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P3D2: Visualizing space and time

These slides map to the R example slides on Day 25 or P3D11

At 3:45 we have a Zoom meeting with a SafeGraph employee.

In his iconic flow map of Napoleon’s catastrophic 1812 invasion of Russia, Charles Joseph Minard blends and bends temporal and spatial representations. The path and number of outward-bound soldiers is represented by an initially thick tan ribbon while the dwindling numbers of retreating survivors are represented by a narrowing black band. In this visualization, the time it took to march into Russia is not clear. The only time stamps shown are for the return trip and progressing from right to left. In a sense, time is “bent” to advance the impact of the visual narrative.

Small multiples (facets) and time

Animated

Questions and data

Spatial mapping

Amelia McNamara and spatial polygons

  1. 2:37 Gelman on maps and variables
  2. 11:07 Modifiable areal unit problem
  3. 12:11 Histogram example
  4. 14:20 Washinton Post on Gerrymandering
  5. 14:20 John Oliver on Gerrymandering has some crude language after 6 minutes.
  6. 16:45 The redistricting game
  7. 17:40 Talismanic Redistricting
  8. 19:27 Dasymetric Maps
  9. 21:10 Mapping upscaling interactive example
  10. 22:08 Side Scaling: Nested Polygons and redrawing the states
  11. 23:20 Misaligned Polygons and Flint. Zipcodes are problematic.
  12. 26:19 Tobler’s First Law
  13. 27:08 Pycno Package

Handling SafeGraph Data

SafeGraphR 

library(tidyverse)
library(sf)
library(jsonlite)

json_to_tibble <- function(x){
    if(is.na(x))  return(x)
    parse_json(x) %>%
    enframe() %>%
    unnest(value)
}

bracket_to_tibble <- function(x){
    value <- str_replace_all(x, "\\[|\\]", "") %>%
        str_split(",", simplify = TRUE) %>%
        as.numeric()

    name <- seq_len(length(value))

    tibble::tibble(name = name, value = value)
}

Changing json dictionaries to nested tibbles

dat <- read_csv("SafeGraph - Patterns and Core Data - Chipotle - July 2021/Core Places and Patterns Data/chipotle_core_poi_and_patterns.csv")

datNest <- dat %>%
    slice(1:50) %>% # for the example in class.
    mutate(
        open_hours = map(open_hours, ~json_to_tibble(.x)),
        visits_by_day = map(visits_by_day, ~bracket_to_tibble(.x)),
        visitor_country_of_origin = map(visitor_country_of_origin, ~json_to_tibble(.x)),
        bucketed_dwell_times = map(bucketed_dwell_times, ~json_to_tibble(.x)),
        related_same_day_brand = map(related_same_day_brand, ~json_to_tibble(.x)),
        related_same_month_brand = map(related_same_month_brand, ~json_to_tibble(.x)),
        popularity_by_hour = map(popularity_by_hour, ~json_to_tibble(.x)),
        popularity_by_day = map(popularity_by_day, ~json_to_tibble(.x)),
        device_type = map(device_type, ~json_to_tibble(.x)),
        visitor_home_cbgs = map(visitor_home_cbgs, ~json_to_tibble(.x)),
        visitor_home_aggregation = map(visitor_home_aggregation, ~json_to_tibble(.x)),
        visitor_daytime_cbgs = map(visitor_daytime_cbgs, ~json_to_tibble(.x))
        ) 
datNest <- datNest %>%
    select(placekey, latitude, longitude, street_address,
        city, region, postal_code,  
        raw_visit_counts:visitor_daytime_cbgs, parent_placekey, open_hours)

Python

Some examples

# %%
import pandas as pd
import numpy as np
# %%
raw_url = 'https://raw.githubusercontent.com/ryanfoxsquire/demodata/master/ohioRestaurants/Starter-OH-Restaurants-CORE_POI-2019-08-09/Starter-OH-Restaurants-CORE_POI-2019-08-09.csv'
dat = pd.read_csv(raw_url)