---
title: "dodgr flows"
author: "Mark Padgham"
date: "`r Sys.Date()`"
output: 
    html_document:
        toc: true
        toc_float: true
        number_sections: false
        theme: flatly
vignette: >
  %\VignetteIndexEntry{3 dodgr-flows}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r pkg-load, echo = FALSE, message = FALSE}
library (dodgr)
```

The `dodgr`package includes three functions for allocating and aggregating
flows throughout network, based on defined properties of a set of origin and
destination points. The three primary functions for flows are
[`dodgr_flows_aggregate()`](https://atfutures.github.io/dodgr/reference/dodgr_flows_aggregate.html),
[`dodgr_flows_disperse()`](https://atfutures.github.io/dodgr/reference/dodgr_flows_disperse.html),
and
[`dodgr_flows_si()`](https://atfutures.github.io/dodgr/reference/dodgr_flows_si.html),
each of which is now described in detail.

## 1 Flow Aggregation

The first of the above functions aggregates ''flows'' throughout a
network from a set of origin (`from`) and destination (`to`) points.  Flows
commonly arise in origin-destination matrices used in transport studies, but may
be any kind of generic flows on graphs. A flow matrix specifies the flow between
each pair of origin and destination points, and the
[`dodgr_flows_aggregate()`](https://atfutures.github.io/dodgr/reference/dodgr_flows_aggregate.html)
function aggregates all of these flows throughout a network and assigns a
resultant aggregate flow to each edge.

For a set of `nf` points of origin and `nt` points of destination, flows are
defined by a simple `nf`-by-`nt` matrix of values, as in the following code:
```{r}
graph <- weight_streetnet (hampi, wt_profile = "foot")
from <- sample (graph$from_id, size = 10)
to <- sample (graph$to_id, size = 10)
flows <- matrix (10 * runif (length (from) * length (to)),
                 nrow = length (from))
```
This `flows` matrix is then submitted to
[`dodgr_flows_aggregate()`](https://atfutures.github.io/dodgr/reference/dodgr_flows_aggregate.html),
which simply appends an additional column of `flows` to the submitted `graph`:
```{r}
graph_f <- dodgr_flows_aggregate (graph, from = from, to = to, flows = flows)
head (graph_f)
```
Most flows are zero because they have only been calculated between very few
points in the graph.
```{r}
summary (graph_f$flow)
```

## 2 Flow Dispersal

The second function,
[`dodgr_flows_disperse()`](https://atfutures.github.io/dodgr/reference/dodgr_flows_disperse.html),
uses only a vector a origin (`from`) points, and aggregates flows as they
disperse throughout the network
according to a simple exponential model. In place of the matrix of flows
required by
[`dodgr_flows_aggregate()`](https://atfutures.github.io/dodgr/reference/dodgr_flows_aggregate.html),
dispersal requires an equivalent vector of densities dispersing from all origin
(`from`) points. This is illustrated in
the following code, using the same graph as the previous example.
```{r}
dens <- rep (1, length (from)) # uniform densities
graph_f <- dodgr_flows_disperse (graph, from = from, dens = dens)
summary (graph_f$flow)
```

## 3 Merging directed flows

Note that flows from both
[`dodgr_flows_aggregate()`](https://atfutures.github.io/dodgr/reference/dodgr_flows_aggregate.html)
and
[`dodgr_flows_disperse()`](https://atfutures.github.io/dodgr/reference/dodgr_flows_aggregate.html)
are *directed*, so the flow from 'A' to 'B' will not necessarily equal the flow
from 'B' to 'A'. It is often desirable to aggregate flows in an undirected
manner, for example for visualisations where plotting pairs of directed flows
between each edge if often not feasible for large graphs. Directed flows can be
aggregated to equivalent undirected flows with the `merge_directed_graph()`
function:
```{r}
graph_undir <- merge_directed_graph (graph_f)
```
Resultant graphs produced by
[`merge_directed_graph()`](https://atfutures.github.io/dodgr/reference/merge_directed_graph.html)
only include those edges having non-zero flows, and so:
```{r}
nrow (graph_f); nrow (graph_undir) # the latter is much smaller
```
The resultant graph can readily be merged with the original graph to regain the
original data on vertex coordinates through
```{r}
graph <- graph [graph_undir$edge_id, ]
graph$flow <- graph_undir$flow
```
This graph may then be used to visualise flows with the
[`dodgr_flowmap()`](https://atfutures.github.io/dodgr/reference/dodgr_flowmap.html)
function:
```{r flowmap, eval = FALSE}
graph_f <- graph_f [graph_f$flow > 0, ]
dodgr_flowmap (graph_f, linescale = 5)
```
![](hampi-flowmap.png)
