Algorithmic
City
Planning Lab
A lab creating data-based quantitative indicators for city planning and public policy in partnership with local governments
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» (smaller)
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.
This lab develops quantitative, data-driven tools to support urban planning and public policy. Working in collaboration with local authorities—most notably the Tel Aviv-Yafo Municipality—it builds high-resolution databases based on unique municipal datasets.
By analyzing data on the provision and use of public institutions and commercial services, the lab creates models that help cities make smarter, fairer decisions about where to allocate resources and how to design urban services.
By analyzing data on the provision and use of public institutions and commercial services, the lab creates models that help cities make smarter, fairer decisions about where to allocate resources and how to design urban services.
Publication
Kaufmann, T., (2022). Towards Algorithmic City Planning Data-driven indicators for policies and planning
Direction
Talia Kaufmann
Research
Michael Drogochinsky
Artem Nikitin
Talia Gudkov
Kaufmann, T., (2022). Towards Algorithmic City Planning Data-driven indicators for policies and planning
Direction
Talia Kaufmann
Research
Michael Drogochinsky
Artem Nikitin
Talia Gudkov
We combine high-resolution municipal data, spatial analysis, and predictive modelling to understand how public services are accessed
Contacts
Partnership
approach
approach
We combine high-resolution municipal data, spatial analysis, and predictive modelling to understand how public services are accessed and where gaps exist.
By partnering with local governments, we turn this data into practical tools that inform planning decisions, prioritise equity, and empower communities.
By partnering with local governments, we turn this data into practical tools that inform planning decisions, prioritise equity, and empower communities.
Physical Layer
Highlights spatial resources and gaps that partnerships can address (e.g., improving access to facilities)
Highlights spatial resources and gaps that partnerships can address (e.g., improving access to facilities)
Calculation Layer
Uses overlay analysis, weighted scoring, or spatial statistics to quantify connections between layers
Uses overlay analysis, weighted scoring, or spatial statistics to quantify connections between layers
Special Layer
Maps existing networks and critical services to prioritize collaborative interventions
Maps existing networks and critical services to prioritize collaborative interventions
Social Economic Layer
Identifies demographic and economic conditions that shape partnership needs and opportunities
Identifies demographic and economic conditions that shape partnership needs and opportunities
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