The participation loop: helping citizens to get in Jorge Gustavo Rocha - University of Minho

1. “Cities, Technologies and Planning”
The participation loop:
Helping citizens to get in
Jorge Gustavo Rocha
jgr@di.uminho.pt
Universidade do Minho
Portugal

2. Motivation
● Two contradictory facts:
● Public Participatory GIS*
– people are NOT participating
● User Generated Contents
– people ARE participating
*PPGIS is an approach to getting the public more involved in the planning and decision making process

3. PPGIS interface have improved

4. PPGIS interface have improved

5. PPGIS interface have improved

6. PPGIS problems
● Low participation
● The participation did not improve with more
sophisticated interfaces
● ...but VGI participation has never been so
prolific!
● We should further investigate how VGI
connects and how it can improve PPGIS

7. VGI projects
● Advantages
● Not regulated by public authorities, as PPGIS initiatives
● Make people more aware of their neighbourhood
● Make people more skilled to work with maps: layers,
scales, formats, symbolism, interoperability issues,
meaning, etc
● More aware of current positional technologies (using
more functionalities of the hw and sw, p.e. Mobile
phones)
● Large support community, able to share and improve
knowledge and tools

8. VGI difficulties: the OSM case
● Open Street Map
● Where to start? By doing what? Where to go further?
● Who is in charge? Who tells me what to do?
● One of the greatest OSM advantages is that it is completely
open: no one regulates where, when or what should be
mapped.
● It's also a disadvantage from less skilled communities
● After successful OSM parties, we noticed that some local
communities are able to go on, while others didn't.
● Sometimes, the whole territory, all features, all details, are
simple too much to deal with

9. Lessons learned from FOSS
● Free Open Source Software (FOSS) projects
can became quite large
● FOSS communities are using the well known
divide and conquer strategy to divide the project
into smaller tasks
● Besides tasks, many other issues are helping
the FOSS community to successfully develop
large projects:
● Bug track,
● Milestones, releases,
● Tickets, wish lists, etc.

10. Lessons learned from FOSS
● Example:
● Translation task in Ubuntu’s Launchpad, and how it is
displayed to users.
● The visualization clearly depicts the size of the task and
how much has already been done.
● To be able to address and solve specific tasks is more
rewarding.
● We have more feedback on how the project was before
and after each contribution.

11. Calculate tasks in OSM
● With several different techniques (ETL-
GIS), we calculate well defined and
assignable mapping tasks.
● OSM mappers can choose to pick up a task,
from a list of many generated ones.
● The community has more feedback over what is
already done and what they need to do.
● It is good for their involvement and motivation.
● You can always forget the task pool and do
whatever you want.

12. Simple example
● We start by grabbing all McDonald’s restaurants
from the company’s website
● Parsing techniques are used to extract the
information about each restaurant, from the web
pages
● This information is compared with the restaurants
already mapped in the OSM map
● The difference is converted to mapping tasks,
separated by municipality
● So, one simple task is “map the 1 missing
McDonald’s of the 4 existing in Braga”

13. How to do it: general approach
1.find a suitable (either official or credible) source
of information,
2.get the full list of available features,
3.capture all the necessary (or available)
information about each feature and put it in a
geospatial database,
4.compare the captured data with OSM data using
geographical units (either districts, municipalities,
parish) that are suitable for a task,
5.generating suitable visualization (tabular and
geographic)

14. How to do it: ETL example

15. How to do it: simple query

16. How to do it: visualization

17. Additional Challenges
● When a more complete map does not exist?
● In many places or for some kinds of feature, there
aren’t complete maps or other sources of
information to serve as a basis for task computation
● How do we calculate tasks, if we don’t know how
many features are there in the real world?
● For such cases, we need to estimate the number
and location of the features.
● Using such estimated values, we are able to create
tasks for this class of problems.

18. Additional Challenges
● What if the sources are not always correct and complete?
● In fact, there is no problem at all. The source data is used to calculate
tasks: not to be imported.
● Only data personally captured by volunteers is added to the map.
● Using the McDonald’s example:
● The were restaurants missing from the official website but already mapped in OSM.
● The average location difference between the OSM and McDonald’s website
reported position was almost 200m.
● The maximum difference between the McDonald’s reported position and OSM
mapped position was 540m.
● Even with differences in the number of restaurants (by Nov. 2010, 7
existing restaurants were not reported on the official website), and
positional errors, we were able to create tasks and suggest them to the
community.
● In some cities, for example, the computed McDonald’s OSM coverage percentage
was 133%.

19. Additional Challenges
● How can we track the changes over time?
● Over time some McDonald’s restaurants might close and new ones will appear. The
same happens with ATM machines, recycling facilities, etc.
● How can these changes be addressed by our task calculator? It is not easy.
● Two approaches can be considered:
● The first one, is to periodically check the source website, and check if changes exist. Whenever
changes occur, specific tasks can be re-computed and suggested to OSM users.
● The second approach can use the feature’s date and time of last editing, either to detect low
activity or to check if the feature is still valid.
– What low activity means? Everything is already mapped? The community is not updating the map?
● Right now, we only have done some preliminary work to identify spots of low activity in
OSM.
● This will be a major challenge when OSM will be almost completed: contributions will
be more related with features updates then new ones.

20. Conclusions
● While PPGIS still has low participation, VGI is
engaging users in the spatial realm
● VGI makes people aware of their neighbourhood
● Less skilled users needs some additional support to
became autonomous in VGI
● Case study: how to compute well defined and
assignable mapping tasks is OSM
● The general approach
● Other additional approaches
● The techniques used (mostly scripting) are available
on the OSM Wiki, and can be reproduced.