ValuePerAcre2

A modern Python implementation of the Value-per-Acre analysis tool, designed to create interactive maps for visualizing property tax revenue and land use efficiency.

What is Value-per-Acre and Why?

Value-per-Acre analysis is a method advocated by Strong Towns to evaluate the efficiency of land use by focusing on both its productivity and desirability relative to infrastructure investment.

Looking at a map with properties grouped and colorized based on their taxable value allows us to see which areas:

• Are the most and least productive
• Contribute the most and least revenue to city finances
• May be inefficiently using valuable land

While higher density development typically corresponds with higher taxable value, more desirable areas also attract higher home prices and, consequently, higher taxable value. This effect can compensate for areas of low taxable value like parks that increase overall desirability of neighboring properties.

Demo

This tool was used to compile the map at https://strongtownslangley.org/library/maps/valueperacre.php

Township of Langley Data Sources

For the Township of Langley Data, this was taken from multiple sources:

2024 Property Assessments, from TOL Open Data

Source: https://data-tol.opendata.arcgis.com/datasets/6f63c681511e4856836a7c9f988a0a6f_0/

This dataset seems to now only include Residential assessments, though used to include all types. Archived here as 2024_assessments.geojson

2024 Property Assessments, from TOL ArcGIS

Source: https://mapsvr.tol.ca/arcgisext02/rest/services/GeoSource/DynamicServices/MapServer/1129

This dataset is used by the GeoSource map and includes ALL assessed values, however we use the Open Data set first and fall back to this one. This data is downloaded using the 00_DownloadAssessmentData.py script.

Parcel Data

Source: https://data-tol.opendata.arcgis.com/datasets/cc82def6b6b54586aa7a1e452fcc1f02_0/

This dataset includes all the parcel outlines which is essential for calculating value per acre.

Tax Rates

Archived at: https://strongtownslangley.org/library/maps/maps_sources/2024-Tax-Rates.pdf

Original Tax Rates taken from the TOL Website.

Features

Polygon Mode (Recommended)

Parcel data is used to assign colors to each individual parcel based on its value per acre.

Point Mode

Each block (by default 100m²) is assigned a value based on the properties it contains.

How to Use

This Python implementation consists of a pipeline of scripts that:

1. Create a unified parcels file with tax values from assessment data
2. Convert coordinate systems if needed
3. Generate the web map and associated files

Quick Start

Prerequisites

1. Install Python 3.6 or higher from python.org
2. Install the required libraries using pip:

pip install shapely rtree tqdm pyproj geojson

Note: For rtree installation on Windows, you may need to install the Microsoft C++ Build Tools.

Run the Pipeline

For Township of Langley data:

# Run the full pipeline
python3 01_CreateUnifiedFile_Langley.py
python3 02_GeoJsonConvertToWSG.py unified_parcels.geojson unified_parcels_langley_4326.geojson 
python3 03_GenerateWebMap.py --input unified_parcels_langley_4326.geojson --output-folder langley_json --levels 50 --mode polygons

For Moldovan data (or other data already in the right coordinate system):

python3 01_CreateUnifiedFile_Moldovan.py
python3 03_GenerateWebMap.py --input moldovan_unified_parcels.geojson --output-folder moldovan_json --levels 50 --mode polygons

Understanding the Pipeline

Step 1: Create Unified Parcels File

The first script (01_CreateUnifiedFile_*.py) processes raw assessment and parcel data to create a unified GeoJSON file with tax values attached to each parcel.

python3 01_CreateUnifiedFile_Langley.py

Step 2: Coordinate System Conversion (if needed)

If your data isn't already in WGS84 (EPSG:4326) format needed for web maps, use the conversion script:

python3 02_GeoJsonConvertToWSG.py input.geojson output_4326.geojson

Step 3: Generate Web Map

The final script takes the unified parcels file and generates the web map and all supporting files:

python3 03_GenerateWebMap.py --input unified_parcels_langley_4326.geojson --output-folder output_json --levels 50 --mode polygons

Parameters:

• --input or -i: Path to your unified parcels GeoJSON file (required)
• --output-folder or -o: Where to save the output files (default: "json")
• --levels or -l: Number of levels to group the data into (default: 50)
• --block-size or -b: Size of blocks in meters when using points mode (default: 100.0)
• --mode or -m: Either "points" or "polygons" (default: "polygons")
  • "points" mode treats parcels as points (centroids) and groups them into blocks
  • "polygons" mode uses the actual parcel geometries for visualization

Output Files

The tool generates several files in the output folder:

1. Level-specific GeoJSON files (level_0.json, level_1.json, etc.)
2. A level info summary file (level_info.json)
3. Two HTML visualization files:
   • website.static.html: Contains embedded data (larger file size but works offline)
   • website.dynamic.html: Loads data from the JSON files (smaller file size but requires a web server)

The website.dynamic.html is the preferred method for deployment on the internet as it allows you to customize the page and simply update the level files separately in the future.

Data Format Examples

Unified Parcels GeoJSON Format

This is the output of the first script and input to the third:

{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "id": "0020929006",
      "geometry": {
        "type": "Polygon",
        "coordinates": [...]
      },
      "properties": {
        "FOLIO": "0020929006",
        "PID": "000-561-291",
        "STREET": "204A ST",
        "HOUSE": 2144,
        "CITY": "LANGLEY",
        "TaxableValue": 7157.44,
        "CombinedUnits": 0,
        "CombinedParcels": ""
      }
    },
    ...
  ]
}

Raw Tax Rates Format

If you're creating your own unified parcels file, the tax rates should be in this format:

{
    "Residential": 3.80248,
    "Utilities": 42.53820,
    "SupportiveHousing": 2.37008,
    "MajorIndustry": 8.34595,
    "LightIndustry": 10.24375,
    "Business": 12.02044,
    "ManagedForest": 0,
    "Rec_NonProfit": 6.73828,
    "Farm": 15.97448
}

Raw Assessments Format

Simplified example of the assessments file structure:

{
"type": "FeatureCollection",
"features": [
  { 
    "type": "Feature", 
    "properties": { 
      "Residential_Buildings": 113700, 
      "Residential_Land": 0, 
      "Latitude": 49.004443779630002, 
      "Longitude": -122.64299023049,
      "PID": "000-561-123"
    } 
  },
  ...
]
}

Other Similar Projects

• A commercial firm producing this kind of analysis is Urban3, who produce 3D renderings in addition to factoring in not just the Value but also the Cost-per-Acre.
• Fellow Strong Towns Local Conversation group A Better Cobb created their own set of Value per Acre tools, available at https://github.com/ABetterCobb/ValuePerAcre/

Credits and Technical Details

This tool was programmed by James Hansen ([email protected])

This Python implementation is based on the original C# version (now deprecated) at https://github.com/StrongTownsLangley/ValuePerAcre/

Technologies used:

• Python 3 with libraries:
  • shapely: For geometry operations
  • json: For data processing
  • rtree: For spatial indexing (in the unified file creation)
• Leaflet: For map visualization

Help and Contributing

If you encounter any issues or have suggestions for improvement, please open an issue on the GitHub repository. Pull requests are also welcome.

For additional help, join the Strong Towns Langley discord: https://discord.gg/MuAn3cFd8J and ask in the #🧮do-the-math channel.

License

This program is released under the Apache 2.0 License. If you use it for your website or project, please provide credit to Strong Towns Langley and preferably link to this GitHub.