Statistics and Data Analysis

Following the last post, naturally, we would like to observe these 3.5 million connections directly. But even with 35k connections, the lines can fill the whole map. It makes more sense to show randomly sampled connections while we have checked that the sample is pretty representative of the parent distribution. This post includes the R code in the end. Connection Map The main observation is that shorter trips are more clustered near the city center. Fig 1. 35 K trips (1% of the 3.5 M), shorter trips marked in red and longer trips marked in yellow. (If mark longer trips with the darker color they would cover everything beneath. ) Fig 2. Use only 3.5 k trips (1/10 of fig 1), red color shows shorter trips. Fig 3. Use only 3.5 k trips, but shorter trips are less transparent (higher alpha value) The less transparent (longer) trips can hardly be identified since there are too many short trips covering on top. Density Map The relative density of where do people live...

Revised on Mar.25th, calculation of jobs passed was not correct. 3.21 - 23 (Fri) Since Wednesday I have been working on a problem that looks similar to the global conflict score I calculated before. We have the coordinates of both start points (home) and end poi nts (work) of 3.5 million commuting trips in Chicago. One trip can carry more than one person, the total number of people on all the trips is 3.7 million. So each trip is weighted, but the average number of persons on each trip is merely 1.07. 1. Most trips in the dataset were done by only one person The distribution of the weights: number of people on each trip between each pair of origin and destination are highly skewed to the right: the busiest trip had 109 persons traveling from Indian Village to the University of Chicago. Actually, the top 13 trips (853 persons) all target somewhere in the University of Chicago (coordinate: 41.78937, -87.60285). On the other hand, 95% of the 3.5 million trips have only 1 person...