Numeric Network Analysis (NNA)
(by designju)

NJ Namju Lee and Jung Hyun Woo develop the NNA V2. for Rhino Grasshopper. The Beta 2 version is officially listed on the Food4Rhino in Oct 2020.

Numeric Network Analysis (NNA) offers easy approaches for measuring distances and analyzing various accessibility and centrality concepts with spatial networks. There are new features and functionalities to measure the values of spatial networks, locations, buildings, and travel cost/time.

This add-on calculates a wide range of properties of mobility analysis to accelerate their design thinking and decision-making. Architects, designers, urban designers, and planners who engage with the spatial analyses for their design process can interactively evaluate designated networks, redesigned paths /circulations, and new building location impacts that thoroughly improve the design outcome.

Theories for Network Analysis

Accessibility Analysis (Reach, Gravity, Huff-model)

1. Reach Analysis — Reach Index shows the cumulative opportunities that are accessible within a given radius. For the higher the reach index, the more destination value around each origin.
2. Gravity Analysis — The model considers the general cost (distance decay), the resistance factor of travel of accessibility while reaching to destinations. The result of the Gravity Index is lower than Reach Index due to the distance decay effect.
3. Huff-model — The Huff Index displays the probability as a percentage of consumers visiting within a given radius. The attractiveness of the store and the distance you need to travel is competing. The higher the probability, the more attractive it is to the consumer.

Centrality Analysis (Betweenness, Closeness, Straightness, Degree)

In graph theory, centrality estimates to determine the hierarchy of nodes within a network.

1. Betweenness — The Betweenness Index reflects realistic pedestrian flows in the network. If a target node has a higher betweenness centrality if it shows in many shortest paths to the node.
2. Closeness — The Closeness Index indicates how close an origin is to all other destinations. Lower values indicate an origin node is more closely located to the destination nodes than other origins.
3. Straightness — The higher the straightness index, the higher the efficiency of network connectivity, and the more straightness centrality linking to destinations.
4. Degree — The Degree Centrality Index is a count of the total number of connecting edges. A higher Degree Index means that one node is more connected to the neighborhood nodes.

The toolbox is in the beta version. It is still under development and we are looking forward to listening to your feedback in making results better.

Contact to: axuplatform@gmail.com