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Algorithms and data for optimizing the recycling network in the city of Zurich, Switzerland

Contributors
- Silas Schweizer 0009-0000-1284-7063 author, developer, maintainer
- Jakub Tkaczuk 0000-0001-7997-9423 supervisor, maintainer
- Elizabeth Tilley 0000-0002-2095-9724 supervisor
- Nicolas Seemann-Ricard 0000-0002-0945-7475 technical support

This repository compliments the openly-accessible master’s thesis, available on the

ETH Research Collection.

Project Overview

This project aims to optimize the placement of Recycling Collection Points (RCPs) in Zurich, Switzerland. Using spatial analysis and optimization techniques, we identify optimal locations for new RCPs to improve accessibility for residents while minimizing the total number of facilities needed. The workflow uses a routing engine to calculate actual walking durations using the street network rather than relying on airline distances.

Directory Structure

rcp_project/ ├── config/ # Configuration files │ └── config.yaml # Main config with analysis parameters ├── data/ # Data directory │ ├── derived_data/ # Processed datasets │ │ └── workflow/ # Intermediate and final analysis outputs │ ├── plots/ # Generated visualizations │ └── raw_data/ # Original unprocessed data │ └── geodata_stadt_Zuerich/ # Geographic data from Zurich │ ├── ssz.gwr_stzh_wohnungen.shp # Building statistics │ ├── BEVOELKERUNG_HA_F.shp # Population data │ ├── Gemeindegrenzen_-OGD.gpkg # Municipal boundaries │ └── stzh.poi_sammelstelle_view.shp # Existing RCPs ├── envs/ # Conda environment definitions │ ├── geo_env.yaml # Environment for geospatial analysis │ ├── snake_env.yaml # Environment for running Snakemake │ └── solver_env.yaml # Environment for optimization solvers ├── img/ # Images for documentation ├── logs/ # Logs from workflow execution ├── rules/ # Snakemake workflow rules │ ├── data_preparation.smk # Rules for data preparation │ ├── optimisation_rules.smk # Rules for optimization │ ├── p_analysis_rules.smk # Rules for p-analysis analysis │ └── sensitivity_rules.smk # Rules for sensitivity analysis ├── scripts/ # Python scripts used in workflow └── Snakefile # Main workflow definition

Data Download

This repository is provided without data, which is too big to be stored on Github. However, both raw and derived data are available under this Zenodo entry. A script to automatically download and extract raw and derived data to their directories is also available. To use it, run from the main repo directory the following command in unix terminal: bash download_data.sh If you are using Windows you may need to download the zipped data manually and extract its content to data directory, replacing empty data/raw_data and data/derived_data.

Data Description

Raw Data

• Building Statistics: Detailed information about buildings in Zurich (ssz.gwr_stzh_wohnungen.shp)
• Population Data: Spatial distribution of population in hectare cells (BEVOELKERUNG_HA_F.shp)
• Municipal Boundaries: Administrative boundaries of Zurich (Gemeindegrenzen_-OGD.gpkg)
• Existing RCPs: Current recycling collection points (stzh.poi_sammelstelle_view.shp)

Derived Data

• Population-Allocated Buildings: Buildings with estimated population counts (flats_population.gpkg)
• Suitable RCP Sites: Potential locations for new RCPs (all_pot_sites.gpkg)
• Isochrones: Walking time service areas around RCPs (merged_isochrones.gpkg, isochrones_all.gpkg)
• Walking Duration: Buildings with calculated walking time to nearest RCP (flats_duration_current.gpkg)
• Clustering Results: Results from clustering analyses (rcps_clustering_ors.gpkg, rcps_clustering_iso.gpkg)
• Optimization Results: Solutions from optimization models (rcps_optimisation.gpkg)

Comprehensive Workflow

The project workflow consists of the following main stages:

1. Data Preparation

1. Population Allocation

   • Allocate population to residential buildings based on building size and type
   • Include new buildings (under construction and approved) for future planning bash snakemake --use-conda data/derived_data/workflow/flats_population.gpkg

2. Isochrone Generation

   • Generate walking time service areas (isochrones) around existing RCPs
   • Merge isochrones to identify areas with adequate service coverage bash snakemake --use-conda data/derived_data/workflow/iso_merged.gpkg

3. Suitability Analysis

   • Identify suitable sites for new RCPs based on land use, accessibility, and distance to existing RCPs
   • Apply constraints (no placement in certain zones, minimum distance from buildings) bash snakemake --use-conda data/derived_data/workflow/all_pot_sites.gpkg

4. Demand Point Generation

   • Generate representative demand points using k-means clustering on building locations
   • Weight clusters by population to better represent demand distribution bash snakemake --use-conda data/derived_data/workflow/kmeans_clusters.gpkg

5. Distance Matrix Calculation

   • Calculate walking durations between potential RCP sites and demand points
   • Uses routing services (Valhalla or OpenRouteService) for accurate walking times bash snakemake --use-conda data/derived_data/workflow/distance_matrix.csv

2. Optimization Methods

1. Clustering-Based Optimization with Routing

   • Identify underserved buildings (>10 min walking time to nearest RCP)
   • Cluster underserved buildings to identify potential new RCP locations
   • Select optimal placement of new RCPs within each cluster bash snakemake --use-conda data/derived_data/workflow/rcps_clustering_ors.gpkg

2. Isochrone-Based Optimization

   • Identify areas outside existing service coverage (10-min walking isochrones)
   • Find clusters of underserved buildings
   • Place new RCPs to maximize coverage of underserved population bash snakemake --use-conda data/derived_data/workflow/rcps_clustering_iso.gpkg

3. Linear Optimization

   • Formulate and solve a facility location problem to minimize the number of new RCPs
   • Consider coverage constraints to ensure all buildings are served within threshold time bash snakemake --use-conda data/derived_data/workflow/rcps_optimisation.gpkg

3. Evaluation and Analysis

1. Walking Duration Calculations

   • Calculate walking durations from each building to nearest RCP for each solution
   • Compare solutions by coverage percentage and population impact bash snakemake --use-conda data/derived_data/workflow/flats_duration_opt.gpkg

2. Method Comparison

   • Compare different optimization approaches (clustering vs. linear optimization)
   • Evaluate solutions based on coverage metrics and efficiency bash snakemake --use-conda data/derived_data/workflow/method_comparison.csv

3. Sensitivity Analysis

   • Analyze impact of clustering parameters on solution quality
   • Determine optimal number of clusters for best coverage/cost ratio bash snakemake --use-conda run_sensitivity_analysis

4. Facility Count Analysis (p-analysis)

   • Analyze impact of varying the number of new facilities (p-value)
   • Identify diminishing returns in coverage improvement bash snakemake --use-conda run_p_analysis

4. Visualization

1. Suitability Maps

   • Visualize suitable locations for new RCPs
   • Highlight existing and potential RCP sites

2. Walking Duration Maps

   • Color-coded visualization of walking time to nearest RCP
   • Identify underserved areas (>10 min walking time)

3. Isochrone Maps

   • Visualize service areas based on walking time
   • Show coverage of existing and proposed RCPs

4. Comparison Visualizations

   • Compare different optimization methods
   • Visualize coverage improvements and population impact

Installation and Usage

Prerequisites

• Conda/Miniconda
• Git
• A local instance of Valhalla or OpenRouteService; an ORS API key works too but is too slow for a real analysis
• Gurobi

Setup

1. Clone the repository: bash git clone <repository-url> cd rcp_project

2. Create and activate the base environment: bash conda env create -f envs/snake_env.yaml conda activate snake_env

Running the Workflow

• Complete workflow: bash snakemake --use-conda

• Specific target: bash snakemake --use-conda <target-file>

• Sensitivity analysis: bash snakemake --use-conda run_sensitivity_analysis

• p-Analysis (number of facilities): bash snakemake --use-conda run_p_analysis

Configuration

Adjust parameters in config/config.yaml: - routing_engine: Choose routing service (valhalla or ors) - data_preparation: Parameters for data preparation steps - optimization: Parameters for the optimization models - p_values: Values for p-analysis (number of facilities)

Results and Outputs

The workflow produces the following key outputs:

1. Optimized RCP Locations

   • Proposed locations for new RCPs under different optimization strategies
   • Files: rcps_clustering_ors.gpkg, rcps_clustering_iso.gpkg, rcps_optimisation.gpkg

2. Coverage Analysis

   • Buildings with calculated walking time to nearest RCP
   • Population covered within different time thresholds
   • Files: flats_duration_current.gpkg, etc.

3. Method Comparison

   • Comparison metrics between different optimization approaches
   • Efficiency analysis (population served per new RCP)
   • Files: method_comparison.csv, method_comparison_table.tex

4. Visualization Maps

   • Interactive HTML maps showing analysis results
   • Files in data/plots/ directory

Routing Engines

The project supports two routing engines:

1. Valhalla (default)

   • High-performance open-source routing engine
   • Requires local installation, see config file

2. OpenRouteService (ORS)

   • Alternative routing service with API access
   • Requires API key configuration or local instance, see config file

To switch routing engines, modify the routing_engine parameter in config/config.yaml.

Disclaimer

Important Notice: This code was developed as a master thesis at the Global Health Engineering group in collaboration with ERZ (Entsorgung + Recycling Zürich). It should be considered a research protype. Several known issues exist:

• Routing Engine Selection: The routing engine selection in the configuration file does not work properly. The main branch only works with Valhalla, not with OpenRouteService (ORS). For ORS, use the generate_ors_results branch instead.
• Logging System: The logging functionality is not fully implemented and may not work as expected.
• Data Download: Automatic data download could not be implemented due to time constraints. Data must be manually placed in the appropriate directories.
• Optimization Solver: The linear optimisation only works with the commercial Gurobi solver, as the open source solvers inlcuded in PulP cause the script to break.

These issues may be addressed in future updates.