Service Area Tools

Overview

This repository contains Python scripts aimed at automating the creation of road service areas performing complex spatial and network analyses, specifically tailored for geographical data processing and visualization, focusing on census data integration with network-based spatial analysis.

These tools can be used in standalone projects, simply by including the services folder within your project's repository.

Please read the How-To Guide for a full guide on the use of this tool. For an interactive version with functional links, please download as GitHub currently does not support links within PDFs.

Requirements

• Python 3.9+ (Confirmed to work on 3.9, 3.10, 3.11 & 3.12)
• Jupyter Notebooks (optional)
• Currently these tools utilise offline OSM data .pbf files for the creation of the road networks. The use of tools such as osmconvert or Osmosis can be used to obtain large OSM road network areas.

Installation/Set-up

Cloning

First, clone the GitHub repository. This can be done using gitbash (alternatives are available):

gitbash git clone https://github.com/hularuns/Service-Area-Tools

Installing Dependencies

Install with Pip

All dependencies can be installed using pip with the following command:

bash pip install "python>=3.9" "geopandas<=0.14.3" "pandas<=2.2.2" networkx ipykernel matplotlib pyrosm alphashape faker folium jupyter tqdm

Should you wish to only use the core functionality and not run the example script, you only need to install the following: bash pip install "python>=3.9" "geopandas<=0.14.3" "pandas<=2.2.2" networkx matplotlib pyrosm alphashape faker tqdm

Install with Conda

Optionally, you can also install all dependencies using conda with the following steps:

Using conda terminal navigate to the cloned repository where the environment.yml file is found. and run the following commands:

Create the environment:

bash conda env create -f environment.yml NOTE: If you wish to only use the core functionality and not wish to install jupyter notebook dependencies, replace environment.yml with environment_tools.yml

Activate the environment:

bash conda activate netgeo_env

Using In Your Own Project:

The functions in this script can be added to your project simply by including the entirety of the services/ folder and then imported into your project, for example with similar code to as below:

Example Using In your Own Project

```python from services import nearest_note_and_name services.network_bands.network_start_locations_nearest_node = nearest_node_and_name(graph = G, start_locations = start_locations_gdf, location_name = 'Static Library Name') ```

Example Script:

For a detailed example, see data_analysis.ipynb which is a script showing how to use the tools and some example analysis using data in testEnvironment/Data.

An example script showcasing the WIP shortestpathiterator() function is available in shortestpathexample

Running the Example Script:

• With the netgeo_env conda environment activated, navigate to the cloned repository folder and run the example script from the conda terminal: bash jupyter notebook data_analysis.ipynb
• If you do not want to use a notebook, an equivalent python script with identical code is available which can be ran with netgeo_env activated and ran from the cloned repository using the command: bash python data_analysis_script.py
• If successful an interactive webmap using folium is created in the root folder of the repository called index.html as well as two geopackage files called network_areas.gpkg and network_bands.gpkg.

This directory contains modules that provide various functionalities such as data loading, transformation, spatial analysis, and visualization. Below is an example of how to use the core functions of this repository to create a network service areas.

Please read the How-to guide for a more more in-depth guide on how to install, use and debug the code with specific use cases and examples.

Click Here To Show Example Code Usage

```python import services.network_bands as network_bands # Load the network graph # Ensure all data is in same CRS as pbf (likely EPSG:4326) file_path = '/path/to/city.osm.pbf' G, nodes, edges = network_bands.load_osm_network(file_path=file_path, network_type='driving', graph_type='networkx') # Define start locations and distances for creating service areas # Start locations are the location of each service, e.g. each library, hospital, supermarket. #Example of GeoDataframe of start locations print(start_locations_gdf) | Static Library Name |geometry | |---------------------------------|---------------------------| | Ardoyne Library | POINT (-5.97089 54.61635) | | Ballyhackamore Library | POINT (-5.86641 54.59504) | | Belfast Central Library | POINT (-5.93147 54.60270) | # Obtain the nearest nodes on the Graph start_locations_nearest_node = network_bands.nearest_node_and_name( graph = G, start_locations = start_locations_gdf, location_name = 'Static Library Name') print(start_locations_nearest_node) {'Ardoyne Library': {'nearest_node': 475085580}, 'Ballyhackamore Library': {'nearest_node': 73250694}, 'Belfast Central Library': {'nearest_node': 4513699587}} # Generate service areas ## Define the distance bands in a list. distances = [1000,2000,3000] # Distances in meters ## Create individual service area polygons for each start location and distance. network_areas = network_bands.service_areas(nearest_node_dict = start_locations_nearest_node, graph = G , #networkX graph search_distances = search_distances, alpha_value=500, # Value for alpha shape weight = 'length', # chooses shortest path based off length progress = True, # Prints ongoing progress save_output = True) #Saves output automatically to .gpkg ## Create tidy service area polygons by dissolving and differencing based on attributes. network_service_areas = network_bands.service_bands(geodataframe=network_areas, #output of network_areas() or service areas gdf. dissolve_cat = 'distance', # column to dissolve by aggfunc = 'first', #geopandas aggregate arg show_graph = True, #displays output save_output = True) #Saves output automatically to .gpkg # Do subsequent analysis. See data_analysis.ipynb for an example. ```

Use Cases

The tools within this repository are useful for the following: - Calculates service areas based on network distance: Generates services areas around many start locations based on nearest network nodes within specified distances. The output of which can be dissolved and is useful for visualising and analysing accessibility to a range of services such as healthcare, amenities, education etc.

• Visual representation of accessibility: Assists in modelling how far services can extend from certain points, crucial for resource allocation and planning of new governmental projects or identifying commercial opportunities.

Roadmap

The roadmap provides a glimpse into the current plans and priorities for future releases:

Ongoing Development

• Improving Examples and Documention: Increase the number of examples and detail in the documentation.
• Shortestpathiterator: Increase the efficiency of shortestpathiterator to make it feasible with realistic datasets.

Upcoming Features (v1.1)

• Online Road Network Compatibility: Graph creation will be handled more dynamically with packages such as pyrosm and osmnx

Planned Features (v1.2+)

• Individual Start-to-End Shortest Routes: Create additional tools to handle computationally intensive shortest routes between start and end location. The goal will be to iterate over numerous start and end locations to calculate the shortest route between each start location and end location.
• Develop GUI: A GUI will be developed to enable non-code users to engage with some of the more basic and principal tools.
• Graph Creation of Other Formats: A tool to handle the creation of network graphs, extracting the nodes and edges from other geospatial file formats. Note: This might be expedited to an earlier release

Known Issues & Limitations

Show Issues & Limitations

- **Issue 1**: Currently only handles off-line .pbf files - due to be handled in v1.1 release - **Issue 2**: Graph creation from other file formats not currently supported. - **Issue 3**: Dissolved network service areas currently do not retain parent information - this should change soon. - **Issue 4**: **Shortest_path_iterator()** is incredibly inefficient at calculating the nearest node and path finding, as such is not feasible currently to use and is **NOT** recommended to be used. If you have any issues or are struggling to implement the tools, PLEASE READ THE [HOW-TO GUIDE](Documentation/Service_Area_Tools_Guide) before raising an issue or contacting myself.

Packages Used and References

The following list contains linked and references to all used packages and data as part of the project:

Show packages and data sources

Packages:

• Python >= 3.6
• pyrosm
• osmnx
• geopandas <= 0.14.3
• pandas <= 2.2.2
• networkx
• ipykernel
• matplotlib
• alphashape
• faker
• folium
• notebook
• nb_conda
• jupyter_client
• jupyter_core
• tqdm

Data:

• NISRA Census (Northern Ireland) Statistics
• OpenStreetMap

Contributing

We will be welcoming contributions from the community, particularly with bugs. You can contribute by:

• Submitting bugs and feature requests, and help us verify as they are checked in.

License

This project is licensed under the GNU License - see the LICENSE.md file for details.

Authors

• Sam Groves - hularuns

Acknowledgments

• Thank you stack overflow!

All pointer data has been randomised multiple times to ensure that the data is defunct for any nefarious use and is solely for use as an easy example. A partial example of how this was randomised is included in the services