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Data Mining the City 2019 - Week 1
- 1. Weds 7p-9p 114 Avery | Violet Whitney | Assoc Director, Design Sidewalk Labs data miningthe city
- 5. Platform Society Foursquare 2008 Big Data Widespread Adoption Democratization of data Statistics Bayes Theorem (1763) Regression (1805) Computer Age Turing (1936) Neural Networks (1943) Evolutionary Computation (1965) Databases (1970s) Genetic Algorithms (1975) Data Mining & Simulation Supervised machine learning (1992) Data Science (2001) Cloud Computing Web-Based-Gaming
- 6. Platform SocietyStatistics Computer Age Data Mining
- 14. Unity C# Mapbox SDK Behavior Bricks Open Data Tools we’ll use in the class
- 15. behavior time movement growth policy interaction walls streets infrastructure 3 dimensions 1 moment in time physical
- 16. complexity
- 17. Complexity Theory Agent Based Models Behavioral Economics Nature & Fractals Decision Trees Emergent Behavior Game Theory Spatial Economics Populations Space Time Money Data Subjectivity Bias Logistics & Routing Graph Theory Scheduling Taylorism Operations Research Affordance
- 18. Authors ： Nengjing Deng Danting Luo Shulin Zhang Authors: Yingjun Mou, Shuyang Huang, Yue Dong
- 24. Authors: Adam Robert Swietek Aniket Dikshit Sidharth Chotangada Somana Suying Zhang
- 25. If x occurs then y will occur If agent 1 is within 10’ of agent 2, then agent 1 will move 10’ further
- 26. Experimenting Programming Is Hard Collaboration Attitude
- 29. www.yellkey.com/third Why are you interested in this class? How comfortable are you with programming? (1 not at all, to 5 expert) it relates to my thesis 1 was a tech job or an "alternative" career 3 was a tech job or an "alternative" career 3 was a tech job or an "alternative" career 2 it relates to my thesis 4 was a tech job or an "alternative" career 4 was a tech job or an "alternative" career 2 Interested in what data mining is 1 I LOVE this stuff!! 2 was a tech job or an "alternative" career 3 was a tech job or an "alternative" career 2 I'm interested in urban applications of tech 4 Interested in learning coding and data analytics 2 Wanted to learn about technologies and data 1 I don’t know 1 was a tech job or an "alternative" career 4
- 31. syllabus
- 32. populations
- 35. average data scientist salary (╯°□°）╯︵ ┻━┻
- 37. complexity
- 38. What is complexity? System composed of many components which may interact with each other. behavior is intrinsically difficult to model due to the dependencies, competitions, relationships, and interactions nonlinearity, emergence
- 39. What is cybernetics? Cybernetics is a transdisciplinary approach for exploring regulatory systems—their structures, constraints, and possibilities.
- 40. What is control?
- 43. Why is complexity so relevant now? design a public pool… A-Frame...
- 44. Why is complexity so relevant now? “Immaterial” dimensions scale and emergence
- 45. What is are some “modern behaviors” that are complex? www.yellkey.com/thought Logistics of food delivery services Choices of online shopping channels, including ads from social media or third party links. How recommendation algorithms for consumer products create self-reinforcing choice patterns Rating systems affecting decisions delivery of online shopping Social media and self media， streaming platform, Key Opinion Leaders. Different apps reveal different levels of your identity Internet video content(Netflix ,Amazon) influencing viewer habits Online Shopping (E.g. Amazon Prime), against several parameters such as age groups, geographical location, logistics, articles of consumption (groceries, movies, electronics..etc), frequency, patterns of use How does somebody choose what mode of transport they will use? Public transit versus Uber/cab depends on such parameters as culture, distance, income, weather, last mile connectivity. Bedbug registry When I use some shopping online with some apps like Sense or far Farfetched, the recommendations and history for my shopping in those apps will also appear on Instagram. Things or ADs that modern search engine give you even if you didn’t search this thing. "Like" culture blowing up things: Fyre Festival : put things on social media with no responsibility 2-3 people
- 48. behavior time movement growth policy interaction walls streets infrastructure 3 dimensions 1 moment in time physical
- 53. Crowd simulation
- 65. individuals (agents)— could be humans, parcels of land, vehicles, animals, etc environment— framework in which the interactions occur, i.e. a city, a neighborhood, a floor plan, a room, etc behavior— the procedural rules that define how the individuals behave — i.e. a person move away from other people if a space gets crowded, or a person may be attracted to other people with particular characteristics, etc parameters— characteristics of the agent — i.e. a agent that is a person might have speed, size, distance the agent will stand next to other agents, etc input— parameters that globally drive the model — i.e. population size of your agents, climate or location for your environment, or other data that is put into the model output— global parameters that are the outcome of running your model
- 66. Create a simple model description of your “modern behavior” with diagrams 2-3 people
- 67. For next class: Start your Populations project, do the “Roll a ball tutorial”. Sign up for Medium and Slack. Bring your Medium User Name and your computer/room to the next class. 🔗 Medium website medium.com/data-mining-the-city Class slack shorturl.at/ANP23
Notes de l'éditeur
- I just graduated with my MArch from GSAPP
- Aleppo project at CSR
- sidewalk
- Where we fit into history
- Kings College practiced statistics through engineering The world’s most powerful computer at Watson Lab 1954, Paperless studio (CAD) CBIP - Columbia Building Intelligence Project - data/metric-driven design of the built environment Columbia also hosted Cities Lab and Network Cities Center for Spatial Research - humanitarian mapping This is the best place for technology and architecture
- The computerization of every aspect of life has created a Platform society.
- Today most of our social and economic relations take place through platforms like Facebook and Venmo.
- Tinder’s matching algorithm leads to an increasing number of matches and marriages each year. Ultimately its algorithm will shape the genetic makeup of the human race, as swipes are made, humans are matched and babies are born.
- The filters of StreetEasy and Apartment Finder --literally filter the makeup of --who lives in what neighborhoods-- reprogramming entire city zones.
- Where the Nolli map once exposed accessible public space, Yelp is now telling individuals what spaces they should like, but when everyone sees a different map, recommendation systems algorithmically segregate cities, generating spatialized filter bubbles which choreograph pedestrian flows through siloed canals across the city.
- From Yelp reviews changing movement to Airbnb reprogramming homes
- the invisible code that powers a city’s use may have more drastic influence than any physical invention in the last century.
- Today, computing speed and open spatial data give immense power to designers
- to look at possibilities beyond the rhino model’s 3 dimensions in a fixed moment in time >>> to capture the immaterial dimensions
- Complexity theory understands urban environments as interconnected systems where the simple adjustment of an economic policy may have consequences in dimensions not easily represented in that field: such as its spatial implications. The de-fragmentation of disciplinary methods is required for seeing the connections across our complex environments.
- The class will be broken into modules: populations, space, time and money. Within these categories we will borrow from dynamic simulation methods across economics, transit planning, computer science to thread a needle through these many dimensions and disciplines. Students will develop simulations speculating on modern phenomena (amazon’s impact on cities, autonomous vehicles, co-living, etc)
- Google’s shortest walk path algorithm to create more equitable access to resources.
- Think about autonomous vehicle fleets sizes
- Changing land use policy to emphasize shared uses between neighbors
- Choreographing procedures and appointments in a hospital
- How a street grid impacts evacuation
- How populations change over time do to current factors
- Market forces like rent burden
- We will hypothesize about the relationships of rules and space as a critical understanding of the social, economic, and political dynamics caused by these technologies such as data bias, privacy issues, and naive faith in simplistic models. We’ll aim to make our assumptions and biases explicit and testable, moving simulation out of the black box
- As Professor José van Dijck has described, the computerization of every aspect of life has created a Platform society.
- As Professor José van Dijck has described, the computerization of every aspect of life has created a Platform society.
- As Professor José van Dijck has described, the computerization of every aspect of life has created a Platform society.
- Not complicated Simple rules can produce non-simple behavior
- Out of the same set of theories looking at complexity was cybernetics
- To you...
- So much is dependent on what can’t be seen (movement, change) whether something is scheduled Internet really about information - changes the way we share material things and space Uber, Facebook → Egypt, elections
- In 1978 the economist and (future nobel laureate Thomas Schelling) developed a theory showing how micromotives in neighborhoods might generate a macrobehavior of segregation. He used graph paper to represent lots in a neighborhood and then randomly placed pennies and dimes to represent two distinct cultures and then left a few spot open. He then speculated if 1/3 of the adjacent cells (surrounding neighbors) were not the same, than the coin would move to a random open spot. He called this the “tipping point”. As these moves are made over time the model would further segregate.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds.
- The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.
- individuals (agents) — could be humans, parcels of land, vehicles, animals, etc environment — framework in which the interactions occur, i.e. a city, a neighborhood, a floor plan, a room, etc behavior — the procedural rules that define how the individuals behave — i.e. a person move away from other people if a space gets crowded, or a person may be attracted to other people with particular characteristics, etc parameters — characteristics of the agent — i.e. a agent that is a person might have speed, size, distance the agent will stand next to other agents, etc input — parameters that globally drive the model — i.e. population size of your agents, climate or location for your environment, or other data that is put into the model output — global parameters that are the outcome of running your model
- Simple rules can produce non-simple behavior Boids was an artificial life program developed by Craig Reynolds that used agent behavior to simulate the motion of a flock of birds. The scientist, Christopher Langton one of the founders of the field of artificial life, i.e. life and its behaviors can be simulated. He developed some of the key concepts for cellular automata.