##           group 1           group 2           group 3         group 4
## 1 Kang, Christine     Moore, Allana   Randall, Javion                
## 2     Ong, Alyssa Mahoney, Brigette       Qin, Celine Batson, Anthony
## 3   Leahy, Olivia      Myoung, Sein       Smith, Reid    Mendoza, Ava
## 4  Devir, Lindsey       Pham, Canon Wolfenstein, Luci   Pacheco, Alex
##           group 5
## 1    Barga, Jolie
## 2 Bell, Mary Rose
## 3  Knowles, Genny
## 4 Kang, Christine

• Using the definition provided, how could you gather could you use to measure gentrification in Bay Area neighborhoods?
• Consider custom made and ready made sources

##           group 1           group 2           group 3         group 4
## 1 Kang, Christine     Moore, Allana   Randall, Javion                
## 2     Ong, Alyssa Mahoney, Brigette       Qin, Celine Batson, Anthony
## 3   Leahy, Olivia      Myoung, Sein       Smith, Reid    Mendoza, Ava
## 4  Devir, Lindsey       Pham, Canon Wolfenstein, Luci   Pacheco, Alex
##           group 5
## 1    Barga, Jolie
## 2 Bell, Mary Rose
## 3  Knowles, Genny
## 4 Kang, Christine

• Warm-up: neighborhood change
• Gathering data in the 21st century
• Activity: data modeling
• Data models

• Using APIs
• Web Scraping
• Data Models
• Prediction

• Office Hours: Fridays, 1:30pm-3:00pm (Tyler)
• Tuesdays, 10:30am-12:00pm (Yao)

• Week 4 optional readings
• Final project proposal upcoming!
• Topic/methods open

• What are traditional methods for studying neighborhood change?
• Probabilistic surveys!
• Census
• Qualitative interviews/ethnographic observations
• Question: Does computation change the way we do these methods?

• What are traditional methods for studying neighborhood change?
• Probabilistic surveys! (recruit respondents on social media)
• Census (use R package to pull data)
• Qualitative interviews/ethnographic observations (observe online behavior)
• Question: Does computation change the way we do these methods?

• What are new methods for studying neighborhood change?
• Wiki Surveys (flexible surveys with user input) allourideas.org
• Ecological Momentary Assessments (survey people in real time)
• Gamification (fun surveys)

• Use Application Program Interfaces (APIs) to gather census data tidycensus
• Open-access measures Urban Displacement Project
• Wiki Surveys (flexible surveys with user input) allourideas.org
• Gathering text from online sources (scraping)
• Gathering images or data from online sources
• Link surveys to gathered data

• Takeaway: many computational social science approaches blur the lines between new and old methods
• Let’s look at some examples!

• Much debate about occurrence and extent of gentrification
• However: empirical evidence of neighborhood change is limited (surveys, census data)

• Hwang’s solution: use Google Street View to look at neighborhood change

• Hwang’s solution: use Google Street View to look at neighborhood change
• Combined these data with Census data
• Compared these estimates to earlier Chicago gentrification estimates

• In pairs: discuss whether a Google Street View approach would be effective for studying gentrification in the Bay Area

• The US incarceration system is large (more on this soon)
• Little is known about how people re-enter society after incarceration

• Sugie’s solution: administer cell phones to study participants leaving prison
• Conduct “Ecological Momentary Assessments” (daily surveys) to assess well-being, job search, and more

• Sugie’s solution: administer cell phones to study participants leaving prison
• Conduct “Ecological Momentary Assessments” (daily surveys) to assess well-being, job search, and more

• In pairs: discuss how Sugie’s approach helps us learn more about human behaviors, and any potential challenges to gathering data this way

• Plot some (or all) of the incarceration data
• How would you predict what rates will be in 2030?

##           group 1           group 2           group 3         group 4
## 1 Kang, Christine     Moore, Allana   Randall, Javion                
## 2     Ong, Alyssa Mahoney, Brigette       Qin, Celine Batson, Anthony
## 3   Leahy, Olivia      Myoung, Sein       Smith, Reid    Mendoza, Ava
## 4  Devir, Lindsey       Pham, Canon Wolfenstein, Luci   Pacheco, Alex
##           group 5
## 1    Barga, Jolie
## 2 Bell, Mary Rose
## 3  Knowles, Genny
## 4 Kang, Christine

• As social scientists, we often want to go beyond descriptions of social processes
• We may want to make explanations/generalizations or even predictions
• In our incarceration data, write an example of:

1. A description
2. An explanation/generalization
3. A prediction

1. Description: In the U.S., between 1970 and 2010, the incarceration rate increased from 161 to 731.

1. Description: In the U.S., between 1970 and 2010, the incarceration rate increased from 161 to 731.
2. Explanation/Generalization: As time passes, modern societies become increasingly rely on carceral solutions to social problems, and incarceration rates increase.

1. Description: In the U.S., between 1970 and 2010, the incarceration rate increased from 161 to 731.
2. Explanation/Generalization: As time passes, modern societies become increasingly rely on carceral solutions to social problems, and incarceration rates increase.
3. Prediction: In 2030, the U.S. incarceration rate will reach 1,000 (persons per 100,000).

• Estimate the line that minimizes squared “residuals”

• We could calculate line of best fit from our incarceration data

• Polynomials: quadratic, cubic, etc.
• Tree-based models! Random forests
• Neural networks
• And more

• Data scientists often split their data into training and test sets
• Goal: choose model that is likely to predict well out of sample

• What is the structure of our data? (numeric, character, binary, etc.)
• What do we want to do (describe, explain, predict)?

• This class is short!
• Optional readings have more information on types of modeling

• Week 4 optional readings
• Final project proposal upcoming!
• Topic/methods open

• Writing Exercise: describe one idea for a final project
• What data will you use? (traditional, new, mix)
• What is the structure of your data (numeric, character, binary, etc.)?
• What do you want to do (describe, explain, predict)?
• What type of model will you use? (none, linear, non-linear)?