An evaluation of the use of points versus polygons in public participation geographic information systems using quasi-experimental design and Monte Carlo simulation
The collection of spatial information through public participation geographic information systems (PPGIS) is most frequently implemented using either point or polygon spatial features but the research trade-offs between the two methods are not well-understood. In a quasi-experimental PPGIS design, we collected four attributes (aesthetic, recreation, economic, and biological values) as both point and polygon spatial features in the same PPGIS study. We then used Monte Carlo simulation methods to describe the relationship between the quantity of data collected and the degree of spatial convergence in the two methods for each of the four PPGIS attributes. The results demonstrate that the same PPGIS attributes identified by points and polygons will converge on a collective spatial 'truth' within the study area provided there are enough observations, however, the degree of spatial convergence varies by PPGIS attribute type and the quantity of data collected. The use of points for mapping PPGIS attributes and aggregating areas through density mapping constitutes a conservative approach to spatial inference about place significance, but the data demands for point collection are considerably higher than for polygon features. Based on our results, we provide heuristic guidelines for future PPGIS research when using point or polygon spatial attributes. We argue that other variables such as the cognitive complexity of the PPGIS mapping process and stronger claims of external validity favor the use of point features, but these advantages must be weighed against the significantly higher sampling effort required.
geographic information systems monte carlo simulation public participation gis spatial analysis