PPGIS is a map-based survey method that allows participants to provide both geographic and non-geographic information. This method can bridge “soft” subjective, place-based data (such as human experiences and everyday behaviour) and “hard” objective GIS data. PPGIS has been used as a tool for place-based human-environment research and as a tool for large scale public participation. PPGIS in among the most widely used digital methods of public participation.

Basic Information on the Method
Mode of communication
Group size
31 and more
Geographical scale
Public space, Neighbourhood, City, Region
Skills required
Resources needed
Medium, High
Level of Involvement
Level of involvement
Consult, Involve, Collaborate
Type of knowledge enabled
Additional Criteria
Planning phase
Initiatiion, Planning & Design, Evaluation & Research, Maintenance
Methodological approach
Diagnostic, Expressive, Organisational

How to use the method

Survey theme & design

When designing the survey, careful consideration of the content of the survey is needed. The survey cannot be too long to make sure that the respondents finish filling the survey. The target is to collect only the kind of knowledge that will be eventually used in public participation process. A combination of spatial and non-spatial survey questions are typically used in a PPGIS survey. Non-spatial questions include background questions but can also include other information about the respondents, their preferences and values as well as outcome variables such as perceived wellbeing. Spatial questions can regard e.g. a) spatial values, perceptions, or attitudes; b) spatial behavior patterns, everyday practices and activities; c) spatially defined future preferences or visions or d) mapping of environmental phenomenon and problems (citizen science).  In addition to mapping the spatial attributes, additional open or structured follow-up questions can be asked to describe the mapped attributes. These follow-up questions often appear in a pop-up window in relation to mapped places in the survey.

Participants & data collection strategy

In public participation projects, the PPGIS survey is most often targeted to all participants and at best, large-scale, representative datasets can be collected from thousands of people. The data collection  strategies range from random samples drawn from a national population or household registers to purposive sampling, crowdsourced/volunteer sampling through traditional or social media and the use of internet survey panels. The data collection strategy matters for sample representativeness: random sampling seems to promote good representativeness while through crowdsourced/volunteer sampling it is typically challenging to reach a balanced respondent profile. (See Fagerholm et al 2021).

Data analysis

Depending on the sample size and the quality of PPGIS data, many different data analysis methods exits. Cursory, descriptive analysis can be carried out in the Explore phase. This level of data analysis is supported by some PPGIS services like Maptionnaire and allow the analysis of the place-based data without any specific skills like the ability to use GIS programmes.  This level of data analysis is often enough to support practical management and planning needs. More complex methods are developed within academia: Explain means diagnostic analysis that combine spatial and non-spatial PPGIS data with other geospatial data. Example of these type of analysis are many, e.g. visual and overlay analysis, spatial pattern analysis, proximity-related analysis, analysis across spatial scales, calculation of indices across spatial units, analysis of spatial associations, cluster and multivariate association analysis. In the Predict/Model phase the aim is to generalize and predict mapped attributes to other places and contexts or produce a representation of a system to make inferences. Analysis methods in this phase typically require multiple data sources in addition to PPGIS data and involve multivariate modelling. (See Fagerholm et al 2021).

Data strorage & publishing

PPGIS data can be archived to city-wide GIS databases . This has been done e.g. by the Finnish city of Lahti. This city-wide geospatial database (provided by Trimble) merges data layers from various sources and includes more than 200 layers of conventional passive sensing data (geospatial data on e.g. land use, transport networks) and layers of PPGIS data. The city encourages all sectors to actively use the database, not only the city planning department but also other sectors, like health, social, forest, sport and educational departments. It is also possible to allow open access to PPGIS data.  Here, careful consideration of privacy issues is needed.

What are the outcomes

  • Place-based information from people that is easy to show on a map and to combine with other knowledge bases used in the planning process
  • Knowledge from people can become more equal with other types of knowledge
  • Quantitative and qualitative knowledge
  • Deeper analysis of the associations between certain environmental characteristics and human perceptions/experiences/behaviour

Skills required

Skills required form participants: medium

  • Skills to operate computer or smartphone, and to use digital map.

Resources needed

Resources: medium, high

  • Skills of making a proper survey
  • Resources to promote/market the survey
  • Data analysis skills
  • (possible) purchase commercial tool license

Strengths and weaknesses

  • Enhance effective arrangements of public participation
  • Reach a broad spectrum of people
  • Produce high quality and versatile knowledge
  • Requires certain GIS and data analysing skills
  • Planners can lack the (skills and) institutional motivation to use the data effectively
  • Visualisation requires special care in order to avoid stigmatisation of certain areas

Use cases

Travel behaviour in Turku[1]

In 2020, PPGIS method was used to collect data of citizens’ travel behaviour in Turku, and about 800 residents participated the online survey. After analysing the 474 full responds, four types of travel behaviours were classified. The result of this project can be used both in transportation and land use planning. Ramezani, S. Soinio, L. & Kyttä, M. (2020)


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  3. Kyttä, M. (2021) A methodological framework for analysis of participatory mapping data in research, planning, and management. International Journal of Geographical Information Science, 1-28.
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  6. Kahila-Tani, M., Broberg, A., Kyttä, M., & Tyger, T. (2016). Let the citizens map—public participation GIS as a planning support system in the Helsinki master plan process. Planning Practice & Research, 31(2), 195-214.
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