Crowdsourced Mapping invites users to directly edit or co-create a map that is representative of their perspective and knowledge.
Crowdsourced mapping requires data collection tools to effectively collect and consolidate inputs. Digital platforms could be effective in allowing users to upload information in real time, even though it may not be suitable for issues that require inputs from people who do not have access to such digital platforms. For instance, non-digital tools such as door-to-door surveys through close social groups, or community bulletin boards can be more effective in mapping rural community resources and practices.
While crowdsourced mapping is frequently used for geographic mapping of incidents, it is not limited to literal maps. In particular, it can be an empowerment tool within communities with which residents collaboratively map their various resources, socio-cultural heritage, and everyday practices for their own initiatives such as local election.
|Suitable Subject||Especially suited for self-reported, first-hand experiences, such as geographic locations of incidents witnessed.|
|Participants||Typically there is no eligibility criteria for participation, but the subject itself will call for those who have first-hand relevant experiences.|
|Key Roles||Participants: Provide first hand knowledge or experience
Moderator: Facilitates and mediates participants interaction, encourages further contribution as appropriate and provides assistance in submission
|Essential Tools||Platform: Means for participants to engage and / or submit experiences (often driven by web-based tools)
Map visualization & synthesis: Sharable, communicable format of the map that allow revisions and discussions. Could be part of the platform.
Recognition: Acknowledging learning generated by individual contributors, often in the form of publication for educational purposes
|Outputs||Map reflecting user perspective / knowledge|
|Cost & Time||Cost: Setting up and running the crowdsourcing platform.
Time: Depending on the scale of data you want to collect and the number of people you intend to engage - but factor in the time required for synthesis.
Analyze your challenge and identify what types of information could elicit potential solutions or hidden factors behind the current situation.
Crowdsourced mapping works best if the information you are calling for does not require special skills or knowledge, but can be from anyone who has a relevant experience. It is also important to identify key target communities who can contribute most so you can focus your efforts in engaging them.
Data Collection Platform and Tools
As your challenge may require a specific data type from potential participants, e.g., geographic location of an incident, it is worthwhile to invest in designing the data input platform to allow participants to easily input their information with minimum error. Consider how you can easily collate all the individual data and visualize it in a meaningful, useful manner - especially if you are not relying on an electronic platform. Accessibility and participants’ ability such as literacy need to be considered in choosing and designing data collection tools.
Promotion for Participation
Promote the cause of crowdsourcing and the platform to communities that you are targeting and encourage their participation. If you are working within a closed community such as an urban slum or a rural village, it may be necessary to partner with native local residents or established organizations who have a wide social network. It is also important to ensure broad participation especially if the topic is subjected to power structure or discrimination within the community, to prevent the biased results.
Synthesis & Next Steps
Collate all the data received and clearly visualize the map, filtering out unnecessary or faulty data., then utilize the synthesized map to analyze and come up with next steps. In some cases, the map may reveal issues or new questions that you can investigate further with participants. Crowdsourced maps typically provide insights that emerge out of the large scale data that was invisible before, which requires a synthesis process in order to derive implications for next steps.