Simulated Amenity Location
Predictive Site Selection, Data Mining the City at Columbia GSAPP
Team Members: Jacob Kackley, Yi Liang, Shelly Xu, Olivia Xin Chen
Introduction
With over 147 thousand New York City dog licenses issued since 2014, the rise of the pet population has given way to amenity markets like dog runs and pet care facilities. According to the Trust for Public Land, New York ranks 27th out of the top 100 U.S cities for dog park amenities. Although land is highly limited, we believe this amenity gap will be addressed in the coming years.
For our case study location, we have selected the up and coming neighborhood of Crown Heights Brooklyn. We believe Crown Heights is an untapped resource for households with dogs. It has an ideal proximity to the largest green open space in Brooklyn — Prospect Park, yet a location underserved for dog care facilities. Similar to the patterns of dog runs in the U.S. at large, we anticipate dog care amenities to be in high demand in these coming years.
Intent
Our goal is to simulate the population of the Crown Heights locale, their movement patterns in the neighborhood and amenity locations/gaps in order to identify a potential market location for a new dog care facility and run. Among other strategies, we will be prioritizing amenity demand profiles, agent based simulation, and choice to inform our network analysis and identify a suitable site location.
Amenity Demand Profile (ADP): describes the spatiotemporal distribution of human activities according to the activeness in urban amenities. [1]
Agent Based Models (ABMs): is a simulation system is modeled as a collection of autonomous decision-making entities called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules. [2]
Choice: measures how likely an axial line or a street segment it is to be passed through on all shortest routes from all spaces to all other spaces in the entire system or within a predetermined distance (radius) from each segment. See Betweenness and Segment Angular Choice.
Methodology
What to analyze and where to get data
Before our analysis begins, our team identified parameters that may inform our research, where we may be able to find the information, and what these parameters may eventually lead to.
Our spatial data research is broken into three categories; dog amenity locations, population information, and business locations. To understand the urban fabric, we also sourced the built environment (amenities, street networks, and building footprints) from OpenStreetMaps.
General Network Analysis
Travel score simulation based on standard amenities
To begin our analysis, we acquired the residential and commercial floor area data and assigned this tract’s population and household with dogs proportionally to the buildings in our analysis. A 24-hour weekday activity demand profile simulation reveals two street network locations within Crown Heights have the highest use at the times most people would take their dogs out for walks, 7am and 6pm. These result were met with skepticism as they were weighted with standard procedures with no facilities given weights outside the analysis boundary or specified amenities.
Weighted Network Analysis
Customized ADP analysis
After creating the previous ADP study, we analyze the given amenity weight categories/metrics provided by Urbano.io and DeCodingSpaces. We decided that in order to proceed, a customized ADP analysis with curated amenity demands prioritizing pet amenity use, would be necessary.
In order to weight the simulation properly, business data and location information were necessary to proceed. Using the ESRI ArcGIS API our team extracted point locations for grocery store, pet store, parks, dog runs, and tourist attractions. Using this information, we created a betweenness analysis to prioritize agent choice within the network analysis. Between the ADP study and the API request, we can begin to see the dog amenity gap in the context of Crown Heights Brooklyn.
Example API Request (Pet Store): https://geocode.arcgis.com/arcgis/rest/services/World/GeocodeServer/findAddressCandidates?singleLine=&category=Pet%20Store&outFields=PlaceName,Place_Addr,Type,Distance&location=-73.95118784493545,%2040.66447653773987&f=pjson
Prioritizing the Work Day
Factoring in human lifestyle into location selection
The next step of the analysis continues with prioritizing a dog owners lifestyle. Our analysis assumes that dog owners will only need amenities within small windows throughout the day. Breaking down the 24-hour cycle, we prioritized times before and after work. We extract times between 7–9 am and 6–8 pm. Through this process, three street locations surface as having the most street hits during these times.
Most Street Hits Before Work : Maple St (between Nostrand Avenue and New York Avenue) and Union St (between Toussaint L’ouveture Boulevard and New York Avenue)
Most Street Hits After Work: Nostrand Ave (between Empire Boulevard and Sterling Street)
By prioritizing choice (betweenness) in this network, this analysis aims to identify a location that is along the most likely traveled path between supported amenity locations.
Agent Based Simulation
Micro-analysis to understand movement through isolated network conditions
The next step was to analyze the localized street networks for agent based movement patterns. We wanted to understand how an added amenity location may impact the pedestrian traffic of the neighborhood. The site locations chosen can vary per clients desire, but for our purposes, were selected through the Google Street Viewer. This method was used to understand the most current condition of the neighborhood and which parcels may have available open space for a dog run or amenity.
After a preliminary site was selected, our team implemented the amenities from the previous ADP study and ran an agent based simulation based on the three locations previously identified; Maple St., President St., and Nostrand Ave.
This agent based simulation was completed using the Grasshopper Plugin PedSimPro. Through this simulation tool, we weigh each amenity equally and generate pedestrians midblock throughout the surrounding neighborhood. Our simulation is meant to understand how human traffic flows may be impacted if new amenities are added through the neighborhood.
With this quick micro-analysis, we can see that more granularity comes with increased clarity. Using a heat map, we can visualize specific street edge and corner condition that are impacted with the addition of an amenity. Although the weight of the simulation was general and can always more closely resemble the urban dynamic, the process of creating a digital twin and simulating its urban impact is an exciting addition to the design process.
Conclusion
What we learned
If used in the Architecture, Construction, and Engineering (AEC) industry, the dynamism of a simulation and its corresponding visualizations have tremendous potentials for the design process. Using simulations and strategies deployed above, a designer could be able to more closely capture the human experience and further tailor solutions to fit real life.
Without limiting its use to a neighborhood scale, the understanding of the human dynamic through a digital twin may be especially beneficial in closer analysis than selection and urban networks. Our team expects that it would be of most use simulating retail, office, or cultural interior designs for a more accurate reflection of how users inhabit space. Furthermore, these processes could be strengthened with the pairing of UX strategies (define, empathize, ideate).
The potential to ideate based on user pain points, simulate solutions based on user defined behavior, and continue to gather feedback has tremendous upside. Getting a feedback loop of behavior start before occupancy or use has even started is extremely exciting.
Grasshopper Tools
Data Sources
- NYC Open Data
- ESRI ArcGIS API
- Trust for Public Land
- OpenStreetMaps (Crown Heights)
