Crowdsourced Spatial Participation Project

Image of World Map
Crowdsourced Spatial Mapping Project.

Crowdsourcing

“A type of participative online activity in which an individual, an institution, a nonprofit organization, or company proposes to a group of individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task.”(Peaker)

In understanding crowdsourcing as defined by Alicia Peaker in her article Crowdsourcing and Community Engagement’ I was excited to begin this first assignment which involved working with others on a community-engaged project using for me a new innovative approach to open geographical data. The practical aspect of the assignment called for me to join a global mapping community on Mapswipe and OpenStreetMap. As this was my first outing on either of these platforms I went through a number of tutorials and got up to speed with the editing tools required before I began mapping.

Image of Web Page www.mapswipe.org
www.mapswipe.org

First up, Mapswipe, an open source project,  created and maintained by volunteers.   MapSwipe is a mobile App for crowdsourcing geographic information which is needed by humanitarian organisations like The Red Cross, Doctors without Borders and among others, Missing Maps. The Missing Maps Collaborative’s central objective is to map the areas of the world where humanitarian organisations are working to meet the needs of the most vulnerable people who as they outline are literally ‘Missing’ from any map. Mapswipe was developed by Missing Maps and uses satellite imagery provided by Bing/Microsoft Corporation which will become the base layer of a map that will be further developed by other volunteers.

Image from Mapswipe App outlining mapping instructions.
Fig.1: Mapswipe Instructions for Mission Contribution.

The Mapswipe platform did not call for any mapping skills and allowed for a beginner’s uncertainty. Mapswipe is available for IOS and Android and the installation and registration of the app were minimal. The instructions are very clear and it is very simple to pick a mission, and swipe through satellite imagery tiles while tapping the features such as buildings or roads as you spot them (Fig.1). There are vast areas of the world to be mapped under the Missing Maps Collaborative project. In the article “Big? Smart? Clean? Messy? Data in the Humanities” Christof Schöch outlines Crowdsourcing as one of two ways we can achieve ‘Bigger Smart Data’. Schöch states that   “crowdsourcing relies on breaking down a large task into such small units that each of these little tasks can be performed in a distributed way by a large number of volunteers.” (Schöch) Through crowdsourcing, Missing Maps objectives are being met and communities of volunteers are enriching ‘big data’ (satellite imagery) and are mapping the most vulnerable places in the developing world

Further to this, Schöch outlines that “Various strategies have been developed for breaking up the tasks, for creating incentive structures to motivate volunteers (like “gamification” or “win-win”-constellations), and to reintegrate the added information into the project.”(Schöch) Mapping with Mapswipe most definitely felt like a gamification experience.

Image from Mapswipe App encouraging statements
Fig.2: Motivation from Mapswipe.

The app incorporates a number of simple gamification elements. By employing user levels, using terminology such as “Missions” and motivational statements (e.g. “your effort is helping”, “keep up the good work” and “with every tap you help put a family on the map”) the user is engaged and encouraged (Fig.2) and I even found myself continuing swiping on a Mission in order to reach a higher level.

Image from Mapwsipe App indicating Volunteer gaining Level 5.
Fig.3: Mapswipe example of Gamification

 

OpenStreetMap (OSM) is one of the most well known crowdsourced VGI (Volunteered geographic information) projects. The OpenStreetMap project was founded by Steve Coast and developed at University College London (UCL) in 2004 with the vision of creating a social and technical challenge the output of which would be content rich community generated maps. The OSM geographical data is free for all to use. Every OSM community member has free access to and can visualise, query, edit and download the OSM geographical data for their own purpose under Open Licences. There are only two requirements involved in using the OSM data and that is very simply, to credit OSM and its contributors and to release any improvements made to the geographical data under a similar open license.

In OSM I found the interface relatively easy to use. The set of OSM editing tools (ie iD Editor) that let me add, update and delete geographical features were user-friendly. The step by step detailed tutorials on using the OSM interface were kept deliberately simple for mapping beginners like myself.

Apart from familiarising myself with the concept and workings of OSM, part of my assignment was to participate in a humanitarian project via Humanitarian OpenStreetMap (HOT).” HOT kicked off in 2009 to coordinate the resources resident in the OpenStreetMap community and apply them to assist with humanitarian aid projects. Working both remotely and on the ground, the first large-scale effort undertaken by HOT was mapping in response to the Haiti earthquake in early 2010.“(McCormick)  Hot applies the principles of Open Source and Open Data and has along with grown its contributor base “connected with dozens of governments and NGOs worldwide—such as UNOCHA, UNOSAT, and the World Bank—to promote open data, sharing, transparency, and collaboration to assist in the response to humanitarian crises.”(McCormick)  Up to date information and maps are of critical importance to enable relief organisations to reach those in need and through crowdsourcing, these maps are created and provided. I carried out editing on a number of graphical map tiles in three disaster mapping projects that required attention as detailed on the HOT Tasking Manager webpage.

Being very conscious of my lack of mapping experience I opted to participate in projects that were all High priority but were at a beginner level. Building mapping (Fig.4) is outlined as the most needed and the most common type of mapping HOT and MissingMaps does and the best place for a mapping novice to begin.

Image from HOT OpenStreetMap Tile of buildings that needed mapping.
Fig.4: Satellite Imagery of buildings in HOT OpenStreetMap Tile that required mapping.

When I began work in HOT I was very aware that I was a volunteer mapper, a beginner facing technical barriers due to lack of knowledge of the specialist tools and concepts in mapping. It raised a concern for me about the accuracy of what I was doing as I was working with satellite imagery of an area but without localised knowledge of that area. How could I be sure that even though I followed the given instructions, that I was indeed mapping a building? In order to alleviate my fears when I completed each tile, I requested that another volunteer would check my mapping. I found this to be a great initiative and I did get great feedback and guidance from another community member via email but unfortunately, this was only in reference to work done on one map tile. I had become a contributor, and in becoming so it had instilled in me a sense of responsibility for the quality of the map data I had contributed to. Due to my own very limited exposure to Crowdsourced mapping VGI projects, indeed my limited knowledge of cartography I am left to wonder about the changes I have made. “Is the data I have edited, accurate? Is there consistency in terms of classification of data? Have I misinterpreted the aerial imagery? Is a ground survey carried out to rectify these mistakes?”

When it came to validating the work of others I must admit I was very apprehensive. When validating another’s work, I was able to verify that the markings were correct as per the outlined project instructions. However, I could not confirm without local knowledge or a closer view that what was marked was actually a building. Volunteer mappers such as myself working from satellite imagery, unfortunately, cannot easily collect local knowledge of an area.  I was very reluctant to press the ‘Validate’ tab.

I was much more confident when it came to mapping areas of my home city of Cork on OpenStreetMap.  I felt confident to stand over and verify any edits I made, or points of interest that I marked as I have personal knowledge of the streets and buildings. Also, when I did have a query I had the option to consult Google  Maps, for a “Google street view” for any landmark I needed confirmation of. This detailed resource is not obviously available for any of the HOT task (or there would be no need for the VGI projects in the first place!).

The practical aspect of this assignment was to join the global mapping community of volunteers on OpenStreetMap (OSM). OSM is a very impressive example of what can be achieved with Volunteered Geographic Information (VGI), the map of the city of Cork, for example, is freely available and very detailed and outlining city bus routes.   The Cork city map will hopefully continue to be added to and developed. But in coming to the end of my assignment I ask myself could I become a consistently contributing member of this mapping community for OSM?

The answer is I could and I should. In November of 2017, Keith Collins reported in QUARTZ that Google had been tracking Android users since early in 2017 even with location services turned off. Collins reports that “Since the beginning of 2017, Android phones have been collecting the addresses of nearby cellular towers—even when location services are disabled—and sending that data back to Google. The result is that Google, the unit of Alphabet behind Android, has access to data about individuals’ locations and their movements that go far beyond a reasonable consumer expectation of privacy.

Image from Quartz Article by K Collins showing Android location sent to Google
Fig. 5: A cell-tower location sent to Google from an Android device. (Obtained by Quartz www.qz.com 21/11/17)

Quartz observed the data collection occur and contacted Google, which confirmed the practice.”(Collins) As a result, Google holds more extensive information on an Android user than we might realise. Location History is used to power features like restaurant recommendations or “find the nearest Wi-Fi access point” and Google has embedded location tracking into the software of apps we use every day such as Google Maps, Photos, and of course when we surf the internet on “Google”. Of course, we are aware that location tracking is nothing new for Google, it has been collecting data on users for years as they long realised its commercial value. But this practice Collins highlights “is troubling for people who’d prefer they weren’t tracked, especially for those such as law-enforcement officials or victims of domestic abuse who turn off location services thinking they’re fully concealing their whereabouts.”(Collins) To me, this is very intrusive and moves beyond realising the obvious commercial value of this personal information and enables advertisers to target consumers like me using location data.

OpenStreetMap is open and transparent. As a community member, I am free to download some OpenStreetMap data, or indeed all of a map offline. This means that it’s possible to use OpenStreetMap data to navigate without giving my location away to anyone at all. It’s not just a question of will I continue to be a contributing community member of OpenStreetMap but of what kind of a society I want to live in.

 

 

References:

Collins, Keith. “Google Collects Android Users’ Locations Even When Location Services Are Disabled.” Quartz, https://qz.com/1131515/google-collects-android-users-locations-even-when-location-services-are-disabled/. Accessed 5 Feb. 2018.

McCormick, Coleman. “Bringing Geographic Data Into the Open with OpenStreetMap.” Colemanm.Org, https://www.colemanm.org/post/geographic-data-in-the-open-with-openstreetmap/. Accessed 18 Jan. 2018.

Peaker, A. “Crowdsourcing and Community Engagement.” Educause Review, vol. 50, no. 6, 2015, pp. 90–91.

Schöch, Christof. “Big? Smart? Clean? Messy? Data in the Humanities.” Journal of Digital Humanities, vol. 2, no. 3, 2013, pp. 2–13.