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Diff for: LICENSE

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Copyright (C) 2020, Deloitte Digital. All rights reserved.
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LightOSM.jl can be downloaded from: https://github.com/DeloitteDigitalAPAC/LightOSM.jl
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LightOSM.jl can be downloaded from: https://github.com/DeloitteOptimalReality/LightOSM.jl
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:

Diff for: README.md

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# LightOSM.jl
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[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://deloittedigitalapac.github.io/LightOSM.jl/docs/)
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[![Tutorial](https://img.shields.io/badge/docs-tutorial-informational.svg)](https://deloittedigitalapac.github.io/LightOSM.jl/notebooks/tutorial)
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[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://deloitteoptimalreality.github.io/LightOSM.jl/docs/)
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[![Tutorial](https://img.shields.io/badge/docs-tutorial-informational.svg)](https://deloitteoptimalreality.github.io/LightOSM.jl/notebooks/tutorial)
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[![Build Status](https://github.com/DeloitteOptimalReality/LightOSM.jl/workflows/CI/badge.svg?branch=master)](https://github.com/DeloitteOptimalReality/LightOSM.jl/actions?query=workflow%3ACI+branch%3Amaster)
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[![Codecov](https://codecov.io/gh/DeloitteOptimalReality/LightOSM.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/DeloitteOptimalReality/LightOSM.jl)
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Diff for: docs/make.jl

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)
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deploydocs(
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repo="github.com/DeloitteDigitalAPAC/LightOSM.jl.git",
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repo="github.com/DeloitteOptimalReality/LightOSM.jl.git",
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devurl="docs",
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push_preview=true,
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)

Diff for: docs/src/index.md

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# LightOSM.jl
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**[`LightOSM.jl`](https://github.com/DeloitteDigitalAPAC/LightOSM.jl)** is **[Julia](https://julialang.org/)** package for downloading and analysing geospatial data from **[OpenStreetMap](https://wiki.openstreetmap.org/wiki/Main_Page)** APIs (**[Nominatim](https://nominatim.openstreetmap.org/ui/search.html)** and **[Overpass](https://overpass-api.de)**), such as nodes, ways, relations and building polygons.
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**[`LightOSM.jl`](https://github.com/DeloitteOptimalReality/LightOSM.jl)** is **[Julia](https://julialang.org/)** package for downloading and analysing geospatial data from **[OpenStreetMap](https://wiki.openstreetmap.org/wiki/Main_Page)** APIs (**[Nominatim](https://nominatim.openstreetmap.org/ui/search.html)** and **[Overpass](https://overpass-api.de)**), such as nodes, ways, relations and building polygons.
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## Interface
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## Acknowledgements
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**[`LightOSM.jl`](https://github.com/DeloitteDigitalAPAC/LightOSM.jl)** is inspired by the Python package **[OSMnx](https://github.com/gboeing/osmnx)** for its interface and Overpass query logic. Graph analysis algorithms (connected components and shortest path) are based on **[LightGraphs.jl](https://github.com/JuliaGraphs/LightGraphs.jl)** implementation, but adapted to account for turn restrictions and improve runtime performance.
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**[`LightOSM.jl`](https://github.com/DeloitteOptimalReality/LightOSM.jl)** is inspired by the Python package **[OSMnx](https://github.com/gboeing/osmnx)** for its interface and Overpass query logic. Graph analysis algorithms (connected components and shortest path) are based on **[LightGraphs.jl](https://github.com/JuliaGraphs/LightGraphs.jl)** implementation, but adapted to account for turn restrictions and improve runtime performance.
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Another honourable mention goes to an existing Julia package **[OpenStreetMapX.jl](https://github.com/pszufe/OpenStreetMapX.jl)** as many learnings were taken to improve parsing of raw OpenStreetMap data.
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## Usage
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A comprehensive tutorial can be found found **[here](https://deloittedigitalapac.github.io/LightOSM.jl/notebooks/tutorial)**.
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A comprehensive tutorial can be found found **[here](https://deloitteoptimalreality.github.io/LightOSM.jl/notebooks/tutorial)**.
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## Using `OSMGraph`s in Unit Tests
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## Benchmarks
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Benchmark comparison for shortest path algorithms can be found **[here](https://deloittedigitalapac.github.io/LightOSM.jl/notebooks/benchmarks)**.
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Benchmark comparison for shortest path algorithms can be found **[here](https://deloitteoptimalreality.github.io/LightOSM.jl/notebooks/benchmarks)**.

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# Using LightOSM in Unit Tests
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To avoid having to download graphs within unit tests, it is suggested that something
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similar to the `OSMGraph` stub used in LightOSM's own tests (see [`test/stub.jl`](https://github.com/DeloitteDigitalAPAC/LightOSM.jl/blob/master/test/stub.jl)) is
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similar to the `OSMGraph` stub used in LightOSM's own tests (see [`test/stub.jl`](https://github.com/DeloitteOptimalReality/LightOSM.jl/blob/master/test/stub.jl)) is
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used by your package. This allows you to have explicit control over the structure of
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the graph and therefore to have explicit tests.
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