Upgrade to PRO for Only $50/Year—Limited-Time Offer! 🔥
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Montreal R Users Data Dive - Bike accidents
Search
Corey Chivers
April 03, 2013
0
130
Montreal R Users Data Dive - Bike accidents
Corey Chivers
April 03, 2013
Tweet
Share
More Decks by Corey Chivers
See All by Corey Chivers
Germination Project Data Science at Penn Medicine
cjbayesian
1
320
From Predictions to Decisions
cjbayesian
1
600
NIPS 2017 Summary
cjbayesian
1
1.6k
Validation des prévisions écologiques utilisant VMAPP: Validation métrique appliquée à des prévisions probabilistes
cjbayesian
1
150
From Whale Calls to Dark Matter - Competetive Data Science with R and Python
cjbayesian
0
1.4k
Introduction to Likelihood-based methods
cjbayesian
1
820
Implications of uncertainty: Bayesian modelling of aquatic invasive species spread
cjbayesian
0
370
Future Avenues for Open Data
cjbayesian
0
240
Introduction to Simulation using R
cjbayesian
2
8.3k
Featured
See All Featured
How People are Using Generative and Agentic AI to Supercharge Their Products, Projects, Services and Value Streams Today
helenjbeal
1
81
Building an army of robots
kneath
306
46k
The Power of CSS Pseudo Elements
geoffreycrofte
80
6.1k
More Than Pixels: Becoming A User Experience Designer
marktimemedia
2
260
Dominate Local Search Results - an insider guide to GBP, reviews, and Local SEO
greggifford
PRO
0
17
Large-scale JavaScript Application Architecture
addyosmani
515
110k
Marketing to machines
jonoalderson
1
4.3k
sira's awesome portfolio website redesign presentation
elsirapls
0
89
Java REST API Framework Comparison - PWX 2021
mraible
34
9k
Into the Great Unknown - MozCon
thekraken
40
2.2k
Music & Morning Musume
bryan
46
7k
AI in Enterprises - Java and Open Source to the Rescue
ivargrimstad
0
1.1k
Transcript
Welcome to the Data Dive! Sponsored By:
Cycling Collisions • All accidents in Montreal reported 2006-2010 •
Obtained and compiled by Roberta Rocha at the Gazette https://github.com/cjbayesian/collisions
• Can we predict accident rates? • Spatial patterns? Dangerous
areas? • Does the construction holiday have an effect on accident rates? Some potential questions
Getting Started library(lubridate) library(maptools) library(Hmisc) d<-read.csv('data/Bike Accidents.csv',sep='|') mtl<-readShapePoly('data/montreal_borough_borders.shp') par(bg='black') plot(mtl,col='grey')
points(d$long,d$lat,col='red',pch=20,cex=0.5) https://github.com/cjbayesian/collisions http://youtu.be/hJE2_XMdfTk
• Can we predict accident rates? • Spatial patterns? Dangerous
areas? • Does the construction holiday have an effect on accident rates? Some potential questions