https://github.com/atfutures-labs/osmod

Estimating origin-destination flows from OSM data
https://github.com/atfutures-labs/osmod

Last synced: 3 months ago
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Estimating origin-destination flows from OSM data

• Host: GitHub
• URL: https://github.com/atfutures-labs/osmod
• Owner: atfutures-labs
• License: gpl-3.0
• Created: 2017-04-22T16:27:12.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-10-25T12:00:13.000Z (about 7 years ago)
• Last Synced: 2024-05-21T02:52:35.875Z (6 months ago)
• Language: R
• Size: 22.5 KB
• Stars: 5
• Watchers: 5
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# osmod

Estimating origin-destination flows from OSM data

## TODO List

1. Choose a test location - Briston, U.K.?

2. Use [`osmdata`](https://github.com/osmdatar/osmdata) to extract all building

   polygons ~~and building heights where given~~. For residential buildings:

   - Construct some kind of simple statistical model to estimate heights of

   buildings lacking OSM heights

   ~~- Polygon area times height ~ residential population density, with census

   data as ground truth and calibration.~~

   - Use global population density data to down-sample to OSM building polygons.

   For commercial and industrial buildings:

   - ~~Repeat same procedure for density of employments (likely using

           different models for commercial and industrial).~~

    - Think of a smarter way to estimate employment densities?

3. Define a distance decay function for proportion of people that cycle to work

   as a function of distance to workplace.

4. Define some kind of function defining for a residential location some

   distance from the city centre the probability of that person working in some

   location a specific distance from both the city centre and their residential

   location. This will be some kind of two-parameter distance decay function.

5. Use output of Step#4 to route population density through the street network

   using a probabilistic router to convert that to relative densities of cycle

   traffic. This can be done separately for travel both *to* and *from* work.

## Refinements

1. Include travel not related to work, through connecting residential densities

   with leisure and commercial locations, to reflect recreation and shopping

   activities.

2. Improve the model suggested in Step#4 to reflect not necessarily radial

   urban structure.

3. Use google earth engine to automatically identify bicycles - that could well

   be possible? - and individual people on pavements. Feed that in as an extra

   parameter to reflect the possibility that those locations where more people

   are out and about without cars are also more likely to see more cycling

   activity.

4. Several spatially-defined variables will ultimately be able to be both

   automatically and globally derived, and the analysis could likely be fed into

   a neural network rather than hard-coding a fixed statistical algorithm.

   OSMODMAI? ODOSMAI?