Numeric Network Analysis V2. Basic
Concept Introduction and Interface Overview for NNA V2
Dr. Jung Hyun Woo and Nj Namju Lee have developed the NNA V2. for Rhino Grasshopper. The Beta 2 version is officially listed on the Food4Rhino in Oct 2020.
Download NNA V2: Link
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.
Graphic User Interface
You can see the NNA toolbox V2 in Grasshopper that looks like this.
The first set of tools (Network Utility) deals with assigning a network, cleans the lines, and re-builds new network datasets in Grasshopper.
The second set of tools (Basic Analysis) offers fast-paced site analysis/evaluation and data collection through mobility analysis methods, including accessibility and centrality. The basic version also performs spatial analysis with big data sets integrated with Python or C# code.
The third set of tools (Data) is used for various types for understanding data, including decay analysis, linear regression, data description, normalization, and visualization. You can interpret your mobility research outcome through the Data section.
Theories
for Network Analysis
Accessibility Analysis (Reach, Gravity, Huff-model)
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.
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.
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.
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.
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.
Straightness — The higher the straightness index, the higher the efficiency of network connectivity, and the more straightness centrality linking to destinations.
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
