Optimized Foraging Environment for Pigeons
Qijian Cui, Yanan Zhou, Haozhen Yang, Jiazheng Zhang| Generative Design | Spring 2022 | GSAPP, Columbia University
Introduction
In front of the Low Memorial Library, a grand staircase area is where students frequent in the noon, where they prefer to have their lunch and have a nice talk. But this area is not only for humans but also for other species, like pigeons. In that case, we are trying to figure out a way to use design intervention and generative design to improve the foraging system, not only for humans but also for pigeons.
Methodology
Step-1 Design Feeding Machine
To enable a better foraging system, we designed a feeding machine for pigeons. Through this, students can put the wasting food in the feeding machine after they finish eating lunch, and pigeons can enjoy food collectively.
Step-3 Conclude a Possible Area for Feeding Machine
a.Locate Study Area — Locate the study area to be the grand staircase area.
b. Locate Pedestrian Simulation Area — Extend the simulation area to be a little larger to ensure the pedestrian route’s accuracy.
c.Set Pedestrian Route — Set several most used pedestrian routes in the area to conclude a rather accurate result for further steps.
d.Get a Heat Map — Use PedSim to set up the basic simulation for pedestrians. Put a 1meter-sized grid on the map, and use the data recorder to record how many people pass each grid, then hook the data into an RGB battery to visualize the final result.
e. Dispatch Area — Using dispatch battery to select the grids that few people pass by, which can be the potential area where the feeding machine can be put.
f.Trim Area — Then use the staircase area polyline geometry to split the area and get the final area for further simulation
Step-3 Optimize Potential Positions to Get Final 5 Position
a.Using Populate 2d to generate total 7 points in 6 areas.
b. Form a polyline with 7 points.
c. Set the length of the polyline as objective, and max the objective.
d.Get a first step result for 7 potential positions for feeding machines
e. Use a heat map algorithm and set the area sum of the circles as objective and generate 5 optimized points selection, which is the final positions.
f. Overlay the initial route and the heatmap together to check if the spots conflict with the route
Result — Position for Feeding Machine
Conclusions
In generative design, it is important to control the number of variables and make these variables easily transformed into quantifiable numbers. This means we need to set a few fixed parameters, in this case, the grid sizes for pedestrian simulation are set fixed as well as the pedestrian number for each route. Also, we set the number of tables to be 5 out of 7(initial input number).
There are a few things that we have not thought through, such as the pigeons’ food capacity, speed, numbers and etc. Which would make the project a lot more interesting but also difficult to proceed with.
But on the other hand, we put a lot of thinking into designing intervention and visualization. For example, the visualization of pedestrian simulation can serve better for checking the final result with the original pedestrian route, and the final heatmap provides a mapping option from the pigeon’s perspective instead of human, which is the more common case. And also the feeding machine is designed as a combination of table and feeding distributor which serves both humans and pigeons.
Eventually, our goal is achieved, which is to enable a better foraging environment not only for humans but also for pigeons in this diversified campus that we all love very much.
