Module 13: Visualizing Results II

Lab 13: Divides in Social Infrastructure in Boston

Click here to run the code for this workshop yourself on RStudio.Cloud!

This lab investigates divisions in social infrastructure in neighborhoods in the center of Boston. Social infrastructure refers to the physical spaces in our communities that build social ties between residents. These include community spaces, like libraries or community centers, places of worship, like mosques, synagogues, and churches, social businesses, like cafes, barbershops, and nail salons, and parks, like green belts, squares, and fountains. In our workshop, we investigated variation in total social infrastructure, but what if trends in social infrastructure differ more when we break them down by the type of social infrastructure? You will use mapping in the sf package to visualize and tabulate your data, and then unleash your tidyverse and lm toolkit to analyze trends in one type of social infrastructure.


0. Import Data

First, please import the following data for your analysis.

  • Social Infrastructure Points ("boston_social_infra.geojson")
  • Grid Cell Polygons ("boston_grid.geojson")
  • T Metro Lines ("boston_train_lines.geojson")
  • Census Data for Polygons ("boston_census_data.csv")
  • Block Group Polygons ("boston_block_groups.geojson")
# Import packages
library(tidyverse)
library(viridis)
library(sf)
library(ggspatial)

# Import data
# Get social infrastructure sites (points)
mypoints <- read_sf("boston_social_infra.geojson")
# Get grid cells in Central Boston (polygons)
myshapes <- read_sf("boston_grid.geojson")
# Get T metro lines (lines)
mylines <- read_sf("boston_train_lines.geojson")
# Get average census traits for grid cells (data.frame)
mydata <- read_csv("boston_census_data.csv")
# Get census block groups (polygons)
mybg <- read_sf("boston_block_groups.geojson")



Task 1: Spatial Data Wrangling

In this lab, you will analyze one type of social infrastructure (specified by the group variable in mypoints). Please choose 1 from the following four types of social infrastructure: "Community Spaces", "Places of Worship", "Social Businesses" or "Parks".

  • Part A. First, using your data wrangling tools, please count how many of each type of social infrastructure are in the study region. Which type is most common? Where does your chosen type of social infrastructure rank?

  • Part B. Second, using st_join(), please count the total number of sites of your chosen type of social infrastructure in each of our 73 grid cells in Boston. Save your result as a data.frame called mytally.

  • Part C. Then, using left_join(), please merge mytally with our mydata data.frame. Use the population density variable (pop_density) you gained to calculate for each grid cell the rate of your type of social infrastructure, per 1000 residents per square kilometer. filter() to just grid cells with a population density greater than 0. Please name your final dataset of rates and other demographic variables as myrates.



Task 2: Mapping

Next, combining your new sf toolkit and your ggplot skills, please visualize the rates of your chosen type of social infrastructure in Boston’s 73 grid cells. Be sure to…

  • Color: Make intentional color and design choices.

  • Readability: Crop your map, apply a good theme, and add helpful labels.

  • Storytelling: Give an informative title that summarizes the geographic patterns you find.

  • Context: Integrate census block group polygons (mybg) or T lines (mylines) to help add geographic context for your reader.



Task 3: Analysis

Finally, using your data wrangling toolkit, please analyze the rates of your chosen type of social infrastructure in your myrates data.frame, drawing on the other demographic variables you joined in. Your analysis should include at least 2 parts - a descriptive analysis and a model. Please choose 1 from each of the sections below:

(Note: Remember that you’ll probably need to convert it to a tibble before further analysis.)

Descriptive Analysis Options (Pick 1):

  • Neighborhoods: Calculate the average rate of your type of social infrastructure in each neighborhood in Boston, and visualize it in ggplot(). Which neighborhoods experience the most vs. the lowest of this type of social infrastructure?

  • Correlations: What variables correlate most strongly with your chosen type of social infrastructure? Create an intuitive visual in ggplot() or GGally that describes associations with at least 3 covariates.

  • Inequality: Investigate differences in average rates of your type of social infrastructure for grid cells whose residents are predominantly white vs. grid cells with high rates of Black, Latinx, or Asian residents. (You will need to choose a threshold for what counts as ‘high’ rates. Is it 25% 50%? Briefly justify your choice.) Then, create an intuitive visual in ggplot() that describes this difference.

Modeling Options (Pick 1):

  • Maximize R2: Build a model that explains as high a percentage of variation (R2) in social infrastructure rates as you can. Report your results. What trends do you find?

  • Simulation: Simulate the effect of one key demographic variable (eg. median_income) on the rate of your chosen type of social infrastructure in an average city block, controlling for several relevant demographic variables.



Conclusion

Congratulations! You made it! Finally, please take your results, write it up in an individual lab report, and turn it in. You can work with geographic data, and you just finished your last lab in this class! Hooray!