This project presents a real-time system for scoring and evaluating the design of neighborhoods according to the principles of the "15-minute city", a concept that aims to arrange amenities and services within a 15-minute walking distance of all residents. The system uses data from multiple web-based APIs to automate the extraction of location-based information and provide a quantitative method for calculating the performance of neighborhoods according to the 15-minute city criteria. We created a web-based application that allows for real-time feedback on neighborhood design and can be used to refine designs at the building and tenancy level.

Project Focus 🧿

The focus of this project is on creating a user-friendly online tool that combines multiple GIS data sources to provide a comprehensive and objective assessment of neighborhood design. The tool includes a neighborhood score system that helps users benchmark the performance of existing designs and visualize the potential impacts of new building designs on the overall quality of the neighborhood. The tool allows for a quick and easy assessment of a neighborhood's quality and how it may change over time.

Methodology 📝

We developed a set of indexes to evaluate neighborhoods based on the availability of various urban features and services. These indexes were designed to score neighborhoods according to the principles of the "15-minute city" which emphasizes the importance of having essential services and amenities within a 15-minute walking or cycling distance. To determine which features and services should be included in the analysis, we used an isochrone analysis to define a walking or cycling shed within a 5-15 minute commute time from a starting point. We then searched for relevant urban features that fell within this distance. These features included residential buildings, offices, health services, educational institutions, public facilities, commercial spaces, markets, restaurants, and transportation and utility infrastructure.

Neighborhood Quality Score (NQS)

To calculate the neighbourhood score, the system filters urban features by category. Each category is given a score based on the number of features as well as their inclusion within either a 5 or 15 minutes isochrone from a reference point. If the urban feature is found within a 5-minute transit distance, it scores higher than the 15-minute transit distance. The final score is the aggregation of these values.

Urban Diversity Index

Another important indicator of neighbourhood performance is Urban Diversity. Measuring the distribution of urban features produces a useful indicator to understand the quality of a neighbourhood. The right balance between various urban services within a short distance makes for more vibrant and active communities. For the calculation of this index, Simpson's diversity index was taken as a reference, as it allows to determine the diversity of elements within a group of features.

The Simpson diversity, often used in biology to measure biodiversity, is a quantitative measure that reflects differences in type within a population (or data-set) with a finite number. In the case of the index, urban features found within the isochrone shed area are used.

Descriptive Spatial Metrics

To provide a general description of urban morphology, descriptive statistics are used to identify the mean, minimum and maximum values for building footprint area and building height. These metrics serve as a proxy to understand local urban regulation of the study area as it describes the floor and ceiling values of building form found in the area.

UI development for Building Area Distribution and Building Height Distribution. The results are presented in charts that help visualize the pattern of distribution for height and foot-print area within the isochrone

Implementation 🛠️

In this roject, we developed a platform that allows users to quickly estimate the quality of neighborhoods and inform decision-making in urban development. The platform includes a map interface that allows users to search for specific locations, drop a pin to study an area, and obtain a score for that area. The platform also allows users to generate multiple design scenarios with different program distributions and visualize the impact of these interventions on the neighborhood, making it easy to compare different scenarios and understand the potential improvements that could be made.

This figure illustrates result of the analysis for different neighbourhoods around the world. As data availability varies significantly from city to city, the score accuracy is relative to the quality of the data found for a given area.

Impact ✨

The primary contribution of this project is the development of tools, frameworks, and metrics to help designers, planners, and developers identify missing elements and opportunities to improve neighborhood design. One of the key innovations of the system is its holistic approach to integrating multiple data sources into a user-friendly, platform-independent web system that can be accessed by a broad audience.

Urban Insights: The User Interface for design tool for “15-minute city” planning and development, integrated into our web based design tool, Digital Blue Foam.

Unlike traditional GIS software, which is typically used by experts and can be difficult for non-technical users to understand, our system abstracts the complexity of analytical processes and presents easy-to-interpret results that can be used to facilitate real-time, interactive feedback and collaboration among all stakeholders involved in the planning and design process. By simplifying the process of performing pedestrian accessibility analysis and enabling more effective communication and collaboration, our system helps to transform the "15-minute city" concept from an idea into a practical and repeatable urban development strategy that can be implemented in the design process to improve the quality of urban fabric.

Future Work 💡

This project aims to continue developing the Neighborhood Quality Scoring system by integrating additional location information into the dataset and increasing the precision of the measurement methods. The system will also consider incorporating data on demographics and population density to assign specific weights to certain urban features depending on the needs of the neighborhood. In order to provide more accurate comparisons between different neighborhoods, the system will introduce a distance decay function and include a larger search area beyond the current 15-minute boundary.

Geolocated 3D models can be uploaded to the map platform, and the users can see the changes in the metrics

In the future, the system may also incorporate real-time data from the Internet of Things and visual data from Google Street View Imagery to better understand and communicate the multiple processes occurring in cities. The ultimate goal is to empower residents with access to more information and improve urban life through the use of data.