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slides.Rmd
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---
title: "Jittering and routing options for converting origin-destination data into route networks"
subtitle: "Towards accurate estimates of movement at the street level"
author: "Robin Lovelace, University of Leeds <br>+[Rosa Félix](http://www.rosafelix.bike/), [Dustin Carlino](https://dabreegster.github.io/)"
date: "[FOSS4G 2022](https://2022.foss4g.org/schedule_academic.php)"
output:
xaringan::moon_reader:
nature:
highlightStyle: github
highlightLines: true
countIncrementalSlides: false
---
```{r, include=FALSE}
library(tidyverse)
library(tmap)
tmap_mode("view")
knitr::opts_chunk$set(echo = FALSE, cache = TRUE)
```
---
# Background
- We need to build dense walking/cycling/wheeling networks
- But where?
Source: Cycle Routing Uptake and Scenario Estimation (CRUSE) tool
---
# Tools of the trade
```{r, out.width="30%", fig.show='hold', echo=FALSE}
knitr::include_graphics(c(
"https://docs.ropensci.org/stplanr/reference/figures/stplanr.png",
"https://raw.githubusercontent.com/ropensci/stats19/master/man/figures/logo.png",
"https://github.com/Robinlovelace/geocompr/blob/main/images/geocompr_hex.png?raw=true"
))
```
--
???
- Need for more sustainable transport systems
- Local authorities need data
- We've developed a number of tools that provide this data
---
### Modelling framework and long-standing limitations
.left-column[
Modular
Future proof
Scalable
Vector/
Raster/
Source: Morgan and Lovelace ([2020](https://doi.org/10.1177/2399808320942779 )) Implementation: [stplanr](https://docs.ropensci.org/stplanr/index.html)
]
--
.right-column[
```{r}
knitr::include_graphics("https://user-images.githubusercontent.com/1825120/142071229-81358e26-5e8d-437e-9ef8-91704a4e690f.png")
```
Approach: OD -> Desire Line -> Route -> Route Networks
]
---
## What is Jittering?
Source: Lovelace, R., Félix, R., & Carlino, D. (2022, January 13). Jittering: A computationally efficient method for generating realistic route networks from origin-destination data. Transport Findings, in Press https://doi.org/10.31219/osf.io/qux6g
---
## Current default: centroid-based desire lines (+routes+rnets)
---
## Jittering in action: minimal reproducible example
---
### Jittering a larger dataset
Adding value and detail to existing OD data.
Source: Lovelace, Félix and Carlino ([2022](https://osf.io/qux6g/))
---
## Resulting route network
---
### Validating the approach: MKI
Data from Edinburgh. Source: [GISRUK 2022 conference paper](https://zenodo.org/record/6410196).
--
.pull-left[
See slides [here](https://www.robinlovelace.net/presentations/gisruk2022-jittering.html#1 )
]
.pull-right[
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Just presented new 'jittering' approach to pre-processing origin-destination (OD) data by diversifying simulated O/D locations <a href="https://twitter.com/GISRUK?ref_src=twsrc%5Etfw">@GISRUK</a>🎉<br><br>Great to get these new methods + reproducible implementation in open source software (<a href="https://twitter.com/rustlang?ref_src=twsrc%5Etfw">@rustlang</a> + <a href="https://twitter.com/hashtag/RStats?src=hash&ref_src=twsrc%5Etfw">#RStats</a> + ...) out there, give them a spin! <a href="https://t.co/vaydCHajm8">pic.twitter.com/vaydCHajm8</a></p>— Robin Lovelace (@robinlovelace) <a href="https://twitter.com/robinlovelace/status/1511739257962045440?ref_src=twsrc%5Etfw">April 6, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
]
---
### Model experiments: jittering parameters
---
## Results From Edinburgh
---
# Changing jittering *and* routing params
#### The 'jittering only' approach is assumes perfect routing, not true
#### Model/data discrepancies may be more due to routing than jittering/OD parameters
Enter Lisbon!
---
# Network level results
---
# Summary of results
---
#### Next steps
Exploring the parameter space: different origin and destination points + weights, routing 'engines', disaggregation.
.pull-left[
#### Selection of input data: open options
- Traffic count data
- Urban Observatory type data (Newcastle, Birmingham, Manchester)
- Faceboook and Google open mobility data
- 'OSM2od' - spatial interaction model
- Modelled data
- jittering: spatial disaggregation
- temporal disaggregation
]
.pull-right[
#### Non-open data data
- National Travel Survey
- Mobile Telephone Data
- Large GPS type data (biobank, Google timeline, Straval)
]
---
## Appendix: Reproducible code I: Rust implementation
See reproducible repo + manuscript here: https://github.com/Robinlovelace/odnet
System command line implementation (compile Rust code):
```bash
cargo install --git https://github.com/dabreegster/odjitter
odjitter jitter --od-csv-path od_iz_ed.csv \
--zones-path iz_zones11_ed.geojson \
--subpoints-path road_network_ed.geojson \
--max-per-od 10 --output-path output_max50.geojson
```
---
# Reproducible code II: R implementation
(See code in slides.Rmd in robinlovelace/foss4g22 for code to get data)
```{r}
if(!file.exists("cycle_counts_edinburgh_summary_2020-03-02-2022-01-05.geojson")) {
system("wget https://github.com/Robinlovelace/odnet/releases/download/0/cycle_counts_edinburgh_summary_2020-03-02-2022-01-05.geojson")
system("wget https://github.com/ITSLeeds/od/releases/download/v0.3.1/od_iz_ed.csv")
system("wget https://github.com/ITSLeeds/od/releases/download/v0.3.1/iz_zones11_ed.geojson")
system("wget https://github.com/Robinlovelace/odnet/releases/download/0/road_network_ed.geojson")
}
```
```{r, eval=FALSE, echo=TRUE}
remotes::install_github("dabreegster/odjitter", subdir = "r")
```
```{r, echo=TRUE, message=FALSE}
od = read_csv("od_iz_ed.csv")
zones = sf::read_sf("iz_zones11_ed.geojson")
```
```{r, echo=TRUE}
od_jittered = odjitter::jitter(
od = od,
zones = zones,
subpoints = sf::read_sf("road_network_ed.geojson")
)
od_jittered2 = odjitter::jitter(
od = od,
zones = zones,
subpoints = sf::read_sf("road_network_ed.geojson"),
disaggregation_key = "all",
disaggregation_threshold = 10
)
```
---
### Results of reprex 1
```{r, echo=TRUE, message=FALSE}
od_sf = od::od_to_sf(
od,
zones
)
nrow(od_sf)
nrow(od_jittered)
nrow(od_jittered2)
```
---
# Plot of unjittered data
```{r, message=FALSE, echo=TRUE}
library(dplyr)
plot(od_sf$geometry,
lwd = od_sf$all / 500)
```
---
### Results of reprex 2
```{r, echo=TRUE}
plot(od_jittered$geometry,
lwd = od_jittered$all / 500)
```
---
### Results of reprex 3
```{r, echo=TRUE}
plot(od_jittered2$geometry,
lwd = od_jittered2$all / 50)
```
---
### Alternative validation datasets: OA-WPZ data
There are 17,848,366 OA to WPZ records, 170k OAs, 54k WPZ
For 5km buffer around London, 1.5 million OD pairs with destinations
[](https://rpubs.com/RobinLovelace/863109)
---
### Reproducible example
.left-column[
```{r, echo=TRUE}
u = "https://github.com/ITSLeeds/od/releases/download/v0.3.1/od_intra_top_sf.geojson"
desire_lines_oa_wpz_1k = sf::read_sf(u)
oas_in_buffer = sf::read_sf("https://github.com/ITSLeeds/od/releases/download/v0.3.1/oas_in_buffer.geojson")
wpz_in_buffer = sf::read_sf("https://github.com/ITSLeeds/od/releases/download/v0.3.1/wpz_in_buffer.geojson")
library(tmap)
tmap_mode("view")
m = tm_shape(desire_lines_oa_wpz_1k) +
tm_lines() +
tm_shape(oas_in_buffer) + tm_dots(col = "darkgreen") +
tm_shape(wpz_in_buffer) + tm_dots(col = "darkred")
```
]
.right-column[
See [here](https://rpubs.com/anon-user/887139) for map
```{r, eval=FALSE}
m
```
]