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Spatial Statistics

Aino performs advanced spatial statistics on vector data to support real estate and urban planning decisions. Each task combines open data (like OpenStreetMap) with user-uploaded datasets.

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Density and Clustering

Goal: Identify where development or property activity is most concentrated.

Inputs: Building footprints or point events (transactions, permits). Outputs: Density maps, cluster polygons, hotspot areas.

How to do:

  1. Upload your dataset or transaction layer, e.g, Cafes.

  1. Prompt: “Show clusters of Cafes”

  1. Review the cluster layer and summary stats.

Methodology: Aino analyzes feature proximity using DBSCAN clustering (ST_ClusterDBSCAN) and creates polygons from grouped points. Density per unit area is computed via spatial joins, and clusters are styled by concentration intensity.

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Urban Form and Connectivity

Goal: Evaluate walkability and connectivity of neighborhoods.

Inputs: Street network, intersections, building footprints, research area. Outputs: Intersection density, block size, connectivity index.

How to do:

  1. Retrieve the road network: “Get roads and intersections inside this research area.”

  1. Then prompt: “Calculate intersection density and average block size.”

  1. View the map of connectivity and walkability results.

Methodology: Aino detects intersections using ST_Node and ST_Intersection, measures road lengths (ST_Length), and constructs block polygons (ST_Polygonize). It then calculates intersection density and block metrics for a walkability index.

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Floor Area Ratio (FAR)

Goal: Estimate development intensity (floor area / parcel area).

Inputs: Building footprints (with height or levels), parcel polygons. Outputs: FAR per parcel, color map, and average by land-use.

How to do:

  1. Draw or upload your research area .

  2. Prompt: “Retrieve buildings within Polygon from OSM.”

  1. Then: “Calculate FAR for all buildings and within the Polygon”

Methodology: Aino overlays building footprints on parcels (ST_Intersection), estimates total floor area using height or level attributes, and divides by parcel area (ST_Area). The resulting FAR values are visualized in a color-scaled map for easy comparison.

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