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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Jittering and routing options for converting origin-destination data into route networks</title>
<meta charset="utf-8" />
<meta name="author" content="Robin Lovelace, University of Leeds +Rosa Félix, Dustin Carlino" />
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<textarea id="source">
class: center, middle, inverse, title-slide
.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>+<a href="http://www.rosafelix.bike/">Rosa Félix</a>, <a href="https://dabreegster.github.io/">Dustin Carlino</a>
]
.date[
### <a href="https://2022.foss4g.org/schedule_academic.php">FOSS4G 2022</a>
]
---
---
# 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
<img src="https://docs.ropensci.org/stplanr/reference/figures/stplanr.png" width="30%" /><img src="https://raw.githubusercontent.com/ropensci/stats19/master/man/figures/logo.png" width="30%" /><img src="https://github.com/Robinlovelace/geocompr/blob/main/images/geocompr_hex.png?raw=true" width="30%" />
--
???
- 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[
<!-- -->
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 &#39;jittering&#39; 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&amp;ref_src=twsrc%5Etfw">#RStats</a> + ...) out there, give them a spin! <a href="https://t.co/vaydCHajm8">pic.twitter.com/vaydCHajm8</a></p>&mdash; 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
remotes::install_github("dabreegster/odjitter", subdir = "r")
```
```r
od = read_csv("od_iz_ed.csv")
zones = sf::read_sf("iz_zones11_ed.geojson")
```
```r
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
od_sf = od::od_to_sf(
od,
zones
)
nrow(od_sf)
```
```
## [1] 10394
```
```r
nrow(od_jittered)
```
```
## [1] 10394
```
```r
nrow(od_jittered2)
```
```
## [1] 22432
```
---
# Plot of unjittered data
```r
library(dplyr)
plot(od_sf$geometry,
lwd = od_sf$all / 500)
```
<!-- -->
---
### Results of reprex 2
```r
plot(od_jittered$geometry,
lwd = od_jittered$all / 500)
```
<!-- -->
---
### Results of reprex 3
```r
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
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")
```
```
## tmap mode set to interactive viewing
```
```r
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
]
</textarea>
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