Algorithmic
City
Planning Lab
Harnessing urban data into actionable insights, we build simple, high-resolution indicators for planning and policy in partnership with public sector entities
מעבדה ליצירת מדדים כמותיים מבוססי נתונים לתכנון עירוני ומדיניות ציבורית בשיתוף עם רשויות מקומיות
مختبر لإنشاء مؤشرات كمية قائمة على البيانات للتخطيط الحضري والسياسة العامة بالشراكة مع الحكومات المحلية
About us
Partnership Approach
«ACP lab bridges this gap with algorithmic tools that turn data into just, actionable planning»
Talia Kaufmann
head of ACP lab
head of ACP lab
Cities struggle to allocate public services equitably, leaving vulnerable communities underserved. Despite rich urban data, planning decisions often ignore real patterns of service use.
We combine high-resolution municipal data, spatial analysis, and predictive modelling to understand how public services
are accessed.
We combine high-resolution municipal data, spatial analysis, and predictive modelling to understand how public services
are accessed.
Partnership approach
Search and Explore
Spatial Distribution
We create an interactive experience to help decision makers discover and navigate the city’s network of amenities.
Through a searchable map interface, users can explore amenities by neighbourhood or location, accessing detailed profiles for each one. These profiles highlight the full range of services offered.The experience is designed to make local support more visible, accessible, and easier to engage with.
We create an interactive experience to help decision makers discover and navigate the city’s network of amenities.
Through a searchable map interface, users can explore amenities by neighbourhood or location, accessing detailed profiles for each one. These profiles highlight the full range of services offered.The experience is designed to make local support more visible, accessible, and easier to engage with.
Accessibility Evaluation
and Users Profile
We explore how easily residents can reach community centres based on spatial distribution, pedestrian flows, and neighbourhood connectivity. By mapping catchment areas, the tool identifies service coverage across the city and highlights disparities in access.
and Users Profile
We explore how easily residents can reach community centres based on spatial distribution, pedestrian flows, and neighbourhood connectivity. By mapping catchment areas, the tool identifies service coverage across the city and highlights disparities in access.
Policy Making Insights
We provide policy-making insights by integrating three layers of spatial analysis.
We provide policy-making insights by integrating three layers of spatial analysis.
- Physical Layer Highlights spatial resources and gaps that partnerships can address (e.g., improving access to facilities)
- Social Economic Layer Identifies demographic and economic conditions that shape partnership needs and opportunities
- Calculation Layer Uses overlay analysis, weighted scoring, or spatial statistics to quantify connections between layers
Our Partners
Publications
Variation in access to urban parks across six OECD countries, Kaufmann et al., (2025)
Towards Algorithmic City Planning Data-driven indicators for policies and planning, Kaufmann, T., (2022)
Scaling of urban amenities: generative statistics and implications for urban planning, Kaufmann et al., (2022)
Director
Talia Kaufmann
Variation in access to urban parks across six OECD countries, Kaufmann et al., (2025)
Towards Algorithmic City Planning Data-driven indicators for policies and planning, Kaufmann, T., (2022)
Scaling of urban amenities: generative statistics and implications for urban planning, Kaufmann et al., (2022)
Director
Talia Kaufmann
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