mie-lab / bike_network_evaluation

Bike Network Evaluation Metric Ontology (VeloNEMO)

We have developed VeloNEMO, an ontology to standardize and streamline bike network evaluation metrics from a set of existing scholarly papers. We formalized these metrics according to VeloNEMO and created a graph-database as well as tools to query and visualize the overview of different metric properties.

Setup Instructions

Ensure you have Python 3.11 installed. If not, download it from Python's official website. Clone this repository and navigate into the project directory. Run pip install -r requirements.txt to install required external dependencies.

To set up a graph database (Blazegraph) on Windows:

1. blazegraph.jar needs to be downloaded from https://github.com/blazegraph/database/releases and stored in a directory.
2. Create a run_db.bat file in the same folder as the blazegraph.jar with the following content: java -server -Xmx4g -jar blazegraph.jar.
3. Run the run_db.bat file to start the database. The database can then be accessed via an url and updated via a web endpoint with the generated nquad file.

File description

• paths_example.ini: example config file with: 1) input folder with the csv data for metrics, 2) output folder where generated metric nquads and descriptive plots will be saved to, and 3) the url for graph-database endpoint. You need to have paths.ini with your specific paths and endpoint.
• VeloNEMO.owl: VeloNEMO ontology .owl file.
• onto_manager.py: file containing concept constants for VeloNEMO, Units of Measure Ontology (OM) and Foundation Ontology for Global City Indicators (GCI).
• metrics_to_nquads.py: Functions to generate nquads representing metrics in the input file according to VeloNEMO.
• write_nquads.py: generates nquads that can be then stored in a graph database.
• metric_visualisation.py: Functions to query the graph database for different metrics and to plot descriptive overview.
• plot_metric_overview.py: queries the graph database for metrics and its properties and plots descriptive overview. At this point, the database needs to be preloaded with metric nquads and running.

Input data example

We provide an example input file metrics.xlsx in the example_data folder that gives a better idea of how the input data should look like. Specifically, following column names are necessary:EvaluationMetric (metric name, preferably reused), MetricType (one of the six thematic metrics types from VeloNEMO), EvaluationMethod (DOI or other identifier), EvaluationCriterion (a qualitative criteria such as Accessibility, Stress, Connectivity, etc., preferably from VeloNEMO), RepresentationFeature (geometric feature to which metric values are linked or aggregated on), ScoringFunction (e.g., Likert scale, rank between 1-10, etc.), MeasurementScale (nominal, ordinal, interval, ratio), Unit (refer to OM unit instances), UnitType (refer to OM unit types), Function (refer to OM - e.g., om:sum, om:average, om:mean), Buffer (numeric buffer value within which the metric values are aggregated, e.g., 200), BufferUnit (refer to OM unit instances, but likely om:metre of om:kilometere), Comment (original metric description), Parts (metric names for composite metric parts).