Nate-Wessel

library("RPostgreSQL")
driver <- dbDriver("PostgreSQL")
connection <- dbConnect(driver, dbname="", password="", user="", host="localhost")

# get the full result set from the table
d <- dbGetQuery(connection,"SELECT t1, t2, ST_Azimuth(ST_StartPoint(line)::geography, ST_EndPoint(line)::geography) AS azimuth, ST_Distance(ST_StartPoint(line)::geography,ST_EndPoint(line)::geography)/1000 AS distance, white AS commuters FROM wbcommute_38300 WHERE t1 != t2 AND t1 = 684;")

dbDisconnect(connection)

# this is the number of commuter-kilometers

Nate-Wessel

# make dead-ends ratio map

library('ks')
library('raster')

# connect to database
library("RPostgreSQL")
driver <- dbDriver("PostgreSQL")
connection <- dbConnect(driver, dbname="", password="", user="", host="localhost")

Nate-Wessel

/*
	generic areal apportionment of average
*/

--table1.id
--table1.geom
--table1.value

--table2.geom
--table2.weight

Nate-Wessel

/*
	generic areal apportionment of SUM
*/

--table1.id
--table1.geom
--table1.value

--table2.geom
--table2.weight

Nate-Wessel

# run tarjan with igraph

# name of the table
city = 'helsinki'

modes = c('car','bike','foot')

# connect to database
library("RPostgreSQL")
driver <- dbDriver("PostgreSQL")

Nate-Wessel

# toy code for playing with discrete choice models and logistic regression

# sample size
n = 2000
# generate random independents
x1 = scale(runif(n))
x2 = scale(runif(n))
x3 = scale(runif(n))
# 'actual' utility function that I made up
uy = -3 + -2 * x1 + 2 * x2 + 0.5 * x3

Nate-Wessel

DROP TABLE IF EXISTS gtfs_ttc_agency;
CREATE TABLE gtfs_ttc_agency(
	agency_id int,
	agency_name text,
	agency_url text,
	agency_timezone text,
	agency_lang text,
	agency_phone text,
	agency_fare_url text
);

Nate-Wessel

library('deldir')	# voronoi stuff
library('rgdal')	# postgis and spatial stuff
library('sp') # spatial stuff
library('raster') # more spatial stuff
library('splancs') # csr() for generating random points

# function from http://carsonfarmer.com/2009/09/voronoi-polygons-with-r/
voronoipolygons = function(layer,bounding_box) {
    require(deldir)
    crds = layer@coords

Nate-Wessel

# assign a random location to every person in greater cinci
# inside their home census block

import psycopg2, random
from shapely import wkb
from shapely.geometry import Point

# connect to the census DB
conn_string = ("host='localhost' dbname='' user='' password=''")
census_conn = psycopg2.connect(conn_string)

Nate-Wessel

# this script takes a set of points, in this case bus stops
# and does a centroidal voronoi tesselation weighted by 
# those points from an initial uniform random seeding. The effect 
# is an even-ish distribution of sample points accross the space.
# My goal is to use these as representative ODs for a travel time 
# matrix that effectively samples a whole transit network.

library('deldir')	# voronoi stuff
library('rgdal')	# projections
library('sp') # spatial data types