing does not mean it cannot be used for another purpose. Combine apps together if necessary. And if there’s no app out there to do what you want, maybe consider making our own! It may not be trivial but there are many guides and courses to get you started. And if you’re thinking about undergraduate study, the Geocomputation and Spatial Analysis pathway at King’s will give you some of the skills you need to do this too!


Aspect-Slope Maps in QGIS

While working with Naru to design our new 2nd year GIS methods training course (with parallel QGIS and ArcGIS streams!), I came across a rather striking map on the ESRI blog that managed to combine both slope (steepness) and aspect (direction) in a single representation. This post explains both a problem with the way that the colour scheme was specified and how to replicate this type of map in QGIS (with style sheet).

The Inspiration

Here’s Aileen Buckley’s Aspect-Slope map in all its glory – this is a the area around Crater Lake, Oregon, and you can see that it neatly captures both the direction of slopes (aspect) and their steepness (degree). So features like the crater stand out really clearly, as do what I assume is evidence of lava flows and such, while lesser features gradually fade towards grey, which means flat.

aspect-slope_map

So these maps combine two properties:

  • The direction of the slope is expressed in the hue – different directions are different colours.
  • The steepness of the slope is expressed by its saturation – steeper slopes are brighter colours.

Rather than just jump into providing you with a style sheet, I think it’s useful to trace this back to its constituent parts as it turns out that ESRI has made a mistake in setting up their colour maps.

Aspect Mapping

Aspect maps give the viewer a sense of the direction in which various slopes derived from  a Digital Terrain Model (DTM) lie – typically, we do this by dividing the angle of the slope into eight quadrants: North, Northwest, West, Southwest, South… well, you get the idea.

Here’s an example of what the standard aspect map out of ArcMap looks like as posted by the Rural Management and Development Department of Sikkim:

10sikkim-village-aspect

This, helpfully, gives us the ranges that we’ll need for our aspect-slope map. Note, however, that we don’t really have any idea how steep any of these obvious hills are.

Slope Mapping

Slopes maps are, obviously, intended to fill in the gap in terms of how steep an area is. Typically, we can measure this as either a degree value from one raster cell to the next of the DTM or as a percent/ratio (1-in-10 gradient = 10%). Here’s a nice example looking at the link between coffee bean growing areas and slope in Costa Rica:

costarica_bean_atlas_slope-rb-new

Unlike the aspect map, the divisions used in the slope map seem to be largely arbitrary with no real consensus on the mapping between measured steepness and terminology. The clearest guidance that I could find came from The Barcelona Field Studies Centre and looked like this:

Slope (%) Approx. Degrees Terminology
0.0–0.5 0.0 Level
0.5–2.0 0.3–1.1 Nearly level
2.0–5.0 1.1–3.0 Very gentle slope
5.0–9.0 3.0–5.0 Gentle slope
9.0–15.0 5.0–8.5 Moderate slope
15.0–30.0 8.5–16.5 Strong slope
30.0–45.0 16.5–24.0 Very strong slope
45.0–70.0 24.0–35.0 Extreme slope
70.0–100.0 35.0–45.0 Steep slope
> 100.0 > 45.0 Very steep slope

A Better Aspect-Slope Map Scheme

In order to create an aspect-slope map, we need to combine the two data ranges into a single number that we can use as a classification, and  this is where the ESRI blog approach goes a bit off the rails. In their approach, the ‘tens column’ (i.e. 10, 20, 30, …) represents the steepness – so 0–5 percent slope=10; 5–20 percent slope=20; and 20–40 percent slope=30 – and the ‘units columns’ (i.e. 0–8) represents aspect – so 0–22.5 degrees=1; 22.5–67.5 degrees=2; etc.

The problem with this approach is that you have a lot of problems if you want to add or remove a steepness category: in their example the highest value is 48, which means ‘highest value’ and an aspect of Northwest. But what if decide to insert a class break at a 30 percent slope to distinguish more easily between ‘Extreme’ and ‘Steep’? Well, then I need to redo the entire classification above 30… which is really tedious.

If we switch this around such that aspect is in the tens column (10–80) and steepness in the units column (0–9) then this becomes trivial: I just add or remove breaks within each group of 10 (10–19, 20–29, etc.). No matter how many breaks I have within each aspect class, the overall range remains exactly the same (10–89 if you use the full scale) regardless of the steepness classification that I’m using. It’s not just easier to modify, it’s easier to read as well.

Implementation in QGIS

For all of this to work in QGIS, you need to generate and then reclassify a slope and an aspect analysis from the same DTM. You can do this using outputs from the raster Terrain Analysis plugin (that’s the point-and-click way), or you can build a model in the Processing Toolbox (that’s the visual programming way). I personally prefer the model approach now that I’ve finally had a moment to understand how they work (that’s a topic for another post), but one way or the other you need to get to this point.

Regardless of the approach you take (manual or toolbox), once you’ve got your two output rasters you then need to reclassify them and then combine them. Here’s the mapping that I used to reclassify the two rasters as part of a model. You would copy these lines into text files and then use the GRASS GIS reclassify geoalgorithim while specifying the appropriate reclassification file.

Aspect-Reclassify.txt

0.0 thru 22.499 = 10
22.5 thru 67.499 = 20
67.5 thru 112.499 = 30 
112.5 thru 157.499 = 40
157.5 thru 202.499 = 50
202.5 thru 247.499 = 60
247.5 thru 292.499 = 70
292.5 thru 337.499 = 80
337.5 thru 360.5 = 10

Slope-Reclassify.txt (for percentage change)

0.0 thru 4.999 = 0
5.0 thru 14.999 = 2
15.0 thru 29.999 = 4
30.0 thru 44.999 = 6
45.0 thru 100.0 = 8

So that’s a 5-class steepness classification, but you could easily set up more (or fewer) if you needed them.

Once you’ve reclassified the two rasters it’s a relatively simple matter of raster layer addition: add the reclassified slope raster to the reclassified aspect raster and you should get numbers in the range 10–88.

Here’s the model that I set up (as I said above, more on models in another post):

Aspect-Slope-Model

Specifying a Colour Map

Taking the ‘Aspect Slope Map’ output, all we need to do now is specify a colour map. I took the colours posted by ESRI in the colour wheel (as opposed to the ones specified in the text) and converted them to hexadecimal since that was the easiest way to copy-paste colours. I think, however, that I’ve ended up with a slightly ‘muddier’ set of colours than are in the original Crater Lake set as you’ll see with my ‘Sussex Aspect-Slope Map’ below:

Sussex Aspect Slope Map

And, finally, the QGIS style sheet file is here (sorry about the zip format but .QML is not a recognised style type):

Aspect Slope Style – Close to Original.qml

Wrap-Up

I’m sure that this style sheet could be further improved (and may even try to do so myself, though I’d also welcome submissions from anyone with some time on their hands), but at least this gives users and easy way to combine representations of slope and aspect in a single map using a reclassification scheme that is simple to extend/truncate according to analytical or representational need. Enjoy!