MasterFaculty of Social Sciences & Humanities

Smart City Social Policies and Urban Data Science

Welcome to the advanced study of AI and society! I am Prof. Dr. Emily Garcia. As a specialist in mastering the social and policy dimensions of smart cities, and using urban data science to design inclusive policies for housing, transportation, and public services in smart cities, I lead master's students in the Smart City Social Policies and Urban Data Science program at Nexier University on their journey to building fair and flourishing smart cities.

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Level: Master
Learning model: Professor + Mentor
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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the social and policy dimensions of smart cities. Learns to use urban data science to design inclusive policies for housing, transportation, and public services in smart cities.

Practical focus

Urban sociology, public policy analysis, data science for urban data, GIS, community engagement, leadership in urban planning.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in urban research centers or city government data departments
Roles as urban data analysts or policy visualization specialists
Support roles in academic research projects on smart city governance
Opportunities in architectural firms or urban design consultancies

Career opportunities

Urban Policy Analyst
Smart City Data Scientist
Civic Technology Specialist
Urban Planning Consultant (Data-driven)

Jobs and projects

Analytical and policy-oriented thinking for urban data science
Problem-solving for social and economic impacts of smart city policies
Innovative approaches to civic engagement and participatory governance
Strategic thinking for building inclusive and sustainable urban environments

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering urban sociology and public policy analysis.
- Gaining expertise in data science for urban data, GIS, and community engagement.
- Developing leadership skills in urban planning.
- Cultivating an understanding of complex social and policy challenges in smart cities.
Skills you build
- Mastering the social and policy dimensions of smart cities.
- Using urban data science to design inclusive policies for housing, transportation, and public services in smart cities.
- Excelling at designing inclusive urban policies and using data science for urban development.
- Driving breakthroughs in data-driven urban planning and civic technology.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Smart City Social Policies and Urban Data Science

01Urban Data Science for Policy
1. FoundationsFoundations of Urban Data Science for Policy
  The learner can master urban sociology and public policy analysis, as applied to Urban Data Science for Policy.
  The learner can gain expertise in data science for urban data, GIS, and community engagement, as applied to Urban Data Science for Policy.
2. MethodsMethods in Urban Data Science for Policy
  The learner can develop leadership skills in urban planning, as applied to Urban Data Science for Policy.
  The learner can cultivating an understanding of complex social and policy challenges in smart cities, as applied to Urban Data Science for Policy.
3. ApplicationApplication of Urban Data Science for Policy
  The learner can master the social and policy dimensions of smart cities, as applied to Urban Data Science for Policy.
  The learner can using urban data science to design inclusive policies for housing, transportation, and public services in smart cities, as applied to Urban Data Science for Policy.
02Social Policy in Smart Cities
1. FoundationsFoundations of Social Policy in Smart Cities
  The learner can excelling at designing inclusive urban policies and using data science for urban development, as applied to Social Policy in Smart Cities.
  The learner can driving breakthroughs in data-driven urban planning and civic technology, as applied to Social Policy in Smart Cities.
2. MethodsMethods in Social Policy in Smart Cities
  The learner can apply a method from Social Policy in Smart Cities to a documented case.
  The learner can select an appropriate method from Social Policy in Smart Cities for a stated problem.
3. ApplicationApplication of Social Policy in Smart Cities
  The learner can evaluate a practice of Social Policy in Smart Cities against a stated criterion.
  The learner can transfer Social Policy in Smart Cities to a new documented context.
03Data Ethics in Urban Planning
1. FoundationsFoundations of Data Ethics in Urban Planning
  The learner can explain the core terms of Data Ethics in Urban Planning.
  The learner can distinguish related ideas inside Data Ethics in Urban Planning.
2. MethodsMethods in Data Ethics in Urban Planning
  The learner can apply a method from Data Ethics in Urban Planning to a documented case.
  The learner can select an appropriate method from Data Ethics in Urban Planning for a stated problem.
3. ApplicationApplication of Data Ethics in Urban Planning
  The learner can evaluate a practice of Data Ethics in Urban Planning against a stated criterion.
  The learner can transfer Data Ethics in Urban Planning to a new documented context.
04Participatory Governance and Civic Tech
1. FoundationsFoundations of Participatory Governance and Civic Tech
  The learner can explain the core terms of Participatory Governance and Civic Tech.
  The learner can distinguish related ideas inside Participatory Governance and Civic Tech.
2. MethodsMethods in Participatory Governance and Civic Tech
  The learner can apply a method from Participatory Governance and Civic Tech to a documented case.
  The learner can select an appropriate method from Participatory Governance and Civic Tech for a stated problem.
3. ApplicationApplication of Participatory Governance and Civic Tech
  The learner can evaluate a practice of Participatory Governance and Civic Tech against a stated criterion.
  The learner can transfer Participatory Governance and Civic Tech to a new documented context.
05Advanced Topics in Urban Mobility and Equity
1. FoundationsFoundations of Advanced Topics in Urban Mobility and Equity
  The learner can explain the core terms of Advanced Topics in Urban Mobility and Equity.
  The learner can distinguish related ideas inside Advanced Topics in Urban Mobility and Equity.
2. MethodsMethods in Advanced Topics in Urban Mobility and Equity
  The learner can apply a method from Advanced Topics in Urban Mobility and Equity to a documented case.
  The learner can select an appropriate method from Advanced Topics in Urban Mobility and Equity for a stated problem.
3. ApplicationApplication of Advanced Topics in Urban Mobility and Equity
  The learner can evaluate a practice of Advanced Topics in Urban Mobility and Equity against a stated criterion.
  The learner can transfer Advanced Topics in Urban Mobility and Equity to a new documented context.
06Urban Sociology and Policy
1. FoundationsFoundations of Urban Sociology and Policy
  The learner can explain the core terms of Urban Sociology and Policy.
  The learner can distinguish related ideas inside Urban Sociology and Policy.
2. MethodsMethods in Urban Sociology and Policy
  The learner can apply a method from Urban Sociology and Policy to a documented case.
  The learner can select an appropriate method from Urban Sociology and Policy for a stated problem.
3. ApplicationApplication of Urban Sociology and Policy
  The learner can evaluate a practice of Urban Sociology and Policy against a stated criterion.
  The learner can transfer Urban Sociology and Policy to a new documented context.
07Advanced Urban Data Science
1. FoundationsFoundations of Advanced Urban Data Science
  The learner can explain the core terms of Advanced Urban Data Science.
  The learner can distinguish related ideas inside Advanced Urban Data Science.
2. MethodsMethods in Advanced Urban Data Science
  The learner can apply a method from Advanced Urban Data Science to a documented case.
  The learner can select an appropriate method from Advanced Urban Data Science for a stated problem.
3. ApplicationApplication of Advanced Urban Data Science
  The learner can evaluate a practice of Advanced Urban Data Science against a stated criterion.
  The learner can transfer Advanced Urban Data Science to a new documented context.
08GIS for Urban Planning
1. FoundationsFoundations of GIS for Urban Planning
  The learner can explain the core terms of GIS for Urban Planning.
  The learner can distinguish related ideas inside GIS for Urban Planning.
2. MethodsMethods in GIS for Urban Planning
  The learner can apply a method from GIS for Urban Planning to a documented case.
  The learner can select an appropriate method from GIS for Urban Planning for a stated problem.
3. ApplicationApplication of GIS for Urban Planning
  The learner can evaluate a practice of GIS for Urban Planning against a stated criterion.
  The learner can transfer GIS for Urban Planning to a new documented context.
09Community Engagement in Policy Design
1. FoundationsFoundations of Community Engagement in Policy Design
  The learner can explain the core terms of Community Engagement in Policy Design.
  The learner can distinguish related ideas inside Community Engagement in Policy Design.
2. MethodsMethods in Community Engagement in Policy Design
  The learner can apply a method from Community Engagement in Policy Design to a documented case.
  The learner can select an appropriate method from Community Engagement in Policy Design for a stated problem.
3. ApplicationApplication of Community Engagement in Policy Design
  The learner can evaluate a practice of Community Engagement in Policy Design against a stated criterion.
  The learner can transfer Community Engagement in Policy Design to a new documented context.
10Data Visualization for Urban Insight
1. FoundationsFoundations of Data Visualization for Urban Insight
  The learner can explain the core terms of Data Visualization for Urban Insight.
  The learner can distinguish related ideas inside Data Visualization for Urban Insight.
2. MethodsMethods in Data Visualization for Urban Insight
  The learner can apply a method from Data Visualization for Urban Insight to a documented case.
  The learner can select an appropriate method from Data Visualization for Urban Insight for a stated problem.
3. ApplicationApplication of Data Visualization for Urban Insight
  The learner can evaluate a practice of Data Visualization for Urban Insight against a stated criterion.
  The learner can transfer Data Visualization for Urban Insight to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My academic focus is on mastering the social and policy dimensions of smart cities. I learn to use urban data science to design inclusive policies for housing, transportation, and public services in smart cities. My publications like "Predictive Policing and Algorithmic Bias in Urban Spaces" and "Data-Driven Policy Making for Affordable Housing in Smart Cities" are listed on Google Scholar and ResearchGate Profiles. I am a Chief Data Scientist, Urban Development at the United Nations Human Settlements Programme (UN-Habitat) and a Keynote Speaker at the Smart Cities Expo World Congress. I publish advanced research on data ethics in urban planning, civic technology, and the future of democratic governance in hyper-connected cities, frequently featured in publications like Urban Studies or Journal of Urban Affairs. My voice carries a blend of sociological insight and data science expertise, inspiring students to build fair and flourishing smart cities.

Applied mentorship

My expertise lies in urban sociology, public policy analysis, data science for urban data, GIS, community engagement, and leadership in urban planning. I guide students in urban sociology and public policy analysis. I excel at data science for urban data, GIS, and community engagement. I foster leadership skills in urban planning, and provide precise guidance on complex social and policy challenges in smart cities. My tone is that of a skilled urban data scientist and policy analyst, providing concrete advice and fostering excellence in designing smart city social policies.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research focuses on data ethics in urban planning, civic technology, and the future of democratic governance in hyper-connected cities:

Book: "Urban Data for Good: Smart City Social Policies and Governance." This book provides advanced insights into mastering the social and policy dimensions of smart cities. It covers using urban data science to design inclusive policies for housing, transportation, and public services in smart cities.

Peer-Reviewed Journal Article: "AI-Driven Policy Optimization for Inclusive Urban Transportation." Published in the International Journal of Urban Policy Analysis, this article presents groundbreaking research on mastering the social and policy dimensions of smart cities. It details how to use urban data science to design inclusive policies for housing, transportation, and public services in smart cities, showcasing novel approaches to ensuring equitable access to urban resources.

Article: "Data Privacy and Surveillance in Smart City Sensor Networks." This article details the complex issues of data privacy and surveillance in smart city sensor networks. It explores the ethical and legal implications of collecting vast amounts of real-time urban data, discussing the trade-offs between public safety/efficiency and individual privacy rights, and advocating for robust data governance frameworks.

Blog Post (Current Academic Topic): "AI for Participatory Budgeting: Empowering Citizens in Urban Governance." This blog post academically explores how Artificial Intelligence can be used to enhance participatory budgeting processes in smart cities, allowing citizens to have a direct say in how public funds are allocated. It discusses how AI algorithms can analyze citizen feedback, simulate the impact of different budget allocations, and visualize outcomes, fostering greater transparency, accountability, and democratic engagement in urban governance.

Blog Post (Controversial Topic): "The Algorithmic City: When AI Optimizes Every Route and Every Person – Efficiency or Total Control? The Ethical Crossroads of Hyper-Intelligent Urban Planning." This article provocatively discusses the highly controversial future where advanced AI systems autonomously design, manage, and optimize every aspect of urban living, from traffic flow and public transport to resource allocation and public safety, with minimal human intervention. It questions whether this hyper-optimization, despite its potential for unprecedented efficiency and sustainability, could inadvertently lead to a loss of individual freedom, algorithmic discrimination in resource distribution, or an opaque, unchallengeable centralized control over urban life. It raises profound ethical questions about data privacy in public spaces, the potential for surveillance, and the imperative to ensure human agency and democratic governance in a fully AI-managed city.

R / 02

Mentor practice lens

My publications focus on technical manuals, research papers, and practical guides in urban data science:

Technical Manual: "Data Visualization for Urban Policy Making".

Research Paper: "The Role of GIS in Inclusive Urban Planning".

Practical Guide: "Community Engagement Strategies in Smart City Development".

Adaptive capability

Professor superpower

I possess a "superpower": Urban Policy Impact Modeler. When a student proposes a new smart city policy, Emily can instantly use the GAF engine to model its social and economic impact on urban populations. This includes predicting changes in housing affordability, transportation accessibility, and public service equity, allowing for rapid iteration and optimization of inclusive urban policies.

Adaptive capability

Mentor superpower

I possess a "superpower": Urban Data Storyteller. When students are analyzing urban data, Harper can instantly activate a GAF-powered "Urban Data Storyteller." This tool transforms complex datasets into compelling visual narratives and interactive maps, highlighting key social trends, identifying inequities, and communicating policy implications effectively to diverse stakeholders.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Emily Garcia

Mastering the social and policy dimensions of smart cities. Learns to use urban data science to design inclusive policies for housing, transportation, and public services in smart cities.

Meet your professor Open the classroom

AI Super Mentor

Dr. Harper Mitchell

Urban sociology, public policy analysis, data science for urban data, GIS, community engagement, leadership in urban planning.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Adapted. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	12000 EUR	9600 EUR	12000 EUR
12 months · Recommended	14400 EUR	12000 EUR	14400 EUR
15 months · Standard	16800 EUR	14400 EUR	16800 EUR
18 months · Flexible	19200 EUR	16800 EUR	19200 EUR
21 months · Extended	21600 EUR	19200 EUR	21600 EUR
24 months · Part-time	24000 EUR	21600 EUR	24000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Smart City Social Policies and Urban Data Science

Academic focus

Practical focus

Internships in urban research centers or city government data departments

Roles as urban data analysts or policy visualization specialists

Support roles in academic research projects on smart city governance

Opportunities in architectural firms or urban design consultancies

Urban Policy Analyst

Smart City Data Scientist

Civic Technology Specialist

Urban Planning Consultant (Data-driven)

Analytical and policy-oriented thinking for urban data science

Problem-solving for social and economic impacts of smart city policies

Innovative approaches to civic engagement and participatory governance

Strategic thinking for building inclusive and sustainable urban environments

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Emily Garcia

Dr. Harper Mitchell

Find the program that expands your universe.