https://github.com/josiahparry/anime
Approximate Network Integration, Matching, and Enrichment
https://github.com/josiahparry/anime
python r-spatial rstats rust transportation-network
Last synced: 2 months ago
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Approximate Network Integration, Matching, and Enrichment
- Host: GitHub
- URL: https://github.com/josiahparry/anime
- Owner: JosiahParry
- License: mit
- Created: 2024-12-20T14:54:59.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-07-18T18:31:51.000Z (3 months ago)
- Last Synced: 2025-07-30T01:48:19.224Z (3 months ago)
- Topics: python, r-spatial, rstats, rust, transportation-network
- Language: Rust
- Homepage:
- Size: 24.7 MB
- Stars: 17
- Watchers: 3
- Forks: 4
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ANIME: Approximate Network Integration, Matching, and Enrichment
ANIME is a fast and efficient algorithm to perform partial linestring
matching and attribute interpolation between two sets of linestrings.
The core algorithm is implemented in the `anime` Rust crate ([see Rust
docs](https://docs.rs/anime/)) with bindings to the algorithm in both R
and Python.
## What it does
The `anime` algorithm calculates the approximate shared length between
two sets of linestrings within a specified **distance** and **angle**
tolerance.
For each source linestring, the distance of overlap with a target
linestring is stored. The distance of overlap is then used to perform
intensive or extensive interpolation of attributes.
## Example
``` r
library(sf)
library(dplyr)
library(anime)
library(ggplot2)
# get sample lines from package
targets_fp <- system.file("extdata", "maine-osm-targets.fgb", package = "anime")
sources_fp <- system.file("extdata", "maine-tigris-sources.fgb", package = "anime")
# read files
targets <- read_sf(targets_fp)
sources <- read_sf(sources_fp)
matches <- anime(
sources,
targets,
10, 5
)
# extract matches as a data.frame
match_tbl <- get_matches(matches)
match_tbl
```
# A data frame: 23 × 5
target_id source_id shared_len source_weighted target_weighted
*
1 1 1 73.5 1.00 1.25
2 2 1 50.0 0.680 0.933
3 3 1 73.5 1.00 0.991
4 4 2 0 0 0
5 6 1 0 0 0
6 7 2 0 0 0
7 8 2 0 0 0
8 11 1 18.7 0.255 0.752
9 12 2 170. 0.895 0.894
10 13 3 132. 0.983 0.993
# ℹ 13 more rows
``` r
# find most matched source
most_matched_source <- count(match_tbl, source_id, sort = TRUE) |>
slice(1) |>
pull(source_id)
# find the matched targets in the sf object
matched_tars <- match_tbl |>
filter(source_id == most_matched_source, shared_len > 0) |>
inner_join(transmute(targets, target_id = row_number())) |>
st_as_sf()
# visualize them
ggplot() +
geom_sf(aes(color = shared_len), matched_tars, lwd = 2) +
geom_sf(data = sources[most_matched_source, ], lty = 2) +
scale_color_binned() +
theme_void()
```
