Mappa Quartiere

A data application consisting of visualizations and numerical analyses to monitor the liveability of Italian cities neighbourhoods. We aim to measure the availability of some publicly-organised services in the different city areas, as well as to evaluate vitality levels among them. The output metrics are built on open data collected from various sources (ISTAT, Ministries of Education, Health and Culture, local municipalities).

Currently under development.

Kubernetes Getting Started

kubectl apply -f mappa-quartiere-<environment>.yaml

Files

• /data contains the source files. raw and interim are used for preprocessing. processed contains the standardized data as it is loaded into the model and output contains the model outputs in the format needed for the visualization
• /notebooks has the Jupyter notebooks that can be used to interactively run the data pipeline steps
• /references contains the application-wide settings that are used in the pipeline, such as paths and available cities details
• /src/models has the python libraries to obtain the neighbourhood-level KPI
• /src/visualization has the js libraries of the front-end visualization
• /src/testing has the testing modules for the codebase and the processed data
• run_model.py is a script that can be run to compute all the outputs for every available city
• neighbourhood_kpi.tex is a short paper describing the mathematical model used to evaluate KPI for geolocalized services
• requirements.txt is the python env specification

Production data source files are stored in an external repo as a temporary replacement for an API data source. The server repo can be found here.

TODO: DAF integration

In a future version, data input and output could be integrated in DAF (e.g. via API).

• API calls can be defined in the module references/data_io.py, which collects all the read and write interfaces for the model
• Example source files for existing cities and services are available in data/processed/daf_sources folder

Use

Visualization

To see the visualization first follow instructions in the server repo (this will start a server at localhost:4000), then cd into src/visualization/ and then run npm i and npm start. You should see a dashboard at localhost:3000.

Running underlying model

After setting up an environment with Python 3.5.4 and the packages listed in requirements.txt, the main script is run_model.py. There is also a main notebook named CityServicesModel: it can be used to step through all the computational pipeline for the city selected at the beginning.

Running tests

Code tests are implemented via the standard unittest framework and stored in the src/testing folder.

Additional info

The files and folder structure is designed to follow the specs at: https://github.com/drivendata/cookiecutter-data-science The visualization uses React and has been built with Create React App.

Use

To see the visualization first follow instructions in this repo (this will start a server at localhost:4000), then cd into src/visualization/ and then run npm i and npm start. You should see a dashboard at localhost:3000.