A methodological framework for analyzing PPGIS data collected in 3D

Figures & data

Figure 1. The study area Aninkainen city block in the city of Turku in southwestern Finland. The oldest building (northern part) is a nursing home and was built in the 1890s. The Concert Hall (white oval building) and educational buildings (at the eastern part) from the 1950s will receive new functionalities in the current urban renewal process. Also new buildings are planned.

Figure 2. A screen capture from the 3D PPGIS survey. The 3D map view showed terrain, buildings, streets, trees and also the planned new buildings. The mapping task asked ‘What could be located in this area in the future?’ and the respondent could locate different development preferences as point markers in the 3D view.

Figure 3. Relative share of different development preferences mapped in the 3D PPGIS survey across the Aninkainen area.

Figure 4. Learning data from city of Turku and the output extent.

Figure 5. Exemplary outputs from some of the macro and micro scale metrics calculated with 3D PPGIS data.

Table 1. Analytical framework: Macro and micro-scale analytical metrics for 3D PPGIS data.

Scale	Category	Metric	Usage area	Questions to ask from the data	Required data (^V= vector data, ^R= raster data)
Macro	Distribution	Feature count	Built up areas	How is the data distributed across study area? Where do we have high and low concentration of data?	Spatial enumeration unit (e.g. block, building footprint) ^V
	Distribution	Vertical diversity	Built up areas	How is the data distributed per altitude? Where can areas with interesting vertical variations be identified?	Spatial enumeration unit (e.g. block, building footprint) ^V, 3D PPGIS data ^V
	Distribution	Classified vertical diversity (height-adjusted)	Built up areas	Which marking types have the highest and lowest altitude variations?	Building heights, 3D PPGIS data ^V
	Distribution	Vertical divergence	Unbuilt areas	Where are areas of significant altitude difference between PPGIS mappings and the terrain?	LiDAR or DEM^R, 3D PPGIS data^V
	Distribution	Volume ratio	Built up areas	What are the 3D extents of markings per unit? (For example per building or block)	3D enumeration unit or footprint with height information ^V, 3D PPGIS data ^V
	Distribution	Classified volume ratio	Built up areas	Measure the 3D extents markings per unit per marking type	3D enumeration unit or footprint with height information ^V, 3D PPGIS data ^V
	Diversity	Type density	Built up areas	Allows comparing the relative frequency of a certain marking type across enumeration units	3D enumeration unit or footprint with height information ^V, PPGIS data ^V
	Diversity	Volume richness density	Built up areas	Determines the diversity of marking types	3D enumeration unit or footprint with height information ^V, 3D PPGIS data ^V
Micro	Distribution	Classified vertical diversity	Built up areas	Which marking types have the highest and lowest altitude variations in a specific area?	3D PPGIS data ^V
	Distribution	Average PPGIS marking type height from ground	Built up and unbuilt areas	Which marking types are placed close to ground? Which marking types are further from the ground?	Elevation data ^R, V, 3D PPGIS data ^V
	Shape	3D pattern index (3DPI)	Built up and unbuilt areas	What form of an object do the markings refer to? (Wall, surface, mass)	3D PPGIS data ^V
	Measurement	Volume weighted average	Built up and unbuilt areas	What is the mean value of the numeric variable X in based on volume of markings?	3D PPGIS data ^V
	Measurement	Volume overlap	Built up and unbuilt areas	Do the mapped 3D volumes of different type overlap?	3D PPGIS data ^V
	Distribution	Floor estimation	Built up areas	On which floor of a building is the marking located?	Building footprint ^V, building height, floor height (or average)
Macro-Micro	Measurement	Largest class by volume	Built up and unbuilt areas	What is the dominant marking type by volume in the given unit? (e.g. in the building)	Spatial enumeration unit (e.g. block, building footprint) ^V,3D PPGIS data ^V
	Diversity	Shannon’s diversity volume index (Kedron, Zhao, and Frazier 2019)	Built up and unbuilt areas	How diverse are the marking types based on volumes?	3D PPGIS data ^V
	Shape	Surface to volume ratio (SA:V)	Built up and unbuilt areas	What is the overall shape of the 3D mapped marking like?	3D PPGIS data ^V

Figure 6. Application of 3D PPGIS metrics in Aninkainen study area: (A) Vertical diversity expressed as the deviation of mean Z for points within the building, (B) An overview of markings within the block area, and (C) Average heights per marking type and classified vertical diversity

Figure 7. Application of 3D PPGIS metrics in Aninkainen study area: (A) Volume ratio and (B) type density measurements.

Figure 8. Clusters and 3D objects in Aninkainen. A: Surface to volume ratio (SA:V)., B: 3D pattern index (3DPI).

Kedron, Peter, Yun Zhao, and Amy E. Frazier. 2019. “Three Dimensional (3D) Spatial Metrics for Objects.” Landscape Ecology 34 (9): 2123–2132. https://doi.org/10.1007/S10980-019-00861-4/FIGURES/4.

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