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CourseCivil, Rail & Smart Cities Applications open

Big Data course applied to Urban Mobility

Online course of 300 hours in big data applied to urban mobility: 6 modules covering mobility big data sources, big data infrastructure, spatio-temporal analysis, applications to urban management, privacy, ethics and governance, and a mobility big data project, with an assigned technical tutor.

Duration3 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Identify large-scale mobility data sources and their limitations.

02

Design distributed storage and processing architectures.

03

Apply spatio-temporal analysis techniques to traces and flows.

04

Build origin-destination matrices from large-scale data.

05

Apply the results to urban planning and management.

06

Ensure data privacy and governance.

07

Develop a complete analytical project on mobility big data.

Profile

Who it's for

  • Mobility data engineers
  • Urban data scientists
  • Transport analysts
  • Data platform architects
  • Smart city consultants
  • Data governance managers
  • Engineering or science graduates looking to move into the public sector
Standards & tools

What you'll work with

  • Regulation (EU) 2016/679 (General Data Protection Regulation)
  • Regulation (EU) 2022/868 (Data Governance Act)
  • Regulation (EU) 2023/2854 (Data Act)
  • Directive (EU) 2019/1024 (open data and re-use of public sector information)
  • Organic Law 3/2018 (Spanish Personal Data Protection Act)
  • Directive 2010/40/EU (intelligent transport systems)
  • ISO/IEC 20889 (de-identification techniques)
  • Apache Spark
  • Apache Kafka
  • PostGIS
  • Kepler.gl
  • Python (GeoPandas)
Syllabus

6 modules

  1. 1.1The five Vs of big data in mobility
  2. 1.2Mobile phone data and CDRs
  3. 1.3GPS traces from floating vehicles
  4. 1.4Public transport validations
  5. 1.5Shared bicycle and e-scooter data
  6. 1.6Urban IoT sensors
  7. 1.7Social media and crowdsourced data
  8. 1.8Payment and card data
  9. 1.9Quality, bias and representativeness
  10. 1.10Case study: catalogue of a city's data sources
  1. 2.1Lambda and kappa architectures
  2. 2.2Distributed storage and data lakes
  3. 2.3Processing with Apache Spark
  4. 2.4Streaming ingestion with Kafka
  5. 2.5PostGIS spatial databases
  6. 2.6Parquet and GeoParquet formats
  7. 2.7Cloud services for urban data
  8. 2.8Workflow orchestration with Airflow
  9. 2.9Data governance and catalogue
  10. 2.10Lab: processing GPS traces with PySpark
  1. 3.1Map matching of GPS traces
  2. 3.2Stop and trip detection
  3. 3.3Origin-destination matrices from mobile phone data
  4. 3.4Spatial indexing with H3
  5. 3.5Hourly and seasonal patterns
  6. 3.6Analysis of flows and desire lines
  7. 3.7Anomaly detection
  8. 3.8Trajectory clustering
  9. 3.9Visualisation of large volumes of data
  10. 3.10Practical exercise: origin-destination matrix from mobile phone data
  1. 4.1Transport network planning
  2. 4.2Evaluation of mobility policies
  3. 4.3Congestion analysis using speeds
  4. 4.4Tourist mobility studies
  5. 4.5Management of events and crowds
  6. 4.6Cycling infrastructure planning
  7. 4.7Location of services and facilities
  8. 4.8The INE mobility study based on mobile phone data
  9. 4.9Urban digital twins
  10. 4.10Case study: the Ministry's open mobility data
  1. 5.1Anonymisation and pseudonymisation
  2. 5.2Re-identification risks
  3. 5.3Differential privacy in aggregated data
  4. 5.4Aggregation and minimum thresholds
  5. 5.5European mobility data spaces
  6. 5.6Data-sharing agreements
  7. 5.7Open data and licences
  8. 5.8Transparency and public trust
  9. 5.9Urban data sovereignty
  10. 5.10Workshop: anonymisation protocol for traces
  1. 6.1Research question and scope
  2. 6.2Selection of data sources
  3. 6.3Processing architecture
  4. 6.4Cleaning and integration
  5. 6.5Spatio-temporal data analysis
  6. 6.6Visualisation of results
  7. 6.7Privacy assessment
  8. 6.8Recommendations for management
  9. 6.9Report and presentation
  10. 6.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

Delivered 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so it fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Spark, Apache Kafka, PostGIS, Kepler.gl and Python (GeoPandas), in free, academic or evaluation versions. Practical work is based on real procedures: processing of large GPS trace datasets, building origin-destination matrices and anonymisation of mobility data. Assessment is continuous by module, plus the final project.

Standards covered: Regulation (EU) 2016/679; Regulation (EU) 2022/868; Regulation (EU) 2023/2854; Directive (EU) 2019/1024; Organic Law 3/2018; Directive 2010/40/EU; ISO/IEC 20889.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the GDPR (Regulation (EU) 2016/679), the Data Governance Act (Regulation (EU) 2022/868) and the Data Act (Regulation (EU) 2023/2854) applied to real cases, not as a theoretical annex.
Industry software:Apache Spark, Apache Kafka, PostGIS, Kepler.gl and Python (GeoPandas), in free, academic or evaluation versions.
Final project:a complete mobility big data project, from data source selection and processing architecture to spatio-temporal analysis and privacy assessment, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

City data source catalogue
Compile and assess the mobility data sources available for a real city.
GPS trace processing
Process large volumes of GPS traces with PySpark.
Origin-destination matrix
Build an origin-destination matrix from mobile phone data.
Anonymisation protocol
Design an anonymisation protocol for mobility traces.
Final project
Deliver a complete mobility big data analysis with visualisations, privacy assessment and management recommendations.
Career opportunities

Where this takes you

Mobility data engineerUrban data scientistTransport analystData platform architectSmart city consultantData governance manager
Admissions & scholarships

Requirements

  • No prior qualification is required.
  • An internet connection and a computer able to run the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Apache Spark, Apache Kafka, PostGIS, Kepler.gl and Python (GeoPandas), using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

No prior qualification is required: you need an internet connection, a computer able to run the programme software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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