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UrbanMapper

Enrich Urban Layers Given Urban Datasets

with ease-of-use API and Sklearn-alike Shareable & Reproducible Urban Pipeline

Beartype compliant UV compliant RUFF compliant Jupyter Python 3.10+ Compilation Status

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UrbanMapper, In a Nutshell

UrbanMapper lets you link your data to spatial features—matching, for example, traffic events to streets—to enrich each location with meaningful, location-based information. Formally, it defines a spatial enrichment function \(f(X, Y) = X \bowtie Y\), where \(X\) represents urban layers (e.g., Streets, Sidewalks, Intersections and more) and \(Y\) is a user-provided dataset (e.g., traffic events, sensor data). The operator \(\bowtie\) performs a spatial join, enriching each feature in \(X\) with relevant attributes from \(Y\).

In short, UrbanMapper is a Python toolkit that enriches typically plain urban layers with datasets in a reproducible, shareable, and easily updatable way using minimal code. For example, given traffic accident data and a streets layer from OpenStreetMap, you can compute accidents per street with a Scikit-Learn–style pipeline called the Urban Pipeline—in under 15 lines of code. As your data evolves or team members want new analyses, you can share and update the Urban Pipeline like a trained model, enabling others to run or extend the same workflow without rewriting code.

There are more to UrbanMapper, explore!

See a trailer-style video below to get a quick overview of UrbanMapper and its features:

Urban Layers Currently Supported

UrbanMapper currently supports the following urban layers:

  • Streets Roads – Loads street road networks from OpenStreetMap (OSM) using OSMNx.
  • Streets Intersections – Loads street intersections from OSM using OSMNx.
  • Sidewalks – Loads sidewalks via Tile2Net using Deep Learning for automated mapping of pedestrian infrastructure from aerial imagery.
  • Cross Walks – Loads crosswalks via Tile2Net using Deep Learning for automated mapping of pedestrian infrastructure from aerial imagery.
  • Cities' Features – Loads OSM city features such as buildings, parks, bike lanes, etc., via OSMNx API.
  • Region Neighborhoods – Loads neighborhood boundaries from OSM using OSMNx Features module.
  • Region Cities – Loads city boundaries from OSM using OSMNx Features module.
  • Region States – Loads state boundaries from OSM using OSMNx Features module.
  • Region Countries – Loads country boundaries from OSM using OSMNx Features module.
  • Subway/Tube – Planned support for loading subway/tube networks.

More urban layers will be added in the future. Suggestions? Open an issue or pull request on our GitHub repository.

UrbanMapper – Use Cases by Urban Layer

UrbanMapper is a flexible tool for addressing a wide range of urban analysis challenges. This non-exhaustive list of practical use cases showcases its capabilities in transportation, safety, environment, demographics, and urban planning scenarios among others based on each urban layer supported.

  • Analyse traffic congestion patterns
    Load traffic sensor data, filter by peak hours, and enrich with road type information to visualise congestion on streets roads.

  • Optimise traffic signal timings
    Use real-time traffic data to dynamically adjust signal timings on streets roads, reducing congestion and improving flow.

  • Map air pollution levels
    Overlay air quality sensor data onto streets roads to identify high-pollution zones and target emissions reduction efforts.

  • Map taxi pickup/dropoff patterns
    Analyse taxi activity to identify high-traffic street intersections for optimising ride-sharing hubs or traffic flow.

  • Analyse collision hotspots
    Pinpoint street intersections with frequent accidents to implement safety measures like better signage or signal adjustments.

  • Evaluate vehicle wait times
    Study wait times at street intersections to optimise traffic management and reduce delays.

  • Evaluate pedestrian safety
    Map accident or complaint data to sidewalks to identify hazardous areas needing maintenance or infrastructure upgrades.

  • Study the effect of sidewalk quality on pedestrian traffic
    Correlate pedestrian volume with sidewalk conditions (e.g., width, surface quality) to prioritise improvements.

  • Assess walkability in urban areas
    Analyse sidewalk networks and proximity to amenities to calculate walkability scores for different zones.

  • Analyse collision hotspots around cross walks
    Map crash data to cross walks to identify accident-prone locations and improve pedestrian safety measures.

  • Optimise pedestrian signal timings
    Use pedestrian traffic data at cross walks to adjust signal timings for better flow and safety.

  • Evaluate crosswalk accessibility
    Assess the distribution and condition of cross walks to ensure equitable access for all pedestrians.

  • Assess the impact of bike lanes on traffic flow
    Study how bike lanes affect vehicle speeds and accident rates on adjacent roads.

  • Plan urban green spaces
    Analyse the distribution of parks to identify areas lacking accessible green spaces for future development.

  • Analyse noise pollution near building footprints
    Overlay noise data onto building footprints to identify residential areas needing soundproofing or noise barriers.

🏘️ Neighborhoods:

  • Evaluate public transportation coverage
    Map transit stops to neighborhoods to identify underserved areas and plan service improvements.
  • Enrich data with demographic information
    Overlay census data on neighborhoods to reveal population trends, income levels, or age distributions for targeted urban planning.
  • Analyse tourist greenery
    Map remarkable trees or green spaces to neighborhoods to assess their impact on tourism and urban greening.

🌆 Cities:

  • Compare urban density
    Use building footprints or population data to assess and compare density across cities for regional planning.
  • Analyse economic activity
    Map business locations or employment data to cities to identify economic hubs and growth opportunities.
  • Study transportation connectivity
    Analyse road or rail networks across cities to optimise infrastructure and reduce congestion.

🌍 States:

  • Study environmental impacts
    Overlay climate or pollution data across states to compare conditions and plan statewide initiatives.
  • Analyse transportation networks
    Map highway or rail networks across states to optimise connectivity and prioritise infrastructure investments.
  • Evaluate policy effectiveness
    Compare demographic or economic data across states to assess the impact of state-level policies.

🌐 Countries:

  • Analyse global urban trends
    Compare urbanization rates or infrastructure development across countries for international studies.
  • Map international trade routes
    Overlay trade data onto countries to visualise global economic connections and dependencies.
  • Study climate resilience
    Analyse temperature changes or natural disaster data across countries to assess vulnerability and plan mitigation strategies.

Where To Get Started ?