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).
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.